> ## Documentation Index
> Fetch the complete documentation index at: https://docs.lyzr.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Knowledge Base

> Overview of Lyzr's Knowledge Base (KB), its capabilities, supported formats, and RAG integration for AI-powered retrieval and response generation.

## Introduction

The **Knowledge Base (KB)** in Lyzr empowers AI agents to retrieve and utilize both structured and unstructured information for accurate, context-aware responses. It supports various file formats, advanced chunking strategies, and multiple retrieval methods to ensure high-quality information extraction.4

<Card title="Knowledge Base Guide" icon="database" href="https://www.youtube.com/watch?v=uYr0tyluWQ4">
    Learn how to manage data sources for your agents.
</Card>

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## Creating and Managing a Knowledge Base

Lyzr provides a streamlined interface via **Lyzr Studio** to manage Knowledge Bases:

### Create a Knowledge Base

* Configure embedding, LLM, and vector store credentials.
* Set retrieval and chunking strategies.
* Define a unique name and description.

### Manage Content

* **Add content**: Upload documents, enter text, or provide URLs.
* **Delete content**: Remove outdated or irrelevant entries.
* **Update configuration**: Change retrieval types or chunk settings anytime.

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## Supported File Types

The following formats can be uploaded to a Lyzr KB:

* `.pdf`
* `.doc`, `.docx`
* `.txt`
* Website URLs (via scraping)

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## Upload Limitations

To ensure optimal performance:

* Max **5 files at a time**
* Each file must be **less than 15MB**
* For better results, **prefer batch-wise uploading**

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## Chunking Strategy

Chunking splits documents into smaller parts for better semantic indexing.

### Parameters:

* **Number of chunks**: Number of sections generated.
* **Chunk size**: Controls the length of each chunk.
* **Overlap**: Adds context continuity across chunks.

This improves both retrieval quality and answer coherence.

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## Available Retrieval Types

Lyzr offers multiple retrieval mechanisms to suit different information needs:

### a) **Basic Retrieval**

* Default vector similarity-based retrieval.
* Great for general knowledge lookups.

### b) **MMR (Maximal Marginal Relevance)**

* Balances diversity and relevance.
* Reduces duplicate content in retrieved results.

### c) **HyDE (Hypothetical Document Embeddings)**

* Generates synthetic documents to simulate context.
* Boosts open-ended query results.

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## Retrieval-Augmented Generation (RAG)

Lyzr seamlessly integrates **RAG** to generate more accurate and grounded answers using knowledge base content.

### RAG Workflow

1. **Query Reception**\
   Agent receives a user question or instruction.
2. **Document Retrieval**\
   Top-N relevant documents are fetched using vector similarity.
3. **Reranking & Filtering**\
   Results are optionally refined for relevance.
4. **Prompt Assembly**\
   Retrieved context is combined with the original question.
5. **Generation**\
   LLM generates a grounded response using the assembled prompt.
6. **Citation & Delivery**\
   Output includes references to source documents for transparency.

### Core Components

* **Vector Store**: Stores semantic vectors (e.g., Pinecone, FAISS, Qdrant)
* **Embedding Model**: Transforms content into vectors (e.g., OpenAI, Cohere)
* **Reranker**: Improves result ordering (optional)
* **Prompt Template**: Defines how context + question are structured
* **Citation Module**: Appends references to the output

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## Simulator Testing

Once a Knowledge Base is created and populated:

1. Navigate to the **Agent Simulator** in Lyzr Studio.
2. Select the agent connected to your KB.
3. Enter test prompts to evaluate:
   * Retrieval accuracy
   * Answer relevance
   * Citation correctness
4. Adjust retrieval type, chunking, or KB content as needed.

Testing helps validate that the agent understands and uses the KB effectively before production deployment.

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## Conclusion

Lyzr’s Knowledge Base system is a robust tool for enabling intelligent, grounded, and flexible AI responses. With support for diverse file types, retrieval strategies, and RAG integration, it provides a powerful foundation for domain-specific agents.

Optimize your AI workflows by:

* Configuring proper chunking
* Choosing the right retrieval type
* Uploading high-quality content in batches
* Testing thoroughly with the simulator