From 5b383d040654d01a463f8826c5ee2c738e608652 Mon Sep 17 00:00:00 2001 From: Louis Choquel <8851983+lchoquel@users.noreply.github.com> Date: Sun, 14 Dec 2025 21:58:24 +0100 Subject: [PATCH 01/10] Use Gateway (local dep) --- .env.example | 2 +- my_project/hello_world.py | 9 +- pyproject.toml | 4 + uv.lock | 249 ++++++++++++++++++++++++++++++++++++-- 4 files changed, 250 insertions(+), 14 deletions(-) diff --git a/.env.example b/.env.example index 22b29d7..cbc7ad2 100644 --- a/.env.example +++ b/.env.example @@ -1 +1 @@ -PIPELEX_INFERENCE_API_KEY= \ No newline at end of file +PIPELEX_GATEWAY_API_KEY= \ No newline at end of file diff --git a/my_project/hello_world.py b/my_project/hello_world.py index 5614816..7249a14 100644 --- a/my_project/hello_world.py +++ b/my_project/hello_world.py @@ -17,8 +17,11 @@ async def hello_world(): # Print the output pretty_print(pipe_output, title="Your first Pipelex output") + # get the generated text + generated_text = pipe_output.main_stuff_as_str + pretty_print(generated_text, title="Generated text") + # start Pipelex -Pipelex.make() -# run sample using asyncio -asyncio.run(hello_world()) +with Pipelex.make(): + asyncio.run(hello_world()) diff --git a/pyproject.toml b/pyproject.toml index 1cfd5d7..821ce0d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -19,6 +19,10 @@ dependencies = [ "pipelex[mistralai,anthropic,google,google-genai,bedrock,fal]==0.17.3", ] +[tool.uv.sources] +pipelex = { path = "../pipelex", editable = true } + + [tool.setuptools] packages = ["my_project"] include-package-data = true diff --git a/uv.lock b/uv.lock index f5923b6..66406c0 100644 --- a/uv.lock +++ b/uv.lock @@ -343,6 +343,15 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/68/b6/f624f5143bc5f7a66b79fc40e67b30b9584471a6143062af420bc83ae887/botocore_stubs-1.41.6-py3-none-any.whl", hash = "sha256:859e4147b5b14dc5eb64fc84fa02424839354368a0fea41da52c7a1d06427e37", size = 66748, upload-time = "2025-12-01T04:14:12.833Z" }, ] +[[package]] 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"https://files.pythonhosted.org/packages/2e/54/647ade08bf0db230bfea292f893923872fd20be6ac6f53b2b936ba839d75/zipp-3.23.0-py3-none-any.whl", hash = "sha256:071652d6115ed432f5ce1d34c336c0adfd6a884660d1e9712a256d3d3bd4b14e", size = 10276, upload-time = "2025-06-08T17:06:38.034Z" }, +] From d79a90a3cb6d21b131862e4e3ef2aedf95a9f380 Mon Sep 17 00:00:00 2001 From: Louis Choquel <8851983+lchoquel@users.noreply.github.com> Date: Mon, 12 Jan 2026 17:07:01 +0100 Subject: [PATCH 02/10] Use feature/Chicago --- .pipelex/inference/backends.toml | 109 +++++ .pipelex/inference/backends/anthropic.toml | 100 +++++ .pipelex/inference/backends/azure_openai.toml | 213 ++++++++++ .pipelex/inference/backends/bedrock.toml | 120 ++++++ .pipelex/inference/backends/blackboxai.toml | 240 +++++++++++ .pipelex/inference/backends/fal.toml | 107 +++++ .pipelex/inference/backends/google.toml | 94 +++++ .pipelex/inference/backends/groq.toml | 129 ++++++ .pipelex/inference/backends/huggingface.toml | 43 ++ .pipelex/inference/backends/internal.toml | 37 ++ .pipelex/inference/backends/mistral.toml | 164 ++++++++ .pipelex/inference/backends/ollama.toml | 63 +++ .pipelex/inference/backends/openai.toml | 208 +++++++++ .../inference/backends/pipelex_gateway.toml | 41 ++ .../inference/backends/pipelex_inference.toml | 205 +++++++++ .pipelex/inference/backends/portkey.toml | 263 ++++++++++++ .pipelex/inference/backends/scaleway.toml | 67 +++ .pipelex/inference/backends/vertexai.toml | 54 +++ .pipelex/inference/backends/xai.toml | 56 +++ .pipelex/inference/deck/base_deck.toml | 202 +++++++++ .pipelex/inference/deck/overrides.toml | 19 + .pipelex/inference/routing_profiles.toml | 173 ++++++++ .pipelex/pipelex.toml | 162 +++++++ .pipelex/pipelex_service.toml | 19 + .pipelex/telemetry.toml | 92 ++++ Makefile | 6 +- crazy/__init__.py | 0 crazy/bundle.plx | 42 ++ crazy/bundle_view.html | 111 +++++ crazy/bundle_view.svg | 397 ++++++++++++++++++ crazy/inputs.json | 1 + crazy/run_generate_crazy_image.py | 19 + crazy/structures/__init__.py | 0 .../crazy_image_generation_ImagePrompt.py | 22 + my_project/hello_world.py | 2 +- pyproject.toml | 6 +- uv.lock | 164 ++++---- 37 files changed, 3666 insertions(+), 84 deletions(-) create mode 100644 .pipelex/inference/backends.toml create mode 100644 .pipelex/inference/backends/anthropic.toml create mode 100644 .pipelex/inference/backends/azure_openai.toml create mode 100644 .pipelex/inference/backends/bedrock.toml create mode 100644 .pipelex/inference/backends/blackboxai.toml create mode 100644 .pipelex/inference/backends/fal.toml create mode 100644 .pipelex/inference/backends/google.toml create mode 100644 .pipelex/inference/backends/groq.toml create mode 100644 .pipelex/inference/backends/huggingface.toml create mode 100644 .pipelex/inference/backends/internal.toml create mode 100644 .pipelex/inference/backends/mistral.toml create mode 100644 .pipelex/inference/backends/ollama.toml create mode 100644 .pipelex/inference/backends/openai.toml create mode 100644 .pipelex/inference/backends/pipelex_gateway.toml create mode 100644 .pipelex/inference/backends/pipelex_inference.toml create mode 100644 .pipelex/inference/backends/portkey.toml create mode 100644 .pipelex/inference/backends/scaleway.toml create mode 100644 .pipelex/inference/backends/vertexai.toml create mode 100644 .pipelex/inference/backends/xai.toml create mode 100644 .pipelex/inference/deck/base_deck.toml create mode 100644 .pipelex/inference/deck/overrides.toml create mode 100644 .pipelex/inference/routing_profiles.toml create mode 100644 .pipelex/pipelex.toml create mode 100644 .pipelex/pipelex_service.toml create mode 100644 .pipelex/telemetry.toml create mode 100644 crazy/__init__.py create mode 100644 crazy/bundle.plx create mode 100644 crazy/bundle_view.html create mode 100644 crazy/bundle_view.svg create mode 100644 crazy/inputs.json create mode 100644 crazy/run_generate_crazy_image.py create mode 100644 crazy/structures/__init__.py create mode 100644 crazy/structures/crazy_image_generation_ImagePrompt.py diff --git a/.pipelex/inference/backends.toml b/.pipelex/inference/backends.toml new file mode 100644 index 0000000..46cbb79 --- /dev/null +++ b/.pipelex/inference/backends.toml @@ -0,0 +1,109 @@ +#################################################################################################### +# Pipelex Inference Backends Configuration +#################################################################################################### +# +# This file configures the inference backends available to Pipelex. +# Each backend connects to a different AI service provider (OpenAI, Anthropic, Google, etc.). +# +# Documentation: https://docs.pipelex.com +# Support: https://go.pipelex.com/discord +# +#################################################################################################### + +[pipelex_gateway] +display_name = "⭐ Pipelex Gateway" +enabled = true # Enable after accepting terms via `pipelex init config` +api_key = "${PIPELEX_GATEWAY_API_KEY}" + +[anthropic] +enabled = false +api_key = "${ANTHROPIC_API_KEY}" +valued_constraints = { max_output_tokens_limit = 8192 } + +[azure_openai] +display_name = "Azure OpenAI" +enabled = false +endpoint = "${AZURE_API_BASE}" +api_key = "${AZURE_API_KEY}" +api_version = "${AZURE_API_VERSION}" + +[bedrock] +display_name = "Amazon Bedrock" +enabled = false +aws_region = "${AWS_REGION}" + +[blackboxai] +display_name = "BlackBox AI" +enabled = false +endpoint = "https://api.blackbox.ai/v1" +api_key = "${BLACKBOX_API_KEY}" + +[fal] +display_name = "FAL" +enabled = false +api_key = "${FAL_API_KEY}" + +[google] +display_name = "Google AI" +enabled = false +api_key = "${GOOGLE_API_KEY}" + +[groq] +display_name = "Groq" +enabled = false +endpoint = "https://api.groq.com/openai/v1" +api_key = "${GROQ_API_KEY}" + +[huggingface] +display_name = "Hugging Face" +enabled = false +api_key = "${HF_TOKEN}" + +[mistral] +display_name = "Mistral AI" +enabled = false +api_key = "${MISTRAL_API_KEY}" + +[ollama] +enabled = false +endpoint = "http://localhost:11434/v1" + +[openai] +display_name = "OpenAI" +enabled = false +api_key = "${OPENAI_API_KEY}" + +[portkey] +display_name = "Portkey" +enabled = false +endpoint = "https://api.portkey.ai/v1" +api_key = "${PORTKEY_API_KEY}" + +[scaleway] +display_name = "Scaleway" +enabled = false +endpoint = "${SCALEWAY_ENDPOINT}" +api_key = "${SCALEWAY_API_KEY}" + +[vertexai] +display_name = "Google Vertex AI" +enabled = false # This is the only one we disable beacuse setting it up requires internet access just to get credentials so it fails in CI sandboxes +gcp_project_id = "${GCP_PROJECT_ID}" +gcp_location = "${GCP_LOCATION}" +gcp_credentials_file_path = "${GCP_CREDENTIALS_FILE_PATH}" + +[xai] +display_name = "xAI" +enabled = false +endpoint = "https://api.x.ai/v1" +api_key = "${XAI_API_KEY}" + +[internal] # software-only backend, runs internally, without AI +enabled = true + +# Deprecated +[pipelex_inference] +display_name = "🛑 Legacy Pipelex Inference" +enabled = false +endpoint = "https://inference.pipelex.com/v1" +api_key = "${PIPELEX_INFERENCE_API_KEY}" diff --git a/.pipelex/inference/backends/anthropic.toml b/.pipelex/inference/backends/anthropic.toml new file mode 100644 index 0000000..0f04f4d --- /dev/null +++ b/.pipelex/inference/backends/anthropic.toml @@ -0,0 +1,100 @@ +################################################################################ +# Anthropic Backend Configuration +################################################################################ +# +# This file defines the model specifications for Anthropic Claude models. +# It contains model definitions for various Claude language models +# accessible through the Anthropic API. +# +# Configuration structure: +# - Each model is defined in its own section with the model name as the header +# - Headers with dots must be quoted (e.g., ["claude-3.5-sonnet"]) +# - Model costs are in USD per million tokens (input/output) +# +# Documentation: https://docs.pipelex.com +# Support: https://go.pipelex.com/discord +# +################################################################################ + +################################################################################ +# MODEL DEFAULTS +################################################################################ + +[defaults] +model_type = "llm" +sdk = "anthropic" +prompting_target = "anthropic" +structure_method = "instructor/anthropic_tools" + +################################################################################ +# LANGUAGE MODELS +################################################################################ + +# --- Claude 3 Series ---------------------------------------------------------- +[claude-3-haiku] +model_id = "claude-3-haiku-20240307" +max_tokens = 4096 +inputs = ["text", "images"] +outputs = ["text", "structured"] +max_prompt_images = 100 +costs = { input = 0.25, output = 1.25 } + +# --- Claude 3.7 Series -------------------------------------------------------- +["claude-3.7-sonnet"] +model_id = "claude-3-7-sonnet-20250219" +max_tokens = 8192 +inputs = ["text", "images"] +outputs = ["text", "structured"] +max_prompt_images = 100 +costs = { input = 3.0, output = 15.0 } + +# --- Claude 4 Series ---------------------------------------------------------- +[claude-4-sonnet] +model_id = "claude-sonnet-4-20250514" +max_tokens = 64000 +inputs = ["text", "images"] +outputs = ["text", "structured"] +max_prompt_images = 100 +costs = { input = 3.0, output = 15.0 } + +[claude-4-opus] +model_id = "claude-opus-4-20250514" +max_tokens = 32000 +inputs = ["text", "images"] +outputs = ["text", "structured"] +max_prompt_images = 100 +costs = { input = 3.0, output = 15.0 } + +# --- Claude 4.1 Series -------------------------------------------------------- +["claude-4.1-opus"] +model_id = "claude-opus-4-1-20250805" +max_tokens = 32000 +inputs = ["text", "images"] +outputs = ["text", "structured"] +max_prompt_images = 100 +costs = { input = 3.0, output = 15.0 } + +# --- Claude 4.5 Series -------------------------------------------------------- +["claude-4.5-sonnet"] +model_id = "claude-sonnet-4-5-20250929" +max_tokens = 64000 +inputs = ["text", "images"] +outputs = ["text", "structured"] +max_prompt_images = 100 +costs = { input = 3.0, output = 15.0 } + +["claude-4.5-haiku"] +model_id = "claude-haiku-4-5-20251001" +max_tokens = 64000 +inputs = ["text", "images"] +outputs = ["text", "structured"] +max_prompt_images = 100 +costs = { input = 1.0, output = 5.0 } + +["claude-4.5-opus"] +model_id = "claude-opus-4-5-20251101" +max_tokens = 64000 +inputs = ["text", "images"] +outputs = ["text", "structured"] +max_prompt_images = 100 +costs = { input = 5.0, output = 25.0 } diff --git a/.pipelex/inference/backends/azure_openai.toml b/.pipelex/inference/backends/azure_openai.toml new file mode 100644 index 0000000..8a89898 --- /dev/null +++ b/.pipelex/inference/backends/azure_openai.toml @@ -0,0 +1,213 @@ +################################################################################ +# Azure OpenAI Backend Configuration +################################################################################ +# +# This file defines the model specifications for Azure OpenAI models. +# It contains model definitions for OpenAI models deployed on Azure +# accessible through the Azure OpenAI API. +# +# Configuration structure: +# - Each model is defined in its own section with the model name as the header +# - Headers with dots must be quoted (e.g., ["gpt-4.1"]) +# - Model costs are in USD per million tokens (input/output) +# +# Documentation: https://docs.pipelex.com +# Support: https://go.pipelex.com/discord +# +################################################################################ + +################################################################################ +# MODEL DEFAULTS +################################################################################ + +[defaults] +model_type = "llm" +sdk = "azure_openai_responses" +prompting_target = "openai" +structure_method = "instructor/openai_responses_tools" + +################################################################################ +# LANGUAGE MODELS +################################################################################ + +# --- GPT-4o Series ------------------------------------------------------------ +[gpt-4o] +model_id = "gpt-4o-2024-11-20" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 2.5, output = 10.0 } + +[gpt-4o-mini] +model_id = "gpt-4o-mini-2024-07-18" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 0.15, output = 0.6 } + +# --- GPT-4.1 Series ----------------------------------------------------------- +["gpt-4.1"] +model_id = "gpt-4.1-2025-04-14" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 2, output = 8 } + +["gpt-4.1-mini"] +model_id = "gpt-4.1-mini-2025-04-14" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 0.4, output = 1.6 } + +["gpt-4.1-nano"] +model_id = "gpt-4.1-nano-2025-04-14" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 0.1, output = 0.4 } + +# --- o Series ---------------------------------------------------------------- +[o1-mini] +model_id = "o1-mini-2024-09-12" +inputs = ["text"] +outputs = ["text", "structured"] +costs = { input = 3.0, output = 12.0 } +valued_constraints = { fixed_temperature = 1 } + +[o1] +model_id = "o1-2024-12-17" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 15.0, output = 60.0 } +valued_constraints = { fixed_temperature = 1 } + +[o3-mini] +model_id = "o3-mini-2025-01-31" +inputs = ["text"] +outputs = ["text", "structured"] +costs = { input = 1.1, output = 4.4 } +valued_constraints = { fixed_temperature = 1 } + +[o3] +model_id = "o3-2025-04-16" +inputs = ["text"] +outputs = ["text", "structured"] +costs = { input = 2, output = 8 } +valued_constraints = { fixed_temperature = 1 } + +# --- GPT-5 Series ------------------------------------------------------------- +[gpt-5-mini] +model_id = "gpt-5-mini-2025-08-07" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 0.25, output = 2.0 } +valued_constraints = { fixed_temperature = 1 } + +[gpt-5-nano] +model_id = "gpt-5-nano-2025-08-07" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 0.05, output = 0.4 } +valued_constraints = { fixed_temperature = 1 } + +[gpt-5-chat] +model_id = "gpt-5-chat-2025-08-07" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 1.25, output = 10.0 } +valued_constraints = { fixed_temperature = 1 } + +[gpt-5] +model_id = "gpt-5-2025-08-07" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 1.25, output = 10.0 } +valued_constraints = { fixed_temperature = 1 } + +# --- GPT-5.1 Series ------------------------------------------------------------- +["gpt-5.1"] +model_id = "gpt-5.1-2025-11-13" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 1.25, output = 10.0 } +valued_constraints = { fixed_temperature = 1 } + +["gpt-5.1-chat"] +model_id = "gpt-5.1-chat-2025-11-13" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 1.25, output = 10.0 } +valued_constraints = { fixed_temperature = 1 } + +["gpt-5.1-codex"] +model_id = "gpt-5.1-codex-2025-11-13" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 1.25, output = 10.0 } +valued_constraints = { fixed_temperature = 1 } + +# --- GPT-5.2 Series ------------------------------------------------------------- +["gpt-5.2"] +model_id = "gpt-5.2-2025-12-11" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 1.75, output = 14.0 } + +["gpt-5.2-chat"] +model_id = "gpt-5.2-chat-2025-12-11" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 1.25, output = 10.0 } +valued_constraints = { fixed_temperature = 1 } + +################################################################################ +# IMAGE GENERATION MODELS +################################################################################ + +# --- OpenAI Image Generation -------------------------------------------------- +[gpt-image-1] +sdk = "azure_rest_img_gen" +model_type = "img_gen" +model_id = "gpt-image-1-2025-04-15" +inputs = ["text"] +outputs = ["image"] +costs = { input = 10, output = 40 } + +[gpt-image-1.rules] +prompt = "positive_only" +num_images = "gpt" +aspect_ratio = "gpt" +background = "gpt" +inference = "gpt" +safety_checker = "unavailable" +output_format = "gpt" + +[gpt-image-1-mini] +sdk = "azure_rest_img_gen" +model_type = "img_gen" +model_id = "gpt-image-1-mini-2025-10-06" +inputs = ["text"] +outputs = ["image"] +costs = { input = 2.5, output = 8 } + +[gpt-image-1-mini.rules] +prompt = "positive_only" +num_images = "gpt" +aspect_ratio = "gpt" +background = "gpt" +inference = "gpt" +safety_checker = "unavailable" +output_format = "gpt" + +["gpt-image-1.5"] +sdk = "azure_rest_img_gen" +model_type = "img_gen" +model_id = "gpt-image-1.5-2025-12-16" +inputs = ["text"] +outputs = ["image"] +costs = { input = 8, output = 32 } + +["gpt-image-1.5".rules] +prompt = "positive_only" +num_images = "gpt" +aspect_ratio = "gpt" +background = "gpt" +inference = "gpt" +safety_checker = "unavailable" +output_format = "gpt" diff --git a/.pipelex/inference/backends/bedrock.toml b/.pipelex/inference/backends/bedrock.toml new file mode 100644 index 0000000..c4ab176 --- /dev/null +++ b/.pipelex/inference/backends/bedrock.toml @@ -0,0 +1,120 @@ +################################################################################ +# Amazon Bedrock Backend Configuration +################################################################################ +# +# This file defines the model specifications for Amazon Bedrock models. +# It contains model definitions for various language models +# accessible through the Amazon Bedrock service. +# +# Configuration structure: +# - Each model is defined in its own section with the model name as the header +# - Headers with dots must be quoted (e.g., ["claude-3.5-sonnet"]) +# - Model costs are in USD per million tokens (input/output) +# +# Documentation: https://docs.pipelex.com +# Support: https://go.pipelex.com/discord +# +################################################################################ + +################################################################################ +# MODEL DEFAULTS +################################################################################ + +[defaults] +model_type = "llm" +sdk = "bedrock_aioboto3" +prompting_target = "anthropic" + +################################################################################ +# LANGUAGE MODELS +################################################################################ + +# --- Mistral Models ----------------------------------------------------------- +[bedrock-mistral-large] +model_id = "mistral.mistral-large-2407-v1:0" +max_tokens = 8192 +inputs = ["text"] +outputs = ["text"] +costs = { input = 4.0, output = 12.0 } + +# --- Meta Llama Models -------------------------------------------------------- +[bedrock-meta-llama-3-3-70b-instruct] +model_id = "us.meta.llama3-3-70b-instruct-v1:0" +max_tokens = 8192 +inputs = ["text"] +outputs = ["text"] +# TODO: find out the actual cost per million tokens for llama3 on bedrock +costs = { input = 3.0, output = 15.0 } + +# --- Amazon Nova Models ------------------------------------------------------- +[bedrock-nova-pro] +model_id = "us.amazon.nova-pro-v1:0" +max_tokens = 5120 +inputs = ["text"] +outputs = ["text"] +# TODO: find out the actual cost per million tokens for nova on bedrock +costs = { input = 3.0, output = 15.0 } + +# --- Claude LLMs -------------------------------------------------------------- +["claude-3.7-sonnet"] +sdk = "bedrock_anthropic" +model_id = "us.anthropic.claude-3-7-sonnet-20250219-v1:0" +max_tokens = 8192 +inputs = ["text", "images"] +outputs = ["text", "structured"] +max_prompt_images = 100 +costs = { input = 3.0, output = 15.0 } + +[claude-4-sonnet] +sdk = "bedrock_anthropic" +model_id = "us.anthropic.claude-sonnet-4-20250514-v1:0" +max_tokens = 64000 +inputs = ["text", "images"] +outputs = ["text", "structured"] +max_prompt_images = 100 +costs = { input = 3.0, output = 15.0 } + +[claude-4-opus] +sdk = "bedrock_anthropic" +model_id = "us.anthropic.claude-opus-4-20250514-v1:0" +max_tokens = 32000 +inputs = ["text", "images"] +outputs = ["text", "structured"] +max_prompt_images = 100 +costs = { input = 3.0, output = 15.0 } + +["claude-4.1-opus"] +sdk = "bedrock_anthropic" +model_id = "us.anthropic.claude-opus-4-1-20250805-v1:0" +max_tokens = 32000 +inputs = ["text", "images"] +outputs = ["text", "structured"] +max_prompt_images = 100 +costs = { input = 3.0, output = 15.0 } + +["claude-4.5-sonnet"] +sdk = "bedrock_anthropic" +model_id = "us.anthropic.claude-sonnet-4-5-20250929-v1:0" +max_tokens = 8192 +inputs = ["text", "images"] +outputs = ["text", "structured"] +max_prompt_images = 100 +costs = { input = 3.0, output = 15.0 } + +["claude-4.5-haiku"] +sdk = "bedrock_anthropic" +model_id = "us.anthropic.claude-haiku-4-5-20251001-v1:0" +max_tokens = 8192 +inputs = ["text", "images"] +outputs = ["text", "structured"] +max_prompt_images = 100 +costs = { input = 1.0, output = 5.0 } + +["claude-4.5-opus"] +sdk = "bedrock_anthropic" +model_id = "global.anthropic.claude-opus-4-5-20251101-v1:0" +max_tokens = 8192 +inputs = ["text", "images"] +outputs = ["text", "structured"] +max_prompt_images = 100 +costs = { input = 5.0, output = 25.0 } diff --git a/.pipelex/inference/backends/blackboxai.toml b/.pipelex/inference/backends/blackboxai.toml new file mode 100644 index 0000000..9ee0433 --- /dev/null +++ b/.pipelex/inference/backends/blackboxai.toml @@ -0,0 +1,240 @@ +################################################################################ +# BlackBoxAI Backend Configuration +################################################################################ +# +# This file defines the model specifications for BlackBoxAI models. +# It contains model definitions for various language models from different providers +# accessible through the BlackBoxAI API. +# +# Configuration structure: +# - Each model is defined in its own section with the model name as the header +# - Headers with dots must be quoted (e.g., ["gpt-4.5-preview"]) +# - Model costs are in USD per million tokens (input/output) +# +# Documentation: https://docs.pipelex.com +# Support: https://go.pipelex.com/discord +# +################################################################################ + +################################################################################ +# MODEL DEFAULTS +################################################################################ + +[defaults] +model_type = "llm" +sdk = "openai" +structure_method = "instructor/openai_tools" + +################################################################################ +# LANGUAGE MODELS +################################################################################ + +# --- OpenAI Models ------------------------------------------------------------ +[gpt-4o-mini] +model_id = "blackboxai/openai/gpt-4o-mini" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 0.15, output = 0.60 } + +[gpt-4o] +model_id = "blackboxai/openai/gpt-4o" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 2.50, output = 10.00 } + +[o1-mini] +model_id = "blackboxai/openai/o1-mini" +inputs = ["text"] +outputs = ["text", "structured"] +costs = { input = 1.10, output = 4.40 } + +[o4-mini] +model_id = "blackboxai/openai/o4-mini" +inputs = ["text"] +outputs = ["text", "structured"] +costs = { input = 1.10, output = 4.40 } + +# --- Claude LLMs -------------------------------------------------------------- +["claude-3.5-haiku"] +model_id = "blackboxai/anthropic/claude-3.5-haiku" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 0.80, output = 4.00 } + +["claude-3.5-sonnet"] +model_id = "blackboxai/anthropic/claude-3.5-sonnet" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 3.00, output = 15.00 } + +["claude-3.7-sonnet"] +model_id = "blackboxai/anthropic/claude-3.7-sonnet" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 3.00, output = 15.00 } + +[claude-opus-4] +model_id = "blackboxai/anthropic/claude-opus-4" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 15.00, output = 75.00 } + +[claude-4-sonnet] +model_id = "blackboxai/anthropic/claude-sonnet-4" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 3.00, output = 15.00 } + +["claude-4.5-sonnet"] +model_id = "blackboxai/anthropic/claude-sonnet-4.5" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 0.28, output = 1.10 } + +# --- Google Models ------------------------------------------------------------ +["gemini-2.5-flash"] +model_id = "blackboxai/google/gemini-2.5-flash" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 0.30, output = 2.50 } + +["gemini-2.5-pro"] +model_id = "blackboxai/google/gemini-2.5-pro" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 1.25, output = 10.00 } + +["gemini-flash-1.5-8b"] +model_id = "blackboxai/google/gemini-flash-1.5-8b" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 0.04, output = 0.15 } + +# --- Mistral Models ----------------------------------------------------------- +[mistral-large] +model_id = "blackboxai/mistralai/mistral-large" +inputs = ["text"] +outputs = ["text", "structured"] +costs = { input = 2.00, output = 6.00 } + +[pixtral-large-2411] +model_id = "blackboxai/mistralai/pixtral-large-2411" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 2.00, output = 6.00 } + +# --- Meta Llama Models -------------------------------------------------------- +["llama-3.3-70b-instruct"] +model_id = "blackboxai/meta-llama/llama-3.3-70b-instruct" +inputs = ["text"] +outputs = ["text", "structured"] +costs = { input = 0.04, output = 0.12 } + +["llama-3.2-11b-vision-instruct"] +model_id = "blackboxai/meta-llama/llama-3.2-11b-vision-instruct" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 0.05, output = 0.05 } + +# --- Qwen Models -------------------------------------------------------------- +["qwen-2.5-72b-instruct"] +model_id = "blackboxai/qwen/qwen-2.5-72b-instruct" +inputs = ["text"] +outputs = ["text", "structured"] +costs = { input = 0.12, output = 0.39 } + +["qwen2.5-vl-72b-instruct"] +model_id = "blackboxai/qwen/qwen2.5-vl-72b-instruct" +inputs = ["text", "images"] +outputs = ["text"] +costs = { input = 0.25, output = 0.75 } + +# --- Amazon Nova Models ------------------------------------------------------- +[nova-micro-v1] +model_id = "blackboxai/amazon/nova-micro-v1" +inputs = ["text"] +outputs = ["text", "structured"] +costs = { input = 0.04, output = 0.14 } + +[nova-lite-v1] +model_id = "blackboxai/amazon/nova-lite-v1" +inputs = ["text"] +outputs = ["text", "structured"] +costs = { input = 0.06, output = 0.24 } + +################################################################################ +# FREE MODELS +################################################################################ + +# --- DeepSeek Free Models ----------------------------------------------------- +[deepseek-chat] +model_id = "blackboxai/deepseek/deepseek-chat:free" +inputs = ["text"] +outputs = ["text", "structured"] +costs = { input = 0.00, output = 0.00 } + +[deepseek-r1] +model_id = "blackboxai/deepseek/deepseek-r1:free" +inputs = ["text"] +outputs = ["text", "structured"] +costs = { input = 0.00, output = 0.00 } + +# --- Meta Llama Free Models --------------------------------------------------- +["llama-3.3-70b-instruct-free"] +model_id = "blackboxai/meta-llama/llama-3.3-70b-instruct:free" +inputs = ["text"] +outputs = ["text", "structured"] +costs = { input = 0.00, output = 0.00 } + + +################################################################################ +# IMAGE GENERATION MODELS +################################################################################ + +[flux-pro] +model_type = "img_gen" +sdk = "blackboxai_img_gen" +model_id = "blackboxai/black-forest-labs/flux-pro" +inputs = ["text"] +outputs = ["image"] +costs = { input = 0.0, output = 0.04 } + +["flux-pro/v1.1"] +model_type = "img_gen" +sdk = "blackboxai_img_gen" +model_id = "blackboxai/black-forest-labs/flux-1.1-pro" +inputs = ["text"] +outputs = ["image"] +costs = { input = 0.0, output = 0.04 } + +["flux-pro/v1.1-ultra"] +model_type = "img_gen" +sdk = "blackboxai_img_gen" +model_id = "blackboxai/black-forest-labs/flux-1.1-pro-ultra" +inputs = ["text"] +outputs = ["image"] +costs = { input = 0.0, output = 0.06 } + +[fast-lightning-sdxl] +model_type = "img_gen" +sdk = "blackboxai_img_gen" +model_id = "blackboxai/bytedance/sdxl-lightning-4step" +inputs = ["text"] +outputs = ["image"] +costs = { input = 0.0, output = 0.0014 } + +[nano-banana] +model_type = "img_gen" +sdk = "blackboxai_img_gen" +model_id = "blackboxai/google/nano-banana" +inputs = ["text"] +outputs = ["image"] +costs = { input = 0.0, output = 0.039 } + +[nano-banana-pro] +model_type = "img_gen" +sdk = "blackboxai_img_gen" +model_id = "blackboxai/google/nano-banana-pro" +inputs = ["text"] +outputs = ["image"] +costs = { input = 0.0, output = 0.039 } diff --git a/.pipelex/inference/backends/fal.toml b/.pipelex/inference/backends/fal.toml new file mode 100644 index 0000000..3433f99 --- /dev/null +++ b/.pipelex/inference/backends/fal.toml @@ -0,0 +1,107 @@ +################################################################################ +# FAL Backend Configuration +################################################################################ +# +# This file defines the model specifications for FAL (Fast AI Labs) models. +# It contains model definitions for various image generation models +# accessible through the FAL API. +# +# Configuration structure: +# - Each model is defined in its own section with the model name as the header +# - Headers with dots must be quoted (e.g., ["flux-pro/v1.1"]) +# - Model costs are in USD per million tokens (input/output) +# +# Documentation: https://docs.pipelex.com +# Support: https://go.pipelex.com/discord +# +################################################################################ + +################################################################################ +# MODEL DEFAULTS +################################################################################ + +[defaults] +model_type = "img_gen" +sdk = "fal" +prompting_target = "fal" + +################################################################################ +# IMAGE GENERATION MODELS +################################################################################ + +# --- Flux Pro Series ---------------------------------------------------------- +[flux-pro] +model_id = "fal-ai/flux-pro" +inputs = ["text"] +outputs = ["image"] +costs = { input = 0.05, output = 0.0 } + +[flux-pro.rules] +prompt = "positive_only" +num_images = "fal" +aspect_ratio = "flux" +inference = "flux" +safety_checker = "available" +output_format = "flux_1" +specific = "fal" + +["flux-pro/v1.1"] +model_id = "fal-ai/flux-pro/v1.1" +inputs = ["text"] +outputs = ["image"] +costs = { input = 0.05, output = 0.0 } + +["flux-pro/v1.1".rules] +prompt = "positive_only" +num_images = "fal" +aspect_ratio = "flux" +inference = "flux" +safety_checker = "available" +output_format = "flux_1" +specific = "fal" + +["flux-pro/v1.1-ultra"] +model_id = "fal-ai/flux-pro/v1.1-ultra" +inputs = ["text"] +outputs = ["image"] +costs = { input = 0.06, output = 0.0 } + +["flux-pro/v1.1-ultra".rules] +prompt = "positive_only" +num_images = "fal" +aspect_ratio = "flux_11_ultra" +inference = "flux_11_ultra" +safety_checker = "available" +output_format = "flux_1" +specific = "fal" + +[flux-2] +model_id = "fal-ai/flux-2" +inputs = ["text"] +outputs = ["image"] +costs = { input = 0.05, output = 0.0 } + +[flux-2.rules] +prompt = "positive_only" +num_images = "fal" +aspect_ratio = "flux" +inference = "flux" +safety_checker = "available" +output_format = "flux_2" +specific = "fal" + +# --- SDXL models -------------------------------------------------------------- +[fast-lightning-sdxl] +model_id = "fal-ai/fast-lightning-sdxl" +inputs = ["text"] +outputs = ["image"] +costs = { input = 0.0003, output = 0.0 } + +[fast-lightning-sdxl.rules] +prompt = "positive_only" +num_images = "fal" +aspect_ratio = "flux" +inference = "sdxl_lightning" +safety_checker = "unavailable" +output_format = "sdxl" +specific = "fal" diff --git a/.pipelex/inference/backends/google.toml b/.pipelex/inference/backends/google.toml new file mode 100644 index 0000000..36e19c2 --- /dev/null +++ b/.pipelex/inference/backends/google.toml @@ -0,0 +1,94 @@ +################################################################################ +# Google Gemini API Backend Configuration +################################################################################ +# +# This file defines the model specifications for Google Gemini API models. +# It contains model definitions for Gemini language models +# accessible through the Google Gemini API (not VertexAI). +# +# Configuration structure: +# - Each model is defined in its own section with the model name as the header +# - Headers with dots must be quoted (e.g., ["gemini-2.0-flash"]) +# - Model costs are in USD per million tokens (input/output) +# +# Documentation: https://docs.pipelex.com +# Support: https://go.pipelex.com/discord +# +################################################################################ + +################################################################################ +# MODEL DEFAULTS +################################################################################ + +[defaults] +model_type = "llm" +sdk = "google" +prompting_target = "gemini" +structure_method = "instructor/genai_tools" + +################################################################################ +# LANGUAGE MODELS +################################################################################ + +# --- Gemini 2.0 Series ---------------------------------------- +["gemini-2.0-flash"] +model_id = "gemini-2.0-flash" +inputs = ["text", "images"] +outputs = ["text", "structured"] +max_prompt_images = 3000 +costs = { input = 0.10, output = 0.40 } + +# --- Gemini 2.5 Series ---------------------------------------- +["gemini-2.5-pro"] +model_id = "gemini-2.5-pro" +inputs = ["text", "images"] +outputs = ["text", "structured"] +max_prompt_images = 3000 +costs = { input = 1.25, output = 10.0 } + +["gemini-2.5-flash"] +model_id = "gemini-2.5-flash" +inputs = ["text", "images"] +outputs = ["text", "structured"] +max_prompt_images = 3000 +costs = { input = 0.30, output = 2.50 } + +["gemini-2.5-flash-lite"] +model_id = "gemini-2.5-flash-lite" +inputs = ["text", "images"] +outputs = ["text", "structured"] +max_prompt_images = 3000 +costs = { input = 0.10, output = 0.40 } + +# --- Gemini 3.0 Series ---------------------------------------- +["gemini-3.0-pro"] +model_id = "gemini-3-pro-preview" +inputs = ["text", "images"] +outputs = ["text", "structured"] +max_prompt_images = 3000 +costs = { input = 2, output = 12.0 } + +["gemini-3.0-flash-preview"] +model_id = "gemini-3-flash-preview" +inputs = ["text", "images"] +outputs = ["text", "structured"] +max_prompt_images = 3000 +costs = { input = 0.5, output = 3.0 } + +################################################################################ +# IMAGE GENERATION MODELS (Nano Banana) +################################################################################ + +[nano-banana] +model_type = "img_gen" +model_id = "gemini-2.5-flash-image" +inputs = ["text"] +outputs = ["image"] +costs = { input = 0.0, output = 0.039 } + +[nano-banana-pro] +model_type = "img_gen" +model_id = "gemini-3-pro-image-preview" +inputs = ["text"] +outputs = ["image"] +costs = { input = 0.0, output = 0.039 } diff --git a/.pipelex/inference/backends/groq.toml b/.pipelex/inference/backends/groq.toml new file mode 100644 index 0000000..72bdae3 --- /dev/null +++ b/.pipelex/inference/backends/groq.toml @@ -0,0 +1,129 @@ +################################################################################ +# Groq Backend Configuration +################################################################################ +# +# This file defines the model specifications for Groq models. +# It contains model definitions for various LLM models accessible through +# the Groq API, including text-only and vision-capable models. +# +# Configuration structure: +# - Each model is defined in its own section with the model name as the header +# - Headers with dots or slashes must be quoted (e.g., ["meta-llama/llama-4-scout"]) +# - Model costs are in USD per million tokens (input/output) +# - Vision models support max 5 images per request, 33MP max resolution +# +# Documentation: https://docs.pipelex.com +# Support: https://go.pipelex.com/discord +# +################################################################################ + +################################################################################ +# MODEL DEFAULTS +################################################################################ + +[defaults] +model_type = "llm" +sdk = "openai" +structure_method = "instructor/json" + +################################################################################ +# PRODUCTION TEXT MODELS +################################################################################ + +# --- Meta Llama 3.x Series ---------------------------------------------------- +["llama-3.1-8b-instant"] +model_id = "llama-3.1-8b-instant" +max_tokens = 131072 +inputs = ["text"] +outputs = ["text", "structured"] +costs = { input = 0.05, output = 0.08 } + +["llama-3.3-70b-versatile"] +model_id = "llama-3.3-70b-versatile" +max_tokens = 32768 +inputs = ["text"] +outputs = ["text", "structured"] +costs = { input = 0.59, output = 0.79 } + +# --- Meta Llama Guard --------------------------------------------------------- +[llama-guard-4-12b] +model_id = "meta-llama/llama-guard-4-12b" +max_tokens = 1024 +inputs = ["text"] +outputs = ["text", "structured"] +costs = { input = 0.20, output = 0.20 } + +# --- OpenAI GPT-OSS Models ---------------------------------------------------- +[gpt-oss-20b] +model_id = "openai/gpt-oss-20b" +max_tokens = 65536 +inputs = ["text"] +outputs = ["text", "structured"] +costs = { input = 0.075, output = 0.30 } + +[gpt-oss-120b] +model_id = "openai/gpt-oss-120b" +max_tokens = 65536 +inputs = ["text"] +outputs = ["text", "structured"] +costs = { input = 0.15, output = 0.60 } + +# --- Groq Compound Systems ---------------------------------------------------- +["groq/compound"] +model_id = "groq/compound" +max_tokens = 8192 +inputs = ["text"] +outputs = ["text", "structured"] +costs = { input = 0.15, output = 0.45 } + +["groq/compound-mini"] +model_id = "groq/compound-mini" +max_tokens = 8192 +inputs = ["text"] +outputs = ["text", "structured"] +costs = { input = 0.10, output = 0.30 } + +################################################################################ +# PREVIEW MODELS +################################################################################ + +# --- Meta Llama 4 Vision Models (Preview) ------------------------------------- +[llama-4-scout-17b-16e-instruct] +model_id = "meta-llama/llama-4-scout-17b-16e-instruct" +max_tokens = 8192 +inputs = ["text", "images"] +outputs = ["text", "structured"] +max_prompt_images = 5 +costs = { input = 0.11, output = 0.34 } + +[llama-4-maverick-17b-128e-instruct] +model_id = "meta-llama/llama-4-maverick-17b-128e-instruct" +max_tokens = 8192 +inputs = ["text", "images"] +outputs = ["text", "structured"] +max_prompt_images = 5 +costs = { input = 0.20, output = 0.60 } + +# --- Moonshot Kimi K2 --------------------------------------------------------- +[kimi-k2-instruct-0905] +model_id = "moonshotai/kimi-k2-instruct-0905" +max_tokens = 16384 +inputs = ["text"] +outputs = ["text", "structured"] +costs = { input = 1.00, output = 3.00 } + +# --- OpenAI Safety Model ------------------------------------------------------ +[gpt-oss-safeguard-20b] +model_id = "openai/gpt-oss-safeguard-20b" +max_tokens = 65536 +inputs = ["text"] +outputs = ["text", "structured"] +costs = { input = 0.075, output = 0.30 } + +# --- Qwen 3 ------------------------------------------------------------------- +[qwen3-32b] +model_id = "qwen/qwen3-32b" +max_tokens = 40960 +inputs = ["text"] +outputs = ["text", "structured"] +costs = { input = 0.29, output = 0.59 } diff --git a/.pipelex/inference/backends/huggingface.toml b/.pipelex/inference/backends/huggingface.toml new file mode 100644 index 0000000..1a79638 --- /dev/null +++ b/.pipelex/inference/backends/huggingface.toml @@ -0,0 +1,43 @@ +################################################################################ +# Hugging Face Backend Configuration +################################################################################ +# +# This file defines the model specifications for Hugging Face models. +# It contains model definitions for various image generation models +# accessible through the Hugging Face Inference API with provider="auto". +# +# Configuration structure: +# - Each model is defined in its own section with the model name as the header +# - Headers with dots or slashes must be quoted (e.g., ["stabilityai/stable-diffusion-2-1"]) +# - Model costs are in USD per million tokens (input/output) +# +# Documentation: https://docs.pipelex.com +# Support: https://go.pipelex.com/discord +# +################################################################################ + +################################################################################ +# MODEL DEFAULTS +################################################################################ + +[defaults] +model_type = "img_gen" +sdk = "huggingface_img_gen" + +################################################################################ +# IMAGE GENERATION MODELS +################################################################################ + +# --- Qwen Image Models -------------------------------------------------- +[qwen-image] +model_id = "Qwen/Qwen-Image" +inputs = ["text"] +outputs = ["image"] +costs = { input = 0.0, output = 0.0 } +variant = "fal-ai" +# variant = "replicate" + +[qwen-image.rules] +prompt = "with_negative" +aspect_ratio = "qwen_image" +inference = "qwen_image" diff --git a/.pipelex/inference/backends/internal.toml b/.pipelex/inference/backends/internal.toml new file mode 100644 index 0000000..e44b222 --- /dev/null +++ b/.pipelex/inference/backends/internal.toml @@ -0,0 +1,37 @@ +################################################################################ +# Internal Backend Configuration +################################################################################ +# +# This file defines the model specifications for internal software-only models. +# These models run internally without external APIs or AI services. +# +# Configuration structure: +# - Each model is defined in its own section with the model name as the header +# - Model costs are in USD per million tokens (input/output) +# +# Documentation: https://docs.pipelex.com +# Support: https://go.pipelex.com/discord +# +################################################################################ + +################################################################################ +# TEXT EXTRACTION MODELS +################################################################################ + +# --- PyPDFium2 Text Extractor ------------------------------------------------- +[pypdfium2-extract-pdf] +model_type = "text_extractor" +sdk = "pypdfium2" +model_id = "extract-text" +inputs = ["pdf"] +outputs = ["pages"] +costs = {} + +# --- Docling Text Extractor --------------------------------------------------- +[docling-extract-text] +model_type = "text_extractor" +sdk = "docling_sdk" +model_id = "extract-text" +inputs = ["pdf", "image"] +outputs = ["pages"] +costs = {} diff --git a/.pipelex/inference/backends/mistral.toml b/.pipelex/inference/backends/mistral.toml new file mode 100644 index 0000000..b695ac7 --- /dev/null +++ b/.pipelex/inference/backends/mistral.toml @@ -0,0 +1,164 @@ +################################################################################ +# Mistral Backend Configuration +################################################################################ +# +# This file defines the model specifications for Mistral AI models. +# It contains model definitions for various Mistral language models and specialized models +# accessible through the Mistral API. +# +# Configuration structure: +# - Each model is defined in its own section with the model name as the header +# - Headers with dots must be quoted (e.g., ["ministral-3b"]) +# - Model costs are in USD per million tokens (input/output) +# +# Documentation: https://docs.pipelex.com +# Support: https://go.pipelex.com/discord +# +################################################################################ + +################################################################################ +# MODEL DEFAULTS +################################################################################ + +[defaults] +model_type = "llm" +sdk = "mistral" +prompting_target = "mistral" +structure_method = "instructor/mistral_tools" + +################################################################################ +# LANGUAGE MODELS +################################################################################ + +# --- Ministral Series --------------------------------------------------------- +[ministral-3b] +model_id = "ministral-3b-latest" +max_tokens = 131072 +inputs = ["text"] +outputs = ["text", "structured"] +costs = { input = 0.04, output = 0.04 } + +[ministral-8b] +model_id = "ministral-8b-latest" +max_tokens = 131072 +inputs = ["text"] +outputs = ["text", "structured"] +costs = { input = 0.1, output = 0.1 } + +# --- Mistral 7B Series -------------------------------------------------------- +[mistral-7b-2312] +model_id = "mistral-large-2402" +max_tokens = 32768 +inputs = ["text"] +outputs = ["text", "structured"] +costs = { input = 0.25, output = 0.25 } + +# --- Mistral 8x7B Series ------------------------------------------------------ +[mistral-8x7b-2312] +model_id = "open-mixtral-8x7b" +max_tokens = 32768 +inputs = ["text"] +outputs = ["text"] +costs = { input = 0.7, output = 0.7 } + +# --- Mistral Codestral Series ------------------------------------------------- +[mistral-codestral-2405] +model_id = "codestral-2405" +max_tokens = 262144 +inputs = ["text"] +outputs = ["text"] +costs = { input = 1.0, output = 3.0 } + +# --- Mistral Large Series ----------------------------------------------------- +[mistral-large-2402] +model_id = "mistral-large-2402" +max_tokens = 32768 +inputs = ["text"] +outputs = ["text", "structured"] +costs = { input = 4.0, output = 12.0 } + +[mistral-large] +model_id = "mistral-large-latest" +max_tokens = 131072 +inputs = ["text"] +outputs = ["text", "structured"] +costs = { input = 4.0, output = 12.0 } + +# --- Mistral Small Series ----------------------------------------------------- +[mistral-small-2402] +model_id = "mistral-small-2402" +max_tokens = 32768 +inputs = ["text"] +outputs = ["text", "structured"] +costs = { input = 1.0, output = 3.0 } + +[mistral-small] +model_id = "mistral-small-latest" +max_tokens = 32768 +inputs = ["text"] +outputs = ["text", "structured"] +costs = { input = 1.0, output = 3.0 } + +# --- Pixtral Series ----------------------------------------------------------- +[pixtral-12b] +model_id = "pixtral-12b-latest" +max_tokens = 131072 +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 0.15, output = 0.15 } + +[pixtral-large] +model_id = "pixtral-large-latest" +max_tokens = 131072 +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 2.0, output = 6.0 } + +# --- Mistral Medium Series ---------------------------------------------------- +[mistral-medium] +model_id = "mistral-medium-latest" +max_tokens = 128000 +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 0.4, output = 2.0 } + +[mistral-medium-2508] +model_id = "mistral-medium-2508" +max_tokens = 128000 +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 0.4, output = 2.0 } + +################################################################################ +# EXTRACTION MODELS +################################################################################ + +# TODO: add support to pricing per page + +[mistral-ocr-2503] +model_type = "text_extractor" +model_id = "mistral-ocr-2503" +max_tokens = 16384 +inputs = ["pdf", "image"] +outputs = ["pages"] + +[mistral-ocr-2505] +model_type = "text_extractor" +model_id = "mistral-ocr-2505" +max_tokens = 16384 +inputs = ["pdf", "image"] +outputs = ["pages"] + +[mistral-ocr-2512] +model_type = "text_extractor" +model_id = "mistral-ocr-2512" +max_tokens = 16384 +inputs = ["pdf", "image"] +outputs = ["pages"] + +[mistral-ocr] +model_type = "text_extractor" +model_id = "mistral-ocr-latest" +max_tokens = 16384 +inputs = ["pdf", "image"] +outputs = ["pages"] diff --git a/.pipelex/inference/backends/ollama.toml b/.pipelex/inference/backends/ollama.toml new file mode 100644 index 0000000..397e9ac --- /dev/null +++ b/.pipelex/inference/backends/ollama.toml @@ -0,0 +1,63 @@ +################################################################################ +# Ollama Backend Configuration +################################################################################ +# +# This file defines the model specifications for Ollama models. +# It contains model definitions for local language models +# accessible through the Ollama API. +# +# Configuration structure: +# - Each model is defined in its own section with the model name as the header +# - Headers with dots must be quoted (e.g., ["mistral-small3.1-24b"]) +# - Model costs are in USD per million tokens (input/output) +# +# Documentation: https://docs.pipelex.com +# Support: https://go.pipelex.com/discord +# +################################################################################ + +################################################################################ +# MODEL DEFAULTS +################################################################################ + +[defaults] +model_type = "llm" +sdk = "openai" +prompting_target = "anthropic" +structure_method = "instructor/openai_tools" + +################################################################################ +# LANGUAGE MODELS +################################################################################ + +# --- Gemma Models ------------------------------------------------------------- +[gemma3-4b] +model_id = "gemma3:4b" +inputs = ["text"] +outputs = ["text"] +max_prompt_images = 3000 +costs = { input = 0, output = 0 } + +# --- Llama Models ------------------------------------------------------------- +[llama4-scout] +model_id = "llama4:scout" +inputs = ["text"] +outputs = ["text"] +max_prompt_images = 3000 +costs = { input = 0, output = 0 } + +# --- Mistral Models ----------------------------------------------------------- +["mistral-small3.1-24b"] +model_id = "mistral-small3.1:24b" +inputs = ["text"] +outputs = ["text"] +max_prompt_images = 3000 +costs = { input = 0, output = 0 } + +# --- Qwen Models -------------------------------------------------------------- +[qwen3-8b] +model_id = "qwen3:8b" +inputs = ["text"] +outputs = ["text"] +costs = { input = 0, output = 0 } +# TODO: support tokens diff --git a/.pipelex/inference/backends/openai.toml b/.pipelex/inference/backends/openai.toml new file mode 100644 index 0000000..e61d52e --- /dev/null +++ b/.pipelex/inference/backends/openai.toml @@ -0,0 +1,208 @@ +################################################################################ +# OpenAI Backend Configuration +################################################################################ +# +# This file defines the model specifications for OpenAI models. +# It contains model definitions for various LLM and image generation models +# accessible through the OpenAI API. +# +# Configuration structure: +# - Each model is defined in its own section with the model name as the header +# - Headers with dots must be quoted (e.g., ["gpt-4.1"]) +# - Model costs are in USD per million tokens (input/output) +# +# Documentation: https://docs.pipelex.com +# Support: https://go.pipelex.com/discord +# +################################################################################ + +################################################################################ +# MODEL DEFAULTS +################################################################################ + +[defaults] +model_type = "llm" +sdk = "openai_responses" +prompting_target = "openai" +structure_method = "instructor/openai_responses_tools" + +################################################################################ +# LANGUAGE MODELS +################################################################################ + +# --- GPT-3.5 Series ----------------------------------------------------------- +["gpt-3.5-turbo"] +model_id = "gpt-3.5-turbo-1106" +inputs = ["text"] +outputs = ["text", "structured"] +costs = { input = 0.5, output = 1.5 } + +# --- GPT-4 Series ------------------------------------------------------------- +[gpt-4] +inputs = ["text"] +outputs = ["text"] +costs = { input = 30.0, output = 60.0 } + +[gpt-4-turbo] +inputs = ["text"] +outputs = ["text", "structured"] +costs = { input = 10.0, output = 30.0 } + +# --- GPT-4o Series ------------------------------------------------------------ +[gpt-4o-2024-11-20] +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 2.5, output = 10.0 } + +[gpt-4o] +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 2.5, output = 10.0 } + +[gpt-4o-mini-2024-07-18] +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 0.15, output = 0.6 } + +[gpt-4o-mini] +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 0.15, output = 0.6 } + +# --- GPT-4.1 Series ----------------------------------------------------------- +["gpt-4.1"] +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 2, output = 8 } + +["gpt-4.1-mini"] +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 0.4, output = 1.6 } + +["gpt-4.1-nano"] +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 0.1, output = 0.4 } + +# --- o Series ---------------------------------------------------------------- +[o1] +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 15.0, output = 60.0 } +valued_constraints = { fixed_temperature = 1 } + +[o3-mini] +inputs = ["text"] +outputs = ["text", "structured"] +costs = { input = 1.1, output = 4.4 } +valued_constraints = { fixed_temperature = 1 } + +[o3] +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 10.0, output = 40.0 } +valued_constraints = { fixed_temperature = 1 } + +[o4-mini] +inputs = ["text"] +outputs = ["text", "structured"] +costs = { input = 1.1, output = 4.4 } +valued_constraints = { fixed_temperature = 1 } + +# --- GPT-5 Series ------------------------------------------------------------- +[gpt-5] +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 1.25, output = 10.0 } +valued_constraints = { fixed_temperature = 1 } + +[gpt-5-mini] +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 0.25, output = 2.0 } +valued_constraints = { fixed_temperature = 1 } + +[gpt-5-nano] +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 0.05, output = 0.4 } +valued_constraints = { fixed_temperature = 1 } + +[gpt-5-chat] +model_id = "gpt-5-chat-latest" +inputs = ["text", "images"] +outputs = ["text"] +costs = { input = 1.25, output = 10.0 } +valued_constraints = { fixed_temperature = 1 } + +[gpt-5-codex] +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 1.25, output = 10.0 } +valued_constraints = { fixed_temperature = 1 } + +# --- GPT-5.1 Series ------------------------------------------------------------- +["gpt-5.1"] +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 1.25, output = 10.0 } + +["gpt-5.1-chat"] +model_id = "gpt-5.1-chat-latest" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 1.25, output = 10.0 } +valued_constraints = { fixed_temperature = 1 } + +["gpt-5.1-codex"] +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 1.25, output = 10.0 } +valued_constraints = { fixed_temperature = 1 } + +["gpt-5.1-codex-max"] +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 1.25, output = 10.0 } +valued_constraints = { fixed_temperature = 1 } + +# --- GPT-5.2 Series ------------------------------------------------------------- +["gpt-5.2"] +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 1.75, output = 14.0 } + +["gpt-5.2-chat"] +model_id = "gpt-5.2-chat-latest" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 1.75, output = 14.0 } +valued_constraints = { fixed_temperature = 1 } + +################################################################################ +# IMAGE GENERATION MODELS +################################################################################ + +# --- OpenAI Image Generation -------------------------------------------------- +[gpt-image-1] +sdk = "openai_img_gen" +model_type = "img_gen" +inputs = ["text"] +outputs = ["image"] +costs = { input = 10, output = 40 } + +[gpt-image-1-mini] +sdk = "openai_img_gen" +model_type = "img_gen" +inputs = ["text"] +outputs = ["image"] +costs = { input = 2.5, output = 8 } + +["gpt-image-1.5"] +sdk = "openai_img_gen" +model_type = "img_gen" +model_id = "gpt-image-1.5" +inputs = ["text"] +outputs = ["image"] +costs = { input = 8, output = 32 } diff --git a/.pipelex/inference/backends/pipelex_gateway.toml b/.pipelex/inference/backends/pipelex_gateway.toml new file mode 100644 index 0000000..bca075b --- /dev/null +++ b/.pipelex/inference/backends/pipelex_gateway.toml @@ -0,0 +1,41 @@ +################################################################################ +# Pipelex Gateway Local Overrides +################################################################################ +# +# TELEMETRY NOTICE: +# +# Using Pipelex Gateway enables identified telemetry tied to your API key +# (hashed for security). This is independent from your telemetry.toml settings. +# +# We collect only technical data (model names, token counts, latency, error rates). +# We do NOT collect prompts, completions, pipe codes, or business data. +# +# This allows us to monitor service quality, enforce fair usage, and support you. +# +################################################################################ +# +# WARNING: USE AT YOUR OWN RISK! +# +# The actual model configuration is fetched remotely from Pipelex servers. +# Any override in this file may cause unexpected behavior or failures, +# as the remote configuration may change at any time. +# +# If you must override, you may ONLY use these keys per model: +# - sdk +# - structure_method +# +# All other keys will be ignored. +# +# If you need custom configurations, consider using your own API keys +# with direct provider backends (openai, anthropic, etc.) instead. +# +# Documentation: +# https://docs.pipelex.com/home/7-configuration/config-technical/inference-backend-config/ +# Support: https://go.pipelex.com/discord +# +################################################################################ + +# Per-model overrides example: +# [gpt-4o] +# sdk = "gateway_completions" +# structure_method = "instructor/openai_tools" diff --git a/.pipelex/inference/backends/pipelex_inference.toml b/.pipelex/inference/backends/pipelex_inference.toml new file mode 100644 index 0000000..751c570 --- /dev/null +++ b/.pipelex/inference/backends/pipelex_inference.toml @@ -0,0 +1,205 @@ +################################################################################ +# Pipelex Inference Backend Configuration +################################################################################ +# +# This file defines the model specifications for the Pipelex Inference backend. +# It contains model definitions for various LLM and image generation models +# accessible through the Pipelex unified inference API. +# +# Configuration structure: +# - Each model is defined in its own section with the model name as the header +# - Headers with dots must be quoted (e.g., ["gpt-4.1"]) +# - Model costs are in USD per million tokens (input/output) +# +# Documentation: https://docs.pipelex.com +# Support: https://go.pipelex.com/discord +# +################################################################################ + +################################################################################ +# MODEL DEFAULTS +################################################################################ + +[defaults] +model_type = "llm" +sdk = "openai" +prompting_target = "anthropic" +structure_method = "instructor/openai_tools" + +################################################################################ +# LANGUAGE MODELS +################################################################################ + +# --- OpenAI LLMs -------------------------------------------------------------- +[gpt-4o] +model_id = "pipelex/gpt-4o" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 2.75, output = 11.00 } +sdk = "openai_responses" +structure_method = "instructor/openai_responses_tools" + +[gpt-4o-mini] +model_id = "pipelex/gpt-4o-mini" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 0.17, output = 0.66 } +sdk = "openai_responses" +structure_method = "instructor/openai_responses_tools" + +["gpt-4.1"] +model_id = "pipelex/gpt-4.1" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 2, output = 8 } +sdk = "openai_responses" +structure_method = "instructor/openai_responses_tools" + +["gpt-4.1-mini"] +model_id = "pipelex/gpt-4.1-mini" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 0.4, output = 1.6 } +sdk = "openai_responses" +structure_method = "instructor/openai_responses_tools" + +["gpt-4.1-nano"] +model_id = "pipelex/gpt-4.1-nano" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 0.1, output = 0.4 } +sdk = "openai_responses" +structure_method = "instructor/openai_responses_tools" + +[gpt-5-nano] +model_id = "pipelex/gpt-5-nano" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 0.05, output = 0.40 } +sdk = "openai_responses" +structure_method = "instructor/openai_responses_tools" + +[gpt-5-mini] +model_id = "pipelex/gpt-5-mini" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 0.25, output = 2.00 } +sdk = "openai_responses" +structure_method = "instructor/openai_responses_tools" + +[gpt-5-chat] +model_id = "pipelex/gpt-5-chat" +inputs = ["text", "images"] +outputs = ["text"] +costs = { input = 1.25, output = 10.00 } +sdk = "openai_responses" +structure_method = "instructor/openai_responses_tools" + +[gpt-5] +model_id = "pipelex/gpt-5" +inputs = ["text", "images"] +outputs = ["text"] +costs = { input = 1.25, output = 10.00 } +sdk = "openai_responses" +structure_method = "instructor/openai_responses_tools" + +["gpt-5.1"] +model_id = "pipelex/gpt-5.1" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 1.25, output = 10.00 } +sdk = "openai_responses" +structure_method = "instructor/openai_responses_tools" + +["gpt-5.1-chat"] +model_id = "pipelex/gpt-5.1-chat" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 1.25, output = 10.00 } +sdk = "openai_responses" +structure_method = "instructor/openai_responses_tools" + +# --- Claude LLMs -------------------------------------------------------------- +["claude-4-sonnet"] +model_id = "pipelex/claude-4-sonnet" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 3, output = 15 } + +["claude-4.1-opus"] +model_id = "pipelex/claude-4.1-opus" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 15, output = 75 } + +["claude-4.5-sonnet"] +model_id = "pipelex/claude-4.5-sonnet" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 3, output = 15 } + +["claude-4.5-haiku"] +model_id = "pipelex/claude-4.5-haiku" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 1, output = 5 } + +["claude-4.5-opus"] +model_id = "pipelex/claude-4.5-opus" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 5, output = 25 } + +# --- Gemini LLMs -------------------------------------------------------------- +["gemini-2.0-flash"] +model_id = "pipelex/gemini-2.0-flash" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 0.10, output = 0.40 } + +["gemini-2.5-pro"] +model_id = "pipelex/gemini-2.5-pro" +inputs = ["text", "images"] +outputs = ["text", "structured"] +max_prompt_images = 3000 +costs = { input = 1.25, output = 10.0 } + +["gemini-2.5-flash"] +model_id = "pipelex/gemini-2.5-flash" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 0.30, output = 2.50 } + +["gemini-2.5-flash-lite"] +model_id = "pipelex/gemini-2.5-flash-lite" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 0.10, output = 0.40 } + +["gemini-3.0-pro"] +model_id = "pipelex/gemini-3.0-pro" +inputs = ["text", "images"] +outputs = ["text", "structured"] +max_prompt_images = 3000 +costs = { input = 2, output = 12.0 } + +# --- XAI LLMs -------------------------------------------------------------- + +[grok-3] +model_id = "grok-3" +inputs = ["text"] +outputs = ["text"] +costs = { input = 3, output = 15 } + +[grok-3-mini] +model_id = "grok-3-mini" +inputs = ["text"] +outputs = ["text"] +costs = { input = 0.3, output = 0.5 } + +################################################################################ +# OCR and IMAGE GENERATION MODELS +################################################################################ + +# We are still working in giving you acces to OCR and image generation models +# and to the best models from Mistral through the Pipelex Inference backend. diff --git a/.pipelex/inference/backends/portkey.toml b/.pipelex/inference/backends/portkey.toml new file mode 100644 index 0000000..75e2574 --- /dev/null +++ b/.pipelex/inference/backends/portkey.toml @@ -0,0 +1,263 @@ +################################################################################ +# Portkey Configuration +################################################################################ +# +# This file defines the model specifications for the Portkey backend. +# It contains model definitions for various AI models. +# +# Configuration structure: +# - Each model is defined in its own section with the model name as the header +# - Headers with dots must be quoted (e.g., ["gpt-4.1"]) +# - Model costs are in USD per million tokens (input/output) +# +# Documentation: https://docs.pipelex.com +# Support: https://go.pipelex.com/discord +# +################################################################################ + +################################################################################ +# MODEL DEFAULTS +################################################################################ + +[defaults] +model_type = "llm" +sdk = "portkey_completions" +structure_method = "instructor/openai_tools" +prompting_target = "anthropic" + +################################################################################ +# LANGUAGE MODELS +################################################################################ + +# --- OpenAI LLMs -------------------------------------------------------------- +[gpt-4o-mini] +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 0.15, output = 0.6 } +sdk = "portkey_responses" +structure_method = "instructor/openai_responses_tools" +x-portkey-provider = "@openai" + +[gpt-4o] +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 2.5, output = 10.0 } +sdk = "portkey_responses" +structure_method = "instructor/openai_responses_tools" +x-portkey-provider = "@openai" + +["gpt-4.1-nano"] +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 0.1, output = 0.4 } +sdk = "portkey_responses" +structure_method = "instructor/openai_responses_tools" +x-portkey-provider = "@openai" + +["gpt-4.1-mini"] +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 0.4, output = 1.6 } +sdk = "portkey_responses" +structure_method = "instructor/openai_responses_tools" +x-portkey-provider = "@openai" + +["gpt-4.1"] +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 2, output = 8 } +sdk = "portkey_responses" +structure_method = "instructor/openai_responses_tools" +x-portkey-provider = "@openai" + +[o1] +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 15.0, output = 60.0 } +valued_constraints = { fixed_temperature = 1 } +sdk = "portkey_responses" +structure_method = "instructor/openai_responses_tools" +x-portkey-provider = "@openai" + +[o3-mini] +inputs = ["text"] +outputs = ["text", "structured"] +costs = { input = 1.1, output = 4.4 } +valued_constraints = { fixed_temperature = 1 } +sdk = "portkey_responses" +structure_method = "instructor/openai_responses_tools" +x-portkey-provider = "@openai" + +[o3] +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 2, output = 8 } +valued_constraints = { fixed_temperature = 1 } +sdk = "portkey_responses" +structure_method = "instructor/openai_responses_tools" +x-portkey-provider = "@openai" + +[o4-mini] +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 1.1, output = 4.4 } +valued_constraints = { fixed_temperature = 1 } +sdk = "portkey_responses" +structure_method = "instructor/openai_responses_tools" +x-portkey-provider = "@openai" + +[gpt-5-nano] +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 0.05, output = 0.4 } +valued_constraints = { fixed_temperature = 1 } +sdk = "portkey_responses" +structure_method = "instructor/openai_responses_tools" +x-portkey-provider = "@openai" + +[gpt-5-mini] +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 0.25, output = 2.0 } +valued_constraints = { fixed_temperature = 1 } +sdk = "portkey_responses" +structure_method = "instructor/openai_responses_tools" +x-portkey-provider = "@openai" + +[gpt-5] +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 1.25, output = 10.0 } +valued_constraints = { fixed_temperature = 1 } +sdk = "portkey_responses" +structure_method = "instructor/openai_responses_tools" +x-portkey-provider = "@openai" + +["gpt-5.1"] +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 1.25, output = 10.0 } +valued_constraints = { fixed_temperature = 1 } +sdk = "portkey_responses" +structure_method = "instructor/openai_responses_tools" +x-portkey-provider = "@openai" + +["gpt-5.1-codex"] +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 1.25, output = 10.0 } +valued_constraints = { fixed_temperature = 1 } +sdk = "portkey_responses" +structure_method = "instructor/openai_responses_tools" +x-portkey-provider = "@openai" + +# --- Claude LLMs -------------------------------------------------------------- +[claude-3-haiku] +model_id = "claude-3-haiku-20240307" +max_tokens = 4096 +inputs = ["text", "images"] +outputs = ["text", "structured"] +max_prompt_images = 100 +costs = { input = 0.25, output = 1.25 } +x-portkey-provider = "@anthropic" + +["claude-3.7-sonnet"] +model_id = "claude-3-7-sonnet-20250219" +max_tokens = 8192 +inputs = ["text", "images"] +outputs = ["text", "structured"] +max_prompt_images = 100 +costs = { input = 3.0, output = 15.0 } +x-portkey-provider = "@anthropic" + +[claude-4-sonnet] +model_id = "claude-sonnet-4-20250514" +max_tokens = 64000 +inputs = ["text", "images"] +outputs = ["text", "structured"] +max_prompt_images = 100 +costs = { input = 3.0, output = 15.0 } +x-portkey-provider = "@anthropic" + +[claude-4-opus] +model_id = "claude-opus-4-20250514" +max_tokens = 32000 +inputs = ["text", "images"] +outputs = ["text", "structured"] +max_prompt_images = 100 +costs = { input = 3.0, output = 15.0 } +x-portkey-provider = "@anthropic" + +["claude-4.1-opus"] +model_id = "claude-opus-4-1-20250805" +max_tokens = 32000 +inputs = ["text", "images"] +outputs = ["text", "structured"] +max_prompt_images = 100 +costs = { input = 3.0, output = 15.0 } +x-portkey-provider = "@anthropic" + +["claude-4.5-sonnet"] +model_id = "claude-sonnet-4-5-20250929" +max_tokens = 64000 +inputs = ["text", "images"] +outputs = ["text", "structured"] +max_prompt_images = 100 +costs = { input = 3.0, output = 15.0 } +x-portkey-provider = "@anthropic" + +["claude-4.5-haiku"] +model_id = "claude-haiku-4-5-20251001" +max_tokens = 64000 +inputs = ["text", "images"] +outputs = ["text", "structured"] +max_prompt_images = 100 +costs = { input = 1.0, output = 5.0 } +x-portkey-provider = "@anthropic" + +["claude-4.5-opus"] +model_id = "claude-opus-4-5-20251101" +max_tokens = 64000 +inputs = ["text", "images"] +outputs = ["text", "structured"] +max_prompt_images = 100 +costs = { input = 5.0, output = 25.0 } +x-portkey-provider = "@anthropic" + +# --- Gemini LLMs -------------------------------------------------------------- +["gemini-2.0-flash"] +model_id = "gemini-2.0-flash" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 0.10, output = 0.40 } +x-portkey-provider = "@google" + +["gemini-2.5-pro"] +model_id = "gemini-2.5-pro" +inputs = ["text", "images"] +outputs = ["text", "structured"] +max_prompt_images = 3000 +costs = { input = 1.25, output = 10.0 } +x-portkey-provider = "@google" + +["gemini-2.5-flash"] +model_id = "gemini-2.5-flash" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 0.30, output = 2.50 } +x-portkey-provider = "@google" + +["gemini-2.5-flash-lite"] +model_id = "gemini-2.5-flash-lite" +inputs = ["text", "images"] +outputs = ["text", "structured"] +costs = { input = 0.10, output = 0.40 } +x-portkey-provider = "@google" + +["gemini-3.0-pro"] +model_id = "gemini-3-pro-preview" +inputs = ["text", "images"] +outputs = ["text", "structured"] +max_prompt_images = 3000 +costs = { input = 2, output = 12.0 } +x-portkey-provider = "@google" diff --git a/.pipelex/inference/backends/scaleway.toml b/.pipelex/inference/backends/scaleway.toml new file mode 100644 index 0000000..20fe792 --- /dev/null +++ b/.pipelex/inference/backends/scaleway.toml @@ -0,0 +1,67 @@ +################################################################################ +# Groq Backend Configuration +################################################################################ +# +# This file defines the model specifications for Scaleway models. +# It contains model definitions for various LLM models accessible through +# the Groq API, including text-only and vision-capable models. +# +# Configuration structure: +# - Each model is defined in its own section with the model name as the header +# - Headers with dots or slashes must be quoted (e.g., ["meta-llama/llama-4-scout"]) +# - Model costs are in USD per million tokens (input/output) +# - Vision models support max 5 images per request, 33MP max resolution +# +# Documentation: https://docs.pipelex.com +# Support: https://go.pipelex.com/discord +# +################################################################################ + +################################################################################ +# MODEL DEFAULTS +################################################################################ + +[defaults] +model_type = "llm" +sdk = "openai" +structure_method = "instructor/json" + +# --- DeepSeek Models ---------------------------------------------------------- +[deepseek-r1-distill-llama-70b] +max_tokens = 32768 +inputs = ["text"] +outputs = ["text", "structured"] +costs = { input = 0.90, output = 0.90 } + +# --- Meta Llama 3.x Series ---------------------------------------------------- +["llama-3.1-8b-instruct"] +max_tokens = 131072 +inputs = ["text"] +outputs = ["text", "structured"] +costs = { input = 0.2, output = 0.2 } + +["llama-3.3-70b-instruct"] +max_tokens = 32768 +inputs = ["text"] +outputs = ["text", "structured"] +costs = { input = 0.90, output = 0.90 } + +# --- OpenAI GPT-OSS Models ---------------------------------------------------- +[gpt-oss-120b] +max_tokens = 65536 +inputs = ["text"] +outputs = ["text", "structured"] +costs = { input = 0.15, output = 0.60 } + +# --- Qwen 3 ------------------------------------------------------------------- +[qwen3-235b-a22b-instruct-2507] +max_tokens = 40960 +inputs = ["text"] +outputs = ["text", "structured"] +costs = { input = 0.75, output = 2.25 } + +[qwen3-coder-30b-a3b-instruct] +max_tokens = 40960 +inputs = ["text"] +outputs = ["text", "structured"] +costs = { input = 0.20, output = 0.80 } diff --git a/.pipelex/inference/backends/vertexai.toml b/.pipelex/inference/backends/vertexai.toml new file mode 100644 index 0000000..1ebab79 --- /dev/null +++ b/.pipelex/inference/backends/vertexai.toml @@ -0,0 +1,54 @@ +################################################################################ +# VertexAI Backend Configuration +################################################################################ +# +# This file defines the model specifications for Google VertexAI models. +# It contains model definitions for Gemini language models +# accessible through the Google VertexAI API. +# +# Configuration structure: +# - Each model is defined in its own section with the model name as the header +# - Headers with dots must be quoted (e.g., ["gemini-2.0-flash"]) +# - Model costs are in USD per million tokens (input/output) +# +# Documentation: https://docs.pipelex.com +# Support: https://go.pipelex.com/discord +# +################################################################################ + +################################################################################ +# MODEL DEFAULTS +################################################################################ + +[defaults] +model_type = "llm" +sdk = "openai" +prompting_target = "gemini" +structure_method = "instructor/vertexai_tools" + +################################################################################ +# LANGUAGE MODELS +################################################################################ + +# --- Gemini 2.0 Series -------------------------------------------------------- +["gemini-2.0-flash"] +model_id = "google/gemini-2.0-flash" +inputs = ["text", "images"] +outputs = ["text", "structured"] +max_prompt_images = 3000 +costs = { input = 0.1, output = 0.4 } + +# --- Gemini 2.5 Series -------------------------------------------------------- +["gemini-2.5-pro"] +model_id = "google/gemini-2.5-pro" +inputs = ["text", "images"] +outputs = ["text", "structured"] +max_prompt_images = 3000 +costs = { input = 1.25, output = 10.0 } + +["gemini-2.5-flash"] +model_id = "google/gemini-2.5-flash" +inputs = ["text", "images"] +outputs = ["text", "structured"] +max_prompt_images = 3000 +costs = { input = 0.30, output = 2.50 } diff --git a/.pipelex/inference/backends/xai.toml b/.pipelex/inference/backends/xai.toml new file mode 100644 index 0000000..3045344 --- /dev/null +++ b/.pipelex/inference/backends/xai.toml @@ -0,0 +1,56 @@ +################################################################################ +# XAI Backend Configuration +################################################################################ +# +# This file defines the model specifications for XAI (formerly Twitter AI) models. +# It contains model definitions for Grok language models +# accessible through the XAI API. +# +# Configuration structure: +# - Each model is defined in its own section with the model name as the header +# - Headers with dots must be quoted (e.g., ["grok-3"]) +# - Model costs are in USD per million tokens (input/output) +# +# Documentation: https://docs.pipelex.com +# Support: https://go.pipelex.com/discord +# +################################################################################ + +################################################################################ +# MODEL DEFAULTS +################################################################################ + +[defaults] +model_type = "llm" +sdk = "openai" +prompting_target = "anthropic" +structure_method = "instructor/openai_tools" + +################################################################################ +# LANGUAGE MODELS +################################################################################ + +# --- Grok 3 Series ------------------------------------------------------------ +[grok-3] +model_id = "grok-3" +inputs = ["text"] +outputs = ["text"] +costs = { input = 3, output = 15 } + +[grok-3-mini] +model_id = "grok-3-mini" +inputs = ["text"] +outputs = ["text"] +costs = { input = 0.3, output = 0.5 } + +[grok-3-fast] +model_id = "grok-3-fast-latest" +inputs = ["text"] +outputs = ["text"] +costs = { input = 5, output = 25 } + +[grok-3-mini-fast] +model_id = "grok-3-mini-fast-latest" +inputs = ["text"] +outputs = ["text"] +costs = { input = 0.15, output = 4 } diff --git a/.pipelex/inference/deck/base_deck.toml b/.pipelex/inference/deck/base_deck.toml new file mode 100644 index 0000000..63ab39e --- /dev/null +++ b/.pipelex/inference/deck/base_deck.toml @@ -0,0 +1,202 @@ +#################################################################################################### +# Pipelex Model Deck - Base Configuration +#################################################################################################### +# +# This file defines model aliases and presets for: +# - LLMs (language models for text generation and structured output) +# - Image generation models (for creating images from text prompts) +# - Document extraction models (OCR and text extraction from PDFs/images) +# +# Documentation: https://docs.pipelex.com +# Support: https://go.pipelex.com/discord +# +#################################################################################################### + +#################################################################################################### +# Aliases +#################################################################################################### + +[aliases] +base-claude = "claude-4.5-sonnet" +base-gpt = "gpt-4o" +base-gemini = "gemini-2.5-flash" +base-mistral = "mistral-medium" +base-groq = "llama-3.3-70b-versatile" +base-grok = "grok-4-fast-non-reasoning" + +best-gpt = "gpt-5.1" +best-claude = "claude-4.5-opus" +best-gemini = "gemini-3.0-pro" +best-mistral = "mistral-medium" + +# Groq-specific aliases +fast-groq = "llama-3.1-8b-instant" +vision-groq = "llama-4-scout-17b-16e-instruct" + +# Image generation aliases +base-img-gen = "flux-pro/v1.1" +best-img-gen = "flux-2" +fast-img-gen = "fast-lightning-sdxl" + +#################################################################################################### +# Waterfalls +#################################################################################################### + +[waterfalls] + +# --- Waterfalls for LLMs --------------------------------------------------------------------- +smart_llm = [ + "claude-4.5-opus", + "claude-4.5-sonnet", + "gemini-3.0-pro", + "gpt-5.1", + "claude-4.1-opus", + "gemini-2.5-pro", + "claude-4-sonnet", + "grok-4-fast-non-reasoning", +] +smart_llm_with_vision = [ + "claude-4.5-opus", + "claude-4.5-sonnet", + "gemini-3.0-pro", + "gpt-5.1", + "claude-4.1-opus", + "gemini-2.5-pro", + "claude-4-sonnet", + "grok-4-fast-non-reasoning", +] +smart_llm_for_structured = [ + "claude-4.5-opus", + "claude-4.5-sonnet", + "gemini-3.0-pro", + "gpt-5.1", + "claude-4.1-opus", + "claude-4-sonnet", +] +llm_for_creativity = [ + "claude-4.5-opus", + "claude-4.1-opus", + "gemini-2.5-pro", + "gpt-5.1", +] +llm_for_large_codebase = [ + "gemini-2.5-pro", + "claude-4.5-sonnet", + "gemini-3.0-pro", + "gpt-5.1", + "gemini-2.5-flash", + "grok-4-fast-non-reasoning", +] +cheap_llm = [ + "gpt-4o-mini", + "gemini-2.5-flash-lite", + "mistral-small", + "claude-3-haiku", + "grok-3-mini", +] +cheap_llm_for_vision = [ + "gemini-2.5-flash-lite", + "gpt-4o-mini", + "claude-3-haiku", +] +cheap_llm_for_structured = ["gpt-4o-mini", "mistral-small", "claude-3-haiku"] +cheap_llm_for_creativity = [ + "gemini-2.5-flash", + "grok-3-mini", + "gpt-4o-mini", + "claude-4.5-haiku", +] + +# --- Waterfalls for Extracts --------------------------------------------------------------------- +pdf_text_extractor = [ + "azure-document-intelligence", + "mistral-ocr", + "pypdfium2-extract-pdf", +] +image_text_extractor = ["mistral-ocr"] + +#################################################################################################### +# LLM Presets +#################################################################################################### + +[llm.presets] + +# LLM Presets — Specific skills ------------------------------------------------------------- + +# Generation skills +llm_for_factual_writing = { model = "base-gpt", temperature = 0.1 } +llm_for_creative_writing = { model = "base-gpt", temperature = 0.9 } +llm_for_writing_cheap = { model = "gpt-4o-mini", temperature = 0.3 } + +# Retrieve and answer questions skills +llm_to_answer_questions_cheap = { model = "gpt-4o-mini", temperature = 0.3 } +llm_to_answer_questions = { model = "base-claude", temperature = 0.3 } +llm_to_retrieve = { model = "base-claude", temperature = 0.1 } + +# Engineering skills +llm_to_engineer = { model = "smart_llm_for_structured", temperature = 0.2 } +llm_to_code = { model = "base-claude", temperature = 0.1 } +llm_to_analyze_large_codebase = { model = "base-claude", temperature = 0.1 } + +# Vision skills +llm_for_img_to_text_cheap = { model = "gpt-4o-mini", temperature = 0.1 } +llm_for_img_to_text = { model = "base-claude", temperature = 0.1 } +llm_for_diagram_to_text = { model = "best-claude", temperature = 0.3 } +llm_for_table_to_text = { model = "base-claude", temperature = 0.3 } + +# Image generation prompting skills +llm_to_prompt_img_gen = { model = "base-claude", temperature = 0.2 } +llm_to_prompt_img_gen_cheap = { model = "gpt-4o-mini", temperature = 0.5 } + +# Groq-specific presets (fast inference, low cost) +llm_groq_fast_text = { model = "fast-groq", temperature = 0.7 } +llm_groq_balanced = { model = "base-groq", temperature = 0.5 } +llm_groq_vision = { model = "vision-groq", temperature = 0.3 } + +# LLM Presets — For Testing --------------------------------------------------------------------- + +llm_for_testing_gen_text = { model = "cheap_llm", temperature = 0.5 } +llm_for_testing_gen_object = { model = "cheap_llm_for_structured", temperature = 0.1 } +llm_for_testing_vision = { model = "cheap_llm_for_vision", temperature = 0.5 } +llm_for_testing_vision_structured = { model = "cheap_llm_for_vision", temperature = 0.5 } + +#################################################################################################### +# LLM Choices +#################################################################################################### + +[llm.choice_defaults] +for_text = "cheap_llm" +for_object = "cheap_llm_for_structured" + +#################################################################################################### +# Extract Presets +#################################################################################################### + +[extract] +choice_default = "extract_ocr_from_document" + +[extract.presets] +extract_ocr_from_document = { model = "azure-document-intelligence", max_nb_images = 100, image_min_size = 50 } +extract_basic_from_pdf = { model = "pypdfium2-extract-pdf", max_nb_images = 100, image_min_size = 50 } + +#################################################################################################### +# Image Generation Presets +#################################################################################################### + +[img_gen] +choice_default = "gen_image_basic" + +[img_gen.presets] + +# General purpose +gen_image_basic = { model = "base-img-gen", quality = "medium", guidance_scale = 7.5, is_moderated = true, safety_tolerance = 3 } +gen_image_fast = { model = "fast-img-gen", nb_steps = 4, guidance_scale = 5.0, is_moderated = true, safety_tolerance = 3 } +gen_image_high_quality = { model = "best-img-gen", quality = "high", guidance_scale = 8.0, is_moderated = true, safety_tolerance = 3 } +gen_image_openai_low_quality = { model = "gpt-image-1", quality = "low" } + +# Specific skills +img_gen_for_art = { model = "best-img-gen", quality = "high", guidance_scale = 9.0, is_moderated = false, safety_tolerance = 5 } +img_gen_for_diagram = { model = "base-img-gen", quality = "medium", guidance_scale = 7.0, is_moderated = true, safety_tolerance = 2 } +img_gen_for_mockup = { model = "base-img-gen", quality = "medium", guidance_scale = 6.5, is_moderated = true, safety_tolerance = 3 } +img_gen_for_product = { model = "best-img-gen", quality = "high", guidance_scale = 8.5, is_moderated = true, safety_tolerance = 2 } +img_gen_for_testing = { model = "fast-img-gen", nb_steps = 4, guidance_scale = 4.0, is_moderated = true, safety_tolerance = 4 } diff --git a/.pipelex/inference/deck/overrides.toml b/.pipelex/inference/deck/overrides.toml new file mode 100644 index 0000000..08814db --- /dev/null +++ b/.pipelex/inference/deck/overrides.toml @@ -0,0 +1,19 @@ +#################################################################################################### +# Pipelex Model Deck - Overrides +#################################################################################################### +# +# This file allows you to override the default model choices defined in base_deck.toml. +# You can customize presets for LLMs, image generation, and document extraction models. +# +# Documentation: https://docs.pipelex.com +# Support: https://go.pipelex.com/discord +# +#################################################################################################### + +#################################################################################################### +# LLM Deck overrides +#################################################################################################### + +[llm.choice_overrides] +for_text = "disabled" +for_object = "disabled" diff --git a/.pipelex/inference/routing_profiles.toml b/.pipelex/inference/routing_profiles.toml new file mode 100644 index 0000000..bf40281 --- /dev/null +++ b/.pipelex/inference/routing_profiles.toml @@ -0,0 +1,173 @@ +# Routing profile library - Routes models to their backends +# ========================================================================================= +# This file controls which backend serves which model. +# Simply change the 'active' field to switch profiles, +# or you can add your own custom profiles. +# +# Documentation: https://docs.pipelex.com +# Support: https://go.pipelex.com/discord +# ========================================================================================= + +# Which profile to use (change this to switch routing) +active = "pipelex_gateway_first" + +# We recommend using the "pipelex_gateway_first" profile to get a head start with all models. +# To use the Pipelex Gateway backend: +# 1. Join our Discord community to get your free API key (no credit card required): +# Visit https://go.pipelex.com/discord and request your key in the appropriate channel +# 2. Set the environment variable (or add it to your .env file): +# - Linux/macOS: export PIPELEX_GATEWAY_API_KEY="your-api-key" +# - Windows CMD: set PIPELEX_GATEWAY_API_KEY=your-api-key +# - Windows PowerShell: $env:PIPELEX_GATEWAY_API_KEY="your-api-key" +# 3. The .pipelex/inference/backends.toml is already configured with api_key = "${PIPELEX_GATEWAY_API_KEY}" +# which will get the key from the environment variable. + +# ========================================================================================= +# Routing Profiles +# ========================================================================================= + +[profiles.pipelex_gateway_first] +description = "Use Pipelex Gateway backend for all its supported models" +default = "pipelex_gateway" +fallback_order = [ + "pipelex_gateway", + "azure_openai", + "bedrock", + "google", + "blackboxai", + "mistral", + "fal", +] + +[profiles.pipelex_gateway_first.routes] +# Pattern matching: "model-pattern" = "backend-name" + +[profiles.pipelex_gateway_first.optional_routes] # Each optional route is considered only if its backend is available +"gpt-*" = "pipelex_gateway" +"gpt-image-1" = "openai" +"claude-*" = "pipelex_gateway" +"grok-*" = "pipelex_gateway" +"gemini-*" = "pipelex_gateway" +"*-sdxl" = "fal" +"flux-*" = "fal" +"mistral-ocr" = "mistral" + +[profiles.all_pipelex_gateway] +description = "Use Pipelex Gateway for all its supported models" +default = "pipelex_gateway" + +[profiles.all_anthropic] +description = "Use Anthropic backend for all its supported models" +default = "anthropic" + +[profiles.all_azure_openai] +description = "Use Azure OpenAI backend for all its supported models" +default = "azure_openai" + +[profiles.all_bedrock] +description = "Use Bedrock backend for all its supported models" +default = "bedrock" + +[profiles.all_blackboxai] +description = "Use BlackBoxAI backend for all its supported models" +default = "blackboxai" + +[profiles.all_fal] +description = "Use FAL backend for all its supported models" +default = "fal" + +[profiles.all_google] +description = "Use Google GenAI backend for all its supported models" +default = "google" + +[profiles.all_groq] +description = "Use groq backend for all its supported models" +default = "groq" + +[profiles.all_huggingface] +description = "Use HuggingFace backend for all its supported models" +default = "huggingface" + +[profiles.all_mistral] +description = "Use Mistral backend for all its supported models" +default = "mistral" + +[profiles.all_ollama] +description = "Use Ollama backend for all its supported models" +default = "ollama" + +[profiles.all_openai] +description = "Use OpenAI backend for all its supported models" +default = "openai" + +[profiles.all_portkey] +description = "Use Portkey backend for all its supported models" +default = "portkey" + +[profiles.all_scaleway] +description = "Use Scaleway backend for all its supported models" +default = "scaleway" + +[profiles.all_xai] +description = "Use xAI backend for all its supported models" +default = "xai" + +[profiles.all_internal] +description = "Use internal backend for all its supported models" +default = "internal" + +# ========================================================================================= +# Custom Profiles +# ========================================================================================= +# Add your own profiles below following the same pattern: +# +# [profiles.your_profile_name] +# description = "What this profile does" +# default = "backend-name" # Where to route models by default +# [profiles.your_profile_name.routes] +# "model-pattern" = "backend-name" # Specific routing rules +# +# Pattern matching supports: +# - Exact names: "gpt-4o-mini" +# - Wildcards: "claude-*" (matches all models starting with claude-) +# - Partial wildcards: "*-sonnet" (matches all sonnet variants) + +# ========================================================================================= +# Example of a custom routing profile with mostly pattern matching and one specific model +# ========================================================================================= +[profiles.example_routing_using_patterns] +description = "Example routing profile using patterns" +default = "pipelex_gateway" + +[profiles.example_routing_using_patterns.routes] +# Pattern matching: "model-pattern" = "backend-name" +"gpt-*" = "azure_openai" +"claude-*" = "bedrock" +"gemini-*" = "google" +"grok-*" = "xai" +"*-sdxl" = "fal" +"flux-*" = "fal" +"gpt-image-1" = "openai" + +# ========================================================================================= +# Example of a custom routing profile with specific model matching +# ========================================================================================= + +[profiles.example_routing_using_specific_models] +description = "Example routing profile using specific models" + +[profiles.example_routing_using_specific_models.routes] +"gpt-5-nano" = "pipelex_gateway" +"gpt-4o-mini" = "blackboxai" +"gpt-5-mini" = "openai" +"gpt-5-chat" = "azure_openai" + +"claude-4-sonnet" = "pipelex_gateway" +"claude-3.7-sonnet" = "blackboxai" + +"gemini-2.5-flash-lite" = "pipelex_gateway" +"gemini-2.5-flash" = "blackboxai" +"gemini-2.5-pro" = "vertexai" + +"grok-3" = "pipelex_gateway" +"grok-3-mini" = "xai" diff --git a/.pipelex/pipelex.toml b/.pipelex/pipelex.toml new file mode 100644 index 0000000..4a2ea38 --- /dev/null +++ b/.pipelex/pipelex.toml @@ -0,0 +1,162 @@ +#################################################################################################### +# Pipelex Configuration File +#################################################################################################### +# +# This configuration file is copied to client projects' .pipelex/ directory when running: +# `pipelex init config` +# +# Purpose: +# - This file allows to override Pipelex's default settings for specific projects +# - Feel free to modify any settings below to suit your needs +# - You can add any configuration sections that exist in the main pipelex.toml +# +# Finding Available Settings: +# - See the full default configuration in: pipelex/pipelex.toml (in the Pipelex package) +# - See the configuration structure classes in: pipelex/config.py and pipelex/cogt/config_cogt.py +# +# Common customizations are proposed below, such as: +# - Logging levels and behavior +# - Excluded directories for scanning +# - LLM prompt dumping for debugging +# - Feature flags for tracking and reporting +# - Observer and reporting output directories +# +# Documentation: https://docs.pipelex.com +# Support: https://go.pipelex.com/discord +# +#################################################################################################### + +[pipelex.pipeline_execution_config] +# Uncomment to disable conversion of data URLs to pipelex-storage:// URIs +# is_normalize_data_urls_to_storage = false + +[pipelex.pipeline_execution_config.graph_config.data_inclusion] +# Uncomment to include stuff data in graph outputs: +stuff_json_content = true +stuff_text_content = true +stuff_html_content = true +error_stack_traces = true + +[pipelex.pipeline_execution_config.graph_config.graphs_inclusion] +# Uncomment to customize which graph outputs are generated (all enabled by default): +# graphspec_json = false +# mermaidflow_mmd = false +# mermaidflow_html = false +# reactflow_viewspec = false +# reactflow_html = false + +[pipelex.pipeline_execution_config.graph_config.reactflow_config] +# Uncomment to customize ReactFlow graph rendering: +# edge_type = "bezier" # Options: "bezier", "smoothstep", "step", "straight" +# nodesep = 50 # Horizontal spacing between nodes +# ranksep = 80 # Vertical spacing between ranks/levels +# initial_zoom = 1.0 # Initial zoom level (1.0 = 100%) +# pan_to_top = true # Pan to show top of graph on load + +[pipelex.storage_config] +# Storage method: "local" (default), "in_memory", "s3", or "gcp" +# method = "local" + +# Whether to fetch remote HTTP URLs and store them locally +# is_fetch_remote_content_enabled = true + +# --- Local Storage Configuration --- +# Uncomment to customize local storage settings: +[pipelex.storage_config.local] +# uri_format = "{primary_id}/{secondary_id}/{hash}.{extension}" +# local_storage_path = ".pipelex/storage" + +# --- AWS S3 Storage Configuration --- +# Uncomment to use S3 storage (requires boto3: `pip install pipelex[s3]`): +[pipelex.storage_config.s3] +# uri_format = "{primary_id}/{secondary_id}/{hash}.{extension}" +# bucket_name = "your-bucket-name" +# region = "us-east-1" +# signed_urls_lifespan_seconds = 3600 # Set to "disabled" for public URLs + +# --- Google Cloud Storage Configuration --- +# Uncomment to use GCP storage (requires google-cloud-storage: `pip install pipelex[gcp-storage]`): +[pipelex.storage_config.gcp] +# uri_format = "{primary_id}/{secondary_id}/{hash}.{extension}" +# bucket_name = "your-bucket-name" +# project_id = "your-project-id" +# signed_urls_lifespan_seconds = 3600 # Set to "disabled" for public URLs + +[pipelex.scan_config] +# Uncomment to customize the excluded directories for scanning +# excluded_dirs = [ +# ".venv", +# "venv", +# "env", +# ".env", +# "virtualenv", +# ".virtualenv", +# ".git", +# "__pycache__", +# ".pytest_cache", +# ".mypy_cache", +# ".ruff_cache", +# "node_modules", +# "results", +# ] + +[pipelex.builder_config] +# Uncomment to change where the generated pipelines are saved: +# default_output_dir = "." +# default_bundle_file_name = "bundle" +# default_directory_base_name = "pipeline" + +[pipelex.log_config] +# Uncomment to change the default log level: +# default_log_level = "INFO" + +# Uncomment to log to stderr instead of stdout +# console_log_target = "stderr" +# console_print_target = "stderr" + +[pipelex.log_config.package_log_levels] +# Uncomment to change the log level for specific packages: +# pipelex = "INFO" + +[pipelex.observer_config] +# Uncomment to change the directory where the observer will save its results: +# observer_dir = "results/observer" + +[pipelex.feature_config] +# WIP/Experimental feature flags: +# is_pipeline_tracking_enabled = false +# is_reporting_enabled = true + +[pipelex.reporting_config] +# Uncomment to customize the reporting configuration: +# is_log_costs_to_console = false +# is_generate_cost_report_file_enabled = false +# cost_report_dir_path = "reports" +# cost_report_base_name = "cost_report" +# cost_report_extension = "csv" +# cost_report_unit_scale = 1.0 + +[cogt] +[cogt.model_deck_config] +# Uncomment to disable model fallback: it will raise errors instead of using secondary model options: +# is_model_fallback_enabled = false +# Uncomment to change the reaction to missing presets: "raise" (default), "log" or "none" +# missing_presets_reaction = "raise" + +[cogt.tenacity_config] +# Uncomment to change those values as needed: +# max_retries = 50 # Maximum number of retry attempts before giving up +# wait_multiplier = 0.2 # Multiplier applied to the wait time between retries (in seconds) +# wait_max = 20 # Maximum wait time between retries (in seconds) +# wait_exp_base = 1.3 # Base for exponential backoff calculation + +[cogt.llm_config] +# Uncomment any of these to enable dumping the inputs or outputs of text-generation with an LLM: +# is_dump_text_prompts_enabled = true +# is_dump_response_text_enabled = true + +[cogt.llm_config.instructor_config] +# Uncomment any of these to enable dumping the kwargs, response or errors when generating structured content: +# is_dump_kwargs_enabled = true +# is_dump_response_enabled = true +# is_dump_error_enabled = true diff --git a/.pipelex/pipelex_service.toml b/.pipelex/pipelex_service.toml new file mode 100644 index 0000000..afe39a2 --- /dev/null +++ b/.pipelex/pipelex_service.toml @@ -0,0 +1,19 @@ +#################################################################################################### +# Pipelex Service Configuration +#################################################################################################### +# +# This file stores settings related to Pipelex managed services. +# Currently used for Pipelex Gateway terms acceptance. +# +# Documentation: https://docs.pipelex.com +# Support: https://go.pipelex.com/discord +# +#################################################################################################### + +[agreement] +# Set to true after accepting Pipelex terms of service. +terms_accepted = true + +# Note: when using pipelex_gateway, telemetry is enabled to monitor service usage. +# We collect technical data (model, pipe type...) and quantitative data (token counts...) +# but NOT your content, pipe codes, or output class names. diff --git a/.pipelex/telemetry.toml b/.pipelex/telemetry.toml new file mode 100644 index 0000000..eb2c537 --- /dev/null +++ b/.pipelex/telemetry.toml @@ -0,0 +1,92 @@ +#################################################################################################### +# Custom Telemetry Configuration +#################################################################################################### +# +# This file controls YOUR custom telemetry settings for observability and analytics. +# Configure your own PostHog, Langfuse, or OTLP-compatible backends here. +# +# NOTE: When using Pipelex Gateway, identified telemetry is automatically enabled +# (tied to your Gateway API key, hashed for security). This allows us to monitor +# service quality, enforce fair usage, and provide you with better support. +# Gateway telemetry operates independently from your settings below - you can have both! +# +# To disable all telemetry, set the DO_NOT_TRACK=1 environment variable. +# +# Documentation: https://docs.pipelex.com +# Support: https://go.pipelex.com/discord +# +#################################################################################################### + +# ────────────────────────────────────────────────────────────────────────────── +# PostHog Configuration (Event tracking + AI span tracing) +# ────────────────────────────────────────────────────────────────────────────── + +[custom_posthog] +mode = "off" # Values: "off" | "anonymous" | "identified" +# user_id = "your_user_id" # Required when mode = "identified" +endpoint = "${POSTHOG_ENDPOINT}" # Default: https://us.i.posthog.com (or https://eu.i.posthog.com for EU) +api_key = "${POSTHOG_API_KEY}" # Get from PostHog Project Settings +geoip = true # Enable GeoIP lookup +debug = false # Enable PostHog debug mode +redact_properties = [ + "prompt", + "system_prompt", + "response", + "file_path", + "url", +] # Event properties to redact + +# AI span tracing to YOUR PostHog (does NOT affect Langfuse/OTLP - they receive full data) +[custom_posthog.tracing] +enabled = false # Send AI spans to your PostHog + +# Privacy controls for data sent to YOUR PostHog only +[custom_posthog.tracing.capture] +content = false # Capture prompt/completion content +# content_max_length = 1000 # Max length for captured content (omit for unlimited) +pipe_codes = false # Include pipe codes in span names/attributes +output_class_names = false # Include output class names in span names/attributes + +# ────────────────────────────────────────────────────────────────────────────── +# Portkey SDK Configuration +# ────────────────────────────────────────────────────────────────────────────── + +[custom_portkey] +force_debug_enabled = false +force_tracing_enabled = false + +# ────────────────────────────────────────────────────────────────────────────── +# Langfuse Integration +# Note: Langfuse receives FULL span data (no redaction) +# ────────────────────────────────────────────────────────────────────────────── + +[langfuse] +enabled = false +# endpoint = "https://cloud.langfuse.com" # Override for self-hosted Langfuse +# public_key = "${LANGFUSE_PUBLIC_KEY}" # Langfuse public key +# secret_key = "${LANGFUSE_SECRET_KEY}" # Langfuse secret key + +# ────────────────────────────────────────────────────────────────────────────── +# Additional OTLP Exporters (array for multiple) +# Note: OTLP exporters receive FULL span data (no redaction) +# ────────────────────────────────────────────────────────────────────────────── + +# [[otlp]] +# name = "my-collector" # Identifier for logging +# endpoint = "https://..." # OTLP endpoint URL +# headers = { Authorization = "Bearer ${OTLP_AUTH_TOKEN}" } # Headers for OTLP export + +# ────────────────────────────────────────────────────────────────────────────── +# Custom Telemetry Allowed Modes +# Controls which integration modes can use custom telemetry settings above. +# ────────────────────────────────────────────────────────────────────────────── + +[telemetry_allowed_modes] +ci = false # CI environments don't use custom telemetry +cli = true # CLI usage allows custom telemetry +docker = true # Docker deployments allow custom telemetry +fastapi = true # FastAPI integrations allow custom telemetry +mcp = true # MCP integrations allow custom telemetry +n8n = true # n8n integrations allow custom telemetry +pytest = false # Tests don't use custom telemetry +python = false # Direct Python SDK usage doesn't use custom telemetry by default diff --git a/Makefile b/Makefile index 803118d..b3f7eb7 100644 --- a/Makefile +++ b/Makefile @@ -6,7 +6,7 @@ VIRTUAL_ENV := $(CURDIR)/.venv PROJECT_NAME := $(shell grep '^name = ' pyproject.toml | sed -E 's/name = "(.*)"/\1/') # The "?" is used to make the variable optional, so that it can be overridden by the user. -PYTHON_VERSION ?= 3.11 +PYTHON_VERSION ?= 3.13 VENV_PYTHON := $(VIRTUAL_ENV)/bin/python VENV_PYTEST := $(VIRTUAL_ENV)/bin/pytest VENV_RUFF := $(VIRTUAL_ENV)/bin/ruff @@ -16,7 +16,7 @@ VENV_PIPELEX := $(VIRTUAL_ENV)/bin/pipelex UV_MIN_VERSION = $(shell grep -m1 'required-version' pyproject.toml | sed -E 's/.*= *"([^<>=, ]+).*/\1/') -USUAL_PYTEST_MARKERS := "(dry_runnable or not (inference or llm or imgg or ocr)) and not (needs_output or pipelex_api)" +USUAL_PYTEST_MARKERS := "(dry_runnable or not inference) and not (needs_output or pipelex_api)" define PRINT_TITLE $(eval PROJECT_PART := [$(PROJECT_NAME)]) @@ -117,7 +117,7 @@ env: check-uv $(call PRINT_TITLE,"Creating virtual environment") @if [ ! -d $(VIRTUAL_ENV) ]; then \ echo "Creating Python virtual env in \`${VIRTUAL_ENV}\`"; \ - uv venv $(VIRTUAL_ENV) --python 3.11; \ + uv venv $(VIRTUAL_ENV) --python $(PYTHON_VERSION); \ else \ echo "Python virtual env already exists in \`${VIRTUAL_ENV}\`"; \ fi diff --git a/crazy/__init__.py b/crazy/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/crazy/bundle.plx b/crazy/bundle.plx new file mode 100644 index 0000000..af706cd --- /dev/null +++ b/crazy/bundle.plx @@ -0,0 +1,42 @@ +domain = "crazy_image_generation" +description = "Imagining and rendering absurd, funny images with unexpected surreal elements" +main_pipe = "generate_crazy_image" + +[concept.ImagePrompt] +description = """ +A detailed textual description of a scene to be used as input for an image generation model, including subjects, setting, style, and visual details. +""" +refines = "Text" + +[pipe.generate_crazy_image] +type = "PipeSequence" +description = """ +Main pipeline that orchestrates the full crazy image generation flow - imagines a wild, absurd scene concept and renders it as an image +""" +output = "Image" +steps = [ + { pipe = "imagine_scene", result = "image_prompt" }, + { pipe = "render_image", result = "crazy_image" }, +] + +[pipe.imagine_scene] +type = "PipeLLM" +description = """ +Generates a creative, absurd, and hilarious image concept combining unexpected elements in surreal ways - think flying spaghetti monsters, penguins in business suits at a disco, or a T-Rex doing yoga on the moon +""" +output = "ImagePrompt" +model = "cheap_llm_for_creativity" +system_prompt = """ +You are a wildly creative visual concept artist specializing in absurd, surreal, and hilarious imagery. Your task is to generate a structured image prompt that combines unexpected elements in surprising and funny ways. Think outside the box - the more unexpected the combination, the better! +""" +prompt = """ +Generate a creative, absurd, and funny image concept. Combine unexpected elements in a surreal way that would make viewers laugh or do a double-take. Be VERY concise and focus on vivid, specific visual details that work well for image generation. +""" + +[pipe.render_image] +type = "PipeImgGen" +description = "Generates the absurd image based on the creative scene description" +inputs = { image_prompt = "ImagePrompt" } +output = "Image" +prompt = "$image_prompt" +model = "gen_image_high_quality" diff --git a/crazy/bundle_view.html b/crazy/bundle_view.html new file mode 100644 index 0000000..0e294d7 --- /dev/null +++ b/crazy/bundle_view.html @@ -0,0 +1,111 @@ + + + + + + + + 
Domain: crazy_image_generation
+
+Description: Imagining and rendering absurd, funny images with unexpected surreal elements
+
+Main Pipe: generate_crazy_image
+
+
+
+                                              Concepts                                              
+┌──────────────────────────────────────────────────────────────────────────────────────────────────┐
+│ Concept: ImagePrompt                                                                             │
+│ Refines: Text                                                                                    │
+│                                                                                                  │
+│ Description: A detailed textual description of a scene to be used as input for an image          │
+│ generation model, including subjects, setting, style, and visual details.                        │
+│                                                                                                  │
+└──────────────────────────────────────────────────────────────────────────────────────────────────┘
+
+
+                                               Pipes                                                
+┌──────────────────────────────────────────────────────────────────────────────────────────────────┐
+│ Pipe: generate_crazy_image                                                                       │
+│                                                                                                  │
+│ Type: PipeSequence (PipeController)                                                              │
+│                                                                                                  │
+│ Description: Main pipeline that orchestrates the full crazy image generation flow - imagines a   │
+│ wild, absurd scene concept and renders it as an image                                            │
+│                                                                                                  │
+│                                                                                                  │
+│ No inputs                                                                                        │
+│                                                                                                  │
+│ Output: Image                                                                                    │
+│                                                                                                  │
+│ Sequence Steps:                                                                                  │
+│ ┏━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┓                                                          │
+│ ┃ Step ┃ Pipe          ┃ Result name  ┃                                                          │
+│ ┡━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━┩                                                          │
+│ │    1 │ imagine_scene │ image_prompt │                                                          │
+│ ├──────┼───────────────┼──────────────┤                                                          │
+│ │    2 │ render_image  │ crazy_image  │                                                          │
+│ └──────┴───────────────┴──────────────┘                                                          │
+├──────────────────────────────────────────────────────────────────────────────────────────────────┤
+│ Pipe: imagine_scene                                                                              │
+│                                                                                                  │
+│ Type: PipeLLM (PipeOperator)                                                                     │
+│                                                                                                  │
+│ Description: Generates a creative, absurd, and hilarious image concept combining unexpected      │
+│ elements in surreal ways - think flying spaghetti monsters, penguins in business suits at a      │
+│ disco, or a T-Rex doing yoga on the moon                                                         │
+│                                                                                                  │
+│                                                                                                  │
+│ No inputs                                                                                        │
+│                                                                                                  │
+│ Output: ImagePrompt                                                                              │
+│                                                                                                  │
+│ LLM Skill: cheap_llm_for_creativity                                                              │
+│                                                                                                  │
+│ ╭─ System Prompt ──────────────────────────────────────────────────────────────────────────────╮ │
+│ │ You are a wildly creative visual concept artist specializing in absurd, surreal, and         │ │
+│ │ hilarious imagery. Your task is to generate a structured image prompt that combines          │ │
+│ │ unexpected elements in surprising and funny ways. Think outside the box - the more           │ │
+│ │ unexpected the combination, the better!                                                      │ │
+│ ╰──────────────────────────────────────────────────────────────────────────────────────────────╯ │
+│                                                                                                  │
+│ ╭─ Prompt ─────────────────────────────────────────────────────────────────────────────────────╮ │
+│ │ Generate a creative, absurd, and funny image concept. Combine unexpected elements in a       │ │
+│ │ surreal way that would make viewers laugh or do a double-take. Be VERY concise and focus on  │ │
+│ │ vivid, specific visual details that work well for image generation.                          │ │
+│ ╰──────────────────────────────────────────────────────────────────────────────────────────────╯ │
+├──────────────────────────────────────────────────────────────────────────────────────────────────┤
+│ Pipe: render_image                                                                               │
+│                                                                                                  │
+│ Type: PipeImgGen (PipeOperator)                                                                  │
+│                                                                                                  │
+│ Description: Generates the absurd image based on the creative scene description                  │
+│                                                                                                  │
+│                                                                                                  │
+│ Input: image_prompt (ImagePrompt)                                                                │
+│                                                                                                  │
+│ Output: Image                                                                                    │
+│                                                                                                  │
+│ Image Generation Skill: gen_image_high_quality                                                   │
+└──────────────────────────────────────────────────────────────────────────────────────────────────┘
+
