AI Engineer

5 minute read

Leveraging VespaStore in LangChain: A Comprehensive Guide to Scalable Vector Search Implementation

Vector search has become a cornerstone technology in modern AI applications, enabling efficient retrieval of semantically similar content. When building applications with LangChain, the VespaStore integration offers a powerful solution for implementing scalable vector search capabilities. This article explores how to effectively implement and utilize Vespa’s vector store within your LangChain applications.

Introduction to VespaStore

VespaStore is LangChain’s integration with Vespa, an open-source big data serving engine that provides real-time, scalable storage and search capabilities. The integration allows developers to leverage Vespa’s advanced search features while maintaining the simplicity and flexibility of the LangChain ecosystem.

Before diving in, you’ll need to install the PyVespa client:

pip install pyvespa

Getting Started with VespaStore

Initialization

To begin using VespaStore in your LangChain application, you first need to initialize it with a PyVespa client:

from langchain_community.vectorstores import VespaStore
from langchain_openai import OpenAIEmbeddings
from pyvespa import Vespa

# Create a PyVespa client
vespa_app = Vespa("https://your-vespa-instance:8080")

# Initialize VespaStore with the client and embedding model
embeddings = OpenAIEmbeddings()
vector_store = VespaStore(
    app=vespa_app,
    embedding_function=embeddings,
    page_content_field="content",
    embedding_field="embedding",
    metadata_fields=["author", "date", "category"]
)

The initialization parameters include:

  • app: Your PyVespa client instance
  • embedding_function: The embedding model to use (optional)
  • page_content_field: Field name for document content (optional)
  • embedding_field: Field name for the vector embeddings (optional)
  • input_field: Field name for input data (optional)
  • metadata_fields: List of metadata field names to include (optional)

Adding Documents to VespaStore

From Documents

You can add documents to the vector store using the add_documents method:

from langchain.schema import Document

# Create some sample documents
documents = [
    Document(page_content="LangChain provides tools for working with language models", metadata={"category": "AI"}),
    Document(page_content="Vector stores enable efficient similarity search", metadata={"category": "Search"}),
    Document(page_content="Vespa is a powerful search and recommendation engine", metadata={"category": "Search"})
]

# Add documents to the vector store
doc_ids = vector_store.add_documents(documents)
print(f"Added documents with IDs: {doc_ids}")

From Text

Alternatively, you can add raw text with associated metadata:

texts = [
    "LangChain integrates with various vector stores",
    "Vespa provides real-time big data serving capabilities",
    "Vector search is essential for semantic retrieval"
]

metadatas = [
    {"author": "Alice", "category": "LangChain"},
    {"author": "Bob", "category": "Vespa"},
    {"author": "Charlie", "category": "Search"}
]

# Add texts to the vector store
text_ids = vector_store.add_texts(texts=texts, metadatas=metadatas)
print(f"Added texts with IDs: {text_ids}")

Creating VespaStore From Documents

For convenience, you can also create a new VespaStore instance directly from documents:

# Create VespaStore from documents
new_vector_store = VespaStore.from_documents(
    documents=documents,
    embedding=embeddings,
    app=vespa_app,
    page_content_field="content"
)

The most common operation is performing a similarity search based on a query string:

# Perform a similarity search
query = "How do language models work with vector search?"
results = vector_store.similarity_search(query, k=3)

for doc in results:
    print(f"Content: {doc.page_content}")
    print(f"Metadata: {doc.metadata}")
    print("---")

Similarity Search with Scores

If you need relevance scores along with the results:

# Get documents with relevance scores
results_with_scores = vector_store.similarity_search_with_score(query, k=3)

for doc, score in results_with_scores:
    print(f"Content: {doc.page_content}")
    print(f"Relevance: {score}")
    print("---")

Searching by Vector

You can also search directly using embedding vectors:

# Generate embedding for the query
query_embedding = embeddings.embed_query(query)

# Search by vector
vector_results = vector_store.similarity_search_by_vector(query_embedding, k=3)

# With scores
vector_results_with_scores = vector_store.similarity_search_by_vector_with_score(query_embedding, k=3)

Advanced Search Techniques

Maximal Marginal Relevance (MMR)

MMR search balances relevance with diversity in search results:

# MMR search
mmr_results = vector_store.max_marginal_relevance_search(
    query=query,
    k=3,
    fetch_k=10,
    lambda_mult=0.5  # 0 for maximum diversity, 1 for maximum relevance
)

