from langchain_openai.chat_models import chatopenai 这行代码在 LangChain 中是不正确的。 LangChain 是一个用于开发基于语言模型的应用程序的框架,它提供了与各种语言模型交互的接口。然而,根据你提供的代码片段,from langchain_openai.chat_models import chatopenai 这行代码似乎试图从一个不存在的模块中导入一...
from langchain_openai import ChatOpenAI llm = ChatOpenAI( api_key="ollama", model="llama3:8b-instruct-fp16", base_url="http://localhost:11434/v1", ) Description Using Model from Ollama in ChatOpenAI doesnt invoke the tools with bind_tools System Info .. 3 Replies...
from langchain.chains import create_sql_query_chain from langchain_openai import ChatOpenAI llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0) from langchain_community.tools.sql_database.tool import QuerySQLDataBaseTool # 执行查询动作 execute_query = QuerySQLDataBaseTool(db=db) # 获取sql...
步骤1:创建上下文压缩检索器 from langchain.retrievers import ContextualCompressionRetriever from langchain.retrievers.document_compressors import LLMChainExtractor # 包装我们的向量存储 llm = OpenAI(temperature=0) compressor = LLMChainExtractor.from_llm(llm) compression_retriever = ContextualCompressionRetriever(...
接下来我们需要先加载一下在之前的博客 让Langchain与你的数据对话(二):向量存储 与嵌入中我们在本地创建的关于吴恩达老师的机器学习课程cs229课程讲义(pdf)的向量数据库: from langchain.vectorstores import Chroma from langchain.embeddings.openai import OpenAIEmbeddings persist_directory = 'docs/chroma/' embed...
from fastapi import FastAPI, Depends, Request, Response from typing import Any, Dict, List, Generator import asyncio from langchain.llms import OpenAI from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler from langchain.schema import LLMResult, HumanMessage, SystemMessage from ...
from langchain.embeddings.openai import OpenAIEmbeddings embedding = OpenAIEmbeddings(openai_api_key=api_key) db = Chroma(persist_directory="embeddings\\",embedding_function=embedding) The embedding_function parameter accepts OpenAI embedding object that serves the purpose. ...
from langchain.chat_models.openai import ChatOpenAI from langchain.utilities import GoogleSearchAPIWrapper os.environ[“OPENAI_API_KEY”] = ‘my_key’ vectorstore = Chroma(embedding_function=OpenAIEmbeddings(),persist_directory=“./chroma_db_oai”) ...
Finally, you can build a chain for the RAG pipeline, chaining together the retriever, the prompt template and the LLM. Once the RAG chain is defined, you can invoke it. from langchain.chat_models import ChatOpenAI from langchain.schema.runnable import RunnablePassthrough ...
from langchain.chains.openai_functions.openapi import get_openapi_chain chain = get_openapi_chain("https://www.klarna.com/us/shopping/public/openai/v0/api-docs/") chain("What are some options for a men's large blue button down shirt") Error message when running with pydantic 1 Unable to...