llm = OpenAI(model_name=modal,openai_api_key=api_key,openai_api_base=api_url) llm_with_stop = llm.bind(stop=["\nObservation"]) 使用duckduckgo搜索引擎创建在线搜索工具 # 在线搜索工具 # pip install duckduckgo-search search = DuckDuckGoSearchRun() search_tool = Tool( name = "Web Search", ...
chat_models import ChatOpenAI chat_model = ChatOpenAI(temperature=0, model='gpt-3.5-turbo', openai_api_key=openai_api_key) 代码语言:javascript 复制 # Vanilla Extraction instructions = """ You will be given a sentence with fruit names, extract those fruit names and assign an emoji to ...
1）LLM：将文本字符串作为输入并返回文本字符串的模型，类似 OpenAI 的 text-davinci-003 2）Chat Mo...
openai_api_base=config.get("api_base_url", fschat_openai_api_address()), model_name=model_name, temperature=temperature, max_tokens=max_tokens, openai_proxy=config.get("openai_proxy"), **kwargs ) 在这里指定了fastchat的openai_api接口地址,这样就获得了指定接口地址的langchain ChatOpenAI对象 ...
fromlangchain.chainsimportSimpleSequentialChain# Initialize the language modelllm = ChatOpenAI(temperature=0.9, model=llm_model)# Prompt template 1: Suggest a company namefirst_prompt = ChatPromptTemplate.from_template("What is the best name to describe a company that makes {product}?") ...
# LLMllm= ChatOpenAI(model_name="gpt-4-1106-preview", temperature=0, streaming=True)# Embedding Modelembed_model = OpenAIEmbedding(model="text-embedding-3-small", embed_batch_size=100)# Set Llamaindex ConfigsSettings.llm = llmSettings....
2.1.1 原生 openai 模块调用大模型 如果你是在国外或者学会了科学上网,并且你在openai注册了key,你就可以使用opanai的模块进行调用 pip install openai==0.28 1. import os import openai def get_completion(prompt,model="gpt-3.5-turbo"): messages=[{"role":"user","content":prompt}] ...
llm = ChatOpenAI(model_name="gpt-3.5-turbo") toolkit = SQLDatabaseToolkit(db=db,llm=llm) agent_executor = create_sql_agent( llm=llm, toolkit=toolkit, verbose=True) agent_executor.run("using the teachers table, find the first_name and last name of teachers who earn less the mean salary...
embed_model = OpenAIEmbedding( model="text-embedding-3-small", embed_batch_size=100 ) # Set Llamaindex Configs Settings.llm = llm Settings.embed_model = embed_model 然后利用LlamaIndex的索引和检索功能为文档定义单独的查询引擎。 #Building Indexes for each of the Documents ...
通过文档目录我们可以看到,Langchain由6个module组成,分别是Model IO、Retrieval、Chains、Memory、Agents和Callbacks。 Model IO:AI应用的核心部分,其中包括输入、Model和输出。 Retrieval:“检索“——该功能与向量数据密切库相关,是在向量数据库中搜索与问题相关的文档内容。