Page Index

From the release notes

The PageIndex SDK, local or cloud — vectorless, reasoning-based RAG, end to end. Much faster indexing — the PageIndex Flash engine gets the tree from layout stats: no LLM involved for the structure generation itself, LLMs only write the node summaries, and tree expansion proposes a wave of nodes concurrently. client = PageIndexClient() client.submit_document("report.pdf") client.chat("What does the report conclude?") Index, to chat, to agent integration, one client. Local mode needs no server, no vector DB, no PageIndex API key. Highlights Flash engine: the local default (mode="standard" keeps the classic LLM pipeline). Embedded bookmarks are consumed when trustworthy, and tree optimization is on by default — optimize="merge" for the deterministic LLM-free pass, "full" (default) adds LLM expand, which runs a wave of nodes concurrently instead of one round-trip at a time. One complete surface, local and cloud: PageIndexLocalClient(storage_path=...) is the same client as cloud — submit, tree, page content, chat, and agent tools all present in both modes, so code moves between them unchanged. Cloud documents, your own model: api_key decides where your documents live; a configured chat model decides who answers — and the two combine. PageIndexClient(api_key="pi-...", chat_model="openai/gpt-5.2") runs the same in-process document-QA engine over the live cloud tool set. Page content flows through your process to your provider on your credentials; doc_id targets at the prompt level; enable_citations stays with the managed chat. Agent integration: the cloud MCP tool contract, in-process — client.agent_tools() (plain functions), as_openai_tools(), as_anthropic_tools(), as_claude_mcp(), plus one-call openai_agent_config() / anthropic_runner_config() / claude_agent_config() bundles and agent_instructions() for the system prompt. Cloud clients get the live server tool set over the MCP bridge (read-only endpoint by default); local clients get the in-process subset with the same schemas and envelopes — agent prompts port unchanged. Chat surfaces: chat() — question in, answer out, on any backend; chat(stream=True) shows the run as it happens, thinking and tool calls woven into the text, or as typed events via .events; chat(protocol="responses" | "messages") drives the OpenAI Responses or Anthropic Messages API natively with that protocol's own shapes, and chat_completions() keeps the OpenAI-compatible envelope — all with doc_id targeting, streaming, honest usage accounting, and prompt-cache continuity across turns. Transcripts append verbatim: a protocol lane's output goes back into the next request unchanged. Model & connection knobs: index_model / chat_model, index_backend / chat_backend (and per-call backend) passed verbatim to each lane — LiteLLM-routed providers, keyless OpenAI-compatible servers, Azure/Bedrock/Vertex included. index= / chat= slots: the grouped spelling of the flat arguments — a string shorthand or a mapping (index={"model": ..

