biochatter

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]"   # local models via 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).