Summarize Tables with Custom Prompts

Vectara supports table summarization using LLMs during document upload. This lets you define custom prompt templates that control how the LLM interprets and summarizes table data during extraction. By customizing the prompt_template, you can tailor summaries for domain-specific language, analytical perspectives, or formatting preferences.

The summarization engine supports OpenAI-compatible models (for example, gpt-4o) and is configured through the table_extraction_config parameter. This configuration also lets you specify model parameters like temperature and token limits.

Prompt templates use Apache Velocity syntax and have access to structured table data via these built-in variables:

• $vectaraTable: The root object representing the extracted table.
• $vectaraTable.title(): The title of the table.
• $vectaraTable.description(): A description or summary of the table data.
• $vectaraTable.json(): The table contents as a JSON object.
• $vectaraTable.markdown(): The table contents in Markdown format.

Leverage $vectaraTable.markdown() for LLM-friendly formatting and $vectaraTable.json() if you want precise structured access to table contents. You can define role-based prompt structures (system and user roles) in JSON format to match OpenAI-style chat templates.

Enable table extraction and custom summarization

To enable table extraction and custom summarization, pass the table_extraction_config in the multipart form data of your upload request.

{
  "extract_tables": true,
  "extractor": {
    "name": "gmft"
  },
  "generation": {
    "llm_name": "gpt-4o-mini",
    "prompt_template": "Adopt the perspective of a data analyst and summarize the table below:\n\n$vectaraTable.markdown()",
    "model_parameters": {
      "temperature": 0.7,
      "max_tokens": 1024
    }
  }
}

• The gmft extractor extracts table data.
• The gpt-4o-mini model is used for summarization.
• The prompt_template uses Velocity to refer to the table in markdown format.
• The model_parameters customize the generation behavior (creativity and response length).

Example curl request

You can either pass the configuration inline or via a JSON file. Inline JSON is a cleaner option for single-use or dynamic configs. Using a config file for table_extraction_config can be helpful if you plan to upload multiple files using the same config. You must include the type=application/json suffix when setting the -F parameter—otherwise, the content may not parse correctly.

Inline JSON example

Inline JSON is a cleaner option for single-use or dynamic configs.

curl -i -L -X POST 'https://api.vectara.io/v2/corpora/<corpus_id>/upload_file' \
-H 'Content-Type: multipart/form-data' \
-H 'Accept: application/json' \
-H "x-api-key: <your-api-key>" \
-F 'file=@"/path/to/document.pdf"' \
-F 'table_extraction_config={"extract_tables":true,"extractor":{"name":"gmft"}};type=application/json'

Using a config file

curl -i -L -X POST 'https://api.vectara.io/v2/corpora/<corpus_id>/upload_file' \
-H 'Content-Type: multipart/form-data' \
-H 'Accept: application/json' \
-H "x-api-key: <your-api-key>" \
-F 'file=@"/path/to/document.pdf"' \
-F 'table_extraction_config=@"/path/to/config-extractor.json";type=application/json'

Where config-extractor.json contains:

{
  "extract_tables": true,
  "extractor": {
    "name": "gmft"
  }
}