From 279ee160a961747ae684b94f895f5e39720c858d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=BC=A0=E9=B8=A3=E5=A4=A9?= Date: Thu, 20 Aug 2026 05:32:36 +0100 Subject: [PATCH 01/25] improve readme --- README.md | 319 +++++++++++++++++++++++++----------------------------- 1 file changed, 150 insertions(+), 169 deletions(-) diff --git a/README.md b/README.md index b3a123466..186341934 100644 --- a/README.md +++ b/README.md @@ -26,43 +26,27 @@ -```bash -pip install pageindex -export OPENAI_API_KEY=... # your own LLM key β€” PageIndex runs on it -``` - -```python -from pageindex import PageIndexClient -client = PageIndexClient() # local mode -doc_id = client.submit_document("report.pdf")["doc_id"] # build the tree index -print(client.chat("What was the 2023 operating margin?", doc_id=doc_id)) -``` - -No vector DB, no chunking, no embedding model β€” the whole thing runs on your machine, calling your own LLM provider. [Jump to the quickstart ↓](#-quickstart)
-

πŸ“’ Updates

 +

Updates

 - πŸ”₯ [**PageIndex SDK**](#-quickstart) β€” `pip install pageindex` now ships **local mode**: index, retrieve, and chat entirely on your machine with your own LLM key, or point the same client at PageIndex Cloud with an API key. - ⚑ [**PageIndex Flash**](#-step-1-build-the-tree-index) β€” tree structure generation from PDFs in seconds, with structure extracted heuristically instead of by an LLM. -- [**Agentic Vectorless RAG**](examples/agentic_vectorless_rag_demo.py) β€” a simple agentic, vectorless RAG example with *self-hosted PageIndex*, using OpenAI Agents SDK. -- [**Scale PageIndex to Millions of Documents**](https://pageindex.ai/blog/pageindex-filesystem) β€” *PageIndex File System* is a file-level tree indexing layer that lets PageIndex reason over an entire corpus, not just a single document. -- [PageIndex Chat](https://chat.pageindex.ai) β€” Human-like document analysis agent [platform](https://chat.pageindex.ai) for professional long documents. Also available via [MCP](https://pageindex.ai/developer) or [API](https://pageindex.ai/developer). -- [PageIndex Framework](https://pageindex.ai/blog/pageindex-intro) β€” Deep dive into PageIndex: an *agentic, in-context tree index* that enables LLMs to perform *reasoning-based, context-aware retrieval* over long documents. +- [PageIndex Chat](https://app.pageindex.ai) β€” Human-like document analysis agent [platform](https://chat.pageindex.ai) for professional long documents. Also available via [MCP](https://pageindex.ai/developer) or [API](https://pageindex.ai/developer).
-# πŸ“‘ What is PageIndex? +# What is PageIndex? Are you frustrated with vector database retrieval accuracy for long professional documents? Vector-based RAG retrieves by semantic **similarity**. But **similarity β‰  relevance** β€” what retrieval actually needs is relevance, and relevance requires **reasoning**. On professional documents that demand contextual understanding, domain expertise, and multi-step reasoning, similarity search misses what is relevant but not similar, and returns what is similar but not relevant. Inspired by AlphaGo, **[PageIndex](https://vectify.ai/pageindex)** replaces the vector index with a **hierarchical tree index** and lets an LLM **reason** its way through it β€” the way a human expert flips to the right section of a long report. Retrieval happens in two steps: -1. **Index** β€” turn the document into a β€œtable-of-contents” **tree structure** -2. **Retrieve** β€” **search that tree** with LLM reasoning, agentically +1. **Index** β€” generate **tree structure** index for each document +2. **Retrieve** β€” retrieve infomation via LLM-based **tree search**
@@ -71,9 +55,7 @@ Inspired by AlphaGo, **[PageIndex](https://vectify.ai/pageindex)** replaces the
-### 🎯 Why it works - -> PageIndex is a vectorless, reasoning-based RAG engine that mirrors how humans read, delivering traceable, explainable, and context-aware retrieval, without vector databases or chunking. +### Compare with Vector RAG | | Vector RAG | **PageIndex** | |---|---|---| @@ -85,78 +67,126 @@ Inspired by AlphaGo, **[PageIndex](https://vectify.ai/pageindex)** replaces the It is ideal for financial reports, legal documents, regulatory filings, technical manuals, medical literature, academic textbooks β€” any long, complex professional document. -PageIndex achieved **state-of-the-art** [98.7% accuracy](https://github.com/VectifyAI/Mafin2.5-FinanceBench) on FinanceBench (financial document QA benchmark), vastly outperforming vector-based RAG β€” see [Benchmarks](#-benchmarks). - +> PageIndex achieved **state-of-the-art** [98.7% accuracy](https://github.com/VectifyAI/Mafin2.5-FinanceBench) on FinanceBench (financial document QA benchmark), vastly outperforming vector-based RAG β€” see [Benchmarks](#-benchmarks). -# ⚑ Quickstart -### 1. Install +# Quickstart ```bash -pip install pageindex +pip install -U pageindex ``` -Local mode and the agent surfaces below require `pageindex >= 0.2.10`. Python 3.10+. -### 2. Set your LLM key +```python +import os +from pageindex import PageIndexClient +import os -Local mode needs **your own** LLM provider key β€” PageIndex calls it both to index (node summaries) and to answer. Export it, or put it in a `.env` file next to your script: +os.environ["OPENAI_API_KEY"] = "your-openai-key" -```bash -export OPENAI_API_KEY=your_openai_key_here -``` +client = PageIndexClient( + index_model="gpt-5.6-luna", # model to build the tree index + chat_model="gpt-5.6-sol", # model to search the tree +) +doc_id = client.submit_document("report.pdf")["doc_id"] -```bash -# .env -OPENAI_API_KEY=your_openai_key_here +answer = client.chat("What was the 2023 operating margin, and where is it stated?", + doc_id=doc_id) +print(answer) ``` -Any provider [LiteLLM](https://docs.litellm.ai/docs/providers) supports works β€” set that provider's key (`ANTHROPIC_API_KEY`, …) and pass a provider-prefixed model name (`anthropic/claude-sonnet-4-6`, `bedrock/…`). For a self-hosted OpenAI-compatible server (vLLM, TGI, Ollama), point `OPENAI_BASE_URL` at it and set `OPENAI_API_KEY` to whatever token it expects. -> This is *not* a PageIndex key β€” local mode never talks to our servers. A PageIndex [API key](https://dash.pageindex.ai/api-keys) is only for [cloud mode](#or-use-pageindex-cloud), where the managed LLM is included. +### Model Recommendations + +- **`index_model` β€” a basic model is sufficient.** The index model generates the document's tree index. A basic model is sufficient to produce a good tree structure. +- **`chat_model` β€” use the best model you can afford.** The chat model searches the tree to retrieve information. See [Benchmarks](#-benchmarks) for the trade-off between cost and accuracy. + + +# Benchmarks + +### Indexing cost + +Building a tree locally runs **about $0.001 per page** with `index_model="gpt-5.6-luna"` β€” so a 1,000-page textbook costs a little over a dollar and a few minutes, once, and every later question reuses it. + +**The index model is not the bottleneck.** PageIndex is designed not to rely heavily on the model used at index time, so in our experiments a cheap model does not hurt quality. + + + + Indexing cost against document length, log-log, for nine PDFs from 9 to 1,098 pages. Points track a $0.0011-per-page reference line; the spread around it is text density, not length. + + + + +### Query Cost and Accuracy + +[**PageIndex-OSS-Benchmark**](https://github.com/VectifyAI/PageIndex-OSS-Benchmark) measures exactly the setup in the quickstart above β€” `PageIndexClient()` in local mode, flash indexing, no OCR β€” on 62 lookup questions over 34 PDFs (1,945 pages) drawn from [MMLongBench-Doc-V2](https://github.com/VectifyAI/MMLongBench-Doc-V2). Every question's answer is a fact stated in running text, so a wrong answer is a **retrieval or reading failure**, not a reasoning one. + + + + Accuracy against average cost per question. Each model forms a near-vertical reasoning-effort ladder; moving between models costs an order of magnitude a step. + + + +Full results, data, and the runner are in the [benchmark repo](https://github.com/VectifyAI/PageIndex-OSS-Benchmark). + -### 3. Index a document, then ask + + +
+

