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@ -148,3 +148,5 @@ $ python startup.py -a
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### 公众号
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🎉 Langchain-Chatchat 项目官方公众号，欢迎扫码关注。
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<u>[Langchain-Chatchat](https://github.com/chatchat-space/Langchain-Chatchat)</u>:基于 ChatGLM 等大语言模型与 Langchain 等应用框架实现，开源、可离线部署的检索增强生成(RAG)大模型知识库项目
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@ -6,8 +6,6 @@ import langchain
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log_verbose = False
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langchain.verbose = False
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# 是否保存聊天记录
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SAVE_CHAT_HISTORY = False
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# 通常情况下不需要更改以下内容
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Binary file not shown.
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Before Width: | Height: | Size: 188 KiB |
@ -1,6 +1,6 @@
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langchain>=0.0.324
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langchain>=0.0.326
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langchain-experimental>=0.0.30
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fschat[model_worker]==0.2.31
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fschat[model_worker]==0.2.32
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xformers>=0.0.22.post4
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openai>=0.28.1
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sentence_transformers
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@ -48,7 +48,7 @@ pandas~=2.0.3
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streamlit>=1.26.0
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streamlit-option-menu>=0.3.6
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streamlit-antd-components>=0.1.11
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streamlit-chatbox==1.1.11
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streamlit-chatbox>=1.1.11
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streamlit-aggrid>=0.3.4.post3
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httpx[brotli,http2,socks]>=0.25.0
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watchdog
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@ -1,10 +1,10 @@
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langchain>=0.0.324
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langchain>=0.0.326
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langchain-experimental>=0.0.30
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fschat[model_worker]==0.2.31
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fschat[model_worker]==0.2.32
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xformers>=0.0.22.post4
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openai>=0.28.1
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sentence_transformers>=2.2.2
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transformers>=4.34
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transformers>=4.35
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torch>=2.1
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torchvision
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torchaudio
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@ -1,12 +1,12 @@
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langchain>=0.0.324
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fschat>=0.2.31
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langchain>=0.0.326
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fschat>=0.2.32
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openai
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# sentence_transformers
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# transformers>=4.33.0
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# transformers>=4.35.0
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# torch>=2.0.1
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# torchvision
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# torchaudio
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fastapi>=0.103.1
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fastapi>=0.104.1
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python-multipart
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nltk~=3.8.1
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uvicorn~=0.23.1
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@ -71,15 +71,16 @@ bigdata,大数据的就业情况如何
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这些数据库是你能访问的，冒号之前是他们的名字，冒号之后是他们的功能，你应该参考他们的功能来帮助你思考
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{database_names}
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你的回答格式应该按照下面的内容，请注意```text 等标记都必须输出，这是我用来提取答案的标记。
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不要输出中文的逗号，不要输出引号。
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Question: ${{用户的问题}}
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```text
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${{知识库名称,查询问题,不要带有任何除了,之外的符号}}
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${{知识库名称,查询问题,不要带有任何除了,之外的符号,比如不要输出中文的逗号，不要输出引号}}
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```output
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数据库查询的结果
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@ -165,7 +166,11 @@ class LLMKnowledgeChain(LLMChain):
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lines = cleaned_input_str.split("\n")
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# 使用逗号分割每一行，然后形成一个（数据库，查询）元组的列表
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try:
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queries = [(line.split(",")[0].strip(), line.split(",")[1].strip()) for line in lines]
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except:
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queries = [(line.split("，")[0].strip(), line.split("，")[1].strip()) for line in lines]
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print(queries)
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run_manager.on_text("知识库查询询内容:\n\n" + str(queries) + " \n\n", color="blue", verbose=self.verbose)
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output = self._evaluate_expression(queries)
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run_manager.on_text("\nAnswer: ", verbose=self.verbose)
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@ -193,7 +198,10 @@ class LLMKnowledgeChain(LLMChain):
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cleaned_input_str = (
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expression.replace("\"", "").replace("“", "").replace("”", "").replace("```", "").strip())
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lines = cleaned_input_str.split("\n")
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try:
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queries = [(line.split(",")[0].strip(), line.split(",")[1].strip()) for line in lines]
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except:
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queries = [(line.split("，")[0].strip(), line.split("，")[1].strip()) for line in lines]
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await run_manager.on_text("知识库查询询内容:\n\n" + str(queries) + " \n\n", color="blue",
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verbose=self.verbose)
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@ -20,21 +20,25 @@ tools = [
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func=weathercheck,
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name="天气查询工具",
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description="无需访问互联网，使用这个工具查询中国各地未来24小时的天气",
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args_schema=WhetherSchema,
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),
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Tool.from_function(
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func=shell,
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name="shell工具",
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description="使用命令行工具输出",
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args_schema=ShellInput,
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),
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Tool.from_function(
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func=knowledge_search_more,
