> ## Documentation Index
> Fetch the complete documentation index at: https://docs-v1.agno.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Combined Knowledge Base

## Code

```python theme={null}
from pathlib import Path

from agno.agent import Agent
from agno.knowledge.combined import CombinedKnowledgeBase
from agno.knowledge.csv import CSVKnowledgeBase
from agno.knowledge.pdf import PDFKnowledgeBase
from agno.knowledge.pdf_url import PDFUrlKnowledgeBase
from agno.knowledge.website import WebsiteKnowledgeBase
from agno.vectordb.pgvector import PgVector

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

# Create CSV knowledge base
csv_kb = CSVKnowledgeBase(
    path=Path("data/csvs"),
    vector_db=PgVector(
        table_name="csv_documents",
        db_url=db_url,
    ),
)

# Create PDF URL knowledge base
pdf_url_kb = PDFUrlKnowledgeBase(
    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
    vector_db=PgVector(
        table_name="pdf_documents",
        db_url=db_url,
    ),
)

# Create Website knowledge base
website_kb = WebsiteKnowledgeBase(
    urls=["https://docs-v1.agno.com/introduction"],
    max_links=10,
    vector_db=PgVector(
        table_name="website_documents",
        db_url=db_url,
    ),
)

# Create Local PDF knowledge base
local_pdf_kb = PDFKnowledgeBase(
    path="data/pdfs",
    vector_db=PgVector(
        table_name="pdf_documents",
        db_url=db_url,
    ),
)

# Combine knowledge bases
knowledge_base = CombinedKnowledgeBase(
    sources=[
        csv_kb,
        pdf_url_kb,
        website_kb,
        local_pdf_kb,
    ],
    vector_db=PgVector(
        table_name="combined_documents",
        db_url=db_url,
    ),
)

# Initialize the Agent with the combined knowledge base
agent = Agent(
    knowledge=knowledge_base,
    search_knowledge=True,
)

knowledge_base.load(recreate=False)

# Use the agent
agent.print_response("Ask me about something from the knowledge base", markdown=True)
```

## Usage

<Steps>
  <Snippet file="create-venv-step.mdx" />

  <Step title="Install libraries">
    ```bash theme={null}
    pip install -U sqlalchemy 'psycopg[binary]' pgvector pypdf agno
    ```
  </Step>

  <Step title="Run PgVector">
    ```bash theme={null}
    docker run -d \
      -e POSTGRES_DB=ai \
      -e POSTGRES_USER=ai \
      -e POSTGRES_PASSWORD=ai \
      -e PGDATA=/var/lib/postgresql/data/pgdata \
      -v pgvolume:/var/lib/postgresql/data \
      -p 5532:5432 \
      --name pgvector \
      agnohq/pgvector:16
    ```
  </Step>

  <Step title="Run Agent">
    <CodeGroup>
      ```bash Mac theme={null}
      python cookbook/agent_concepts/knowledge/combined_kb.py
      ```

      ```bash Windows theme={null}
      python cookbook/agent_concepts/knowledge/combined_kb.py
      ```
    </CodeGroup>
  </Step>
</Steps>