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

# Document Knowledge Base

The **DocumentKnowledgeBase** reads **local docs** files, converts them into vector embeddings and loads them to a vector databse.

## Usage

<Note>
  We are using a local PgVector database for this example. [Make sure it's running](https://docs.phidata.com/vectordb/pgvector)
</Note>

```shell theme={null}
pip install textract
```

```python theme={null}
from phi.knowledge.document import DocumentKnowledgeBase
from phi.vectordb.pgvector import PgVector

knowledge_base = DocumentKnowledgeBase(
    path="data/docs",
    # Table name: ai.documents
    vector_db=PgVector(
        table_name="documents",
        db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
    ),
)
```

Then use the `knowledge_base` with an `Agent`:

```python theme={null}
from phi.agent import Agent
from knowledge_base import knowledge_base

agent = Agent(
    knowledge=knowledge_base,
    search_knowledge=True,
)
agent.knowledge.load(recreate=False)

agent.print_response("Ask me about something from the knowledge base")
```

## Params

| Parameter           | Type               | Default             | Description                                                                                     |
| ------------------- | ------------------ | ------------------- | ----------------------------------------------------------------------------------------------- |
| `documents`         | `List[Document]`   | -                   | List of documents to load into the vector database.                                             |
| `vector_db`         | `VectorDb`         | -                   | Vector Database for the Knowledge Base.                                                         |
| `reader`            | `Reader`           | -                   | A `Reader` that converts the content of the documents into `Documents` for the vector database. |
| `num_documents`     | `int`              | `5`                 | Number of documents to return on search.                                                        |
| `optimize_on`       | `int`              | -                   | Number of documents to optimize the vector db on.                                               |
| `chunking_strategy` | `ChunkingStrategy` | `FixedSizeChunking` | The chunking strategy to use.                                                                   |