一、基本chain
from langchain_core.output_parsers import StrOutputParser from langchain_core.prompts import ChatPromptTemplate from langchain_community.llms.chatglm3 import ChatGLM3 import os os.environ["LANGCHAIN_TRACING_V2"] ="true" os.environ["LANGCHAIN_API_KEY"]="ls__96d567894428421db2d42dec4edde0b4" #llm endpoint_url = "https://u4378-ad4b-55fa9b48.westb.seetacloud.com:8443/v1/chat/completions" llm = ChatGLM3( endpoint_url=endpoint_url, max_tokens=8000, top_p=0.9, timeout=999 ) prompt = ChatPromptTemplate.from_template("tell me a short joke about {topic}") output_parser = StrOutputParser() chain = prompt | llm | output_parser chain.invoke({"topic": "ice cream"})
二、rag search example
from langchain_community.vectorstores import DocArrayInMemorySearch from langchain_core.output_parsers import StrOutputParser from langchain_core.prompts import ChatPromptTemplate from langchain_core.runnables import RunnableParallel, RunnablePassthrough from langchain_openai import OpenAIEmbeddings from langchain_community.llms.chatglm3 import ChatGLM3 from langchain_community.embeddings import HuggingFaceBgeEmbeddings import os os.environ["LANGCHAIN_TRACING_V2"] ="true" os.environ["LANGCHAIN_API_KEY"]="ls__96d567894428421db2d42dec4edde0b4" #llm endpoint_url = "https://u4378-ad4b-55fa9b48.westb.seetacloud.com:8443/v1/chat/completions" llm = ChatGLM3( endpoint_url=endpoint_url, max_tokens=8000, top_p=0.9, timeout=999 ) #embeded knowledge model_name = "/home/jsxyhelu/CODES/bge-large-zh-v1.5" model_kwargs = {'device': 'cpu'} encode_kwargs = {'normalize_embeddings': True} embedding = HuggingFaceBgeEmbeddings( model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs, query_instruction="为这个句子生成表示以用于检索相关文章:" ) #start vectorstore = DocArrayInMemorySearch.from_texts( ["harrison worked at kensho", "bears like to eat honey"], embedding=embedding, ) retriever = vectorstore.as_retriever() template = """Answer the question based only on the following context: {context} Question: {question} """ prompt = ChatPromptTemplate.from_template(template) output_parser = StrOutputParser() setup_and_retrieval = RunnableParallel( {"context": retriever, "question": RunnablePassthrough()} ) chain = setup_and_retrieval | prompt | llm | output_parser chain.invoke("where did harrison work?")
更精简、更现代,应该是被选择的方法。
我认为掌握到这个水平完全是足够 了。
标签:endpoint,output,kwargs,langchain,具体,llm,import,LCEL,实验 From: https://www.cnblogs.com/jsxyhelu/p/18127331