mistralai-search-toolkit-plugins-vespa 0.0.9

Vespa integration for mistralai-search-toolkit

Vespa Plugin for Search Toolkit

Vespa integration plugin for mistralai-search-toolkit.

This plugin provides a production-ready Vespa search backend implementation for the Search Toolkit, enabling powerful vector, keyword, and hybrid search capabilities.

Installation

pip install mistralai-search-toolkit-plugins-vespa

Or as an optional dependency of the core package:

pip install mistralai-search-toolkit[vespa]

Quick Start

1. Bootstrap Your Application

Create the application structure with an initial migration:

uv run mistral-vespa generate-migration --app-dir ./vespa_app initial_schema

This creates the ./vespa_app/ directory and generates a migration file. Fill it with your schema definition:

from mistralai.search.toolkit.plugins.vespa.app.schemas.app import SearchMode
from mistralai.search.toolkit.plugins.vespa.migration import VespaMigration, create_default_schema, set_app_name

class InitialSchema(VespaMigration):
    def migrate(self) -> None:
        set_app_name("articles")
        create_default_schema(
            name="articles",
            mode=SearchMode.INDEX,
            embedding_dimensions=1024,  # Adjust based on your embedder
            schema_version=1,
        )

2. Start a Local Vespa Instance

uv run mistral-vespa local up --query-port 18080 --config-port 19171 --name vespa-dev

3. Deploy Your Application

Deploy the migrations to generate the vespa_app module:

uv run mistral-vespa migrate \
  --app-dir ./vespa_app \
  --config-server http://localhost:19171 \
  --query-port 18080

This generates the vespa_app Python module that you can now import.

4. Index Documents

import os
from mistralai.search.toolkit.ingestion.pipelines import Pipeline
from mistralai.search.toolkit.ingestion.loaders import FilesystemFileLoader
from mistralai.search.toolkit.ingestion.text_splitters import CharacterTextSplitter
from mistralai.search.toolkit.embedders import MistralEmbedder, MODEL_1024_EMBEDDING
from mistralai.client import Mistral
from mistralai.search.toolkit.plugins.vespa import VespaClientConfig
from vespa_app import app

# Setup
mistral_client = Mistral(api_key=os.environ.get("MISTRAL_API_KEY"))
vespa_config = VespaClientConfig(
    endpoint=os.environ.get("VESPA_ENDPOINT", "http://localhost:18080"),
)
collection_name = "articles"

# Connect to Vespa
vector_store = app.get_search_index(vespa_config, collection_name=collection_name)

# Index documents
pipeline = Pipeline(
    loader=FilesystemFileLoader(),
    text_splitter=CharacterTextSplitter(chunk_size=512),
    embedder=MistralEmbedder(client=mistral_client, model_name=MODEL_1024_EMBEDDING),
    stores=vector_store,
)

num_chunks = await pipeline.run(documents=["doc1.pdf", "doc2.pdf"])

4. Search

from mistralai.search.toolkit.embedders import MistralEmbedder, MODEL_1024_EMBEDDING
from mistralai.search.toolkit.retrieval import QueryEngine
from mistralai.search.toolkit.retrieval.retrievers import VectorRetriever

# Setup search
embedder = MistralEmbedder(client=mistral_client, model_name=MODEL_1024_EMBEDDING)
query_engine = QueryEngine(
    retriever=[VectorRetriever(client=vector_store, embedder=embedder)],
)

# Search documents
results = await query_engine.search(query="What is machine learning?", top_k=10)

# Display results
for result in results.results:
    print(f"Score: {result.score}")
    print(f"Content: {result.content}\n")

Configuration

Quick Setup

Use app.get_search_index() for the common case where a single endpoint serves both query and feed APIs:

import os
from mistralai.search.toolkit.plugins.vespa import VespaClientConfig
from vespa_app import app

vespa_config = VespaClientConfig(
    endpoint=os.environ.get("VESPA_ENDPOINT", "http://localhost:18080"),
)
vector_store = app.get_search_index(vespa_config, collection_name="articles")

Advanced Setup

Use separate query and feed endpoints for production deployments:

from mistralai.search.toolkit.plugins.vespa import VespaClientConfig
from vespa_app import app

client_config = VespaClientConfig(
    query_endpoint=os.environ.get("VESPA_QUERY_ENDPOINT", "https://query.vespa.example.com"),
    feed_endpoint=os.environ.get("VESPA_FEED_ENDPOINT", "https://feed.vespa.example.com"),
)
vector_store = app.get_search_index(
    client_config=client_config,
    collection_name="articles",
)

License

This plugin is licensed under the Apache License 2.0.

Support

For issues related to the Search Toolkit, refer to the Search Toolkit documentation.

For Vespa-specific questions, visit Vespa documentation.