Define agent groups in standalone json file. Add simple api key protection.

.gitignore

@@ -160,3 +160,5 @@ cython_debug/
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 .idea/
+
+/dev-compose.yml

Dockerfile

@@ -0,0 +1,6 @@
+FROM python:3.12
+WORKDIR /app
+COPY requirements.txt .
+RUN pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple
+COPY . .
+CMD ["/bin/bash","-c","./run.sh"]

README.md

@@ -1,36 +1,43 @@
 # Streaming Autogen with FastAPI 
 This is an example FastAPI server that streams messages from the Autogen framework
 
-## Installation
+## Installation & Running
+Clone the repo and build the docker image, set your LLM model and API key in the docker-compose.yml file.
 ```sh
-git clone https://github.com/LineaLabs/autogen-fastapi.git
+git clone https://github.com/0000sir/autogen-fastapi.git
 cd autogen-fastapi
-conda create -n autogen python=3.10
-conda activate autogen
-pip install -r requirements.txt
+docker compose build
+docker compose up
 ```
 
-## Running the server
-Make sure to set `OPENAI_API_KEY` in your environment variables or in `.env` file. You can get an API key from https://platform.openai.com/account/api-keys
-```sh
-./run.sh
-```
-
-
-## Querying the server
+## Documentation
+Navigate to http://localhost:8000/autogen to see the docs. 
 
