openindex 0.1.7

Compile PDFs into a queryable wiki.

OpenIndex

Overview

OpenIndex parses PDF documents into a hierarchical section tree and compiles them into a persistent, cross-linked wiki that agents can query.

It combines two projects:

• PageIndex — LLM-based hierarchical section extraction from PDFs
• OpenKB — compiles documents into a queryable wiki with cross-document concept pages

Unlike traditional RAG (which rediscovers knowledge on every query), OpenIndex compiles once: sections are indexed, summaries generated, concept pages created with bidirectional links, and a structured wiki is written to disk. An agent can then search the wiki to answer questions precisely.

Table of Contents

• Installation
• Usage
  • Index a PDF
  • Return types
  • Return wiki as dict
  • Index a Markdown / plain-text file
  • Query the wiki
  • Async usage
• License

Installation

From PyPI:

pip install openindex

From source:

uv pip install git+https://github.com/hienhayho/openindex.git

Usage

Set environment variables (or use a .env file):

OPENAI_MODEL_NAME=...
OPENAI_BASE_URL=https://api.openai.com/v1
OPENAI_API_KEY=
OPENAI_EXTRA_BODY={}

Note: openindex works with any OpenAI-compatible API server (OpenAI, vLLM, Ollama, LM Studio, etc.). Set OPENAI_BASE_URL to point to your server.

Index a PDF

Runs the full pipeline: section extraction → verification → tree building → summaries → wiki generation.

import os
import json
from dotenv import load_dotenv
from openindex import WikiIndex, TreeConfig

load_dotenv()

index = WikiIndex(
    model_name=os.getenv("OPENAI_MODEL_NAME"),
    base_url=os.getenv("OPENAI_BASE_URL"),
    api_key=os.getenv("OPENAI_API_KEY"),
    extra_body=json.loads(os.getenv("OPENAI_EXTRA_BODY", "{}")),
    config=TreeConfig(max_parallel_llm_calls=8),
)

result = index.build_wiki_sync("paper.pdf", "./wiki")  # returns BuildResult
WikiIndex.print_result(result)

See tools/index.py for a full example.

Output wiki structure:

wiki/
├── index.md              # master catalog
├── summaries/<doc>.md    # section tree with page ranges
├── concepts/<slug>.md    # cross-document concept pages
└── sources/<doc>.json    # full per-page text

Return types

All build_wiki / build_wiki_sync calls return a BuildResult pydantic model:

from openindex import BuildResult, WikiDict
from openindex.models import SectionNode

result: BuildResult = index.build_wiki_sync("paper.pdf", "./wiki")

result.title        # str — document stem, e.g. "paper"
result.doc_name     # str — filename, e.g. "paper.pdf"
result.description  # str — one-paragraph document summary
result.nodes        # list[SectionNode] — nested section tree
result.pages        # dict[int, str] — 1-based page index → page text
result.wiki         # WikiDict — compiled wiki artifacts (always set)

result.wiki is a WikiDict:

result.wiki.doc_name     # str — document stem
result.wiki.description  # str — one-paragraph document summary
result.wiki.summary      # str — full section tree as Markdown
result.wiki.sources      # list[SourcePage] — per-page content
result.wiki.concepts     # dict[str, ConceptEntry] — concept pages keyed by slug
result.wiki.related      # list[str] — related concept slugs (from LLM planner)
result.wiki.index        # str — index.md content for this document

WikiDict is JSON-serializable via .model_dump() or .model_dump_json() for database storage.

Sample BuildResult:

BuildResult(
    title="paper",
    doc_name="paper.pdf",
    description="This paper introduces a novel attention mechanism for transformer models...",
    nodes=[
        SectionNode(
            title="Introduction",
            start_index=1,
            end_index=3,
            depth=0,
            summary="Introduces the problem of efficient attention in long sequences.",
            children=[
                SectionNode(
                    title="Motivation",
                    start_index=1,
                    end_index=2,
                    depth=1,
                    summary="Existing approaches scale quadratically with sequence length.",
                    children=[],
                ),
            ],
        ),
        SectionNode(title="Method", start_index=4, end_index=8, depth=0, summary="...", children=[]),
    ],
    pages={
        1: "Page 1 text...",
        2: "Page 2 text...",
    },
    wiki=WikiDict(
        doc_name="paper",
        description="This paper introduces a novel attention mechanism...",
        summary="---\ndoc_type: pageindex\nfull_text: sources/paper.json\n---\n\n# Introduction (pages 1–3)\n...",
        sources=[
            SourcePage(page=1, content="Page 1 text...", images=[]),
            SourcePage(page=2, content="Page 2 text...", images=[]),
        ],
        concepts={
            "attention-mechanism": ConceptEntry(
                brief="A mechanism for focusing on relevant parts of the input.",
                content="## Attention Mechanism\n\nAttention allows models to...",
            ),
        },
        related=["transformer"],
        index="# Knowledge Base Index\n\n## Documents\n\n- [[summaries/paper]] (pageindex)...",
    ),
)

