Published March 9, 2026

How to Build Legal Contract Search and Insights with RAG Using Unified's Storage and KMS APIs

February 13, 2026

Retrieval-Augmented Generation (RAG) is one of the most common patterns for building AI systems grounded in enterprise data.

In legal teams, RAG is often used to:

Search contracts across multiple repositories
Answer clause-level questions
Surface renewal terms and termination language
Ground AI responses in actual agreement text

But legal RAG systems fail when they:

Index documents without respecting permissions
Mix tenant data
Ignore document structure
Treat legal insight as inference instead of retrieval

Unified's Storage and Knowledge Management (KMS) APIs provide the normalized, real-time data layer required to implement contract RAG pipelines correctly.

This guide shows how to build a permission-aware, tenant-isolated, incrementally synchronized RAG pipeline for legal contracts using Unified.

What RAG Means in a Legal Context

RAG (Retrieval-Augmented Generation) is not model training.

It is a pattern:

Retrieve relevant contract content.
Provide that content as context.
Generate a response grounded in the retrieved text.

For legal use cases, RAG must be:

Deterministic
Permission-aware
Metadata-driven
Non-inferential

Legal RAG answers questions like:

Which contracts include indemnification clauses?
Which agreements auto-renew?
Which vendor contracts expire next quarter?
Where is termination for convenience defined?

It does not determine compliance or legal validity.

Unified provides the data retrieval layer for this architecture. You own embeddings, vector storage, and model behavior.

Where Legal Contracts Live in SaaS Systems

Enterprise contracts typically exist in:

File Storage (PDF contracts)
Knowledge Management systems (Notion, Confluence contract pages)

Unified normalizes both.

StorageFile (File Storage API)

Contracts stored as files are represented as StorageFile.

Relevant fields:

id
created_at
updated_at
name
parent_id
user_id
size
type (FILE or FOLDER)
mime_type
permissions[]
download_url
hash
version
web_url

Important RAG implications:

download_url expires after 1 hour (must re-fetch when reprocessing)
permissions[] includes:
- user_id
- group_id
- roles (OWNER, READ, WRITE)
Explicit permission metadata is available only for file objects
Unified does not enforce access — your RAG system must

This makes StorageFile ideal for embedding contract PDFs with permission metadata attached.

KmsPage (Knowledge Management API)

Contracts stored as structured pages use KmsPage.

Relevant fields:

id
created_at
updated_at
title
type (HTML, MARKDOWN, TEXT, OTHER)
space_id
parent_id
is_active
user_id
download_url
metadata[]
has_children
web_url

Important RAG implications:

No explicit permissions[] structure
Access must be enforced at the application layer
Content must be fetched via download_url

RAG pipelines must treat KMS content differently than file storage content.

Step 1: Connect Contract Data Sources

Authorize integrations using Embedded Auth.

Each integration produces a connection_id.

In RAG systems, connection_id must be treated as the tenant boundary.

All Unified endpoints require connection_id. Unified enforces isolation at the connection level.

Your vector database must also filter by connection_id.

RAG systems require freshness.

Unified supports:

Native webhooks (push-based)
Virtual webhooks (polling-based)

Subscribe to:

File created
File updated
Page created
Page updated

Deletion event support varies by provider. If unavailable, use incremental sync via updated_gte.

For legal RAG, webhook-driven indexing is strongly preferred over polling.

Step 3: Retrieve Metadata First

Example (Storage):

const files = await sdk.storage.listStorageFiles({
  connectionId,
  limit: 100,
  fields: 'id,name,type,mime_type,updated_at,permissions'
});

Example (KMS):

const pages = await sdk.kms.listKmsPages({
  connectionId,
  limit: 100,
  fields: 'id,title,space_id,updated_at'
});

List endpoints return metadata only.

Full content must be retrieved explicitly.

Step 4: Retrieve Full Contract Content

Storage:

const file = await sdk.storage.getStorageFile({
  connectionId,
  id: fileId,
  fields: 'download_url'
});

KMS:

const page = await sdk.kms.getKmsPage({
  connectionId,
  id: pageId,
  fields: 'download_url'
});

Fetch the content using the returned download_url.

Unified does not inline contract bodies.

Step 5: Chunk Contracts for RAG

Legal RAG quality depends on chunking.

Chunk by:

Section headings
Clause boundaries
Defined term groupings
Structured paragraphs

Attach metadata to each chunk:

connection_id
object_type
object_id
updated_at
permissions (for files)
space_id / parent_id

Unified normalizes the schema. You manage embedding and vector storage.

Step 6: Store in Your Vector Database

Unified does not:

Store embeddings
Cache payloads
Maintain vector indexes

Store embeddings in your infrastructure.

Use metadata to support:

Tenant isolation
Permission enforcement
Update replacement
Space/folder scoping

Step 7: Permission-Aware RAG Retrieval

When a user queries:

Embed the query.
Retrieve top-matching chunks.
Filter by:
- connection_id
- permissions[] (for files)
- Application-level user rules (for pages)
Pass filtered context to the model.
Generate response.