> For the complete documentation index, see [llms.txt](https://docs.projectbit.ca/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.projectbit.ca/documentation/knowledges/s3-pdf-knowledge-base.md).

# S3 PDF Knowledge Base

The **S3PDFKnowledgeBase** reads **PDF** files from an S3 bucket, converts them into vector embeddings and loads them to a vector databse.

### [​](https://docs.phidata.com/knowledge/s3_pdf#usage)Usage <a href="#usage" id="usage"></a>

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

```python
from bitca.knowledge.s3.pdf import S3PDFKnowledgeBase
from bitca.vectordb.pgvector import PgVector

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

knowledge_base = S3PDFKnowledgeBase(
    bucket_name="bitca-public",
    key="recipes/ThaiRecipes.pdf",
    vector_db=PgVector(table_name="recipes", db_url=db_url),
)
```

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

```python
from bitca.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 Thai curry?")
```

### [​](https://docs.phidata.com/knowledge/s3_pdf#params)Params <a href="#params" id="params"></a>

| Parameter           | Type               | Default             | Description                                                                        |
| ------------------- | ------------------ | ------------------- | ---------------------------------------------------------------------------------- |
| `reader`            | `S3PDFReader`      | `S3PDFReader()`     | A `S3PDFReader` that converts the `PDFs` 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.                                                      |