Chat SDK

💬 PageIndex Chat (beta)

The PageIndex Chat Completions SDK (beta) allows you have conversations with your PageIndex-processed documents, with the ability to scope to specific documents.

Chat with Documents

Parameters

Name	Type	Required	Description	Default
messages	`List[Dict[str, str]]`	yes	Conversation messages with ‘role’ and ‘content’ keys.	-
stream	`boolean`	no	Enable streaming responses for real-time output.	False
doc_id	`string` or `List[string]`	no	Document ID(s) to scope the conversation. Can be a single ID or list of IDs.	None
temperature	`float`	no	Sampling temperature (0.0 to 1.0). Lower is more deterministic.	None
stream_metadata	`boolean`	no	If True with stream=True, return raw chunks with metadata instead of just text.	False

Non-Streaming Chat

Get non-streaming response from PageIndex Chat.

Example Request


from pageindex import PageIndexClient
 
pi_client = PageIndexClient(api_key="YOUR_PAGEINDEX_API_KEY")
 
# Chat without document context
response = pi_client.chat_completions(
    messages=[
        {"role": "user", "content": "What is the contribution of the first paper?"}
    ]
)
 
print(response["choices"][0]["message"]["content"])

Example Request with Single Document


# Chat with a specific document
response = pi_client.chat_completions(
    messages=[
        {"role": "user", "content": "What are the key findings in this document?"}
    ],
    doc_id="pi-abc123def456"
)
 
print(response["choices"][0]["message"]["content"])

Example Request with Multiple Documents


# Chat with multiple documents
response = pi_client.chat_completions(
    messages=[
        {"role": "user", "content": "Compare the findings across these documents"}
    ],
    doc_id=["pi-abc123def456", "pi-xyz789ghi012"]
)
 
print(response["choices"][0]["message"]["content"])

Example Response


{
  "id": "chatcmpl-xyz789",
  "object": "chat.completion",
  "created": 1234567890,
  "choices": [{
    "index": 0,
    "message": {
      "role": "assistant",
      "content": "Based on the document, the key findings are..."
    },
    "finish_reason": "end_turn"
  }],
  "usage": {
    "prompt_tokens": 1234,
    "completion_tokens": 567,
    "total_tokens": 1801
  }
}

Streaming Chat

Stream PageIndex Chat responses in real-time for better user experience.

Example Request


from pageindex import PageIndexClient
 
pi_client = PageIndexClient(api_key="YOUR_PAGEINDEX_API_KEY")
 
# Streaming chat with document
for chunk in pi_client.chat_completions(
    messages=[
        {"role": "user", "content": "Summarize this document"}
    ],
    doc_id="pi-abc123def456",
    stream=True
):
    print(chunk, end='', flush=True)

Example with Multi-Turn Conversation


# Continue a conversation
messages = [
    {"role": "user", "content": "What is the main topic of this document?"},
    {"role": "assistant", "content": "The main topic is climate change and its effects on agriculture."},
    {"role": "user", "content": "What solutions does it propose?"}
]
 
for chunk in pi_client.chat_completions(
    messages=messages,
    doc_id="pi-abc123def456",
    stream=True
):
    print(chunk, end='', flush=True)

Advanced: Streaming with Intermediate Metadata

Track tool calls and intermediate steps using raw streaming mode.

Example Request


from pageindex import PageIndexClient
 
pi_client = PageIndexClient(api_key="YOUR_PAGEINDEX_API_KEY")
 
# Stream with metadata about tool calls
for chunk in pi_client.chat_completions_stream_raw(
    messages=[
        {"role": "user", "content": "What are the main findings?"}
    ],
    doc_id="pi-abc123def456",
    stream=True,
    stream_metadata=True
):
    # Check for metadata about tool calls
    metadata = chunk.get("block_metadata", {})
    if metadata:
        block_type = metadata.get("type")
 
        # Tool call started
        if block_type == "mcp_tool_use_start":
            tool_name = metadata.get("tool_name")
            print(f"\n[Searching document using {tool_name}]\n", flush=True)
 
        # Tool result received
        elif block_type == "mcp_tool_result_start":
            print(f"\n[Retrieved relevant content]\n", flush=True)
 
    # Get content
    content = chunk.get("choices", [{}])[0].get("delta", {}).get("content", "")
    if content:
        print(content, end='', flush=True)

Available Block Types

text_block_start / text_stop - Text content blocks
mcp_tool_use_start / mcp_tool_use_stop - PageIndex tool being called
mcp_tool_result_start / mcp_tool_result_stop - Tool results received

Document Management

Check document status and metadata before chatting.

Get Document Metadata

Retrieve document information including processing status, page count, and creation time.

Method: pi_client.get_document(doc_id)

Parameters

Name	Type	Required	Description
doc_id	string	yes	Document ID

Returns

Field	Type	Description
id	string	Document ID
name	string	Document filename
description	string	Document description (if available)
status	string	Processing status: “queued”, “processing”, “completed”, “failed”
createdAt	string	Creation timestamp in ISO 8601 format
pageNum	int	Number of pages in the document

Response Example


{
  "id": "pi-abc123def456",
  "name": "research_paper.pdf",
  "description": "Machine Learning Research Paper",
  "status": "completed",
  "createdAt": "2024-01-15T10:30:00.000Z",
  "pageNum": 42
}

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