Querdex

Reasoning-first document intelligence system.

Querdex indexes any document into a hierarchical tree, then uses a two-tier LLM search to answer questions with cited sources. It works without an LLM (keyword heuristics), and optionally plugs in Anthropic or OpenAI for higher-quality results.

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Table of Contents

• How it works
• Installation
• Quick Start (CLI)
• LLM Setup
• Structured Extraction
• CLI Reference
• Python API
• Supported File Types
• Environment Variables

---

How it works

Document
   │
   ▼
Ingestion ──► parse into pages/sections (Section[])
   │
   ▼
Indexing ───► build hierarchical tree (TreeNode) + entity map + knowledge graph
   │
   ▼
Storage ────► persist to SQLite (sections, tree, entities, graph, query cache)
   │
   ▼
Query
  ├─ Tier 1: LLM (or keyword) batch-prune of tree nodes
  ├─ Tier 2: LLM (or heuristic) per-node relevance scoring
  ├─ Retrieval: pull section text for selected nodes
  └─ Answer: LLM synthesizes answer with source citations
   │
   ▼
Adaptive ───► update node summaries based on query feedback (runs in background)

Three query routes are selected automatically:

• single_doc — standard hierarchical search on one document
• multi_doc — virtual super-tree across up to 3 documents
• graph — entity-seeded graph walk for relationship queries ("how does X relate to Y?")

---

Installation

Base install (no LLM, uses keyword heuristics):

pip install querdex

With Anthropic (Claude):

pip install querdex[anthropic]

With OpenAI (GPT):

pip install querdex[openai]

Development:

git clone <repo>
cd querdex
uv sync --extra dev
# or with an LLM provider:
uv sync --extra dev --extra anthropic
uv sync --extra dev --extra openai

Requirements: Python 3.11+

---

Quick Start (CLI)

1. Index a document

querdex index ./report.pdf --doc-id annual-report

Output:

Indexed doc_id=annual-report version=1
Nodes=12 max_depth=3

2. Query it

querdex query --doc-id annual-report --query "What was the Q3 revenue?"

Output:

Query ID: 3f8a1c...
Intent: single_doc | Cache hit: False
Q3 revenue was $1.2B, up 8% year-over-year (Revenue Analysis, pages 4-6).

3. Multi-turn conversation (session)

# First turn
querdex query --doc-id annual-report \
  --query "What were the risk factors?" \
  --session-id session_001

# Second turn — context from first turn is carried over
querdex query --doc-id annual-report \
  --query "Which of those risks materialised?" \
  --session-id session_001

4. Re-index an updated document

When the document changes, Querdex only rebuilds the affected parts:

querdex index ./report_v2.pdf --doc-id annual-report

5. Delete a document

querdex delete --doc-id annual-report

Custom database path

By default the database is stored at ./index_store/querdex.db. To change it:

querdex --db /path/to/my.db index ./report.pdf --doc-id demo
querdex --db /path/to/my.db query --doc-id demo --query "summary?"

---

LLM Setup

Without any LLM configured, Querdex falls back to keyword/heuristic matching — it always produces an answer, just less precise.

Anthropic (Claude)

export QUERDEX_LLM_PROVIDER=anthropic
export QUERDEX_LLM_API_KEY=sk-ant-...

# Optional: override model defaults
export QUERDEX_LLM_TIER1_MODEL=claude-haiku-4-5-20251001   # fast, cheap (batch prune)
export QUERDEX_LLM_TIER2_MODEL=claude-sonnet-4-6            # powerful (deep reasoning + answers)

OpenAI (GPT)

export QUERDEX_LLM_PROVIDER=openai
export QUERDEX_LLM_API_KEY=sk-...

# Optional: override model defaults
export QUERDEX_LLM_TIER1_MODEL=gpt-4o-mini   # fast, cheap
export QUERDEX_LLM_TIER2_MODEL=gpt-4o         # powerful

How the two tiers are used:

Tier	Model	Purpose
Tier 1	cheap/fast	Single batched call to prune all tree nodes to the relevant few
Tier 2	powerful	Per-node deep reasoning to confirm relevance + score confidence
Answer	powerful	Synthesise a cited answer from the retrieved section text

---

Structured Extraction

Pull structured facts out of any indexed document — with source grounding: every extraction carries the exact section, page, and character span it came from, so nothing is silently hallucinated.

The schema is defined by example, not by code. Describe what you want and (optionally) show one or two examples:

querdex extract --doc-id demo \
  --prompt "Extract revenue figures and executive names" \
  --examples examples.json \
  --html review.html

examples.json:

[
  {
    "text": "Alice Chen reported revenue of $5M in Q1.",
    "extractions": [
      {"extraction_class": "metric", "extraction_text": "revenue of $5M", "attributes": {"period": "Q1"}},
      {"extraction_class": "person", "extraction_text": "Alice Chen"}
    ]
  }
]

The classes and attribute keys in your examples define the output schema. Every result is aligned back to the source text (exact → fuzzy matching); model output that cannot be located is kept but flagged unaligned so you can review it instead of trusting it.

--html writes a self-contained review page: the full document with color-coded highlights per extraction class, toggleable legend, attribute tooltips, and a click-to-jump list of all extractions.

Long documents are chunked and processed in parallel; use --passes 2 to trade extra LLM calls for higher recall. Without an LLM configured, extraction degrades to literal matching of your example texts.

