> ## 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.

# RAG Prepare

> Prepare documents for LLM context windows with token management and citations

<Frame>
  <img src="https://mintcdn.com/mixpeek/TwtTrae3Fi3EFJ72/assets/retrievers/rag-prepare.svg?fit=max&auto=format&n=TwtTrae3Fi3EFJ72&q=85&s=d27d7a01f367d2643a5d9bf2737a654a" alt="RAG Prepare stage showing document formatting for LLM context" width="1000" height="400" data-path="assets/retrievers/rag-prepare.svg" />
</Frame>

The RAG Prepare stage formats search results for LLM consumption by managing token budgets, formatting documents, and adding citations. This is a **preparation stage that does NOT call an LLM** - it prepares content for downstream LLM stages or external LLM calls.

<Note>
  **Stage Category**: APPLY

  **Transformation**:

  * `single_context` mode: N documents → 1 combined context document
  * `formatted_list` mode: N documents → N formatted documents
</Note>

## When to Use

| Use Case                    | Description                               |
| --------------------------- | ----------------------------------------- |
| **Before LLM generation**   | Prepare context for summarization or Q\&A |
| **Token budget management** | Fit multiple docs into context window     |
| **Citation tracking**       | Enable source attribution in responses    |
| **Consistent formatting**   | Standardize document format for LLM input |

## When NOT to Use

| Scenario                     | Recommended Alternative       |
| ---------------------------- | ----------------------------- |
| Want LLM to generate summary | `summarize` stage (calls LLM) |
| Don't need token management  | Pass documents directly       |
| Simple pass-through          | Skip this stage               |

## Parameters

| Parameter             | Type    | Default                                   | Description                         |
| --------------------- | ------- | ----------------------------------------- | ----------------------------------- |
| `max_tokens`          | integer | `8000`                                    | Maximum tokens for combined output  |
| `tokenizer`           | string  | `cl100k_base`                             | Tokenizer to use (GPT-4 compatible) |
| `truncation_strategy` | string  | `priority_truncate`                       | How to handle token overflow        |
| `output_mode`         | string  | `single_context`                          | Output format                       |
| `document_template`   | string  | `[{{CONTEXT.INDEX}}] {{DOC.content}}\n\n` | Template for each document          |
| `content_field`       | string  | `content`                                 | Field to extract content from       |
| `separator`           | string  | `\n`                                      | Separator between documents         |
| `citation`            | object  | `{style: "numbered"}`                     | Citation configuration              |

### Truncation Strategies

| Strategy            | Behavior                                          |
| ------------------- | ------------------------------------------------- |
| `priority_truncate` | Include docs in score order, truncate last to fit |
| `proportional`      | Give each doc proportional token budget           |
| `drop_last`         | Include complete docs until limit, drop remaining |

### Output Modes

| Mode             | Output                                     | Use Case          |
| ---------------- | ------------------------------------------ | ----------------- |
| `single_context` | 1 document with combined `context` string  | Direct LLM input  |
| `formatted_list` | N documents with `formatted_content` field | Custom processing |

## Configuration Examples

<CodeGroup>
  ```json Basic RAG Context theme={null}
  {
    "stage_name": "rag_prepare",
    "stage_type": "apply",
    "config": {
      "stage_id": "rag_prepare",
      "parameters": {
        "max_tokens": 8000,
        "output_mode": "single_context"
      }
    }
  }
  ```

  ```json With Numbered Citations theme={null}
  {
    "stage_name": "rag_prepare",
    "stage_type": "apply",
    "config": {
      "stage_id": "rag_prepare",
      "parameters": {
        "max_tokens": 4000,
        "document_template": "[{{CONTEXT.INDEX}}] {{DOC.metadata.title}}\n{{DOC.content}}\n\n",
        "truncation_strategy": "priority_truncate",
        "citation": {
          "style": "numbered",
          "include_title": true
        }
      }
    }
  }
  ```

  ```json Large Context Window (GPT-4 Turbo) theme={null}
  {
    "stage_name": "rag_prepare",
    "stage_type": "apply",
    "config": {
      "stage_id": "rag_prepare",
      "parameters": {
        "max_tokens": 32000,
        "tokenizer": "cl100k_base",
        "truncation_strategy": "proportional"
      }
    }
  }
  ```

  ```json Formatted List Mode theme={null}
  {
    "stage_name": "rag_prepare",
    "stage_type": "apply",
    "config": {
      "stage_id": "rag_prepare",
      "parameters": {
        "output_mode": "formatted_list",
        "document_template": "Source: {{DOC.metadata.source}}\n{{DOC.content}}"
      }
    }
  }
  ```

  ```json Custom Template with URL theme={null}
  {
    "stage_name": "rag_prepare",
    "stage_type": "apply",
    "config": {
      "stage_id": "rag_prepare",
      "parameters": {
        "max_tokens": 8000,
        "document_template": "Document {{CONTEXT.INDEX}}:\nTitle: {{DOC.metadata.title}}\nURL: {{DOC.metadata.url}}\n\n{{DOC.content}}\n\n---\n",
        "citation": {
          "style": "bracketed",
          "include_url": true
        }
      }
    }
  }
  ```
</CodeGroup>

## Template Placeholders

| Placeholder            | Description                                                            |
| ---------------------- | ---------------------------------------------------------------------- |
| `{{CONTEXT.INDEX}}`    | 1-based position in result set (1, 2, 3...)                            |
| `{{CONTEXT.CITATION}}` | Citation marker based on citation.style                                |
| `{{DOC.*}}`            | Any document field (e.g., `{{DOC.content}}`, `{{DOC.metadata.title}}`) |

## Citation Styles

| Style       | Output              | Example                |
| ----------- | ------------------- | ---------------------- |
| `numbered`  | `[1]`, `[2]`, `[3]` | Default, clean         |
| `bracketed` | `[doc_id]`          | Document ID references |
| `footnote`  | Superscript numbers | Academic style         |
| `none`      | No citations        | When not needed        |

## Output Schema

### single\_context Mode

```json theme={null}
{
  "rag_context": "[1] First document content...\n\n[2] Second document content...",
  "citations": [
    {"index": 1, "title": "Document Title", "document_id": "doc_123"},
    {"index": 2, "title": "Another Title", "document_id": "doc_456"}
  ]
}
```

### formatted\_list Mode

Each document gets:

```json theme={null}
{
  "document_id": "doc_123",
  "formatted_content": "[1] Title\nContent here...",
  "original_content": "Content here...",
  "metadata": {...}
}
```

## Performance

| Metric             | Value                    |
| ------------------ | ------------------------ |
| **Latency**        | \< 10ms                  |
| **Token counting** | Uses tiktoken (accurate) |
| **No LLM calls**   | Pure formatting          |

<Tip>
  This stage does NOT call an LLM. It only formats content for LLM consumption. Use the `summarize` stage if you want LLM-generated summaries.
</Tip>

## Common Pipeline Patterns

### Search + Prepare + External LLM

```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": 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": "rag_prepare",
    "stage_type": "apply",
    "config": {
      "stage_id": "rag_prepare",
      "parameters": {
        "max_tokens": 8000,
        "output_mode": "single_context",
        "citation": {"style": "numbered"}
      }
    }
  }
]
```

The output `rag_context` can then be passed to an external LLM call.

### vs Summarize Stage

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

## Related

* [Summarize](/retrieval/stages/summarize) - LLM-powered summarization
* [JSON Transform](/retrieval/stages/json-transform) - Custom document formatting
* [LLM Enrich](/retrieval/stages/llm-enrich) - Extract structured data with LLM