Vald

Vald is a highly scalable distributed fast approximate nearest neighbor (ANN) dense vector search engine.

This notebook shows how to use functionality related to the Vald database.

To run this notebook you need a running Vald cluster. Check Get Started for more information.

See the installation instructions.

%pip install -qU  vald-client-python langchain-community

Basic Example

from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import Vald
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_text_splitters import CharacterTextSplitter

raw_documents = TextLoader("state_of_the_union.txt").load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
documents = text_splitter.split_documents(raw_documents)
model_name = "sentence-transformers/all-mpnet-base-v2"
embeddings = HuggingFaceEmbeddings(model_name=model_name)
db = Vald.from_documents(documents, embeddings, host="localhost", port=8080)

query = "What did the president say about Ketanji Brown Jackson"
docs = db.similarity_search(query)
docs[0].page_content

Similarity search by vector

embedding_vector = embeddings.embed_query(query)
docs = db.similarity_search_by_vector(embedding_vector)
docs[0].page_content

Similarity search with score

docs_and_scores = db.similarity_search_with_score(query)
docs_and_scores[0]

Maximal Marginal Relevance Search (MMR)

In addition to using similarity search in the retriever object, you can also use mmr as retriever.

retriever = db.as_retriever(search_type="mmr")
retriever.invoke(query)

Or use max_marginal_relevance_search directly:

db.max_marginal_relevance_search(query, k=2, fetch_k=10)

Example of using secure connection

In order to run this notebook, it is necessary to run a Vald cluster with secure connection.

Here is an example of a Vald cluster with the following configuration using Athenz authentication.

ingress(TLS) -> authorization-proxy(Check athenz-role-auth in grpc metadata) -> vald-lb-gateway

import grpc

with open("test_root_cacert.crt", "rb") as root:
    credentials = grpc.ssl_channel_credentials(root_certificates=root.read())

# Refresh is required for server use
with open(".ztoken", "rb") as ztoken:
    token = ztoken.read().strip()

metadata = [(b"athenz-role-auth", token)]

from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import Vald
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_text_splitters import CharacterTextSplitter

raw_documents = TextLoader("state_of_the_union.txt").load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
documents = text_splitter.split_documents(raw_documents)
model_name = "sentence-transformers/all-mpnet-base-v2"
embeddings = HuggingFaceEmbeddings(model_name=model_name)

db = Vald.from_documents(
    documents,
    embeddings,
    host="localhost",
    port=443,
    grpc_use_secure=True,
    grpc_credentials=credentials,
    grpc_metadata=metadata,
)

query = "What did the president say about Ketanji Brown Jackson"
docs = db.similarity_search(query, grpc_metadata=metadata)
docs[0].page_content

Similarity search by vector

embedding_vector = embeddings.embed_query(query)
docs = db.similarity_search_by_vector(embedding_vector, grpc_metadata=metadata)
docs[0].page_content

Similarity search with score

docs_and_scores = db.similarity_search_with_score(query, grpc_metadata=metadata)
docs_and_scores[0]

Maximal Marginal Relevance Search (MMR)

retriever = db.as_retriever(
    search_kwargs={"search_type": "mmr", "grpc_metadata": metadata}
)
retriever.invoke(query, grpc_metadata=metadata)

Or:

db.max_marginal_relevance_search(query, k=2, fetch_k=10, grpc_metadata=metadata)