This LLM-based method extracts repeating data in a document based on your description of the list’s overall contents and each individual item. Data such as the work history or skills on a resume, the vehicles on an auto insurance policy, or the line items on an invoice are best suited for this method.

This method is an alternative to the NLP Table method, when the data you want can appear either as a table or as another layout.

The List method can find:

data in paragraphs of free text or in more structured layouts, such as key/value pairs or tables.
data in other extracted fields, when you configure the Source Fields parameter. In other words, you can extract data with one prompt, then apply further prompts to the extracted data.

Limitations

Sensible can output lists of different maximum lengths depending on how you configure the LLM Engine and Single LLM Completion parameters. If the extracted list exceeds the limit, Sensible truncates the list.
For highly complex repeating layouts, such as insurance loss run documents, use the Sections method.

Prompt tips

The list description describes the overall contents for the list, while each property is a single description of an item that repeats in the list.
For more information about how to write descriptions, or "prompts", see Query Group extraction tips.
For advanced options, see Advanced LLM prompt configuration.

For information about how this method works, see Notes.

Parameters
Examples
Notes

Parameters

Note: For additional parameters available for this method, see Global parameters for methods. The following table only shows parameters most relevant to or specific to this method.

Note You can configure some of the following parameters in both the NLP preprocessor and in a field's method. If you configure both, the field's parameter overrides the NLP preprocessor's parameter.

key	value	description	interactions
id (required)	`list`
description (required)	string	A prompt describing the list's subject matter as a whole.
properties (required)	object	An array of objects with the following parameters: -`id` (required): A user-friendly ID for the data in the extraction output. -`description` (required): A prompt describing the list item that you want to extract. You can prompt the LLM to reformat or filter the data. For example, provide prompts like `" transaction amount. return the absolute value"` or `"vehicle make (not model)"`. -`type`: The list item's type. For more information, see types.
		*CONTEXT COMPLETNESS*
llmEngine	object	Configures the LLM model to which Sensible submits the full prompt. Contains the following parameters: `provider`: Contains the following options: - `open-ai` (default): Sensible uses OpenAI's models. - `anthropic`: Sensible uses Anthropic's models. Select this option to troubleshoot situations in which Sensible correctly identifies the part of the document that contains the answers to your prompts, but the LLM's answer contains problems. For example, Sensible returns an LLM error because the answer isn't properly formatted, or the LLM doesn't follow instructions in your prompt. `mode`: Contains the following options - `fast` (default): Sensible uses a faster LLM model (GPT-4o mini or (Claude 3.5 Haiku, depending on the provider) . - `thorough`: Sensible uses a slower and more powerful LLM model (GPT-4o or Claude 3.5 Sonnet, depending on the provider). Sensible can take several minutes to return the list. Use this option if the Fast parameter results in incomplete extractions for multi-page lists. - `long`: Sensible uses a faster LLM model (GPT-4o mini or Claude 3.5 Haiku, depending on the provider). If you set this value, then Sensible can output lists extracted from up to 100 potentially nonconsecutive, 1-page source chunks. Otherwise Sensible by default extracts from 20 1-page chunks. If the list in the document is longer than the number of source chunks, Sensible truncates the list. For more information, see Notes.	If you set the Mode parameter to Long, then Sensible sets the Chunk Count parameter to 100.
singleLLMCompletion	boolean. default: false	If Sensible returns incomplete or duplicate results in a list that's under ~20 pages long, set this parameter to True to troubleshoot. If true, Sensible concatenates the top-scoring chunks into a single batch to send as context to the LLM, instead of batching calls to the LLM. By avoiding splitting context, you can avoid problems such as the LLM failing to recognize the end of one list and the start of another. See Notes for more information. - The following limits apply: - If the extracted list exceeds the output limit for a single API response from an LLM engine (~16k tokens or ~20 pages for all supported LLM engines), Sensible truncates the list. - Rarely, Sensible fails to truncate a list at ~16k token output and returns an error. This can happen if the source text in the context has a very small font size. To avoid this type of error, set this parameter to false.	Don't set this parameter to true if you set the LLM Engine parameter to Long. The Single LLM Completion parameter limits output to ~16k tokens, so it'll truncate longer lists.
		*FIND CONTEXT*
source_ids	array of field IDs in the current config	If specified, prompts an LLM to extract data from another field's output. For example, if you extract a field `_checking_transactions` and specify it in this parameter, then Sensible answers the prompts `rank deposits by size` and `reformat withdrawals with a minus sign if they're formatted with parentheses` using `_checking_transactions` rather than searching the whole document to locate the context. Note that the `_checking_transactions` field must precede the `transactions_frequencies` field in the fields array in this example. Use this parameter to: - reformat or otherwise transform the outputs of other fields. - narrow down the context for your prompts to a specific part of the document. - troubleshoot or simplify complex prompts that aren't performing reliably. Break the prompt into several simpler parts, and chain them together using successive Source Ids in the fields array. For an example, see Examples.	If you configure this parameter, then generally don't configure: - LLM Engine parameter - Single LLM Completion parameter - If you configure this parameter, Sensible doesn't support the following parameters: - Anchor parameter in the field - Context Description parameter - Multimodal Engine parameter - Chunk Scoring Text parameter - Search By Summarization parameter - Page Hinting parameter - Chunk Count parameter - Chunk Size parameter - Chunk Overlap Percentage parameter - Page Range parameter
searchBySummarization	boolean. default: false	Set this to true to troubleshoot situations in which Sensible misidentifies the part of the document that contains the answers to your prompts. This parameter is compatible with documents up to 1,280 pages long. When true, Sensible uses a completion-only retrieval-augmented generation (RAG) strategy: Sensible prompts an LLM to summarize each page in the document, prompts a second LLM to return the pages most relevant to your prompt based on the summaries, and extracts the answers to your prompts from those pages.	If you set this parameter to true, then Sensible sets the following for chunk-related parameters and ignores any configured values: - Chunk Size parameter: 1 - Chunk Overlap Percentage parameter: 0 - Chunk Count parameter: 5
		*GLOBAL PARAMETERS*
contextDescription		For information about this parameter, see Advanced LLM prompt configuration
pageHinting		For information about this parameter, see Advanced LLM prompt configuration
chunkCount		For information about this parameter, see Advanced LLM prompt configuration
chunkSize		For information about this parameter, see Advanced LLM prompt configuration
chunkOverlapPercentage		For information about this parameter, see Advanced LLM prompt configuration
pageRange		For information about this parameter, see Advanced LLM prompt configuration.

