Merge branch 'feat_werewolf' of github.com:better629/MetaGPT into feat_werewolf

examples/rag_pipeline.py

@@ -8,7 +8,6 @@ from metagpt.const import DATA_PATH, EXAMPLE_DATA_PATH
 from metagpt.logs import logger
 from metagpt.rag.engines import SimpleEngine
 from metagpt.rag.schema import (
-    BM25RetrieverConfig,
     ChromaIndexConfig,
     ChromaRetrieverConfig,
     ElasticsearchIndexConfig,
@@ -51,7 +50,7 @@ class RAGExample:
         if not self._engine:
             self._engine = SimpleEngine.from_docs(
                 input_files=[DOC_PATH],
-                retriever_configs=[FAISSRetrieverConfig(), BM25RetrieverConfig()],
+                retriever_configs=[FAISSRetrieverConfig()],
                 ranker_configs=[LLMRankerConfig()],
             )
         return self._engine
@@ -61,7 +60,7 @@ class RAGExample:
         self._engine = value
 
     async def run_pipeline(self, question=QUESTION, print_title=True):
-        """This example run rag pipeline, use faiss&bm25 retriever and llm ranker, will print something like:
+        """This example run rag pipeline, use faiss retriever and llm ranker, will print something like:
 
         Retrieve Result:
         0. Productivi..., 10.0

examples/stream_output_via_api.py

@@ -0,0 +1,117 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+"""
+@Time    : 2024/3/27 9:44
+@Author  : leiwu30
+@File    : stream_output_via_api.py
+@Description    : Stream log information and communicate over the network via web api.
+"""
+import asyncio
+import json
+import socket
+import threading
+from contextvars import ContextVar
+
+from flask import Flask, Response, jsonify, request, send_from_directory
+
+from metagpt.const import TUTORIAL_PATH
+from metagpt.logs import logger, set_llm_stream_logfunc
+from metagpt.roles.tutorial_assistant import TutorialAssistant
+from metagpt.utils.stream_pipe import StreamPipe
+
+app = Flask(__name__)
+
+
+def stream_pipe_log(content):
+    print(content, end="")
+    stream_pipe = stream_pipe_var.get(None)
+    if stream_pipe:
+        stream_pipe.set_message(content)
+
+
+def write_tutorial(message):
+    async def main(idea, stream_pipe):
+        stream_pipe_var.set(stream_pipe)
+        role = TutorialAssistant()
+        await role.run(idea)
+
+    def thread_run(idea: str, stream_pipe: StreamPipe = None):
+        """
+        Convert asynchronous function to thread function
+        """
+        asyncio.run(main(idea, stream_pipe))
+
+    stream_pipe = StreamPipe()
+    thread = threading.Thread(
+        target=thread_run,
+        args=(
+            message["content"],
+            stream_pipe,
+        ),
+    )
+    thread.start()
+
+    while thread.is_alive():
+        msg = stream_pipe.get_message()
+        yield stream_pipe.msg2stream(msg)
+
+
+@app.route("/v1/chat/completions", methods=["POST"])
+def completions():
+    """
+    data: {
+        "model": "write_tutorial",
+        "stream": true,
+        "messages": [
+            {
+                "role": "user",
+                "content": "Write a tutorial about MySQL"
+            }
+        ]
+    }
+    """
+
+    data = json.loads(request.data)
+    logger.info(json.dumps(data, indent=4, ensure_ascii=False))
+
+    # Non-streaming interfaces are not supported yet
+    stream_type = True if data.get("stream") else False
+    if not stream_type:
+        return jsonify({"status": 400, "msg": "Non-streaming requests are not supported, please use `stream=True`."})
+
+    # Only accept the last user information
+    # openai['model'] ~ MetaGPT['agent']
+    last_message = data["messages"][-1]
+    model = data["model"]
+
+    # write_tutorial
+    if model == "write_tutorial":
+        return Response(write_tutorial(last_message), mimetype="text/plain")
+    else:
+        return jsonify({"status": 400, "msg": "No suitable agent found."})
+
+
+@app.route("/download/<path:filename>")
+def download_file(filename):
+    return send_from_directory(TUTORIAL_PATH, filename, as_attachment=True)
+
+
+if __name__ == "__main__":
+    """
+    curl https://$server_address:$server_port/v1/chat/completions -X POST -d '{
+        "model": "write_tutorial",
+        "stream": true,
+        "messages": [
+          {
+               "role": "user",
+               "content": "Write a tutorial about MySQL"
+          }
+        ]
+    }'
+    """
+    server_port = 7860
+    server_address = socket.gethostbyname(socket.gethostname())
+
+    set_llm_stream_logfunc(stream_pipe_log)
+    stream_pipe_var: ContextVar[StreamPipe] = ContextVar("stream_pipe")
+    app.run(port=server_port, host=server_address)