+ + diff --git a/crazy/bundle_view.svg b/crazy/bundle_view.svg new file mode 100644 index 0000000..a7dbf52 --- /dev/null +++ b/crazy/bundle_view.svg @@ -0,0 +1,397 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Rich + + + + + + + + + + Domain: crazy_image_generation + +Description: Imagining and rendering absurd, funny images with unexpected surreal elements + +Main Pipe: generate_crazy_image + + + +                                              Concepts                                               +┌──────────────────────────────────────────────────────────────────────────────────────────────────┐ +│Concept: ImagePrompt│ +│Refines: Text│ +││ +│Description: A detailed textual description of a scene to be used as input for an image │ +│generation model, including subjects, setting, style, and visual details.│ +││ +└──────────────────────────────────────────────────────────────────────────────────────────────────┘ + + +                                               Pipes                                                 +┌──────────────────────────────────────────────────────────────────────────────────────────────────┐ +│Pipe: generate_crazy_image│ +││ +│Type: PipeSequence (PipeController)                                                             │ +││ +│Description: Main pipeline that orchestrates the full crazy image generation flow - imagines a │ +│wild, absurd scene concept and renders it as an image│ +││ +││ +│No inputs                                                                                       │ +││ +│Output: Image│ +││ +│Sequence Steps:                        │ +│┏━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┓│ +│┃Step┃Pipe         ┃Result name ┃│ +│┡━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━┩│ +││   1│imagine_scene│image_prompt││ +│├──────┼───────────────┼──────────────┤│ +││   2│render_image │crazy_image ││ +│└──────┴───────────────┴──────────────┘│ +├──────────────────────────────────────────────────────────────────────────────────────────────────┤ +│Pipe: imagine_scene│ +││ +│Type: PipeLLM (PipeOperator)                                                                    │ +││ +│Description: Generates a creative, absurd, and hilarious image concept combining unexpected │ +│elements in surreal ways - think flying spaghetti monsters, penguins in business suits at a │ +│disco, or a T-Rex doing yoga on the moon│ +││ +││ +│No inputs                                                                                       │ +││ +│Output: ImagePrompt│ +││ +│LLM Skill: cheap_llm_for_creativity│ +││ +│╭─ System Prompt ──────────────────────────────────────────────────────────────────────────────╮│ +││You are a wildly creative visual concept artist specializing in absurd, surreal, and        ││ +││hilarious imagery. Your task is to generate a structured image prompt that combines         ││ +││unexpected elements in surprising and funny ways. Think outside the box - the more          ││ +││unexpected the combination, the better!                                                     ││ +│╰──────────────────────────────────────────────────────────────────────────────────────────────╯│ +││ +│╭─ Prompt ─────────────────────────────────────────────────────────────────────────────────────╮│ +││Generate a creative, absurd, and funny image concept. Combine unexpected elements in a      ││ +││surreal way that would make viewers laugh or do a double-take. Be VERY concise and focus on ││ +││vivid, specific visual details that work well for image generation.                         ││ +│╰──────────────────────────────────────────────────────────────────────────────────────────────╯│ +├──────────────────────────────────────────────────────────────────────────────────────────────────┤ +│Pipe: render_image│ +││ +│Type: PipeImgGen (PipeOperator)                                                                 │ +││ +│Description: Generates the absurd image based on the creative scene description│ +││ +││ +│Input: image_prompt (ImagePrompt)                                                               │ +││ +│Output: Image│ +││ +│Image Generation Skill: gen_image_high_quality│ +└──────────────────────────────────────────────────────────────────────────────────────────────────┘ + + + + diff --git a/crazy/inputs.json b/crazy/inputs.json new file mode 100644 index 0000000..9e26dfe --- /dev/null +++ b/crazy/inputs.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/crazy/run_generate_crazy_image.py b/crazy/run_generate_crazy_image.py new file mode 100644 index 0000000..2d0f24d --- /dev/null +++ b/crazy/run_generate_crazy_image.py @@ -0,0 +1,19 @@ +import asyncio + +from pipelex.core.stuffs.image_content import ImageContent +from pipelex.pipelex import Pipelex +from pipelex.pipeline.execute import execute_pipeline + + +async def run_generate_crazy_image() -> ImageContent: + pipe_output = await execute_pipeline( + pipe_code="generate_crazy_image", + ) + return pipe_output.main_stuff_as(content_type=ImageContent) + + +if __name__ == "__main__": + # Initialize Pipelex + with Pipelex.make(): + # Run the pipeline + result = asyncio.run(run_generate_crazy_image()) diff --git a/crazy/structures/__init__.py b/crazy/structures/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/crazy/structures/crazy_image_generation_ImagePrompt.py b/crazy/structures/crazy_image_generation_ImagePrompt.py new file mode 100644 index 0000000..46b45ae --- /dev/null +++ b/crazy/structures/crazy_image_generation_ImagePrompt.py @@ -0,0 +1,22 @@ +""" +AUTOGENERATED CODE - DO NOT EDIT + +If you want to customize this structure: + 1. Copy this file to your own module + 2. Remove the 'structure' or 'refines' declaration from the concept in the PLX file + and declare it in inline mode (see https://docs.pipelex.com/home/6-build-reliable-ai-workflows/concepts/define_your_concepts/#basic-concept-definition) + 3. Make sure your custom class is importable and registered + +To regenerate: pipelex build structures +""" + +from enum import Enum +from typing import Any, Dict, List, Literal, Optional + +from pipelex.core.stuffs.structured_content import StructuredContent +from pipelex.core.stuffs.text_content import TextContent +from pydantic import Field + + +class ImagePrompt(TextContent): + """Generated ImagePrompt class""" diff --git a/my_project/hello_world.py b/my_project/hello_world.py index 7249a14..f70cd9e 100644 --- a/my_project/hello_world.py +++ b/my_project/hello_world.py @@ -23,5 +23,5 @@ async def hello_world(): # start Pipelex -with Pipelex.make(): +with Pipelex.make(library_dirs=["my_project"]): asyncio.run(hello_world()) diff --git a/pyproject.toml b/pyproject.toml index 821ce0d..c8bb40a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,15 +12,17 @@ classifiers = [ "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", "Programming Language :: 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"https://github.com/Pipelex/pipelex.git?branch=feature%2FChicago" }, + { name = "pipelex", extras = ["mistralai", "anthropic", "google", "google-genai", "bedrock", "fal"], git = "https://github.com/Pipelex/pipelex.git?branch=feature%2FChicago" }, { name = "pyright", marker = "extra == 'dev'", specifier = ">=1.1.405" }, { name = "pytest", marker = "extra == 'dev'", specifier = ">=9.0.1" }, { name = "pytest-asyncio", marker = "extra == 'dev'", specifier = ">=0.24.0" }, @@ -1891,7 +1962,7 @@ wheels = [ [[package]] name = "pipelex" version = "0.17.3" -source = { editable = "../pipelex" } +source = { git = "https://github.com/Pipelex/pipelex.git?branch=feature%2FChicago#88f1cefb1901f0759e91b06a2ba2f580291a29f2" } dependencies = [ { name = "aiofiles" }, { name = "backports-strenum", marker = "python_full_version < '3.11'" }, @@ -1947,69 +2018,6 @@ mistralai = [ { name = "mistralai" }, ] -[package.metadata] -requires-dist = [ - { name = "aioboto3", marker = "extra == 'bedrock'", specifier = ">=13.4.0" }, - { name = "aiofiles", specifier = ">=23.2.1" }, - { name = "anthropic", marker = "extra == 'anthropic'", specifier = ">=0.60.0" }, - { name = "backports-strenum", marker = "python_full_version < '3.11'", specifier = ">=1.3.0" }, - { name = "boto3", marker = "extra == 'bedrock'", specifier = ">=1.34.131" }, - { name = "boto3-stubs", marker = "extra == 'dev'", specifier = ">=1.35.24" }, - { name = "docling", marker = "extra == 'docling'", specifier = ">=2.64.0" }, - { name = "fal-client", marker = "extra == 'fal'", specifier = ">=0.4.1" }, - { name = "filetype", specifier = ">=1.2.0" }, - { name = "google-auth-oauthlib", marker = "extra == 'google'", specifier = ">=1.2.1" }, - { name = "google-genai", marker = "extra == 'google-genai'" }, - { name = "httpx", specifier = ">=0.23.0,<1.0.0" }, - { name = "instructor", specifier = ">=1.8.3,!=1.11.*,!=1.12.*" }, - { name = "instructor", extras = ["google-genai"], marker = "extra == 'google-genai'" }, - { name = "jinja2", specifier = ">=3.1.4" }, - { name = "json2html", specifier = ">=1.3.0" }, - { name = "kajson", specifier = "==0.3.1" }, - { name = "markdown", specifier = ">=3.6" }, - { name = "mistralai", marker = "extra == 'mistralai'", specifier = "==1.5.2" }, - { name = "mkdocs", marker = "extra == 'docs'", specifier = "==1.6.1" }, - { name = "mkdocs-glightbox", marker = "extra == 'docs'", specifier = "==0.4.0" }, - { name = "mkdocs-material", marker = "extra == 'docs'", specifier = "==9.6.14" }, - { name = "mkdocs-meta-manager", marker = "extra == 'docs'", specifier = "==1.1.0" }, - { name = "mypy", marker = "extra == 'dev'", specifier = ">=1.11.2" }, - { name = "networkx", specifier = ">=3.4.2" }, - { name = "openai", specifier = ">=1.108.1" }, - { name = "opentelemetry-api" }, - { name = "opentelemetry-exporter-otlp-proto-http" }, - { name = "opentelemetry-sdk" }, - { name = "opentelemetry-semantic-conventions" }, - { name = "pillow", specifier = ">=11.2.1" }, - { name = "polyfactory", specifier = ">=2.21.0" }, - { name = "portkey-ai", specifier = ">=2.1.0" }, - { name = "posthog", specifier = ">=6.7.0" }, - { name = "pydantic", specifier = ">=2.10.6,<3.0.0" }, - { name = "pylint", marker = "extra == 'dev'", specifier = ">=3.3.8" }, - { name = "pypdfium2", specifier = ">=4.30.0,!=4.30.1" }, - { name = "pyright", marker = "extra == 'dev'", specifier = ">=1.1.405" }, - { name = "pytest", marker = "extra == 'dev'", specifier = ">=9.0.1" }, - { name = "pytest-asyncio", marker = "extra == 'dev'", specifier = ">=0.24.0" }, - { name = "pytest-cov", marker = "extra == 'dev'", specifier = ">=6.1.1" }, - { name = "pytest-mock", marker = "extra == 'dev'", specifier = ">=3.14.0" }, - { name = "pytest-sugar", marker = "extra == 'dev'", specifier = ">=1.0.0" }, - { name = "pytest-xdist", marker = "extra == 'dev'", specifier = ">=3.6.1" }, - { name = "python-dotenv", specifier = ">=1.0.1" }, - { name = "pyyaml", specifier = ">=6.0.2" }, - { name = "rich", specifier = ">=13.8.1" }, - { name = "ruff", marker = "extra == 'dev'", specifier = ">=0.6.8" }, - { name = "shortuuid", specifier = ">=1.0.13" }, - { name = "tomli", specifier = ">=2.3.0" }, - { name = "tomlkit", specifier = ">=0.13.2" }, - { name = "typer", specifier = ">=0.16.0" }, - { name = "types-aioboto3", extras = ["bedrock", "bedrock-runtime"], marker = "extra == 'dev'", specifier = ">=13.4.0" }, - { name = "types-aiofiles", marker = "extra == 'dev'", specifier = ">=24.1.0.20240626" }, - { name = "types-markdown", marker = "extra == 'dev'", specifier = ">=3.6.0.20240316" }, - { name = "types-networkx", marker = "extra == 'dev'", specifier = ">=3.3.0.20241020" }, - { name = "types-pyyaml", marker = "extra == 'dev'", specifier = ">=6.0.12.20250326" }, - { name = "typing-extensions", specifier = ">=4.13.2" }, -] -provides-extras = ["anthropic", "bedrock", "docling", "fal", "google", "google-genai", "mistralai", "docs", "dev"] - [[package]] name = "platformdirs" version = "4.5.0" From 92be946afd2502858429a84657d036f329fa5b32 Mon Sep 17 00:00:00 2001 From: Louis Choquel Date: Mon, 19 Jan 2026 11:22:53 +0100 Subject: [PATCH 03/10] Update agent rules. Cleanup config files and test pipeline. --- .blackboxrules | 233 ++++++---- .cursor/rules/run_pipelex.mdc | 6 +- .cursor/rules/write_pipelex.mdc | 149 ++++++- .github/copilot-instructions.md | 233 ++++++---- .pipelex/inference/backends.toml | 109 ----- .pipelex/inference/backends/anthropic.toml | 100 ----- .pipelex/inference/backends/azure_openai.toml | 213 ---------- .pipelex/inference/backends/bedrock.toml | 120 ------ .pipelex/inference/backends/blackboxai.toml | 240 ----------- .pipelex/inference/backends/fal.toml | 107 ----- .pipelex/inference/backends/google.toml | 94 ----- .pipelex/inference/backends/groq.toml | 129 ------ .pipelex/inference/backends/huggingface.toml | 43 -- .pipelex/inference/backends/internal.toml | 37 -- .pipelex/inference/backends/mistral.toml | 164 -------- .pipelex/inference/backends/ollama.toml | 63 --- .pipelex/inference/backends/openai.toml | 208 --------- .../inference/backends/pipelex_gateway.toml | 41 -- .../inference/backends/pipelex_inference.toml | 205 --------- .pipelex/inference/backends/portkey.toml | 263 ------------ .pipelex/inference/backends/scaleway.toml | 67 --- .pipelex/inference/backends/vertexai.toml | 54 --- .pipelex/inference/backends/xai.toml | 56 --- .pipelex/inference/deck/base_deck.toml | 202 --------- .pipelex/inference/deck/overrides.toml | 19 - .pipelex/inference/routing_profiles.toml | 173 -------- .pipelex/pipelex.toml | 162 ------- .pipelex/pipelex_service.toml | 19 - .pipelex/telemetry.toml | 92 ---- .windsurfrules.md | 233 ++++++---- AGENTS.md | 233 ++++++---- CLAUDE.md | 233 ++++++---- crazy/__init__.py | 0 crazy/bundle.plx | 42 -- crazy/bundle_view.html | 111 ----- crazy/bundle_view.svg | 397 ------------------ crazy/inputs.json | 1 - crazy/run_generate_crazy_image.py | 19 - crazy/structures/__init__.py | 0 .../crazy_image_generation_ImagePrompt.py | 22 - 40 files changed, 870 insertions(+), 4022 deletions(-) delete mode 100644 .pipelex/inference/backends.toml delete mode 100644 .pipelex/inference/backends/anthropic.toml delete mode 100644 .pipelex/inference/backends/azure_openai.toml delete mode 100644 .pipelex/inference/backends/bedrock.toml delete mode 100644 .pipelex/inference/backends/blackboxai.toml delete mode 100644 .pipelex/inference/backends/fal.toml delete mode 100644 .pipelex/inference/backends/google.toml delete mode 100644 .pipelex/inference/backends/groq.toml delete mode 100644 .pipelex/inference/backends/huggingface.toml delete mode 100644 .pipelex/inference/backends/internal.toml delete mode 100644 .pipelex/inference/backends/mistral.toml delete mode 100644 .pipelex/inference/backends/ollama.toml delete mode 100644 .pipelex/inference/backends/openai.toml delete mode 100644 .pipelex/inference/backends/pipelex_gateway.toml delete mode 100644 .pipelex/inference/backends/pipelex_inference.toml delete mode 100644 .pipelex/inference/backends/portkey.toml delete mode 100644 .pipelex/inference/backends/scaleway.toml delete mode 100644 .pipelex/inference/backends/vertexai.toml delete mode 100644 .pipelex/inference/backends/xai.toml delete mode 100644 .pipelex/inference/deck/base_deck.toml delete mode 100644 .pipelex/inference/deck/overrides.toml delete mode 100644 .pipelex/inference/routing_profiles.toml delete mode 100644 .pipelex/pipelex.toml delete mode 100644 .pipelex/pipelex_service.toml delete mode 100644 .pipelex/telemetry.toml delete mode 100644 crazy/__init__.py delete mode 100644 crazy/bundle.plx delete mode 100644 crazy/bundle_view.html delete mode 100644 crazy/bundle_view.svg delete mode 100644 crazy/inputs.json delete mode 100644 crazy/run_generate_crazy_image.py delete mode 100644 crazy/structures/__init__.py delete mode 100644 crazy/structures/crazy_image_generation_ImagePrompt.py diff --git a/.blackboxrules b/.blackboxrules index 6125caa..af4572e 100644 --- a/.blackboxrules +++ b/.blackboxrules @@ -23,10 +23,10 @@ A pipeline file has three main sections: #### Domain Statement ```plx -domain = "domain_name" +domain = "domain_code" description = "Description of the domain" # Optional ``` -Note: The domain name usually matches the plx filename for single-file domains. For multi-file domains, use the subdirectory name. +Note: The domain code usually matches the plx filename for single-file domains. For multi-file domains, use the subdirectory name. #### Concept Definitions @@ -62,7 +62,7 @@ For details on how to structure concepts with fields, see the "Structuring Model ### Pipe Base Definition ```plx -[pipe.your_pipe_name] +[pipe.your_pipe_code] type = "PipeLLM" description = "A description of what your pipe does" inputs = { input_1 = "ConceptName1", input_2 = "ConceptName2" } @@ -471,7 +471,7 @@ The PipeExtract operator is used to extract text and images from an image or a P [pipe.extract_info] type = "PipeExtract" description = "extract the information" -inputs = { document = "PDF" } # or { image = "Image" } if it's an image. This is the only input. +inputs = { document = "Document" } # or { image = "Image" } if it's an image. This is the only input. output = "Page" ``` @@ -480,7 +480,7 @@ Using Extract Model Settings: [pipe.extract_with_model] type = "PipeExtract" description = "Extract with specific model" -inputs = { document = "PDF" } +inputs = { document = "Document" } output = "Page" model = "base_extract_mistral" # Use predefined extract preset or model alias ``` @@ -588,15 +588,16 @@ $sales_rep.phone | $sales_rep.email """ ``` -#### Key Parameters +#### Key Parameters (Template Mode) -- `template`: Inline template string (mutually exclusive with template_name) +- `template`: Inline template string (mutually exclusive with template_name and construct) - `template_name`: Name of a predefined template (mutually exclusive with template) - `template_category`: Template type ("llm_prompt", "html", "markdown", "mermaid", etc.) - `templating_style`: Styling options for template rendering - `extra_context`: Additional context variables for template For more control, you can use a nested `template` section instead of the `template` field: + - `template.template`: The template string - `template.category`: Template type - `template.templating_style`: Styling options @@ -604,9 +605,143 @@ For more control, you can use a nested `template` section instead of the `templa #### Template Variables Use the same variable insertion rules as PipeLLM: + - `@variable` for block insertion (multi-line content) - `$variable` for inline insertion (short text) +#### Construct Mode (for StructuredContent Output) + +PipeCompose can also generate `StructuredContent` objects using the `construct` section. This mode composes field values from fixed values, variable references, templates, or nested structures. + +**When to use construct mode:** + +- You need to output a structured object (not just Text) +- You want to deterministically compose fields from existing data +- No LLM is needed - just data composition and templating + +##### Basic Construct Usage + +```plx +[concept.SalesSummary] +description = "A structured sales summary" + +[concept.SalesSummary.structure] +report_title = { type = "text", description = "Title of the report" } +customer_name = { type = "text", description = "Customer name" } +deal_value = { type = "number", description = "Deal value" } +summary_text = { type = "text", description = "Generated summary text" } + +[pipe.compose_summary] +type = "PipeCompose" +description = "Compose a sales summary from deal data" +inputs = { deal = "Deal" } +output = "SalesSummary" + +[pipe.compose_summary.construct] +report_title = "Monthly Sales Report" +customer_name = { from = "deal.customer_name" } +deal_value = { from = "deal.amount" } +summary_text = { template = "Deal worth $deal.amount with $deal.customer_name" } +``` + +##### Field Composition Methods + +There are four ways to define field values in a construct: + +**1. Fixed Value (literal)** + +Use a literal value directly: + +```plx +[pipe.compose_report.construct] +report_title = "Annual Report" +report_year = 2024 +is_draft = false +``` + +**2. Variable Reference (`from`)** + +Get a value from working memory using a dotted path: + +```plx +[pipe.compose_report.construct] +customer_name = { from = "deal.customer_name" } +total_amount = { from = "order.total" } +street_address = { from = "customer.address.street" } +``` + +**3. Template (`template`)** + +Render a Jinja2 template with variable substitution: + +```plx +[pipe.compose_report.construct] +invoice_number = { template = "INV-$order.id" } +summary = { template = "Deal worth $deal.amount with $deal.customer_name on {{ current_date }}" } +``` + +**4. Nested Construct** + +For nested structures, use a TOML subsection: + +```plx +[pipe.compose_invoice.construct] +invoice_number = { template = "INV-$order.id" } +total = { from = "order.total_amount" } + +[pipe.compose_invoice.construct.billing_address] +street = { from = "customer.address.street" } +city = { from = "customer.address.city" } +country = "France" +``` + +##### Complete Construct Example + +```plx +domain = "invoicing" + +[concept.Address] +description = "A postal address" + +[concept.Address.structure] +street = { type = "text", description = "Street address" } +city = { type = "text", description = "City name" } +country = { type = "text", description = "Country name" } + +[concept.Invoice] +description = "An invoice document" + +[concept.Invoice.structure] +invoice_number = { type = "text", description = "Invoice number" } +total = { type = "number", description = "Total amount" } + +[pipe.compose_invoice] +type = "PipeCompose" +description = "Compose an invoice from order and customer data" +inputs = { order = "Order", customer = "Customer" } +output = "Invoice" + +[pipe.compose_invoice.construct] +invoice_number = { template = "INV-$order.id" } +total = { from = "order.total_amount" } + +[pipe.compose_invoice.construct.billing_address] +street = { from = "customer.address.street" } +city = { from = "customer.address.city" } +country = "France" +``` + +##### Key Parameters (Construct Mode) + +- `construct`: Dictionary mapping field names to their composition rules +- Each field can be: + - A literal value (string, number, boolean) + - A dict with `from` key for variable reference + - A dict with `template` key for template rendering + - A nested dict for nested structures + +**Note:** You must use either `template` or `construct`, not both. They are mutually exclusive. + ### PipeImgGen operator The PipeImgGen operator is used to generate images using AI image generation models. @@ -952,13 +1087,13 @@ So here are a few concrete examples of calls to execute_pipeline with various wa }, ) -## Here we have a single input and it's a PDF. -## Because PDFContent is a native concept, we can use it directly as a value, +## Here we have a single input and it's a document. +## Because DocumentContent is a native concept, we can use it directly as a value, ## the system knows what content it corresponds to: pipe_output = await execute_pipeline( pipe_code="power_extractor_dpe", inputs={ - "document": PDFContent(url=pdf_url), + "document": DocumentContent(url=pdf_url), }, ) @@ -1081,82 +1216,4 @@ result_list = pipe_output.main_stuff_as_items(item_type=GanttChart) ``` --- - -## Rules to choose LLM models used in PipeLLMs. - -### LLM Configuration System - -In order to use it in a pipe, an LLM is referenced by its llm_handle (alias) and possibly by an llm_preset. -LLM configurations are managed through the new inference backend system with files located in `.pipelex/inference/`: - -- **Model Deck**: `.pipelex/inference/deck/base_deck.toml` and `.pipelex/inference/deck/overrides.toml` -- **Backends**: `.pipelex/inference/backends.toml` and `.pipelex/inference/backends/*.toml` -- **Routing**: `.pipelex/inference/routing_profiles.toml` - -### LLM Handles - -An llm_handle can be either: -1. **A direct model name** (like "gpt-4o-mini", "claude-3-sonnet") - automatically available for all models loaded by the inference backend system -2. **An alias** - user-defined shortcuts that map to model names, defined in the `[aliases]` section: - -```toml -[aliases] -base-claude = "claude-4.5-sonnet" -base-gpt = "gpt-5" -base-gemini = "gemini-2.5-flash" -base-mistral = "mistral-medium" -``` - -The system first looks for direct model names, then checks aliases if no direct match is found. The system handles model routing through backends automatically. - -### Using an LLM Handle in a PipeLLM - -Here is an example of using an llm_handle to specify which LLM to use in a PipeLLM: - -```plx -[pipe.hello_world] -type = "PipeLLM" -description = "Write text about Hello World." -output = "Text" -model = { model = "gpt-5", temperature = 0.9 } -prompt = """ -Write a haiku about Hello World. -""" -``` - -As you can see, to use the LLM, you must also indicate the temperature (float between 0 and 1) and max_tokens (either an int or the string "auto"). - -### LLM Presets - -Presets are meant to record the choice of an llm with its hyper parameters (temperature and max_tokens) if it's good for a particular task. LLM Presets are skill-oriented. - -Examples: -```toml -llm_to_engineer = { model = "base-claude", temperature = 1 } -llm_to_extract_invoice = { model = "claude-4.5-sonnet", temperature = 0.1, max_tokens = "auto" } -``` - -The interest is that these presets can be used to set the LLM choice in a PipeLLM, like this: - -```plx -[pipe.extract_invoice] -type = "PipeLLM" -description = "Extract invoice information from an invoice text transcript" -inputs = { invoice_text = "InvoiceText" } -output = "Invoice" -model = "llm_to_extract_invoice" -prompt = """ -Extract invoice information from this invoice: - -The category of this invoice is: $invoice_details.category. - -@invoice_text -""" -``` - -The setting here `model = "llm_to_extract_invoice"` works because "llm_to_extract_invoice" has been declared as an llm_preset in the deck. -You must not use an LLM preset in a PipeLLM that does not exist in the deck. If needed, you can add llm presets. - - -You can override the predefined llm presets by setting them in `.pipelex/inference/deck/overrides.toml`. diff --git a/.cursor/rules/run_pipelex.mdc b/.cursor/rules/run_pipelex.mdc index 8387838..7650051 100644 --- a/.cursor/rules/run_pipelex.mdc +++ b/.cursor/rules/run_pipelex.mdc @@ -99,13 +99,13 @@ So here are a few concrete examples of calls to execute_pipeline with various wa }, ) -# Here we have a single input and it's a PDF. -# Because PDFContent is a native concept, we can use it directly as a value, +# Here we have a single input and it's a document. +# Because DocumentContent is a native concept, we can use it directly as a value, # the system knows what content it corresponds to: pipe_output = await execute_pipeline( pipe_code="power_extractor_dpe", inputs={ - "document": PDFContent(url=pdf_url), + "document": DocumentContent(url=pdf_url), }, ) diff --git a/.cursor/rules/write_pipelex.mdc b/.cursor/rules/write_pipelex.mdc index 9f23faf..93422cc 100644 --- a/.cursor/rules/write_pipelex.mdc +++ b/.cursor/rules/write_pipelex.mdc @@ -27,10 +27,10 @@ A pipeline file has three main sections: ### Domain Statement ```plx -domain = "domain_name" +domain = "domain_code" description = "Description of the domain" # Optional ``` -Note: The domain name usually matches the plx filename for single-file domains. For multi-file domains, use the subdirectory name. +Note: The domain code usually matches the plx filename for single-file domains. For multi-file domains, use the subdirectory name. ### Concept Definitions @@ -66,7 +66,7 @@ For details on how to structure concepts with fields, see the "Structuring Model ## Pipe Base Definition ```plx -[pipe.your_pipe_name] +[pipe.your_pipe_code] type = "PipeLLM" description = "A description of what your pipe does" inputs = { input_1 = "ConceptName1", input_2 = "ConceptName2" } @@ -475,7 +475,7 @@ The PipeExtract operator is used to extract text and images from an image or a P [pipe.extract_info] type = "PipeExtract" description = "extract the information" -inputs = { document = "PDF" } # or { image = "Image" } if it's an image. This is the only input. +inputs = { document = "Document" } # or { image = "Image" } if it's an image. This is the only input. output = "Page" ``` @@ -484,7 +484,7 @@ Using Extract Model Settings: [pipe.extract_with_model] type = "PipeExtract" description = "Extract with specific model" -inputs = { document = "PDF" } +inputs = { document = "Document" } output = "Page" model = "base_extract_mistral" # Use predefined extract preset or model alias ``` @@ -592,15 +592,16 @@ $sales_rep.phone | $sales_rep.email """ ``` -### Key Parameters +### Key Parameters (Template Mode) -- `template`: Inline template string (mutually exclusive with template_name) +- `template`: Inline template string (mutually exclusive with template_name and construct) - `template_name`: Name of a predefined template (mutually exclusive with template) - `template_category`: Template type ("llm_prompt", "html", "markdown", "mermaid", etc.) - `templating_style`: Styling options for template rendering - `extra_context`: Additional context variables for template For more control, you can use a nested `template` section instead of the `template` field: + - `template.template`: The template string - `template.category`: Template type - `template.templating_style`: Styling options @@ -608,9 +609,143 @@ For more control, you can use a nested `template` section instead of the `templa ### Template Variables Use the same variable insertion rules as PipeLLM: + - `@variable` for block insertion (multi-line content) - `$variable` for inline insertion (short text) +### Construct Mode (for StructuredContent Output) + +PipeCompose can also generate `StructuredContent` objects using the `construct` section. This mode composes field values from fixed values, variable references, templates, or nested structures. + +**When to use construct mode:** + +- You need to output a structured object (not just Text) +- You want to deterministically compose fields from existing data +- No LLM is needed - just data composition and templating + +#### Basic Construct Usage + +```plx +[concept.SalesSummary] +description = "A structured sales summary" + +[concept.SalesSummary.structure] +report_title = { type = "text", description = "Title of the report" } +customer_name = { type = "text", description = "Customer name" } +deal_value = { type = "number", description = "Deal value" } +summary_text = { type = "text", description = "Generated summary text" } + +[pipe.compose_summary] +type = "PipeCompose" +description = "Compose a sales summary from deal data" +inputs = { deal = "Deal" } +output = "SalesSummary" + +[pipe.compose_summary.construct] +report_title = "Monthly Sales Report" +customer_name = { from = "deal.customer_name" } +deal_value = { from = "deal.amount" } +summary_text = { template = "Deal worth $deal.amount with $deal.customer_name" } +``` + +#### Field Composition Methods + +There are four ways to define field values in a construct: + +**1. Fixed Value (literal)** + +Use a literal value directly: + +```plx +[pipe.compose_report.construct] +report_title = "Annual Report" +report_year = 2024 +is_draft = false +``` + +**2. Variable Reference (`from`)** + +Get a value from working memory using a dotted path: + +```plx +[pipe.compose_report.construct] +customer_name = { from = "deal.customer_name" } +total_amount = { from = "order.total" } +street_address = { from = "customer.address.street" } +``` + +**3. Template (`template`)** + +Render a Jinja2 template with variable substitution: + +```plx +[pipe.compose_report.construct] +invoice_number = { template = "INV-$order.id" } +summary = { template = "Deal worth $deal.amount with $deal.customer_name on {{ current_date }}" } +``` + +**4. Nested Construct** + +For nested structures, use a TOML subsection: + +```plx +[pipe.compose_invoice.construct] +invoice_number = { template = "INV-$order.id" } +total = { from = "order.total_amount" } + +[pipe.compose_invoice.construct.billing_address] +street = { from = "customer.address.street" } +city = { from = "customer.address.city" } +country = "France" +``` + +#### Complete Construct Example + +```plx +domain = "invoicing" + +[concept.Address] +description = "A postal address" + +[concept.Address.structure] +street = { type = "text", description = "Street address" } +city = { type = "text", description = "City name" } +country = { type = "text", description = "Country name" } + +[concept.Invoice] +description = "An invoice document" + +[concept.Invoice.structure] +invoice_number = { type = "text", description = "Invoice number" } +total = { type = "number", description = "Total amount" } + +[pipe.compose_invoice] +type = "PipeCompose" +description = "Compose an invoice from order and customer data" +inputs = { order = "Order", customer = "Customer" } +output = "Invoice" + +[pipe.compose_invoice.construct] +invoice_number = { template = "INV-$order.id" } +total = { from = "order.total_amount" } + +[pipe.compose_invoice.construct.billing_address] +street = { from = "customer.address.street" } +city = { from = "customer.address.city" } +country = "France" +``` + +#### Key Parameters (Construct Mode) + +- `construct`: Dictionary mapping field names to their composition rules +- Each field can be: + - A literal value (string, number, boolean) + - A dict with `from` key for variable reference + - A dict with `template` key for template rendering + - A nested dict for nested structures + +**Note:** You must use either `template` or `construct`, not both. They are mutually exclusive. + ## PipeImgGen operator The PipeImgGen operator is used to generate images using AI image generation models. diff --git a/.github/copilot-instructions.md b/.github/copilot-instructions.md index 6125caa..af4572e 100644 --- a/.github/copilot-instructions.md +++ b/.github/copilot-instructions.md @@ -23,10 +23,10 @@ A pipeline file has three main sections: #### Domain Statement ```plx -domain = "domain_name" +domain = "domain_code" description = "Description of the domain" # Optional ``` -Note: The domain name usually matches the plx filename for single-file domains. For multi-file domains, use the subdirectory name. +Note: The domain code usually matches the plx filename for single-file domains. For multi-file domains, use the subdirectory name. #### Concept Definitions @@ -62,7 +62,7 @@ For details on how to structure concepts with fields, see the "Structuring Model ### Pipe Base Definition ```plx -[pipe.your_pipe_name] +[pipe.your_pipe_code] type = "PipeLLM" description = "A description of what your pipe does" inputs = { input_1 = "ConceptName1", input_2 = "ConceptName2" } @@ -471,7 +471,7 @@ The PipeExtract operator is used to extract text and images from an image or a P [pipe.extract_info] type = "PipeExtract" description = "extract the information" -inputs = { document = "PDF" } # or { image = "Image" } if it's an image. This is the only input. +inputs = { document = "Document" } # or { image = "Image" } if it's an image. This is the only input. output = "Page" ``` @@ -480,7 +480,7 @@ Using Extract Model Settings: [pipe.extract_with_model] type = "PipeExtract" description = "Extract with specific model" -inputs = { document = "PDF" } +inputs = { document = "Document" } output = "Page" model = "base_extract_mistral" # Use predefined extract preset or model alias ``` @@ -588,15 +588,16 @@ $sales_rep.phone | $sales_rep.email """ ``` -#### Key Parameters +#### Key Parameters (Template Mode) -- `template`: Inline template string (mutually exclusive with template_name) +- `template`: Inline template string (mutually exclusive with template_name and construct) - `template_name`: Name of a predefined template (mutually exclusive with template) - `template_category`: Template type ("llm_prompt", "html", "markdown", "mermaid", etc.) - `templating_style`: Styling options for template rendering - `extra_context`: Additional context variables for template For more control, you can use a nested `template` section instead of the `template` field: + - `template.template`: The template string - `template.category`: Template type - `template.templating_style`: Styling options @@ -604,9 +605,143 @@ For more control, you can use a nested `template` section instead of the `templa #### Template Variables Use the same variable insertion rules as PipeLLM: + - `@variable` for block insertion (multi-line content) - `$variable` for inline insertion (short text) +#### Construct Mode (for StructuredContent Output) + +PipeCompose can also generate `StructuredContent` objects using the `construct` section. This mode composes field values from fixed values, variable references, templates, or nested structures. + +**When to use construct mode:** + +- You need to output a structured object (not just Text) +- You want to deterministically compose fields from existing data +- No LLM is needed - just data composition and templating + +##### Basic Construct Usage + +```plx +[concept.SalesSummary] +description = "A structured sales summary" + +[concept.SalesSummary.structure] +report_title = { type = "text", description = "Title of the report" } +customer_name = { type = "text", description = "Customer name" } +deal_value = { type = "number", description = "Deal value" } +summary_text = { type = "text", description = "Generated summary text" } + +[pipe.compose_summary] +type = "PipeCompose" +description = "Compose a sales summary from deal data" +inputs = { deal = "Deal" } +output = "SalesSummary" + +[pipe.compose_summary.construct] +report_title = "Monthly Sales Report" +customer_name = { from = "deal.customer_name" } +deal_value = { from = "deal.amount" } +summary_text = { template = "Deal worth $deal.amount with $deal.customer_name" } +``` + +##### Field Composition Methods + +There are four ways to define field values in a construct: + +**1. Fixed Value (literal)** + +Use a literal value directly: + +```plx +[pipe.compose_report.construct] +report_title = "Annual Report" +report_year = 2024 +is_draft = false +``` + +**2. Variable Reference (`from`)** + +Get a value from working memory using a dotted path: + +```plx +[pipe.compose_report.construct] +customer_name = { from = "deal.customer_name" } +total_amount = { from = "order.total" } +street_address = { from = "customer.address.street" } +``` + +**3. Template (`template`)** + +Render a Jinja2 template with variable substitution: + +```plx +[pipe.compose_report.construct] +invoice_number = { template = "INV-$order.id" } +summary = { template = "Deal worth $deal.amount with $deal.customer_name on {{ current_date }}" } +``` + +**4. Nested Construct** + +For nested structures, use a TOML subsection: + +```plx +[pipe.compose_invoice.construct] +invoice_number = { template = "INV-$order.id" } +total = { from = "order.total_amount" } + +[pipe.compose_invoice.construct.billing_address] +street = { from = "customer.address.street" } +city = { from = "customer.address.city" } +country = "France" +``` + +##### Complete Construct Example + +```plx +domain = "invoicing" + +[concept.Address] +description = "A postal address" + +[concept.Address.structure] +street = { type = "text", description = "Street address" } +city = { type = "text", description = "City name" } +country = { type = "text", description = "Country name" } + +[concept.Invoice] +description = "An invoice document" + +[concept.Invoice.structure] +invoice_number = { type = "text", description = "Invoice number" } +total = { type = "number", description = "Total amount" } + +[pipe.compose_invoice] +type = "PipeCompose" +description = "Compose an invoice from order and customer data" +inputs = { order = "Order", customer = "Customer" } +output = "Invoice" + +[pipe.compose_invoice.construct] +invoice_number = { template = "INV-$order.id" } +total = { from = "order.total_amount" } + +[pipe.compose_invoice.construct.billing_address] +street = { from = "customer.address.street" } +city = { from = "customer.address.city" } +country = "France" +``` + +##### Key Parameters (Construct Mode) + +- `construct`: Dictionary mapping field names to their composition rules +- Each field can be: + - A literal value (string, number, boolean) + - A dict with `from` key for variable reference + - A dict with `template` key for template rendering + - A nested dict for nested structures + +**Note:** You must use either `template` or `construct`, not both. They are mutually exclusive. + ### PipeImgGen operator The PipeImgGen operator is used to generate images using AI image generation models. @@ -952,13 +1087,13 @@ So here are a few concrete examples of calls to execute_pipeline with various wa }, ) -## Here we have a single input and it's a PDF. -## Because PDFContent is a native concept, we can use it directly as a value, +## Here we have a single input and it's a document. +## Because DocumentContent is a native concept, we can use it directly as a value, ## the system knows what content it corresponds to: pipe_output = await execute_pipeline( pipe_code="power_extractor_dpe", inputs={ - "document": PDFContent(url=pdf_url), + "document": DocumentContent(url=pdf_url), }, ) @@ -1081,82 +1216,4 @@ result_list = pipe_output.main_stuff_as_items(item_type=GanttChart) ``` --- - -## Rules to choose LLM models used in PipeLLMs. - -### LLM Configuration System - -In order to use it in a pipe, an LLM is referenced by its llm_handle (alias) and possibly by an llm_preset. -LLM configurations are managed through the new inference backend system with files located in `.pipelex/inference/`: - -- **Model Deck**: `.pipelex/inference/deck/base_deck.toml` and `.pipelex/inference/deck/overrides.toml` -- **Backends**: `.pipelex/inference/backends.toml` and `.pipelex/inference/backends/*.toml` -- **Routing**: `.pipelex/inference/routing_profiles.toml` - -### LLM Handles - -An llm_handle can be either: -1. **A direct model name** (like "gpt-4o-mini", "claude-3-sonnet") - automatically available for all models loaded by the inference backend system -2. **An alias** - user-defined shortcuts that map to model names, defined in the `[aliases]` section: - -```toml -[aliases] -base-claude = "claude-4.5-sonnet" -base-gpt = "gpt-5" -base-gemini = "gemini-2.5-flash" -base-mistral = "mistral-medium" -``` - -The system first looks for direct model names, then checks aliases if no direct match is found. The system handles model routing through backends automatically. - -### Using an LLM