07. Vespa

Introduction to Vespa

Vespa is an open-source, scalable search and AI platform designed for efficient vector search, recommendation systems, and retrieval-augmented generation (RAG). It supports large-scale indexing and query processing while allowing hybrid search capabilities that combine vector and keyword-based retrieval.

Setting Up Vespa

1. Installing Vespa

To use Vespa, you need to set up a Vespa instance. You can run it locally using Docker:

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docker run -d --name vespa -p 8080:8080 vespaengine/vespa

This will start a Vespa instance locally, accessible on http://localhost:8080.

2. Creating a Vespa Client

Once installed, initialize a Vespa client in Python:

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import vespa

from vespa.application import Vespa

app = Vespa(url="http://localhost:8080")

If you're using Vespa Cloud, replace localhost with your cloud endpoint and provide authentication credentials.

Integrating Vespa with LangChain

LangChain provides seamless integration with Vespa for vector-based storage and retrieval. The Vespa wrapper in LangChain simplifies adding and retrieving vector embeddings.

1. Defining a Vespa Schema

Before storing vectors, define a schema in Vespa:

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schema langchain_docs {
    document langchain_docs {
        field id type string {
            indexing: attribute | summary
        }
        field embedding type tensor<float>(x[1536]) {
            indexing: attribute | index
        }
    }
    fieldset default {
        fields: id, embedding
    }
}

Deploy this schema to your Vespa instance.

2. Storing Embeddings in Vespa

To store vectors, first generate embeddings using an embedding model (e.g., OpenAI or Hugging Face):

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from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Vespa

embeddings = OpenAIEmbeddings()
vector_db = Vespa(app=app, index_name="langchain_docs", embeddings=embeddings)

Now, store some text data in Vespa:

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documents = ["This is a sample document.", "LangChain makes working with LLMs easier."]
vector_db.add_texts(texts=documents)

3. Performing Similarity Search

Retrieve documents similar to a given query:

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query = "How does LangChain help with LLMs?"
results = vector_db.similarity_search(query, k=2)

for result in results:
    print(result.page_content)

This fetches the top 2 documents that are most semantically similar to the query.

Best Practices and Optimization

• Hybrid Search: Leverage Vespa's ability to combine keyword and vector search for more accurate retrieval.

• Efficient Indexing: Optimize schema configurations for better performance.

• Scaling: Deploy Vespa on Kubernetes for large-scale AI workloads.

• Cloud Deployment: Consider using Vespa Cloud for automatic scaling and management.

Conclusion

Vespa is a powerful, open-source search platform that offers robust vector and hybrid search capabilities. Its integration with LangChain enables efficient storage and retrieval of embeddings, making it an excellent choice for scalable AI applications. With proper setup and optimization, you can leverage Vespa for tasks such as search, recommendations, and retrieval-augmented generation (RAG) applications.