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    "title": "RAG Text Chunker (Markdown to Chunks, Token Counts)",
    "description": "Split Markdown or text into RAG-ready chunks with exact OpenAI token counts (o200k/cl100k): heading-aware, code blocks kept whole, sentence-boundary overlap, heading path on every chunk. Chunks any dataset from Website to Markdown, PDF to Markdown or your own texts.",
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        "type": "object",
        "properties": {
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            "title": "Input dataset ID",
            "type": "string",
            "description": "Dataset of another run to chunk, e.g. from Website to Markdown, llms.txt Generator or PDF to Markdown. The text column is found automatically (`markdown`, `text`, `content`, `body`, `transcript`)."
          },
          "textField": {
            "title": "Text column",
            "type": "string",
            "description": "Optional: the dataset column holding the text, if it is not one of the usual names."
          },
          "documents": {
            "title": "Documents",
            "type": "array",
            "description": "Or paste documents as JSON: `[{\"url\": \"...\", \"title\": \"...\", \"text\": \"...\"}]`."
          },
          "texts": {
            "title": "Texts",
            "type": "array",
            "description": "Or paste plain texts or Markdown, one document per item.",
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              "type": "string"
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          },
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            "title": "Chunk size (tokens)",
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          },
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          },
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          },
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          },
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            "default": true
          },
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            "title": "Columns to keep",
            "type": "array",
            "description": "Optional: dataset or document fields copied onto every chunk, e.g. `lang`, `crawledAt`.",
            "items": {
              "type": "string"
            }
          }
        }
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                "type": "string",
                "format": "date-time",
                "example": "2025-01-08T00:00:00.000Z"
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              "status": {
                "type": "string",
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                  "userAgent": {
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              },
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