Detailed Usage Guide

 + +
+ +## βš™οΈ Step 1: Initialize the client + +Create a local client and choose the models used for indexing and retrieval: ```python from pageindex import PageIndexClient -client = PageIndexClient( # local mode β€” uses OPENAI_API_KEY - index_model="gpt-5.6-luna", # builds the tree: a cheap model is enough - chat_model="gpt-5.6-sol", # answers questions: this is what drives accuracy +client = PageIndexClient( + index_model="gpt-5.6-luna", + chat_model="gpt-5.6-sol", + storage_path=".pageindex", ) -doc_id = client.submit_document("report.pdf")["doc_id"] - -answer = client.chat("What was the 2023 operating margin, and where is it stated?", - doc_id=doc_id) -print(answer) ``` -Those two are also the defaults, so `PageIndexClient()` on its own gives you the same thing β€” but the two roles are worth naming, because the rule of thumb is **spend on the chat model, not the index model**: +- **`index_model`** builds the tree index. A basic model is sufficient. -- **`index_model` β€” a weak model is fine.** Flash reads the hierarchy out of the PDF's own layout, so the model is not inventing the structure; it only writes the node summaries. Dropping to a cheaper model barely changes the tree, and it is a one-off cost per document either way. -- **`chat_model` β€” use the best you can afford.** This is the model that reasons over the tree, decides what to open, and reads the pages. It is where accuracy actually comes from, and it is billed per question (see [Benchmarks](#-benchmarks)). +- **`chat_model`** searches the tree and answers questions. Use the best model you can afford. -What just happened: +- **`storage_path`** specifies where indexed documents are stored locally. -- `submit_document` parsed the PDF and built its **tree index**, stored under `./.pageindex`. Indexing is synchronous and one-off β€” later questions reuse the same `doc_id`. -- `chat` ran a **document-QA agent** over that tree: it read the table of contents, reasoned about which sections could hold the answer, opened only those pages, and answered from them. -- The answer is grounded in real sections and page numbers, so you can check it. +### Model naming conventions -### Or use PageIndex Cloud +Model names follow [LiteLLM's naming convention](https://docs.litellm.ai/docs/providers). Choose the format that matches your provider: -Same client, same methods β€” pass an [API key](https://dash.pageindex.ai/api-keys) and the work happens on our servers, with the production OCR, tree-building, and retrieval pipeline behind it: +**OpenAI** β€” use the model name directly and set `OPENAI_API_KEY`: ```python -client = PageIndexClient(api_key="pi-...") -doc_id = client.submit_document("report.pdf", wait=True)["doc_id"] -print(client.chat("What was the 2023 operating margin?", doc_id=doc_id)) +os.environ["OPENAI_API_KEY"] = "your-openai-api-key" +chat_model="gpt-5.6-sol" ``` +**Anthropic** β€” prefix the model name with `anthropic/` and set `ANTHROPIC_API_KEY`: -# πŸ› οΈ Using PageIndex +```python +os.environ["ANTHROPIC_API_KEY"] = "your-anthropic-api-key" +chat_model="anthropic/claude-sonnet-4-6" +``` -## 🌲 Step 1: Build the tree index +**OpenRouter** β€” prefix the provider and model name with `openrouter/` and set `OPENROUTER_API_KEY`: + +```python +os.environ["OPENROUTER_API_KEY"] = "your-openrouter-api-key" +chat_model="openrouter/anthropic/claude-sonnet-4-6" +``` + +For model names and API key settings for other providers, see the [LiteLLM provider documentation](https://docs.litellm.ai/docs/providers). + + +## 🌲 Step 2: Build the tree index `submit_document` defaults to **Flash** indexing: the structure is extracted from the PDF's own layout (no LLM), and a model is called only for node summaries and the tree-optimization expansion pass. It takes seconds. @@ -201,117 +231,111 @@ A PageIndex tree looks like this β€” a table of contents optimized for LLMs and See more example [documents](https://github.com/VectifyAI/PageIndex/tree/main/examples/documents) and generated [tree structures](https://github.com/VectifyAI/PageIndex/tree/main/examples/documents/results). -
-Naming models, and where documents are stored -
-```python -client = PageIndexClient( - index_model="gpt-5.6-luna", # tree: node summaries + expansion pass - chat_model="anthropic/claude-sonnet-4-6",# answering, on another provider - storage_path=".pageindex", # where indexed documents live -) -``` -Model names mean what [LiteLLM](https://docs.litellm.ai/docs/providers) says they mean: a **bare name** goes to an OpenAI-compatible backend (selected by `OPENAI_API_KEY` / `OPENAI_BASE_URL`), and a **`provider/model`** name reaches that provider directly with its own key. Write `openai/Qwen/...` for a self-hosted server that itself serves slashed ids. -The two roles can sit on different providers. `model="..."` sets both at once, and a per-call `model=` on the chat surfaces overrides `chat_model` for that question. +## πŸ’¬ Step 3: Ask questions -
+`chat()` is the one-line surface. Underneath it is a document-QA agent, and you can talk to it over whichever protocol your stack already speaks: -
-Just the tree, without the client -
+**Get a simple answer with `chat()`:** ```python -from pageindex import page_index_flash, page_index - -result = page_index_flash("report.pdf") # heuristic structure + LLM summaries -result = page_index("report.pdf") # full LLM-built tree +client.chat("What changed in the risk factors?", doc_id=doc_id) ``` -Or from the command line, in this repo: +Pass a string or role/content history and get the answer back. -```bash -python3 run_pageindex.py --pdf_path /path/to/your/document.pdf -python3 run_pageindex.py --md_path /path/to/your/document.md # markdown, by "#" heading level +**Stream the answer:** + +```python +client.chat(question, doc_id=doc_id, stream=True) ``` -
-Optional CLI parameters -
+Returns the answer as text chunks. -The structure-tuning flags from `--toc-check-pages` down require `--mode standard`; `--optimize` requires Flash mode. +**Use the OpenAI Chat Completions format:** +```python +client.chat_completions(messages, doc_id=doc_id) ``` ---mode Processing mode: flash (default) or standard ---index-model LLM model used to index the document (default: gpt-5.6-luna) ---optimize Tree optimization for retrieval: full (default), merge, or off ---toc-check-pages Pages to check for table of contents (default: 20) ---max-pages-per-node Max pages per node (default: 10) ---max-tokens-per-node Max tokens per node (default: 20000) ---if-add-node-id Add node ID (yes/no, default: yes) ---if-add-node-summary Add node summary (yes/no, default: yes) ---if-add-doc-description Add doc description (yes/no, default: yes) -``` -
-> **Markdown mode note:** headings are read from `#` levels, so the file must be correctly formatted. If your Markdown was converted from PDF or HTML, most converters destroy the hierarchy β€” use [PageIndex OCR](https://pageindex.ai/blog/ocr), which is built to preserve it. +Returns the full envelope, including token usage, streaming metadata, and `finish_reason`. -
+**Use the OpenAI Responses format:** -## πŸ’¬ Step 2: Ask questions +```python +client.responses("...", doc_id=doc_id, reasoning={"effort": "high"}) +``` -`chat()` is the one-line surface. Underneath it is a document-QA agent, and you can talk to it over whichever protocol your stack already speaks: +Returns the agent's process transcript in `items`. Append those items to the next call's `input` to preserve memory and benefit from provider prompt caching. This requires a Responses-compatible backend in local mode. + +**Use the Anthropic Messages format:** ```python -client.chat("What changed in the risk factors?", doc_id=doc_id) # β†’ answer string -client.chat(question, doc_id=doc_id, stream=True) # β†’ text chunks -client.chat_completions(messages, doc_id=doc_id) # OpenAI Chat Completions envelope -client.responses("...", doc_id=doc_id, reasoning={"effort": "high"}) # OpenAI Responses (agentic) -client.messages("...", model="claude-sonnet-4-6", doc_id=doc_id) # Anthropic Messages +client.messages("...", model="claude-sonnet-4-6", doc_id=doc_id) ``` -- **`chat`** β€” sugar over `chat_completions`: pass a string or a role/content history, get the answer back. -- **`chat_completions`** β€” the same engine with the full envelope: token usage, streaming metadata, `finish_reason`. -- **`responses`** β€” the agentic surface. The whole process transcript (including tool outputs) rides in `items`; append it to your next call's `input` to keep the agent's memory and the provider's prompt cache warm. Local mode, Responses-capable backends. -- **`messages`** β€” Claude-native, via the Anthropic SDK's tool runner (`pip install 'pageindex[anthropic]'`). Local mode. +Uses Anthropic's native Messages API and tool runner. Install it with `pip install 'pageindex[anthropic]'`. Pass a list of ids to `doc_id` to search several documents at once, and keep it identical across a conversation's calls. -## πŸ€– Step 3: Put PageIndex inside your own agent +## πŸ€– Integrate PageIndex with your own agent Instead of calling PageIndex's agent, hand PageIndex's tools to yours. One call fills every slot: +**OpenAI Agents SDK:** + ```python -# OpenAI Agents SDK from agents import Agent, Runner + agent = Agent(**client.openai_agent_config(doc_id=doc_id)) result = Runner.run_sync(agent, "Summarize the auditor's concerns.") +``` + +`openai_agent_config()` provides the instructions and tools required by an OpenAI agent. + +**Anthropic SDK tool runner:** -# Anthropic SDK tool runner (pip install 'pageindex[anthropic]') +```python runner = anthropic_client.beta.messages.tool_runner( **client.anthropic_runner_config(model="claude-sonnet-4-6", doc_id=doc_id), messages=[{"role": "user", "content": "Summarize the auditor's concerns."}], ) +``` + +`anthropic_runner_config()` configures Anthropic's native tool runner. Install the integration with `pip install 'pageindex[anthropic]'`. -# Claude Agent SDK (pip install 'pageindex[claude]') +**Claude Agent SDK:** + +```python options = ClaudeAgentOptions(**client.claude_agent_config(doc_id=doc_id)) +``` + +`claude_agent_config()` creates the options for the Claude Agent SDK. Install the integration with `pip install 'pageindex[claude]'`. + +**Other agent frameworks:** -# Anything else β€” plain Python functions for LangChain, PydanticAI, ... +```python tools = client.agent_tools() ``` +`agent_tools()` returns plain Python functions that work with LangChain, PydanticAI, and other agent frameworks. + Each `*_config` helper is sugar over the explicit pieces β€” `client.agent_instructions()` for the system prompt and `client.as_openai_tools()` / `as_anthropic_tools()` / `as_claude_mcp()` for the tools β€” so you can swap in your own prompt whenever you need to. Locally, `doc_id` is enforced at the tool layer, not just prompted: out-of-scope lookups return `NOT_FOUND`. +
-```bash -python3 examples/agentic_vectorless_rag_demo.py -``` +# PageIndex Cloud -## ☁️ Local or Cloud +Same client, same methods β€” pass an [API key](https://dash.pageindex.ai/api-keys) and the work happens on our servers, with the production OCR, tree-building, and retrieval pipeline behind it: + +```python +client = PageIndexClient(api_key="pi-...") +doc_id = client.submit_document("report.pdf", wait=True)["doc_id"] +print(client.chat("What was the 2023 operating margin?", doc_id=doc_id)) +``` -The same `PageIndexClient` runs both. Omit `api_key` for local, pass one for cloud. | | **Local** (this repo) | **Cloud** ([API key](https://dash.pageindex.ai/api-keys)) | |---|---|---| @@ -324,59 +348,16 @@ The same `PageIndexClient` runs both. Omit `api_key` for local, pass one for clo | Citations & references | page-level | line-level | | Extras | β€” | folders, hosted search, MCP server | -`PageIndexCloudClient` / `PageIndexLocalClient` pin the mode explicitly if you would rather not infer it from `api_key`. - -The cloud service is also available as a ChatGPT-style [chat platform](https://chat.pageindex.ai), or via [MCP](https://pageindex.ai/developer) and [API](https://pageindex.ai/developer). For dedicated or private deployment (VPC, on-prem), [contact us](https://ii2abc2jejf.typeform.com/to/gVv7qkaN) or [book a demo](https://calendly.com/pageindex/meet). - +For dedicated or private deployment (VPC, on-prem), [contact us](https://ii2abc2jejf.typeform.com/to/gVv7qkaN) or [book a demo](https://calendly.com/pageindex/meet). -# πŸ“Š Benchmarks -### What indexing costs - -Building a tree locally runs **about $0.001 per page** with `index_model="gpt-5.6-luna"` β€” so a 1,000-page textbook costs a little over a dollar and a few minutes, once, and every later question reuses it. Measured over nine PDFs, from a 9-page whitepaper to a 1,098-page textbook, 2,800 pages in total. - -**The index model is not the bottleneck.** PageIndex is designed not to rely heavily on the model used at index time, so in our experiments a cheap model does not hurt quality. - - - - Indexing cost against document length, log-log, for nine PDFs from 9 to 1,098 pages. Points track a $0.0011-per-page reference line; the spread around it is text density, not length. - - - - -### Open-source PageIndex, running locally - -[**PageIndex-OSS-Benchmark**](https://github.com/VectifyAI/PageIndex-OSS-Benchmark) measures exactly the setup in the quickstart above β€” `PageIndexClient()` in local mode, flash indexing, no OCR β€” on 62 lookup questions over 34 PDFs (1,945 pages) drawn from [MMLongBench-Doc-V2](https://github.com/VectifyAI/MMLongBench-Doc-V2). Every question's answer is a fact stated in running text, so a wrong answer is a **retrieval or reading failure**, not a reasoning one. - - - - Accuracy against average cost per question. Each model forms a near-vertical reasoning-effort ladder; moving between models costs an order of magnitude a step. - - - -Full results, data, and the runner are in the [benchmark repo](https://github.com/VectifyAI/PageIndex-OSS-Benchmark). - -### PageIndex leads a finance QA benchmark - -Mafin 2.5, a reasoning-based RAG system for financial document analysis powered by PageIndex, reached a state-of-the-art [**98.7% accuracy**](https://vectify.ai/blog/Mafin2.5) on [FinanceBench](https://arxiv.org/abs/2311.11944), far ahead of vector-based RAG systems on SEC filings and earnings disclosures. - -
- - - -
- -Explore the full [benchmark results](https://github.com/VectifyAI/Mafin2.5-FinanceBench) and the [blog post](https://vectify.ai/blog/Mafin2.5). # 🧭 Resources * πŸ“ [Blog](https://pageindex.ai/blog): technical articles, research insights, and product updates. * πŸ”§ [Developer](https://pageindex.ai/developer): MCP setup, API docs, and integration guides. -* πŸ§ͺ [Cookbooks](https://docs.pageindex.ai/cookbook): hands-on, runnable examples and advanced use cases β€” try the [Vectorless RAG](https://colab.research.google.com/github/VectifyAI/PageIndex/blob/main/cookbook/pageindex_RAG_simple.ipynb) and the OCR-free, vision-based [Vision RAG](https://colab.research.google.com/github/VectifyAI/PageIndex/blob/main/cookbook/vision_RAG_pageindex.ipynb) notebooks in Colab. -* πŸ“– [Tutorials](https://docs.pageindex.ai/tutorials): practical guides and strategies, including *Document Search* and *Tree Search*. - --- # ⭐ Support Us From 4a3347bacdc84be8191c93f6d50d97a1ce3c719d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=BC=A0=E9=B8=A3=E5=A4=A9?= Date: Thu, 20 Aug 2026 05:36:58 +0100 Subject: [PATCH 02/25] improve readme --- README.md | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 186341934..93f9d25cb 100644 --- a/README.md +++ b/README.md @@ -107,9 +107,7 @@ print(answer) ### Indexing cost -Building a tree locally runs **about $0.001 per page** with `index_model="gpt-5.6-luna"` β€” so a 1,000-page textbook costs a little over a dollar and a few minutes, once, and every later question reuses it. - -**The index model is not the bottleneck.** PageIndex is designed not to rely heavily on the model used at index time, so in our experiments a cheap model does not hurt quality. +Building a tree locally runs **about $0.001 per page** with `index_model="gpt-5.6-luna"` β€” so a 1,000-page textbook costs a little over a dollar and a few minutes, once, and every later question reuses it. PageIndex is designed not to rely heavily on the model used at index time, so in our experiments a basic model does not hurt quality. @@ -118,7 +116,7 @@ Building a tree locally runs **about $0.001 per page** with `index_model="gpt-5. -### Query Cost and Accuracy +### Query cost and accuracy [**PageIndex-OSS-Benchmark**](https://github.com/VectifyAI/PageIndex-OSS-Benchmark) measures exactly the setup in the quickstart above β€” `PageIndexClient()` in local mode, flash indexing, no OCR β€” on 62 lookup questions over 34 PDFs (1,945 pages) drawn from [MMLongBench-Doc-V2](https://github.com/VectifyAI/MMLongBench-Doc-V2). Every question's answer is a fact stated in running text, so a wrong answer is a **retrieval or reading failure**, not a reasoning one. From 315e2920d12d4dd5f7633e4e79ca3c56af6b04ad Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=BC=A0=E9=B8=A3=E5=A4=A9?= Date: Thu, 20 Aug 2026 05:53:34 +0100 Subject: [PATCH 03/25] improve readme --- README.md | 29 +++++++++++++---------------- 1 file changed, 13 insertions(+), 16 deletions(-) diff --git a/README.md b/README.md index 93f9d25cb..271e74a00 100644 --- a/README.md +++ b/README.md @@ -100,7 +100,7 @@ print(answer) ### Model Recommendations - **`index_model` β€” a basic model is sufficient.** The index model generates the document's tree index. A basic model is sufficient to produce a good tree structure. -- **`chat_model` β€” use the best model you can afford.** The chat model searches the tree to retrieve information. See [Benchmarks](#-benchmarks) for the trade-off between cost and accuracy. +- **`chat_model` β€” use the best model you can afford.** The chat model searches the tree to retrieve information. See [Query cost and accuracy](#query-cost-and-accuracy). # Benchmarks @@ -326,7 +326,9 @@ Each `*_config` helper is sugar over the explicit pieces β€” `client.agent_instr # PageIndex Cloud -Same client, same methods β€” pass an [API key](https://dash.pageindex.ai/api-keys) and the work happens on our servers, with the production OCR, tree-building, and retrieval pipeline behind it: +The open-source version is designed for text-heavy PDFs. For scanned documents or PDFs with many images, use PageIndex Cloud. + +Same client, same methods β€” pass a PageIndex [API key](https://dash.pageindex.ai/api-keys) and the work happens on our servers, with the production OCR, tree-building, and retrieval pipeline behind it: ```python client = PageIndexClient(api_key="pi-...") @@ -334,28 +336,23 @@ doc_id = client.submit_document("report.pdf", wait=True)["doc_id"] print(client.chat("What was the 2023 operating margin?", doc_id=doc_id)) ``` - | | **Local** (this repo) | **Cloud** ([API key](https://dash.pageindex.ai/api-keys)) | |---|---|---| -| Parsing | standard PDF text extraction | [PageIndex OCR](https://pageindex.ai/blog/ocr) β€” built to preserve document hierarchy | -| Scanned / image-only PDFs | not supported | supported | -| LLM | yours β€” bring a provider key (`OPENAI_API_KEY`, …) and pay that provider | managed, included with your PageIndex key | -| Storage | `./.pageindex` on disk | hosted library, folders, search | -| Where data goes | never leaves your machine | PageIndex Cloud | -| Image retrieval & understanding | not supported β€” text layer only | supported | +| Parsing | text extraction | hosted OCR | +| Data Storage | local | cloud | | Citations & references | page-level | line-level | -| Extras | β€” | folders, hosted search, MCP server | - -For dedicated or private deployment (VPC, on-prem), [contact us](https://ii2abc2jejf.typeform.com/to/gVv7qkaN) or [book a demo](https://calendly.com/pageindex/meet). - +| Image retrieval & understanding | β€” | βœ… | +| File system | β€” | βœ… | +| MCP server | β€” | βœ… | +- [Scale PageIndex to Millions of Documents](https://pageindex.ai/blog/pageindex-filesystem) β€” **PageIndex File System** is a Cloud-only, file-level tree indexing layer that lets PageIndex reason over an entire corpus, not just a single document. +- [Developer Dashboard](https://developer.pageindex.ai/) β€” manage your API keys and projects. +- [PageIndex Cloud documentation](https://docs.vectify.ai/) β€” explore API guides and reference documentation. +For dedicated or private deployment (VPC, on-prem), [contact us](https://ii2abc2jejf.typeform.com/to/gVv7qkaN) or [book a demo](https://calendly.com/pageindex/meet). -# 🧭 Resources -* πŸ“ [Blog](https://pageindex.ai/blog): technical articles, research insights, and product updates. -* πŸ”§ [Developer](https://pageindex.ai/developer): MCP setup, API docs, and integration guides. --- # ⭐ Support Us From cb12a06bca4ebe3854fe3d122b2f953830c970ec Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=BC=A0=E9=B8=A3=E5=A4=A9?= Date: Thu, 20 Aug 2026 05:55:12 +0100 Subject: [PATCH 04/25] improve readme --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 271e74a00..bfed7400e 100644 --- a/README.md +++ b/README.md @@ -31,8 +31,8 @@

