| name | biochatter |
| description | Connect biomedical LLM chat to knowledge graphs, APIs, and document RAG with BioChatter. Use for natural-language Cypher over Neo4j, BioCypherPromptEngine query generation, DatabaseAgent, or general biomedical conversation with structured backends. Trigger on BioChatter, NL graph queries, Cypher generation, KG chat, schema_info.yaml querying, or conversational AI over biomedical data. |
BioChatter: LLM layer for biomedical apps
BioChatter is the harness — LLM connectivity, prompts, and agents. It does not build graphs; it queries graphs, APIs, and documents that already exist.
pip install biochatter
pip install "biochatter[ollama]"
Docs: https://biochatter.org/
Choose a mode
| Task | Entry point |
|---|
| NL query over a Neo4j KG | DatabaseAgent — see references/kg-query.md |
| Cypher generation only | BioCypherPromptEngine.generate_query() |
| General biomedical chat | GptConversation / LangChainConversation + query() |
| Document RAG | DocumentEmbedder + vectorstore — docs/features/rag.md |
| External API (OncoKB, etc.) | APIAgent — docs/api-docs/api-calling-base.md |
Knowledge graph querying (primary)
Prerequisites: running graph DB + schema_info.yaml (from BioCypher write_schema_info()).
Read references/kg-query.md for full DatabaseAgent and BioCypherPromptEngine setup.
Always show the generated Cypher to the user. Prefer use_reflexion=False unless they ask for iterative refinement.
General conversation
from biochatter.llm_connect import GptConversation
conversation = GptConversation(model_name="gpt-4o", prompts={})
conversation.set_api_key(api_key="...")
msg, token_usage, correction = conversation.query("Explain CRISPR off-target effects.")
Supply domain system prompts via prompts={} or append_system_message() — raw passthrough to the LLM is rarely enough for production use.
Providers
OpenAI, Anthropic, Google/Gemini, Azure, Ollama, Xinference, OpenRouter — each has a *Conversation class in biochatter.llm_connect. Set API keys / base URLs per provider docs.
House rules
- BioChatter does not fix bad graphs — empty or wrong answers often mean upstream schema or id issues.
- One mode per task; don't pull in RAG/API agents unless the user asked.
- For web UI: BioChatter Light (Streamlit) or BioChatter Next (FastAPI + Next.js).
