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Description
We maintain two separate projects: scatlas-builder, the core single-cell analysis platform, and rag-gene-discovery-assistant (ragbio), the RAG-LLM–based biological knowledge extraction tool.
The objective is to integrate ragbio into scatlas-builder to generate biologically grounded, citation-rich summaries for cell clusters.
This integration is a key step toward evolving scatlas-builder into a full scientific discovery platform that combines computational analysis with literature-backed biological interpretation.
Acceptance Criteria
1. New API Endpoint
Create a new FastAPI endpoint to provide LLM-augmented summaries for cluster marker genes.
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Path:
/summarize/cluster/{dataset_id} -
Method:
POST -
Input:
JSON containing thedataset_idand a dictionary of cluster markers, e.g.:{ "Cluster 0": ["GeneA", "GeneB", "GeneC"], "Cluster 1": ["GeneD", "GeneE"] } -
Output:
JSON with biological interpretations and PubMed-derived citations for each cluster.
2. Service Layer Integration
The new endpoint must call a dedicated service function—for example:
llm_service.run_rag_summary()—that invokes the ragbio RAG pipeline and returns structured summaries.
3. Deployment Updates
Modify:
docker/Dockerfilerequirements.txt
to ensure that ragbio and its dependencies (Ollama runtime, FAISS index, BioBERT embeddings, etc.) are available within the scatlas-builder deployment environment.
Alternatively, configure scatlas-builder to communicate with an external Ollama/RAG service.
4. Diagram + Architectural Explanation
Provide a clear architectural explanation and a System Diagram showing how:
- client requests flow through the new FastAPI route,
- marker genes reach the analysis layer (Scanpy),
- results pass into the LLM orchestration layer,
ragbioperforms retrieval and LLM reasoning,- summaries return to the client.
System Integration Diagram (Reference Implementation)
This diagram illustrates the expected data and control flow needed to integrate the ragbio knowledge layer into the scatlas-builder analysis layer.