Examples

The following example shows using the List method to extract information from a menu about listed menu items.

Config

{
  "fields": [
    {
      /* the id is a user-friendly name for the target list */
      "id": "dinners",
      "type": "table",
      "method": {
        "id": "list",
        /* overall description of list's contents */
        "description": "dinner special menu items",
        "properties": [
          {
            /* for each item in the list, provide a user-friendly ID and 
               description of the data you want to extract
               and optional instructions to filter or reformat the data */
            "id": "dinner_description",
            "description": "entree description"
          },
          /* optional: target data is a currency */
          {
            "id": "price",
            "type": "currency",
            "description": "dinner price"
          },
        ]
      }
    },
    {
      "id": "desserts",
      "type": "table",
      "method": {
        "id": "list",
        "description": "dessert special menu items",
        "properties": [
          {
            "id": "dessert_description",
            "description": "dessert description"
          },
          {
            "id": "price",
            "type":"currency",
            "description": "dessert price"
          },
        ]
      }
    },
    /* lists can span roughly a document page, if the list exceeds that,
      Sensible may truncate the extracted list */
    {
      "id": "wines",
      "type": "table",
      "method": {
        "id": "list",
        /* optional: try restricting the list to white wines,
           or try making a new list for liquors and their serving sizes */
        "description": "red wines and white wines (not other drinks such as beers or liquors)",
        "properties": [
          {
            "id": "wine_name",
            "description": "wine brand name"
          },
          {
            "id": "wine_type",
            "description": "wine varietal name (not brand), for example, return 'Red:cabernet savignon' or 'white:varietal not found'"
          },
          {
            "id": "wine_description",
            "description": "wine description"
          },
          {
            "id": "smallest_serving_price",
            "description": "smallest wine serving size and its dollar price, formatted like '6 oz: $11'"
          },
          {
            "id": "second_smallest_serving_price",
            "description": "second-smallest wine serving size and its dollar price, formatted like '6 oz: $11'"
          },
          {
            "id": "bottle_price",
            "type":"currency",
            "description": "price per bottle, in dollars"
          },
        ]
      }
    },
  ],
  "computed_fields": [
    /* optional: for cleaner output, zip each list */
    {
      "id": "dinners_zipped",
      "method": {
        "id": "zip",
        "source_ids": [
          "dinners",
        ]
      }
    },
    {
      "id": "desserts_zipped",
      "method": {
        "id": "zip",
        "source_ids": [
          "desserts",
        ]
      }
    },
    {
      "id": "wines_zipped",
      "method": {
        "id": "zip",
        "source_ids": [
          "wines",
        ]
      }
    },
    /* optional: for cleaner output, remove the source
       fields. */
    {
      "id": "hide_fields",
      "method": {
        "id": "suppressOutput",
        "source_ids": [
          "dinners",
          "desserts",
          "wines"
        ]
      }
    }
  ]
}

