V0.1.0

src/mindpilot/app/agent/agents_registry.py

@@ -1,5 +1,7 @@
+import typing
 from typing import List, Sequence
 
+import langchain_core
 from langchain import hub
 from langchain.agents import AgentExecutor, create_structured_chat_agent
 from langchain_core.callbacks import BaseCallbackHandler
@@ -20,6 +22,7 @@ def agents_registry(
         prompt = ChatPromptTemplate.from_messages([SystemMessage(content=prompt)])
     else:
         prompt = hub.pull("hwchase17/structured-chat-agent")  # default prompt
+        print(prompt)
     agent = create_structured_chat_agent(llm=llm, tools=tools, prompt=prompt)
 
     agent_executor = AgentExecutor(

src/mindpilot/app/api/chat_routes.py

@@ -1,85 +1,11 @@
 from __future__ import annotations
-
-from typing import Dict, List
-
 from fastapi import APIRouter, Request
-from langchain.prompts.prompt import PromptTemplate
-
-# from app.api.api_schemas import MsgType, OpenAIChatInput
 from ..chat.chat import chat
-from ..utils import (
-    get_OpenAIClient,
-    get_prompt_template,
-    get_tool,
-    get_tool_config,
-)
-
-from .openai_routes import openai_request
 
 chat_router = APIRouter(prefix="/chat", tags=["MindPilot对话"])
 
 chat_router.post(
     "/chat/online",
-    summary="与llm模型对话",
+    summary="以API方式与在线llm模型进行对话",
 )(chat)
 
-
-# @chat_router.post("/chat/completions", summary="兼容 openai 的统一 chat 接口")
-# async def chat_completions(
-#     request: Request,
-#     body: OpenAIChatInput,
-# ) -> Dict:
-#     """
-#     请求参数与 openai.chat.completions.create 一致，可以通过 extra_body 传入额外参数
-#     tools 和 tool_choice 可以直接传工具名称，会根据项目里包含的 tools 进行转换
-#     通过不同的参数组合调用不同的 chat 功能：
-#     - tool_choice
-#         - extra_body 中包含 tool_input: 直接调用 tool_choice(tool_input)
-#         - extra_body 中不包含 tool_input: 通过 agent 调用 tool_choice
-#     - tools: agent 对话
-#     - 其它：LLM 对话
-#     返回与 openai 兼容的 Dict
-#     """
-#     client = get_OpenAIClient(model_name=body.model, is_async=True)
-#     extra = {**body.model_extra} or {}
-#     for key in list(extra):
-#         delattr(body, key)
-#
-#     global global_model_name
-#     global_model_name = body.model
-#
-#     if isinstance(body.tools, list):
-#         for i in range(len(body.tools)):
-#             if isinstance(body.tools[i], str):
-#                 if t := get_tool(body.tools[i]):
-#                     body.tools[i] = {
-#                         "type": "function",
-#                         "function": {
-#                             "name": t.name,
-#                             "description": t.description,
-#                             "parameters": t.args,
-#                         },
-#                     }
-#     # agent chat with tool calls
-#     if body.tools:
-#         chat_model_config = {}
-#         tool_names = [x["function"]["name"] for x in body.tools]
-#         tool_config = {name: get_tool_config(name) for name in tool_names}
-#         # print(tool_config)
-#         result = await chat(
-#             query=body.messages[-1]["content"],
-#             # query="查询北京的天气状况，并搜索互联网给出北京的旅游攻略",
-#             metadata=extra.get("metadata", {}),
-#             conversation_id=extra.get("conversation_id", ""),
-#             # message_id=message_id,
-#             history_len=-1,
-#             history=body.messages[:-1],
-#             stream=body.stream,
-#             chat_model_config=extra.get("chat_model_config", chat_model_config),
-#             tool_config=extra.get("tool_config", tool_config),
-#         )
-#         return result
-#     else:
-#         # TODO 使用用户指定工具
-#         pass
-#

src/mindpilot/app/chat/chat.py

@@ -4,7 +4,7 @@ import uuid
 from typing import AsyncIterable, List
 
 from fastapi import Body
-from langchain.schema.runnable import RunnableSequence
+from langchain.chains import LLMChain
 from langchain.prompts.chat import ChatPromptTemplate
 from langchain_core.messages import AIMessage, HumanMessage, convert_to_messages
 from sse_starlette.sse import EventSourceResponse
@@ -48,7 +48,7 @@ def create_models_from_config(configs, callbacks, stream):
 
 
 def create_models_chains(
-    history, prompts, models, tools, callbacks, metadata
+    history, prompts, models, tools, callbacks, metadata, agent_enable
 ):
     chat_prompt = None
 
@@ -65,22 +65,20 @@ def create_models_chains(
             False
         )
         chat_prompt = ChatPromptTemplate.from_messages([input_msg])
+    print(chat_prompt)
 
     llm = models["llm_model"]
     llm.callbacks = callbacks
+    chain = LLMChain(prompt=chat_prompt, llm=llm)
 
