kb-arena 0.2.1

Benchmark retrieval strategies on your documentation — find which KB architecture fits your data

KB Arena

Which retrieval architecture works best for your documentation?

KB Arena benchmarks 7 retrieval strategies — naive vector, contextual vector, Q&A pairs, knowledge graph, hybrid, RAPTOR, and PageIndex — on your documentation. Bring your docs in any format, run the pipeline, get empirical results. Ships with an AWS Compute corpus (75 questions across 5 difficulty tiers) as a built-in example.

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Try It in 10 Seconds

No API keys. No Docker. Just explore real benchmark results:

pip install kb-arena
kb-arena demo

This launches the dashboard with pre-computed results from the AWS Compute corpus (75 questions, 7 strategies, 5 difficulty tiers).

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How KB Arena Differs from Other RAG Evaluation Tools

Most RAG evaluation tools answer "how well does my pipeline work?" KB Arena answers a different question: "which retrieval architecture works best for my docs?"

	KB Arena	RAGAS	MTEB / BEIR	GraphRAG	DeepEval
Compares multiple architectures	Yes - 7 strategies	No - evaluates your existing pipeline	No - compares embedding models	No - only their own approach	No
Works on your own docs	Yes	Yes	No - fixed public datasets	No - fixed datasets	Yes
Includes graph + vector + hybrid	Yes	Vector/hybrid only	Embeddings only	Graph only	Any
Auto-generates benchmark questions	Yes - 5 difficulty tiers	Manual	Fixed	Fixed	Manual
Interactive comparison UI	Yes - chatbot + benchmark explorer	No	Leaderboard only	No	Dashboard
Chatbot per strategy	Yes	No	No	No	No
Standard IR metrics (NDCG, MRR)	Roadmap	Yes	Yes	Partial	No

If you want to know whether a knowledge graph, Q&A pairs, or plain vector search is the right architecture for your documentation, that's what KB Arena is for.

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Quick Start — I Just Have My Docs

You have documentation files (markdown, HTML, text, PDFs). You want to know which retrieval strategy works best. Here's everything from zero.

Prerequisites

1. Python 3.11+ and pip
2. Docker (for Neo4j — the knowledge graph strategy needs it)
3. API keys for Anthropic (LLM) and OpenAI (embeddings)

That's it. No Neo4j expertise needed. No graph database experience required. KB Arena handles the schema, extraction, and querying.

Step 1: Install

pip install kb-arena

# Optional: install format-specific parsers
pip install kb-arena[pdf]        # PDF support (PyMuPDF)
pip install kb-arena[docx]       # Word documents (mammoth)
pip install kb-arena[web]        # Web scraping (httpx)
pip install kb-arena[all-formats] # All of the above

Step 2: Set API keys

Create a .env file or export directly:

export KB_ARENA_ANTHROPIC_API_KEY=sk-ant-...    # Claude for generation + evaluation
export KB_ARENA_OPENAI_API_KEY=sk-...           # OpenAI for text-embedding-3-large

Step 3: Start Neo4j

KB Arena uses Neo4j for the knowledge graph strategy. One command:

docker compose up neo4j -d

This starts Neo4j on localhost:7687 with default credentials (neo4j / kbarena1). No configuration needed — KB Arena creates the schema automatically.

If you don't have the docker-compose.yml, create one:

services:
  neo4j:
    image: neo4j:5-community
    ports:
      - "7474:7474"
      - "7687:7687"
    environment:
      - NEO4J_AUTH=neo4j/kbarena1
      - NEO4J_PLUGINS=["apoc"]
    volumes:
      - neo4j_data:/data

volumes:
  neo4j_data:

Don't want Docker? KB Arena still works — the vector strategies, RAPTOR, and PageIndex run without Neo4j. Only the knowledge graph and hybrid strategies need it.