Python API:

from querdex.extraction import ExtractionTask, ExtractionExample, ExampleExtraction

task = ExtractionTask(
    description="Extract revenue figures and executive names",
    examples=[...],
)
run = engine.extract_document("demo", task, passes=1)
for e in run.extractions:
    print(e.extraction_class, repr(e.extraction_text), e.section_id, e.char_start, e.alignment)

---

CLI Reference

querdex [--db PATH] <command> [options]

Command	Description
index <file>	Index a document. Auto-detects format from extension.
query	Query an indexed document.
extract	Run schema-by-example structured extraction over an indexed document.
delete	Remove a document and all its data from the store.

index

querdex index <file_path> [--doc-id ID]

Argument	Default	Description
file_path	required	Path to the document to index
--doc-id	auto-generated from filename+hash	Stable identifier for this document

query

querdex query --doc-id ID --query TEXT [--session-id ID]

Argument	Default	Description
--doc-id	required	Document to query
--query	required	Natural language question
--session-id	none	Enables multi-turn context (pass same ID across turns)

extract

querdex extract --doc-id ID --prompt TEXT [--examples FILE] [--passes N] [--html FILE]

Argument	Default	Description
--doc-id	required	Document to extract from
--prompt	required	Natural language description of what to extract
--examples	none	JSON file with few-shot examples (defines the output schema)
--passes	1	Extraction passes; more passes improve recall
--html	none	Write an interactive HTML review page to this path

delete

querdex delete --doc-id ID

---

Python API

For integration into your own application:

import asyncio
from querdex.services import build_engine

# build_engine reads QUERDEX_LLM_* env vars automatically
engine = build_engine("./index_store/querdex.db")

# Index a document
doc = asyncio.run(engine.index_document("./report.pdf", doc_id="annual-report"))
print(f"Indexed: {doc.doc_id} | nodes={doc.stats.total_nodes}")

# Query
result = engine.query_document("annual-report", "What was Q3 revenue?")
print(result.answer)
print(f"Confidence: {result.confidence:.0%}")
for source in result.source_nodes:
    print(f"  Source: {source.title}, pages {source.pages}")

# Multi-turn query
result2 = engine.query_document(
    "annual-report",
    "What caused that increase?",
    session_id="my-session-001",
)

# Re-index after the document changes
doc_v2 = asyncio.run(engine.reindex_document("./report_v2.pdf", doc_id="annual-report"))

# Delete
engine.store.delete_document("annual-report")

# Always close when done
engine.store.close()

Passing an LLM client directly

from querdex.llm.anthropic_client import AnthropicLLMClient
from querdex.services.engine import QuerdexEngine
from querdex.storage import SQLiteStore

llm = AnthropicLLMClient(
    api_key="sk-ant-...",
    tier1_model="claude-haiku-4-5-20251001",
    tier2_model="claude-sonnet-4-6",
)
store = SQLiteStore("./querdex.db")
engine = QuerdexEngine(store, llm_client=llm)

Using the FakeLLMClient in tests

from querdex.llm.fake_client import FakeLLMClient
from querdex.query.answering import AnswerGenerator

fake = FakeLLMClient(
    default='{"answer": "Revenue was $1.2B.", "confidence": 0.9}'
)
gen = AnswerGenerator(llm_client=fake)
answer, confidence, sources = gen.generate("What was revenue?", chunks)

---

Supported File Types

Extension	Parser	Notes
.txt	TextParser	Plain text, split by paragraphs
.md, .markdown	MarkdownParser	Heading-aware section splitting
.html, .htm	HTMLParser	Strips tags, extracts text blocks
.docx	DOCXParser	Microsoft Word, paragraph-level
.pdf	PDFParser	Page-level; OCR optional (see below)
.py	PythonCodeParser	Function/class level chunking
.js, .ts, .jsx, .tsx	JSCodeParser	Function-level chunking
.csv	CSVParser	Row-batched sections
.db, .sqlite	SQLiteParser	Table-level sections
.mp3, .wav, .m4a, .mp4, .mov	AudioVideoParser	Transcript-based (requires Whisper or similar)
.url	URLParser	Fetches and parses the web page at that URL
URL string	URLParser	Pass a URL string directly as the file path

PDF OCR

For scanned PDFs, enable OCR via environment variables:

# Tesseract (local)
export QUERDEX_OCR_ENABLED=true
export QUERDEX_OCR_PROVIDER=tesseract         # default when OCR enabled
export QUERDEX_TESSERACT_CMD=tesseract        # path to tesseract binary

# Cloud OCR (custom endpoint)
export QUERDEX_OCR_ENABLED=true
export QUERDEX_OCR_PROVIDER=cloud
export QUERDEX_OCR_ENDPOINT=https://your-ocr-api.com/v1/ocr
export QUERDEX_OCR_API_KEY=your-key

---

Environment Variables

Variable	Default	Description
QUERDEX_LLM_PROVIDER	(none)	anthropic or openai. If unset, heuristic mode is used.
QUERDEX_LLM_API_KEY	(none)	API key for the selected provider
QUERDEX_LLM_TIER1_MODEL	claude-haiku-4-5-20251001 / gpt-4o-mini	Fast model for batch node pruning
QUERDEX_LLM_TIER2_MODEL	claude-sonnet-4-6 / gpt-4o	Powerful model for deep reasoning and answers
QUERDEX_OCR_ENABLED	false	Enable OCR for scanned PDFs
QUERDEX_OCR_PROVIDER	tesseract	tesseract or cloud
QUERDEX_TESSERACT_CMD	tesseract	Path to Tesseract binary
QUERDEX_OCR_ENDPOINT	(none)	Endpoint URL for cloud OCR provider
QUERDEX_OCR_API_KEY	(none)	API key for cloud OCR provider

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License

MIT