Example document
The following image shows the example document used with this example config:

Click to enlarge

Example document	Download link

Output

{
  "dinners_zipped": [
    {
      "dinner_description": {
        "value": "Grilled salmon with almond rice pilaf and spinach with pine nuts",
        "type": "string"
      },
      "price": {
        "source": "$45",
        "value": 45,
        "unit": "$",
        "type": "currency"
      }
    },
    {
      "dinner_description": {
        "value": "Seared beef with fingerling potatoes and asparagus",
        "type": "string"
      },
      "price": {
        "source": "$56",
        "value": 56,
        "unit": "$",
        "type": "currency"
      }
    }
  ],
  "desserts_zipped": [
    {
      "dessert_description": {
        "value": "Cannoli cake with candied lavender",
        "type": "string"
      },
      "price": {
        "source": "$14",
        "value": 14,
        "unit": "$",
        "type": "currency"
      }
    },
    {
      "dessert_description": {
        "value": "Creme brulee",
        "type": "string"
      },
      "price": {
        "source": "$13",
        "value": 13,
        "unit": "$",
        "type": "currency"
      }
    }
  ],
  "wines_zipped": [
    {
      "wine_name": {
        "value": "House white",
        "type": "string"
      },
      "wine_type": {
        "value": "white:varietal not found",
        "type": "string"
      },
      "wine_description": {
        "value": "Dry with citrus notes reminiscent of grapefruit",
        "type": "string"
      },
      "smallest_serving_price": {
        "value": "6 oz: $10",
        "type": "string"
      },
      "second_smallest_serving_price": {
        "value": "9 oz: $16",
        "type": "string"
      },
      "bottle_price": {
        "source": "32",
        "value": 32,
        "unit": "$",
        "type": "currency"
      }
    },
    {
      "wine_name": {
        "value": "Barone Ventoura",
        "type": "string"
      },
      "wine_type": {
        "value": "white:varietal not found",
        "type": "string"
      },
      "wine_description": {
        "value": "Subtle honey notes with floral aromas",
        "type": "string"
      },
      "smallest_serving_price": {
        "value": "6 oz: $12",
        "type": "string"
      },
      "second_smallest_serving_price": {
        "value": "9 oz: $18",
        "type": "string"
      },
      "bottle_price": {
        "source": "40",
        "value": 40,
        "unit": "$",
        "type": "currency"
      }
    },
    {
      "wine_name": {
        "value": "Val Feuillatte",
        "type": "string"
      },
      "wine_type": {
        "value": "white:varietal not found",
        "type": "string"
      },
      "wine_description": {
        "value": "Bold, distinctive spice notes",
        "type": "string"
      },
      "smallest_serving_price": null,
      "second_smallest_serving_price": null,
      "bottle_price": {
        "source": "65",
        "value": 65,
        "unit": "$",
        "type": "currency"
      }
    },
    {
      "wine_name": {
        "value": "House red",
        "type": "string"
      },
      "wine_type": {
        "value": "red:varietal not found",
        "type": "string"
      },
      "wine_description": {
        "value": "A satisfying blend",
        "type": "string"
      },
      "smallest_serving_price": {
        "value": "6 oz: $11",
        "type": "string"
      },
      "second_smallest_serving_price": {
        "value": "9 oz: $17",
        "type": "string"
      },
      "bottle_price": {
        "source": "33",
        "value": 33,
        "unit": "$",
        "type": "currency"
      }
    },
    {
      "wine_name": {
        "value": "Wrath cabernet sauvignon",
        "type": "string"
      },
      "wine_type": {
        "value": "red:cabernet sauvignon",
        "type": "string"
      },
      "wine_description": {
        "value": "Bold with oak notes",
        "type": "string"
      },
      "smallest_serving_price": null,
      "second_smallest_serving_price": null,
      "bottle_price": {
        "source": "62",
        "value": 62,
        "unit": "$",
        "type": "currency"
      }
    },
    {
      "wine_name": {
        "value": "Turbell Estate Pinot noir",
        "type": "string"
      },
      "wine_type": {
        "value": "red:pinot noir",
        "type": "string"
      },
      "wine_description": {
        "value": "Mellow with dark chocolate notes",
        "type": "string"
      },
      "smallest_serving_price": {
        "value": "6 oz: $13",
        "type": "string"
      },
      "second_smallest_serving_price": {
        "value": "9 oz: $18",
        "type": "string"
      },
      "bottle_price": {
        "source": "35",
        "value": 35,
        "unit": "$",
        "type": "currency"
      }
    },
    {
      "wine_name": {
        "value": "Chappellet Shiraz",
        "type": "string"
      },
      "wine_type": {
        "value": "red:shiraz",
        "type": "string"
      },
      "wine_description": {
        "value": "Complex and subtle",
        "type": "string"
      },
      "smallest_serving_price": null,
      "second_smallest_serving_price": null,
      "bottle_price": {
        "source": "72",
        "value": 72,
        "unit": "$",
        "type": "currency"
      }
    }
  ]
}

