documents = loader.load() text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) texts = text_splitter.split_documents(documents) embeddings = OpenAIEmbeddings() db = FAISS.from_documents(texts, embeddings) retriever = db.as_retriever() docs = retriever.get_relevant_documents("w...
# create embeddings and add to vector store if os.path.exists(dest_embed_dir): update = FAISS.load_local(folder_path=dest_embed_dir, embeddings=embeddings) update.add_texts(texts, metadatas=metadatas) update.save_local(folder_path=dest_embed_dir) else: docsearch = FAISS.from_texts(texts, ...
.0f} characters (smaller pieces)") Now you have 62 documents that have an average of 2,846 characters (smaller pieces) # Embeddings and docstore embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key) docsearch = FAISS.
from langchain.vectorstores import Chroma # 持久化数据 docsearch = Chroma.from_documents(documents, embeddings, persist_directory="D:/vector_store") docsearch.persist() # 加载数据 docsearch = Chroma(persist_directory="D:/vector_store", embedding_function=embeddings) 1. 2. 3. 4. 5. 6. 7. ...
add_default_faker_operators=False, ) 2.匿名化数据 现在,我们可以通过用占位符或标记替换已识别的PII实体来对文本进行匿名化处理: fromlangchain_core.documentsimportDocumentdefprint_colored_pii(string): colored_string = re.sub(r"(<[^>]*>)",lambdam:"\033[31m"+ m.group(1) +"\033[0m", stri...
anonymizer=PresidioReversibleAnonymizer(add_default_faker_operators=False,) 2.匿名化数据 现在,我们可以通过用占位符或标记替换已识别的PII实体来对文本进行匿名化处理: 代码语言:python 代码运行次数:0 复制 Cloud Studio代码运行 fromlangchain_core.documentsimportDocumentdefprint_colored_pii(string):colored_string...
上述代码中,我们分别使用了两种方法来进行文本的向量表示,他们最大不同在于:embed_query()接收一个字符串的输入,而embed_documents可以接收一组字符串。 LangChain集成的文本嵌入模型有: AzureOpenAI、Cohere、Hugging Face Hub、OpenAI、Llama-cpp、SentenceTransformers ...
Docsearch = Pinecone.from_texts([“dog”, “cat”], embeddings) 例如,嵌入式信息可能是 OpenAI 嵌入式信息。 现在,我们可以通过相似性找到与查询最相似的文档: docs = docsearch.similarity_search(“terrier”, include_metadata=True) 然后,我们可以再次查询或将这些文档用于问题回答链,就像我们在第四章的问题...
docs = docsearch.similarity_search(query) # Only using the first two documents to reduce token search size on openai chain.run(docs=docs[:2],question=query) Answer: '\nA lock table is a system-wide, in-memory table maintained by InterSystems IRIS that records all current locks and the pro...
每个文件会作为一个 documentdocuments = loader.load()# 初始化加载器text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=0)# 切割加载的 documentsplit_docs = text_splitter.split_documents(documents) index_name="liaokong-test"# 持久化数据# docsearch = Pinecone.from_texts([t.page_...