Chunk enrichers | Mistral Docs

Chunk enrichers add custom metadata to chunks during ingestion. Use them to attach information from external sources, classifications, tags, or any computed metadata.

Available chunk enrichers

Enricher	Purpose
Summary Enricher	Generate document summaries using an LLM
Custom Enrichers	Add custom metadata from any source

Summary Enricher

SummaryEnricher generates a document summary using an LLM and optionally injects it into chunks and/or document metadata. This can improve retrieval by giving each chunk context about the full document.

By default, SummaryEnricher is non-breaking: if summary generation fails, it logs the failure and returns the original chunks unchanged.

Requirements:

A Mistral API key

Usage:

import os

from mistralai.client import Mistral
from mistralai.search.toolkit.ingestion.enrichment import SummaryEnricher, SummaryConfig, SummarizeRequestConfig
from mistralai.search.toolkit.llm import MistralChat, LLMConfig

# Create LLM provider
mistral_client = Mistral(api_key=os.environ.get("MISTRAL_API_KEY", "your-api-key"))
llm = MistralChat(client=mistral_client, config=LLMConfig(model="mistral-small-latest"))

# Create enricher with default settings
enricher = SummaryEnricher(llm_provider=llm)

# Or customize the summary behavior
enricher = SummaryEnricher(
    llm_provider=llm,
    summary_config=SummaryConfig(
        request_config=SummarizeRequestConfig(
            prompt="Summarize this document in 3 sentences.",
            max_tokens=256,
        ),
    ),
)

import os

from mistralai.client import Mistral
from mistralai.search.toolkit.ingestion.enrichment import SummaryEnricher, SummaryConfig, SummarizeRequestConfig
from mistralai.search.toolkit.llm import MistralChat, LLMConfig

# Create LLM provider
mistral_client = Mistral(api_key=os.environ.get("MISTRAL_API_KEY", "your-api-key"))
llm = MistralChat(client=mistral_client, config=LLMConfig(model="mistral-small-latest"))

# Create enricher with default settings
enricher = SummaryEnricher(llm_provider=llm)

# Or customize the summary behavior
enricher = SummaryEnricher(
    llm_provider=llm,
    summary_config=SummaryConfig(
        request_config=SummarizeRequestConfig(
            prompt="Summarize this document in 3 sentences.",
            max_tokens=256,
        ),
    ),
)

Parameters:

Parameter	Type	Default	Description
`llm_provider`	`ChatLLMProvider`	(required)	LLM provider for summarization
`summary_config`	`SummaryConfig \| None`	`None`	Configuration object (see below)

SummarizeRequestConfig:

Controls the LLM request for summary generation.

Parameter	Type	Default	Description
`model`	`str`	`"mistral-small-latest"`	Model to use for summarization
`prompt`	`str`	`"Summarize the document in less than 5 lines."`	Summarization prompt
`max_tokens`	`int`	`256`	Maximum tokens in the summary
`max_chars_per_call`	`int \| None`	`32768`	Char budget per LLM call; content above this is truncated or map-reduced (see `overflow_strategy`)
`overflow_strategy`	`OverflowStrategy`	`OverflowStrategy.TRUNCATE`	`TRUNCATE` cuts to `max_chars_per_call`; `MAP_REDUCE` splits into parts, summarizes each, then merges
`map_concurrency`	`int`	`10`	Max concurrent map-phase LLM calls (map-reduce only)
`temperature`	`float \| None`	`0.6`	LLM temperature

SummaryRequestOptions:

Controls how the summary is injected into the pipeline output.

Parameter	Type	Default	Description
`include_summary_chunk`	`bool`	`True`	Add a dedicated summary chunk to the chunk list
`propagate_summary_to_chunks`	`bool`	`False`	Prepend the summary to every chunk's content
`populate_document_metadata`	`bool`	`True`	Store the summary in the document's metadata
`fail_on_generation_error`	`bool`	`False`	Raise on summary generation failure instead of logging and continuing

Creating custom enrichers

Implement the ChunkEnricher interface to add custom metadata. ChunkEnricher is a DocumentProcessor: implement enrich_chunk to transform a single chunk, and process fans it out over the document's chunks:

from mistralai.search.toolkit.context import IngestContext
from mistralai.search.toolkit.ingestion.enrichment import ChunkEnricher
from mistralai.search.toolkit.document import DocumentChunk, Document

class EntityTagger(ChunkEnricher):
    """Add entity tags to chunks."""