Updates

 -- πŸ”₯ [**PageIndex SDK**](#-quickstart) β€” `pip install pageindex` now ships **local mode**: index, retrieve, and chat entirely on your machine with your own LLM key, or point the same client at PageIndex Cloud with an API key. -- ⚑ [**PageIndex Flash**](#-step-1-build-the-tree-index) β€” tree structure generation from PDFs in seconds, with structure extracted heuristically instead of by an LLM. +- [2026/08] πŸ”₯ [**PageIndex SDK**](#-quickstart) β€” `pip install pageindex` now ships **local mode**: index, retrieve, and chat entirely on your machine with your own LLM key, or point the same client at PageIndex Cloud with an API key. +- [2026/08] ⚑ [**PageIndex Flash**](#-step-1-build-the-tree-index) β€” tree structure generation from PDFs in seconds, with structure extracted heuristically instead of by an LLM. - [PageIndex Chat](https://app.pageindex.ai) β€” Human-like document analysis agent [platform](https://chat.pageindex.ai) for professional long documents. Also available via [MCP](https://pageindex.ai/developer) or [API](https://pageindex.ai/developer).
From a4abe78e64b1aff214512fc54e005376e024cc7b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=BC=A0=E9=B8=A3=E5=A4=A9?= Date: Thu, 20 Aug 2026 05:59:34 +0100 Subject: [PATCH 05/25] improve readme --- README.md | 55 ++++++++++++++++++++++++++++++------------------------- 1 file changed, 30 insertions(+), 25 deletions(-) diff --git a/README.md b/README.md index bfed7400e..b3ed8abbf 100644 --- a/README.md +++ b/README.md @@ -31,22 +31,22 @@