Similarity Score Threshold

You can filter results based on a minimum similarity threshold:

# Search with threshold
threshold_results = vector_store.search(
    query=query,
    search_type="similarity_score_threshold",
    k=5,
    score_threshold=0.7  # Only return results with relevance score >= 0.7
)

Using VespaStore as a Retriever

VespaStore can be easily converted to a retriever for use in LangChain chains:

from langchain.chains import RetrievalQA
from langchain_openai import ChatOpenAI

# Create a retriever
retriever = vector_store.as_retriever(
    search_type="mmr",
    search_kwargs={
        "k": 5,
        "fetch_k": 10,
        "lambda_mult": 0.7
    }
)

# Use in a QA chain
llm = ChatOpenAI()
qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=retriever
)

# Ask a question
result = qa_chain.run("What are the benefits of using Vespa with LangChain?")
print(result)

Managing Documents

Retrieving Documents by ID

You can retrieve specific documents using their IDs:

# Retrieve documents by IDs
docs = vector_store.get_by_ids(["doc_id_1", "doc_id_2"])

Deleting Documents

To delete documents from the store:

# Delete specific documents
vector_store.delete(ids=["doc_id_1", "doc_id_2"])

# Delete all documents
vector_store.delete()

Asynchronous Operations

VespaStore also supports asynchronous operations, which can be beneficial for high-throughput applications:

import asyncio

async def add_docs_async():
    # Async add documents
    ids = await vector_store.aadd_documents(documents)
    return ids

async def search_async():
    # Async similarity search
    results = await vector_store.asimilarity_search(query, k=3)
    return results

# Run async operations
ids = asyncio.run(add_docs_async())
results = asyncio.run(search_async())

Performance Considerations

When implementing VespaStore in production applications, consider the following:

  1. Batch operations: When adding large numbers of documents, use batch operations to reduce overhead.
  2. Index configuration: Optimize your Vespa schema based on your specific use case.
  3. Embedding dimensionality: Higher-dimensional embeddings provide more semantic information but require more storage and computational resources.
  4. Caching: Implement caching strategies for frequently accessed embeddings and search results.

Example: Building a Document Retrieval System

Let’s put everything together in a complete example:

from langchain_community.vectorstores import VespaStore
from langchain_openai import OpenAIEmbeddings
from langchain.schema import Document
from pyvespa import Vespa
import os

# Initialize Vespa client
vespa_app = Vespa("https://your-vespa-instance:8080")

# Initialize embedding model
embeddings = OpenAIEmbeddings(api_key=os.environ.get("OPENAI_API_KEY"))

# Create VespaStore
vector_store = VespaStore(
    app=vespa_app,
    embedding_function=embeddings,
    page_content_field="content",
    embedding_field="embedding",
    metadata_fields=["source", "author", "date"]
)

# Load and add documents
documents = [
    Document(
        page_content="VespaStore provides efficient vector search capabilities in LangChain",
        metadata={"source": "documentation", "author": "LangChain Team"}
    ),
    Document(
        page_content="Vector embeddings enable semantic search across documents",
        metadata={"source": "blog", "author": "AI Researcher"}
    ),
    Document(
        page_content="Vespa scales to billions of documents with real-time updates",
        metadata={"source": "case study", "author": "Vespa Team"}
    )
]

# Add documents
vector_store.add_documents(documents)

# Create a retriever
retriever = vector_store.as_retriever(
    search_type="similarity",
    search_kwargs={"k": 2}
)

# Perform a search
results = retriever.get_relevant_documents("How does semantic search work?")

for doc in results:
    print(f"Content: {doc.page_content}")
    print(f"Source: {doc.metadata.get('source')}")
    print(f"Author: {doc.metadata.get('author')}")
    print("---")

Conclusion

VespaStore provides a powerful and flexible solution for implementing vector search in LangChain applications. By leveraging Vespa’s capabilities for scalable, real-time search, developers can build sophisticated retrieval systems that efficiently handle large document collections.

The integration offers a comprehensive API for document management, similarity search, and retrieval operations, with both synchronous and asynchronous interfaces. Whether you’re building a simple document retrieval system or a complex question-answering application, VespaStore provides the tools you need to implement efficient vector search capabilities.

As you integrate VespaStore into your applications, remember to optimize your embedding and indexing strategies based on your specific requirements, and leverage the full range of search options to achieve the best balance of relevance, diversity, and performance.

This post was originally written in my native language and then translated using an LLM. I apologize if there are any grammatical inconsistencies.

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