fitz-ai 0.7.0

A modular, production-ready knowledge engine platform with clean architecture and multi-paradigm support (RAG, CLaRa).

fitz-ai

Intelligent, honest RAG in 5 minutes. No infrastructure. No boilerplate.

Quick Start • Installation • Documentation • GitHub

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pip install fitz-ai

fitz quickstart ./docs "What is our refund policy?"

That's it. Your documents are now searchable with AI.

Python SDK → Full SDK Reference

import fitz_ai

fitz_ai.ingest("./docs")
answer = fitz_ai.query("What is our refund policy?")

REST API → Full API Reference

pip install fitz-ai[api]

fitz serve  # http://localhost:8000/docs for interactive API

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About 🧑‍🌾

Solo project by Yan Fitzner (LinkedIn, GitHub).

• ~40k lines of Python
• 1200+ tests, 100% coverage
• Zero LangChain/LlamaIndex dependencies — built from scratch

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📦 What is RAG?

RAG is how ChatGPT's "file search," Notion AI, and enterprise knowledge tools actually work under the hood. Instead of sending all your documents to an AI, RAG:

1. Indexes your documents once — Splits them into chunks, converts to vectors, stores in a database
2. Retrieves only what's relevant — When you ask a question, finds the 5-10 most relevant chunks
3. Sends just those chunks to the LLM — The AI answers based on focused, relevant context

Traditional approach:

  [All 10,000 documents] → LLM → Answer
  ❌ Impossible (too large)
  ❌ Expensive (if possible)
  ❌ Unfocused

RAG approach:

  Question → [Search index] → [5 relevant chunks] → LLM → Answer
  ✅ Works at any scale
  ✅ Costs pennies per query
  ✅ Focused context = better answers

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📦 Why Can't I Just Send My Documents to ChatGPT directly?

You can—but you'll hit walls fast.

Context window limits 🚨

GPT-4 accepts ~128k tokens. That's roughly 300 pages. Your company wiki, codebase, or document archive is likely 10x-100x larger. You physically cannot paste it all.

Cost explosion 💥

Even if you could fit everything, you'd pay for every token on every query. Sending 100k tokens costs ~$1-3 per question. Ask 50 questions a day? That's $50-150 daily—for one user.

No selective retrieval ❌

When you paste documents, the model reads everything equally. It can't focus on what's relevant. Ask about refund policies and it's also processing your hiring guidelines, engineering specs, and meeting notes—wasting context and degrading answers.

No persistence 💢

Every conversation starts fresh. You re-upload, re-paste, re-explain. There's no knowledge base that accumulates and improves.

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Why Fitz?

Super fast setup 🐆

Point at a folder. Ask a question. Get an answer with sources. Even for tables! Everything else is handled by Fitz.

Honest answers ✅

Most RAG tools confidently answer even when the answer isn't in your documents. Ask "What was our Q4 revenue?" when your docs only cover Q1-Q3, and typical RAG hallucinates a number. Fitz says: "I cannot find Q4 revenue figures in the provided documents."

Queries that actually work 📊

Standard RAG fails silently on real queries. Fitz has built-in intelligence: hierarchical summaries for "What are the trends?", exact keyword matching for "Find TC-1000", multi-query decomposition for complex questions, AST-aware chunking for code, and SQL execution for tabular data. No configuration—it just works.

Tabular data that is actually searchable 📈

CSV and table data is a nightmare in most RAG systems—chunked arbitrarily, structure lost, queries fail. Fitz stores tables natively in PostgreSQL alongside your vectors—same database, no sync issues. Auto-detects schema and runs real SQL. Ask "What's the average price by region?" and get an actual computed answer, not fragmented rows.

Other Features at a Glance 🃏

1. [x] Fully local execution possible. Embedded PostgreSQL + Ollama, no API keys required to start.
2. [x] Plugin-based architecture. Swap LLMs, rerankers, and retrieval pipelines via YAML config.
3. [x] Extensible engine system. FitzRAG built-in, with a clean registry for adding custom engines.
4. [X] Incremental ingestion. Only reprocesses changed files, even with new chunking settings.
5. [x] Full provenance. Every answer traces back to the exact chunk and document.
6. [x] Data privacy: No telemetry, no cloud, no external calls except to the LLM provider you configure.

[!TIP] Any questions left? Try fitz on itself:

fitz quickstart ./fitz_ai "How does the chunking pipeline work?"

The codebase speaks for itself.