Updates

 -- [2026/08] πŸ”₯ [**PageIndex SDK**](#-quickstart) β€” `pip install pageindex` now ships **local mode**: index, retrieve, and chat entirely on your machine with your own LLM key, or point the same client at PageIndex Cloud with an API key. -- [2026/08] ⚑ [**PageIndex Flash**](#-step-1-build-the-tree-index) β€” tree structure generation from PDFs in seconds, with structure extracted heuristically instead of by an LLM. -- [PageIndex Chat](https://app.pageindex.ai) β€” Human-like document analysis agent [platform](https://chat.pageindex.ai) for professional long documents. Also available via [MCP](https://pageindex.ai/developer) or [API](https://pageindex.ai/developer). +- [2026/08] πŸ”₯ [**PageIndex SDK**](#quickstart) β€” `pip install -U pageindex` now ships **local mode**: index, retrieve, and chat entirely on your machine with your own LLM key, or point the same client at PageIndex Cloud with an API key. +- [2026/08] ⚑ [**PageIndex Flash**](#step-2-build-the-tree-index) β€” tree structure generation from PDFs in seconds, with structure extracted heuristically instead of by an LLM. +- [PageIndex Chat](https://chat.pageindex.ai) β€” a human-like document analysis agent for long professional documents. Also available via [MCP](https://pageindex.ai/developer) or [API](https://pageindex.ai/developer).
-# What is PageIndex? +## What is PageIndex? Are you frustrated with vector database retrieval accuracy for long professional documents? Vector-based RAG retrieves by semantic **similarity**. But **similarity β‰  relevance** β€” what retrieval actually needs is relevance, and relevance requires **reasoning**. On professional documents that demand contextual understanding, domain expertise, and multi-step reasoning, similarity search misses what is relevant but not similar, and returns what is similar but not relevant. Inspired by AlphaGo, **[PageIndex](https://vectify.ai/pageindex)** replaces the vector index with a **hierarchical tree index** and lets an LLM **reason** its way through it β€” the way a human expert flips to the right section of a long report. Retrieval happens in two steps: -1. **Index** β€” generate **tree structure** index for each document -2. **Retrieve** β€” retrieve infomation via LLM-based **tree search** +1. **Index** β€” generate a **tree-structure index** for each document +2. **Retrieve** β€” retrieve information via LLM-based **tree search**
@@ -67,11 +67,11 @@ Inspired by AlphaGo, **[PageIndex](https://vectify.ai/pageindex)** replaces the It is ideal for financial reports, legal documents, regulatory filings, technical manuals, medical literature, academic textbooks β€” any long, complex professional document. -> PageIndex achieved **state-of-the-art** [98.7% accuracy](https://github.com/VectifyAI/Mafin2.5-FinanceBench) on FinanceBench (financial document QA benchmark), vastly outperforming vector-based RAG β€” see [Benchmarks](#-benchmarks). +> PageIndex achieved **state-of-the-art** [98.7% accuracy](https://github.com/VectifyAI/Mafin2.5-FinanceBench) on FinanceBench (financial document QA benchmark), vastly outperforming vector-based RAG β€” see [Benchmarks](#benchmarks). -# Quickstart +## Quickstart ```bash pip install -U pageindex @@ -81,7 +81,6 @@ pip install -U pageindex ```python import os from pageindex import PageIndexClient -import os os.environ["OPENAI_API_KEY"] = "your-openai-key" @@ -102,8 +101,10 @@ print(answer) - **`index_model` β€” a basic model is sufficient.** The index model generates the document's tree index. A basic model is sufficient to produce a good tree structure. - **`chat_model` β€” use the best model you can afford.** The chat model searches the tree to retrieve information. See [Query cost and accuracy](#query-cost-and-accuracy). +See the [Detailed Usage Guide](#detailed-usage-guide) to configure other models and integrate PageIndex with your own agent. + -# Benchmarks +## Benchmarks ### Indexing cost @@ -131,17 +132,19 @@ Full results, data, and the runner are in the [benchmark repo](https://github.co +
-

Detailed Usage Guide

 +

Detailed Usage Guide
-## βš™οΈ Step 1: Initialize the client +### βš™οΈ Step 1: Initialize the client Create a local client and choose the models used for indexing and retrieval: ```python from pageindex import PageIndexClient +import os client = PageIndexClient( index_model="gpt-5.6-luna", @@ -156,7 +159,7 @@ client = PageIndexClient( - **`storage_path`** specifies where indexed documents are stored locally. -### Model naming conventions +#### Model naming conventions Model names follow [LiteLLM's naming convention](https://docs.litellm.ai/docs/providers). Choose the format that matches your provider: @@ -164,27 +167,29 @@ Model names follow [LiteLLM's naming convention](https://docs.litellm.ai/docs/pr ```python os.environ["OPENAI_API_KEY"] = "your-openai-api-key" -chat_model="gpt-5.6-sol" +chat_model = "gpt-5.6-sol" ``` **Anthropic** β€” prefix the model name with `anthropic/` and set `ANTHROPIC_API_KEY`: ```python os.environ["ANTHROPIC_API_KEY"] = "your-anthropic-api-key" -chat_model="anthropic/claude-sonnet-4-6" +chat_model = "anthropic/claude-sonnet-4-6" ``` **OpenRouter** β€” prefix the provider and model name with `openrouter/` and set `OPENROUTER_API_KEY`: ```python os.environ["OPENROUTER_API_KEY"] = "your-openrouter-api-key" -chat_model="openrouter/anthropic/claude-sonnet-4-6" +chat_model = "openrouter/anthropic/claude-sonnet-4-6" ``` For model names and API key settings for other providers, see the [LiteLLM provider documentation](https://docs.litellm.ai/docs/providers). -## 🌲 Step 2: Build the tree index + + +### 🌲 Step 2: Build the tree index `submit_document` defaults to **Flash** indexing: the structure is extracted from the PDF's own layout (no LLM), and a model is called only for node summaries and the tree-optimization expansion pass. It takes seconds. @@ -232,7 +237,7 @@ See more example [documents](https://github.com/VectifyAI/PageIndex/tree/main/ex -## πŸ’¬ Step 3: Ask questions +### πŸ’¬ Step 3: Ask questions `chat()` is the one-line surface. Underneath it is a document-QA agent, and you can talk to it over whichever protocol your stack already speaks: @@ -278,7 +283,7 @@ Uses Anthropic's native Messages API and tool runner. Install it with `pip insta Pass a list of ids to `doc_id` to search several documents at once, and keep it identical across a conversation's calls. -## πŸ€– Integrate PageIndex with your own agent +### πŸ€– Integrate PageIndex with your own agent Instead of calling PageIndex's agent, hand PageIndex's tools to yours. One call fills every slot: @@ -324,11 +329,11 @@ Each `*_config` helper is sugar over the explicit pieces β€” `client.agent_instr
-# PageIndex Cloud +## PageIndex Cloud The open-source version is designed for text-heavy PDFs. For scanned documents or PDFs with many images, use PageIndex Cloud. -Same client, same methods β€” pass a PageIndex [API key](https://dash.pageindex.ai/api-keys) and the work happens on our servers, with the production OCR, tree-building, and retrieval pipeline behind it: +Same client, same methods β€” pass a [PageIndex API key](https://dash.pageindex.ai/api-keys) and the work happens on our servers, with the production OCR, tree-building, and retrieval pipeline behind it: ```python client = PageIndexClient(api_key="pi-...") @@ -339,15 +344,15 @@ print(client.chat("What was the 2023 operating margin?", doc_id=doc_id)) | | **Local** (this repo) | **Cloud** ([API key](https://dash.pageindex.ai/api-keys)) | |---|---|---| | Parsing | text extraction | hosted OCR | -| Data Storage | local | cloud | +| Data storage | local | cloud | | Citations & references | page-level | line-level | | Image retrieval & understanding | β€” | βœ… | -| File system | β€” | βœ… | +| PageIndex File System | β€” | βœ… | | MCP server | β€” | βœ… | - [Scale PageIndex to Millions of Documents](https://pageindex.ai/blog/pageindex-filesystem) β€” **PageIndex File System** is a Cloud-only, file-level tree indexing layer that lets PageIndex reason over an entire corpus, not just a single document. - [Developer Dashboard](https://developer.pageindex.ai/) β€” manage your API keys and projects. -- [PageIndex Cloud documentation](https://docs.vectify.ai/) β€” explore API guides and reference documentation. +- [PageIndex Cloud documentation](https://docs.pageindex.ai/) β€” explore API guides and reference documentation. For dedicated or private deployment (VPC, on-prem), [contact us](https://ii2abc2jejf.typeform.com/to/gVv7qkaN) or [book a demo](https://calendly.com/pageindex/meet). @@ -355,7 +360,7 @@ For dedicated or private deployment (VPC, on-prem), [contact us](https://ii2abc2 --- -# ⭐ Support Us +## ⭐ Support Us Leave us a star 🌟 if you like our project. Thank you! From 523ef0845f161abffd28e927a249d6c23793a56e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=BC=A0=E9=B8=A3=E5=A4=A9?= Date: Thu, 20 Aug 2026 06:01:16 +0100 Subject: [PATCH 06/25] improve readme --- README.md | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index b3ed8abbf..258d66adb 100644 --- a/README.md +++ b/README.md @@ -134,7 +134,11 @@ Full results, data, and the runner are in the [benchmark repo](https://github.co
-

