v0.2.15

- 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.

```python
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**: https://github.com/VectifyAI/PageIndex/compare/v0.2.14...v0.2.15