Step 4: Run the pipeline

# Scaffold a new corpus
kb-arena init-corpus my-docs

# Drop your docs into the raw/ directory
cp ~/my-documentation/*.md datasets/my-docs/raw/
# Supports: .md, .html, .txt, .pdf, .docx, .csv, .tsv — auto-detected

# Parse into the unified Document model (JSONL intermediate files)
kb-arena ingest datasets/my-docs/raw/ --corpus my-docs

# Or ingest directly from a URL or GitHub repo
kb-arena ingest https://docs.example.com --corpus my-docs
kb-arena ingest github:owner/repo --corpus my-docs

# Build the knowledge graph in Neo4j (entities + relationships)
kb-arena build-graph --corpus my-docs

# Build vector indexes in ChromaDB (local, no server needed)
kb-arena build-vectors --corpus my-docs

# Auto-generate benchmark questions from your docs (10 per difficulty tier)
kb-arena generate-questions --corpus my-docs --count 50

# Run the benchmark (each question x 7 strategies, 4-pass evaluation)
kb-arena benchmark --corpus my-docs

# Launch the web UI to explore results
kb-arena serve

Open http://localhost:8000 for the API, http://localhost:3000 for the dashboard.

Step 5: Read the results

The benchmark produces:

• Accuracy by tier — which strategy handles simple lookups vs multi-hop architecture questions
• Latency percentiles — p50, p95, p99 per strategy
• Cost per query — token usage and API cost
• Composite ranking — 0.5 * accuracy + 0.3 * reliability + 0.2 * latency

Results are saved to results/ as JSON and displayed in the web dashboard.

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Full Stack with Docker Compose

Run everything — Neo4j, the API server, and the frontend — in one command:

# Set your API keys
export ANTHROPIC_API_KEY=sk-ant-...
export OPENAI_API_KEY=sk-...

# Start all services
docker compose up -d

# Open the dashboard
open http://localhost:3000

The compose file starts Neo4j (port 7474/7687), the FastAPI backend (port 8000), and the Next.js frontend (port 3000).

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Using the Built-in AWS Example

The AWS Compute corpus ships ready to use (75 questions across 5 difficulty tiers):

kb-arena ingest ./datasets/aws-compute/raw/ --corpus aws-compute
kb-arena build-graph --corpus aws-compute
kb-arena build-vectors --corpus aws-compute
kb-arena benchmark --corpus aws-compute
kb-arena serve

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Screenshots

Home — Overview of the 7 strategies, difficulty tiers, and evaluation methodology.

Strategy comparison — Ask the same question to all 7 strategies simultaneously. Compare answers, sources, latency, and cost side-by-side.

Benchmark results — Accuracy table by tier with grouped bar chart.

Knowledge graph — Interactive force-directed visualization of entities extracted from your docs.

Live graph build — Watch entities and relationships stream in as the extractor runs.

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Documentation Tools

Beyond benchmarking, KB Arena includes three standalone tools that work on any documentation corpus.

All three tools are available as CLI commands and through the web UI at /tools.

Q&A Generator

Generate Q&A pairs from your docs — use them for chatbot training, FAQ pages, or search indexes. Only needs an Anthropic key (no embeddings, no vector DB).

kb-arena generate-qa --corpus my-docs
# Outputs: datasets/my-docs/qa-pairs/qa_pairs.jsonl

CLI

Web UI

Docs Gap Analyzer

Find what's missing in your documentation before your users complain about it. Generates Q&A pairs per section, self-evaluates them, and classifies each section as strong (>=70%), weak (30-70%), or gap (<30%).

kb-arena audit --corpus my-docs

CLI

Web UI

Fix My Docs

Get actionable recommendations with draft content to improve your docs. Runs the audit internally, then generates prioritized fixes for weak and gap sections.

kb-arena fix --corpus my-docs --max-fixes 5

CLI

Web UI

Pipeline: generate-qa → audit → fix — each command builds on the previous. Or run them independently via CLI or the web UI.

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Benchmark Results (AWS Compute Corpus)

Real numbers from 75 questions across 5 difficulty tiers, evaluated with a 4-pass system (structural checks + LLM-as-judge):

Strategy	Overall	T1 Lookup	T2 How-To	T3 Comparison	T4 Integration	T5 Architecture	Avg Latency	Cost
Q&A Pairs	79.2%	79%	85%	83%	84%	66%	9.0s	$0.48
Knowledge Graph	71.5%	72%	69%	61%	77%	79%	20.3s	$1.37
Hybrid	64.7%	39%	81%	61%	80%	62%	41.5s	$3.02
RAPTOR	25.3%	30%	16%	15%	36%	30%	7.2s	$0.69
Naive Vector	20.7%	27%	15%	14%	26%	22%	6.4s	$0.33
Contextual Vector	16.5%	25%	11%	9%	26%	11%	5.1s	$0.29
PageIndex	14.3%	19%	12%	7%	21%	12%	10.9s	$0.29