Detailed Usage Guide

 + + +# Detailed Usage Guide + +
From 8cf55f923b03937a1a9f742573c576400dab181f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=BC=A0=E9=B8=A3=E5=A4=A9?= Date: Thu, 20 Aug 2026 06:03:17 +0100 Subject: [PATCH 07/25] improve readme --- README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 258d66adb..5c394f404 100644 --- a/README.md +++ b/README.md @@ -333,7 +333,7 @@ Each `*_config` helper is sugar over the explicit pieces β€” `client.agent_instr
-## PageIndex Cloud +# PageIndex Cloud The open-source version is designed for text-heavy PDFs. For scanned documents or PDFs with many images, use PageIndex Cloud. @@ -354,6 +354,8 @@ print(client.chat("What was the 2023 operating margin?", doc_id=doc_id)) | PageIndex File System | β€” | βœ… | | MCP server | β€” | βœ… | +### More About PageIndex Cloud + - [Scale PageIndex to Millions of Documents](https://pageindex.ai/blog/pageindex-filesystem) β€” **PageIndex File System** is a Cloud-only, file-level tree indexing layer that lets PageIndex reason over an entire corpus, not just a single document. - [Developer Dashboard](https://developer.pageindex.ai/) β€” manage your API keys and projects. - [PageIndex Cloud documentation](https://docs.pageindex.ai/) β€” explore API guides and reference documentation. From c8a33b540ce2397fb9a8e3b5726564215e0908fa Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=BC=A0=E9=B8=A3=E5=A4=A9?= Date: Thu, 20 Aug 2026 06:04:10 +0100 Subject: [PATCH 08/25] improve readme --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 5c394f404..97ff5436c 100644 --- a/README.md +++ b/README.md @@ -136,7 +136,7 @@ Full results, data, and the runner are in the [benchmark repo](https://github.co
-# Detailed Usage Guide +## Detailed Usage Guide @@ -333,7 +333,7 @@ Each `*_config` helper is sugar over the explicit pieces β€” `client.agent_instr
-# PageIndex Cloud +## PageIndex Cloud The open-source version is designed for text-heavy PDFs. For scanned documents or PDFs with many images, use PageIndex Cloud. From 747e05017ff1e84183ef884663e5104c495e19ea Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=BC=A0=E9=B8=A3=E5=A4=A9?= Date: Thu, 20 Aug 2026 09:48:11 +0100 Subject: [PATCH 09/25] docs: clarify PageIndex README copy --- README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 97ff5436c..0be4f2745 100644 --- a/README.md +++ b/README.md @@ -41,7 +41,7 @@ ## What is PageIndex? -Are you frustrated with vector database retrieval accuracy for long professional documents? Vector-based RAG retrieves by semantic **similarity**. But **similarity β‰  relevance** β€” what retrieval actually needs is relevance, and relevance requires **reasoning**. On professional documents that demand contextual understanding, domain expertise, and multi-step reasoning, similarity search misses what is relevant but not similar, and returns what is similar but not relevant. +Are you frustrated with vector database retrieval accuracy for long and complex documents? Vector-based RAG retrieves by semantic **similarity**. But **similarity β‰  relevance** β€” what retrieval actually needs is relevance, and relevance requires **reasoning**. On professional documents that demand contextual understanding, domain expertise, and multi-step reasoning, similarity search misses what is relevant but not similar, and returns what is similar but not relevant. Inspired by AlphaGo, **[PageIndex](https://vectify.ai/pageindex)** replaces the vector index with a **hierarchical tree index** and lets an LLM **reason** its way through it β€” the way a human expert flips to the right section of a long report. Retrieval happens in two steps: @@ -59,10 +59,10 @@ Inspired by AlphaGo, **[PageIndex](https://vectify.ai/pageindex)** replaces the | | Vector RAG | **PageIndex** | |---|---|---| -| **Index** | embeddings in a vector DB | tree structure of the document itself | +| **Index** | vector index | tree index | | **Unit** | fixed-size chunks | natural sections | -| **Retrieval** | approximate similarity search | LLM reasoning over the tree | -| **Result** | opaque, β€œvibe retrieval” | traceable to explicit sections and page numbers | +| **Retrieval** | semantic similarity search | LLM-based relevance search | +| **Result** | opaque, β€œvibe retrieval” | traceable to explicit references | | **Context** | query embedding only | full context: conversation history, domain knowledge | It is ideal for financial reports, legal documents, regulatory filings, technical manuals, medical literature, academic textbooks β€” any long, complex professional document. From 69f1c59f9d065ee6e5933498f212fd95171ac016 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=BC=A0=E9=B8=A3=E5=A4=A9?= Date: Thu, 20 Aug 2026 11:46:03 +0100 Subject: [PATCH 10/25] improve readme --- README.md | 32 +++++++++++++++++++++----------- 1 file changed, 21 insertions(+), 11 deletions(-) diff --git a/README.md b/README.md index 0be4f2745..a52d30ae6 100644 --- a/README.md +++ b/README.md @@ -335,32 +335,42 @@ Each `*_config` helper is sugar over the explicit pieces β€” `client.agent_instr ## PageIndex Cloud -The open-source version is designed for text-heavy PDFs. For scanned documents or PDFs with many images, use PageIndex Cloud. +The open-source version is ideal for text-heavy PDFs and local workflows. Use PageIndex Cloud when you need OCR for scanned documents, image understanding, hosted storage, or search across a large document library. -Same client, same methods β€” pass a [PageIndex API key](https://dash.pageindex.ai/api-keys) and the work happens on our servers, with the production OCR, tree-building, and retrieval pipeline behind it: +Moving from Local to Cloud only requires a [PageIndex API key](https://developer.pageindex.ai/): ```python +# Local +client = PageIndexClient() + +# Cloud client = PageIndexClient(api_key="pi-...") + +# The rest of your code stays the same doc_id = client.submit_document("report.pdf", wait=True)["doc_id"] print(client.chat("What was the 2023 operating margin?", doc_id=doc_id)) ``` -| | **Local** (this repo) | **Cloud** ([API key](https://dash.pageindex.ai/api-keys)) | +| Capability | **Local** (this repo) | **Cloud** ([get an API key](https://developer.pageindex.ai/)) | |---|---|---| -| Parsing | text extraction | hosted OCR | -| Data storage | local | cloud | -| Citations & references | page-level | line-level | -| Image retrieval & understanding | β€” | βœ… | -| PageIndex File System | β€” | βœ… | +| Best for | text-heavy PDFs and local workflows | scanned, image-heavy, and large document collections | +| Parsing | local text extraction | production OCR | +| Storage | local filesystem | managed document library | +| Citations | page-level | line-level | +| Image understanding | β€” | βœ… | +| Multi-document scale | manual | PageIndex File System | | MCP server | β€” | βœ… | ### More About PageIndex Cloud - [Scale PageIndex to Millions of Documents](https://pageindex.ai/blog/pageindex-filesystem) β€” **PageIndex File System** is a Cloud-only, file-level tree indexing layer that lets PageIndex reason over an entire corpus, not just a single document. -- [Developer Dashboard](https://developer.pageindex.ai/) β€” manage your API keys and projects. -- [PageIndex Cloud documentation](https://docs.pageindex.ai/) β€” explore API guides and reference documentation. -For dedicated or private deployment (VPC, on-prem), [contact us](https://ii2abc2jejf.typeform.com/to/gVv7qkaN) or [book a demo](https://calendly.com/pageindex/meet). +### Ready to Try It? + +- [Get a PageIndex API Key](https://developer.pageindex.ai/) +- [Read the PageIndex Cloud Documentation](https://docs.pageindex.ai/) + +For dedicated deployment (VPC or on-premises), [contact us](https://ii2abc2jejf.typeform.com/to/gVv7qkaN) or [book a demo](https://calendly.com/pageindex/meet). From 2c95224d15414286d16c78a2735d1ad41fa757dc Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=BC=A0=E9=B8=A3=E5=A4=A9?= Date: Thu, 20 Aug 2026 21:39:55 +0100 Subject: [PATCH 11/25] improve readme --- README.md | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index a52d30ae6..04c0256d5 100644 --- a/README.md +++ b/README.md @@ -287,7 +287,16 @@ Uses Anthropic's native Messages API and tool runner. Install it with `pip insta Pass a list of ids to `doc_id` to search several documents at once, and keep it identical across a conversation's calls. -### πŸ€– Integrate PageIndex with your own agent + + +
+ + +## Integrate PageIndex with your own agent + + + +
Instead of calling PageIndex's agent, hand PageIndex's tools to yours. One call fills every slot: @@ -330,6 +339,7 @@ tools = client.agent_tools() `agent_tools()` returns plain Python functions that work with LangChain, PydanticAI, and other agent frameworks. Each `*_config` helper is sugar over the explicit pieces β€” `client.agent_instructions()` for the system prompt and `client.as_openai_tools()` / `as_anthropic_tools()` / `as_claude_mcp()` for the tools β€” so you can swap in your own prompt whenever you need to. Locally, `doc_id` is enforced at the tool layer, not just prompted: out-of-scope lookups return `NOT_FOUND`. +
From 720c2faf6b468564ad5f2d889fc5fc00faffc7a4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=BC=A0=E9=B8=A3=E5=A4=A9?= Date: Wed, 26 Aug 2026 09:42:36 +0100 Subject: [PATCH 12/25] fix more --- README.md | 27 +++++++++++++++++---------- 1 file changed, 17 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index 04c0256d5..7927a17a6 100644 --- a/README.md +++ b/README.md @@ -85,8 +85,8 @@ from pageindex import PageIndexClient os.environ["OPENAI_API_KEY"] = "your-openai-key" client = PageIndexClient( - index_model="gpt-5.6-luna", # model to build the tree index - chat_model="gpt-5.6-sol", # model to search the tree + index="gpt-5.6-luna", # model to build the tree index + chat="gpt-5.6-sol", # model to search the tree ) doc_id = client.submit_document("report.pdf")["doc_id"] @@ -345,16 +345,22 @@ Each `*_config` helper is sugar over the explicit pieces β€” `client.agent_instr ## PageIndex Cloud -The open-source version is ideal for text-heavy PDFs and local workflows. Use PageIndex Cloud when you need OCR for scanned documents, image understanding, hosted storage, or search across a large document library. +The open-source version is ideal for text-heavy PDFs and local workflows. With **PageIndex Cloud, document indexing and storage run in the cloud**: PageIndex handles parsing, OCR, image understanding, tree-index construction, and managed storage for you. The chat and retrieval layer remains **compatible with your model**, so you can search the cloud-hosted index using the model provider your application already uses. -Moving from Local to Cloud only requires a [PageIndex API key](https://developer.pageindex.ai/): +Moving indexing and storage from Local to Cloud only requires a [PageIndex API key](https://developer.pageindex.ai/): ```python -# Local -client = PageIndexClient() +import os +from pageindex import PageIndexClient + +os.environ["PageIndex_API_KEY"] = "your-pageindex-key" +os.environ["OPENAI_API_KEY"] = "your-openai-key" -# Cloud -client = PageIndexClient(api_key="pi-...") + +client = PageIndexClient( + index="cloud", # build and store the index in PageIndex Cloud + chat="gpt-5.6-sol", # use your preferred compatible model for chat +) # The rest of your code stays the same doc_id = client.submit_document("report.pdf", wait=True)["doc_id"] @@ -364,8 +370,9 @@ print(client.chat("What was the 2023 operating margin?", doc_id=doc_id)) | Capability | **Local** (this repo) | **Cloud** ([get an API key](https://developer.pageindex.ai/)) | |---|---|---| | Best for | text-heavy PDFs and local workflows | scanned, image-heavy, and large document collections | -| Parsing | local text extraction | production OCR | -| Storage | local filesystem | managed document library | +| Indexing | runs locally | runs in PageIndex Cloud, with production OCR and image understanding | +| Storage | local | managed in PageIndex Cloud | +| Chat model | your model | your compatible model | | Citations | page-level | line-level | | Image understanding | β€” | βœ… | | Multi-document scale | manual | PageIndex File System | From cac4a0336104dd67fb931832ed63f8351db5a36b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=BC=A0=E9=B8=A3=E5=A4=A9?= Date: Wed, 26 Aug 2026 10:31:27 +0100 Subject: [PATCH 13/25] fix more --- README.md | 9 ++++++++- assets/index-time-dark.png | Bin 0 -> 140246 bytes assets/index-time-light.png | Bin 0 -> 135104 bytes 3 files changed, 8 insertions(+), 1 deletion(-) create mode 100644 assets/index-time-dark.png create mode 100644 assets/index-time-light.png diff --git a/README.md b/README.md index 7927a17a6..7b64935ea 100644 --- a/README.md +++ b/README.md @@ -106,7 +106,7 @@ See the [Detailed Usage Guide](#detailed-usage-guide) to configure other models ## Benchmarks -### Indexing cost +### Indexing cost and time Building a tree locally runs **about $0.001 per page** with `index_model="gpt-5.6-luna"` β€” so a 1,000-page textbook costs a little over a dollar and a few minutes, once, and every later question reuses it. PageIndex is designed not to rely heavily on the model used at index time, so in our experiments a basic model does not hurt quality. @@ -115,6 +115,13 @@ Building a tree locally runs **about $0.001 per page** with `index_model="gpt-5. Indexing cost against document length, log-log, for nine PDFs from 9 to 1,098 pages. Points track a $0.0011-per-page reference line; the spread around it is text density, not length. +Indexing time also scales predictably with document length. In the same local setup, the benchmark documentsβ€”from 9 to 1,098 pagesβ€”finished in roughly **13 seconds to 4.5 minutes**. + + + + Indexing time against document length, log-log, for nine PDFs from 9 to 1,098 pages. The measured indexing times range from about 13 seconds to 4.5 minutes and increase predictably with document length. + + ### Query cost and accuracy diff --git a/assets/index-time-dark.png b/assets/index-time-dark.png new file mode 100644 index 0000000000000000000000000000000000000000..699287ef2f9183a6930c6ab1f5080720b5104b2d GIT binary patch literal 140246 zcmeEu^`<`?}OCePV>t#7UKUPDcplAMto0)bG%RTMNK5aKTo2oXCeAvjVO zM1g`3Kp=1hS#8hJwG_9{v;$M$yZ1WQhY8)6H#U^4cy16af1xESCDJI(xtp!6cSA$A zMnU5SHFdU+&*8#eve%_(ZL_(D?(Oqso8;*3S)%rd>c_9ud{3W3<5fxMDM;Qny%hNO z&+(@049EBX`v1Q}h19S9^Z0-L`F`;KdTJ9n$Nzm<)5|Y%@BaHO{{PGV`;3K9|DUr8 zPRR?7pZ{}KDRf95{;xAhEB8Mapy}oROD{W9XD1C}CzYT&qBRwuuX7iu5|Z3v|9kgs z(-~#PHuXzY|Ik<{8KlQ;LVzMXbBpUjIxrIkfduymc3w_Be@0CVM^noo@P$mL`m$)K zEcIoh>Hg(@kP2E5aZB!9LdTS&Pe*{P2Hq7fco}MQb@x(SzGwOUSDn4r5?r3o{w?o# z%W~L*UD`pJ3qNNLS?U%E?d{7G?uqNc)N+>ep-?ykQZlsaUdtIv8jrZ--j$#ymnCI! z7f0YP`Q=rqgH7nMRyf?f_D8Kw{*7tSHWld3VefG)?US4Dmul?GM?|9@Q_MD+K8i8d zMMFbP*4)L7JOVhcU6EZ1HNjx9R%qyBiuJ=&hS2v)S(iddSFof65!gsy1tb*yh>vel zFe4Losq2Ch;-Z#A(hxwlx3|9w&R=eQ{l@h5UBCkWO%P&iTPzc9pL3{W??b9 zP8HU?v3sod)e>3jUR1lWa=bspl$I#kJMDOOHCj!OOE{@>*|o-?Sf@yDu-LHnc{i)L z$GW$5#|v5NE$@Go$1-ES zhpO2#D>YF+9>o%VU`@lq!m$jY$L%xoq(MDy=hIRV>Q-&w&?r=SIpIeDSs(rCvy=ajTP>NIc)BFhGSek4E-7TOHd*g;xi3cxNB3G*1YFkUHf(z@ z_vMgKdT*t8OeP8Yi6LeO4lgCRIf5yg>n={>u!y81U#Y70dDmeA2p43iIOmn|)j^Uc zQ9c$wf=MV>hKdcxHV=>ejP}$ey3a)hSn-0o@hsBz2KC-Zg_ z|4ta`E6yy!z`qgv5+X}YSNV(@GGXDFFdR-3+E&Kg?&O~IvV6odH5aCe)JA@|I2g;< z%2ivRIGs?xdYv(JJKFt)ENSku)c!)b+UW?9anS)vvytk@<8vh)RLgc0RV zStJp+`IbhDl%b3{JS!!!^z6YD@>3HfY^{&DAepd?=WC9l3dpmhpJq3SIokikwUtD& z3K32i&8vQj=hN*k^64&r-R(Tys8b_{tYr~*e{r|OdY%}E+WFXpOp`qj>y zlqWn>MRQ2Vruw|GR15dG{$JSFj4C;usy6fqV_?|zH&<(dlz6zN^KHNHj& zfqaqy>E6X)muq^9^s7Q5ZqI70UmCkmsjm4hO)Gmvt&dfyZyME`S*9=1Hot+qtUi#I zl#F?@-}caS_UuCTEy<^QpA@Kx5qM%*m9jH7ZPMLe3h34kEStODn~j$jadBog&uA0_ zxxQxntAfNscE!R;c1>qjq%V$44>u-iJXY_u4^>iWS0QK=Z$ZK)>~DDzVFF*0uJf#q zeYG0NJT53v!wC6wN%yN;ViunAH)@v3h5SM=N;_Vja|JI%=C+?5U+YS1+-mR53W)px z;h6Kf_|4zAChhoU@i-Oy-a=h@G&CE^zRWzf>tBUsAt%1hCe@cAFFwcNDwYx`^LNNt z{mX;XgvfiERSg#>Ng4w5q$Fb%_9=8+su0D2n9li4cCV{%n>HK9{_QUsMaA0SOzW^{ z<^|CWwj#O=>g}W)nItaov45M=5VuH!(sB17Z?O5zVb`-prHAE@F1rTp#=y1e4x{o| z8%9zS8xE0y8pRv1`|E7>emXnR-sYGd9iJXE<-8`%{*T>m651>0AF)=K8!oqWBas