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Retrieval Intelligence

Most RAG implementations are naive vector search—they fail silently on real-world queries. Fitz has built-in intelligence that handles edge cases automatically:

Feature	Query	Naive RAG Problem	FitzRAG Solution
epistemic-honesty	"What was our Q4 revenue?"	❌ Hallucinated number — Info doesn't exist, but LLM won't admit it	✅ "I don't know"
keyword-vocabulary	"Find TC_1000"	❌ Wrong test case — Embeddings see TC_1000 ≈ TC_2000 (semantically similar)	✅ Exact keyword matching
hybrid-search	"X100 battery specs"	❌ Returns Y200 docs — Semantic search misses exact model numbers	✅ Hybrid search (dense + sparse)
multi-hop	"Who wrote the paper cited by the 2023 review?"	❌ Returns the review only — Single-step search can't traverse references	✅ Iterative retrieval
hierarchical-rag	"What are the design principles?"	❌ Random fragments — Answer is spread across docs; no single chunk contains it	✅ Hierarchical summaries
tabular-data-routing	"What's the timeout for CAN?" (table)	❌ Fragmented rows — Tables chunked arbitrarily, structure lost	✅ SQL on structured data
multi-query	[User pastes 500-char test report] "What failed and why?"	❌ Vaguely related chunks — Long input → averaged embedding → matches nothing specifically	✅ Multi-query decomposition
comparison-queries	"Compare React vs Vue performance"	❌ Incomplete comparison — Only retrieves one entity, missing the other	✅ Multi-entity retrieval
temporal-queries	"What changed between Q1 and Q2?"	❌ Random chunks — No awareness of time periods in query	✅ Temporal query handling
aggregation-queries	"List all the test cases that failed"	❌ Partial list — No mechanism for comprehensive retrieval	✅ Aggregation query handling
freshness-authority	"What does the official spec say?"	❌ Returns notes — Can't distinguish authoritative vs informal sources	✅ Freshness/authority boosting
query-expansion	"How do I fetch the db config?"	❌ No matches — User says "fetch", docs say "retrieve"; "db" vs "database"	✅ Query expansion
query-rewriting	"Tell me more about it" (after discussing TechCorp)	❌ Lost context — Pronouns like "it" reference nothing, retrieval fails	✅ Conversational context resolution
hyde	"What's TechCorp's approach to sustainability?"	❌ Poor recall — Abstract queries don't embed close to concrete documents	✅ Hypothetical document generation
code-aware-chunking	"How does the auth module work?" (code)	❌ Broken code fragments — Naive chunking splits functions mid-body	✅ Complete functions
contextual-embeddings	"When does it expire?"	❌ Ambiguous chunk — "It expires in 24h" embedded without context; "it" = ?	✅ Summary-prefixed embeddings

[!IMPORTANT] These features are always on—no configuration needed. Fitz automatically detects when to use each capability.

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📦 Fitz vs LangChain vs LlamaIndex

Fitz opts for a deliberately narrower approach.

LangChain and LlamaIndex are powerful LLM application frameworks designed to help developers build complex, end-to-end AI systems. Fitz provides a minimal, replaceable RAG engine with strong epistemic guarantees — without locking users into a framework, ecosystem, or long-term architectural commitment.

Fitz is not a competitor in scope.
It is an infrastructure primitive.

Core philosophical differences ⚖️

Dimension	Fitz	LangChain	LlamaIndex
Primary role	RAG engine	LLM application framework	LLM data framework
User commitment	No framework lock-in	High	High
Engine coupling	Swappable in one line	Deep	Deep
Design goal	Correctness & honesty	Flexibility	Data integration
Long-term risk	Low	Migration-heavy	Migration-heavy

Epistemic behavior (truth over fluency) 🎯

Aspect	Fitz	LangChain / LlamaIndex
“I don’t know”	First-class behavior	Not guaranteed
Hallucination handling	Designed-in	Usually prompt-level
Confidence signaling	Explicit	Implicit

Fitz treats uncertainty as a feature, not a failure.
If the system cannot support an answer with retrieved evidence, it says so.

Transparency & provenance 🔎

Capability	Fitz	LangChain / LlamaIndex
Source attribution	Mandatory	Optional
Retrieval trace	Explicit & structured	Often opaque
Debuggability	Built-in	Tool-dependent

Every answer in Fitz is fully auditable down to the retrieval step.

