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Documentation Index

Fetch the complete documentation index at: https://docs.knowledgestack.ai/llms.txt

Use this file to discover all available pages before exploring further.

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Overview

Knowledge Stack uses Qdrant as its dedicated vector search engine. When you upload documents, Knowledge Stack splits them into chunks, generates vector embeddings, and stores them in Qdrant. This powers semantic search — finding content by meaning rather than exact keyword matches.

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How It Works

  1. Documents are ingested through the standard upload pipeline
  2. Chunks are embedded — each chunk of text is converted into a 1536-dimensional vector using OpenAI’s text-embedding-3-small model
  3. Vectors are stored in Qdrant along with metadata (tenant, tags, folder path, version status)
  4. Search queries are also embedded, and Qdrant finds the most semantically similar chunks
PostgreSQL remains the source of truth for all data. Qdrant is a derived search index that is kept in sync automatically.

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Search Capabilities

Search your knowledge base by meaning: 
POST /v1/search
The search API accepts a natural-language query and returns the most relevant content chunks, ranked by semantic similarity.

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Metadata Filtering

You can narrow search results using metadata filters, all applied at the vector search level for maximum performance:
FilterDescription
Folder scopeSearch within a specific folder and all its subfolders
Document scopeSearch within a specific document
TagsFilter by one or more tags (inherited from parent folders)
Content typeFilter by chunk type: text, table, or image
Time rangeFilter by when content was ingested
Active version onlySearch only the current version of documents (default)

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Path-Based Scoping

Every chunk stores its full ancestor path — from the root folder down to the chunk itself. This means you can scope searches to any level of the hierarchy: 
/shared/engineering/api-spec.pdf/v1/Chapter 1/chunk_42
  • Search within /shared/engineering/ to find all chunks under the engineering folder
  • Search within a specific document to limit results to that document
  • Search within a specific version to compare across versions

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Tag Inheritance

Tags are inherited down the content hierarchy. If you tag a folder, all documents and chunks within that folder are automatically included when you search by that tag. This makes it easy to organize and search large knowledge bases.

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Multi-Tenant Isolation

Knowledge Stack ensures complete data isolation between tenants:
  • Every search query is automatically scoped to your tenant
  • Per-tenant search indexes provide optimal performance
  • No cross-tenant data leakage is possible at the vector search level

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Data Consistency

The system is eventually consistent. When you make changes in Knowledge Stack (move folders, update tags, upload new versions), the search index updates automatically:
  • Simple changes (deletions, version switches) are applied immediately
  • Complex changes (folder moves, tag updates across many documents) are processed asynchronously and converge quickly
  • A reconciliation process runs periodically to detect and fix any drift between the database and the search index

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Version Management

When you set a new active version for a document, the search index automatically:
  1. Deactivates all chunks from the previous version
  2. Activates chunks from the new version
By default, searches only return results from active versions.

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Self-Hosted Deployment

If you are self-hosting Knowledge Stack, you need to run a Qdrant instance. The minimum required version is v1.16.3.

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Docker Compose

qdrant:
  image: qdrant/qdrant:v1.16.3
  ports:
    - "6333:6333"   # REST API
    - "6334:6334"   # gRPC
  volumes:
    - qdrant_storage:/qdrant/storage

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Configuration

SettingDefaultEnvironment VariableDescription
Qdrant URLhttp://localhost:6333QDRANT_URLQdrant server address
Qdrant API Key(none)QDRANT_API_KEYAPI key for authenticated access