> ## 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.

# Classic Knowledge Base

Lyzr Studio’s **Classic Knowledge Base** feature allows you to create a no-code RAG pipeline for fast, searchable document understanding. It's ideal for building lightweight Q\&A systems from files, URLs, and plain text sources — optimized for quick setup and cost-efficient usage.

## 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* **Delete content**: Remove outdated or irrelevant entries.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.

***

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

## 1. Choose Knowledge Base Type

Before creation, you'll be prompted to choose the type of Knowledge Base:

* Classic (for general documents and text)
* Knowledge Graph (for relationship-heavy data)
* Semantic Model (for structured databases)

Select **Classic Knowledge Base** to proceed.

<img src="https://mintlify.s3.us-west-1.amazonaws.com/lyzrinc/assets/images/studio/kb2.png" alt="" />

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## 2. Create a Classic Knowledge Base

Define your new KB by entering essential details:

* **Name**: A meaningful title (e.g., "Marketing FAQs").
* **Description**: Briefly explain the purpose of this KB.
* **Vector Database**: Choose where embeddings will be stored — options include **Qdrant**, **Weaviate**, or others integrated with Lyzr.

Click **Create** to initialize your Classic Knowledge Base.

<img src="https://mintlify.s3.us-west-1.amazonaws.com/lyzrinc/assets/images/studio/kb3.png" alt="" />

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## 3. Add Content from Multiple Sources

Lyzr supports uploading or linking multiple data types. Content types supported:

* **File Upload**: PDF, DOCX, TXT, CSV, JSON.
* **Web Links**: Direct URLs of documentation pages or websites.
* **Copy-paste text**: Manually input chunks of content.

Lyzr auto-detects file formats and applies the right parser. Behind the scenes:

* Content is split into semantically coherent chunks.
* Embeddings are generated using LLMs.
* The vector store indexes these chunks for fast retrieval.

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## 4. Query & Test

Once your Classic KB is populated, test it directly inside Studio:

* **Search Input**: Ask natural language questions.
* **Chunk Count**: Control number of results returned (default: 10).
* **Retrieval Type**: Basic (similarity-based).
* **Score Threshold**: Filter out low-score responses for higher precision.

You can continue uploading more files or editing your KB over time. Lyzr will automatically update the index.

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## 5. Integrate with Agents

The Classic KB can now be connected to Lyzr agents:

* Choose the KB as a data source during agent creation.
* The agent will use this KB to perform Retrieval-Augmented Generation (RAG).
* No coding required — fully visual interface for mapping knowledge and deploying agents.

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## Summary

| Feature                  | Description                                               |
| ------------------------ | --------------------------------------------------------- |
| Fast Setup               | Upload or link content in minutes.                        |
| Cost-Effective Retrieval | Optimized for quick queries and basic document Q\&A.      |
| No-Code Interface        | Simple visual UI for all KB operations.                   |
| Agent-Ready              | Seamlessly connect to agents for real-time semantic Q\&A. |

***

The **Classic Knowledge Base** is best suited when you want to get started fast with document-based Q\&A and lightweight RAG — no database or complex configuration needed.