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

# Chat with PDF Agents

> Learn how to create AI agents that can intelligently chat with PDF documents using vector databases for efficient information retrieval.

A PDF-centric workflow where Chat agents interact with vector databases to store and retrieve information from PDF documents, enabling natural conversations and intelligent question-answering capabilities.

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import Agent

agent = Agent(
    name="researcher",
    instructions="Answer questions using the uploaded PDF",
    knowledge=True,
)

agent.start("Summarise chapter 2 of the document")
```

The user uploads a PDF and asks questions; answers cite the indexed content.

```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
graph LR
    subgraph "Chat with PDF"
        PDF[📋 PDF Document] --> Agent[🤖 PDF Chat Agent]
        Agent --> VDB[💾 Vector DB]
        VDB --> Out[✅ Response]
    end

    classDef input fill:#6366F1,stroke:#7C90A0,color:#fff
    classDef agent fill:#8B0000,stroke:#7C90A0,color:#fff
    classDef tool fill:#189AB4,stroke:#7C90A0,color:#fff
    classDef success fill:#10B981,stroke:#7C90A0,color:#fff
    class PDF input
    class Agent agent
    class VDB tool
    class Out success
```

## Quick Start

<Steps>
  <Step title="Install Package">
    Install PraisonAI Agents with PDF chat support:

    ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
    pip install "praisonaiagents[knowledge]"
    ```
  </Step>

  <Step title="Set API Key">
    Set your OpenAI API key:

    ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
    export OPENAI_API_KEY="${OPENAI_API_KEY:?Set OPENAI_API_KEY in your shell}"
    ```
  </Step>

  <Step title="Create Script">
    Create a new file `chat_with_pdf.py`:

    ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
    from praisonaiagents import Agent

    agent = Agent(
        name="PDF Chat Agent",
        instructions="You answer questions based on the provided PDF document.",
        knowledge=["document.pdf"], # PDF Indexing
    )

    agent.start("What is the main topic of this PDF?") # Chat Query
    ```
  </Step>
</Steps>

## PDF Processing and Chat Agents

<Note>
  PDF processing involves indexing the document content for efficient retrieval during chat.
</Note>

```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
flowchart LR
    subgraph In[Input]
        PDF[PDF Documents]
    end

    subgraph Router[Vector Store]
        DB[(Vector DB)]
    end
    
    subgraph Out[Chat Agents]
        A1[Chat Agent 1]
        A2[Chat Agent 2]
        A3[Chat Agent 3]
    end

    In --> Router
    Router --> A1
    Router --> A2
    Router --> A3

    style In fill:#8B0000,color:#fff
    style Router fill:#189AB4,color:#fff
    style Out fill:#8B0000,color:#fff
```

The simplest way to create a PDF chat agent is without any configuration:

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import Agent

agent = Agent(
    name="PDF Chat Agent",
    instructions="You answer questions based on the provided PDF document.",
    knowledge=["document.pdf"] # PDF Indexing
)

agent.start("What are the key points in this document?") # Chat Query
```

### Advanced Configuration

For more control over the knowledge base, you can specify a configuration:

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import Agent

config = {
    "vector_store": {
        "provider": "chroma",
        "config": {
            "collection_name": "praison",
            "path": ".praison",
        }
    }
}

agent = Agent(
    name="PDF Chat Agent",
    instructions="You answer questions based on the provided PDF document.",
    knowledge={
        "sources": ["document.pdf"],
        **config
    }
)

