> ## Documentation Index
> Fetch the complete documentation index at: https://neuraltrust-92b43583-develop.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Overview

> Knowledge base connectors used to generate RAG and functional tests

A `KnowledgeBase` is a collection of documents used to generate test cases for a domain or task — typically the vector store behind retrieval-augmented generation (RAG).

Documents are grouped by topics. If you do not pass `seed_topics`, the connector clusters embeddings and names topics with an LLM.

`trusttest.knowledge_base` only re-exports `KnowledgeBase`, `InMemoryKnowledgeBase`, and `Document`. Azure, Neo4j, Postgres, and Upstash must be imported from their submodules.

## Connectors

### AzureKnowledgeBase

Import: `from trusttest.knowledge_base.azure_search import AzureKnowledgeBase`

Leverages **Azure AI Search** and is best suited for:

* Cloud-based document indexing and storage
* Full-text search with advanced filtering and ranking
* Integration with Microsoft’s AI-powered search stack

[Azure connector →](/trusttest/create/knowledge-base/connectors/azure)

### Neo4jKnowledgeBase

Import: `from trusttest.knowledge_base.neo4j import Neo4jKnowledgeBase`

Built on **Neo4j**, this connector excels at:

* Handling complex document relationships
* Graph-based querying and clustering
* Constructing dynamic knowledge graphs

[Neo4j connector →](/trusttest/create/knowledge-base/connectors/neo4j)

### PgVectorKnowledgeBase

Import: `from trusttest.knowledge_base.pgvector import PgVectorKnowledgeBase`

Leverages **PostgreSQL** with **pgvector**:

* Semantic vector search on your existing Postgres tables
* Configurable `connection_string`, `table_name`, and `fields_mapping`

[PostgreSQL connector →](/trusttest/create/knowledge-base/connectors/postgres)

### UpstashKnowledgeBase

Import: `from trusttest.knowledge_base.upstash import UpstashKnowledgeBase`

Serverless **Upstash Vector** store for:

* Managed similarity search without operating a cluster
* URL + token authentication (`UPSTASH_URL` / `UPSTASH_TOKEN`)

[Upstash connector →](/trusttest/create/knowledge-base/connectors/upstash)

### InMemoryKnowledgeBase

Import: `from trusttest.knowledge_base import InMemoryKnowledgeBase, Document`

A minimal, no-dependency implementation designed for:

* Prototyping and local testing
* Lightweight, quick-start environments
* Small-scale document classification

[In-memory connector →](/trusttest/create/knowledge-base/connectors/in-memory)

## Topic Creation Process

The topic creation pipeline groups unlabeled documents using embeddings, UMAP, HDBSCAN, and an LLM summarizer. It is **not** Azure-only — any vector connector that embeds documents can run it.

<Note>This process is triggered when no predefined (`seed_topics`) list is provided. Configure `embeddings` and `topic_summarizer` with `set_config` (or pass `embeddings_model` / `llm_client`).</Note>

1. **Document Retrieval**
   * Pulls documents using the mapped `id` and `content` fields.
   * Filters out empty or whitespace-only content.
   * Optionally detects language on ingested documents (`trusttest[language-detection]`).

2. **Embedding Generation**
   * Applies an embedding model to each document’s content (truncated to `3 * max_tokens`).
   * Produces high-dimensional semantic vectors for clustering.

3. **Dimensionality Reduction**
   * Uses **UMAP** to reduce embedding vectors. `n_neighbors`, `n_components`, and initialization scale with document count.

4. **Topic Clustering**
   * Runs **HDBSCAN** over the reduced vectors. Noise and outliers are discarded (`label = -1`).

5. **LLM-based Topic Naming**
   * Names each cluster with the `topic_summarizer` client.
   * Uses up to `max_docs` samples per topic, truncated to `max_doc_length`.

6. **Return Structure**
   * A dictionary mapping topic names to documents, plus flat lists of topic names and documents.