ComponentsVector storesWeaviateOn this pageWeaviateWeaviate is an open-source vector database. It allows you to store data objects and vector embeddings from your favorite ML-models, and scale seamlessly into billions of data objects.This notebook shows how to use functionality related to the Weaviatevector database.See the Weaviate installation instructions.pip install weaviate-client    Requirement already satisfied: weaviate-client in /workspaces/langchain/.venv/lib/python3.9/site-packages (3.19.1)    Requirement already satisfied: requests<2.29.0,>=2.28.0 in /workspaces/langchain/.venv/lib/python3.9/site-packages (from weaviate-client) (2.28.2)    Requirement already satisfied: validators<=0.21.0,>=0.18.2 in /workspaces/langchain/.venv/lib/python3.9/site-packages (from weaviate-client) (0.20.0)    Requirement already satisfied: tqdm<5.0.0,>=4.59.0 in /workspaces/langchain/.venv/lib/python3.9/site-packages (from weaviate-client) (4.65.0)    Requirement already satisfied: authlib>=1.1.0 in /workspaces/langchain/.venv/lib/python3.9/site-packages (from weaviate-client) (1.2.0)    Requirement already satisfied: cryptography>=3.2 in /workspaces/langchain/.venv/lib/python3.9/site-packages (from authlib>=1.1.0->weaviate-client) (40.0.2)    Requirement already satisfied: charset-normalizer<4,>=2 in /workspaces/langchain/.venv/lib/python3.9/site-packages (from requests<2.29.0,>=2.28.0->weaviate-client) (3.1.0)    Requirement already satisfied: idna<4,>=2.5 in /workspaces/langchain/.venv/lib/python3.9/site-packages (from requests<2.29.0,>=2.28.0->weaviate-client) (3.4)    Requirement already satisfied: urllib3<1.27,>=1.21.1 in /workspaces/langchain/.venv/lib/python3.9/site-packages (from requests<2.29.0,>=2.28.0->weaviate-client) (1.26.15)    Requirement already satisfied: certifi>=2017.4.17 in /workspaces/langchain/.venv/lib/python3.9/site-packages (from requests<2.29.0,>=2.28.0->weaviate-client) (2023.5.7)    Requirement already satisfied: decorator>=3.4.0 in /workspaces/langchain/.venv/lib/python3.9/site-packages (from validators<=0.21.0,>=0.18.2->weaviate-client) (5.1.1)    Requirement already satisfied: cffi>=1.12 in /workspaces/langchain/.venv/lib/python3.9/site-packages (from cryptography>=3.2->authlib>=1.1.0->weaviate-client) (1.15.1)    Requirement already satisfied: pycparser in /workspaces/langchain/.venv/lib/python3.9/site-packages (from cffi>=1.12->cryptography>=3.2->authlib>=1.1.0->weaviate-client) (2.21)We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.import osimport getpassos.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")WEAVIATE_URL = getpass.getpass("WEAVIATE_URL:")os.environ["WEAVIATE_API_KEY"] = getpass.getpass("WEAVIATE_API_KEY:")WEAVIATE_API_KEY = os.environ["WEAVIATE_API_KEY"]from langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import Weaviatefrom langchain.document_loaders import TextLoaderfrom langchain.document_loaders import TextLoaderloader = TextLoader("../../../state_of_the_union.txt")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)docs = text_splitter.split_documents(documents)embeddings = OpenAIEmbeddings()db = Weaviate.from_documents(docs, embeddings, weaviate_url=WEAVIATE_URL, by_text=False)query = "What did the president say about Ketanji Brown Jackson"docs = db.similarity_search(query)print(docs[0].page_content)    Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections.         Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.         One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.         And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.Authentication​Weaviate instances have authentication enabled by default. You can use either a username/password combination or API key.  import weaviateclient = weaviate.Client(url=WEAVIATE_URL, auth_client_secret=weaviate.AuthApiKey(WEAVIATE_API_KEY))# client = weaviate.Client(#     url=WEAVIATE_URL,#     auth_client_secret=weaviate.AuthClientPassword(#         username = "WCS_USERNAME",  # Replace w/ your WCS username#         password = "WCS_PASSWORD",  # Replace w/ your WCS password#     ),# )vectorstore = Weaviate.from_documents(documents, embeddings, client=client, by_text=False)    <langchain.vectorstores.weaviate.Weaviate object at 0x107f46550>Similarity search with score​Sometimes we might want to perform the search, but also obtain a relevancy score to know how good is a particular result.
