Building a RAG-Powered AI Chatbot for Single-Cell Analysis - Part 1: Foundations
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Motivation
AI applications like ChatGPT have acted as capable personal assistants during my single-cell analysis projects, whether helping me debug a Scanpy error at midnight or brainstorming strategies for integrating scRNA and scATAC-seq data.
However, despite their versatility, hallucination is still a major problem for base LLM applications. Here’s a real example (abbreviated) I encountered: Q: How can I compute a gene activity matrix from a scATAC-seq dataset in scanpy/muon ecosystem? A: You can use ComputeGeneActivity() from mu.atac module. => this function doesn’t exist anywhere in the codebase or documentation!
Imagine trusting a nonexistant function in a pipeline and wasting hour debugging it… Therefore, this inspired me to build scOracle, a domain-specific assistant powered by Retrieval-Augmented Generation (RAG) and enriched with a knowledge base of curated single-cell documentation, tutorials, and source code. By grounding LLM responses on real, trusted documents, scOracle aims to eliminate hallucinations and deliver verifiable, context-aware answers.
Intended features
- Target audience: Scientists or students navigating single-cell workflows
- Ingests real docs, notebooks, and repo source code
- Responds to natural language questions (e.g., “How do I run Leiden clustering?”)
- Offers code snippets, parameter guidance, and troubleshooting tips based on ingested knowledge
- Modular backend: ability to swap models, tune parameters, and filter retrieved knowledge
App Structure
Single Cell packages to include in the knowledge base
- Umbrella analysis framework: Scanpy & Seurat ==> MVP for first iteration
- Can be extended to whole scVerse ecosystem
- Upstream processing: Cell Ranger, Alevin-Fry, and NF-Core
- scATAC-seq analysis: Signac & ArchR
- Gene regulatory network inference: SCENIC & scPRINT
- Spatial Transcriptomics: squidpy
- Awesome Single Cell repo and much more!
Planned Tech Stack
| Component | Choice | Function |
|---|---|---|
| LLM API | GPT-4o mini | Generates natural language responses conditioned on retrieved context |
| Retrieval | LlamaIndex | Manages document ingestion, chunking, indexing, and context retrieval for RAG |
| Embedding | all-MiniLM-L6-v2 (SBERT) | Converts text into dense vector representations for semantic search |
| Vector DB | Chroma | Stores and retrieves embeddings efficiently to support fast similarity search |
| UI | Streamlit | Provides a simple, interactive web interface for user input and chatbot output |
The modular design allows easy upgrades, such as swapping to better embedding models or scaling with a production-grade vector store like Qdrant.