mistralai-search-toolkit-storage-s3 0.0.9

AWS S3 (and S3-compatible) ObjectStorage plugin for mistralai-search-toolkit

AWS S3 Storage Plugin for Search Toolkit

AWS S3 (and S3-compatible) object storage backend for mistralai-search-toolkit.

This plugin implements the Search Toolkit's ObjectStorage interface, enabling the ingestion pipeline to load files directly from S3.

Installation

pip install mistralai-search-toolkit-storage-s3

Or as an optional dependency of the core package:

pip install mistralai-search-toolkit[storage-s3]

Quick Start: Load Files from S3 in Ingestion Pipeline

1. Upload a File to S3

import asyncio
from mistralai.search.toolkit.plugins.storage.s3 import S3BlobStorage

async def upload_file():
    storage = S3BlobStorage(
        bucket_name="your-bucket",
        region_name="us-east-1",
    )

    # Upload a file
    with open("document.pdf", "rb") as f:
        data = f.read()

    await storage.put(key="documents/document.pdf", data=data)

asyncio.run(upload_file())

2. Load Files from S3 in Ingestion Pipeline

import asyncio
import os
from mistralai.search.toolkit.ingestion.loaders import FileLoader
from mistralai.search.toolkit.ingestion.pipelines import Pipeline
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.storage.s3 import S3BlobStorage
from mistralai.search.toolkit.plugins.vespa import VespaClientConfig
from vespa_app import app

async def ingest_from_s3():
    # Create S3 storage factory
    def s3_storage_factory():
        return S3BlobStorage(
            bucket_name="your-bucket",
            region_name="us-east-1",
        )

    # Create FileLoader backed by S3
    file_loader = FileLoader(storage_factory=s3_storage_factory)

    # Create ingestion pipeline
    mistral_client = Mistral(api_key=os.environ.get("MISTRAL_API_KEY"))
    vespa_config = VespaClientConfig(
        endpoint=os.environ.get("VESPA_ENDPOINT", "http://localhost:8080"),
    )
    vector_store = app.get_search_index(vespa_config, collection_name="articles")

    pipeline = Pipeline(
        loader=file_loader,
        text_splitter=CharacterTextSplitter(chunk_size=512),
        embedder=MistralEmbedder(client=mistral_client, model_name=MODEL_1024_EMBEDDING),
        stores=vector_store,
    )

    # Ingest documents from S3
    num_chunks = await pipeline.run(documents=[
        "documents/document1.pdf",
        "documents/document2.pdf",
    ])

    print(f"Indexed {num_chunks} chunks")

asyncio.run(ingest_from_s3())

Configuration

Basic Setup

storage = S3BlobStorage(
    bucket_name="your-bucket",
    region_name="us-east-1",
)

With Credentials

storage = S3BlobStorage(
    bucket_name="your-bucket",
    region_name="us-east-1",
    access_key="your-access-key",
    secret_key="your-secret-key",
)

S3-Compatible Services

Works with MinIO, DigitalOcean Spaces, and other S3-compatible services:

storage = S3BlobStorage(
    bucket_name="bucket",
    endpoint_url="https://minio.example.com",
    access_key="minioadmin",
    secret_key="minioadmin",
)

Local Development

For testing without AWS, use MinIO:

docker run -p 9000:9000 -p 9001:9001 minio/minio server /data

Configure to use local MinIO:

storage = S3BlobStorage(
    bucket_name="documents",
    endpoint_url="http://localhost:9000",
    access_key="minioadmin",
    secret_key="minioadmin",
)

License

This plugin is licensed under the Apache License 2.0.

Support

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

For AWS S3 documentation, visit AWS S3 Docs.