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Document Data Structuring

Munging files into a structured data format helps preserve relationships between bits of data, retains special meaning of specific data types, and enables users to query the data with filters.

Let's use this National Institute of Health PDF as an example:

www.techtransfer.nih.gov_tech_tab-3843.pdf

Vectara offers a structured data format where users can convert PDFs to a format like the following structure:

{
  "documentId": "TAB‑3843",
  "title": "Engineered Cell‑Penetrating Monoclonal Antibody for Universal Inuenza Immunotherapy",
  "description": "Home » Tech » Engineered Cell‑Penetrating Monoclonal Antibody for Universal Inuenza Immunotherapy",
  "metadataJson": "{\"developmentStatus\":\"Pre‑Clinical\",\"isAntibodiesProduct\":true,\"date\":\"2023‑05‑17\",\"patentSeriesCode\":63,\"patentApplicationNumber\":365841}",
  "section": [{
    "title": "body",
    "text": "Influenza remains a burden on public health..."
  }, {
    "title": "Clinical treatment",
    "text": "Clinical Treatment꞉ CPP‑mAbs against influenza NP may...",
    "metadataJson": "{\"clinicalTreatment\"꞉\"CPP‑mAbs against influenza NP may...\"}",
  }, {
    "text": "Current vaccines remain effective for a short time period..."
  }]
}

This data structure is built upon three core concepts:

  • Document
  • Metadata
  • Sections

Document​

The document format provides high-level information that gets encoded into Vectara and allows users to retrieve this information using semantic search, keyword-based search, and hybrid search:

"documentId": "TAB‑3843",
  "title": "Engineered Cell‑Penetrating Monoclonal Antibody for Universal Inuenza Immunotherapy",
  "description": "Home » Tech » Engineered Cell‑Penetrating Monoclonal Antibody for Universal Inuenza Immunotherapy",
  • documentId specifies a unique identifier for the document.
  • title specifies the heading of the document.
  • description provides additional context about the document.

Metadata​

The document has metadata attached to it with the metadataJson property. This property expects to be assigned a stringified JSON object that consists of arbitrary key-value pairs which accept text, numeric, and boolean values.

In our example document, we selected different properties from the original PDF that are useful for the following scenarios:

  • Filtering through different documents
  • Cross-referencing a document with other data sources
  • Comparing and grouping results

Defining metadata properties on the document level instead of the section level helps you retrieve the entire document rather than just a part of it. Let's look at these metadata properties in more detail.

Example Metadata Properties​

"metadataJson": "{\"developmentStatus\":\"Pre‑Clinical\",\"isAntibodiesProduct\":true,\"date\":\"2023‑05‑17\",\"patentSeriesCode\":63,\"patentApplicationNumber\":365841}",
  • developmentStatus specifies status of the patent, such as pre-clinical.
  • isAntibodiesProduct indicates whether the patent applies to "antibodies-related" products, which is the domain we care about in this contrived example.
  • date specifies the date this document was created.
  • patentSeriesCode specifies the patent series code number.
  • patentApplicationNumber specifies the patent application number.

Metadata can also be attached to sections, which are an organization unit for grouping related bodies of text.

Sections​

When Vectara ingests a document, it splits the text in these sections into chunks and encodes them in vectors. This enables queries to retrieve them based on semantic similarity.

"section": [{
    "title": "body",
    "text": "Influenza remains a burden on public health..."
  }, {
    "title": "Clinical treatment",
    "text": "Clinical Treatment꞉ CPP‑mAbs against influenza NP may...",
    "metadataJson": "{\"clinicalTreatment\"꞉\"CPP‑mAbs against influenza NP may...\"}",
  }, {
    "text": "Current vaccines remain effective for a short time period..."
  }]
  • text specifies the body of text.
  • title specifies an optional name for identifying the body of text. This is like a heading in a document.
  • metadataJson specifies an optional stringified JSON object, which can be configured as flexibly as the root-level document metadata.
  • sections specifies an optional array of child sections. Those sections can also have their own child sections.

Nested Sections​

You can also nest sections within sections, which also have their own titles, text, and metadata, as shown in our King Lear example. This example document is structured with a top-level section array that contains the parent sections, which are plays titled King Lear and Antony and Cleopatra.

King Lear has nested sections for Act 1 and Act II, which contain additional text and metadata, while Antony and Cleopatra directly contains the content for this parent section. This example demonstrates the flexibility of the document structure that Vectara can ingest.

Special Document Metadata​

Vectara Console recognizes special metadata which have proven useful across many use cases.

date​

If you define date in the document's metadata, it appears in the Console Corpus Search interface. This can be useful for tracking the recency of a document, since older docs can lose relevance in some scenarios.

url​

If you define url in the document's metadata, it appears in the Console Corpus Search interface as a clickable link. This can be useful for enabling users to click through to the document's original resource, such a web page or downloadable artifact.

ts_create​

If you define ts_create and define a creation date in epoch seconds, it appears in the Console Corpus Search interface as the document's date of creation.

author​

If you define author and then define either a string or an array of strings, these values appear in the Console Corpus Search interface as the document's author(s).