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zu)w<3fG#mZ0lu?dsV3Y>r-;_m4`N1(whHh70RzfBE9m8HJp-q6ZOpnJcp&wwYZhnk zBIb9Mwar|RlyaWUR%k?23T}kTN%ZvhTQUAlIoYP7q~sdSb@r^pO!XT6p2bY2;`tn7=X1`3Mg=WsVbDI*_J zG@ne|nry#!h^E22Y;7`CdG-y@zVy790_+HDU3hlig1bfazTFRJNqG1|P}U*3r2hU! zc5zvAgN{n^2T8Cqkx+Sm_!JY>e0B0xb|&#%sy(FnTok_P-g(JY^^OgHmu$Q?pN^Yk zJh*g9FN?^@JHpNJ@^o8RzDtp;>ofv2VMX{Q)B|>En&Y;&I?eV63$yx5+45JWNw~%z3opckzghZhnAc zM(?azEl%#JFZtAVC8i-`^1=M+na7E7zT(;fSGSA4+|a+fzAI8#|B*9C3zPip%#|+8 zp;Rd^?E;4AQ``$iN>7Osw=~&;xgh=~a{+TPxolTRFHV2$w!BJf`|r%bK4xG9FN$<ksBq7Ab3X2Qm-!hcLNg#Y}a)E;f6qfTN573#nD1LxLygN z?aW#-4I}#*dwRKk;<32zqCB>nq&@VKHqUfp$MfV{*>+Rgh5Z(Qe>ZjcDcZM8D(&RJ z3;*z4Ae8*@!m#Ce?SJDoknnT!5&3|Bd|dz4SM@*gd;Pz>?SDPk|Fz5QKS$k=%ELEr W Date: Wed, 26 Aug 2026 10:35:08 +0100 Subject: [PATCH 14/25] fix more --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 7b64935ea..ca2c9be16 100644 --- a/README.md +++ b/README.md @@ -379,7 +379,7 @@ print(client.chat("What was the 2023 operating margin?", doc_id=doc_id)) | Best for | text-heavy PDFs and local workflows | scanned, image-heavy, and large document collections | | Indexing | runs locally | runs in PageIndex Cloud, with production OCR and image understanding | | Storage | local | managed in PageIndex Cloud | -| Chat model | your model | your compatible model | +| Chat model | your model | your model | | Citations | page-level | line-level | | Image understanding | β€” | βœ… | | Multi-document scale | manual | PageIndex File System | From 52c72fb31f4f3c1095562f6888c454637545aefc Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=BC=A0=E9=B8=A3=E5=A4=A9?= Date: Wed, 26 Aug 2026 11:24:50 +0100 Subject: [PATCH 15/25] fix more --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index ca2c9be16..d23289351 100644 --- a/README.md +++ b/README.md @@ -360,7 +360,7 @@ Moving indexing and storage from Local to Cloud only requires a [PageIndex API k import os from pageindex import PageIndexClient -os.environ["PageIndex_API_KEY"] = "your-pageindex-key" +os.environ["PAGEINDEX_API_KEY"] = "your-pageindex-key" os.environ["OPENAI_API_KEY"] = "your-openai-key" From f874150200eeb464e453c7fbcaf7115c0283c5f6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=BC=A0=E9=B8=A3=E5=A4=A9?= Date: Wed, 26 Aug 2026 14:02:10 +0100 Subject: [PATCH 16/25] fix more --- README.md | 24 +++++++++++++++++++++++- 1 file changed, 23 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index d23289351..b87af70cd 100644 --- a/README.md +++ b/README.md @@ -95,7 +95,6 @@ answer = client.chat("What was the 2023 operating margin, and where is it stated print(answer) ``` - ### Model Recommendations - **`index_model` β€” a basic model is sufficient.** The index model generates the document's tree index. A basic model is sufficient to produce a good tree structure. @@ -103,6 +102,29 @@ print(answer) See the [Detailed Usage Guide](#detailed-usage-guide) to configure other models and integrate PageIndex with your own agent. +To request inline page-level citations, pass a system message together with the question: + +```python +messages = [ + { + "role": "system", + "content": ( + 'Cite only statements supported by tool outputs using ' + '' + ), + }, + {"role": "user", "content": "Summarize the document."}, +] + +answer = client.chat(messages, doc_id=doc_id) +``` + +The model fills in the document name and page number, for example: + +```text +Revenue increased during the reporting period. +``` + ## Benchmarks From 450a780ec64ffa8a41ddd59270d947d01e81dc7f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=BC=A0=E9=B8=A3=E5=A4=A9?= Date: Wed, 26 Aug 2026 14:03:22 +0100 Subject: [PATCH 17/25] fix more --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index b87af70cd..e470f52b5 100644 --- a/README.md +++ b/README.md @@ -102,6 +102,8 @@ print(answer) See the [Detailed Usage Guide](#detailed-usage-guide) to configure other models and integrate PageIndex with your own agent. +### Request Answers with Citations + To request inline page-level citations, pass a system message together with the question: ```python From 31d428d2eb90f934ae6b46d0a8e4227b1d48a9e8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=BC=A0=E9=B8=A3=E5=A4=A9?= Date: Wed, 26 Aug 2026 14:05:19 +0100 Subject: [PATCH 18/25] fix more --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index e470f52b5..66bff422b 100644 --- a/README.md +++ b/README.md @@ -102,7 +102,7 @@ print(answer) See the [Detailed Usage Guide](#detailed-usage-guide) to configure other models and integrate PageIndex with your own agent. -### Request Answers with Citations +### Get Answers with Citations To request inline page-level citations, pass a system message together with the question: From 6dd7a26c437ccd83e17c23fb7c5b63f77e25035a Mon Sep 17 00:00:00 2001 From: Ray Date: Thu, 27 Aug 2026 02:37:24 +0800 Subject: [PATCH 19/25] docs: restore reasoning positioning, surface managed chat, fix quickstart cross-links - Retrieve step and the comparison table say LLM reasoning again, and the 'Why it works' quote returns above the table - Model Recommendations use the quickstart's index=/chat= spellings; the usage guide notes the flat spellings are interchangeable - the own-agent integration block gets an anchor and the quickstart pointer links to it instead of claiming it lives in the Detailed Usage Guide - cloud table shows the managed chat included with the key; wait=True explained; em-dash spacing and heading whitespace --- README.md | 25 ++++++++++++++++--------- 1 file changed, 16 insertions(+), 9 deletions(-) diff --git a/README.md b/README.md index 66bff422b..4ad798b45 100644 --- a/README.md +++ b/README.md @@ -46,7 +46,7 @@ Are you frustrated with vector database retrieval accuracy for long and complex Inspired by AlphaGo, **[PageIndex](https://vectify.ai/pageindex)** replaces the vector index with a **hierarchical tree index** and lets an LLM **reason** its way through it β€” the way a human expert flips to the right section of a long report. Retrieval happens in two steps: 1. **Index** β€” generate a **tree-structure index** for each document -2. **Retrieve** β€” retrieve information via LLM-based **tree search** +2. **Retrieve** β€” **search that tree** with LLM reasoning, agentically

