> ## Documentation Index > Fetch the complete documentation index at: https://docs.mixpeek.com/docs/llms.txt > Use this file to discover all available pages before exploring further. # Summarize > Generate LLM-powered summaries from document sets 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 ```json Basic RAG Summary theme={null} { "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 } } } ``` ```json Detailed Summary theme={null} { "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 } } } ``` ```json Per-Category Summaries theme={null} { "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" } } } ``` ```json Executive Brief theme={null} { "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 } } } ``` ```json Q&A with Sources theme={null} { "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`) ```json theme={null} { "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: ```json theme={null} [ { "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 ```json theme={null} [ { "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 ```json theme={null} [ { "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 ```json theme={null} [ { "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 | ## Related * [RAG Prepare](/retrieval/stages/rag-prepare) - Format for LLM without calling * [LLM Enrich](/retrieval/stages/llm-enrich) - Structured extraction * [Rerank](/retrieval/stages/rerank) - Improve input quality first