> ## 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 Bases Overview

> Build RAG-powered agents with knowledge bases

Knowledge bases enable Retrieval Augmented Generation (RAG) by storing and querying documents. Agents can use knowledge bases to answer questions based on your documents, websites, and other content.

## Quick Start

```python theme={null}
from lyzr import Studio

studio = Studio(api_key="your-api-key")

# Create a knowledge base
kb = studio.create_knowledge_base(
    name="product_docs",
    vector_store="qdrant",
    embedding_model="text-embedding-3-large"
)

# Add documents
kb.add_pdf("manual.pdf")
kb.add_website("https://docs.example.com", max_pages=50)

# Create an agent with the knowledge base
agent = studio.create_agent(
    name="Support Bot",
    provider="gpt-4o",
    role="Customer support",
    goal="Answer questions using documentation",
    instructions="Use the knowledge base to answer questions accurately"
)

# Query with the knowledge base
response = agent.run(
    "How do I reset my password?",
    knowledge_bases=[kb]
)
print(response.response)
```

## What is a Knowledge Base?

A knowledge base is a vector database that stores your documents as embeddings. When an agent receives a question, it:

1. **Searches** the knowledge base for relevant content
2. **Retrieves** the most relevant chunks
3. **Generates** a response using the retrieved context

This is called Retrieval Augmented Generation (RAG).

## Supported Document Types

| Type    | Method          | Description             |
| ------- | --------------- | ----------------------- |
| PDF     | `add_pdf()`     | PDF documents           |
| DOCX    | `add_docx()`    | Word documents          |
| TXT     | `add_txt()`     | Plain text files        |
| Website | `add_website()` | Web pages with crawling |
| Text    | `add_text()`    | Raw text strings        |

## Vector Store Providers

| Provider  | ID          | Description                               |
| --------- | ----------- | ----------------------------------------- |
| Qdrant    | `qdrant`    | Default, high-performance vector database |
| Weaviate  | `weaviate`  | Open-source vector search engine          |
| PG Vector | `pg_vector` | PostgreSQL with vector extension          |
| Milvus    | `milvus`    | Scalable vector database                  |
| Neptune   | `neptune`   | Amazon Neptune graph database             |

## Key Operations

### Create

```python theme={null}
kb = studio.create_knowledge_base(
    name="my_kb",
    vector_store="qdrant"
)
```

### Add Documents

```python theme={null}
kb.add_pdf("document.pdf")
kb.add_website("https://example.com")
kb.add_text("Custom content", source="custom")
```

### Query

```python theme={null}
results = kb.query("search term", top_k=5)
for result in results:
    print(f"{result.score:.2f}: {result.text}")
```

### Use with Agent

```python theme={null}
response = agent.run(
    "What is the return policy?",
    knowledge_bases=[kb]
)
```

## Retrieval Types

| Type         | Description                                  |
| ------------ | -------------------------------------------- |
| `basic`      | Standard vector similarity search            |
| `mmr`        | Maximal Marginal Relevance (diverse results) |
| `hyde`       | Hypothetical Document Embeddings             |
| `time_aware` | Time-decay weighted retrieval                |

## Next Steps

<Card title="Creating Knowledge Bases" icon="plus" href="/lyzr-adk/knowledge-bases/creating-kb">
  Learn all creation options and configuration
</Card>

<Card title="Adding Documents" icon="file-plus" href="/lyzr-adk/knowledge-bases/adding-documents">
  Add PDFs, websites, and other content
</Card>

<Card title="Querying" icon="magnifying-glass" href="/lyzr-adk/knowledge-bases/querying">
  Search and retrieve from knowledge bases
</Card>

<Card title="Managing" icon="gear" href="/lyzr-adk/knowledge-bases/managing-kb">
  Update, delete, and manage documents
</Card>