Summarize

Summarize stage showing LLM-powered document summarization

The Summarize stage uses language models to generate summaries from document sets. It can create single summaries from multiple documents, per-document summaries, or answer questions based on the retrieved content.

Stage Category: REDUCE (Aggregates documents)Transformation: N documents → 1 summary document (or N documents with summaries)

When to Use

Use Case	Description
RAG summarization	Generate answers from search results
Document synthesis	Combine multiple sources into one summary
Key points extraction	Distill long documents to essentials
Question answering	Answer user questions from retrieved docs

When NOT to Use

Scenario	Recommended Alternative
Just formatting for LLM	`rag_prepare` (no LLM call)
Extracting structured data	`llm_enrich`
Real-time low-latency	Pre-compute summaries

Parameters

Parameter	Type	Default	Description
`prompt`	string	Required	Summarization instructions (must include `{{DOCUMENTS}}`)
`provider`	string	`google`	LLM provider: `openai`, `google`, `anthropic`
`model_name`	string	provider default	Specific LLM model to use
`content_field`	string	`content`	Field containing text to summarize
`group_by`	string	none	Field to group by (one summary per group); omit for a single summary
`max_input_tokens`	integer	`8000`	Max tokens to send to LLM
`include_sources`	boolean	`true`	Add source document IDs to output
`output_field`	string	`summary`	Field for summary output

Available Models

Set provider and model_name together. If provider is omitted, it is inferred from model_name (defaults to google / gemini-2.5-flash-lite).

Provider	`model_name` examples	Speed	Quality
`google`	`gemini-2.5-flash-lite`	Fast	Good
`openai`	`gpt-4o-mini`	Fast	Good
`openai`	`gpt-4o`	Medium	Excellent
`anthropic`	`claude-haiku-4-5-20251001`	Fast	Good

Configuration Examples

{
  "stage_name": "summarize",
  "stage_type": "reduce",
  "config": {
    "stage_id": "summarize",
    "parameters": {
      "provider": "openai",
      "model_name": "gpt-4o-mini",
      "prompt": "Based on the provided documents, answer the user's question: {{INPUT.query}}\n\n{{DOCUMENTS}}",
      "include_sources": true
    }
  }
}

{
  "stage_name": "summarize",
  "stage_type": "reduce",
  "config": {
    "stage_id": "summarize",
    "parameters": {
      "provider": "openai",
      "model_name": "gpt-4o",
      "prompt": "Synthesize the following documents into a comprehensive summary. Include key findings, important details, and any contradictions between sources.\n\n{{DOCUMENTS}}",
      "max_input_tokens": 16000,
      "include_sources": true
    }
  }
}

{
  "stage_name": "summarize",
  "stage_type": "reduce",
  "config": {
    "stage_id": "summarize",
    "parameters": {
      "provider": "openai",
      "model_name": "gpt-4o-mini",
      "prompt": "Summarize the documents for '{{GROUP_VALUE}}' in 2-3 sentences, focusing on the main points.\n\n{{DOCUMENTS}}",
      "group_by": "metadata.category",
      "output_field": "category_summary"
    }
  }
}

{
  "stage_name": "summarize",
  "stage_type": "reduce",
  "config": {
    "stage_id": "summarize",
    "parameters": {
      "provider": "anthropic",
      "model_name": "claude-haiku-4-5-20251001",
      "prompt": "Create an executive summary of these documents. Include: key takeaways (3-5 bullet points), recommendations, and any risks or concerns mentioned.\n\n{{DOCUMENTS}}",
      "include_sources": true
    }
  }
}

{
  "stage_name": "summarize",
  "stage_type": "reduce",
  "config": {
    "stage_id": "summarize",
    "parameters": {
      "provider": "openai",
      "model_name": "gpt-4o",
      "prompt": "Answer the following question based only on the provided documents. If the answer cannot be found in the documents, say so. Question: {{INPUT.question}}\n\n{{DOCUMENTS}}",
      "include_sources": true,
      "max_input_tokens": 12000
    }
  }
}

Grouping

Single Summary (default)

With no group_by, all documents are combined into one summary (N→1):

[Doc1, Doc2, Doc3] → "Combined summary of all documents..."

Per-Group Summaries

Set group_by to a field path to produce one summary per unique group value (N→M). Use {{GROUP_VALUE}} in the prompt to reference the current group:

group_by: "metadata.category"
[Doc1(A), Doc2(A), Doc3(B)] → ["A" summary, "B" summary]

Output Schema

The summary is written to output_field (default summary). When include_sources is true, source_document_ids is added; when include_metadata is true, document_count and tokens_used are added.

Single Summary (no `group_by`)

{
  "summary": "Based on the documents, the answer is...",
  "source_document_ids": ["doc_123", "doc_456"],
  "document_count": 2,
  "tokens_used": 1250
}

Per-Group (with `group_by`)

One summary document per unique group value:

[
  {
    "summary": "Summary for the electronics category...",
    "source_document_ids": ["doc_123", "doc_456"],
    "document_count": 2
  },
  {
    "summary": "Summary for the clothing category...",
    "source_document_ids": ["doc_789"],
    "document_count": 1
  }
]

Performance

Metric	Value
Latency	500-2000ms
Token usage	Depends on input size
Max input	Model context window
Streaming	Supported

Summarization calls the LLM and incurs API costs. Use rag_prepare if you only need to format content for external LLM calls.