-You can query the autogen agents using the following command: 
-```sh
+## Call different agent groups with different model definitions
+We can define different agent groups for different purposes in a single json file (app/agent_configs.json).
+After defined, we can call the agent group with the there id as model parameter.
+For example we have a definition as follows:
+```json
+{
+  "article_writer": {
+    "name": "ArticleWriter",
+    # other parameters ...
+  }
+}
+```
+We can call the agent group with the following command, notice the `model` parameter:
+```bash
 curl -X 'POST' \
   'http://localhost:8000/autogen/api/v1/chat/completions' \
   -H 'accept: application/json' \
   -H 'Content-Type: application/json' \
+  -H 'Authorization: Bearer 976707f2ab39ebee343034b4b33db6f9' \
   -d '{
-  "model": "model_name_v0.1",
+  "model": "article_writer",
   "messages": [
     {
       "role": "user",
-      "content": "Hey Mitch, can you start us off with a joke?"
+      "content": "写一篇向小学生介绍明朝历史的文章，涵盖明朝的建立、兴盛、衰落的过程，大约3000字。"
     }
   ],
   "temperature": 1,
@@ -40,8 +47,5 @@ curl -X 'POST' \
   "stream": true
 }'
 ```
-Note that you must provide the entire conversation history to the backend, as the server expects input in OpenAI format. 
-
-## Documentation
-Navigate to http://localhost:8000/autogen to see the docs. 
 
+For available models, please refer to the mode list API [http://localhost:8000/autogen/api/v1/models](http://localhost:8000/autogen/api/v1/models).

app/agent_configs.json

@@ -0,0 +1,24 @@
+{
+  "article_writer": {
+    "name": "ArticleWriter",
+    "description": "默认的文章生成器，先根据需求描述生成文章提纲，然后根据提纲生成文章。",
+    "agents": [
+        {
+            "type": "ConversableAgent",
+            "name": "writer",
+            "system_message": "根据editor提供的文章主题和大纲内容写作文章正文，正文应该是一段或多段文字，而不是大纲式列表，写作完成后将内容提交给editor检查，如果editor提出修改建议，则按要求修改，直到文章完成。",
+            "human_input_mode": "NEVER",
+            "code_execution_config": false,
+            "llm_config": {}
+        },
+        {
+            "type": "ConversableAgent",
+            "name": "editor",
+            "system_message": "分析并理解user提出的文章撰写要求，构思文章大纲和标题，然后通过多次对话依次将大纲的每个部分交给writer，要求writer根据大纲撰写该章节内容。writer写作完每一部分后你都要检查是否符合主题和字数要求，不符合要求的提出修改建议，如此依次重复直到全部章节写作完成。文章完成后将写作文章的全文汇总并包含在<article></article>标记中输出，然后结束对话并输出 TERMINATE",
+            "human_input_mode": "NEVER",
+            "code_execution_config": false,
+            "llm_config": {}
+        }
+    ]
+  }
+}

app/autogen_agents.py

@@ -0,0 +1,69 @@
+from autogen import ChatResult, GroupChat, Agent, OpenAIWrapper, ConversableAgent, UserProxyAgent, GroupChatManager
+from data_model import ModelInformation
+import os
+# 导入json依赖库
+import json
+
+config_list = [{
+  "model": os.getenv("LLM_MODEL", "qwen-plus"),
+  "base_url": os.getenv("BASE_URL","https://dashscope.aliyuncs.com/compatible-mode/v1"),
+  "api_key": os.getenv("API_KEY","EMPTY"),
+  "price" : [0.004, 0.012]
+}]
+
+llm_config = {"config_list": config_list, "cache_seed": 42, "timeout": 180}
+
+# 读取 JSON 文件，初始化agent_configs
+def load_agent(file_path):
+    with open(file_path, 'r', encoding='utf-8') as file:
+        agent_configs = json.load(file)
+        for agent_group_name, config in agent_configs.items():
+            for agent in config['agents']:
+                agent['llm_config'] = llm_config
+    return agent_configs
+
+agent_configs = load_agent('/app/app/agent_configs.json')
+
+models = {}
+
+for k, v in agent_configs.items():
+    models[k] = ModelInformation(
+        id=k,
+        name=v["name"],
+        description=v["description"],
+        pricing={
+            "prompt": "0.00",
+            "completion": "0.00",
+            "image": "0.00",
+            "request": "0.00",
+        },
+        context_length=1024 * 1000,
+        architecture={
+            "modality": "text",
+            "tokenizer": "text2vec-openai",
+            "instruct_type": "InstructGPT",
+        },
+        top_provider={"max_completion_tokens": None, "is_moderated": False},
+        per_request_limits=None
+    )
+
+def build_agents(agent_id):
+    """ Must return a user_proxy agent at first place """
+    agents = []
+    agents.append(ConversableAgent(
+      name="user",
+      system_message="提出写作要求的用户。",
+      is_termination_msg=lambda x: x.get("content", "") and x.get("content", "").rstrip().endswith("TERMINATE"),
+      code_execution_config= False,
+      human_input_mode="NEVER"
+    ))
+    for config in agent_configs[agent_id]["agents"]:
+      _agent = ConversableAgent(
+              name=config["name"],
+              system_message=config["system_message"],
+              human_input_mode=config["human_input_mode"],
+              code_execution_config=config["code_execution_config"],
+              llm_config=config["llm_config"],
+          )
+      agents.append(_agent)
+    return agents

app/autogen_server.py

@@ -20,7 +20,7 @@
 def serve_autogen(inp: Input):
     model_dump = inp.model_dump()
     model_messages = model_dump["messages"]
-    workflow = AutogenWorkflow()
+    workflow = AutogenWorkflow(model_dump["model"])
 
     if inp.stream:
         queue = Queue()
@@ -59,7 +59,7 @@ def return_streaming_response(inp: Input, queue: Queue):
             usage=empty_usage,
             model=inp.model,
         )
-        yield f"data: {json.dumps(chunk.model_dump())}\n\n"
+        yield f"data: {json.dumps(chunk.model_dump(), ensure_ascii=False)}\n\n"
         queue.task_done()
 
 

app/autogen_workflow.py

@@ -10,6 +10,7 @@
 from autogen.code_utils import content_str
 from autogen.io import IOStream
 from termcolor import colored
+from autogen_agents import build_agents, llm_config
 
 
 def streamed_print_received_message(
@@ -121,53 +122,16 @@ def streamed_print_received_message(
     )
 
 
-llm_config = {"model": "gpt-4", "api_key": os.environ["OPENAI_API_KEY"]}
-
-
 class AutogenWorkflow:
-    def __init__(self):
+    def __init__(self, agent_id: str):
         self.queue: Queue | None = None
-        self.user_proxy = UserProxyAgent(
-            name="UserProxy",
-            system_message="You are the UserProxy. You are the user in this conversation.",
-            human_input_mode="NEVER",
-            code_execution_config=False,
-            llm_config=llm_config,