From the release notes

The PageIndex SDK, local or cloud — vectorless, reasoning-based RAG, end to end. Much faster indexing — the PageIndex Flash engine gets the tree from layout stats: no LLM involved for the structure generation itself, LLMs only write the node summaries, and tree expansion proposes a wave of nodes concurrently. client = PageIndexClient() client.submit_document("report.pdf") client.chat("What does the report conclude?") Index, to chat, to agent integration, one client. Local mode needs no server, no vector DB, no PageIndex API key. Highlights Flash engine: the local default (mode="standard" keeps the classic LLM pipeline). Embedded bookmarks are consumed when trustworthy, and tree optimization is on by default — optimize="merge" for the deterministic LLM-free pass, "full" (default) adds LLM expand, which runs a wave of nodes concurrently instead of one round-trip at a time. One complete surface, local and cloud: PageIndexLocalClient(storage_path=...) is the same client as cloud — submit, tree, page content, chat, and agent tools all present in both modes, so code moves between them unchanged. Cloud documents, your own model: api_key decides where your documents live; a configured chat model decides who answers — and the two combine. PageIndexClient(api_key="pi-...", chat_model="openai/gpt-5.2") runs the same in-process document-QA engine over the live cloud tool set. Page content flows through your process to your provider on your credentials; doc_id targets at the prompt level; enable_citations stays with the managed chat. Agent integration: the cloud MCP tool contract, in-process — client.agent_tools() (plain functions), as_openai_tools(), as_anthropic_tools(), as_claude_mcp(), plus one-call openai_agent_config() / anthropic_runner_config() / claude_agent_config() bundles and agent_instructions() for the system prompt. Cloud clients get the live server tool set over the MCP bridge (read-only endpoint by default); local clients get the in-process subset with the same schemas and envelopes — agent prompts port unchanged. Chat surfaces: chat() — question in, answer out, on any backend; chat(stream=True) shows the run as it happens, thinking and tool calls woven into the text, or as typed events via .events; chat(protocol="responses" | "messages") drives the OpenAI Responses or Anthropic Messages API natively with that protocol's own shapes, and chat_completions() keeps the OpenAI-compatible envelope — all with doc_id targeting, streaming, honest usage accounting, and prompt-cache continuity across turns. Transcripts append verbatim: a protocol lane's output goes back into the next request unchanged. Model & connection knobs: index_model / chat_model, index_backend / chat_backend (and per-call backend) passed verbatim to each lane — LiteLLM-routed providers, keyless OpenAI-compatible servers, Azure/Bedrock/Vertex included. index= / chat= slots: the grouped spelling of the flat arguments — a string shorthand or a mapping (index={"model": ..

From the release notes

The PageIndex SDK, local or cloud — vectorless, reasoning-based RAG, end to end. Much faster indexing — the PageIndex Flash engine gets the tree from layout stats: no LLM involved for the structure generation itself, LLMs only write the node summaries, and tree expansion proposes a wave of nodes concurrently. client = PageIndexClient() client.submit_document("report.pdf") client.chat("What does the report conclude?") Index, to chat, to agent integration, one client. Local mode needs no server, no vector DB, no PageIndex API key. Highlights Flash engine: the local default (mode="standard" keeps the classic LLM pipeline). Embedded bookmarks are consumed when trustworthy, and tree optimization is on by default — optimize="merge" for the deterministic LLM-free pass, "full" (default) adds LLM expand, which runs a wave of nodes concurrently instead of one round-trip at a time. One complete surface, local and cloud: PageIndexLocalClient(storage_path=...) is the same client as cloud — submit, tree, page content, chat, and agent tools all present in both modes, so code moves between them unchanged. Cloud documents, your own model: api_key decides where your documents live; a configured chat model decides who answers — and the two combine. PageIndexClient(api_key="pi-...", chat_model="openai/gpt-5.2") runs the same in-process document-QA engine over the live cloud tool set. Page content flows through your process to your provider on your credentials; doc_id targets at the prompt level; enable_citations stays with the managed chat. Agent integration: the cloud MCP tool contract, in-process — client.agent_tools() (plain functions), as_openai_tools(), as_anthropic_tools(), as_claude_mcp(), plus one-call openai_agent_config() / anthropic_runner_config() / claude_agent_config() bundles and agent_instructions() for the system prompt. Cloud clients get the live server tool set over the MCP bridge (read-only endpoint by default); local clients get the in-process subset with the same schemas and envelopes — agent prompts port unchanged. Chat surfaces: chat() — question in, answer out, on any backend; chat(stream=True) shows the run as it happens, thinking and tool calls woven into the text, or as typed events via .events; chat(protocol="responses" | "messages") drives the OpenAI Responses or Anthropic Messages API natively with that protocol's own shapes, and chat_completions() keeps the OpenAI-compatible envelope — all with doc_id targeting, streaming, honest usage accounting, and prompt-cache continuity across turns. Transcripts append verbatim: a protocol lane's output goes back into the next request unchanged. Model & connection knobs: index_model / chat_model, index_backend / chat_backend (and per-call backend) passed verbatim to each lane — LiteLLM-routed providers, keyless OpenAI-compatible servers, Azure/Bedrock/Vertex included. index= / chat= slots: the grouped spelling of the flat arguments — a string shorthand or a mapping (index={"model": ..