Example: Extract data from other fields

The following example shows how to prompt an LLM to get repeating data about another field's output.

Config

{
  "fields": [
    {
      /* define section group containing businesses */
      "id": "_businesses",
      "type": "sections",
      "range": {
        "anchor": {
          /* each business section starts with "Business #" */
          "match": {
            "type": "regex",
            "pattern": "Business [0-9]"
          }
        }
      },
      /* return each business as object containing names, sales, and selected classification */
      "fields": [
        {
          "id": "name",
          "anchor": "name",
          "method": {
            "id": "passthrough",
          }
        },
        {
          "id": "sales",
          "anchor": "sales",
          "method": {
            "id": "passthrough",
          }
        },
        { /* get checkbox status for 'individual' classification */
          "id": "individual",
          "type": "boolean",
          "method": {
            "id": "nearestCheckbox",
            "position": "left"
          },
          "anchor": "individual"
        },
        { /* get checkbox status for 'parternship' classification */
          "id": "partnernship",
          "type": "boolean",
          "method": {
            "id": "nearestCheckbox",
            "position": "left"
          },
          "anchor": "partnership"
        },
        { /* get checkbox status for 'llc' classification */
          "id": "llc",
          "type": "boolean",
          "method": {
            "id": "nearestCheckbox",
            "position": "left"
          },
          "anchor": "limited"
        },
        {
          /* return the selected classification for the business */
          "id": "selected_classification",
          "method": {
            "id": "pickValues",
            // select match:one for a group of radio buttons where the user can select a single item
            "match": "one",
            "source_ids": [
              "individual",
              "partnership",
              "llc"
            ]
          }
        }
      ]
    },
    /* prompt an LLM to find answers in the `_business` section output */
    {
      "id": "_business_sales",
      "type": "table",
      "method": {
        "id": "list",
        /* use _businesses ID as context for prompts */
        "source_ids": [
          "_businesses"
        ],
        "description": "business attributes",
        "properties": [
          {
            "id": "name",
            /* clean up source output, e.g., transform
            "Name: ACME co" to "AMCE co" */
            "description": "name of the business"
          },
          {
            /* compare sales rankings */
            "id": "sales_rank",
            "description": "relative rank in annual sales compared to other businesses in this context, i.e., return 1st, 2nd, 3rd"
          }
        ]
      }
    },
    {
      /* count instances of individual, parternship, and llc classified businesses */
      "id": "_business_classifications_frequency",
      "type": "table",
      "method": {
        "id": "list",
        "source_ids": [
          /* count instances in the _businesses output */
          "_businesses"
        ],
        "description": "business classifications",
        "properties": [
          {
            "id": "business classification",
            "description": "business classification, ie. llc or solo proprietor"
          },
          {
            "id": "frequency",
            "description": "how many times the business classification was selected"
       
