> 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/combined-knowledgebase.md).

# Combined KnowledgeBase

The **CombinedKnowledgeBase** combines multiple knowledge bases into 1 and is used when your app needs information using multiple sources.

### [​](https://docs.phidata.com/knowledge/combined#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 pypdf bs4
```

knowledge\_base.py

```python
from bitca.knowledge.combined import CombinedKnowledgeBase
from bitca.vectordb.pgvector import PgVector

url_pdf_knowledge_base = PDFUrlKnowledgeBase(
    urls=["pdf_url"],
    # Table name: ai.pdf_documents
    vector_db=PgVector(
        table_name="pdf_documents",
        db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
    ),
)

website_knowledge_base = WebsiteKnowledgeBase(
    urls=["https://docs.bitcadata.com/introduction"],
    # Number of links to follow from the seed URLs
    max_links=10,
    # Table name: ai.website_documents
    vector_db=PgVector(
        table_name="website_documents",
        db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
    ),
)

local_pdf_knowledge_base = PDFKnowledgeBase(
    path="data/pdfs",
    # Table name: ai.pdf_documents
    vector_db=PgVector(
        table_name="pdf_documents",
        db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
    ),
    reader=PDFReader(chunk=True),
)

knowledge_base = CombinedKnowledgeBase(
    sources=[
        url_pdf_knowledge_base,
        website_knowledge_base,
        local_pdf_knowledge_base,
    ],
    vector_db=PgVector(
        # Table name: ai.combined_documents
        table_name="combined_documents",
        db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
    ),
)
```

Then use the `knowledge_base` with an Agent:

agent.py

```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/combined#params)Params <a href="#params" id="params"></a>

| Parameter           | Type                   | Default             | Description                                                                                     |
| ------------------- | ---------------------- | ------------------- | ----------------------------------------------------------------------------------------------- |
| `sources`           | `List[AgentKnowledge]` | -                   | List of Agent knowledge bases.                                                                  |
| `reader`            | `Reader`               | -                   | A `Reader` that converts the content of the documents 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.                                                                   |