from langchain.embeddings.openai import OpenAIEmbeddings from langchain.text_splitter import CharacterTextSplitter from langchain.vectorstores import Chroma # pku.txt内容:<https://www.pku.edu.cn/about.html> with open('./pku.txt') as f: state_of_the_union = f.read() text_splitter = Charact...
chunk_overlap=0) texts = text_splitter.split_documents(documents) from langchain.embeddings import OpenAIEmbeddings embeddings = OpenAIEmbeddings() from langchain.vectorstores import Chroma db = Chroma.from_documents(texts, embeddings) retriever = db.as...
embeddings=OpenAIEmbeddings() #vectorstore=Chroma.from_documents(documents,embeddings) vectorstore=FAISS.from_texts(texts,embeddings) 嵌入向量直接存储在一个向量数据库中。有许多可用的向量数据库,如 Pinecone、FAISS 等。在这里,我们将使用 FAISS。 prompt_template="""Usethefollowingpiecesofcontexttoanswertheque...
embeddings = OpenAIEmbeddings() # vectorstore = Chroma.from_documents(documents, embeddings) vectorstore = FAISS.from_texts(texts, embeddings) 1. 2. 3. 嵌入向量直接存储在一个向量数据库中。有许多可用的向量数据库,如 Pinecone、FAISS 等。在这里,我们将使用 FAISS。 prompt_template = """Use the f...
vector_store=Chroma(), embeddings_model=OpenAIEmbeddings(), example_prompt=example_prompt ) # 最后用FewShotPromptTemplate 来创建一个提示词模板,该模板将输入变量作为输入,并将其格式化为包含示例的提示词。 prompt = FewShotPromptTemplate( example_selector=example_selector, ...
如今各类AI模型层出不穷,百花齐放,大佬们开发的速度永远遥遥领先于学习者的学习速度。。为了解放生产力,不让应用层开发人员受限于各语言模型的生产部署中..LangChain横空出世界。
db = Chroma() db.add_documents(embeddings, split_docs) # 5. 检索 query = "你的查询内容" similar_docs = db.similarity_search(query) for doc in similar_docs: print(doc.content) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11.
vectorstore = Chroma.from_documents(documents=docs, embedding=OpenAIEmbeddings()) 文档(或在第四章中看到的拆分)将被嵌入并存储在 Chroma 向量数据库中。我们将在本章的另一部分讨论文档加载程序。我们可以使用其他嵌入集成,或者像这样提供嵌入: vector_store = Chroma()# Add vectors to the vector store:vect...
From what I understand, you raised an issue regarding theChroma.from_documentsmethod inlangchain'schroma.pyfile. The problem is that thepersist_directoryargument is not correctly used when storing the database. I responded and suggested that the issue lies in thechroma.pyfile, and provided guida...
Issue you'd like to raise. I was trying to store different vectorstore to chroma with different ids like "vectorstore = Chroma.from_documents(documents, embeddings, ids="test1",)", but it keeps telling Number of embeddings 9 must match n...