Handle in a PipeLLM - -Here is an example of using an llm_handle to specify which LLM to use in a PipeLLM: - -```plx -[pipe.hello_world] -type = "PipeLLM" -description = "Write text about Hello World." -output = "Text" -model = { model = "gpt-5", temperature = 0.9 } -prompt = """ -Write a haiku about Hello World. -""" -``` - -As you can see, to use the LLM, you must also indicate the temperature (float between 0 and 1) and max_tokens (either an int or the string "auto"). - -### LLM Presets - -Presets are meant to record the choice of an llm with its hyper parameters (temperature and max_tokens) if it's good for a particular task. LLM Presets are skill-oriented. - -Examples: -```toml -llm_to_engineer = { model = "base-claude", temperature = 1 } -llm_to_extract_invoice = { model = "claude-4.5-sonnet", temperature = 0.1, max_tokens = "auto" } -``` - -The interest is that these presets can be used to set the LLM choice in a PipeLLM, like this: - -```plx -[pipe.extract_invoice] -type = "PipeLLM" -description = "Extract invoice information from an invoice text transcript" -inputs = { invoice_text = "InvoiceText" } -output = "Invoice" -model = "llm_to_extract_invoice" -prompt = """ -Extract invoice information from this invoice: - -The category of this invoice is: $invoice_details.category. - -@invoice_text -""" -``` - -The setting here `model = "llm_to_extract_invoice"` works because "llm_to_extract_invoice" has been declared as an llm_preset in the deck. -You must not use an LLM preset in a PipeLLM that does not exist in the deck. If needed, you can add llm presets. - - -You can override the predefined llm presets by setting them in `.pipelex/inference/deck/overrides.toml`. diff --git a/.pipelex/inference/backends.toml b/.pipelex/inference/backends.toml deleted file mode 100644 index 46cbb79..0000000 --- a/.pipelex/inference/backends.toml +++ /dev/null @@ -1,109 +0,0 @@ -#################################################################################################### -# Pipelex Inference Backends Configuration -#################################################################################################### -# -# This file configures the inference backends available to Pipelex. -# Each backend connects to a different AI service provider (OpenAI, Anthropic, Google, etc.). -# -# Documentation: https://docs.pipelex.com -# Support: https://go.pipelex.com/discord -# -#################################################################################################### - -[pipelex_gateway] -display_name = "⭐ Pipelex Gateway" -enabled = true # Enable after accepting terms via `pipelex init config` -api_key = "${PIPELEX_GATEWAY_API_KEY}" - -[anthropic] -enabled = false -api_key = "${ANTHROPIC_API_KEY}" -valued_constraints = { max_output_tokens_limit = 8192 } - -[azure_openai] -display_name = "Azure OpenAI" -enabled = false -endpoint = "${AZURE_API_BASE}" -api_key = "${AZURE_API_KEY}" -api_version = "${AZURE_API_VERSION}" - -[bedrock] -display_name = "Amazon Bedrock" -enabled = false -aws_region = "${AWS_REGION}" - -[blackboxai] -display_name = "BlackBox AI" -enabled = false -endpoint = "https://api.blackbox.ai/v1" -api_key = "${BLACKBOX_API_KEY}" - -[fal] -display_name = "FAL" -enabled = false -api_key = "${FAL_API_KEY}" - -[google] -display_name = "Google AI" -enabled = false -api_key = "${GOOGLE_API_KEY}" - -[groq] -display_name = "Groq" -enabled = false -endpoint = "https://api.groq.com/openai/v1" -api_key = "${GROQ_API_KEY}" - -[huggingface] -display_name = "Hugging Face" -enabled = false -api_key = "${HF_TOKEN}" - -[mistral] -display_name = "Mistral AI" -enabled = false -api_key = "${MISTRAL_API_KEY}" - -[ollama] -enabled = false -endpoint = "http://localhost:11434/v1" - -[openai] -display_name = "OpenAI" -enabled = false -api_key = "${OPENAI_API_KEY}" - -[portkey] -display_name = "Portkey" -enabled = false -endpoint = "https://api.portkey.ai/v1" -api_key = "${PORTKEY_API_KEY}" - -[scaleway] -display_name = "Scaleway" -enabled = false -endpoint = "${SCALEWAY_ENDPOINT}" -api_key = "${SCALEWAY_API_KEY}" - -[vertexai] -display_name = "Google Vertex AI" -enabled = false # This is the only one we disable beacuse setting it up requires internet access just to get credentials so it fails in CI sandboxes -gcp_project_id = "${GCP_PROJECT_ID}" -gcp_location = "${GCP_LOCATION}" -gcp_credentials_file_path = "${GCP_CREDENTIALS_FILE_PATH}" - -[xai] -display_name = "xAI" -enabled = false -endpoint = "https://api.x.ai/v1" -api_key = "${XAI_API_KEY}" - -[internal] # software-only backend, runs internally, without AI -enabled = true - -# Deprecated -[pipelex_inference] -display_name = "🛑 Legacy Pipelex Inference" -enabled = false -endpoint = "https://inference.pipelex.com/v1" -api_key = "${PIPELEX_INFERENCE_API_KEY}" diff --git a/.pipelex/inference/backends/anthropic.toml b/.pipelex/inference/backends/anthropic.toml deleted file mode 100644 index 0f04f4d..0000000 --- a/.pipelex/inference/backends/anthropic.toml +++ /dev/null @@ -1,100 +0,0 @@ -################################################################################ -# Anthropic Backend Configuration -################################################################################ -# -# This file defines the model specifications for Anthropic Claude models. -# It contains model definitions for various Claude language models -# accessible through the Anthropic API. -# -# Configuration structure: -# - Each model is defined in its own section with the model name as the header -# - Headers with dots must be quoted (e.g., ["claude-3.5-sonnet"]) -# - Model costs are in USD per million tokens (input/output) -# -# Documentation: https://docs.pipelex.com -# Support: https://go.pipelex.com/discord -# -################################################################################ - -################################################################################ -# MODEL DEFAULTS -################################################################################ - -[defaults] -model_type = "llm" -sdk = "anthropic" -prompting_target = "anthropic" -structure_method = "instructor/anthropic_tools" - -################################################################################ -# LANGUAGE MODELS -################################################################################ - -# --- Claude 3 Series ---------------------------------------------------------- -[claude-3-haiku] -model_id = "claude-3-haiku-20240307" -max_tokens = 4096 -inputs = ["text", "images"] -outputs = ["text", "structured"] -max_prompt_images = 100 -costs = { input = 0.25, output = 1.25 } - -# --- Claude 3.7 Series -------------------------------------------------------- -["claude-3.7-sonnet"] -model_id = "claude-3-7-sonnet-20250219" -max_tokens = 8192 -inputs = ["text", "images"] -outputs = ["text", "structured"] -max_prompt_images = 100 -costs = { input = 3.0, output = 15.0 } - -# --- Claude 4 Series ---------------------------------------------------------- -[claude-4-sonnet] -model_id = "claude-sonnet-4-20250514" -max_tokens = 64000 -inputs = ["text", "images"] -outputs = ["text", "structured"] -max_prompt_images = 100 -costs = { input = 3.0, output = 15.0 } - -[claude-4-opus] -model_id = "claude-opus-4-20250514" -max_tokens = 32000 -inputs = ["text", "images"] -outputs = ["text", "structured"] -max_prompt_images = 100 -costs = { input = 3.0, output = 15.0 } - -# --- Claude 4.1 Series -------------------------------------------------------- -["claude-4.1-opus"] -model_id = "claude-opus-4-1-20250805" -max_tokens = 32000 -inputs = ["text", "images"] -outputs = ["text", "structured"] -max_prompt_images = 100 -costs = { input = 3.0, output = 15.0 } - -# --- Claude 4.5 Series -------------------------------------------------------- -["claude-4.5-sonnet"] -model_id = "claude-sonnet-4-5-20250929" -max_tokens = 64000 -inputs = ["text", "images"] -outputs = ["text", "structured"] -max_prompt_images = 100 -costs = { input = 3.0, output = 15.0 } - -["claude-4.5-haiku"] -model_id = "claude-haiku-4-5-20251001" -max_tokens = 64000 -inputs = ["text", "images"] -outputs = ["text", "structured"] -max_prompt_images = 100 -costs = { input = 1.0, output = 5.0 } - -["claude-4.5-opus"] -model_id = "claude-opus-4-5-20251101" -max_tokens = 64000 -inputs = ["text", "images"] -outputs = ["text", "structured"] -max_prompt_images = 100 -costs = { input = 5.0, output = 25.0 } diff --git a/.pipelex/inference/backends/azure_openai.toml b/.pipelex/inference/backends/azure_openai.toml deleted file mode 100644 index 8a89898..0000000 --- a/.pipelex/inference/backends/azure_openai.toml +++ /dev/null @@ -1,213 +0,0 @@ -################################################################################ -# Azure OpenAI Backend Configuration -################################################################################ -# -# This file defines the model specifications for Azure OpenAI models. -# It contains model definitions for OpenAI models deployed on Azure -# accessible through the Azure OpenAI API. -# -# Configuration structure: -# - Each model is defined in its own section with the model name as the header -# - Headers with dots must be quoted (e.g., ["gpt-4.1"]) -# - Model costs are in USD per million tokens (input/output) -# -# Documentation: https://docs.pipelex.com -# Support: https://go.pipelex.com/discord -# -################################################################################ - -################################################################################ -# MODEL DEFAULTS -################################################################################ - -[defaults] -model_type = "llm" -sdk = "azure_openai_responses" -prompting_target = "openai" -structure_method = "instructor/openai_responses_tools" - -################################################################################ -# LANGUAGE MODELS -################################################################################ - -# --- GPT-4o Series ------------------------------------------------------------ -[gpt-4o] -model_id = "gpt-4o-2024-11-20" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 2.5, output = 10.0 } - -[gpt-4o-mini] -model_id = "gpt-4o-mini-2024-07-18" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 0.15, output = 0.6 } - -# --- GPT-4.1 Series ----------------------------------------------------------- -["gpt-4.1"] -model_id = "gpt-4.1-2025-04-14" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 2, output = 8 } - -["gpt-4.1-mini"] -model_id = "gpt-4.1-mini-2025-04-14" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 0.4, output = 1.6 } - -["gpt-4.1-nano"] -model_id = "gpt-4.1-nano-2025-04-14" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 0.1, output = 0.4 } - -# --- o Series ---------------------------------------------------------------- -[o1-mini] -model_id = "o1-mini-2024-09-12" -inputs = ["text"] -outputs = ["text", "structured"] -costs = { input = 3.0, output = 12.0 } -valued_constraints = { fixed_temperature = 1 } - -[o1] -model_id = "o1-2024-12-17" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 15.0, output = 60.0 } -valued_constraints = { fixed_temperature = 1 } - -[o3-mini] -model_id = "o3-mini-2025-01-31" -inputs = ["text"] -outputs = ["text", "structured"] -costs = { input = 1.1, output = 4.4 } -valued_constraints = { fixed_temperature = 1 } - -[o3] -model_id = "o3-2025-04-16" -inputs = ["text"] -outputs = ["text", "structured"] -costs = { input = 2, output = 8 } -valued_constraints = { fixed_temperature = 1 } - -# --- GPT-5 Series ------------------------------------------------------------- -[gpt-5-mini] -model_id = "gpt-5-mini-2025-08-07" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 0.25, output = 2.0 } -valued_constraints = { fixed_temperature = 1 } - -[gpt-5-nano] -model_id = "gpt-5-nano-2025-08-07" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 0.05, output = 0.4 } -valued_constraints = { fixed_temperature = 1 } - -[gpt-5-chat] -model_id = "gpt-5-chat-2025-08-07" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 1.25, output = 10.0 } -valued_constraints = { fixed_temperature = 1 } - -[gpt-5] -model_id = "gpt-5-2025-08-07" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 1.25, output = 10.0 } -valued_constraints = { fixed_temperature = 1 } - -# --- GPT-5.1 Series ------------------------------------------------------------- -["gpt-5.1"] -model_id = "gpt-5.1-2025-11-13" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 1.25, output = 10.0 } -valued_constraints = { fixed_temperature = 1 } - -["gpt-5.1-chat"] -model_id = "gpt-5.1-chat-2025-11-13" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 1.25, output = 10.0 } -valued_constraints = { fixed_temperature = 1 } - -["gpt-5.1-codex"] -model_id = "gpt-5.1-codex-2025-11-13" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 1.25, output = 10.0 } -valued_constraints = { fixed_temperature = 1 } - -# --- GPT-5.2 Series ------------------------------------------------------------- -["gpt-5.2"] -model_id = "gpt-5.2-2025-12-11" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 1.75, output = 14.0 } - -["gpt-5.2-chat"] -model_id = "gpt-5.2-chat-2025-12-11" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 1.25, output = 10.0 } -valued_constraints = { fixed_temperature = 1 } - -################################################################################ -# IMAGE GENERATION MODELS -################################################################################ - -# --- OpenAI Image Generation -------------------------------------------------- -[gpt-image-1] -sdk = "azure_rest_img_gen" -model_type = "img_gen" -model_id = "gpt-image-1-2025-04-15" -inputs = ["text"] -outputs = ["image"] -costs = { input = 10, output = 40 } - -[gpt-image-1.rules] -prompt = "positive_only" -num_images = "gpt" -aspect_ratio = "gpt" -background = "gpt" -inference = "gpt" -safety_checker = "unavailable" -output_format = "gpt" - -[gpt-image-1-mini] -sdk = "azure_rest_img_gen" -model_type = "img_gen" -model_id = "gpt-image-1-mini-2025-10-06" -inputs = ["text"] -outputs = ["image"] -costs = { input = 2.5, output = 8 } - -[gpt-image-1-mini.rules] -prompt = "positive_only" -num_images = "gpt" -aspect_ratio = "gpt" -background = "gpt" -inference = "gpt" -safety_checker = "unavailable" -output_format = "gpt" - -["gpt-image-1.5"] -sdk = "azure_rest_img_gen" -model_type = "img_gen" -model_id = "gpt-image-1.5-2025-12-16" -inputs = ["text"] -outputs = ["image"] -costs = { input = 8, output = 32 } - -["gpt-image-1.5".rules] -prompt = "positive_only" -num_images = "gpt" -aspect_ratio = "gpt" -background = "gpt" -inference = "gpt" -safety_checker = "unavailable" -output_format = "gpt" diff --git a/.pipelex/inference/backends/bedrock.toml b/.pipelex/inference/backends/bedrock.toml deleted file mode 100644 index c4ab176..0000000 --- a/.pipelex/inference/backends/bedrock.toml +++ /dev/null @@ -1,120 +0,0 @@ -################################################################################ -# Amazon Bedrock Backend Configuration -################################################################################ -# -# This file defines the model specifications for Amazon Bedrock models. -# It contains model definitions for various language models -# accessible through the Amazon Bedrock service. -# -# Configuration structure: -# - Each model is defined in its own section with the model name as the header -# - Headers with dots must be quoted (e.g., ["claude-3.5-sonnet"]) -# - Model costs are in USD per million tokens (input/output) -# -# Documentation: https://docs.pipelex.com -# Support: https://go.pipelex.com/discord -# -################################################################################ - -################################################################################ -# MODEL DEFAULTS -################################################################################ - -[defaults] -model_type = "llm" -sdk = "bedrock_aioboto3" -prompting_target = "anthropic" - -################################################################################ -# LANGUAGE MODELS -################################################################################ - -# --- Mistral Models ----------------------------------------------------------- -[bedrock-mistral-large] -model_id = "mistral.mistral-large-2407-v1:0" -max_tokens = 8192 -inputs = ["text"] -outputs = ["text"] -costs = { input = 4.0, output = 12.0 } - -# --- Meta Llama Models -------------------------------------------------------- -[bedrock-meta-llama-3-3-70b-instruct] -model_id = "us.meta.llama3-3-70b-instruct-v1:0" -max_tokens = 8192 -inputs = ["text"] -outputs = ["text"] -# TODO: find out the actual cost per million tokens for llama3 on bedrock -costs = { input = 3.0, output = 15.0 } - -# --- Amazon Nova Models ------------------------------------------------------- -[bedrock-nova-pro] -model_id = "us.amazon.nova-pro-v1:0" -max_tokens = 5120 -inputs = ["text"] -outputs = ["text"] -# TODO: find out the actual cost per million tokens for nova on bedrock -costs = { input = 3.0, output = 15.0 } - -# --- Claude LLMs -------------------------------------------------------------- -["claude-3.7-sonnet"] -sdk = "bedrock_anthropic" -model_id = "us.anthropic.claude-3-7-sonnet-20250219-v1:0" -max_tokens = 8192 -inputs = ["text", "images"] -outputs = ["text", "structured"] -max_prompt_images = 100 -costs = { input = 3.0, output = 15.0 } - -[claude-4-sonnet] -sdk = "bedrock_anthropic" -model_id = "us.anthropic.claude-sonnet-4-20250514-v1:0" -max_tokens = 64000 -inputs = ["text", "images"] -outputs = ["text", "structured"] -max_prompt_images = 100 -costs = { input = 3.0, output = 15.0 } - -[claude-4-opus] -sdk = "bedrock_anthropic" -model_id = "us.anthropic.claude-opus-4-20250514-v1:0" -max_tokens = 32000 -inputs = ["text", "images"] -outputs = ["text", "structured"] -max_prompt_images = 100 -costs = { input = 3.0, output = 15.0 } - -["claude-4.1-opus"] -sdk = "bedrock_anthropic" -model_id = "us.anthropic.claude-opus-4-1-20250805-v1:0" -max_tokens = 32000 -inputs = ["text", "images"] -outputs = ["text", "structured"] -max_prompt_images = 100 -costs = { input = 3.0, output = 15.0 } - -["claude-4.5-sonnet"] -sdk = "bedrock_anthropic" -model_id = "us.anthropic.claude-sonnet-4-5-20250929-v1:0" -max_tokens = 8192 -inputs = ["text", "images"] -outputs = ["text", "structured"] -max_prompt_images = 100 -costs = { input = 3.0, output = 15.0 } - -["claude-4.5-haiku"] -sdk = "bedrock_anthropic" -model_id = "us.anthropic.claude-haiku-4-5-20251001-v1:0" -max_tokens = 8192 -inputs = ["text", "images"] -outputs = ["text", "structured"] -max_prompt_images = 100 -costs = { input = 1.0, output = 5.0 } - -["claude-4.5-opus"] -sdk = "bedrock_anthropic" -model_id = "global.anthropic.claude-opus-4-5-20251101-v1:0" -max_tokens = 8192 -inputs = ["text", "images"] -outputs = ["text", "structured"] -max_prompt_images = 100 -costs = { input = 5.0, output = 25.0 } diff --git a/.pipelex/inference/backends/blackboxai.toml b/.pipelex/inference/backends/blackboxai.toml deleted file mode 100644 index 9ee0433..0000000 --- a/.pipelex/inference/backends/blackboxai.toml +++ /dev/null @@ -1,240 +0,0 @@ -################################################################################ -# BlackBoxAI Backend Configuration -################################################################################ -# -# This file defines the model specifications for BlackBoxAI models. -# It contains model definitions for various language models from different providers -# accessible through the BlackBoxAI API. -# -# Configuration structure: -# - Each model is defined in its own section with the model name as the header -# - Headers with dots must be quoted (e.g., ["gpt-4.5-preview"]) -# - Model costs are in USD per million tokens (input/output) -# -# Documentation: https://docs.pipelex.com -# Support: https://go.pipelex.com/discord -# -################################################################################ - -################################################################################ -# MODEL DEFAULTS -################################################################################ - -[defaults] -model_type = "llm" -sdk = "openai" -structure_method = "instructor/openai_tools" - -################################################################################ -# LANGUAGE MODELS -################################################################################ - -# --- OpenAI Models ------------------------------------------------------------ -[gpt-4o-mini] -model_id = "blackboxai/openai/gpt-4o-mini" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 0.15, output = 0.60 } - -[gpt-4o] -model_id = "blackboxai/openai/gpt-4o" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 2.50, output = 10.00 } - -[o1-mini] -model_id = "blackboxai/openai/o1-mini" -inputs = ["text"] -outputs = ["text", "structured"] -costs = { input = 1.10, output = 4.40 } - -[o4-mini] -model_id = "blackboxai/openai/o4-mini" -inputs = ["text"] -outputs = ["text", "structured"] -costs = { input = 1.10, output = 4.40 } - -# --- Claude LLMs -------------------------------------------------------------- -["claude-3.5-haiku"] -model_id = "blackboxai/anthropic/claude-3.5-haiku" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 0.80, output = 4.00 } - -["claude-3.5-sonnet"] -model_id = "blackboxai/anthropic/claude-3.5-sonnet" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 3.00, output = 15.00 } - -["claude-3.7-sonnet"] -model_id = "blackboxai/anthropic/claude-3.7-sonnet" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 3.00, output = 15.00 } - -[claude-opus-4] -model_id = "blackboxai/anthropic/claude-opus-4" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 15.00, output = 75.00 } - -[claude-4-sonnet] -model_id = "blackboxai/anthropic/claude-sonnet-4" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 3.00, output = 15.00 } - -["claude-4.5-sonnet"] -model_id = "blackboxai/anthropic/claude-sonnet-4.5" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 0.28, output = 1.10 } - -# --- Google Models ------------------------------------------------------------ -["gemini-2.5-flash"] -model_id = "blackboxai/google/gemini-2.5-flash" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 0.30, output = 2.50 } - -["gemini-2.5-pro"] -model_id = "blackboxai/google/gemini-2.5-pro" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 1.25, output = 10.00 } - -["gemini-flash-1.5-8b"] -model_id = "blackboxai/google/gemini-flash-1.5-8b" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 0.04, output = 0.15 } - -# --- Mistral Models ----------------------------------------------------------- -[mistral-large] -model_id = "blackboxai/mistralai/mistral-large" -inputs = ["text"] -outputs = ["text", "structured"] -costs = { input = 2.00, output = 6.00 } - -[pixtral-large-2411] -model_id = "blackboxai/mistralai/pixtral-large-2411" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 2.00, output = 6.00 } - -# --- Meta Llama Models -------------------------------------------------------- -["llama-3.3-70b-instruct"] -model_id = "blackboxai/meta-llama/llama-3.3-70b-instruct" -inputs = ["text"] -outputs = ["text", "structured"] -costs = { input = 0.04, output = 0.12 } - -["llama-3.2-11b-vision-instruct"] -model_id = "blackboxai/meta-llama/llama-3.2-11b-vision-instruct" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 0.05, output = 0.05 } - -# --- Qwen Models -------------------------------------------------------------- -["qwen-2.5-72b-instruct"] -model_id = "blackboxai/qwen/qwen-2.5-72b-instruct" -inputs = ["text"] -outputs = ["text", "structured"] -costs = { input = 0.12, output = 0.39 } - -["qwen2.5-vl-72b-instruct"] -model_id = "blackboxai/qwen/qwen2.5-vl-72b-instruct" -inputs = ["text", "images"] -outputs = ["text"] -costs = { input = 0.25, output = 0.75 } - -# --- Amazon Nova Models ------------------------------------------------------- -[nova-micro-v1] -model_id = "blackboxai/amazon/nova-micro-v1" -inputs = ["text"] -outputs = ["text", "structured"] -costs = { input = 0.04, output = 0.14 } - -[nova-lite-v1] -model_id = "blackboxai/amazon/nova-lite-v1" -inputs = ["text"] -outputs = ["text", "structured"] -costs = { input = 0.06, output = 0.24 } - -################################################################################ -# FREE MODELS -################################################################################ - -# --- DeepSeek Free Models ----------------------------------------------------- -[deepseek-chat] -model_id = "blackboxai/deepseek/deepseek-chat:free" -inputs = ["text"] -outputs = ["text", "structured"] -costs = { input = 0.00, output = 0.00 } - -[deepseek-r1] -model_id = "blackboxai/deepseek/deepseek-r1:free" -inputs = ["text"] -outputs = ["text", "structured"] -costs = { input = 0.00, output = 0.00 } - -# --- Meta Llama Free Models --------------------------------------------------- -["llama-3.3-70b-instruct-free"] -model_id = "blackboxai/meta-llama/llama-3.3-70b-instruct:free" -inputs = ["text"] -outputs = ["text", "structured"] -costs = { input = 0.00, output = 0.00 } - - -################################################################################ -# IMAGE GENERATION MODELS -################################################################################ - -[flux-pro] -model_type = "img_gen" -sdk = "blackboxai_img_gen" -model_id = "blackboxai/black-forest-labs/flux-pro" -inputs = ["text"] -outputs = ["image"] -costs = { input = 0.0, output = 0.04 } - -["flux-pro/v1.1"] -model_type = "img_gen" -sdk = "blackboxai_img_gen" -model_id = "blackboxai/black-forest-labs/flux-1.1-pro" -inputs = ["text"] -outputs = ["image"] -costs = { input = 0.0, output = 0.04 } - -["flux-pro/v1.1-ultra"] -model_type = "img_gen" -sdk = "blackboxai_img_gen" -model_id = "blackboxai/black-forest-labs/flux-1.1-pro-ultra" -inputs = ["text"] -outputs = ["image"] -costs = { input = 0.0, output = 0.06 } - -[fast-lightning-sdxl] -model_type = "img_gen" -sdk = "blackboxai_img_gen" -model_id = "blackboxai/bytedance/sdxl-lightning-4step" -inputs = ["text"] -outputs = ["image"] -costs = { input = 0.0, output = 0.0014 } - -[nano-banana] -model_type = "img_gen" -sdk = "blackboxai_img_gen" -model_id = "blackboxai/google/nano-banana" -inputs = ["text"] -outputs = ["image"] -costs = { input = 0.0, output = 0.039 } - -[nano-banana-pro] -model_type = "img_gen" -sdk = "blackboxai_img_gen" -model_id = "blackboxai/google/nano-banana-pro" -inputs = ["text"] -outputs = ["image"] -costs = { input = 0.0, output = 0.039 } diff --git a/.pipelex/inference/backends/fal.toml b/.pipelex/inference/backends/fal.toml deleted file mode 100644 index 3433f99..0000000 --- a/.pipelex/inference/backends/fal.toml +++ /dev/null @@ -1,107 +0,0 @@ -################################################################################ -# FAL Backend Configuration -################################################################################ -# -# This file defines the model specifications for FAL (Fast AI Labs) models. -# It contains model definitions for various image generation models -# accessible through the FAL API. -# -# Configuration structure: -# - Each model is defined in its own section with the model name as the header -# - Headers with dots must be quoted (e.g., ["flux-pro/v1.1"]) -# - Model costs are in USD per million tokens (input/output) -# -# Documentation: https://docs.pipelex.com -# Support: https://go.pipelex.com/discord -# -################################################################################ - -################################################################################ -# MODEL DEFAULTS -################################################################################ - -[defaults] -model_type = "img_gen" -sdk = "fal" -prompting_target = "fal" - -################################################################################ -# IMAGE GENERATION MODELS -################################################################################ - -# --- Flux Pro Series ---------------------------------------------------------- -[flux-pro] -model_id = "fal-ai/flux-pro" -inputs = ["text"] -outputs = ["image"] -costs = { input = 0.05, output = 0.0 } - -[flux-pro.rules] -prompt = "positive_only" -num_images = "fal" -aspect_ratio = "flux" -inference = "flux" -safety_checker = "available" -output_format = "flux_1" -specific = "fal" - -["flux-pro/v1.1"] -model_id = "fal-ai/flux-pro/v1.1" -inputs = ["text"] -outputs = ["image"] -costs = { input = 0.05, output = 0.0 } - -["flux-pro/v1.1".rules] -prompt = "positive_only" -num_images = "fal" -aspect_ratio = "flux" -inference = "flux" -safety_checker = "available" -output_format = "flux_1" -specific = "fal" - -["flux-pro/v1.1-ultra"] -model_id = "fal-ai/flux-pro/v1.1-ultra" -inputs = ["text"] -outputs = ["image"] -costs = { input = 0.06, output = 0.0 } - -["flux-pro/v1.1-ultra".rules] -prompt = "positive_only" -num_images = "fal" -aspect_ratio = "flux_11_ultra" -inference = "flux_11_ultra" -safety_checker = "available" -output_format = "flux_1" -specific = "fal" - -[flux-2] -model_id = "fal-ai/flux-2" -inputs = ["text"] -outputs = ["image"] -costs = { input = 0.05, output = 0.0 } - -[flux-2.rules] -prompt = "positive_only" -num_images = "fal" -aspect_ratio = "flux" -inference = "flux" -safety_checker = "available" -output_format = "flux_2" -specific = "fal" - -# --- SDXL models -------------------------------------------------------------- -[fast-lightning-sdxl] -model_id = "fal-ai/fast-lightning-sdxl" -inputs = ["text"] -outputs = ["image"] -costs = { input = 0.0003, output = 0.0 } - -[fast-lightning-sdxl.rules] -prompt = "positive_only" -num_images = "fal" -aspect_ratio = "flux" -inference = "sdxl_lightning" -safety_checker = "unavailable" -output_format = "sdxl" -specific = "fal" diff --git a/.pipelex/inference/backends/google.toml b/.pipelex/inference/backends/google.toml deleted file mode 100644 index 36e19c2..0000000 --- a/.pipelex/inference/backends/google.toml +++ /dev/null @@ -1,94 +0,0 @@ -################################################################################ -# Google Gemini API Backend Configuration -################################################################################ -# -# This file defines the model specifications for Google Gemini API models. -# It contains model definitions for Gemini language models -# accessible through the Google Gemini API (not VertexAI). -# -# Configuration structure: -# - Each model is defined in its own section with the model name as the header -# - Headers with dots must be quoted (e.g., ["gemini-2.0-flash"]) -# - Model costs are in USD per million tokens (input/output) -# -# Documentation: https://docs.pipelex.com -# Support: https://go.pipelex.com/discord -# -################################################################################ - -################################################################################ -# MODEL DEFAULTS -################################################################################ - -[defaults] -model_type = "llm" -sdk = "google" -prompting_target = "gemini" -structure_method = "instructor/genai_tools" - -################################################################################ -# LANGUAGE MODELS -################################################################################ - -# --- Gemini 2.0 Series ---------------------------------------- -["gemini-2.0-flash"] -model_id = "gemini-2.0-flash" -inputs = ["text", "images"] -outputs = ["text", "structured"] -max_prompt_images = 3000 -costs = { input = 0.10, output = 0.40 } - -# --- Gemini 2.5 Series ---------------------------------------- -["gemini-2.5-pro"] -model_id = "gemini-2.5-pro" -inputs = ["text", "images"] -outputs = ["text", "structured"] -max_prompt_images = 3000 -costs = { input = 1.25, output = 10.0 } - -["gemini-2.5-flash"] -model_id = "gemini-2.5-flash" -inputs = ["text", "images"] -outputs = ["text", "structured"] -max_prompt_images = 3000 -costs = { input = 0.30, output = 2.50 } - -["gemini-2.5-flash-lite"] -model_id = "gemini-2.5-flash-lite" -inputs = ["text", "images"] -outputs = ["text", "structured"] -max_prompt_images = 3000 -costs = { input = 0.10, output = 0.40 } - -# --- Gemini 3.0 Series ---------------------------------------- -["gemini-3.0-pro"] -model_id = "gemini-3-pro-preview" -inputs = ["text", "images"] -outputs = ["text", "structured"] -max_prompt_images = 3000 -costs = { input = 2, output = 12.0 } - -["gemini-3.0-flash-preview"] -model_id = "gemini-3-flash-preview" -inputs = ["text", "images"] -outputs = ["text", "structured"] -max_prompt_images = 3000 -costs = { input = 0.5, output = 3.0 } - -################################################################################ -# IMAGE GENERATION MODELS (Nano Banana) -################################################################################ - -[nano-banana] -model_type = "img_gen" -model_id = "gemini-2.5-flash-image" -inputs = ["text"] -outputs = ["image"] -costs = { input = 0.0, output = 0.039 } - -[nano-banana-pro] -model_type = "img_gen" -model_id = "gemini-3-pro-image-preview" -inputs = ["text"] -outputs = ["image"] -costs = { input = 0.0, output = 0.039 } diff --git a/.pipelex/inference/backends/groq.toml b/.pipelex/inference/backends/groq.toml deleted file mode 100644 index 72bdae3..0000000 --- a/.pipelex/inference/backends/groq.toml +++ /dev/null @@ -1,129 +0,0 @@ -################################################################################ -# Groq Backend Configuration -################################################################################ -# -# This file defines the model specifications for Groq models. -# It contains model definitions for various LLM models accessible through -# the Groq API, including text-only and vision-capable models. -# -# Configuration structure: -# - Each model is defined in its own section with the model name as the header -# - Headers with dots or slashes must be quoted (e.g., ["meta-llama/llama-4-scout"]) -# - Model costs are in USD per million tokens (input/output) -# - Vision models support max 5 images per request, 33MP max resolution -# -# Documentation: https://docs.pipelex.com -# Support: https://go.pipelex.com/discord -# -################################################################################ - -################################################################################ -# MODEL DEFAULTS -################################################################################ - -[defaults] -model_type = "llm" -sdk = "openai" -structure_method = "instructor/json" - -################################################################################ -# PRODUCTION TEXT MODELS -################################################################################ - -# --- Meta Llama 3.x Series ---------------------------------------------------- -["llama-3.1-8b-instant"] -model_id = "llama-3.1-8b-instant" -max_tokens = 131072 -inputs = ["text"] -outputs = ["text", "structured"] -costs = { input = 0.05, output = 0.08 } - -["llama-3.3-70b-versatile"] -model_id = "llama-3.3-70b-versatile" -max_tokens = 32768 -inputs = ["text"] -outputs = ["text", "structured"] -costs = { input = 0.59, output = 0.79 } - -# --- Meta Llama Guard --------------------------------------------------------- -[llama-guard-4-12b] -model_id = "meta-llama/llama-guard-4-12b" -max_tokens = 1024 -inputs = ["text"] -outputs = ["text", "structured"] -costs = { input = 0.20, output = 0.20 } - -# --- OpenAI GPT-OSS Models ---------------------------------------------------- -[gpt-oss-20b] -model_id = "openai/gpt-oss-20b" -max_tokens = 65536 -inputs = ["text"] -outputs = ["text", "structured"] -costs = { input = 0.075, output = 0.30 } - -[gpt-oss-120b] -model_id = "openai/gpt-oss-120b" -max_tokens = 65536 -inputs = ["text"] -outputs = ["text", "structured"] -costs = { input = 0.15, output = 0.60 } - -# --- Groq Compound Systems ---------------------------------------------------- -["groq/compound"] -model_id = "groq/compound" -max_tokens = 8192 -inputs = ["text"] -outputs = ["text", "structured"] -costs = { input = 0.15, output = 0.45 } - -["groq/compound-mini"] -model_id = "groq/compound-mini" -max_tokens = 8192 -inputs = ["text"] -outputs = ["text", "structured"] -costs = { input = 0.10, output = 0.30 } - -################################################################################ -# PREVIEW MODELS -################################################################################ - -# --- Meta Llama 4 Vision Models (Preview) ------------------------------------- -[llama-4-scout-17b-16e-instruct] -model_id = "meta-llama/llama-4-scout-17b-16e-instruct" -max_tokens = 8192 -inputs = ["text", "images"] -outputs = ["text", "structured"] -max_prompt_images = 5 -costs = { input = 0.11, output = 0.34 } - -[llama-4-maverick-17b-128e-instruct] -model_id = "meta-llama/llama-4-maverick-17b-128e-instruct" -max_tokens = 8192 -inputs = ["text", "images"] -outputs = ["text", "structured"] -max_prompt_images = 5 -costs = { input = 0.20, output = 0.60 } - -# --- Moonshot Kimi K2 --------------------------------------------------------- -[kimi-k2-instruct-0905] -model_id = "moonshotai/kimi-k2-instruct-0905" -max_tokens = 16384 -inputs = ["text"] -outputs = ["text", "structured"] -costs = { input = 1.00, output = 3.00 } - -# --- OpenAI Safety Model ------------------------------------------------------ -[gpt-oss-safeguard-20b] -model_id = "openai/gpt-oss-safeguard-20b" -max_tokens = 65536 -inputs = ["text"] -outputs = ["text", "structured"] -costs = { input = 0.075, output = 0.30 } - -# --- Qwen 3 ------------------------------------------------------------------- -[qwen3-32b] -model_id = "qwen/qwen3-32b" -max_tokens = 40960 -inputs = ["text"] -outputs = ["text", "structured"] -costs = { input = 0.29, output = 0.59 } diff --git a/.pipelex/inference/backends/huggingface.toml b/.pipelex/inference/backends/huggingface.toml deleted file mode 100644 index 1a79638..0000000 --- a/.pipelex/inference/backends/huggingface.toml +++ /dev/null @@ -1,43 +0,0 @@ -################################################################################ -# Hugging Face Backend Configuration -################################################################################ -# -# This file defines the model specifications for Hugging Face models. -# It contains model definitions for various image generation models -# accessible through the Hugging Face Inference API with provider="auto". -# -# Configuration structure: -# - Each model is defined in its own section with the model name as the header -# - Headers with dots or slashes must be quoted (e.g., ["stabilityai/stable-diffusion-2-1"]) -# - Model costs are in USD per million tokens (input/output) -# -# Documentation: https://docs.pipelex.com -# Support: https://go.pipelex.com/discord -# -################################################################################ - -################################################################################ -# MODEL DEFAULTS -################################################################################ - -[defaults] -model_type = "img_gen" -sdk = "huggingface_img_gen" - -################################################################################ -# IMAGE GENERATION MODELS -################################################################################ - -# --- Qwen Image Models -------------------------------------------------- -[qwen-image] -model_id = "Qwen/Qwen-Image" -inputs = ["text"] -outputs = ["image"] -costs = { input = 0.0, output = 0.0 } -variant = "fal-ai" -# variant = "replicate" - -[qwen-image.rules] -prompt = "with_negative" -aspect_ratio = "qwen_image" -inference = "qwen_image" diff --git a/.pipelex/inference/backends/internal.toml b/.pipelex/inference/backends/internal.toml deleted file mode 100644 index e44b222..0000000 --- a/.pipelex/inference/backends/internal.toml +++ /dev/null @@ -1,37 +0,0 @@ -################################################################################ -# Internal Backend Configuration -################################################################################ -# -# This file defines the model specifications for internal software-only models. -# These models run internally without external APIs or AI services. -# -# Configuration structure: -# - Each model is defined in its own section with the model name as the header -# - Model costs are in USD per million tokens (input/output) -# -# Documentation: https://docs.pipelex.com -# Support: https://go.pipelex.com/discord -# -################################################################################ - -################################################################################ -# TEXT EXTRACTION MODELS -################################################################################ - -# --- PyPDFium2 Text Extractor ------------------------------------------------- -[pypdfium2-extract-pdf] -model_type = "text_extractor" -sdk = "pypdfium2" -model_id = "extract-text" -inputs = ["pdf"] -outputs = ["pages"] -costs = {} - -# --- Docling Text Extractor --------------------------------------------------- -[docling-extract-text] -model_type = "text_extractor" -sdk = "docling_sdk" -model_id = "extract-text" -inputs = ["pdf", "image"] -outputs = ["pages"] -costs = {} diff --git a/.pipelex/inference/backends/mistral.toml b/.pipelex/inference/backends/mistral.toml deleted file mode 100644 index b695ac7..0000000 --- a/.pipelex/inference/backends/mistral.toml +++ /dev/null @@ -1,164 +0,0 @@ -################################################################################ -# Mistral Backend Configuration -################################################################################ -# -# This file defines the model specifications for Mistral AI models. -# It contains model definitions for various Mistral language models and specialized models -# accessible through the Mistral API. -# -# Configuration structure: -# - Each model is defined in its own section with the model name as the header -# - Headers with dots must be quoted (e.g., ["ministral-3b"]) -# - Model costs are in USD per million tokens (input/output) -# -# Documentation: https://docs.pipelex.com -# Support: https://go.pipelex.com/discord -# -################################################################################ - -################################################################################ -# MODEL DEFAULTS -################################################################################ - -[defaults] -model_type = "llm" -sdk = "mistral" -prompting_target = "mistral" -structure_method = "instructor/mistral_tools" - -################################################################################ -# LANGUAGE MODELS -################################################################################ - -# --- Ministral Series --------------------------------------------------------- -[ministral-3b] -model_id = "ministral-3b-latest" -max_tokens = 131072 -inputs = ["text"] -outputs = ["text", "structured"] -costs = { input = 0.04, output = 0.04 } - -[ministral-8b] -model_id = "ministral-8b-latest" -max_tokens = 131072 -inputs = ["text"] -outputs = ["text", "structured"] -costs = { input = 0.1, output = 0.1 } - -# --- Mistral 7B Series -------------------------------------------------------- -[mistral-7b-2312] -model_id = "mistral-large-2402" -max_tokens = 32768 -inputs = ["text"] -outputs = ["text", "structured"] -costs = { input = 0.25, output = 0.25 } - -# --- Mistral 8x7B Series ------------------------------------------------------ -[mistral-8x7b-2312] -model_id = "open-mixtral-8x7b" -max_tokens = 32768 -inputs = ["text"] -outputs = ["text"] -costs = { input = 0.7, output = 0.7 } - -# --- Mistral Codestral Series ------------------------------------------------- -[mistral-codestral-2405] -model_id = "codestral-2405" -max_tokens = 262144 -inputs = ["text"] -outputs = ["text"] -costs = { input = 1.0, output = 3.0 } - -# --- Mistral Large Series ----------------------------------------------------- -[mistral-large-2402] -model_id = "mistral-large-2402" -max_tokens = 32768 -inputs = ["text"] -outputs = ["text", "structured"] -costs = { input = 4.0, output = 12.0 } - -[mistral-large] -model_id = "mistral-large-latest" -max_tokens = 131072 -inputs = ["text"] -outputs = ["text", "structured"] -costs = { input = 4.0, output = 12.0 } - -# --- Mistral Small Series ----------------------------------------------------- -[mistral-small-2402] -model_id = "mistral-small-2402" -max_tokens = 32768 -inputs = ["text"] -outputs = ["text", "structured"] -costs = { input = 1.0, output = 3.0 } - -[mistral-small] -model_id = "mistral-small-latest" -max_tokens = 32768 -inputs = ["text"] -outputs = ["text", "structured"] -costs = { input = 1.0, output = 3.0 } - -# --- Pixtral Series ----------------------------------------------------------- -[pixtral-12b] -model_id = "pixtral-12b-latest" -max_tokens = 131072 -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 0.15, output = 0.15 } - -[pixtral-large] -model_id = "pixtral-large-latest" -max_tokens = 131072 -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 2.0, output = 6.0 } - -# --- Mistral Medium Series ---------------------------------------------------- -[mistral-medium] -model_id = "mistral-medium-latest" -max_tokens = 128000 -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 0.4, output = 2.0 } - -[mistral-medium-2508] -model_id = "mistral-medium-2508" -max_tokens = 128000 -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 0.4, output = 2.0 } - -################################################################################ -# EXTRACTION MODELS -################################################################################ - -# TODO: add support to pricing per page - -[mistral-ocr-2503] -model_type = "text_extractor" -model_id = "mistral-ocr-2503" -max_tokens = 16384 -inputs = ["pdf", "image"] -outputs = ["pages"] - -[mistral-ocr-2505] -model_type = "text_extractor" -model_id = "mistral-ocr-2505" -max_tokens = 16384 -inputs = ["pdf", "image"] -outputs = ["pages"] - -[mistral-ocr-2512] -model_type = "text_extractor" -model_id = "mistral-ocr-2512" -max_tokens = 16384 -inputs = ["pdf", "image"] -outputs = ["pages"] - -[mistral-ocr] -model_type = "text_extractor" -model_id = "mistral-ocr-latest" -max_tokens = 16384 -inputs = ["pdf", "image"] -outputs = ["pages"] diff --git a/.pipelex/inference/backends/ollama.toml b/.pipelex/inference/backends/ollama.toml deleted file mode 100644 index 397e9ac..0000000 --- a/.pipelex/inference/backends/ollama.toml +++ /dev/null @@ -1,63 +0,0 @@ -################################################################################ -# Ollama Backend Configuration -################################################################################ -# -# This file defines the model specifications for Ollama models. -# It contains model definitions for local language models -# accessible through the Ollama API. -# -# Configuration structure: -# - Each model is defined in its own section with the model name as the header -# - Headers with dots must be quoted (e.g., ["mistral-small3.1-24b"]) -# - Model costs are in USD per million tokens (input/output) -# -# Documentation: https://docs.pipelex.com -# Support: https://go.pipelex.com/discord -# -################################################################################ - -################################################################################ -# MODEL DEFAULTS -################################################################################ - -[defaults] -model_type = "llm" -sdk = "openai" -prompting_target = "anthropic" -structure_method = "instructor/openai_tools" - -################################################################################ -# LANGUAGE MODELS -################################################################################ - -# --- Gemma Models ------------------------------------------------------------- -[gemma3-4b] -model_id = "gemma3:4b" -inputs = ["text"] -outputs = ["text"] -max_prompt_images = 3000 -costs = { input = 0, output = 0 } - -# --- Llama Models ------------------------------------------------------------- -[llama4-scout] -model_id = "llama4:scout" -inputs = ["text"] -outputs = ["text"] -max_prompt_images = 3000 -costs = { input = 0, output = 0 } - -# --- Mistral Models ----------------------------------------------------------- -["mistral-small3.1-24b"] -model_id = "mistral-small3.1:24b" -inputs = ["text"] -outputs = ["text"] -max_prompt_images = 3000 -costs = { input = 0, output = 0 } - -# --- Qwen Models -------------------------------------------------------------- -[qwen3-8b] -model_id = "qwen3:8b" -inputs = ["text"] -outputs = ["text"] -costs = { input = 0, output = 0 } -# TODO: support tokens diff --git a/.pipelex/inference/backends/openai.toml b/.pipelex/inference/backends/openai.toml deleted file mode 100644 index e61d52e..0000000 --- a/.pipelex/inference/backends/openai.toml +++ /dev/null @@ -1,208 +0,0 @@ -################################################################################ -# OpenAI Backend Configuration -################################################################################ -# -# This file defines the model specifications for OpenAI models. -# It contains model definitions for various LLM and image generation models -# accessible through the OpenAI API. -# -# Configuration structure: -# - Each model is defined in its own section with the model name as the header -# - Headers with dots must be quoted (e.g., ["gpt-4.1"]) -# - Model costs are in USD per million tokens (input/output) -# -# Documentation: https://docs.pipelex.com -# Support: https://go.pipelex.com/discord -# -################################################################################ - -################################################################################ -# MODEL DEFAULTS -################################################################################ - -[defaults] -model_type = "llm" -sdk = "openai_responses" -prompting_target = "openai" -structure_method = "instructor/openai_responses_tools" - -################################################################################ -# LANGUAGE MODELS -################################################################################ - -# --- GPT-3.5 Series ----------------------------------------------------------- -["gpt-3.5-turbo"] -model_id = "gpt-3.5-turbo-1106" -inputs = ["text"] -outputs = ["text", "structured"] -costs = { input = 0.5, output = 1.5 } - -# --- GPT-4 Series ------------------------------------------------------------- -[gpt-4] -inputs = ["text"] -outputs = ["text"] -costs = { input = 30.0, output = 60.0 } - -[gpt-4-turbo] -inputs = ["text"] -outputs = ["text", "structured"] -costs = { input = 10.0, output = 30.0 } - -# --- GPT-4o Series ------------------------------------------------------------ -[gpt-4o-2024-11-20] -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 2.5, output = 10.0 } - -[gpt-4o] -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 2.5, output = 10.0 } - -[gpt-4o-mini-2024-07-18] -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 0.15, output = 0.6 } - -[gpt-4o-mini] -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 0.15, output = 0.6 } - -# --- GPT-4.1 Series ----------------------------------------------------------- -["gpt-4.1"] -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 2, output = 8 } - -["gpt-4.1-mini"] -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 0.4, output = 1.6 } - -["gpt-4.1-nano"] -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 0.1, output = 0.4 } - -# --- o Series ---------------------------------------------------------------- -[o1] -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 15.0, output = 60.0 } -valued_constraints = { fixed_temperature = 1 } - -[o3-mini] -inputs = ["text"] -outputs = ["text", "structured"] -costs = { input = 1.1, output = 4.4 } -valued_constraints = { fixed_temperature = 1 } - -[o3] -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 10.0, output = 40.0 } -valued_constraints = { fixed_temperature = 1 } - -[o4-mini] -inputs = ["text"] -outputs = ["text", "structured"] -costs = { input = 1.1, output = 4.4 } -valued_constraints = { fixed_temperature = 1 } - -# --- GPT-5 Series ------------------------------------------------------------- -[gpt-5] -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 1.25, output = 10.0 } -valued_constraints = { fixed_temperature = 1 } - -[gpt-5-mini] -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 0.25, output = 2.0 } -valued_constraints = { fixed_temperature = 1 } - -[gpt-5-nano] -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 0.05, output = 0.4 } -valued_constraints = { fixed_temperature = 1 } - -[gpt-5-chat] -model_id = "gpt-5-chat-latest" -inputs = ["text", "images"] -outputs = ["text"] -costs = { input = 1.25, output = 10.0 } -valued_constraints = { fixed_temperature = 1 } - -[gpt-5-codex] -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 1.25, output = 10.0 } -valued_constraints = { fixed_temperature = 1 } - -# --- GPT-5.1 Series ------------------------------------------------------------- -["gpt-5.1"] -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 1.25, output = 10.0 } - -["gpt-5.1-chat"] -model_id = "gpt-5.1-chat-latest" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 1.25, output = 10.0 } -valued_constraints = { fixed_temperature = 1 } - -["gpt-5.1-codex"] -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 1.25, output = 10.0 } -valued_constraints = { fixed_temperature = 1 } - -["gpt-5.1-codex-max"] -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 1.25, output = 10.0 } -valued_constraints = { fixed_temperature = 1 } - -# --- GPT-5.2 Series ------------------------------------------------------------- -["gpt-5.2"] -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 1.75, output = 14.0 } - -["gpt-5.2-chat"] -model_id = "gpt-5.2-chat-latest" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 1.75, output = 14.0 } -valued_constraints = { fixed_temperature = 1 } - -################################################################################ -# IMAGE GENERATION MODELS -################################################################################ - -# --- OpenAI Image Generation -------------------------------------------------- -[gpt-image-1] -sdk = "openai_img_gen" -model_type = "img_gen" -inputs = ["text"] -outputs = ["image"] -costs = { input = 10, output = 40 } - -[gpt-image-1-mini] -sdk = "openai_img_gen" -model_type = "img_gen" -inputs = ["text"] -outputs = ["image"] -costs = { input = 2.5, output = 8 } - -["gpt-image-1.5"] -sdk = "openai_img_gen" -model_type = "img_gen" -model_id = "gpt-image-1.5" -inputs = ["text"] -outputs = ["image"] -costs = { input = 8, output = 32 } diff --git a/.pipelex/inference/backends/pipelex_gateway.toml b/.pipelex/inference/backends/pipelex_gateway.toml deleted file mode 100644 index bca075b..0000000 --- a/.pipelex/inference/backends/pipelex_gateway.toml +++ /dev/null @@ -1,41 +0,0 @@ -################################################################################ -# Pipelex Gateway Local Overrides -################################################################################ -# -# TELEMETRY NOTICE: -# -# Using Pipelex Gateway enables identified telemetry tied to your API key -# (hashed for security). This is independent from your telemetry.toml settings. -# -# We collect only technical data (model names, token counts, latency, error rates). -# We do NOT collect prompts, completions, pipe codes, or business data. -# -# This allows us to monitor service quality, enforce fair usage, and support you. -# -################################################################################ -# -# WARNING: USE AT YOUR OWN RISK! -# -# The actual model configuration is fetched remotely from Pipelex servers. -# Any override in this file may cause unexpected behavior or failures, -# as the remote configuration may change at any time. -# -# If you must override, you may ONLY use these keys per model: -# - sdk -# - structure_method -# -# All other keys will be ignored. -# -# If you need custom configurations, consider using your own API keys -# with direct provider backends (openai, anthropic, etc.) instead. -# -# Documentation: -# https://docs.pipelex.com/home/7-configuration/config-technical/inference-backend-config/ -# Support: https://go.pipelex.com/discord -# -################################################################################ - -# Per-model overrides example: -# [gpt-4o] -# sdk = "gateway_completions" -# structure_method = "instructor/openai_tools" diff --git a/.pipelex/inference/backends/pipelex_inference.toml b/.pipelex/inference/backends/pipelex_inference.toml deleted file mode 100644 index 751c570..0000000 --- a/.pipelex/inference/backends/pipelex_inference.toml +++ /dev/null @@ -1,205 +0,0 @@ -################################################################################ -# Pipelex Inference Backend Configuration -################################################################################ -# -# This file defines the model specifications for the Pipelex Inference backend. -# It contains model definitions for various LLM and image generation models -# accessible through the Pipelex unified inference API. -# -# Configuration structure: -# - Each model is defined in its own section with the model name as the header -# - Headers with dots must be quoted (e.g., ["gpt-4.1"]) -# - Model costs are in USD per million tokens (input/output) -# -# Documentation: https://docs.pipelex.com -# Support: https://go.pipelex.com/discord -# -################################################################################ - -################################################################################ -# MODEL DEFAULTS -################################################################################ - -[defaults] -model_type = "llm" -sdk = "openai" -prompting_target = "anthropic" -structure_method = "instructor/openai_tools" - -################################################################################ -# LANGUAGE MODELS -################################################################################ - -# --- OpenAI LLMs -------------------------------------------------------------- -[gpt-4o] -model_id = "pipelex/gpt-4o" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 2.75, output = 11.00 } -sdk = "openai_responses" -structure_method = "instructor/openai_responses_tools" - -[gpt-4o-mini] -model_id = "pipelex/gpt-4o-mini" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 0.17, output = 0.66 } -sdk = "openai_responses" -structure_method = "instructor/openai_responses_tools" - -["gpt-4.1"] -model_id = "pipelex/gpt-4.1" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 2, output = 8 } -sdk = "openai_responses" -structure_method = "instructor/openai_responses_tools" - -["gpt-4.1-mini"] -model_id = "pipelex/gpt-4.1-mini" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 0.4, output = 1.6 } -sdk = "openai_responses" -structure_method = "instructor/openai_responses_tools" - -["gpt-4.1-nano"] -model_id = "pipelex/gpt-4.1-nano" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 0.1, output = 0.4 } -sdk = "openai_responses" -structure_method = "instructor/openai_responses_tools" - -[gpt-5-nano] -model_id = "pipelex/gpt-5-nano" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 0.05, output = 0.40 } -sdk = "openai_responses" -structure_method = "instructor/openai_responses_tools" - -[gpt-5-mini] -model_id = "pipelex/gpt-5-mini" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 0.25, output = 2.00 } -sdk = "openai_responses" -structure_method = "instructor/openai_responses_tools" - -[gpt-5-chat] -model_id = "pipelex/gpt-5-chat" -inputs = ["text", "images"] -outputs = ["text"] -costs = { input = 1.25, output = 10.00 } -sdk = "openai_responses" -structure_method = "instructor/openai_responses_tools" - -[gpt-5] -model_id = "pipelex/gpt-5" -inputs = ["text", "images"] -outputs = ["text"] -costs = { input = 1.25, output = 10.00 } -sdk = "openai_responses" -structure_method = "instructor/openai_responses_tools" - -["gpt-5.1"] -model_id = "pipelex/gpt-5.1" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 1.25, output = 10.00 } -sdk = "openai_responses" -structure_method = "instructor/openai_responses_tools" - -["gpt-5.1-chat"] -model_id = "pipelex/gpt-5.1-chat" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 1.25, output = 10.00 } -sdk = "openai_responses" -structure_method = "instructor/openai_responses_tools" - -# --- Claude LLMs -------------------------------------------------------------- -["claude-4-sonnet"] -model_id = "pipelex/claude-4-sonnet" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 3, output = 15 } - -["claude-4.1-opus"] -model_id = "pipelex/claude-4.1-opus" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 15, output = 75 } - -["claude-4.5-sonnet"] -model_id = "pipelex/claude-4.5-sonnet" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 3, output = 15 } - -["claude-4.5-haiku"] -model_id = "pipelex/claude-4.5-haiku" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 1, output = 5 } - -["claude-4.5-opus"] -model_id = "pipelex/claude-4.5-opus" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 5, output = 25 } - -# --- Gemini LLMs -------------------------------------------------------------- -["gemini-2.0-flash"] -model_id = "pipelex/gemini-2.0-flash" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 0.10, output = 0.40 } - -["gemini-2.5-pro"] -model_id = "pipelex/gemini-2.5-pro" -inputs = ["text", "images"] -outputs = ["text", "structured"] -max_prompt_images = 3000 -costs = { input = 1.25, output = 10.0 } - -["gemini-2.5-flash"] -model_id = "pipelex/gemini-2.5-flash" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 0.30, output = 2.50 } - -["gemini-2.5-flash-lite"] -model_id = "pipelex/gemini-2.5-flash-lite" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 0.10, output = 0.40 } - -["gemini-3.0-pro"] -model_id = "pipelex/gemini-3.0-pro" -inputs = ["text", "images"] -outputs = ["text", "structured"] -max_prompt_images = 3000 -costs = { input = 2, output = 12.0 } - -# --- XAI LLMs -------------------------------------------------------------- - -[grok-3] -model_id = "grok-3" -inputs = ["text"] -outputs = ["text"] -costs = { input = 3, output = 15 } - -[grok-3-mini] -model_id = "grok-3-mini" -inputs = ["text"] -outputs = ["text"] -costs = { input = 0.3, output = 0.5 } - -################################################################################ -# OCR and IMAGE GENERATION MODELS -################################################################################ - -# We are still working in giving you acces to OCR and image generation models -# and to the best models from Mistral through the Pipelex Inference backend. diff --git a/.pipelex/inference/backends/portkey.toml b/.pipelex/inference/backends/portkey.toml deleted file mode 100644 index 75e2574..0000000 --- a/.pipelex/inference/backends/portkey.toml +++ /dev/null @@ -1,263 +0,0 @@ -################################################################################ -# Portkey Configuration -################################################################################ -# -# This file defines the model specifications for the Portkey backend. -# It contains model definitions for various AI models. -# -# Configuration structure: -# - Each model is defined in its own section with the model name as the header -# - Headers with dots must be quoted (e.g., ["gpt-4.1"]) -# - Model costs are in USD per million tokens (input/output) -# -# Documentation: https://docs.pipelex.com -# Support: https://go.pipelex.com/discord -# -################################################################################ - -################################################################################ -# MODEL DEFAULTS -################################################################################ - -[defaults] -model_type = "llm" -sdk = "portkey_completions" -structure_method = "instructor/openai_tools" -prompting_target = "anthropic" - -################################################################################ -# LANGUAGE MODELS -################################################################################ - -# --- OpenAI LLMs -------------------------------------------------------------- -[gpt-4o-mini] -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 0.15, output = 0.6 } -sdk = "portkey_responses" -structure_method = "instructor/openai_responses_tools" -x-portkey-provider = "@openai" - -[gpt-4o] -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 2.5, output = 10.0 } -sdk = "portkey_responses" -structure_method = "instructor/openai_responses_tools" -x-portkey-provider = "@openai" - -["gpt-4.1-nano"] -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 0.1, output = 0.4 } -sdk = "portkey_responses" -structure_method = "instructor/openai_responses_tools" -x-portkey-provider = "@openai" - -["gpt-4.1-mini"] -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 0.4, output = 1.6 } -sdk = "portkey_responses" -structure_method = "instructor/openai_responses_tools" -x-portkey-provider = "@openai" - -["gpt-4.1"] -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 2, output = 8 } -sdk = "portkey_responses" -structure_method = "instructor/openai_responses_tools" -x-portkey-provider = "@openai" - -[o1] -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 15.0, output = 60.0 } -valued_constraints = { fixed_temperature = 1 } -sdk = "portkey_responses" -structure_method = "instructor/openai_responses_tools" -x-portkey-provider = "@openai" - -[o3-mini] -inputs = ["text"] -outputs = ["text", "structured"] -costs = { input = 1.1, output = 4.4 } -valued_constraints = { fixed_temperature = 1 } -sdk = "portkey_responses" -structure_method = "instructor/openai_responses_tools" -x-portkey-provider = "@openai" - -[o3] -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 2, output = 8 } -valued_constraints = { fixed_temperature = 1 } -sdk = "portkey_responses" -structure_method = "instructor/openai_responses_tools" -x-portkey-provider = "@openai" - -[o4-mini] -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 1.1, output = 4.4 } -valued_constraints = { fixed_temperature = 1 } -sdk = "portkey_responses" -structure_method = "instructor/openai_responses_tools" -x-portkey-provider = "@openai" - -[gpt-5-nano] -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 0.05, output = 0.4 } -valued_constraints = { fixed_temperature = 1 } -sdk = "portkey_responses" -structure_method = "instructor/openai_responses_tools" -x-portkey-provider = "@openai" - -[gpt-5-mini] -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 0.25, output = 2.0 } -valued_constraints = { fixed_temperature = 1 } -sdk = "portkey_responses" -structure_method = "instructor/openai_responses_tools" -x-portkey-provider = "@openai" - -[gpt-5] -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 1.25, output = 10.0 } -valued_constraints = { fixed_temperature = 1 } -sdk = "portkey_responses" -structure_method = "instructor/openai_responses_tools" -x-portkey-provider = "@openai" - -["gpt-5.1"] -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 1.25, output = 10.0 } -valued_constraints = { fixed_temperature = 1 } -sdk = "portkey_responses" -structure_method = "instructor/openai_responses_tools" -x-portkey-provider = "@openai" - -["gpt-5.1-codex"] -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 1.25, output = 10.0 } -valued_constraints = { fixed_temperature = 1 } -sdk = "portkey_responses" -structure_method = "instructor/openai_responses_tools" -x-portkey-provider = "@openai" - -# --- Claude LLMs -------------------------------------------------------------- -[claude-3-haiku] -model_id = "claude-3-haiku-20240307" -max_tokens = 4096 -inputs = ["text", "images"] -outputs = ["text", "structured"] -max_prompt_images = 100 -costs = { input = 0.25, output = 1.25 } -x-portkey-provider = "@anthropic" - -["claude-3.7-sonnet"] -model_id = "claude-3-7-sonnet-20250219" -max_tokens = 8192 -inputs = ["text", "images"] -outputs = ["text", "structured"] -max_prompt_images = 100 -costs = { input = 3.0, output = 15.0 } -x-portkey-provider = "@anthropic" - -[claude-4-sonnet] -model_id = "claude-sonnet-4-20250514" -max_tokens = 64000 -inputs = ["text", "images"] -outputs = ["text", "structured"] -max_prompt_images = 100 -costs = { input = 3.0, output = 15.0 } -x-portkey-provider = "@anthropic" - -[claude-4-opus] -model_id = "claude-opus-4-20250514" -max_tokens = 32000 -inputs = ["text", "images"] -outputs = ["text", "structured"] -max_prompt_images = 100 -costs = { input = 3.0, output = 15.0 } -x-portkey-provider = "@anthropic" - -["claude-4.1-opus"] -model_id = "claude-opus-4-1-20250805" -max_tokens = 32000 -inputs = ["text", "images"] -outputs = ["text", "structured"] -max_prompt_images = 100 -costs = { input = 3.0, output = 15.0 } -x-portkey-provider = "@anthropic" - -["claude-4.5-sonnet"] -model_id = "claude-sonnet-4-5-20250929" -max_tokens = 64000 -inputs = ["text", "images"] -outputs = ["text", "structured"] -max_prompt_images = 100 -costs = { input = 3.0, output = 15.0 } -x-portkey-provider = "@anthropic" - -["claude-4.5-haiku"] -model_id = "claude-haiku-4-5-20251001" -max_tokens = 64000 -inputs = ["text", "images"] -outputs = ["text", "structured"] -max_prompt_images = 100 -costs = { input = 1.0, output = 5.0 } -x-portkey-provider = "@anthropic" - -["claude-4.5-opus"] -model_id = "claude-opus-4-5-20251101" -max_tokens = 64000 -inputs = ["text", "images"] -outputs = ["text", "structured"] -max_prompt_images = 100 -costs = { input = 5.0, output = 25.0 } -x-portkey-provider = "@anthropic" - -# --- Gemini LLMs -------------------------------------------------------------- -["gemini-2.0-flash"] -model_id = "gemini-2.0-flash" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 0.10, output = 0.40 } -x-portkey-provider = "@google" - -["gemini-2.5-pro"] -model_id = "gemini-2.5-pro" -inputs = ["text", "images"] -outputs = ["text", "structured"] -max_prompt_images = 3000 -costs = { input = 1.25, output = 10.0 } -x-portkey-provider = "@google" - -["gemini-2.5-flash"] -model_id = "gemini-2.5-flash" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 0.30, output = 2.50 } -x-portkey-provider = "@google" - -["gemini-2.5-flash-lite"] -model_id = "gemini-2.5-flash-lite" -inputs = ["text", "images"] -outputs = ["text", "structured"] -costs = { input = 0.10, output = 0.40 } -x-portkey-provider = "@google" - -["gemini-3.0-pro"] -model_id = "gemini-3-pro-preview" -inputs = ["text", "images"] -outputs = ["text", "structured"] -max_prompt_images = 3000 -costs = { input = 2, output = 12.0 } -x-portkey-provider = "@google" diff --git a/.pipelex/inference/backends/scaleway.toml b/.pipelex/inference/backends/scaleway.toml deleted file mode 100644 index 20fe792..0000000 --- a/.pipelex/inference/backends/scaleway.toml +++ /dev/null @@ -1,67 +0,0 @@ -################################################################################ -# Groq Backend Configuration -################################################################################ -# -# This file defines the model specifications for Scaleway models. -# It contains model definitions for various LLM models accessible through -# the Groq API, including text-only and vision-capable models. -# -# Configuration structure: -# - Each model is defined in its own section with the model name as the header -# - Headers with dots or slashes must be quoted (e.g., ["meta-llama/llama-4-scout"]) -# - Model costs are in USD per million tokens (input/output) -# - Vision models support max 5 images per request, 33MP max resolution -# -# Documentation: https://docs.pipelex.com -# Support: https://go.pipelex.com/discord -# -################################################################################ - -################################################################################ -# MODEL DEFAULTS -################################################################################ - -[defaults] -model_type = "llm" -sdk = "openai" -structure_method = "instructor/json" - -# --- DeepSeek Models ---------------------------------------------------------- -[deepseek-r1-distill-llama-70b] -max_tokens = 32768 -inputs = ["text"] -outputs = ["text", "structured"] -costs = { input = 0.90, output = 0.90 } - -# --- Meta Llama 3.x Series ---------------------------------------------------- -["llama-3.1-8b-instruct"] -max_tokens = 131072 -inputs = ["text"] -outputs = ["text", "structured"] -costs = { input = 0.2, output = 0.2 } - -["llama-3.3-70b-instruct"] -max_tokens = 32768 -inputs = ["text"] -outputs = ["text", "structured"] -costs = { input = 0.90, output = 0.90 } - -# --- OpenAI GPT-OSS Models ---------------------------------------------------- -[gpt-oss-120b] -max_tokens = 65536 -inputs = ["text"] -outputs = ["text", "structured"] -costs = { input = 0.15, output = 0.60 } - -# --- Qwen 3 ------------------------------------------------------------------- -[qwen3-235b-a22b-instruct-2507] -max_tokens = 40960 -inputs = ["text"] -outputs = ["text", "structured"] -costs = { input = 0.75, output = 2.25 } - -[qwen3-coder-30b-a3b-instruct] -max_tokens = 40960 -inputs = ["text"] -outputs = ["text", "structured"] -costs = { input = 0.20, output = 0.80 } diff --git a/.pipelex/inference/backends/vertexai.toml b/.pipelex/inference/backends/vertexai.toml deleted file mode 100644 index 1ebab79..0000000 --- a/.pipelex/inference/backends/vertexai.toml +++ /dev/null @@ -1,54 +0,0 @@ -################################################################################ -# VertexAI Backend Configuration -################################################################################ -# -# This file defines the model specifications for Google VertexAI models. -# It contains model definitions for Gemini language models -# accessible through the Google VertexAI API. -# -# Configuration structure: -# - Each model is defined in its own section with the model name as the header -# - Headers with dots must be quoted (e.g., ["gemini-2.0-flash"]) -# - Model costs are in USD per million tokens (input/output) -# -# Documentation: https://docs.pipelex.com -# Support: https://go.pipelex.com/discord -# -################################################################################ - -################################################################################ -# MODEL DEFAULTS -################################################################################ - -[defaults] -model_type = "llm" -sdk = "openai" -prompting_target = "gemini" -structure_method = "instructor/vertexai_tools" - -################################################################################ -# LANGUAGE MODELS -################################################################################ - -# --- Gemini 2.0 Series -------------------------------------------------------- -["gemini-2.0-flash"] -model_id = "google/gemini-2.0-flash" -inputs = ["text", "images"] -outputs = ["text", "structured"] -max_prompt_images = 3000 -costs = { input = 0.1, output = 0.4 } - -# --- Gemini 2.5 Series -------------------------------------------------------- -["gemini-2.5-pro"] -model_id = "google/gemini-2.5-pro" -inputs = ["text", "images"] -outputs = ["text", "structured"] -max_prompt_images = 3000 -costs = { input = 1.25, output = 10.0 } - -["gemini-2.5-flash"] -model_id = "google/gemini-2.5-flash" -inputs = ["text", "images"] -outputs = ["text", "structured"] -max_prompt_images = 3000 -costs = { input = 0.30, output = 2.50 } diff --git a/.pipelex/inference/backends/xai.toml b/.pipelex/inference/backends/xai.toml deleted file mode 100644 index 3045344..0000000 --- a/.pipelex/inference/backends/xai.toml +++ /dev/null @@ -1,56 +0,0 @@ -################################################################################ -# XAI Backend Configuration -################################################################################ -# -# This file defines the model specifications for XAI (formerly Twitter AI) models. -# It contains model definitions for Grok language models -# accessible through the XAI API. -# -# Configuration structure: -# - Each model is defined in its own section with the model name as the header -# - Headers with dots must be quoted (e.g., ["grok-3"]) -# - Model costs are in USD per million tokens (input/output) -# -# Documentation: https://docs.pipelex.com -# Support: https://go.pipelex.com/discord -# -################################################################################ - -################################################################################ -# MODEL DEFAULTS -################################################################################ - -[defaults] -model_type = "llm" -sdk = "openai" -prompting_target = "anthropic" -structure_method = "instructor/openai_tools" - -################################################################################ -# LANGUAGE MODELS -################################################################################ - -# --- Grok 3 Series ------------------------------------------------------------ -[grok-3] -model_id = "grok-3" -inputs = ["text"] -outputs = ["text"] -costs = { input = 3, output = 15 } - -[grok-3-mini] -model_id = "grok-3-mini" -inputs = ["text"] -outputs = ["text"] -costs = { input = 0.3, output = 0.5 } - -[grok-3-fast] -model_id = "grok-3-fast-latest" -inputs = ["text"] -outputs = ["text"] -costs = { input = 5, output = 25 } - -[grok-3-mini-fast] -model_id = "grok-3-mini-fast-latest" -inputs = ["text"] -outputs = ["text"] -costs = { input = 0.15, output = 4 } diff --git a/.pipelex/inference/deck/base_deck.toml b/.pipelex/inference/deck/base_deck.toml deleted file mode 100644 index 63ab39e..0000000 --- a/.pipelex/inference/deck/base_deck.toml +++ /dev/null @@ -1,202 +0,0 @@ -#################################################################################################### -# Pipelex Model Deck - Base Configuration -#################################################################################################### -# -# This file defines model aliases and presets for: -# - LLMs (language models for text generation and structured output) -# - Image generation models (for creating images from text prompts) -# - Document extraction models (OCR and text extraction from PDFs/images) -# -# Documentation: https://docs.pipelex.com -# Support: https://go.pipelex.com/discord -# -#################################################################################################### - -#################################################################################################### -# Aliases -#################################################################################################### - -[aliases] -base-claude = "claude-4.5-sonnet" -base-gpt = "gpt-4o" -base-gemini = "gemini-2.5-flash" -base-mistral = "mistral-medium" -base-groq = "llama-3.3-70b-versatile" -base-grok = "grok-4-fast-non-reasoning" - -best-gpt = "gpt-5.1" -best-claude = "claude-4.5-opus" -best-gemini = "gemini-3.0-pro" -best-mistral = "mistral-medium" - -# Groq-specific aliases -fast-groq = "llama-3.1-8b-instant" -vision-groq = "llama-4-scout-17b-16e-instruct" - -# Image generation aliases -base-img-gen = "flux-pro/v1.1" -best-img-gen = "flux-2" -fast-img-gen = "fast-lightning-sdxl" - -#################################################################################################### -# Waterfalls -#################################################################################################### - -[waterfalls] - -# --- Waterfalls for LLMs --------------------------------------------------------------------- -smart_llm = [ - "claude-4.5-opus", - "claude-4.5-sonnet", - "gemini-3.0-pro", - "gpt-5.1", - "claude-4.1-opus", - "gemini-2.5-pro", - "claude-4-sonnet", - "grok-4-fast-non-reasoning", -] -smart_llm_with_vision = [ - "claude-4.5-opus", - "claude-4.5-sonnet", - "gemini-3.0-pro", - "gpt-5.1", - "claude-4.1-opus", - "gemini-2.5-pro", - "claude-4-sonnet", - "grok-4-fast-non-reasoning", -] -smart_llm_for_structured = [ - "claude-4.5-opus", - "claude-4.5-sonnet", - "gemini-3.0-pro", - "gpt-5.1", - "claude-4.1-opus", - "claude-4-sonnet", -] -llm_for_creativity = [ - "claude-4.5-opus", - "claude-4.1-opus", - "gemini-2.5-pro", - "gpt-5.1", -] -llm_for_large_codebase = [ - "gemini-2.5-pro", - "claude-4.5-sonnet", - "gemini-3.0-pro", - "gpt-5.1", - "gemini-2.5-flash", - "grok-4-fast-non-reasoning", -] -cheap_llm = [ - "gpt-4o-mini", - "gemini-2.5-flash-lite", - "mistral-small", - "claude-3-haiku", - "grok-3-mini", -] -cheap_llm_for_vision = [ - "gemini-2.5-flash-lite", - "gpt-4o-mini", - "claude-3-haiku", -] -cheap_llm_for_structured = ["gpt-4o-mini", "mistral-small", "claude-3-haiku"] -cheap_llm_for_creativity = [ - "gemini-2.5-flash", - "grok-3-mini", - "gpt-4o-mini", - "claude-4.5-haiku", -] - -# --- Waterfalls for Extracts --------------------------------------------------------------------- -pdf_text_extractor = [ - "azure-document-intelligence", - "mistral-ocr", - "pypdfium2-extract-pdf", -] -image_text_extractor = ["mistral-ocr"] - -#################################################################################################### -# LLM Presets -#################################################################################################### - -[llm.presets] - -# LLM Presets — Specific skills ------------------------------------------------------------- - -# Generation skills -llm_for_factual_writing = { model = "base-gpt", temperature = 0.1 } -llm_for_creative_writing = { model = "base-gpt", temperature = 0.9 } -llm_for_writing_cheap = { model = "gpt-4o-mini", temperature = 0.3 } - -# Retrieve and answer questions skills -llm_to_answer_questions_cheap = { model = "gpt-4o-mini", temperature = 0.3 } -llm_to_answer_questions = { model = "base-claude", temperature = 0.3 } -llm_to_retrieve = { model = "base-claude", temperature = 0.1 } - -# Engineering skills -llm_to_engineer = { model = "smart_llm_for_structured", temperature = 0.2 } -llm_to_code = { model = "base-claude", temperature = 0.1 } -llm_to_analyze_large_codebase = { model = "base-claude", temperature = 0.1 } - -# Vision skills -llm_for_img_to_text_cheap = { model = "gpt-4o-mini", temperature = 0.1 } -llm_for_img_to_text = { model = "base-claude", temperature = 0.1 } -llm_for_diagram_to_text = { model = "best-claude", temperature = 0.3 } -llm_for_table_to_text = { model = "base-claude", temperature = 0.3 } - -# Image generation prompting skills -llm_to_prompt_img_gen = { model = "base-claude", temperature = 0.2 } -llm_to_prompt_img_gen_cheap = { model = "gpt-4o-mini", temperature = 0.5 } - -# Groq-specific presets (fast inference, low cost) -llm_groq_fast_text = { model = "fast-groq", temperature = 0.7 } -llm_groq_balanced = { model = "base-groq", temperature = 0.5 } -llm_groq_vision = { model = "vision-groq", temperature = 0.3 } - -# LLM Presets — For Testing --------------------------------------------------------------------- - -llm_for_testing_gen_text = { model = "cheap_llm", temperature = 0.5 } -llm_for_testing_gen_object = { model = "cheap_llm_for_structured", temperature = 0.1 } -llm_for_testing_vision = { model = "cheap_llm_for_vision", temperature = 0.5 } -llm_for_testing_vision_structured = { model = "cheap_llm_for_vision", temperature = 0.5 } - -#################################################################################################### -# LLM Choices -#################################################################################################### - -[llm.choice_defaults] -for_text = "cheap_llm" -for_object = "cheap_llm_for_structured" - -#################################################################################################### -# Extract Presets -#################################################################################################### - -[extract] -choice_default = "extract_ocr_from_document" - -[extract.presets] -extract_ocr_from_document = { model = "azure-document-intelligence", max_nb_images = 100, image_min_size = 50 } -extract_basic_from_pdf = { model = "pypdfium2-extract-pdf", max_nb_images = 100, image_min_size = 50 } - -#################################################################################################### -# Image Generation Presets -#################################################################################################### - -[img_gen] -choice_default = "gen_image_basic" - -[img_gen.presets] - -# General purpose -gen_image_basic = { model = "base-img-gen", quality = "medium", guidance_scale = 7.5, is_moderated = true, safety_tolerance = 3 } -gen_image_fast = { model = "fast-img-gen", nb_steps = 4, guidance_scale = 5.0, is_moderated = true, safety_tolerance = 3 } -gen_image_high_quality = { model = "best-img-gen", quality = "high", guidance_scale = 8.0, is_moderated = true, safety_tolerance = 3 } -gen_image_openai_low_quality = { model = "gpt-image-1", quality = "low" } - -# Specific skills -img_gen_for_art = { model = "best-img-gen", quality = "high", guidance_scale = 9.0, is_moderated = false, safety_tolerance = 5 } -img_gen_for_diagram = { model = "base-img-gen", quality = "medium", guidance_scale = 7.0, is_moderated = true, safety_tolerance = 2 } -img_gen_for_mockup = { model = "base-img-gen", quality = "medium", guidance_scale = 6.5, is_moderated = true, safety_tolerance = 3 } -img_gen_for_product = { model = "best-img-gen", quality = "high", guidance_scale = 8.5, is_moderated = true, safety_tolerance = 2 } -img_gen_for_testing = { model = "fast-img-gen", nb_steps = 4, guidance_scale = 4.0, is_moderated = true, safety_tolerance = 4 } diff --git a/.pipelex/inference/deck/overrides.toml b/.pipelex/inference/deck/overrides.toml deleted file mode 100644 index 08814db..0000000 --- a/.pipelex/inference/deck/overrides.toml +++ /dev/null @@ -1,19 +0,0 @@ -#################################################################################################### -# Pipelex Model Deck - Overrides -#################################################################################################### -# -# This file allows you to override the default model choices defined in base_deck.toml. -# You can customize presets for LLMs, image generation, and document extraction models. -# -# Documentation: https://docs.pipelex.com -# Support: https://go.pipelex.com/discord -# -#################################################################################################### - -#################################################################################################### -# LLM Deck overrides -#################################################################################################### - -[llm.choice_overrides] -for_text = "disabled" -for_object = "disabled" diff --git a/.pipelex/inference/routing_profiles.toml b/.pipelex/inference/routing_profiles.toml deleted file mode 100644 index bf40281..0000000 --- a/.pipelex/inference/routing_profiles.toml +++ /dev/null @@ -1,173 +0,0 @@ -# Routing profile library - Routes models to their backends -# ========================================================================================= -# This file controls which backend serves which model. -# Simply change the 'active' field to switch profiles, -# or you can add your own custom profiles. -# -# Documentation: https://docs.pipelex.com -# Support: https://go.pipelex.com/discord -# ========================================================================================= - -# Which profile to use (change this to switch routing) -active = "pipelex_gateway_first" - -# We recommend using the "pipelex_gateway_first" profile to get a head start with all models. -# To use the Pipelex Gateway backend: -# 1. Join our Discord community to get your free API key (no credit card required): -# Visit https://go.pipelex.com/discord and request your key in the appropriate channel -# 2. Set the environment variable (or add it to your .env file): -# - Linux/macOS: export PIPELEX_GATEWAY_API_KEY="your-api-key" -# - Windows CMD: set PIPELEX_GATEWAY_API_KEY=your-api-key -# - Windows PowerShell: $env:PIPELEX_GATEWAY_API_KEY="your-api-key" -# 3. The .pipelex/inference/backends.toml is already configured with api_key = "${PIPELEX_GATEWAY_API_KEY}" -# which will get the key from the environment variable. - -# ========================================================================================= -# Routing Profiles -# ========================================================================================= - -[profiles.pipelex_gateway_first] -description = "Use Pipelex Gateway backend for all its supported models" -default = "pipelex_gateway" -fallback_order = [ - "pipelex_gateway", - "azure_openai", - "bedrock", - "google", - "blackboxai", - "mistral", - "fal", -] - -[profiles.pipelex_gateway_first.routes] -# Pattern matching: "model-pattern" = "backend-name" - -[profiles.pipelex_gateway_first.optional_routes] # Each optional route is considered only if its backend is available -"gpt-*" = "pipelex_gateway" -"gpt-image-1" = "openai" -"claude-*" = "pipelex_gateway" -"grok-*" = "pipelex_gateway" -"gemini-*" = "pipelex_gateway" -"*-sdxl" = "fal" -"flux-*" = "fal" -"mistral-ocr" = "mistral" - -[profiles.all_pipelex_gateway] -description = "Use Pipelex Gateway for all its supported models" -default = "pipelex_gateway" - -[profiles.all_anthropic] -description = "Use Anthropic backend for all its supported models" -default = "anthropic" - -[profiles.all_azure_openai] -description = "Use Azure OpenAI backend for all its supported models" -default = "azure_openai" - -[profiles.all_bedrock] -description = "Use Bedrock backend for all its supported models" -default = "bedrock" - -[profiles.all_blackboxai] -description = "Use BlackBoxAI backend for all its supported models" -default = "blackboxai" - -[profiles.all_fal] -description = "Use FAL backend for all its supported models" -default = "fal" - -[profiles.all_google] -description = "Use Google GenAI backend for all its supported models" -default = "google" - -[profiles.all_groq] -description = "Use groq backend for all its supported models" -default = "groq" - -[profiles.all_huggingface] -description = "Use HuggingFace backend for all its supported models" -default = "huggingface" - -[profiles.all_mistral] -description = "Use Mistral backend for all its supported models" -default = "mistral" - -[profiles.all_ollama] -description = "Use Ollama backend for all its supported models" -default = "ollama" - -[profiles.all_openai] -description = "Use OpenAI backend for all its supported models" -default = "openai" - -[profiles.all_portkey] -description = "Use Portkey backend for all its supported models" -default = "portkey" - -[profiles.all_scaleway] -description = "Use Scaleway backend for all its supported models" -default = "scaleway" - -[profiles.all_xai] -description = "Use xAI backend for all its supported models" -default = "xai" - -[profiles.all_internal] -description = "Use internal backend for all its supported models" -default = "internal" - -# ========================================================================================= -# Custom Profiles -# ========================================================================================= -# Add your own profiles below following the same pattern: -# -# [profiles.your_profile_name] -# description = "What this profile does" -# default = "backend-name" # Where to route models by default -# [profiles.your_profile_name.routes] -# "model-pattern" = "backend-name" # Specific routing rules -# -# Pattern matching supports: -# - Exact names: "gpt-4o-mini" -# - Wildcards: "claude-*" (matches all models starting with claude-) -# - Partial wildcards: "*-sonnet" (matches all sonnet variants) - -# ========================================================================================= -# Example of a custom routing profile with mostly pattern matching and one specific model -# ========================================================================================= -[profiles.example_routing_using_patterns] -description = "Example routing profile using patterns" -default = "pipelex_gateway" - -[profiles.example_routing_using_patterns.routes] -# Pattern matching: "model-pattern" = "backend-name" -"gpt-*" = "azure_openai" -"claude-*" = "bedrock" -"gemini-*" = "google" -"grok-*" = "xai" -"*-sdxl" = "fal" -"flux-*" = "fal" -"gpt-image-1" = "openai" - -# ========================================================================================= -# Example of a custom routing profile with specific model matching -# ========================================================================================= - -[profiles.example_routing_using_specific_models] -description = "Example routing profile using specific models" - -[profiles.example_routing_using_specific_models.routes] -"gpt-5-nano" = "pipelex_gateway" -"gpt-4o-mini" = "blackboxai" -"gpt-5-mini" = "openai" -"gpt-5-chat" = "azure_openai" - -"claude-4-sonnet" = "pipelex_gateway" -"claude-3.7-sonnet" = "blackboxai" - -"gemini-2.5-flash-lite" = "pipelex_gateway" -"gemini-2.5-flash" = "blackboxai" -"gemini-2.5-pro" = "vertexai" - -"grok-3" = "pipelex_gateway" -"grok-3-mini" = "xai" diff --git a/.pipelex/pipelex.toml b/.pipelex/pipelex.toml deleted file mode 100644 index 4a2ea38..0000000 --- a/.pipelex/pipelex.toml +++ /dev/null @@ -1,162 +0,0 @@ -#################################################################################################### -# Pipelex Configuration File -#################################################################################################### -# -# This configuration file is copied to client projects' .pipelex/ directory when running: -# `pipelex init config` -# -# Purpose: -# - This file allows to override Pipelex's default settings for specific projects -# - Feel free to modify any settings below to suit your needs -# - You can add any configuration sections that exist in the main pipelex.toml -# -# Finding Available Settings: -# - See the full default configuration in: pipelex/pipelex.toml (in the Pipelex package) -# - See the configuration structure classes in: pipelex/config.py and pipelex/cogt/config_cogt.py -# -# Common customizations are proposed below, such as: -# - Logging levels and behavior -# - Excluded directories for scanning -# - LLM prompt dumping for debugging -# - Feature flags for tracking and reporting -# - Observer and reporting output directories -# -# Documentation: https://docs.pipelex.com -# Support: https://go.pipelex.com/discord -# -#################################################################################################### - -[pipelex.pipeline_execution_config] -# Uncomment to disable conversion of data URLs to pipelex-storage:// URIs -# is_normalize_data_urls_to_storage = false - -[pipelex.pipeline_execution_config.graph_config.data_inclusion] -# Uncomment to include stuff data in graph outputs: -stuff_json_content = true -stuff_text_content = true -stuff_html_content = true -error_stack_traces = true - -[pipelex.pipeline_execution_config.graph_config.graphs_inclusion] -# Uncomment to customize which graph outputs are generated (all enabled by default): -# graphspec_json = false -# mermaidflow_mmd = false -# mermaidflow_html = false -# reactflow_viewspec = false -# reactflow_html = false - -[pipelex.pipeline_execution_config.graph_config.reactflow_config] -# Uncomment to customize ReactFlow graph rendering: -# edge_type = "bezier" # Options: "bezier", "smoothstep", "step", "straight" -# nodesep = 50 # Horizontal spacing between nodes -# ranksep = 80 # Vertical spacing between ranks/levels -# initial_zoom = 1.0 # Initial zoom level (1.0 = 100%) -# pan_to_top = true # Pan to show top of graph on load - -[pipelex.storage_config] -# Storage method: "local" (default), "in_memory", "s3", or "gcp" -# method = "local" - -# Whether to fetch remote HTTP URLs and store them locally -# is_fetch_remote_content_enabled = true - -# --- Local Storage Configuration --- -# Uncomment to customize local storage settings: -[pipelex.storage_config.local] -# uri_format = "{primary_id}/{secondary_id}/{hash}.{extension}" -# local_storage_path = ".pipelex/storage" - -# --- AWS S3 Storage Configuration --- -# Uncomment to use S3 storage (requires boto3: `pip install pipelex[s3]`): -[pipelex.storage_config.s3] -# uri_format = "{primary_id}/{secondary_id}/{hash}.{extension}" -# bucket_name = "your-bucket-name" -# region = "us-east-1" -# signed_urls_lifespan_seconds = 3600 # Set to "disabled" for public URLs - -# --- Google Cloud Storage Configuration --- -# Uncomment to use GCP storage (requires google-cloud-storage: `pip install pipelex[gcp-storage]`): -[pipelex.storage_config.gcp] -# uri_format = "{primary_id}/{secondary_id}/{hash}.{extension}" -# bucket_name = "your-bucket-name" -# project_id = "your-project-id" -# signed_urls_lifespan_seconds = 3600 # Set to "disabled" for public URLs - -[pipelex.scan_config] -# Uncomment to customize the excluded directories for scanning -# excluded_dirs = [ -# ".venv", -# "venv", -# "env", -# ".env", -# "virtualenv", -# ".virtualenv", -# ".git", -# "__pycache__", -# ".pytest_cache", -# ".mypy_cache", -# ".ruff_cache", -# "node_modules", -# "results", -# ] - -[pipelex.builder_config] -# Uncomment to change where the generated pipelines are saved: -# default_output_dir = "." -# default_bundle_file_name = "bundle" -# default_directory_base_name = "pipeline" - -[pipelex.log_config] -# Uncomment to change the default log level: -# default_log_level = "INFO" - -# Uncomment to log to stderr instead of stdout -# console_log_target = "stderr" -# console_print_target = "stderr" - -[pipelex.log_config.package_log_levels] -# Uncomment to change the log level for specific packages: -# pipelex = "INFO" - -[pipelex.observer_config] -# Uncomment to change the directory where the observer will save its results: -# observer_dir = "results/observer" - -[pipelex.feature_config] -# WIP/Experimental feature flags: -# is_pipeline_tracking_enabled = false -# is_reporting_enabled = true - -[pipelex.reporting_config] -# Uncomment to customize the reporting configuration: -# is_log_costs_to_console = false -# is_generate_cost_report_file_enabled = false -# cost_report_dir_path = "reports" -# cost_report_base_name = "cost_report" -# cost_report_extension = "csv" -# cost_report_unit_scale = 1.0 - -[cogt] -[cogt.model_deck_config] -# Uncomment to disable model fallback: it will raise errors instead of using secondary model options: -# is_model_fallback_enabled = false -# Uncomment to change the reaction to missing presets: "raise" (default), "log" or "none" -# missing_presets_reaction = "raise" - -[cogt.tenacity_config] -# Uncomment to change those values as needed: -# max_retries = 50 # Maximum number of retry attempts before giving up -# wait_multiplier = 0.2 # Multiplier applied to the wait time between retries (in seconds) -# wait_max = 20 # Maximum wait time between retries (in seconds) -# wait_exp_base = 1.3 # Base for exponential backoff calculation - -[cogt.llm_config] -# Uncomment any of these to enable dumping the inputs or outputs of text-generation with an LLM: -# is_dump_text_prompts_enabled = true -# is_dump_response_text_enabled = true - -[cogt.llm_config.instructor_config] -# Uncomment any of these to enable dumping the kwargs, response or errors when generating structured content: -# is_dump_kwargs_enabled = true -# is_dump_response_enabled = true -# is_dump_error_enabled = true diff --git a/.pipelex/pipelex_service.toml b/.pipelex/pipelex_service.toml deleted file mode 100644 index afe39a2..0000000 --- a/.pipelex/pipelex_service.toml +++ /dev/null @@ -1,19 +0,0 @@ -#################################################################################################### -# Pipelex Service Configuration -#################################################################################################### -# -# This file stores settings related to Pipelex managed services. -# Currently used for Pipelex Gateway terms acceptance. -# -# Documentation: https://docs.pipelex.com -# Support: https://go.pipelex.com/discord -# -#################################################################################################### - -[agreement] -# Set to true after accepting Pipelex terms of service. -terms_accepted = true - -# Note: when using pipelex_gateway, telemetry is enabled to monitor service usage. -# We collect technical data (model, pipe type...) and quantitative data (token counts...) -# but NOT your content, pipe codes, or output class names. diff --git a/.pipelex/telemetry.toml b/.pipelex/telemetry.toml deleted file mode 100644 index eb2c537..0000000 --- a/.pipelex/telemetry.toml +++ /dev/null @@ -1,92 +0,0 @@ -#################################################################################################### -# Custom Telemetry Configuration -#################################################################################################### -# -# This file controls YOUR custom telemetry settings for observability and analytics. -# Configure your own PostHog, Langfuse, or OTLP-compatible backends here. -# -# NOTE: When using Pipelex Gateway, identified telemetry is automatically enabled -# (tied to your Gateway API key, hashed for security). This allows us to monitor -# service quality, enforce fair usage, and provide you with better support. -# Gateway telemetry operates independently from your settings below - you can have both! -# -# To disable all telemetry, set the DO_NOT_TRACK=1 environment variable. -# -# Documentation: https://docs.pipelex.com -# Support: https://go.pipelex.com/discord -# -#################################################################################################### - -# ────────────────────────────────────────────────────────────────────────────── -# PostHog Configuration (Event tracking + AI span tracing) -# ────────────────────────────────────────────────────────────────────────────── - -[custom_posthog] -mode = "off" # Values: "off" | "anonymous" | "identified" -# user_id = "your_user_id" # Required when mode = "identified" -endpoint = "${POSTHOG_ENDPOINT}" # Default: https://us.i.posthog.com (or https://eu.i.posthog.com for EU) -api_key = "${POSTHOG_API_KEY}" # Get from PostHog Project Settings -geoip = true # Enable GeoIP lookup -debug = false # Enable PostHog debug mode -redact_properties = [ - "prompt", - "system_prompt", - "response", - "file_path", - "url", -] # Event properties to redact - -# AI span tracing to YOUR PostHog (does NOT affect Langfuse/OTLP - they receive full data) -[custom_posthog.tracing] -enabled = false # Send AI spans to your PostHog - -# Privacy controls for data sent to YOUR PostHog only -[custom_posthog.tracing.capture] -content = false # Capture prompt/completion content -# content_max_length = 1000 # Max length for captured content (omit for unlimited) -pipe_codes = false # Include pipe codes in span names/attributes -output_class_names = false # Include output class names in span names/attributes - -# ────────────────────────────────────────────────────────────────────────────── -# Portkey SDK Configuration -# ────────────────────────────────────────────────────────────────────────────── - -[custom_portkey] -force_debug_enabled = false -force_tracing_enabled = false - -# ────────────────────────────────────────────────────────────────────────────── -# Langfuse Integration -# Note: Langfuse receives FULL span data (no redaction) -# ────────────────────────────────────────────────────────────────────────────── - -[langfuse] -enabled = false -# endpoint = "https://cloud.langfuse.com" # Override for self-hosted Langfuse -# public_key = "${LANGFUSE_PUBLIC_KEY}" # Langfuse public key -# secret_key = "${LANGFUSE_SECRET_KEY}" # Langfuse secret key - -# ────────────────────────────────────────────────────────────────────────────── -# Additional OTLP Exporters (array for multiple) -# Note: OTLP exporters receive FULL span data (no redaction) -# ────────────────────────────────────────────────────────────────────────────── - -# [[otlp]] -# name = "my-collector" # Identifier for logging -# endpoint = "https://..." # OTLP endpoint URL -# headers = { Authorization = "Bearer ${OTLP_AUTH_TOKEN}" } # Headers for OTLP export - -# ────────────────────────────────────────────────────────────────────────────── -# Custom Telemetry Allowed Modes -# Controls which integration modes can use custom telemetry settings above. -# ────────────────────────────────────────────────────────────────────────────── - -[telemetry_allowed_modes] -ci = false # CI environments don't use custom telemetry -cli = true # CLI usage allows custom telemetry -docker = true # Docker deployments allow custom telemetry -fastapi = true # FastAPI integrations allow custom telemetry -mcp = true # MCP integrations allow custom telemetry -n8n = true # n8n integrations allow custom telemetry -pytest = false # Tests don't use custom telemetry -python = false # Direct Python SDK usage doesn't use custom telemetry by default diff --git a/.windsurfrules.md b/.windsurfrules.md index 6125caa..af4572e 100644 --- a/.windsurfrules.md +++ b/.windsurfrules.md @@ -23,10 +23,10 @@ A pipeline file has three main sections: #### Domain Statement ```plx -domain = "domain_name" +domain = "domain_code" description = "Description of the domain" # Optional ``` -Note: The domain name usually matches the plx filename for single-file domains. For multi-file domains, use the subdirectory name. +Note: The domain code usually matches the plx filename for single-file domains. For multi-file domains, use the subdirectory name. #### Concept Definitions @@ -62,7 +62,7 @@ For details on how to structure concepts with fields, see the "Structuring Model ### Pipe Base Definition ```plx -[pipe.your_pipe_name] +[pipe.your_pipe_code] type = "PipeLLM" description = "A description of what your pipe does" inputs = { input_1 = "ConceptName1", input_2 = "ConceptName2" } @@ -471,7 +471,7 @@ The PipeExtract operator is used to extract text and images from an image or a P [pipe.extract_info] type = "PipeExtract" description = "extract the information" -inputs = { document = "PDF" } # or { image = "Image" } if it's an image. This is the only input. +inputs = { document = "Document" } # or { image = "Image" } if it's an image. This is the only input. output = "Page" ``` @@ -480,7 +480,7 @@ Using Extract Model Settings: [pipe.extract_with_model] type = "PipeExtract" description = "Extract with specific model" -inputs = { document = "PDF" } +inputs = { document = "Document" } output = "Page" model = "base_extract_mistral" # Use predefined extract preset or model alias ``` @@ -588,15 +588,16 @@ $sales_rep.phone | $sales_rep.email """ ``` -#### Key Parameters +#### Key Parameters (Template Mode) -- `template`: Inline template string (mutually exclusive with template_name) +- `template`: Inline template string (mutually exclusive with template_name and construct) - `template_name`: Name of a predefined template (mutually exclusive with template) - `template_category`: Template type ("llm_prompt", "html", "markdown", "mermaid", etc.) - `templating_style`: Styling options for template rendering - `extra_context`: Additional context variables for template For more control, you can use a nested `template` section instead of the `template` field: + - `template.template`: The template string - `template.category`: Template type - `template.templating_style`: Styling options @@ -604,9 +605,143 @@ For more control, you can use a nested `template` section instead of the `templa #### Template Variables Use the same variable insertion rules as PipeLLM: + - `@variable` for block insertion (multi-line content) - `$variable` for inline insertion (short text) +#### Construct Mode (for StructuredContent Output) + +PipeCompose can also generate `StructuredContent` objects using the `construct` section. This mode composes field values from fixed values, variable references, templates, or nested structures. + +**When to use construct mode:** + +- You need to output a structured object (not just Text) +- You want to deterministically compose fields from existing data +- No LLM is needed - just data composition and templating + +##### Basic Construct Usage + +```plx +[concept.SalesSummary] +description = "A structured sales summary" + +[concept.SalesSummary.structure] +report_title = { type = "text", description = "Title of the report" } +customer_name = { type = "text", description = "Customer name" } +deal_value = { type = "number", description = "Deal value" } +summary_text = { type = "text", description = "Generated summary text" } + +[pipe.compose_summary] +type = "PipeCompose" +description = "Compose a sales summary from deal data" +inputs = { deal = "Deal" } +output = "SalesSummary" + +[pipe.compose_summary.construct] +report_title = "Monthly Sales Report" +customer_name = { from = "deal.customer_name" } +deal_value = { from = "deal.amount" } +summary_text = { template = "Deal worth $deal.amount with $deal.customer_name" } +``` + +##### Field Composition Methods + +There are four ways to define field values in a construct: + +**1. Fixed Value (literal)** + +Use a literal value directly: + +```plx +[pipe.compose_report.construct] +report_title = "Annual Report" +report_year = 2024 +is_draft = false +``` + +**2. Variable Reference (`from`)** + +Get a value from working memory using a dotted path: + +```plx +[pipe.compose_report.construct] +customer_name = { from = "deal.customer_name" } +total_amount = { from = "order.total" } +street_address = { from = "customer.address.street" } +``` + +**3. Template (`template`)** + +Render a Jinja2 template with variable substitution: + +```plx +[pipe.compose_report.construct] +invoice_number = { template = "INV-$order.id" } +summary = { template = "Deal worth $deal.amount with $deal.customer_name on {{ current_date }}" } +``` + +**4. Nested Construct** + +For nested structures, use a TOML subsection: + +```plx +[pipe.compose_invoice.construct] +invoice_number = { template = "INV-$order.id" } +total = { from = "order.total_amount" } + +[pipe.compose_invoice.construct.billing_address] +street = { from = "customer.address.street" } +city = { from = "customer.address.city" } +country = "France" +``` + +##### Complete Construct Example + +```plx +domain = "invoicing" + +[concept.Address] +description = "A postal address" + +[concept.Address.structure] +street = { type = "text", description = "Street address" } +city = { type = "text", description = "City name" } +country = { type = "text", description = "Country name" } + +[concept.Invoice] +description = "An invoice document" + +[concept.Invoice.structure] +invoice_number = { type = "text", description = "Invoice number" } +total = { type = "number", description = "Total amount" } + +[pipe.compose_invoice] +type = "PipeCompose" +description = "Compose an invoice from order and customer data" +inputs = { order = "Order", customer = "Customer" } +output = "Invoice" + +[pipe.compose_invoice.construct] +invoice_number = { template = "INV-$order.id" } +total = { from = "order.total_amount" } + +[pipe.compose_invoice.construct.billing_address] +street = { from = "customer.address.street" } +city = { from = "customer.address.city" } +country = "France" +``` + +##### Key Parameters (Construct Mode) + +- `construct`: Dictionary mapping field names to their composition rules +- Each field can be: + - A literal value (string, number, boolean) + - A dict with `from` key for variable reference + - A dict with `template` key for template rendering + - A nested dict for nested structures + +**Note:** You must use either `template` or `construct`, not both. They are mutually exclusive. + ### PipeImgGen operator The PipeImgGen operator is used to generate images using AI image generation models. @@ -952,13 +1087,13 @@ So here are a few concrete examples of calls to execute_pipeline with various wa }, ) -## Here we have a single input and it's a PDF. -## Because PDFContent is a native concept, we can use it directly as a value, +## Here we have a single input and it's a document. +## Because DocumentContent is a native concept, we can use it directly as a value, ## the system knows what content it corresponds to: pipe_output = await execute_pipeline( pipe_code="power_extractor_dpe", inputs={ - "document": PDFContent(url=pdf_url), + "document": DocumentContent(url=pdf_url), }, ) @@ -1081,82 +1216,4 @@ result_list = pipe_output.main_stuff_as_items(item_type=GanttChart) ``` --- - -## Rules to choose LLM models used in PipeLLMs. - -### LLM Configuration System - -In order to use it in a pipe, an LLM is referenced by its llm_handle (alias) and possibly by an llm_preset. -LLM configurations are managed through the new inference backend system with files located in `.pipelex/inference/`: - -- **Model Deck**: `.pipelex/inference/deck/base_deck.toml` and `.pipelex/inference/deck/overrides.toml` -- **Backends**: `.pipelex/inference/backends.toml` and `.pipelex/inference/backends/*.toml` -- **Routing**: `.pipelex/inference/routing_profiles.toml` - -### LLM Handles - -An llm_handle can be either: -1. **A direct model name** (like "gpt-4o-mini", "claude-3-sonnet") - automatically available for all models loaded by the inference backend system -2. **An alias** - user-defined shortcuts that map to model names, defined in the `[aliases]` section: - -```toml -[aliases] -base-claude = "claude-4.5-sonnet" -base-gpt = "gpt-5" -base-gemini = "gemini-2.5-flash" -base-mistral = "mistral-medium" -``` - -The system first looks for direct model names, then checks aliases if no direct match is found. The system handles model routing through backends automatically. - -### Using an LLM Handle in a PipeLLM - -Here is an example of using an llm_handle to specify which LLM to use in a PipeLLM: - -```plx -[pipe.hello_world] -type = "PipeLLM" -description = "Write text about Hello World." -output = "Text" -model = { model = "gpt-5", temperature = 0.9 } -prompt = """ -Write a haiku about Hello World. -""" -``` - -As you can see, to use the LLM, you must also indicate the temperature (float between 0 and 1) and max_tokens (either an int or the string "auto"). - -### LLM Presets - -Presets are meant to record the choice of an llm with its hyper parameters (temperature and max_tokens) if it's good for a particular task. LLM Presets are skill-oriented. - -Examples: -```toml -llm_to_engineer = { model = "base-claude", temperature = 1 } -llm_to_extract_invoice = { model = "claude-4.5-sonnet", temperature = 0.1, max_tokens = "auto" } -``` - -The interest is that these presets can be used to set the LLM choice in a PipeLLM, like this: - -```plx -[pipe.extract_invoice] -type = "PipeLLM" -description = "Extract invoice information from an invoice text transcript" -inputs = { invoice_text = "InvoiceText" } -output = "Invoice" -model = "llm_to_extract_invoice" -prompt = """ -Extract invoice information from this invoice: - -The category of this invoice is: $invoice_details.category. - -@invoice_text -""" -``` - -The setting here `model = "llm_to_extract_invoice"` works because "llm_to_extract_invoice" has been declared as an llm_preset in the deck. -You must not use an LLM preset in a PipeLLM that does not exist in the deck. If needed, you can add llm presets. - - -You can override the predefined llm presets by setting them in `.pipelex/inference/deck/overrides.toml`. diff --git a/AGENTS.md b/AGENTS.md index 6125caa..af4572e 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -23,10 +23,10 @@ A pipeline file has three main sections: #### Domain Statement ```plx -domain = "domain_name" +domain = "domain_code" description = "Description of the domain" # Optional ``` -Note: The domain name usually matches the plx filename for single-file domains. For multi-file domains, use the subdirectory name. +Note: The domain code usually matches the plx filename for single-file domains. For multi-file domains, use the subdirectory name. #### Concept Definitions @@ -62,7 +62,7 @@ For details on how to structure concepts with fields, see the "Structuring Model ### Pipe Base Definition ```plx -[pipe.your_pipe_name] +[pipe.your_pipe_code] type = "PipeLLM" description = "A description of what your pipe does" inputs = { input_1 = "ConceptName1", input_2 = "ConceptName2" } @@ -471,7 +471,7 @@ The PipeExtract operator is used to extract text and images from an image or a P [pipe.extract_info] type = "PipeExtract" description = "extract the information" -inputs = { document = "PDF" } # or { image = "Image" } if it's an image. This is the only input. +inputs = { document = "Document" } # or { image = "Image" } if it's an image. This is the only input. output = "Page" ``` @@ -480,7 +480,7 @@ Using Extract Model Settings: [pipe.extract_with_model] type = "PipeExtract" description = "Extract with specific model" -inputs = { document = "PDF" } +inputs = { document = "Document" } output = "Page" model = "base_extract_mistral" # Use predefined extract preset or model alias ``` @@ -588,15 +588,16 @@ $sales_rep.phone | $sales_rep.email """ ``` -#### Key Parameters +#### Key Parameters (Template Mode) -- `template`: Inline template string (mutually exclusive with template_name) +- `template`: Inline template string (mutually exclusive with template_name and construct) - `template_name`: Name of a predefined template (mutually exclusive with template) - `template_category`: Template type ("llm_prompt", "html", "markdown", "mermaid", etc.) - `templating_style`: Styling options for template rendering - `extra_context`: Additional context variables for template For more control, you can use a nested `template` section instead of the `template` field: + - `template.template`: The template string - `template.category`: Template type - `template.templating_style`: Styling options @@ -604,9 +605,143 @@ For more control, you can use a nested `template` section instead of the `templa #### Template Variables Use the same variable insertion rules as PipeLLM: + - `@variable` for block insertion (multi-line content) - `$variable` for inline insertion (short text) +#### Construct Mode (for StructuredContent Output) + +PipeCompose can also generate `StructuredContent` objects using the `construct` section. This mode composes field values from fixed values, variable references, templates, or nested structures. + +**When to use construct mode:** + +- You need to output a structured object (not just Text) +- You want to deterministically compose fields from existing data +- No LLM is needed - just data composition and templating + +##### Basic Construct Usage + +```plx +[concept.SalesSummary] +description = "A structured sales summary" + +[concept.SalesSummary.structure] +report_title = { type = "text", description = "Title of the report" } +customer_name = { type = "text", description = "Customer name" } +deal_value = { type = "number", description = "Deal value" } +summary_text = { type = "text", description = "Generated summary text" } + +[pipe.compose_summary] +type = "PipeCompose" +description = "Compose a sales summary from deal data" +inputs = { deal = "Deal" } +output = "SalesSummary" + +[pipe.compose_summary.construct] +report_title = "Monthly Sales Report" +customer_name = { from = "deal.customer_name" } +deal_value = { from = "deal.amount" } +summary_text = { template = "Deal worth $deal.amount with $deal.customer_name" } +``` + +##### Field Composition Methods + +There are four ways to define field values in a construct: + +**1. Fixed Value (literal)** + +Use a literal value directly: + +```plx +[pipe.compose_report.construct] +report_title = "Annual Report" +report_year = 2024 +is_draft = false +``` + +**2. Variable Reference (`from`)** + +Get a value from working memory using a dotted path: + +```plx +[pipe.compose_report.construct] +customer_name = { from = "deal.customer_name" } +total_amount = { from = "order.total" } +street_address = { from = "customer.address.street" } +``` + +**3. Template (`template`)** + +Render a Jinja2 template with variable substitution: + +```plx +[pipe.compose_report.construct] +invoice_number = { template = "INV-$order.id" } +summary = { template = "Deal worth $deal.amount with $deal.customer_name on {{ current_date }}" } +``` + +**4. Nested Construct** + +For nested structures, use a TOML subsection: + +```plx +[pipe.compose_invoice.construct] +invoice_number = { template = "INV-$order.id" } +total = { from = "order.total_amount" } + +[pipe.compose_invoice.construct.billing_address] +street = { from = "customer.address.street" } +city = { from = "customer.address.city" } +country = "France" +``` + +##### Complete Construct Example + +```plx +domain = "invoicing" + +[concept.Address] +description = "A postal address" + +[concept.Address.structure] +street = { type = "text", description = "Street address" } +city = { type = "text", description = "City name" } +country = { type = "text", description = "Country name" } + +[concept.Invoice] +description = "An invoice document" + +[concept.Invoice.structure] +invoice_number = { type = "text", description = "Invoice number" } +total = { type = "number", description = "Total amount" } + +[pipe.compose_invoice] +type = "PipeCompose" +description = "Compose an invoice from order and customer data" +inputs = { order = "Order", customer = "Customer" } +output = "Invoice" + +[pipe.compose_invoice.construct] +invoice_number = { template = "INV-$order.id" } +total = { from = "order.total_amount" } + +[pipe.compose_invoice.construct.billing_address] +street = { from = "customer.address.street" } +city = { from = "customer.address.city" } +country = "France" +``` + +##### Key Parameters (Construct Mode) + +- `construct`: Dictionary mapping field names to their composition rules +- Each field can be: + - A literal value (string, number, boolean) + - A dict with `from` key for variable reference + - A dict with `template` key for template rendering + - A nested dict for nested structures + +**Note:** You must use either `template` or `construct`, not both. They are mutually exclusive. + ### PipeImgGen operator The PipeImgGen operator is used to generate images using AI image generation models. @@ -952,13 +1087,13 @@ So