move wrap_done & get_ChatOpenAI from server.chat.utils to server.utils (#1506)

chains/llmchain_with_history.py

@@ -1,4 +1,4 @@
-from server.chat.utils import get_ChatOpenAI
+from server.utils import get_ChatOpenAI
 from configs.model_config import LLM_MODEL, TEMPERATURE
 from langchain import LLMChain
 from langchain.prompts.chat import (

server/agent/math.py

@@ -1,6 +1,6 @@
 from langchain import PromptTemplate
 from langchain.chains import LLMMathChain
-from server.chat.utils import wrap_done, get_ChatOpenAI
+from server.utils import wrap_done, get_ChatOpenAI
 from configs.model_config import LLM_MODEL, TEMPERATURE
 from langchain.chat_models import ChatOpenAI
 from langchain.callbacks.manager import CallbackManagerForToolRun

server/agent/translator.py

@@ -2,7 +2,7 @@ from langchain import PromptTemplate, LLMChain
 import sys
 import os
 
-from server.chat.utils import get_ChatOpenAI
+from server.utils import get_ChatOpenAI
 
 sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
 from langchain.chains.llm_math.prompt import PROMPT

server/agent/weather.py

@@ -4,7 +4,7 @@ from __future__ import annotations
 import sys
 import os
 
-from server.chat.utils import get_ChatOpenAI
+from server.utils import get_ChatOpenAI
 
 sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
 

server/chat/agent_chat.py

@@ -5,7 +5,7 @@ from server.agent.custom_template import CustomOutputParser, prompt
 from fastapi import Body
 from fastapi.responses import StreamingResponse
 from configs.model_config import LLM_MODEL, TEMPERATURE, HISTORY_LEN
-from server.chat.utils import wrap_done, get_ChatOpenAI
+from server.utils import wrap_done, get_ChatOpenAI
 from langchain import LLMChain
 from langchain.callbacks import AsyncIteratorCallbackHandler
 from langchain.callbacks.streaming_aiter_final_only import AsyncFinalIteratorCallbackHandler

server/chat/chat.py

@@ -1,7 +1,7 @@
 from fastapi import Body
 from fastapi.responses import StreamingResponse
 from configs import LLM_MODEL, TEMPERATURE
-from server.chat.utils import wrap_done, get_ChatOpenAI
+from server.utils import wrap_done, get_ChatOpenAI
 from langchain import LLMChain
 from langchain.callbacks import AsyncIteratorCallbackHandler
 from typing import AsyncIterable

server/chat/knowledge_base_chat.py

@@ -1,7 +1,7 @@
 from fastapi import Body, Request
 from fastapi.responses import StreamingResponse
 from configs import (LLM_MODEL, VECTOR_SEARCH_TOP_K, SCORE_THRESHOLD, TEMPERATURE)
-from server.chat.utils import wrap_done, get_ChatOpenAI
+from server.utils import wrap_done, get_ChatOpenAI
 from server.utils import BaseResponse, get_prompt_template
 from langchain import LLMChain
 from langchain.callbacks import AsyncIteratorCallbackHandler

server/chat/search_engine_chat.py

@@ -4,7 +4,7 @@ from configs import (BING_SEARCH_URL, BING_SUBSCRIPTION_KEY,
 from fastapi import Body
 from fastapi.responses import StreamingResponse
 from fastapi.concurrency import run_in_threadpool
-from server.chat.utils import wrap_done, get_ChatOpenAI
+from server.utils import wrap_done, get_ChatOpenAI
 from server.utils import BaseResponse, get_prompt_template
 from langchain import LLMChain
 from langchain.callbacks import AsyncIteratorCallbackHandler