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name="知识库查询工具",
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description="优先访问知识库来获取答案",
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args_schema=KnowledgeSearchInput,
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),
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Tool.from_function(
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func=search_internet,
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name="互联网查询工具",
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description="如果你无法访问互联网，这个工具可以帮助你访问Bing互联网来解答问题",
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args_schema=SearchInternetInput,
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),
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Tool.from_function(
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func=wolfram,
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@ -76,7 +76,7 @@ async def agent_chat(query: str = Body(..., description="用户输入", examples
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agent = LLMSingleActionAgent(
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llm_chain=llm_chain,
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output_parser=output_parser,
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stop=["\nObservation:", "Observation:", "<|im_end|>", "<|observation|>"],
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stop=["\nObservation:", "Observation:", "<|im_end|>", "<|observation|>"], # Qwen模型中使用这个
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allowed_tools=tool_names,
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)
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# 把history转成agent的memory
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@ -11,7 +11,7 @@ from langchain.prompts.chat import ChatPromptTemplate
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from typing import List, Optional
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from server.chat.utils import History
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from server.utils import get_prompt_template
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from server.db.repository.chat_history_repository import add_chat_history_to_db, update_chat_history
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from server.db.repository import add_chat_history_to_db, update_chat_history
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async def chat(query: str = Body(..., description="用户输入", examples=["恼羞成怒"]),
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@ -0,0 +1,3 @@
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from .chat_history_repository import *
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from .knowledge_base_repository import *
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from .knowledge_file_repository import *
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@ -368,7 +368,7 @@ def recreate_vector_store(
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"code": 200,
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"msg": f"({i + 1} / {len(files)}): {file_name}",
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"total": len(files),
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"finished": i,
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"finished": i + 1,
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"doc": file_name,
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}, ensure_ascii=False)
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kb.add_doc(kb_file, not_refresh_vs_cache=True)
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@ -15,7 +15,6 @@ import httpx
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from typing import Literal, Optional, Callable, Generator, Dict, Any, Awaitable, Union
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async def wrap_done(fn: Awaitable, event: asyncio.Event):
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"""Wrap an awaitable with a event to signal when it's done or an exception is raised."""
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try:
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@ -41,47 +40,7 @@ def get_ChatOpenAI(
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) -> ChatOpenAI:
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## 以下模型是Langchain原生支持的模型，这些模型不会走Fschat封装
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config_models = list_config_llm_models()
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if model_name in config_models.get("langchain", {}):
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config = config_models["langchain"][model_name]
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if model_name == "Azure-OpenAI":
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model = AzureChatOpenAI(
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streaming=streaming,
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verbose=verbose,
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callbacks=callbacks,
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deployment_name=config.get("deployment_name"),
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model_version=config.get("model_version"),
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openai_api_type=config.get("openai_api_type"),
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openai_api_base=config.get("api_base_url"),
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openai_api_version=config.get("api_version"),
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openai_api_key=config.get("api_key"),
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openai_proxy=config.get("openai_proxy"),
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temperature=temperature,
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max_tokens=max_tokens,
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)
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elif model_name == "OpenAI":
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model = ChatOpenAI(
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streaming=streaming,
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verbose=verbose,
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callbacks=callbacks,
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model_name=config.get("model_name"),
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openai_api_base=config.get("api_base_url"),
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openai_api_key=config.get("api_key"),
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openai_proxy=config.get("openai_proxy"),
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temperature=temperature,
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max_tokens=max_tokens,
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)
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elif model_name == "Anthropic":
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model = ChatAnthropic(
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streaming=streaming,
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verbose=verbose,
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callbacks=callbacks,
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model_name=config.get("model_name"),
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anthropic_api_key=config.get("api_key"),
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)
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## TODO 支持其他的Langchain原生支持的模型
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else:
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## 非Langchain原生支持的模型，走Fschat封装
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config = get_model_worker_config(model_name)
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model = ChatOpenAI(
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@ -437,7 +396,6 @@ def get_model_worker_config(model_name: str = None) -> dict:
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config.update(ONLINE_LLM_MODEL.get(model_name, {}).copy())
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config.update(FSCHAT_MODEL_WORKERS.get(model_name, {}).copy())
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if model_name in ONLINE_LLM_MODEL:
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config["online_api"] = True
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if provider := config.get("provider"):
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