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feat: add documentation for websocket AI chat service in README
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@@ -33,6 +33,7 @@
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<li><a href="#testing-strategy">Testing Strategy</a></li>
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<li><a href="#uv-knowledge-and-inspirations">UV knowledge and inspirations</a></li>
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<li><a href="#large-language-model">Integration with local LLM</a></li>
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<li><a href="#websocket-ai-chat-service">Websocket AI chat service</a></li>
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<li><a href="#ha-sample-with-nginx-as-load-balancer">High Availability sample with nginx as load balancer</a></li>
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<li><a href="#performance-profiling-with-pyinstrument">Performance Profiling with Pyinstrument</a></li>
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</ul>
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@@ -179,6 +180,65 @@ ollama run llama3.2
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<p align="right">(<a href="#readme-top">back to top</a>)</p>
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### Websocket AI chat service
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A general-purpose, websocket-based chat service lives at `/v1/chat/ws`, built with FastAPI 0.139.0
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and Pydantic models for every message shape exchanged over the wire. It follows the
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[Pydantic AI chat-app](https://github.com/pydantic/pydantic-ai/blob/main/docs/web.md) interaction
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pattern - a session id, role-labeled messages, and streamed assistant chunks - so a UI can be wired
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up later without changing the backend contract.
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**Module layout**
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- `app/schemas/chat.py` — Pydantic `BaseModel`s for `ChatMessage` (role/content) and the
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client/server websocket event envelopes (discriminated on a `type` field).
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- `app/services/chat_agent.py` — the pluggable async `ChatAgent` adapter `Protocol`, a
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dependency-free `LocalEchoAgent` stub (default, no API keys/network required), and an
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`OllamaChatAgent` example that streams from an OpenAI-compatible `/chat/completions` endpoint.
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- `app/services/chat_session.py` — a small in-memory `ChatSessionManager` tracking one
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`ChatSession` (id + message history) per open websocket connection.
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- `app/api/chat.py` (re-exported as `app/ws.py`) — the `/ws` websocket route implementing the
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chat flow.
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**Lifespan wiring**: `app/main.py`'s `lifespan` builds the configured `ChatAgent` via
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`build_chat_agent(settings.chat)` and a fresh `ChatSessionManager` on startup (`app.chat_agent`
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/ `app.state.chat_sessions`), and calls `chat_agent.aclose()` on shutdown to release any held
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connections.
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**Configuration** (`ChatConfig` in `app/config.py`, all overridable via environment variables):
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| Env var | Default | Description |
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| --- | --- | --- |
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| `CHAT_BACKEND` | `stub` | `stub` (local echo, no network) or `ollama` (OpenAI-compatible streaming) |
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| `CHAT_BASE_URL` | `http://localhost:11434/v1` | Base URL used by the `ollama` backend |
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| `CHAT_MODEL` | `llama3.2` | Model name sent to the `ollama` backend |
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| `CHAT_STREAM_DELAY` | `0.02` | Per-word delay (seconds) the stub uses to emulate token streaming |
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**Example websocket flow** (one JSON object per text frame):
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```
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-> (connect to ws://localhost:8080/v1/chat/ws)
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<- {"type": "connected", "session_id": "..."}
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-> {"type": "start", "system_prompt": "You are a helpful assistant."}
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<- {"type": "started", "session_id": "..."}
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-> {"type": "user_message", "content": "Hello there"}
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<- {"type": "assistant_chunk", "index": 0, "content": "Hello "}
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<- {"type": "assistant_chunk", "index": 1, "content": "there! "}
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<- {"type": "assistant_message", "message": {"role": "assistant", "content": "Hello there! "}}
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-> {"type": "history_request"}
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<- {"type": "history", "messages": [...]}
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-> {"type": "end"}
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```
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**Swapping in a real model**: implement the `ChatAgent` protocol (an async `stream_reply(messages)`
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generator yielding text chunks, plus `aclose()`) in a new class - e.g. wrapping the OpenAI SDK - and
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return it from `build_chat_agent` for a new `backend` value. No changes are needed in
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`app/api/chat.py` or the session manager.
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Run the tests for this service locally with:
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```shell
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pytest tests/api/test_chat_ws.py -vv
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```
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<p align="right">(<a href="#readme-top">back to top</a>)</p>
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### HA sample with nginx as load balancer
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Sample high availability setup with nginx as load balancer and 2 uvicorn instances running on different ports.
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```shell
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