agent.start("What is the main topic of this PDF?") # Chat Query
```

### Multi-Agent Knowledge System

For more complex scenarios, you can create a knowledge-based system with multiple agents:

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import Agent, Task, AgentTeam
import logging
import os

# Configure logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

# Define the configuration for the Knowledge instance
config = {
    "vector_store": {
        "provider": "chroma",
        "config": {
            "collection_name": "praison",
            "path": ".praison",
        }
    }
}

# Create an agent with knowledge capabilities
knowledge_agent = Agent(
    name="KnowledgeAgent",
    role="Information Specialist",
    goal="Store and retrieve knowledge efficiently",
    backstory="Expert in managing and utilizing stored knowledge",
    knowledge={
        "sources": ["sample.pdf"],
        **config
    }
)

# Define a task for the agent
knowledge_task = Task(
    name="knowledge_task",
    description="Who is Mervin Praison?",
    expected_output="Answer to the question",
    agent=knowledge_agent # Agent
)

# Create and start the agents
agents = AgentTeam(
    agents=[knowledge_agent],
    tasks=[knowledge_task],
    process="sequential",
    memory={"user_id": "user1"} # User ID
)

# Start execution
result = agents.start() # Retrieval
```

## How It Works

The user uploads a PDF and asks a question; the agent retrieves the most relevant chunks from the vector store and answers from them.

```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
sequenceDiagram
    participant User
    participant Agent
    participant VectorDB

    User->>Agent: knowledge=["document.pdf"]
    Agent->>VectorDB: Index PDF chunks
    User->>Agent: start("Summarise chapter 2")
    Agent->>VectorDB: Retrieve relevant chunks
    VectorDB-->>Agent: Matching passages
    Agent-->>User: Answer from PDF
```

***

## Understanding PDF Chat Agents

<Card title="What are PDF Chat Agents?" icon="question">
  PDF Chat agents enable:

  * Natural conversation with PDF documents
  * Intelligent information extraction
  * Context-aware document understanding
  * Quick answers to document-specific questions
</Card>

## Features

<CardGroup cols={2}>
  <Card title="PDF Processing" icon="file-pdf">
    Process and index PDF documents efficiently.
  </Card>

  <Card title="Natural Chat" icon="comments">
    Have natural conversations about PDF content.
  </Card>

  <Card title="Smart Retrieval" icon="brain">
    Intelligently retrieve relevant information from PDFs.
  </Card>

  <Card title="Context Awareness" icon="layer-group">
    Maintain context throughout the conversation.
  </Card>
</CardGroup>

## Troubleshooting

<CardGroup cols={2}>
  <Card title="PDF Issues" icon="triangle-exclamation">
    If PDF processing isn't working:

    * Check PDF file format and encoding
    * Verify document accessibility
    * Enable verbose mode for debugging
  </Card>

  <Card title="Chat Issues" icon="gauge-high">
    If chat responses aren't accurate:

    * Check PDF indexing quality
    * Verify question clarity
    * Monitor context retention
  </Card>
</CardGroup>

## Best Practices

<AccordionGroup>
  <Accordion title="Install the knowledge extra">
    Run `pip install "praisonaiagents[knowledge]"` before indexing PDFs. Core install alone lacks Chroma and document parsers needed for retrieval.
  </Accordion>

  <Accordion title="Use text-searchable PDFs">
    Scanned image-only PDFs index poorly. Prefer native text PDFs or run OCR upstream so chunking and embedding capture meaningful content.
  </Accordion>

  <Accordion title="Pin a persistent vector store path">
    Set `path` and `collection_name` in your Chroma config so re-indexing does not wipe prior embeddings between restarts.
  </Accordion>

  <Accordion title="Keep queries specific to the document">
    Ask about sections, figures, or claims in the PDF rather than open-ended prompts — retrieval quality depends on query–chunk alignment.
  </Accordion>
</AccordionGroup>

## Next Steps

<CardGroup cols={2}>
  <Card title="AutoAgents" icon="robot" href="./autoagents">
    Learn about automatically created and managed AI agents
  </Card>

  <Card title="Mini Agents" icon="microchip" href="./mini">
    Explore lightweight, focused AI agents
  </Card>
</CardGroup>

<Note>
  For optimal chat experience, ensure your PDFs are properly formatted and text-searchable.
</Note>

## Related

<CardGroup cols={2}>
  <Card icon="book-open" href="/docs/features/knowledge">
    Give agents a searchable knowledge base from your documents.
  </Card>

  <Card icon="database" href="/docs/features/rag">
    Retrieve relevant chunks from large document sets at query time.
  </Card>
</CardGroup>