+### Why it works + +> PageIndex is a vectorless, reasoning-based RAG engine that mirrors how humans read, delivering traceable, explainable, and context-aware retrieval, without vector databases or chunking. + ### Compare with Vector RAG | | Vector RAG | **PageIndex** | |---|---|---| | **Index** | vector index | tree index | | **Unit** | fixed-size chunks | natural sections | -| **Retrieval** | semantic similarity search | LLM-based relevance search | +| **Retrieval** | semantic similarity search | LLM reasoning over the tree | | **Result** | opaque, β€œvibe retrieval” | traceable to explicit references | | **Context** | query embedding only | full context: conversation history, domain knowledge | @@ -97,10 +101,10 @@ print(answer) ### Model Recommendations -- **`index_model` β€” a basic model is sufficient.** The index model generates the document's tree index. A basic model is sufficient to produce a good tree structure. -- **`chat_model` β€” use the best model you can afford.** The chat model searches the tree to retrieve information. See [Query cost and accuracy](#query-cost-and-accuracy). +- **`index=` β€” a basic model is sufficient.** The index model generates the document's tree index. A basic model is sufficient to produce a good tree structure. +- **`chat=` β€” use the best model you can afford.** The chat model searches the tree to retrieve information. See [Query cost and accuracy](#query-cost-and-accuracy). -See the [Detailed Usage Guide](#detailed-usage-guide) to configure other models and integrate PageIndex with your own agent. +See the [Detailed Usage Guide](#detailed-usage-guide) to configure other models, or [integrate PageIndex with your own agent](#integrate-with-your-own-agent). ### Get Answers with Citations @@ -139,7 +143,7 @@ Building a tree locally runs **about $0.001 per page** with `index_model="gpt-5. Indexing cost against document length, log-log, for nine PDFs from 9 to 1,098 pages. Points track a $0.0011-per-page reference line; the spread around it is text density, not length. -Indexing time also scales predictably with document length. In the same local setup, the benchmark documentsβ€”from 9 to 1,098 pagesβ€”finished in roughly **13 seconds to 4.5 minutes**. +Indexing time also scales predictably with document length. In the same local setup, the benchmark documents β€” from 9 to 1,098 pages β€” finished in roughly **13 seconds to 4.5 minutes**. @@ -194,6 +198,8 @@ client = PageIndexClient( - **`storage_path`** specifies where indexed documents are stored locally. +`index_model=` / `chat_model=` are the flat spellings of the quickstart's `index=` / `chat=` β€” the two are interchangeable. + #### Model naming conventions Model names follow [LiteLLM's naming convention](https://docs.litellm.ai/docs/providers). Choose the format that matches your provider: @@ -320,10 +326,11 @@ Pass a list of ids to `doc_id` to search several documents at once, and keep it +
-## Integrate PageIndex with your own agent +## Integrate PageIndex with your own agent @@ -393,7 +400,7 @@ client = PageIndexClient( chat="gpt-5.6-sol", # use your preferred compatible model for chat ) -# The rest of your code stays the same +# The rest of your code stays the same β€” wait=True because cloud indexing is asynchronous doc_id = client.submit_document("report.pdf", wait=True)["doc_id"] print(client.chat("What was the 2023 operating margin?", doc_id=doc_id)) ``` @@ -403,7 +410,7 @@ print(client.chat("What was the 2023 operating margin?", doc_id=doc_id)) | Best for | text-heavy PDFs and local workflows | scanned, image-heavy, and large document collections | | Indexing | runs locally | runs in PageIndex Cloud, with production OCR and image understanding | | Storage | local | managed in PageIndex Cloud | -| Chat model | your model | your model | +| Chat model | your model | your model, or the managed chat included with your key | | Citations | page-level | line-level | | Image understanding | β€” | βœ… | | Multi-document scale | manual | PageIndex File System | From be68e4511b9207a19c4217b24360f68f4a3c1230 Mon Sep 17 00:00:00 2001 From: Ray Date: Thu, 27 Aug 2026 02:45:39 +0800 Subject: [PATCH 20/25] docs: two new lines drop their connector em dashes --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 4ad798b45..ae0d07795 100644 --- a/README.md +++ b/README.md @@ -198,7 +198,7 @@ client = PageIndexClient( - **`storage_path`** specifies where indexed documents are stored locally. -`index_model=` / `chat_model=` are the flat spellings of the quickstart's `index=` / `chat=` β€” the two are interchangeable. +`index_model=` / `chat_model=` are the flat spellings of the quickstart's `index=` / `chat=`; either spelling works. #### Model naming conventions @@ -400,7 +400,7 @@ client = PageIndexClient( chat="gpt-5.6-sol", # use your preferred compatible model for chat ) -# The rest of your code stays the same β€” wait=True because cloud indexing is asynchronous +# The rest of your code stays the same (wait=True: cloud indexing is asynchronous) doc_id = client.submit_document("report.pdf", wait=True)["doc_id"] print(client.chat("What was the 2023 operating margin?", doc_id=doc_id)) ``` From a4bef126419f132676855d61227edb064a745968 Mon Sep 17 00:00:00 2001 From: Ray Date: Thu, 27 Aug 2026 03:10:29 +0800 Subject: [PATCH 21/25] docs: em dashes give way to plainer punctuation; two wording fixes Every prose em dash converts to a colon, comma, semicolon, or parentheses (label bullets take colons; parenthetical pairs become parentheses). The two table cells that use the dash as a not-available marker stay. Also: "flips to" becomes "goes straight to", and the Context table row gains "etc." --- README.md | 44 ++++++++++++++++++++++---------------------- 1 file changed, 22 insertions(+), 22 deletions(-) diff --git a/README.md b/README.md index ae0d07795..c2faf7949 100644 --- a/README.md +++ b/README.md @@ -31,9 +31,9 @@