-    if "action_model" in models and tools is not None:
+    if agent_enable:
         agent_executor = agents_registry(
             llm=llm, callbacks=callbacks, tools=tools, prompt=None, verbose=True
         )
         full_chain = {"input": lambda x: x["input"]} | agent_executor
     else:
-        full_chain = RunnableSequence(
-            steps=[
-                {"input": lambda x: x["input"]},
-                chat_prompt | llm
-            ]
-        )
+        chain.llm.callbacks = callbacks
+        full_chain = {"input": lambda x: x["input"]} | chain
     return full_chain
 
 
@@ -99,7 +97,8 @@ async def chat(
     ),
     stream: bool = Body(True, description="流式输出"),
     chat_model_config: dict = Body({}, description="LLM 模型配置", examples=[]),
-    tool_config: List[str] = Body([], description="工具配置", examples=["weather_check"]),
+    tool_config: List[str] = Body([], description="工具配置", examples=[]),
+    agent_enable: bool = Body(True, description="是否启用Agent")
 ):
     """Agent 对话"""
 
@@ -121,6 +120,7 @@ async def chat(
             callbacks=callbacks,
             history=history,
             metadata=metadata,
+            agent_enable=agent_enable
         )
 
         _history = [History.from_data(h) for h in history]

src/mindpilot/app/configs/model_config.py

@@ -10,7 +10,7 @@ MODEL_CONFIG = {
             },
             "llm_model": {
                 "glm-4": {
-                    "temperature": 0.8,
+                    "temperature": 0.9,
                     "max_tokens": 4096,
                     "history_len": 10,
                     "prompt_name": "default",
@@ -21,7 +21,7 @@ MODEL_CONFIG = {
                 "glm-4": {
                     "temperature": 0.1,
                     "max_tokens": 4096,
-                    "prompt_name": "GPT-4",
+                    "prompt_name": "ChatGLM3",
                     "callbacks": True,
                 },
             },

src/mindpilot/app/configs/prompt_config.py

@@ -50,39 +50,39 @@ PROMPT_TEMPLATES = {
         "Question:{input}\n"
         "Thought:{agent_scratchpad}\n",
 
-        "ChatGLM3": "You can answer using the tools.Respond to the human as helpfully and accurately as possible.\n"
-        "You have access to the following tools:\n"
-        "{tools}\n"
-        "Use a json blob to specify a tool by providing an action key (tool name)\n"
-        "and an action_input key (tool input).\n"
-        'Valid "action" values: "Final Answer" or  [{tool_names}]\n'
-        "Provide only ONE action per $JSON_BLOB, as shown:\n\n"
-        "```\n"
-        "{{{{\n"
-        '  "action": $TOOL_NAME,\n'
-        '  "action_input": $INPUT\n'
-        "}}}}\n"
-        "```\n\n"
-        "Follow this format:\n\n"
-        "Question: input question to answer\n"
-        "Thought: consider previous and subsequent steps\n"
-        "Action:\n"
-        "```\n"
-        "$JSON_BLOB\n"
-        "```\n"
-        "Observation: action result\n"
-        "... (repeat Thought/Action/Observation N times)\n"
-        "Thought: I know what to respond\n"
-        "Action:\n"
-        "```\n"
-        "{{{{\n"
-        '  "action": "Final Answer",\n'
-        '  "action_input": "Final response to human"\n'
-        "}}}}\n"
-        "Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary.\n"
-        "Respond directly if appropriate. Format is Action:```$JSON_BLOB```then Observation:.\n"
-        "Question: {input}\n\n"
-        "{agent_scratchpad}\n",
+        "ChatGLM3": """You can answer using the tools.Respond to the human as helpfully and accurately as possible.\n
+You have access to the following tools:\n
+{tools}\n
+Use a json blob to specify a tool by providing an action key {tool name}\n
+and an action_input key (tool input).\n
+Valid "action" values: "Final Answer" or  {tool_names}\n
+Provide only ONE action per $JSON_BLOB, as shown:\n\n
+```\n
+{{{{\n
+  "action": $TOOL_NAME,\n
+  "action_input": $INPUT\n
+}}}}\n
+```\n\n
+Follow this format:\n\n
+Question: input question to answer\n
+Thought: consider previous and subsequent steps\n
+Action:\n
+```\n
+$JSON_BLOB\n
+```\n
+Observation: action result\n
+... (repeat Thought/Action/Observation N times)\n
+Thought: I know what to respond\n
+Action:\n
+```\n
+{{{{\n
+  "action": "Final Answer",\n
+  "action_input": "Final response to human"\n
+}}}}\n
+Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary.\n
+Respond directly if appropriate. Format is Action:```$JSON_BLOB```then Observation:.\n
+Question: {input}\n\n
+{agent_scratchpad}\n""",
 
         "qwen": "Answer the following questions as best you can. You have access to the following APIs:\n\n"
         "{tools}\n\n"