Common Pipeline Patterns

Full RAG Pipeline

[
  {
    "stage_name": "hybrid_search",
    "stage_type": "filter",
    "config": {
      "stage_id": "feature_search",
      "parameters": {
        "searches": [
          { "feature_uri": "mixpeek://text_extractor@v1/multilingual_e5_large_instruct_v1", "query": { "input_mode": "text", "value": "{{INPUT.query}}" }, "top_k": 50 }
        ],
        "final_top_k": 50
      }
    }
  },
  {
    "stage_name": "rerank",
    "stage_type": "sort",
    "config": {
      "stage_id": "rerank",
      "parameters": {
        "inference_name": "BAAI__bge_reranker_v2_m3",
        "top_k": 10
      }
    }
  },
  {
    "stage_name": "summarize",
    "stage_type": "reduce",
    "config": {
      "stage_id": "summarize",
      "parameters": {
        "provider": "openai",
        "model_name": "gpt-4o",
        "prompt": "Answer the user's question based on the provided documents: {{INPUT.query}}\n\n{{DOCUMENTS}}",
        "include_sources": true
      }
    }
  }
]

Multi-Document Synthesis

[
  {
    "stage_name": "semantic_search",
    "stage_type": "filter",
    "config": {
      "stage_id": "feature_search",
      "parameters": {
        "searches": [
          { "feature_uri": "mixpeek://text_extractor@v1/multilingual_e5_large_instruct_v1", "query": { "input_mode": "text", "value": "{{INPUT.topic}}" }, "top_k": 20 }
        ],
        "final_top_k": 20
      }
    }
  },
  {
    "stage_name": "structured_filter",
    "stage_type": "filter",
    "config": {
      "stage_id": "attribute_filter",
      "parameters": {
        "conditions": {
          "field": "metadata.type",
          "operator": "eq",
          "value": "research_paper"
        }
      }
    }
  },
  {
    "stage_name": "summarize",
    "stage_type": "reduce",
    "config": {
      "stage_id": "summarize",
      "parameters": {
        "provider": "anthropic",
        "model_name": "claude-haiku-4-5-20251001",
        "prompt": "Synthesize the research findings from these papers on {{INPUT.topic}}. Identify common themes, contradictions, and gaps in the research.\n\n{{DOCUMENTS}}",
        "max_input_tokens": 32000
      }
    }
  }
]

Preview Summaries

[
  {
    "stage_name": "semantic_search",
    "stage_type": "filter",
    "config": {
      "stage_id": "feature_search",
      "parameters": {
        "searches": [
          { "feature_uri": "mixpeek://text_extractor@v1/multilingual_e5_large_instruct_v1", "query": { "input_mode": "text", "value": "{{INPUT.query}}" }, "top_k": 10 }
        ],
        "final_top_k": 10
      }
    }
  },
  {
    "stage_name": "summarize",
    "stage_type": "reduce",
    "config": {
      "stage_id": "summarize",
      "parameters": {
        "provider": "openai",
        "model_name": "gpt-4o-mini",
        "prompt": "Create a one-sentence summary for the '{{GROUP_VALUE}}' group:\n\n{{DOCUMENTS}}",
        "group_by": "metadata.source",
        "output_field": "preview"
      }
    }
  }
]

Comparison: summarize vs rag_prepare

Feature	summarize	rag_prepare
Calls LLM	Yes	No
Output	Generated summary	Formatted context
Latency	500-2000ms	< 10ms
Cost	LLM API costs	Free
Use case	End-to-end RAG	Prepare for external LLM

Error Handling

Error	Behavior
Token limit exceeded	Truncates input, continues
LLM timeout	Retry once, then fail
Rate limit	Automatic backoff
Empty input	Returns empty summary

RAG Prepare - Format for LLM without calling
LLM Enrich - Structured extraction
Rerank - Improve input quality first

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Resources

When to Use

When NOT to Use

Parameters

Available Models

Configuration Examples

Grouping

Single Summary (default)

Per-Group Summaries

Output Schema

Single Summary (no `group_by`)

Per-Group (with `group_by`)

Performance

Common Pipeline Patterns

Full RAG Pipeline

Multi-Document Synthesis

Preview Summaries

Comparison: summarize vs rag_prepare

Error Handling

​When to Use

​When NOT to Use

​Parameters

​Available Models

​Configuration Examples

​Grouping

​Single Summary (default)

​Per-Group Summaries

​Output Schema

​Single Summary (no group_by)

​Per-Group (with group_by)

​Performance

​Common Pipeline Patterns

​Full RAG Pipeline

​Multi-Document Synthesis

​Preview Summaries

​Comparison: summarize vs rag_prepare

​Error Handling

​Related

When to Use

When NOT to Use

Parameters

Available Models

Configuration Examples

Grouping

Single Summary (default)

Per-Group Summaries

Output Schema

Single Summary (no `group_by`)

Per-Group (with `group_by`)

Performance

Common Pipeline Patterns

Full RAG Pipeline

Multi-Document Synthesis

Preview Summaries

Comparison: summarize vs rag_prepare

Error Handling

Related