# Docx Knowledge Base

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

### [​](https://docs.phidata.com/knowledge/docx#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)

```shell
pip install textract
```

```python
from bitca.knowledge.docx import DocxKnowledgeBase
from bitca.vectordb.pgvector import PgVector

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

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

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

| Parameter           | Type               | Default             | Description                                                                           |
| ------------------- | ------------------ | ------------------- | ------------------------------------------------------------------------------------- |
| `path`              | `Union[str, Path]` | -                   | Path to docx files. Can point to a single docx file or a directory of docx files.     |
| `formats`           | `List[str]`        | `[".doc", ".docx"]` | Formats accepted by this knowledge base.                                              |
| `reader`            | `DocxReader`       | `DocxReader()`      | A `DocxReader` that converts the docx 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.                                                         |