---

name: knowledge-base-and-api description: "Use Langchain-Chatchat FastAPI, OpenAI-compatible chat, knowledge-base RAG, file RAG, tools, and vector-store workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Knowledge Base and API

Use this sub-skill when a task asks for Langchain-Chatchat HTTP APIs, RAG modes, knowledge-base document operations, file/temp KB chat, search-engine chat, tools, OpenAI-compatible endpoints, route discovery, or vector-store behavior.

Read first

• references/api-reference.md maps verified route families and request patterns.
• references/rag-workflows.md covers local KB, temp-file, search-engine, direct retrieval, and tool-choice workflows.
• references/vector-store-and-data-formats.md covers document/vector-store settings and supported backends.
• references/troubleshooting.md covers route, provider, streaming, KB, and vector-store failures.
• Run scripts/api_surface_probe.py to list the installed API routes without binding a service port.

Preconditions

1. langchain-chatchat is installed and imports.
2. CHATCHAT_ROOT has been initialized with chatchat init.
3. API server is running for live HTTP calls: chatchat start --api or chatchat start -a.
4. Model provider settings are valid for any route that invokes LLMs or embeddings.
5. For vector rebuilds or retrieval over indexed docs, the embedding model is reachable.

If any precondition is false, route setup/config work to ../server-setup-and-cli/SKILL.md.

Route families

Task	Route family
Unified chat, pure LLM chat, agent/tool chat	/chat/chat/completions
Legacy/specific KB and file chat helpers	/chat/kb_chat, /chat/file_chat
OpenAI-compatible RAG by mode	`/knowledge_base/{local_kb
KB CRUD and document operations	/knowledge_base/list_knowledge_bases, /create_knowledge_base, /delete_knowledge_base, /list_files, /upload_docs, /delete_docs, /update_docs, /search_docs, /download_doc, /recreate_vector_store
Temp docs	/knowledge_base/upload_temp_docs, /search_temp_docs
Summary routes	/knowledge_base/kb_summary_api/*
Tool registry/calls	/tools, /tools/call
OpenAI-compatible provider proxy	/v1/models, /v1/chat/completions, /v1/completions, /v1/embeddings, /v1/images/*, /v1/audio/*, /v1/files*
Server state/prompts	/server/configs, /server/get_prompt_template
MCP connections	/api/v1/mcp_connections/*

Common API patterns

Direct retrieval from local KB without LLM generation

Use OpenAI-compatible RAG route plus return_direct=True in extra_body when the task only needs retrieved documents.

from openai import Client

client = Client(base_url="http://127.0.0.1:7861/knowledge_base/local_kb/samples", api_key="EMPTY")
resp = client.chat.completions.create(
    model="configured-llm-name",
    messages=[{"role": "user", "content": "How do I ask high-quality questions?"}],
    stream=True,
    extra_body={"top_k": 3, "score_threshold": 2.0, "return_direct": True},
)
for chunk in resp:
    print(chunk)

Tool-assisted chat

Fetch /tools first. If the model can choose tools, pass tools. If it cannot reliably infer arguments, pass tool_choice and extra_body.tool_input to make the tool call explicit.

File/temp KB chat

Upload temporary docs first, capture knowledge_id, then use /knowledge_base/temp_kb/{knowledge_id}/chat/completions or the SDK's temp-doc methods.

Verification boundaries

• api_surface_probe.py verifies installed route registration only; it does not prove the API server, provider, or models are running.
• A return_direct=True RAG route still needs retrieval inputs and often an initialized vector store, but it avoids LLM answer generation.
• Live /v1/models depends on provider configuration; failures there are often provider setup failures, not route-definition failures.