Skip to main content

Documents

Track and manage document sources in your memory bank. Documents provide traceability — knowing where memories came from.

Prerequisites

Make sure you've completed the Quick Start and understand how retain works.

What Are Documents?​

Documents are containers for retained content. They help you:

  • Track sources — Know which PDF, conversation, or file a memory came from
  • Update content — Re-retain a document to update its facts
  • Delete in bulk — Remove all memories from a document at once
  • Organize memories — Group related facts by source

Chunks​

When you retain content, Hindsight splits it into chunks before extracting facts. These chunks are stored alongside the extracted memories, preserving the original text segments.

Why chunks matter:

  • Context preservation — Chunks contain the raw text that generated facts, useful when you need the exact wording
  • Richer recall — Including chunks in recall provides surrounding context for matched facts
Include Chunks in Recall

Use include_chunks=True in your recall calls to get the original text chunks alongside fact results. See Recall for details.

Retain with Document ID​

Associate retained content with a document:

# Retain with document ID
client.retain(
    bank_id="my-bank",
    content="Alice presented the Q4 roadmap...",
    document_id="meeting-2024-03-15"
)

# Batch retain for a document
client.retain_batch(
    bank_id="my-bank",
    items=[
        {"content": "Item 1: Product launch delayed to Q2"},
        {"content": "Item 2: New hiring targets announced"},
        {"content": "Item 3: Budget approved for ML team"}
    ],
    document_id="meeting-2024-03-15"
)

Update Documents​

Re-retaining with the same document_id replaces the old content:

# Original
client.retain(
    bank_id="my-bank",
    content="Project deadline: March 31",
    document_id="project-plan"
)

# Update (deletes old facts, creates new ones)
client.retain(
    bank_id="my-bank",
    content="Project deadline: April 15 (extended)",
    document_id="project-plan"
)

Get Document​

Retrieve a document's original text and metadata. This is useful for expanding document context after a recall operation returns memories with document references.

import asyncio
from hindsight_client_api import ApiClient, Configuration
from hindsight_client_api.api import DocumentsApi

async def get_document_example():
    config = Configuration(host="http://localhost:8888")
    api_client = ApiClient(config)
    api = DocumentsApi(api_client)

    # Get document to expand context from recall results
    doc = await api.get_document(
        bank_id="my-bank",
        document_id="meeting-2024-03-15"
    )

    print(f"Document: {doc.id}")
    print(f"Original text: {doc.original_text}")
    print(f"Memory count: {doc.memory_unit_count}")
    print(f"Created: {doc.created_at}")

asyncio.run(get_document_example())

Delete Document​

Remove a document and all its associated memories:

from hindsight_client_api import ApiClient, Configuration
from hindsight_client_api.api import DocumentsApi

async def delete_document_example():
    config = Configuration(host="http://localhost:8888")
    api_client = ApiClient(config)
    api = DocumentsApi(api_client)

    # Delete document and all its memories
    result = await api.delete_document(
        bank_id="my-bank",
        document_id="meeting-2024-03-15"
    )

    print(f"Deleted {result.memory_units_deleted} memories")

asyncio.run(delete_document_example())
warning

Deleting a document permanently removes all memories extracted from it. This action cannot be undone.

Document Response Format​

{
  "id": "meeting-2024-03-15",
  "bank_id": "my-bank",
  "original_text": "Alice presented the Q4 roadmap...",
  "content_hash": "abc123def456",
  "memory_unit_count": 12,
  "created_at": "2024-03-15T14:00:00Z",
  "updated_at": "2024-03-15T14:00:00Z"
}

Next Steps​