这里我们加载了之前保存在本地的向量数据库,并查询了数据库中的文档数量为209,这与我们之前创建该数据库时候的文档数量是一致的,接下来我们提出一个问题:“What are major topics for this class?”,即“ 这门课的主要主题是什么?” 然后用 similarity_search 方法来查询一下与该问题相关的文档块: question="Wha...
fromlangchain.vectorstoresimportChromafromlangchain.embeddings.openaiimportOpenAIEmbeddings#加载本地向量数据库persist_directory='docs/chroma/'embedding=OpenAIEmbeddings()vectordb=Chroma(persist_directory=persist_directory,embedding_function=embedding)#搜索与问题相关的文档question="What are major topics for this ...
movie recommender system with OpenAI embeddings and a vector database\n--\n3\nRecommended from Medium\nKrishna Yogi\nBuilding a question-answering system using LLM on your private data\n--\n6\nRahul Nayak\nin\nTowards Data Science\nHow to Convert Any Text Into a Graph of Concepts\nA method...
作业所需的2个调用和2条指示非常直接：from langchain.chains.question_answering import load_qa_chainfrom langchain import HuggingFaceHub 然后，我们定义了要与我们的访问令牌一起使用的LLM，并告诉python启动我们的相似性搜索请求，该搜索嵌入了我们对所选LLM的问题 llm=HuggingFaceHub(repo_id="google/flan-t5-x...
langchain for Question Answering on own data\nStep-by-step guide to using langchain to chat with own data\n--\n10\nAmogh Agastya\nin\nBetter Programming\nHarnessing Retrieval Augmented Generation With Langchain\nImplementing RAG using Langchain\n--\n6\nAnindyadeep\nHow to integrate custom LLM...
You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don’t know the answer, just say that you don’t know. Use three sentences maximum and keep the answer concise.Question: {question}Context: {context}Answer:** ...
> Question: {{question}} > Context: >>> {{context}} >>> Extracted relevant parts:""" 核心原理还是通过prompt让LLM帮忙压缩信息,提取主干! 3、EnsembleRetriever:前面最常用的Retriever就是计算两个embedding的距离了,这种方式又称“密集检索”,因为embedding的每个维度都有数值,看起来很紧凑和密集,所以叫密集...
"Text-to-SQL is a method that helps users generate SQL queries for their questions about databases. One approach is to use large language models to understand the question and generate the SQL. However, this approach faces challenges with strict SQL syntax rules. Existing methods use examples to...
from langchain.question_answering import load_model from langchain import Document # 加载预训练的问答模型 model = load_model('question-answering') # 问题解析器 def parse_question(question): # 在这个简单的示例中,我们直接返回原始问题 return question # 答案生成器 def generate_answer(question, model...
fromlangchain.evaluation.qaimportQAEvalChain#创建LLMllm=ChatOpenAI(temperature=0)#创建评估chaineval_chain=QAEvalChain.from_llm(llm)#生成评估结果graded_outputs=eval_chain.evaluate(examples,predictions)#统计评估结果fori,eginenumerate(examples):print(f"Example {i}:")print("Question: "+predictions[i]...