The returned distance score is cosine distance. Therefore, a lower score is better.docs = db.similarity_search_with_score(query, by_text=False)docs[0]    (Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'_additional': {'vector': [-0.015289668, -0.011418287, -0.018540842, 0.00274522, 0.008310737, 0.014179829, 0.0080104275, -0.0010217049, -0.022327352, -0.0055002323, 0.018958665, 0.0020548347, -0.0044393567, -0.021609223, -0.013709779, -0.004543812, 0.025722157, 0.01821442, 0.031728342, -0.031388864, -0.01051083, -0.029978717, 0.011555385, 0.0009751897, 0.014675993, -0.02102166, 0.0301354, -0.031754456, 0.013526983, -0.03392191, 0.002800712, -0.0027778621, -0.024259781, -0.006202043, -0.019950991, 0.0176138, -0.0001134321, 0.008343379, 0.034209162, -0.027654583, 0.03149332, -0.0008389079, 0.0053696632, -0.0024644958, -0.016582303, 0.0066720927, -0.005036711, -0.035514854, 0.002942706, 0.02958701, 0.032825127, 0.015694432, -0.019846536, -0.024520919, -0.021974817, -0.0063293483, -0.01081114, -0.0084282495, 0.003025944, -0.010210521, 0.008780787, 0.014793505, -0.006486031, 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0.0327729, -0.00812794, 0.001581521, 0.019350372, 0.016112253, 0.02132197, 0.00043944738, -0.01472822, -0.025735214, -0.03313849, 0.0033817457, 0.028855821, -0.016033912, 0.0050791465, -0.01808385]}, 'source': '../../../state_of_the_union.txt'}),     0.8154189703772676)PersistenceAnything uploaded to weaviate is automatically persistent into the database. You do not need to call any specific method or pass any param for this to happen.Retriever options​This section goes over different options for how to use Weaviate as a retriever.MMR​In addition to using similarity search in the retriever object, you can also use mmr.retriever = db.as_retriever(search_type="mmr")retriever.get_relevant_documents(query)[0]    Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../../state_of_the_union.txt'})Question Answering with Sources​This section goes over how to do question-answering with sources over an Index. It does this by using the RetrievalQAWithSourcesChain, which does the lookup of the documents from an Index. from langchain.chains import RetrievalQAWithSourcesChainfrom langchain.llms import OpenAIwith open("../../../state_of_the_union.txt") as f:    state_of_the_union = f.read()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)texts = text_splitter.split_text(state_of_the_union)docsearch = Weaviate.from_texts(    texts,    embeddings,    weaviate_url=WEAVIATE_URL,    by_text=False,    metadatas=[{"source": f"{i}-pl"} for i in range(len(texts))],)chain = RetrievalQAWithSourcesChain.from_chain_type(    OpenAI(temperature=0), chain_type="stuff", retriever=docsearch.as_retriever())chain(    {"question": "What did the president say about Justice Breyer"},    return_only_outputs=True,)    {'answer': " The president honored Justice Breyer for his service and mentioned his legacy of excellence. He also nominated Circuit Court of Appeals Judge Ketanji Brown Jackson to continue Justice Breyer's legacy.\n",     'sources': '31-pl, 34-pl'}PreviousVespaNextXataAuthenticationSimilarity search with scoreRetriever optionsMMRQuestion Answering with Sources