Give Your RAG Pipeline a Filing System, Not Just a Search Bar
AI extracted Key-Value Fields turn scattered documents into structured, filterable metadata, so specific questions get specific answers instead of a pile of close-enough results.
Define a Schema Once. Let Data Augmentation Agents Extract the Rest.
AI extracted Key-Value Fields let you define a typed schema (vendor, amount, date, currency). The Key-Value Extraction Agent reads every resource, extract the matching values, and validate them against your schema automatically, with no manual tagging and no custom extraction pipeline to maintain.
The Bridge Between Structured Data and Unstructured Content in One Retrieval Layer
AI Key-Value Fields extract specific values out of unstructured content and attach them as validated metadata, so retrieval can narrow to exact matches before semantic search takes over. No spreadsheets, no manual review, no separate database to maintain.
Builds Structure Around Whatever Schema You Define
Reads every document against a schema you set, vendor, amount, date, currency, or any field your use case needs, and writes the extracted values back as structured metadata by:
- Pulling exact values from invoices, contracts, and spreadsheets automatically, with no tagging templates or manual data entry.
- Checking every extracted value against your schema as it's written, so the metadata stays accurate as your content grows.
Lets Structured Filters and Semantic Search Work in the Same Query
Narrows retrieval to records that match specific criteria first, then applies semantic search only to what's left, so answers reflect precise facts instead of approximate matches by:
- Using operators like equals, greater-than, and range to resolve criteria-driven questions as exact filters rather than semantic estimates.
- Running metadata filters and semantic or hybrid search together in a single request, sharpening results without extra engineering work.
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Key Benefits:
Sharper Retrieval, Fewer Irrelevant Results
AI extracted Key-Value Fields narrow retrieval to records that match exact criteria before semantic search runs, so answers pull from the right documents instead of the whole knowledge base.
Less Manual Tagging, More Automatic Structure
Values like vendor, date, and amount are extracted and validated against your schema automatically, removing the need for manual tagging or a separate structured database.
Greater Trust Through Verified, Structured Metadata
Every extracted field is validated at write time, giving compliance and data teams structured, auditable facts behind every retrieved answer.
Frequently Asked Questions
Ready to Get Started?
Index files and documents from internal and external sources to fuel your company use cases.