here are a few concrete examples of calls to execute_pipeline with various wa }, ) -## Here we have a single input and it's a PDF. -## Because PDFContent is a native concept, we can use it directly as a value, +## Here we have a single input and it's a document. +## Because DocumentContent is a native concept, we can use it directly as a value, ## the system knows what content it corresponds to: pipe_output = await execute_pipeline( pipe_code="power_extractor_dpe", inputs={ - "document": PDFContent(url=pdf_url), + "document": DocumentContent(url=pdf_url), }, ) @@ -1081,82 +1216,4 @@ result_list = pipe_output.main_stuff_as_items(item_type=GanttChart) ``` --- - -## Rules to choose LLM models used in PipeLLMs. - -### LLM Configuration System - -In order to use it in a pipe, an LLM is referenced by its llm_handle (alias) and possibly by an llm_preset. -LLM configurations are managed through the new inference backend system with files located in `.pipelex/inference/`: - -- **Model Deck**: `.pipelex/inference/deck/base_deck.toml` and `.pipelex/inference/deck/overrides.toml` -- **Backends**: `.pipelex/inference/backends.toml` and `.pipelex/inference/backends/*.toml` -- **Routing**: `.pipelex/inference/routing_profiles.toml` - -### LLM Handles - -An llm_handle can be either: -1. **A direct model name** (like "gpt-4o-mini", "claude-3-sonnet") - automatically available for all models loaded by the inference backend system -2. **An alias** - user-defined shortcuts that map to model names, defined in the `[aliases]` section: - -```toml -[aliases] -base-claude = "claude-4.5-sonnet" -base-gpt = "gpt-5" -base-gemini = "gemini-2.5-flash" -base-mistral = "mistral-medium" -``` - -The system first looks for direct model names, then checks aliases if no direct match is found. The system handles model routing through backends automatically. - -### Using an LLM Handle in a PipeLLM - -Here is an example of using an llm_handle to specify which LLM to use in a PipeLLM: - -```plx -[pipe.hello_world] -type = "PipeLLM" -description = "Write text about Hello World." -output = "Text" -model = { model = "gpt-5", temperature = 0.9 } -prompt = """ -Write a haiku about Hello World. -""" -``` - -As you can see, to use the LLM, you must also indicate the temperature (float between 0 and 1) and max_tokens (either an int or the string "auto"). - -### LLM Presets - -Presets are meant to record the choice of an llm with its hyper parameters (temperature and max_tokens) if it's good for a particular task. LLM Presets are skill-oriented. - -Examples: -```toml -llm_to_engineer = { model = "base-claude", temperature = 1 } -llm_to_extract_invoice = { model = "claude-4.5-sonnet", temperature = 0.1, max_tokens = "auto" } -``` - -The interest is that these presets can be used to set the LLM choice in a PipeLLM, like this: - -```plx -[pipe.extract_invoice] -type = "PipeLLM" -description = "Extract invoice information from an invoice text transcript" -inputs = { invoice_text = "InvoiceText" } -output = "Invoice" -model = "llm_to_extract_invoice" -prompt = """ -Extract invoice information from this invoice: - -The category of this invoice is: $invoice_details.category. - -@invoice_text -""" -``` - -The setting here `model = "llm_to_extract_invoice"` works because "llm_to_extract_invoice" has been declared as an llm_preset in the deck. -You must not use an LLM preset in a PipeLLM that does not exist in the deck. If needed, you can add llm presets. - - -You can override the predefined llm presets by setting them in `.pipelex/inference/deck/overrides.toml`. diff --git a/CLAUDE.md b/CLAUDE.md index 6125caa..af4572e 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -23,10 +23,10 @@ A pipeline file has three main sections: #### Domain Statement ```plx -domain = "domain_name" +domain = "domain_code" description = "Description of the domain" # Optional ``` -Note: The domain name usually matches the plx filename for single-file domains. For multi-file domains, use the subdirectory name. +Note: The domain code usually matches the plx filename for single-file domains. For multi-file domains, use the subdirectory name. #### Concept Definitions @@ -62,7 +62,7 @@ For details on how to structure concepts with fields, see the "Structuring Model ### Pipe Base Definition ```plx -[pipe.your_pipe_name] +[pipe.your_pipe_code] type = "PipeLLM" description = "A description of what your pipe does" inputs = { input_1 = "ConceptName1", input_2 = "ConceptName2" } @@ -471,7 +471,7 @@ The PipeExtract operator is used to extract text and images from an image or a P [pipe.extract_info] type = "PipeExtract" description = "extract the information" -inputs = { document = "PDF" } # or { image = "Image" } if it's an image. This is the only input. +inputs = { document = "Document" } # or { image = "Image" } if it's an image. This is the only input. output = "Page" ``` @@ -480,7 +480,7 @@ Using Extract Model Settings: [pipe.extract_with_model] type = "PipeExtract" description = "Extract with specific model" -inputs = { document = "PDF" } +inputs = { document = "Document" } output = "Page" model = "base_extract_mistral" # Use predefined extract preset or model alias ``` @@ -588,15 +588,16 @@ $sales_rep.phone | $sales_rep.email """ ``` -#### Key Parameters +#### Key Parameters (Template Mode) -- `template`: Inline template string (mutually exclusive with template_name) +- `template`: Inline template string (mutually exclusive with template_name and construct) - `template_name`: Name of a predefined template (mutually exclusive with template) - `template_category`: Template type ("llm_prompt", "html", "markdown", "mermaid", etc.) - `templating_style`: Styling options for template rendering - `extra_context`: Additional context variables for template For more control, you can use a nested `template` section instead of the `template` field: + - `template.template`: The template string - `template.category`: Template type - `template.templating_style`: Styling options @@ -604,9 +605,143 @@ For more control, you can use a nested `template` section instead of the `templa #### Template Variables Use the same variable insertion rules as PipeLLM: + - `@variable` for block insertion (multi-line content) - `$variable` for inline insertion (short text) +#### Construct Mode (for StructuredContent Output) + +PipeCompose can also generate `StructuredContent` objects using the `construct` section. This mode composes field values from fixed values, variable references, templates, or nested structures. + +**When to use construct mode:** + +- You need to output a structured object (not just Text) +- You want to deterministically compose fields from existing data +- No LLM is needed - just data composition and templating + +##### Basic Construct Usage + +```plx +[concept.SalesSummary] +description = "A structured sales summary" + +[concept.SalesSummary.structure] +report_title = { type = "text", description = "Title of the report" } +customer_name = { type = "text", description = "Customer name" } +deal_value = { type = "number", description = "Deal value" } +summary_text = { type = "text", description = "Generated summary text" } + +[pipe.compose_summary] +type = "PipeCompose" +description = "Compose a sales summary from deal data" +inputs = { deal = "Deal" } +output = "SalesSummary" + +[pipe.compose_summary.construct] +report_title = "Monthly Sales Report" +customer_name = { from = "deal.customer_name" } +deal_value = { from = "deal.amount" } +summary_text = { template = "Deal worth $deal.amount with $deal.customer_name" } +``` + +##### Field Composition Methods + +There are four ways to define field values in a construct: + +**1. Fixed Value (literal)** + +Use a literal value directly: + +```plx +[pipe.compose_report.construct] +report_title = "Annual Report" +report_year = 2024 +is_draft = false +``` + +**2. Variable Reference (`from`)** + +Get a value from working memory using a dotted path: + +```plx +[pipe.compose_report.construct] +customer_name = { from = "deal.customer_name" } +total_amount = { from = "order.total" } +street_address = { from = "customer.address.street" } +``` + +**3. Template (`template`)** + +Render a Jinja2 template with variable substitution: + +```plx +[pipe.compose_report.construct] +invoice_number = { template = "INV-$order.id" } +summary = { template = "Deal worth $deal.amount with $deal.customer_name on {{ current_date }}" } +``` + +**4. Nested Construct** + +For nested structures, use a TOML subsection: + +```plx +[pipe.compose_invoice.construct] +invoice_number = { template = "INV-$order.id" } +total = { from = "order.total_amount" } + +[pipe.compose_invoice.construct.billing_address] +street = { from = "customer.address.street" } +city = { from = "customer.address.city" } +country = "France" +``` + +##### Complete Construct Example + +```plx +domain = "invoicing" + +[concept.Address] +description = "A postal address" + +[concept.Address.structure] +street = { type = "text", description = "Street address" } +city = { type = "text", description = "City name" } +country = { type = "text", description = "Country name" } + +[concept.Invoice] +description = "An invoice document" + +[concept.Invoice.structure] +invoice_number = { type = "text", description = "Invoice number" } +total = { type = "number", description = "Total amount" } + +[pipe.compose_invoice] +type = "PipeCompose" +description = "Compose an invoice from order and customer data" +inputs = { order = "Order", customer = "Customer" } +output = "Invoice" + +[pipe.compose_invoice.construct] +invoice_number = { template = "INV-$order.id" } +total = { from = "order.total_amount" } + +[pipe.compose_invoice.construct.billing_address] +street = { from = "customer.address.street" } +city = { from = "customer.address.city" } +country = "France" +``` + +##### Key Parameters (Construct Mode) + +- `construct`: Dictionary mapping field names to their composition rules +- Each field can be: + - A literal value (string, number, boolean) + - A dict with `from` key for variable reference + - A dict with `template` key for template rendering + - A nested dict for nested structures + +**Note:** You must use either `template` or `construct`, not both. They are mutually exclusive. + ### PipeImgGen operator The PipeImgGen operator is used to generate images using AI image generation models. @@ -952,13 +1087,13 @@ So here are a few concrete examples of calls to execute_pipeline with various wa }, ) -## Here we have a single input and it's a PDF. -## Because PDFContent is a native concept, we can use it directly as a value, +## Here we have a single input and it's a document. +## Because DocumentContent is a native concept, we can use it directly as a value, ## the system knows what content it corresponds to: pipe_output = await execute_pipeline( pipe_code="power_extractor_dpe", inputs={ - "document": PDFContent(url=pdf_url), + "document": DocumentContent(url=pdf_url), }, ) @@ -1081,82 +1216,4 @@ result_list = pipe_output.main_stuff_as_items(item_type=GanttChart) ``` --- - -## Rules to choose LLM models used in PipeLLMs. - -### LLM Configuration System - -In order to use it in a pipe, an LLM is referenced by its llm_handle (alias) and possibly by an llm_preset. -LLM configurations are managed through the new inference backend system with files located in `.pipelex/inference/`: - -- **Model Deck**: `.pipelex/inference/deck/base_deck.toml` and `.pipelex/inference/deck/overrides.toml` -- **Backends**: `.pipelex/inference/backends.toml` and `.pipelex/inference/backends/*.toml` -- **Routing**: `.pipelex/inference/routing_profiles.toml` - -### LLM Handles - -An llm_handle can be either: -1. **A direct model name** (like "gpt-4o-mini", "claude-3-sonnet") - automatically available for all models loaded by the inference backend system -2. **An alias** - user-defined shortcuts that map to model names, defined in the `[aliases]` section: - -```toml -[aliases] -base-claude = "claude-4.5-sonnet" -base-gpt = "gpt-5" -base-gemini = "gemini-2.5-flash" -base-mistral = "mistral-medium" -``` - -The system first looks for direct model names, then checks aliases if no direct match is found. The system handles model routing through backends automatically. - -### Using an LLM Handle in a PipeLLM - -Here is an example of using an llm_handle to specify which LLM to use in a PipeLLM: - -```plx -[pipe.hello_world] -type = "PipeLLM" -description = "Write text about Hello World." -output = "Text" -model = { model = "gpt-5", temperature = 0.9 } -prompt = """ -Write a haiku about Hello World. -""" -``` - -As you can see, to use the LLM, you must also indicate the temperature (float between 0 and 1) and max_tokens (either an int or the string "auto"). - -### LLM Presets - -Presets are meant to record the choice of an llm with its hyper parameters (temperature and max_tokens) if it's good for a particular task. LLM Presets are skill-oriented. - -Examples: -```toml -llm_to_engineer = { model = "base-claude", temperature = 1 } -llm_to_extract_invoice = { model = "claude-4.5-sonnet", temperature = 0.1, max_tokens = "auto" } -``` - -The interest is that these presets can be used to set the LLM choice in a PipeLLM, like this: - -```plx -[pipe.extract_invoice] -type = "PipeLLM" -description = "Extract invoice information from an invoice text transcript" -inputs = { invoice_text = "InvoiceText" } -output = "Invoice" -model = "llm_to_extract_invoice" -prompt = """ -Extract invoice information from this invoice: - -The category of this invoice is: $invoice_details.category. - -@invoice_text -""" -``` - -The setting here `model = "llm_to_extract_invoice"` works because "llm_to_extract_invoice" has been declared as an llm_preset in the deck. -You must not use an LLM preset in a PipeLLM that does not exist in the deck. If needed, you can add llm presets. - - -You can override the predefined llm presets by setting them in `.pipelex/inference/deck/overrides.toml`. diff --git a/crazy/__init__.py b/crazy/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/crazy/bundle.plx b/crazy/bundle.plx deleted file mode 100644 index af706cd..0000000 --- a/crazy/bundle.plx +++ /dev/null @@ -1,42 +0,0 @@ -domain = "crazy_image_generation" -description = "Imagining and rendering absurd, funny images with unexpected surreal elements" -main_pipe = "generate_crazy_image" - -[concept.ImagePrompt] -description = """ -A detailed textual description of a scene to be used as input for an image generation model, including subjects, setting, style, and visual details. -""" -refines = "Text" - -[pipe.generate_crazy_image] -type = "PipeSequence" -description = """ -Main pipeline that orchestrates the full crazy image generation flow - imagines a wild, absurd scene concept and renders it as an image -""" -output = "Image" -steps = [ - { pipe = "imagine_scene", result = "image_prompt" }, - { pipe = "render_image", result = "crazy_image" }, -] - -[pipe.imagine_scene] -type = "PipeLLM" -description = """ -Generates a creative, absurd, and hilarious image concept combining unexpected elements in surreal ways - think flying spaghetti monsters, penguins in business suits at a disco, or a T-Rex doing yoga on the moon -""" -output = "ImagePrompt" -model = "cheap_llm_for_creativity" -system_prompt = """ -You are a wildly creative visual concept artist specializing in absurd, surreal, and hilarious imagery. Your task is to generate a structured image prompt that combines unexpected elements in surprising and funny ways. Think outside the box - the more unexpected the combination, the better! -""" -prompt = """ -Generate a creative, absurd, and funny image concept. Combine unexpected elements in a surreal way that would make viewers laugh or do a double-take. Be VERY concise and focus on vivid, specific visual details that work well for image generation. -""" - -[pipe.render_image] -type = "PipeImgGen" -description = "Generates the absurd image based on the creative scene description" -inputs = { image_prompt = "ImagePrompt" } -output = "Image" -prompt = "$image_prompt" -model = "gen_image_high_quality" diff --git a/crazy/bundle_view.html b/crazy/bundle_view.html deleted file mode 100644 index 0e294d7..0000000 --- a/crazy/bundle_view.html +++ /dev/null @@ -1,111 +0,0 @@ - - - - - - - - 
Domain: crazy_image_generation
-
-Description: Imagining and rendering absurd, funny images with unexpected surreal elements
-
-Main Pipe: generate_crazy_image
-
-
-
-                                              Concepts                                              
-┌──────────────────────────────────────────────────────────────────────────────────────────────────┐
-│ Concept: ImagePrompt                                                                             │
-│ Refines: Text                                                                                    │
-│                                                                                                  │
-│ Description: A detailed textual description of a scene to be used as input for an image          │
-│ generation model, including subjects, setting, style, and visual details.                        │
-│                                                                                                  │
-└──────────────────────────────────────────────────────────────────────────────────────────────────┘
-
-
-                                               Pipes                                                
-┌──────────────────────────────────────────────────────────────────────────────────────────────────┐
-│ Pipe: generate_crazy_image                                                                       │
-│                                                                                                  │
-│ Type: PipeSequence (PipeController)                                                              │
-│                                                                                                  │
-│ Description: Main pipeline that orchestrates the full crazy image generation flow - imagines a   │
-│ wild, absurd scene concept and renders it as an image                                            │
-│                                                                                                  │
-│                                                                                                  │
-│ No inputs                                                                                        │
-│                                                                                                  │
-│ Output: Image                                                                                    │
-│                                                                                                  │
-│ Sequence Steps:                                                                                  │
-│ ┏━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┓                                                          │
-│ ┃ Step ┃ Pipe          ┃ Result name  ┃                                                          │
-│ ┡━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━┩                                                          │
-│ │    1 │ imagine_scene │ image_prompt │                                                          │
-│ ├──────┼───────────────┼──────────────┤                                                          │
-│ │    2 │ render_image  │ crazy_image  │                                                          │
-│ └──────┴───────────────┴──────────────┘                                                          │
-├──────────────────────────────────────────────────────────────────────────────────────────────────┤
-│ Pipe: imagine_scene                                                                              │
-│                                                                                                  │
-│ Type: PipeLLM (PipeOperator)                                                                     │
-│                                                                                                  │
-│ Description: Generates a creative, absurd, and hilarious image concept combining unexpected      │
-│ elements in surreal ways - think flying spaghetti monsters, penguins in business suits at a      │
-│ disco, or a T-Rex doing yoga on the moon                                                         │
-│                                                                                                  │
-│                                                                                                  │
-│ No inputs                                                                                        │
-│                                                                                                  │
-│ Output: ImagePrompt                                                                              │
-│                                                                                                  │
-│ LLM Skill: cheap_llm_for_creativity                                                              │
-│                                                                                                  │
-│ ╭─ System Prompt ──────────────────────────────────────────────────────────────────────────────╮ │
-│ │ You are a wildly creative visual concept artist specializing in absurd, surreal, and         │ │
-│ │ hilarious imagery. Your task is to generate a structured image prompt that combines          │ │
-│ │ unexpected elements in surprising and funny ways. Think outside the box - the more           │ │
-│ │ unexpected the combination, the better!                                                      │ │
-│ ╰──────────────────────────────────────────────────────────────────────────────────────────────╯ │
-│                                                                                                  │
-│ ╭─ Prompt ─────────────────────────────────────────────────────────────────────────────────────╮ │
-│ │ Generate a creative, absurd, and funny image concept. Combine unexpected elements in a       │ │
-│ │ surreal way that would make viewers laugh or do a double-take. Be VERY concise and focus on  │ │
-│ │ vivid, specific visual details that work well for image generation.                          │ │
-│ ╰──────────────────────────────────────────────────────────────────────────────────────────────╯ │
-├──────────────────────────────────────────────────────────────────────────────────────────────────┤
-│ Pipe: render_image                                                                               │
-│                                                                                                  │
-│ Type: PipeImgGen (PipeOperator)                                                                  │
-│                                                                                                  │
-│ Description: Generates the absurd image based on the creative scene description                  │
-│                                                                                                  │
-│                                                                                                  │
-│ Input: image_prompt (ImagePrompt)                                                                │
-│                                                                                                  │
-│ Output: Image                                                                                    │
-│                                                                                                  │
-│ Image Generation Skill: gen_image_high_quality                                                   │
-└──────────────────────────────────────────────────────────────────────────────────────────────────┘
-
- - diff --git a/crazy/bundle_view.svg b/crazy/bundle_view.svg deleted file mode 100644 index a7dbf52..0000000 --- a/crazy/bundle_view.svg +++ /dev/null @@ -1,397 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Rich - - - - - - - - - - Domain: crazy_image_generation - -Description: Imagining and rendering absurd, funny images with unexpected surreal elements - -Main Pipe: generate_crazy_image - - - -                                              Concepts                                               -┌──────────────────────────────────────────────────────────────────────────────────────────────────┐ -│Concept: ImagePrompt│ -│Refines: Text│ -││ -│Description: A detailed textual description of a scene to be used as input for an image │ -│generation model, including subjects, setting, style, and visual details.│ -││ -└──────────────────────────────────────────────────────────────────────────────────────────────────┘ - - -                                               Pipes                                                 -┌──────────────────────────────────────────────────────────────────────────────────────────────────┐ -│Pipe: generate_crazy_image│ -││ -│Type: PipeSequence (PipeController)                                                             │ -││ -│Description: Main pipeline that orchestrates the full crazy image generation flow - imagines a │ -│wild, absurd scene concept and renders it as an image│ -││ -││ -│No inputs                                                                                       │ -││ -│Output: Image│ -││ -│Sequence Steps:                        │ -│┏━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┓│ -│┃Step┃Pipe         ┃Result name ┃│ -│┡━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━┩│ -││   1│imagine_scene│image_prompt││ -│├──────┼───────────────┼──────────────┤│ -││   2│render_image │crazy_image ││ -│└──────┴───────────────┴──────────────┘│ -├──────────────────────────────────────────────────────────────────────────────────────────────────┤ -│Pipe: imagine_scene│ -││ -│Type: PipeLLM (PipeOperator)                                                                    │ -││ -│Description: Generates a creative, absurd, and hilarious image concept combining unexpected │ -│elements in surreal ways - think flying spaghetti monsters, penguins in business suits at a │ -│disco, or a T-Rex doing yoga on the moon│ -││ -││ -│No inputs                                                                                       │ -││ -│Output: ImagePrompt│ -││ -│LLM Skill: cheap_llm_for_creativity│ -││ -│╭─ System Prompt ──────────────────────────────────────────────────────────────────────────────╮│ -││You are a wildly creative visual concept artist specializing in absurd, surreal, and        ││ -││hilarious imagery. Your task is to generate a structured image prompt that combines         ││ -││unexpected elements in surprising and funny ways. Think outside the box - the more          ││ -││unexpected the combination, the better!                                                     ││ -│╰──────────────────────────────────────────────────────────────────────────────────────────────╯│ -││ -│╭─ Prompt ─────────────────────────────────────────────────────────────────────────────────────╮│ -││Generate a creative, absurd, and funny image concept. Combine unexpected elements in a      ││ -││surreal way that would make viewers laugh or do a double-take. Be VERY concise and focus on ││ -││vivid, specific visual details that work well for image generation.                         ││ -│╰──────────────────────────────────────────────────────────────────────────────────────────────╯│ -├──────────────────────────────────────────────────────────────────────────────────────────────────┤ -│Pipe: render_image│ -││ -│Type: PipeImgGen (PipeOperator)                                                                 │ -││ -│Description: Generates the absurd image based on the creative scene description│ -││ -││ -│Input: image_prompt (ImagePrompt)                                                               │ -││ -│Output: Image│ -││ -│Image Generation Skill: gen_image_high_quality│ -└──────────────────────────────────────────────────────────────────────────────────────────────────┘ - - - - diff --git a/crazy/inputs.json b/crazy/inputs.json deleted file mode 100644 index 9e26dfe..0000000 --- a/crazy/inputs.json +++ /dev/null @@ -1 +0,0 @@ -{} \ No newline at end of file diff --git a/crazy/run_generate_crazy_image.py b/crazy/run_generate_crazy_image.py deleted file mode 100644 index 2d0f24d..0000000 --- a/crazy/run_generate_crazy_image.py +++ /dev/null @@ -1,19 +0,0 @@ -import asyncio - -from pipelex.core.stuffs.image_content import ImageContent -from pipelex.pipelex import Pipelex -from pipelex.pipeline.execute import execute_pipeline - - -async def run_generate_crazy_image() -> ImageContent: - pipe_output = await execute_pipeline( - pipe_code="generate_crazy_image", - ) - return pipe_output.main_stuff_as(content_type=ImageContent) - - -if __name__ == "__main__": - # Initialize Pipelex - with Pipelex.make(): - # Run the pipeline - result = asyncio.run(run_generate_crazy_image()) diff --git a/crazy/structures/__init__.py b/crazy/structures/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/crazy/structures/crazy_image_generation_ImagePrompt.py b/crazy/structures/crazy_image_generation_ImagePrompt.py deleted file mode 100644 index 46b45ae..0000000 --- a/crazy/structures/crazy_image_generation_ImagePrompt.py +++ /dev/null @@ -1,22 +0,0 @@ -""" -AUTOGENERATED CODE - DO NOT EDIT - -If you want to customize this structure: - 1. Copy this file to your own module - 2. Remove the 'structure' or 'refines' declaration from the concept in the PLX file - and declare it in inline mode (see https://docs.pipelex.com/home/6-build-reliable-ai-workflows/concepts/define_your_concepts/#basic-concept-definition) - 3. Make sure your custom class is importable and registered - -To regenerate: pipelex build structures -""" - -from enum import Enum -from typing import Any, Dict, List, Literal, Optional - -from pipelex.core.stuffs.structured_content import StructuredContent -from pipelex.core.stuffs.text_content import TextContent -from pydantic import Field - - -class ImagePrompt(TextContent): - """Generated ImagePrompt class""" From 98d422075a55eeb611066fbdf6e36216576ffa78 Mon Sep 17 00:00:00 2001 From: Louis Choquel Date: Mon, 19 Jan 2026 11:56:45 +0100 Subject: [PATCH 04/10] Pipelex dep --- pyproject.toml | 2 +- uv.lock | 8 ++++---- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index c8bb40a..7fbc63f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -22,7 +22,7 @@ dependencies = [ ] [tool.uv.sources] -pipelex = { git = "https://github.com/Pipelex/pipelex.git", branch = "feature/Chicago" } +pipelex = { git = "https://github.com/Pipelex/pipelex.git", branch = "pre-release/v0.18.0b1" } [tool.setuptools] diff --git a/uv.lock b/uv.lock index 87b7dfc..9e419e7 100644 --- a/uv.lock +++ b/uv.lock @@ -1480,8 +1480,8 @@ dev = [ requires-dist = [ { name = "boto3-stubs", marker = "extra == 'dev'", specifier = ">=1.35.24" }, { name = "mypy", marker = "extra == 'dev'", specifier = ">=1.11.2" }, - { name = "pipelex", git = "https://github.com/Pipelex/pipelex.git?branch=feature%2FChicago" }, - { name = "pipelex", extras = ["mistralai", "anthropic", "google", "google-genai", "bedrock", "fal"], git = "https://github.com/Pipelex/pipelex.git?branch=feature%2FChicago" }, + { name = "pipelex", git = "https://github.com/Pipelex/pipelex.git?branch=pre-release%2Fv0.18.0b1" }, + { name = "pipelex", extras = ["mistralai", "anthropic", "google", "google-genai", "bedrock", "fal"], git = "https://github.com/Pipelex/pipelex.git?branch=pre-release%2Fv0.18.0b1" }, { name = "pyright", marker = "extra == 'dev'", specifier = ">=1.1.405" }, { name = "pytest", marker = "extra == 'dev'", specifier = ">=9.0.1" }, { name = "pytest-asyncio", marker = "extra == 'dev'", specifier = ">=0.24.0" }, @@ -1961,8 +1961,8 @@ wheels = [ [[package]] name = "pipelex" -version = "0.17.3" -source = { git = "https://github.com/Pipelex/pipelex.git?branch=feature%2FChicago#88f1cefb1901f0759e91b06a2ba2f580291a29f2" } +version = "0.18.0b1" +source = { git = "https://github.com/Pipelex/pipelex.git?branch=pre-release%2Fv0.18.0b1#c056b6a7015c4c7c96656e4615578277629e8a5e" } dependencies = [ { name = "aiofiles" }, { name = "backports-strenum", marker = "python_full_version < '3.11'" }, From 6e09ecf99fdae48d846b47f7bbb6307aa4374229 Mon Sep 17 00:00:00 2001 From: Louis Choquel Date: Mon, 19 Jan 2026 14:46:38 +0100 Subject: [PATCH 05/10] CI --disable-inference to run without Pipelex Service agreement --- Makefile | 4 ++-- tests/integration/conftest.py | 8 ++++++-- 2 files changed, 8 insertions(+), 4 deletions(-) diff --git a/Makefile b/Makefile index b3f7eb7..bb1c61f 100644 --- a/Makefile +++ b/Makefile @@ -209,12 +209,12 @@ cleanall: cleanderived cleanenv cleanconfig codex-tests: env $(call PRINT_TITLE,"Unit testing for Codex") @echo "• Running unit tests for Codex (excluding inference and codex_disabled)" - $(VENV_PYTEST) --exitfirst --quiet -m "not inference and not codex_disabled" || [ $$? = 5 ] + $(VENV_PYTEST) --disable-inference --exitfirst --quiet -m "not inference and not codex_disabled" || [ $$? = 5 ] gha-tests: env $(call PRINT_TITLE,"Unit testing for github actions") @echo "• Running unit tests for github actions (excluding inference and gha_disabled)" - $(VENV_PYTEST) --exitfirst --quiet -m "not inference and not gha_disabled" || [ $$? = 5 ] + $(VENV_PYTEST) --disable-inference --exitfirst --quiet -m "not inference and not gha_disabled" || [ $$? = 5 ] run-all-tests: env $(call PRINT_TITLE,"Running all unit tests") diff --git a/tests/integration/conftest.py b/tests/integration/conftest.py index 028da5f..ab98a37 100644 --- a/tests/integration/conftest.py +++ b/tests/integration/conftest.py @@ -2,6 +2,8 @@ import pipelex.pipelex import pytest from pipelex.system.configuration.config_check import check_is_initialized +from pipelex.test_extras.shared_pytest_plugins import is_inference_disabled_in_pipelex +from pytest import FixtureRequest from rich import print from rich.console import Console from rich.traceback import Traceback @@ -15,11 +17,13 @@ def check_pipelex_initialized(): @pytest.fixture(scope="module", autouse=True) -def reset_pipelex_config_fixture(): +def reset_pipelex_config_fixture(request: FixtureRequest): # Code to run before each test print("\n[magenta]pipelex setup[/magenta]") try: - pipelex_instance = pipelex.pipelex.Pipelex.make() + pipelex_instance = pipelex.pipelex.Pipelex.make( + disable_inference=is_inference_disabled_in_pipelex(request), + ) except Exception as exc: Console().print(Traceback()) pytest.exit(f"Critical Pipelex setup error: {exc}") From 9451bc7f6c36d7ee6e4a389a510a4e86f6080515 Mon Sep 17 00:00:00 2001 From: Louis Choquel Date: Mon, 19 Jan 2026 14:52:17 +0100 Subject: [PATCH 06/10] Removed the separate "Boot test" step since gha-tests already runs all tests (including TestFundamentals) with the --disable-inference flag. The boot test was redundant - it was running the same tests twice, just with a filter. --- .github/workflows/tests-check.yml | 3 --- 1 file changed, 3 deletions(-) diff --git a/.github/workflows/tests-check.yml b/.github/workflows/tests-check.yml index c94ad16..a347c29 100644 --- a/.github/workflows/tests-check.yml +++ b/.github/workflows/tests-check.yml @@ -43,8 +43,5 @@ jobs: source .venv/bin/activate echo -e "y\nA\n1" |pipelex init - - name: Boot test - run: make tp TEST=TestFundamentals - - name: Run tests run: make gha-tests From 4103dba77c3505543823447be778930aa59734df Mon Sep 17 00:00:00 2001 From: Louis Choquel Date: Wed, 21 Jan 2026 12:08:34 +0100 Subject: [PATCH 07/10] Use Chicago b2 --- pyproject.toml | 2 +- uv.lock | 8 ++++---- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 7fbc63f..4d6f0d4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -22,7 +22,7 @@ dependencies = [ ] [tool.uv.sources] -pipelex = { git = "https://github.com/Pipelex/pipelex.git", branch = "pre-release/v0.18.0b1" } +pipelex = { git = "https://github.com/Pipelex/pipelex.git", branch = "pre-release/v0.18.0b2" } [tool.setuptools] diff --git a/uv.lock b/uv.lock index 9e419e7..ebce1e5 100644 --- a/uv.lock +++ b/uv.lock @@ -1480,8 +1480,8 @@ dev = [ requires-dist = [ { name = "boto3-stubs", marker = "extra == 'dev'", specifier = ">=1.35.24" }, { name = "mypy", marker = "extra == 'dev'", specifier = ">=1.11.2" }, - { name = "pipelex", git = "https://github.com/Pipelex/pipelex.git?branch=pre-release%2Fv0.18.0b1" }, - { name = "pipelex", extras = ["mistralai", "anthropic", "google", "google-genai", "bedrock", "fal"], git = "https://github.com/Pipelex/pipelex.git?branch=pre-release%2Fv0.18.0b1" }, + { name = "pipelex", git = "https://github.com/Pipelex/pipelex.git?branch=pre-release%2Fv0.18.0b2" }, + { name = "pipelex", extras = ["mistralai", "anthropic", "google", "google-genai", "bedrock", "fal"], git = "https://github.com/Pipelex/pipelex.git?branch=pre-release%2Fv0.18.0b2" }, { name = "pyright", marker = "extra == 'dev'", specifier = ">=1.1.405" }, { name = "pytest", marker = "extra == 'dev'", specifier = ">=9.0.1" }, { name = "pytest-asyncio", marker = "extra == 'dev'", specifier = ">=0.24.0" }, @@ -1961,8 +1961,8 @@ wheels = [ [[package]] name = "pipelex" -version = "0.18.0b1" -source = { git = "https://github.com/Pipelex/pipelex.git?branch=pre-release%2Fv0.18.0b1#c056b6a7015c4c7c96656e4615578277629e8a5e" } +version = "0.18.0b2" +source = { git = "https://github.com/Pipelex/pipelex.git?branch=pre-release%2Fv0.18.0b2#06b33904bd697791e058c7bc5b43454d96839f46" } dependencies = [ { name = "aiofiles" }, { name = "backports-strenum", marker = "python_full_version < '3.11'" }, From 497c8cebb49996db93a773b1fe2d8ed3906c56ef Mon Sep 17 00:00:00 2001 From: Louis Choquel Date: Wed, 21 Jan 2026 16:45:17 +0100 Subject: [PATCH 08/10] git ignores --- .gitignore | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index 29670a3..443da68 100644 --- a/.gitignore +++ b/.gitignore @@ -27,7 +27,9 @@ dist/ # Results results/ -# temps temp/ pipelex_super.toml +pipelex_override.toml +telemetry_override.toml base_llm_deck.toml +.pipelex/storage From 9125497ad4566afaf8463a49110794a58e86abda Mon Sep 17 00:00:00 2001 From: Louis Choquel Date: Wed, 21 Jan 2026 16:49:07 +0100 Subject: [PATCH 09/10] Pipelex version dep --- pyproject.toml | 5 +---- uv.lock | 1 - 2 files changed, 1 insertion(+), 5 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 4d6f0d4..382089a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -16,10 +16,7 @@ classifiers = [ "Operating System :: OS Independent", ] -dependencies = [ - "pipelex[mistralai,anthropic,google,google-genai,bedrock,fal]", - "pipelex==0.17.3", -] +dependencies = ["pipelex[mistralai,anthropic,google,google-genai,bedrock,fal]"] [tool.uv.sources] pipelex = { git = "https://github.com/Pipelex/pipelex.git", branch = "pre-release/v0.18.0b2" } diff --git a/uv.lock b/uv.lock index ebce1e5..e33af4a 100644 --- a/uv.lock +++ b/uv.lock @@ -1480,7 +1480,6 @@ dev = [ requires-dist = [ { name = "boto3-stubs", marker = "extra == 'dev'", specifier = ">=1.35.24" }, { name = "mypy", marker = "extra == 'dev'", specifier = ">=1.11.2" }, - { name = "pipelex", git = "https://github.com/Pipelex/pipelex.git?branch=pre-release%2Fv0.18.0b2" }, { name = "pipelex", extras = ["mistralai", "anthropic", "google", "google-genai", "bedrock", "fal"], git = "https://github.com/Pipelex/pipelex.git?branch=pre-release%2Fv0.18.0b2" }, { name = "pyright", marker = "extra == 'dev'", specifier = ">=1.1.405" }, { name = "pytest", marker = "extra == 'dev'", specifier = ">=9.0.1" }, From 2cb68b842044c72e0995ad9e74e81bd5a9afb88e Mon Sep 17 00:00:00 2001 From: Louis Choquel Date: Thu, 12 Feb 2026 00:24:44 +0100 Subject: [PATCH 10/10] Update for Chicago b3 + some cleanup --- .vscode/settings.json | 1 - pyproject.toml | 4 +- uv.lock | 126 ++++++++++++++++++++---------------------- 3 files changed, 61 insertions(+), 70 deletions(-) diff --git a/.vscode/settings.json b/.vscode/settings.json index e908f73..acd31dc 100644 --- a/.vscode/settings.json +++ b/.vscode/settings.json @@ -7,7 +7,6 @@ "mypy.runUsingActiveInterpreter": true, "search.exclude": { ".mypy_cache/*": true, - "**/dev_context": true }, "files.exclude": { "**/__pycache__": true, diff --git a/pyproject.toml b/pyproject.toml index 382089a..fbb9661 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -5,7 +5,7 @@ description = "Replace this with your project description" # authors = [{ name = "Your Name", email = "your.email@example.com" }] license = "MIT" readme = "README.md" -requires-python = ">=3.10" +requires-python = ">=3.10,<3.15" classifiers = [ "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.10", @@ -19,7 +19,7 @@ classifiers = [ dependencies = 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