-            description="The UserProxy is the user in this conversation. They will be interacting with the other agents in the group chat.",
-        )
-        self.mitch_hedberg = ConversableAgent(
-            name="MitchHedberg",
-            system_message="You are the comedian Mitch Hedberg. You are known for your surreal humor and deadpan delivery. Your comedy typically featured short, sometimes one-line jokes mixed with absurd elements and non sequitur",
-            max_consecutive_auto_reply=3,
-            human_input_mode="NEVER",
-            code_execution_config=False,
-            llm_config=llm_config,
-            default_auto_reply="I used to do drugs. I still do, but I used to, too.",
-            description="Mitch Hedberg was an American stand-up comedian known for his surreal humor and deadpan "
-                        "delivery. His comedy typically featured short, sometimes one-line jokes mixed with absurd "
-                        "elements and non sequiturs. Hedberg's comedy and onstage persona gained him a cult "
-                        "following, with audience members sometimes shouting out the punchlines to his jokes before "
-                        "he could finish them.",
-        )
-        self.greg_giraldo = ConversableAgent(
-            name="GregGiraldo",
-            system_message="You are the comedian Greg Giraldo. You are known for your acerbic style of humor and your appearances on Comedy Central's roasts. You are a former lawyer who turned to comedy full-time.",
-            max_consecutive_auto_reply=3,
-            human_input_mode="NEVER",
-            code_execution_config=False,
-            llm_config=llm_config,
-            default_auto_reply="I'm not a good person, but I would like to be better.",
-            description="Greg Giraldo was an American stand-up comedian, television personality, and lawyer. He is known for his acerbic style of humor and his appearances on Comedy Central's roasts. Giraldo was a former lawyer who turned to comedy full-time, and he was known for his sharp wit and biting commentary on a wide range of topics."
-        )
+
+        self.agents = build_agents(agent_id)
 
         self.group_chat_with_introductions = GroupChat(
-            agents=[
-                self.user_proxy,
-                self.mitch_hedberg,
-                self.greg_giraldo,
-            ],
+            agents=self.agents,
             messages=[],
-            max_round=10,
+            max_round=50,
             send_introductions=True,
         )
         self.group_chat_manager_with_intros = GroupChatManager(
@@ -209,8 +173,8 @@ def streamed_print_received_message_with_queue_and_index(
                 streamed_print_received_message_with_queue_and_index,
                 self.group_chat_manager_with_intros,
             )
-
-        chat_history = self.user_proxy.initiate_chat(
+        # agents[0] is the user_proxy agent
+        chat_history = self.agents[0].initiate_chat(
             self.group_chat_manager_with_intros, message=message,
         )
         if stream:

app/main.py

@@ -1,13 +1,32 @@
 from dotenv import load_dotenv
-from fastapi import FastAPI
-from fastapi import HTTPException
+import os
+from fastapi import FastAPI, Depends, HTTPException, Request, Response
 from starlette.responses import RedirectResponse
 
 from autogen_server import serve_autogen
 from data_model import Input, ModelInformation
+from autogen_agents import models
 
 load_dotenv()
 
+# 读取环境变量中的keys
+AUTH_KEYS = os.getenv("AUTH_KEYS").split(",")
+
+# 验证请求头中是否有正确的api_key
+def authorization(req: Request):
+    if(not req.headers.get("Authorization")):
+        raise HTTPException(
+                status_code=401,
+                detail="Unauthorized"
+            )
+    token = req.headers["Authorization"].replace("Bearer ", "")
+    if token not in AUTH_KEYS:
+        raise HTTPException(
+                status_code=401,
+                detail="Unauthorized"
+            )
+    return True
+
 base = "/autogen/"
 prefix = base + "api/v1"
 openapi_url = prefix + "/openapi.json"
@@ -20,27 +39,6 @@
     redoc_url=None,
 )
 
-model_info = ModelInformation(
-    id="model_id_v0.1",
-    name="model_name_v0.1",
-    description="This is a state-of-the-art model.",
-    pricing={
-        "prompt": "0.00",
-        "completion": "0.00",
-        "image": "0.00",
-        "request": "0.00",
-    },
-    context_length=1024 * 1000,
-    architecture={
-        "modality": "text",
-        "tokenizer": "text2vec-openai",
-        "instruct_type": "InstructGPT",
-    },
-    top_provider={"max_completion_tokens": None, "is_moderated": False},
-    per_request_limits=None,
-)
-
-
 @app.get(path=base, include_in_schema=False)
 async def docs_redirect():
     return RedirectResponse(url=docs_url)
@@ -49,17 +47,19 @@ async def docs_redirect():
 @app.get(prefix + "/models")
 async def get_models():
     return {
-        "data": {"data": model_info.dict()}
+        "object": "list",
+        "data": [model.dict(exclude={"agent_configs"}) for model in models.values()]
     }
 
 
 @app.post(prefix + "/chat/completions")
-async def route_query(model_input: Input):
-    model_services = {
-        model_info.name: serve_autogen,
-    }
+async def route_query(model_input: Input, authorized: bool = Depends(authorization)):
+    # model_services = {
+    #     model_info.name: serve_autogen,
+    # }
 
-    service = model_services.get(model_input.model)
+    # service = model_services.get(model_input.model)
+    service = models.get(model_input.model)
     if not service:
         raise HTTPException(status_code=404, detail="Model not found")
-    return service(model_input)
+    return serve_autogen(model_input)

docker-compose.yml

@@ -0,0 +1,13 @@
+services:
+  api:
+    build: .
+    volumes:
+      - ./:/app
+      - ~/.cache:/root/.cache
+    environment:
+      - AUTH_KEYS=YOUR_HTTP_AUTHORIZATION_KEYS_SEPARATED_BY_COMMA
+      - LLM_MODEL=qwen-plus
+      - BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
+      - API_KEY=YOUR_API_KEY
+    ports:
+      - "8000:8000"

run.sh

@@ -1,5 +1,5 @@
 #!/bin/bash
 set -a
 export PYTHONPATH=$PYTHONPATH:$(pwd)/app
-source .env
-uvicorn --reload --log-level debug app.main:app --host 0.0.0.0
+
+uvicorn --reload --log-level debug app.main:app --host 0.0.0.0 --port 8000