---

name: langchain-chatchat description: "Route Langchain-Chatchat setup, RAG/API service, Python SDK, and LangChain adapter workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Langchain-Chatchat Repo Skill

Use this skill when a task involves Langchain-Chatchat: installing or initializing the package, configuring model providers and knowledge bases, operating the FastAPI/WebUI service, calling the RAG/OpenAI-compatible APIs, or using the Python SDK and LangChain adapter objects.

Read references/repo-provenance.md before deciding whether this skill is current for a new checkout. Read references/package-overview.md for the package layout, distribution/import names, and high-level workflow map. Use references/troubleshooting.md for cross-cutting install/import/provider/service failures.

Quick orientation

Langchain-Chatchat is an offline-friendly RAG and Agent application that wraps LLM/embedding providers behind a Chatchat API server and WebUI. The selected core surfaces are:

• langchain-chatchat distribution, importing chatchat and langchain_chatchat.
• chatchat CLI with init, kb, and start subcommands.
• FastAPI routes for chat, knowledge-base management, OpenAI-compatible /v1/*, tools, server state, and MCP connection management.
• A separate SDK import package spelled open_chatcaht in this repository.

Minimal public install and import smoke:

python -m pip install -U langchain-chatchat
python - <<'PY'
import chatchat, langchain_chatchat
print(chatchat.__version__)
print(langchain_chatchat.__all__)
PY
chatchat --help

If the task uses the SDK package, verify the import spelling explicitly:

python - <<'PY'
import open_chatcaht
from open_chatcaht.chatchat_api import ChatChat
print(ChatChat)
PY

Route map

User request shape	Read next
Install, initialize data/config, pick CHATCHAT_ROOT, run chatchat init, rebuild KB files, start API/WebUI, Docker/Xinference deployment, model-provider setup	sub-skills/server-setup-and-cli/SKILL.md
Build or troubleshoot chat/RAG/API calls, /chat/chat/completions, /knowledge_base/*, tools, OpenAI-compatible /v1/*, vector stores, temp-file chat, search-engine RAG	sub-skills/knowledge-base-and-api/SKILL.md
Use the open_chatcaht SDK, ChatChat client, typed request models, streaming generators, ChatPlatformAI, PlatformToolsRunnable, MCP prompt utilities	sub-skills/python-sdk-and-adapters/SKILL.md
Need a safe environment/package smoke from any current directory	Run scripts/chatchat_env_probe.py

Standard operating sequence

1. Decide installation mode: prefer pip install -U langchain-chatchat for users; use editable source installs only for repository development or when matching a specific checkout.
2. Set CHATCHAT_ROOT to the persistent data/config directory if the current working directory is not the intended data root.
3. Run chatchat init once to create data directories, copy samples, initialize the metadata database, and generate YAML templates.
4. Edit model-provider and KB YAML settings before vector rebuild or service startup. Chatchat expects an OpenAI-compatible provider such as Xinference, Ollama, LocalAI, FastChat, One API, OpenAI, or a custom OpenAI endpoint.
5. Run chatchat kb -r only after the embedding provider is reachable. Use chatchat kb --help for incremental/update/prune modes.
6. Start the service with chatchat start --api, chatchat start --webui, or chatchat start -a after provider and KB settings are valid.
7. For API/SDK tasks, confirm the API URL and model names before diagnosing request payloads.

Verification helpers

• scripts/chatchat_env_probe.py checks importability, optional package presence, CLI availability, and selected package metadata without starting a server.
• sub-skills/server-setup-and-cli/scripts/chatchat_config_audit.py inspects an initialized CHATCHAT_ROOT for expected YAML/data directories and key settings.
• sub-skills/knowledge-base-and-api/scripts/api_surface_probe.py lists FastAPI routes using a temp or initialized data root without binding a port.
• sub-skills/python-sdk-and-adapters/scripts/sdk_surface_probe.py lists SDK and adapter signatures without making HTTP calls.

Run each helper with --help first. None of these helpers downloads models, starts long-running services, calls external APIs, or mutates user data unless a documented option says it will create a temporary local root.

Boundaries and cautions

• Do not treat a CPU import as proof that a live LLM/embedding provider, Docker runtime, GPU model server, or external vector DB is working. Those are service/provider checks.
• Do not install Chatchat and a heavy model-serving framework into the same environment by default; the project documentation warns that model providers such as Xinference can conflict with the Chatchat environment.
• Do not run vector rebuilds, live chat tests, Docker Compose, or AutoDL-style scripts unless the user has approved model downloads/services, data mutation, and hardware use.
• Do not copy old command names from stale docs. Current CLI help exposes chatchat init, chatchat kb, and chatchat start.