Key takeaway: Q&A pairs dominate overall because pre-generated answers sidestep retrieval failures. Knowledge graph leads on architecture questions (T5: 79%) where structured graph traversal shines. The hybrid strategy adapts per question type but pays a latency/cost premium. PageIndex — the vectorless, reasoning-based approach — scores comparably to contextual vector at $0.29, demonstrating that LLM tree traversal is a viable alternative to embeddings on well-structured docs. RAPTOR's hierarchical retrieval shows strength at T4/T5 but needs a larger corpus. Pure vector RAG — what most teams ship — scores under 21%. Cost ranges from $0.29 (contextual vector / pageindex) to $3.02 (hybrid) for the full 75-question benchmark.

These are results from the built-in AWS Compute corpus. Your mileage will vary — that's the whole point of running it on your own docs.

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The 7 Strategies

#	Strategy	How it works	Best at
1	Naive Vector	Chunk → embed → cosine similarity → generate	Fast lookups, simple factoid questions
2	Contextual Vector	Chunk + parent context → embed → rank	Disambiguating domain-specific terms
3	Q&A Pairs	LLM pre-generates Q&A at index time → match	Common questions with known answers
4	Knowledge Graph	Entities → Neo4j → Cypher templates → generate	Multi-hop dependencies, cross-topic queries
5	Hybrid	Intent routing → vector or graph or both (RRF)	Adapts per question type
6	RAPTOR	Cluster chunks → LLM topic summaries → recursive tree → query all levels	Cross-document synthesis, broad topic questions
7	PageIndex	Build tree index from doc structure → LLM beam search traversal → no vectors	Well-structured docs, reasoning over hierarchy

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Question Tiers

Questions are organized into 5 difficulty tiers:

Tier	Type	Hops	What it tests
1	Lookup	1	Single-fact lookup from one document
2	How-To	1-2	Multi-step processes, configuration sequences
3	Comparison	2-3	Comparing alternatives, trade-offs between options
4	Integration	3-4	Dependencies and connections between concepts
5	Architecture	3-5	Cross-document synthesis, transitive reasoning

Use kb-arena generate-questions to auto-generate questions from your docs, or write them by hand in YAML.

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Supported Formats

Format	Extensions / Input	Optional Dep	Notes
Markdown	.md, .markdown, .rst	—	Heading hierarchy, code blocks, tables
HTML	.html, .htm	—	Strips nav/footer, extracts structure
Plain text	.txt, .text	—	ALL CAPS heading detection
PDF	.pdf	kb-arena[pdf]	Font-size heading detection, table extraction
Word	.docx	kb-arena[docx]	Converts to HTML, then extracts structure
CSV / TSV	.csv, .tsv	—	Auto-detects delimiter, groups rows into sections
Web URL	https://...	kb-arena[web]	Crawls same-domain pages; uses /llms.txt if available
GitHub	github:owner/repo	—	Shallow clones and ingests all doc files
SEC EDGAR	--format sec-edgar	—	10-K/10-Q filing parser

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Universal Documentation Schema

KB Arena extracts entities and relationships using a universal schema that works for any documentation domain:

5 node types: Topic, Component, Process, Config, Constraint 7 relationship types: DEPENDS_ON, CONTAINS, CONNECTS_TO, TRIGGERS, CONFIGURES, ALTERNATIVE_TO, EXTENDS

No per-domain configuration needed. The LLM maps your documentation concepts to these types automatically.

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CLI Reference

Command	Description
demo	Launch dashboard with pre-computed results (no API keys needed)
init-corpus <name>	Scaffold datasets/{name}/ directories
ingest <path>	Parse docs into JSONL. Accepts files, dirs, URLs, github:owner/repo. Options: --corpus, --format, --dry-run
build-graph	Extract entities/rels into Neo4j. Options: --corpus
build-vectors	Build vector indexes + PageIndex tree. Options: --corpus, --strategy
generate-questions	Auto-generate benchmark questions. Options: --corpus, --count
benchmark	Run evaluation. Options: --corpus, --strategy, --tier, --dry-run
generate-qa	Generate Q&A pairs from your docs as JSONL. Options: --corpus, --output
audit	Find documentation gaps — classifies sections as strong/weak/gap. Options: --corpus, --output, --max-sections
fix	Generate fix recommendations with draft content. Options: --corpus, --max-fixes, --output
report	Generate report. Options: --corpus, --output, --format (rich/json)
serve	Launch API + frontend. Options: --host, --port
health	Pipeline status. Options: --format (rich/json)

All commands are independently re-runnable. Each stage writes to disk so you can re-run any step without repeating earlier ones.