Updates

 -- [2026/08] πŸ”₯ [**PageIndex SDK**](#quickstart) β€” `pip install -U pageindex` now ships **local mode**: index, retrieve, and chat entirely on your machine with your own LLM key, or point the same client at PageIndex Cloud with an API key. -- [2026/08] ⚑ [**PageIndex Flash**](#step-2-build-the-tree-index) β€” tree structure generation from PDFs in seconds, with structure extracted heuristically instead of by an LLM. -- [PageIndex Chat](https://chat.pageindex.ai) β€” a human-like document analysis agent for long professional documents. Also available via [MCP](https://pageindex.ai/developer) or [API](https://pageindex.ai/developer). +- [2026/08] πŸ”₯ [**PageIndex SDK**](#quickstart): `pip install -U pageindex` now ships **local mode**: index, retrieve, and chat entirely on your machine with your own LLM key, or point the same client at PageIndex Cloud with an API key. +- [2026/08] ⚑ [**PageIndex Flash**](#step-2-build-the-tree-index): tree structure generation from PDFs in seconds, with structure extracted heuristically instead of by an LLM. +- [PageIndex Chat](https://chat.pageindex.ai): a human-like document analysis agent for long professional documents. Also available via [MCP](https://pageindex.ai/developer) or [API](https://pageindex.ai/developer).
@@ -41,12 +41,12 @@ ## What is PageIndex? -Are you frustrated with vector database retrieval accuracy for long and complex documents? Vector-based RAG retrieves by semantic **similarity**. But **similarity β‰  relevance** β€” what retrieval actually needs is relevance, and relevance requires **reasoning**. On professional documents that demand contextual understanding, domain expertise, and multi-step reasoning, similarity search misses what is relevant but not similar, and returns what is similar but not relevant. +Are you frustrated with vector database retrieval accuracy for long and complex documents? Vector-based RAG retrieves by semantic **similarity**. But **similarity β‰  relevance**: what retrieval actually needs is relevance, and relevance requires **reasoning**. On professional documents that demand contextual understanding, domain expertise, and multi-step reasoning, similarity search misses what is relevant but not similar, and returns what is similar but not relevant. -Inspired by AlphaGo, **[PageIndex](https://vectify.ai/pageindex)** replaces the vector index with a **hierarchical tree index** and lets an LLM **reason** its way through it β€” the way a human expert flips to the right section of a long report. Retrieval happens in two steps: +Inspired by AlphaGo, **[PageIndex](https://vectify.ai/pageindex)** replaces the vector index with a **hierarchical tree index** and lets an LLM **reason** its way through it, the way a human expert goes straight to the right section of a long report. Retrieval happens in two steps: -1. **Index** β€” generate a **tree-structure index** for each document -2. **Retrieve** β€” **search that tree** with LLM reasoning, agentically +1. **Index**: generate a **tree-structure index** for each document +2. **Retrieve**: **search that tree** with LLM reasoning, agentically
@@ -67,11 +67,11 @@ Inspired by AlphaGo, **[PageIndex](https://vectify.ai/pageindex)** replaces the | **Unit** | fixed-size chunks | natural sections | | **Retrieval** | semantic similarity search | LLM reasoning over the tree | | **Result** | opaque, β€œvibe retrieval” | traceable to explicit references | -| **Context** | query embedding only | full context: conversation history, domain knowledge | +| **Context** | query embedding only | full context: conversation history, domain knowledge, etc. | -It is ideal for financial reports, legal documents, regulatory filings, technical manuals, medical literature, academic textbooks β€” any long, complex professional document. +It is ideal for financial reports, legal documents, regulatory filings, technical manuals, medical literature, academic textbooks, and any other long, complex professional document. -> PageIndex achieved **state-of-the-art** [98.7% accuracy](https://github.com/VectifyAI/Mafin2.5-FinanceBench) on FinanceBench (financial document QA benchmark), vastly outperforming vector-based RAG β€” see [Benchmarks](#benchmarks). +> PageIndex achieved **state-of-the-art** [98.7% accuracy](https://github.com/VectifyAI/Mafin2.5-FinanceBench) on FinanceBench (financial document QA benchmark), vastly outperforming vector-based RAG (see [Benchmarks](#benchmarks)). @@ -101,8 +101,8 @@ print(answer) ### Model Recommendations -- **`index=` β€” a basic model is sufficient.** The index model generates the document's tree index. A basic model is sufficient to produce a good tree structure. -- **`chat=` β€” use the best model you can afford.** The chat model searches the tree to retrieve information. See [Query cost and accuracy](#query-cost-and-accuracy). +- **`index=`: a basic model is sufficient.** The index model generates the document's tree index. A basic model is sufficient to produce a good tree structure. +- **`chat=`: use the best model you can afford.** The chat model searches the tree to retrieve information. See [Query cost and accuracy](#query-cost-and-accuracy). See the [Detailed Usage Guide](#detailed-usage-guide) to configure other models, or [integrate PageIndex with your own agent](#integrate-with-your-own-agent). @@ -136,14 +136,14 @@ Revenue increased during the reporting period. Indexing cost against document length, log-log, for nine PDFs from 9 to 1,098 pages. Points track a $0.0011-per-page reference line; the spread around it is text density, not length. -Indexing time also scales predictably with document length. In the same local setup, the benchmark documents β€” from 9 to 1,098 pages β€” finished in roughly **13 seconds to 4.5 minutes**. +Indexing time also scales predictably with document length. In the same local setup, the benchmark documents (9 to 1,098 pages) finished in roughly **13 seconds to 4.5 minutes**. @@ -154,7 +154,7 @@ Indexing time also scales predictably with document length. In the same local se ### Query cost and accuracy -[**PageIndex-OSS-Benchmark**](https://github.com/VectifyAI/PageIndex-OSS-Benchmark) measures exactly the setup in the quickstart above β€” `PageIndexClient()` in local mode, flash indexing, no OCR β€” on 62 lookup questions over 34 PDFs (1,945 pages) drawn from [MMLongBench-Doc-V2](https://github.com/VectifyAI/MMLongBench-Doc-V2). Every question's answer is a fact stated in running text, so a wrong answer is a **retrieval or reading failure**, not a reasoning one. +[**PageIndex-OSS-Benchmark**](https://github.com/VectifyAI/PageIndex-OSS-Benchmark) measures exactly the setup in the quickstart above (`PageIndexClient()` in local mode, flash indexing, no OCR) on 62 lookup questions over 34 PDFs (1,945 pages) drawn from [MMLongBench-Doc-V2](https://github.com/VectifyAI/MMLongBench-Doc-V2). Every question's answer is a fact stated in running text, so a wrong answer is a **retrieval or reading failure**, not a reasoning one. @@ -204,21 +204,21 @@ client = PageIndexClient( Model names follow [LiteLLM's naming convention](https://docs.litellm.ai/docs/providers). Choose the format that matches your provider: -**OpenAI** β€” use the model name directly and set `OPENAI_API_KEY`: +**OpenAI**: use the model name directly and set `OPENAI_API_KEY`: ```python os.environ["OPENAI_API_KEY"] = "your-openai-api-key" chat_model = "gpt-5.6-sol" ``` -**Anthropic** β€” prefix the model name with `anthropic/` and set `ANTHROPIC_API_KEY`: +**Anthropic**: prefix the model name with `anthropic/` and set `ANTHROPIC_API_KEY`: ```python os.environ["ANTHROPIC_API_KEY"] = "your-anthropic-api-key" chat_model = "anthropic/claude-sonnet-4-6" ``` -**OpenRouter** β€” prefix the provider and model name with `openrouter/` and set `OPENROUTER_API_KEY`: +**OpenRouter**: prefix the provider and model name with `openrouter/` and set `OPENROUTER_API_KEY`: ```python os.environ["OPENROUTER_API_KEY"] = "your-openrouter-api-key" @@ -241,11 +241,11 @@ doc_id = client.submit_document("report.pdf")["doc_id"] Inspect what you got: ```python -tree = client.get_document_structure(doc_id) # titles, page ranges, summaries β€” no text +tree = client.get_document_structure(doc_id) # titles, page ranges, summaries; no text client.list_documents() # everything you have indexed ``` -A PageIndex tree looks like this β€” a table of contents optimized for LLMs and agents: +A PageIndex tree is a table of contents optimized for LLMs and agents: ```jsonc { @@ -376,7 +376,7 @@ tools = client.agent_tools() `agent_tools()` returns plain Python functions that work with LangChain, PydanticAI, and other agent frameworks. -Each `*_config` helper is sugar over the explicit pieces β€” `client.agent_instructions()` for the system prompt and `client.as_openai_tools()` / `as_anthropic_tools()` / `as_claude_mcp()` for the tools β€” so you can swap in your own prompt whenever you need to. Locally, `doc_id` is enforced at the tool layer, not just prompted: out-of-scope lookups return `NOT_FOUND`. +Each `*_config` helper is sugar over the explicit pieces (`client.agent_instructions()` for the system prompt, `client.as_openai_tools()` / `as_anthropic_tools()` / `as_claude_mcp()` for the tools), so you can swap in your own prompt whenever you need to. Locally, `doc_id` is enforced at the tool layer, not just prompted: out-of-scope lookups return `NOT_FOUND`.
@@ -418,7 +418,7 @@ print(client.chat("What was the 2023 operating margin?", doc_id=doc_id)) ### More About PageIndex Cloud -- [Scale PageIndex to Millions of Documents](https://pageindex.ai/blog/pageindex-filesystem) β€” **PageIndex File System** is a Cloud-only, file-level tree indexing layer that lets PageIndex reason over an entire corpus, not just a single document. +- [Scale PageIndex to Millions of Documents](https://pageindex.ai/blog/pageindex-filesystem): **PageIndex File System** is a Cloud-only, file-level tree indexing layer that lets PageIndex reason over an entire corpus, not just a single document. ### Ready to Try It? From 457d5c1193e7a538348722c336e078a90e210089 Mon Sep 17 00:00:00 2001 From: Ray Date: Thu, 27 Aug 2026 03:15:07 +0800 Subject: [PATCH 22/25] docs: turns to, looks like --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index c2faf7949..2048e4d3f 100644 --- a/README.md +++ b/README.md @@ -43,7 +43,7 @@ Are you frustrated with vector database retrieval accuracy for long and complex documents? Vector-based RAG retrieves by semantic **similarity**. But **similarity β‰  relevance**: what retrieval actually needs is relevance, and relevance requires **reasoning**. On professional documents that demand contextual understanding, domain expertise, and multi-step reasoning, similarity search misses what is relevant but not similar, and returns what is similar but not relevant. -Inspired by AlphaGo, **[PageIndex](https://vectify.ai/pageindex)** replaces the vector index with a **hierarchical tree index** and lets an LLM **reason** its way through it, the way a human expert goes straight to the right section of a long report. Retrieval happens in two steps: +Inspired by AlphaGo, **[PageIndex](https://vectify.ai/pageindex)** replaces the vector index with a **hierarchical tree index** and lets an LLM **reason** its way through it, the way a human expert turns to the right section of a long report. Retrieval happens in two steps: 1. **Index**: generate a **tree-structure index** for each document 2. **Retrieve**: **search that tree** with LLM reasoning, agentically @@ -245,7 +245,7 @@ tree = client.get_document_structure(doc_id) # titles, page ranges, summaries client.list_documents() # everything you have indexed ``` -A PageIndex tree is a table of contents optimized for LLMs and agents: +A PageIndex tree looks like a table of contents optimized for LLMs and agents: ```jsonc { From 0f287e54505050eff8f1c41e1d863f8c8ac1f1ee Mon Sep 17 00:00:00 2001 From: Ray Date: Thu, 27 Aug 2026 03:16:26 +0800 Subject: [PATCH 23/25] docs: the expert simile reads the report --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 2048e4d3f..39ec5696b 100644 --- a/README.md +++ b/README.md @@ -43,7 +43,7 @@ Are you frustrated with vector database retrieval accuracy for long and complex documents? Vector-based RAG retrieves by semantic **similarity**. But **similarity β‰  relevance**: what retrieval actually needs is relevance, and relevance requires **reasoning**. On professional documents that demand contextual understanding, domain expertise, and multi-step reasoning, similarity search misses what is relevant but not similar, and returns what is similar but not relevant. -Inspired by AlphaGo, **[PageIndex](https://vectify.ai/pageindex)** replaces the vector index with a **hierarchical tree index** and lets an LLM **reason** its way through it, the way a human expert turns to the right section of a long report. Retrieval happens in two steps: +Inspired by AlphaGo, **[PageIndex](https://vectify.ai/pageindex)** replaces the vector index with a **hierarchical tree index** and lets an LLM **reason** its way through it, the way a human expert reads a long report, turning to the right section. Retrieval happens in two steps: 1. **Index**: generate a **tree-structure index** for each document 2. **Retrieve**: **search that tree** with LLM reasoning, agentically From 1168317494b2aeadcc89c1a284cc578d12a846c9 Mon Sep 17 00:00:00 2001 From: Ray Date: Thu, 27 Aug 2026 03:18:07 +0800 Subject: [PATCH 24/25] docs: turns to and reads --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 39ec5696b..822c5c6c4 100644 --- a/README.md +++ b/README.md @@ -43,7 +43,7 @@ Are you frustrated with vector database retrieval accuracy for long and complex documents? Vector-based RAG retrieves by semantic **similarity**. But **similarity β‰  relevance**: what retrieval actually needs is relevance, and relevance requires **reasoning**. On professional documents that demand contextual understanding, domain expertise, and multi-step reasoning, similarity search misses what is relevant but not similar, and returns what is similar but not relevant. -Inspired by AlphaGo, **[PageIndex](https://vectify.ai/pageindex)** replaces the vector index with a **hierarchical tree index** and lets an LLM **reason** its way through it, the way a human expert reads a long report, turning to the right section. Retrieval happens in two steps: +Inspired by AlphaGo, **[PageIndex](https://vectify.ai/pageindex)** replaces the vector index with a **hierarchical tree index** and lets an LLM **reason** its way through it, the way a human expert turns to and reads the right section of a long report. Retrieval happens in two steps: 1. **Index**: generate a **tree-structure index** for each document 2. **Retrieve**: **search that tree** with LLM reasoning, agentically From bc7db4e6707b65ee01c892b1e4843f7f4b14b4cc Mon Sep 17 00:00:00 2001 From: Ray Date: Thu, 27 Aug 2026 03:20:34 +0800 Subject: [PATCH 25/25] docs: the similarity/relevance dash earns its place --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 822c5c6c4..3170c1f46 100644 --- a/README.md +++ b/README.md @@ -41,7 +41,7 @@ ## What is PageIndex? -Are you frustrated with vector database retrieval accuracy for long and complex documents? Vector-based RAG retrieves by semantic **similarity**. But **similarity β‰  relevance**: what retrieval actually needs is relevance, and relevance requires **reasoning**. On professional documents that demand contextual understanding, domain expertise, and multi-step reasoning, similarity search misses what is relevant but not similar, and returns what is similar but not relevant. +Are you frustrated with vector database retrieval accuracy for long and complex documents? Vector-based RAG retrieves by semantic **similarity**. But **similarity β‰  relevance** β€” what retrieval actually needs is relevance, and relevance requires **reasoning**. On professional documents that demand contextual understanding, domain expertise, and multi-step reasoning, similarity search misses what is relevant but not similar, and returns what is similar but not relevant. Inspired by AlphaGo, **[PageIndex](https://vectify.ai/pageindex)** replaces the vector index with a **hierarchical tree index** and lets an LLM **reason** its way through it, the way a human expert turns to and reads the right section of a long report. Retrieval happens in two steps: