Agentic RAG Chatbot

An agentic Retrieval-Augmented Generation (RAG) chatbot powered by LangGraph, OpenAI’s GPT-3.5-turbo, and a Chroma vector store. It allows you to ask questions about Nikhil Nageshwar Inturi’s background, publications, projects, and qualifications, and get grounded answers sourced from indexed PDFs and other documents.

The app supports one upload at a time; progress bars updates live. More on the steps that go into the pipeline can be found here:

graph TD
    A[Start] --> B[RAG Agent]
    B --> C{tools_condition}
    C -->|tools| D[Retriever Node]
    C -->|END| E[End]
    D --> F{document_quality}
    F -->|generator| G[Generator]
    F -->|rewrite| H[Rewrite]
    G --> E
    H --> B

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Difference b/w Traditional RAG & Agentic RAG

Feature	Traditional RAG	Agentic RAG
Overview	Retriever → Generator; static pipeline grounded in retrieved context.	RAG + intelligent agent; dynamic decision-making, planning & tool use.
Architecture	Fixed sequence: query → embedding → retrieve top-k → prompt → LLM answer.	Loop of “Thought → Tool → Observation”; agent decides when/where/how to retrieve or call tools.
Workflow	One-shot: embed & retrieve → augment prompt → generate answer.	Iterative: plan → retrieve → evaluate → (re-retrieve…) → generate → (optional verify).
Memory & Reasoning	No built-in memory; reasoning is one-pass inside the final LLM call.	Maintains state across steps; chain-of-thought exposes planning, self-correction & long-term memory.
Use Cases	Simple QA on a single knowledge source.	Complex, multi-step tasks; multi-source routing; tool-augmented actions (e.g. charts, APIs).
Trade-offs	Simpler, faster, cheaper, easier to debug; limited adaptability.	More flexible & accurate; handles ambiguity; higher latency, cost & engineering complexity.
**LangChain	Chains: static RetrievalQA/ConversationRetrieval.	Agents: wrap retrievers as tools (LangChain Agents)

Note: Check out the RAG implementation in this repository: NikhilRAG

Features

• Agentic RAG pipeline A state‐graph (graph.py) orchestrates:

  1. A router/agent node that decides whether to call a document retriever tool

  2. A document‐quality checker to route between rewriting or generation

  3. A generator node that synthesizes answers from retrieved context

  4. A rewrite node that reformulates queries when no relevant docs are found

     

• Custom retriever tool Uses Chroma to index and retrieve document chunks (via embed_generator.py → ./chroma_db), exposed as a named tool for the agent.

• Streamlit front-end A chat UI (streamlit_app.py) to interact with your RAG agent in real time:

  • Initializes and persists st.session_state.messages
  • Shows a welcome prompt and conversation history
  • Invokes the compiled LangGraph state‐graph under the hood
  • Displays “Thinking…” spinners and debug info on demand

• Embeddings & indexing embed_generator.py loads PDFs from ./docs, splits into chunks, and persists embeddings in ./chroma_db.

• Retrieval QA demo A standalone retrieval‐QA script (bin/retriever.py) to sanity-check your vector store outside of the state graph.

Getting Started

1. Clone the repo

git clone https://github.com/unikill066/agentic-rag.git
cd agentic-rag

2. Install dependencies

Using pip (via requirements.txt):

pip install uv
uv venv .venv
source .venv/bin/activate
uv pip install -r requirements.txt

Or with Poetry, uv is just 10-100X faster (via pyproject.toml):

poetry install
poetry shell

3. Environment variables

Create a .env(refer to .env.example) in the repo root and set your OpenAI API key (and, if used, Firebase credentials):

OPENAI_API_KEY=sk-...
LANGSMITH_TRACING=true
LANGSMITH_ENDPOINT="https://api.smith.langchain.com"
LANGSMITH_API_KEY=ls-...
LANGSMITH_PROJECT="proj_name"

4. Index your documents

Put any .pdf files you want to query into ./docs/ then run:

python bin/embed_generator.py

This will split and embed your PDFs into ./chroma_db.

5. Run the Streamlit app

streamlit run streamlit_app-local.py

Open your browser at http://localhost:8501 and start asking questions!

Repo Structure

.
├── bin/
│   ├── embed_generator.py     # PDF -> Chroma embedding pipeline
│   └── retriever.py           # Standalone RetrievalQA demo for testing
├── chroma_db
├── constants.py
├── graph.py                   # builds & compiles the LangGraph Agentic RAG agent
├── streamlit_app.py           # Streamlit front‐end & chat UI
├── requirements.txt
├── pyproject.toml
└── README.md

License

This project is licensed under the MIT License.

Built by Nikhil Nageshwar Inturi • 2025-06-22

Contact: Gmail