VectifyAI/PageIndex v0.2.15

on GitHub

• 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": ..., "storage_path": ...}, chat={"model": ..., "backend": ...}). "cloud" / "local" name a side, an optional mode= cross-checks it, and PageIndexLocalClient / PageIndexCloudClient take the same slots. PageIndexCloudClient() reads PAGEINDEX_API_KEY; a bare PageIndexClient() stays local no matter what the environment holds.
• Typed config shapes: IndexConfig / CloudIndexConfig / LocalIndexConfig / ChatConfig, with py.typed shipped so your type checker sees them.
• Nothing fails quietly: unknown keys, mixed sides, empty values and mode/content conflicts refuse at construction with the legal vocabulary in the message; dead credentials or a missing model fail the indexing run instead of storing a document with blank summaries; every cloud error carries its HTTP status.
• Dependencies: Python >= 3.10; openai-agents in the base install (the chat engine); [anthropic] and [claude] extras for those SDKs.

Also in 0.2.15

• Images reach your model. A document's page and figure images (get_document_image) now arrive at the model as images on the own-model chat and the in-process agent integrations: tool results travel as MCP content and each framework renders them itself. mcp joins the base install.
• Chat lanes: extra_body can no longer replace the SDK's own request rows (system, instructions, input, messages, tools), and its sampling fields ride their ModelSettings field on LiteLLM-routed models; on the Responses lane reasoning_effort joins extra_body["reasoning"]; a managed-chat failure reported mid-stream raises after the partial answer; a partial read of .events no longer cancels the run; ChatStream is a real import, so chat()'s type hints resolve at runtime.
• Quieter LiteLLM: its loggers are quieted only while unset, the "Provider List" banner no longer prints mid-answer, and the model cost map is never fetched on import. The README charts now render on PyPI.

Full Changelog: v0.2.14...v0.2.15