CLI Features

Dry run — Preview what a command will do before committing to expensive LLM calls:

kb-arena ingest datasets/my-docs/raw/ --corpus my-docs --dry-run
# Shows: file count by extension, parser assignment, output path

kb-arena benchmark --corpus my-docs --dry-run
# Shows: question count, strategy list, total queries, concurrency settings

JSON output — Pipe structured data to jq, scripts, or CI pipelines:

kb-arena report --corpus my-docs --format json | jq '.corpora'
kb-arena health --format json | jq '.services'

Pipeline hints — After every command, see what to run next:

$ kb-arena ingest datasets/my-docs/raw/ --corpus my-docs
Done. 12 documents, 47 sections → datasets/my-docs/processed/documents.jsonl

Next: kb-arena build-graph --corpus my-docs && kb-arena build-vectors --corpus my-docs

Progress bars — Every long-running command shows real-time progress (extraction sections, Neo4j batch loading, vector index building, question generation tiers).

Cost tracking — Benchmark runs display cumulative API cost in the progress bar and print per-strategy cost/accuracy summaries after completion.

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Environment Variables

All prefixed with KB_ARENA_. Loaded from .env or environment.

Variable	Default	Required	Description
ANTHROPIC_API_KEY	—	Yes	Claude for generation, evaluation, extraction
OPENAI_API_KEY	—	Yes	OpenAI for text-embedding-3-large
NEO4J_URI	bolt://localhost:7687	No	Neo4j connection
NEO4J_USER	neo4j	No	Neo4j username
NEO4J_PASSWORD	—	No	Neo4j password (set to match NEO4J_AUTH in docker-compose)
JUDGE_MODEL	claude-opus-4-6	No	Model used for LLM-as-judge evaluation (default differs from generate model to avoid self-evaluation bias)
CHROMA_PATH	./chroma_data	No	ChromaDB storage path
EMBEDDING_MODEL	text-embedding-3-large	No	OpenAI embedding model
EMBEDDING_DIMENSIONS	3072	No	Embedding vector dimensions
GENERATE_MODEL	claude-sonnet-4-6	No	Generation model
FAST_MODEL	claude-haiku-4-5-20251001	No	Classification model
HOST	0.0.0.0	No	Server bind address
PORT	8000	No	Server port
DEBUG	false	No	Debug mode
BENCHMARK_TEMPERATURE	0.0	No	LLM temperature for benchmarks
BENCHMARK_MAX_CONCURRENT	5	No	Parallel benchmark queries
BENCHMARK_QUERY_TIMEOUT_S	120	No	Per-query timeout (seconds)
BENCHMARK_MAX_RETRIES	2	No	Retry count on failures
PAGEINDEX_BEAM_WIDTH	3	No	Branches to explore per tree level
PAGEINDEX_MAX_DEPTH	4	No	Maximum tree traversal depth
DATASETS_PATH	./datasets	No	Datasets directory
RESULTS_PATH	./results	No	Results output directory

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Development

# Install with dev dependencies
pip install -e '.[dev]'

# Run tests
pytest tests/ -v --ignore=tests/live  # 454 tests

# Lint + format
ruff check . && ruff format --check .

# Frontend
cd web && npm install && npx next build

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Tech Stack

Component	Technology
LLM	Claude Sonnet 4.6 (generation) + Haiku 4.5 (classification)
Embeddings	text-embedding-3-large (3072-dim)
Vector store	ChromaDB 0.5 (local, no server)
Graph store	Neo4j 5 Community
Backend	FastAPI + SSE streaming
Frontend	Next.js 14 + Tailwind + Recharts
Models	Pydantic v2
CLI	Typer + Rich
Testing	pytest (454 tests)

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License

MIT