          }
        ]
      }
    },
    /* zip output for more readable results */
    {
      "id": "business_sales",
      "method": {
        "id": "zip",
        "source_ids": [
          "_business_sales",
        ]
      }
    },
    {
      "id": "business_classifications_frequency",
      "method": {
        "id": "zip",
        "source_ids": [
          "_business_classifications_frequency",
        ]
      }
    },
    /* hide source fields for more readable results */
    {
      "id": "clean_output",
      "method": {
        "id": "suppressOutput",
        "source_ids": [
          "_businesses",
          "_business_classifications_frequency",
          "_business_sales"
        ]
      }
    }
  ]
}

Example document
The following image shows the example document used with this example config:

Click to enlarge

Example document	Download link

Output

{
  "business_sales": [
    {
      "name": {
        "value": "ACME co",
        "type": "string"
      },
      "sales_rank": {
        "value": "2nd",
        "type": "string"
      }
    },
    {
      "name": {
        "value": "Anyco LLM",
        "type": "string"
      },
      "sales_rank": {
        "value": "1st",
        "type": "string"
      }
    },
    {
      "name": {
        "value": "Keystone LLM",
        "type": "string"
      },
      "sales_rank": {
        "value": "3rd",
        "type": "string"
      }
    }
  ],
  "business_classifications_frequency": [
    {
      "business classification": {
        "value": "individual",
        "type": "string"
      },
      "frequency": {
        "value": "1",
        "type": "string"
      }
    },
    {
      "business classification": {
        "value": "llc",
        "type": "string"
      },
      "frequency": {
        "value": "2",
        "type": "string"
      }
    }
  ]
}

Notes

For an overview of how this method works when "searchBySummarization": false and source_ids are unspecified, see the following steps:

Sensible finds the chunks of the document that most likely contain your target data:
- Sensible concatenates all your property descriptions with your overall list description.
- Sensible splits the document into equal-sized chunks.
- Sensible scores your concatenated list descriptions against each chunk using a nonconfigurable OpenAI Embeddings API.
Sensible selects a number of the top-scoring chunks:
1. If you specify Thorough or Long for the Mode parameter in the LLM Engine parameter, the Chunk Count parameter determines the number of top-scoring chunks Sensible selects to submit to the LLM.
2. If you specify Fast for the Mode parameter, 1. Sensible selects a number of top-scoring chunks as determined by the Chunk Count parameter. 2. To improve performance, Sensible removes chunks that are significantly less relevant from the list of top-scoring chunks. The number of chunks Sensible submits to the LLM can therefore be smaller than the number specified by the Chunk Count parameter.
To avoid large language model (LLM)'s input token limits, Sensible batches the chunks into groups. The chunks in each page group can come from non-consecutive pages in the document. If you set the Single LLM Completion parameter to True, then Sensible creates a single group that contains all the top-scoring chunks and sets a larger maximum token input limit for the single group (about 120k tokens) than it does for batched groups.
For each chunk group, Sensible submits a full prompt to an LLM (determined by the LLM Engine parameter) that includes the chunks as context, page-hinting data, and your prompts. For more information about the full prompt, see Advanced LLM prompt configuration. The full prompt instructs the LLM to create a list formatted as a table, based on the context.
Sensible concatenates the results from the LLM for each page group and returns a list, formatted as a table.