Search Toolkit

Search Toolkit is a Python framework for building production-ready Information Retrieval (IR) systems. It provides plug-and-play, extendable components for ingestion, retrieval, and evaluation — with backend-agnostic support and every component swappable.

LLMs are not trained on your private data. To ground their answers in your documents you need a retrieval pipeline that ingests, indexes, and searches your content. Search Toolkit gives you every building block to assemble that pipeline.

Ingestion

• Multi-format extraction: PDF/DOCX/PPTX via Mistral OCR, HTML, spreadsheets, emails, plain text
• File loading: Load from the local filesystem or implement custom loaders for any source
• Flexible chunking: Character, token, markdown-aware, or separator-based splitting
• Enrichment: Enrich documents and chunks with custom metadata or LLM-generated summaries
• Indexing: Index to vector stores for semantic search

Retrieval

• Multiple strategies: Vector (semantic) search with optional reranking
• Query preprocessing: Improve user queries with LLM reformulation or query extension
• Reranking: LLM reranker, cross-encoder reranker, or custom rerankers
• Semantic caching: Cache results by query similarity to skip redundant retrieval

Ingestion transforms raw documents into searchable chunks. A FileLoader reads raw bytes from a source, a DocumentExtractor converts them into a structured document, a TextSplitter divides it into chunks, an optional ChunkEnricher adds metadata, and an Embedder produces vectors for indexing into a vector store.

Retrieval finds relevant chunks for a given query. An optional query preprocessor rewrites or expands the query, a Retriever searches the vector index, and an optional Reranker re-scores the results before returning them.

Both workflows are orchestrated by high-level classes (Pipeline for ingestion, QueryEngine for retrieval) that handle component wiring and execution. Every component is swappable — use the built-in implementations or bring your own.

Components

Install the core package:

uv add mistralai-search-toolkit

uv add mistralai-search-toolkit

Optional extras add specialized functionality:

Install extras with the core package:

uv add "mistralai-search-toolkit[vespa]"
uv add "mistralai-search-toolkit[vespa,extractor-pymupdf]"
uv add "mistralai-search-toolkit[all]"  # Install all extras

uv add "mistralai-search-toolkit[vespa]"
uv add "mistralai-search-toolkit[vespa,extractor-pymupdf]"
uv add "mistralai-search-toolkit[all]"  # Install all extras

All packages are available on PyPI.

• Quickstart — build your first ingestion and retrieval pipeline end to end.
• Search index — set up your vector store.
• Ingestion — load, extract, chunk, enrich, and index your documents.
• Retrieval — configure vector search with optional reranking.