server/chat/utils.py

@@ -1,46 +1,7 @@
-import asyncio
 from pydantic import BaseModel, Field
 from langchain.prompts.chat import ChatMessagePromptTemplate
 from configs import logger, log_verbose
-from server.utils import get_model_worker_config, fschat_openai_api_address
+from typing import List, Tuple, Dict, Union
-from langchain.chat_models import ChatOpenAI
-from typing import Awaitable, List, Tuple, Dict, Union, Callable, Any
-
-
-def get_ChatOpenAI(
-    model_name: str,
-    temperature: float,
-    streaming: bool = True,
-    callbacks: List[Callable] = [],
-    **kwargs: Any,
-) -> ChatOpenAI:
-    config = get_model_worker_config(model_name)
-    model = ChatOpenAI(
-        streaming=streaming,
-        verbose=True,
-        callbacks=callbacks,
-        openai_api_key=config.get("api_key", "EMPTY"),
-        openai_api_base=config.get("api_base_url", fschat_openai_api_address()),
-        model_name=model_name,
-        temperature=temperature,
-        openai_proxy=config.get("openai_proxy"),
-        **kwargs
-    )
-    return model
-
-
-async def wrap_done(fn: Awaitable, event: asyncio.Event):
-    """Wrap an awaitable with a event to signal when it's done or an exception is raised."""
-    try:
-        await fn
-    except Exception as e:
-        # TODO: handle exception
-        msg = f"Caught exception: {e}"
-        logger.error(f'{e.__class__.__name__}: {msg}',
-                     exc_info=e if log_verbose else None)
-    finally:
-        # Signal the aiter to stop.
-        event.set()
 
 
 class History(BaseModel):

server/utils.py

@@ -10,12 +10,50 @@ from configs import (LLM_MODEL, LLM_DEVICE, EMBEDDING_DEVICE,
                     FSCHAT_MODEL_WORKERS)
 import os
 from concurrent.futures import ThreadPoolExecutor, as_completed
-from typing import Literal, Optional, Callable, Generator, Dict, Any, Tuple
+from langchain.chat_models import ChatOpenAI
+from typing import Literal, Optional, Callable, Generator, Dict, Any, Awaitable
 
 
 thread_pool = ThreadPoolExecutor(os.cpu_count())
 
 
+async def wrap_done(fn: Awaitable, event: asyncio.Event):
+    """Wrap an awaitable with a event to signal when it's done or an exception is raised."""
+    try:
+        await fn
+    except Exception as e:
+        # TODO: handle exception
+        msg = f"Caught exception: {e}"
+        logger.error(f'{e.__class__.__name__}: {msg}',
+                     exc_info=e if log_verbose else None)
+    finally:
+        # Signal the aiter to stop.
+        event.set()
+
+
+def get_ChatOpenAI(
+    model_name: str,
+    temperature: float,
+    streaming: bool = True,
+    callbacks: List[Callable] = [],
+    verbose: bool = True,
+    **kwargs: Any,
+) -> ChatOpenAI:
+    config = get_model_worker_config(model_name)
+    model = ChatOpenAI(
+        streaming=streaming,
+        verbose=verbose,
+        callbacks=callbacks,
+        openai_api_key=config.get("api_key", "EMPTY"),
+        openai_api_base=config.get("api_base_url", fschat_openai_api_address()),
+        model_name=model_name,
+        temperature=temperature,
+        openai_proxy=config.get("openai_proxy"),
+        **kwargs
+    )
+    return model
+
+
 class BaseResponse(BaseModel):
     code: int = pydantic.Field(200, description="API status code")
     msg: str = pydantic.Field("success", description="API status message")

tests/agent/test_agent_function.py

@@ -2,7 +2,7 @@ import sys
 import os
 sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
 from configs import LLM_MODEL, TEMPERATURE
-from server.chat.utils import get_ChatOpenAI
+from server.utils import get_ChatOpenAI
 from langchain import LLMChain
 from langchain.agents import LLMSingleActionAgent, AgentExecutor
 from server.agent.tools import tools, tool_names

webui_pages/utils.py

@@ -377,6 +377,7 @@ class ApiRequest:
         else:
             response = self.post("/chat/agent_chat", json=data, stream=True)
             return self._httpx_stream2generator(response)
+
     def knowledge_base_chat(
         self,
         query: str,