    async def enrich_chunk(
        self,
        document: Document,
        chunk: DocumentChunk,
        context: IngestContext = IngestContext(),
    ) -> DocumentChunk:
        entities = await self._extract_entities(chunk.content)
        updated_metadata = chunk.metadata.model_copy(update={"entities": entities})
        return chunk.model_copy(update={"metadata": updated_metadata})

    async def _extract_entities(self, text: str) -> list[str]:
        entities = []
        # ... entity extraction logic ...
        return entities

enricher = EntityTagger()
enriched_document = await enricher.process(document)

for chunk in enriched_document.chunks:
    print(f"Entities: {chunk.metadata.get('entities', [])}")

from mistralai.search.toolkit.context import IngestContext
from mistralai.search.toolkit.ingestion.enrichment import ChunkEnricher
from mistralai.search.toolkit.document import DocumentChunk, Document

class EntityTagger(ChunkEnricher):
    """Add entity tags to chunks."""

    async def enrich_chunk(
        self,
        document: Document,
        chunk: DocumentChunk,
        context: IngestContext = IngestContext(),
    ) -> DocumentChunk:
        entities = await self._extract_entities(chunk.content)
        updated_metadata = chunk.metadata.model_copy(update={"entities": entities})
        return chunk.model_copy(update={"metadata": updated_metadata})

    async def _extract_entities(self, text: str) -> list[str]:
        entities = []
        # ... entity extraction logic ...
        return entities

enricher = EntityTagger()
enriched_document = await enricher.process(document)

for chunk in enriched_document.chunks:
    print(f"Entities: {chunk.metadata.get('entities', [])}")

Batch enrichment patterns

ChunkEnricher.process already fans enrich_chunk out over the document's chunks with a bounded worker pool (concurrency, default 10). For external API calls, keep that work inside enrich_chunk:

from mistralai.search.toolkit.context import IngestContext
from mistralai.search.toolkit.ingestion.enrichment import ChunkEnricher
from mistralai.search.toolkit.document import DocumentChunk, Document

class BatchEnricher(ChunkEnricher):
    """Batch API calls for efficiency."""

    def __init__(self) -> None:
        super().__init__(concurrency=20)  # raise the worker-pool size for external calls

    async def enrich_chunk(
        self,
        document: Document,
        chunk: DocumentChunk,
        context: IngestContext = IngestContext(),
    ) -> DocumentChunk:
        metadata = await self._fetch_metadata(chunk.content)
        return chunk.model_copy(
            update={"metadata": chunk.metadata.model_copy(update=metadata)}
        )

    async def _fetch_metadata(self, text: str) -> dict:
        # Call external API with batching/retries
        ...

from mistralai.search.toolkit.context import IngestContext
from mistralai.search.toolkit.ingestion.enrichment import ChunkEnricher
from mistralai.search.toolkit.document import DocumentChunk, Document

class BatchEnricher(ChunkEnricher):
    """Batch API calls for efficiency."""

    def __init__(self) -> None:
        super().__init__(concurrency=20)  # raise the worker-pool size for external calls

    async def enrich_chunk(
        self,
        document: Document,
        chunk: DocumentChunk,
        context: IngestContext = IngestContext(),
    ) -> DocumentChunk:
        metadata = await self._fetch_metadata(chunk.content)
        return chunk.model_copy(
            update={"metadata": chunk.metadata.model_copy(update=metadata)}
        )

    async def _fetch_metadata(self, text: str) -> dict:
        # Call external API with batching/retries
        ...

Combining multiple enrichers

from mistralai.search.toolkit.ingestion.pipelines import Pipeline

pipeline = Pipeline(
    loader=loader,
    extractor=extractor,
    text_splitter=splitter,
    embedder=embedder,
    stores=vector_store,
    processors=[
        SummaryEnricher(llm_provider=llm),
        EntityTagger(),
        CustomMetadataEnricher(),
    ],
)

from mistralai.search.toolkit.ingestion.pipelines import Pipeline

pipeline = Pipeline(
    loader=loader,
    extractor=extractor,
    text_splitter=splitter,
    embedder=embedder,
    stores=vector_store,
    processors=[
        SummaryEnricher(llm_provider=llm),
        EntityTagger(),
        CustomMetadataEnricher(),
    ],
)

Enrichers are applied sequentially in the order listed. Each enricher receives the output of the previous one.