> ## 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.

# S3 Text Knowledge Base

The **S3TextKnowledgeBase** reads **text** files from an S3 bucket, 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.s3.text import S3TextKnowledgeBase
from phi.vectordb.pgvector import PgVector

db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"

knowledge_base = S3TextKnowledgeBase(
    bucket_name="phi-public",
    key="recipes/recipes.docx",
    vector_db=PgVector(table_name="recipes", db_url=db_url),
)
```

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("How to make Hummus?")
```

## Params

| Parameter           | Type               | Default             | Description                                                                               |
| ------------------- | ------------------ | ------------------- | ----------------------------------------------------------------------------------------- |
| `formats`           | `List[str]`        | `[".doc", ".docx"]` | Formats accepted by this knowledge base.                                                  |
| `reader`            | `S3TextReader`     | `S3TextReader()`    | A `S3TextReader` that converts the `Text` files into `Documents` for the vector database. |
| `vector_db`         | `VectorDb`         | -                   | Vector Database for the Knowledge Base.                                                   |
| `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.                                                             |