Return wiki as dict (Not queryable)

Omit wiki_dir to get wiki artifacts in-memory only — nothing written to disk. The result is not queryable by WikiQueryAgent. Useful for pipelines that process multiple documents before persisting.

result = index.build_wiki_sync("paper.pdf")  # no wiki_dir
wiki: WikiDict = result.wiki

To save to disk later:

from openindex.wiki import save_wiki_dicts_to_dir

wiki1 = index.build_wiki_sync("paper1.pdf").wiki
wiki2 = index.build_wiki_sync("paper2.pdf").wiki

save_wiki_dicts_to_dir([wiki1, wiki2], "./wiki")

To build a unified index string across multiple documents:

from openindex.wiki import build_unified_index

wiki1 = index.build_wiki_sync("paper1.pdf").wiki
wiki2 = index.build_wiki_sync("paper2.pdf").wiki

index_md = build_unified_index([wiki1, wiki2])
print(index_md)

Index a Markdown / plain-text file

Use split_text_to_pages to chunk a .md or .txt file into page-sized strings, then pass them via texts=.

from openindex import WikiIndex, split_text_to_pages

text = open("notes.md").read()
pages = split_text_to_pages(text, words_per_page=500)

result = index.build_wiki_sync(texts=pages, doc_name="notes.md", wiki_dir="./wiki")

split_text_to_pages splits on word count but always extends the boundary forward to the next \n, so lines are never broken mid-sentence. words_per_page controls target chunk size (default 500); actual chunk may be slightly larger.

from openindex import split_text_to_pages

pages = split_text_to_pages("Long document text...", words_per_page=300)
# returns list[str], one entry per "page"

Query the wiki

The query agent searches the compiled wiki to answer questions, always fetching actual page content before responding.

import os
import json
from dotenv import load_dotenv
from openindex import WikiQueryAgent

load_dotenv()

agent = WikiQueryAgent(
    wiki_dir="./wiki",
    model_name=os.getenv("OPENAI_MODEL_NAME"),
    base_url=os.getenv("OPENAI_BASE_URL"),
    api_key=os.getenv("OPENAI_API_KEY"),
    extra_body=json.loads(os.getenv("OPENAI_EXTRA_BODY", "{}")),
    cite=True,  # append \source{doc, p.N} after every factual claim
)

answer = agent.ask_sync("What is RAG?")
print(answer)

cite=True (default) — every factual sentence is followed by an inline source marker:

RAG combines retrieval with generation to ground LLM outputs. \source{rag-paper, p.3}
Two retrieval strategies are used: sparse (BM25) and dense (DPR). \source{rag-paper, p.4-5}

cite=False — plain answer, no source markers:

agent = WikiQueryAgent(..., cite=False)
answer = agent.ask_sync("What is RAG?")

See tools/query.py for a full example.

Async usage

Both WikiIndex and WikiQueryAgent expose async methods directly. Use these inside an existing event loop (FastAPI, async scripts, etc.) to avoid the overhead of asyncio.run().

Index:

import asyncio
import os
import json
from dotenv import load_dotenv
from openindex import WikiIndex, TreeConfig

load_dotenv()

async def main():
    index = WikiIndex(
        model_name=os.getenv("OPENAI_MODEL_NAME"),
        base_url=os.getenv("OPENAI_BASE_URL"),
        api_key=os.getenv("OPENAI_API_KEY"),
        extra_body=json.loads(os.getenv("OPENAI_EXTRA_BODY", "{}")),
        config=TreeConfig(max_parallel_llm_calls=8),
    )

    result = await index.build_wiki("paper.pdf", "./wiki")
    WikiIndex.print_result(result)

asyncio.run(main())

Query:

import asyncio
import os
import json
from dotenv import load_dotenv
from openindex import WikiQueryAgent

load_dotenv()

async def main():
    agent = WikiQueryAgent(
        wiki_dir="./wiki",
        model_name=os.getenv("OPENAI_MODEL_NAME"),
        base_url=os.getenv("OPENAI_BASE_URL"),
        api_key=os.getenv("OPENAI_API_KEY"),
        extra_body=json.loads(os.getenv("OPENAI_EXTRA_BODY", "{}")),
    )

    answer = await agent.ask("What is RAG?")
    print(answer)

asyncio.run(main())

License

Apache 2.0. See LICENSE for details.

This project incorporates code from:

• PageIndex — MIT License
• OpenKB — Apache 2.0 License