Scope & complexity 🪐

Aspect	Fitz	LangChain / LlamaIndex
Chains / agents	❎	✔
Prompt graphs	❎	✔
UI abstractions	❎	Often
Cognitive overhead	Very low	High

Fitz intentionally does less — so it can be trusted more.

Use Fitz if you want:

• A replaceable RAG engine, not a framework marriage
• Strong epistemic guarantees (“I don’t know” is valid output)
• Full provenance for every answer
• A transparent, extensible plugin architecture
• A future-proof ingestion pipeline that survives engine changes

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📦 Features

Swappable RAG Engines 🔄

Your data stays. Your queries stay. Only the engine changes.

       ┌─────────────────────────────────────┐
       │           Your Query                │
       │   "What are the payment terms?"     │
       └──────────────────┬──────────────────┘
                          │
                          ▼
       ┌─────────────────────────────────────┐
       │       engine="..."                  │
       │  ┌─────────┐ ┌─────────┐            │
       │  │ fitz    │ │ custom  │  ...       │
       │  │  _rag   │ │ engine  │            │
       │  └────┬────┘ └────┬────┘            │
       │       └───────────┘                 │
       └──────────────────┬──────────────────┘
                          │
                          ▼
       ┌─────────────────────────────────────┐
       │       Your Ingested Knowledge       │
       │      (unchanged across engines)     │
       └─────────────────────────────────────┘

answer = run("What are the payment terms?", engine="fitz_rag")
answer = run("What are the payment terms?", engine="custom")  # your engine

No migration. No re-ingestion. No new API to learn.

Full Provenance 🗂️

Every answer traces back to its source:

Answer: The refund policy allows returns within 30 days...

Sources:
 [1] policies/refund.md [chunk 3] (score: 0.92)
 [2] faq/payments.md [chunk 1] (score: 0.87)

Incremental Ingestion ⚡ → Ingestion Guide

Fitz tracks file hashes and only re-ingests what changed:

$ fitz ingest ./src

Scanning... 847 files
 → 12 new files
 → 3 modified files
 → 832 unchanged (skipped)

Ingesting 15 files...

Re-running ingestion on a large codebase takes seconds, not minutes. Changed your chunking config? Fitz detects that too and re-processes affected files.

Unified Storage (PostgreSQL) 🐘 → Why PostgreSQL?

Fitz uses PostgreSQL + pgvector instead of dedicated vector databases (Pinecone, Qdrant, FAISS, etc.):

Concern	Dedicated Vector DB	Fitz (PostgreSQL)
Deployment	Separate service to run	Embedded (just pip install)
Structured data	Another DB, or hack it	Native SQL
Sync issues	Vectors ↔ metadata drift	One transaction
Local dev	Docker or cloud account	Zero config

Vector search is <1% of query time (LLM dominates). Trading 10ms of search latency for unified storage, full SQL, and zero infrastructure is the right call.

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📦 Plugin Generator → Plugin Development Guide

Generate plugins with AI 🤖

Fitz can generate fully working plugins from natural language descriptions. Describe what you want, and fitz creates, validates, and saves the plugin automatically.

fitz plugin
? Plugin type: chunker
? Description: sentence-based chunker that splits on periods

Generating...
✓ Syntax valid
✓ Schema valid
✓ Plugin loads correctly
✓ Functional test passed

Created: ~/.fitz/plugins/chunking/sentence_chunker.py

The generated plugin is immediately usable—no manual editing required.

Supported plugin types

Type	Format	Description
llm-chat	YAML	Connect to a chat LLM provider
llm-embedding	YAML	Connect to an embedding provider
llm-rerank	YAML	Connect to a reranking provider
retrieval	YAML	Define a retrieval strategy
chunker	Python	Custom document chunking logic
reader	Python	Custom file format reader
constraint	Python	Epistemic safety guardrail

How it works

1. Prompt building: Fitz loads existing plugin examples and schema definitions
2. Generation: Your configured LLM generates the plugin code
3. Multi-level validation: Syntax → Schema → Integration → Functional tests
4. Auto-retry: If validation fails, fitz feeds the error back and retries (up to 3 attempts)
5. Save: Working plugins are saved to ~/.fitz/plugins/

Generated plugins are auto-discovered by fitz on next run—no registration needed.

Example: Custom chunker

fitz plugin
? Plugin type: chunker
? Description: splits text by paragraphs, keeping code blocks intact

# Creates ~/.fitz/plugins/chunking/paragraph_chunker.py

# Generated plugin is immediately usable
fitz ingest ./docs --chunker paragraph_chunker

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📦 Quick Start

CLI

pip install fitz-ai

fitz quickstart ./docs "Your question here"

Fitz auto-detects your LLM provider:

1. Ollama running? → Uses it automatically (fully local)
2. COHERE_API_KEY or OPENAI_API_KEY set? → Uses it automatically
3. First time? → Guides you through free Cohere signup (2 minutes)

After first run, it's completely zero-friction.

Python SDK

import fitz_ai

fitz_ai.ingest("./docs")
answer = fitz_ai.query("Your question here")

print(answer.text)
for source in answer.provenance:
   print(f"  - {source.source_id}: {source.excerpt[:50]}...")

The SDK provides:

• Module-level functions matching CLI (ingest, query)
• Auto-config creation (no setup required)
• Full provenance tracking
• Same honest RAG as the CLI

For advanced use (multiple collections), use the fitz class directly:

from fitz_ai import fitz

physics = fitz(collection="physics")
physics.ingest("./physics_papers")
answer = physics.query("Explain entanglement")

Fully Local (Ollama)

pip install fitz-ai[local]

ollama pull llama3.2
ollama pull nomic-embed-text

fitz quickstart ./docs "Your question here"

Fitz auto-detects Ollama when running. No API keys needed—no data leaves your machine.

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📦 Real-World Usage

Fitz is a foundation. It handles document ingestion and grounded retrieval—you build whatever sits on top: chatbots, dashboards, alerts, or automation.

Chatbot Backend 🤖

Connect fitz to Slack, Discord, Teams, or your own UI. One function call returns an answer with sources—no hallucinations, full provenance. You handle the conversation flow; fitz handles the knowledge.

Example: A SaaS company plugs fitz into their support bot. Tier-1 questions like "How do I reset my password?" get instant answers. Their support team focuses on edge cases while fitz deflects 60% of incoming tickets.

Internal Knowledge Base 📖

Point fitz at your company's wiki, policies, and runbooks. Employees ask natural language questions instead of hunting through folders or pinging colleagues on Slack.

Example: A 200-person startup ingests their Notion workspace and compliance docs. New hires find answers to "How do I request PTO?" on day one—no more waiting for someone in HR to respond.

Continuous Intelligence & Alerting (Watchdog) 🐶

Pair fitz with cron, Airflow, or Lambda. Ingest data on a schedule, run queries automatically, trigger alerts when conditions match. Fitz provides the retrieval primitive; you wire the automation.

Example: A security team ingests SIEM logs nightly. Every morning, a scheduled job asks "Were there failed logins from unusual locations?" If fitz finds evidence, an alert fires to the on-call channel before anyone checks email.

Web Knowledge Base 🌎

Scrape the web with Scrapy, BeautifulSoup, or Playwright. Save to disk, ingest with fitz. The web becomes a queryable knowledge base.

Example: A football analytics hobbyist scrapes Premier League match reports. After ingesting, they ask "How did Arsenal perform against top 6 teams?" or "What tactics did Liverpool use in away games?"—insights that would take hours to compile manually.

Codebase Search 🐍

Fitz includes built-in AST-aware chunking for code bases. Functions, classes, and modules become individual searchable units with docstrings and imports preserved. Ask questions in natural language; get answers pointing to specific code.

Example: A team inherits a legacy Django monolith—200k lines, sparse docs. They ingest the codebase and ask "Where is user authentication handled?" or "What API endpoints modify the billing table?" New developers onboard in days instead of weeks.

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📦 Architecture → Full Architecture Guide

┌───────────────────────────────────────────────────────────────┐
│                         fitz-ai                               │
├───────────────────────────────────────────────────────────────┤
│  User Interfaces                                              │
│  CLI: quickstart | init | ingest | query | chat | serve       │
│  SDK: fitz_ai.fitz() → ingest() → ask()                       │
│  API: /query | /chat | /ingest | /collections | /health       │
├───────────────────────────────────────────────────────────────┤
│  Engines                                                      │
│  ┌───────────┐  ┌────────────┐                                │
│  │  FitzRAG  │  │  Custom... │  (extensible registry)         │
│  └───────────┘  └────────────┘                                │
├───────────────────────────────────────────────────────────────┤
│  LLM Plugins (YAML-defined)                                   │
│  ┌────────┐ ┌───────────┐ ┌────────┐                          │
│  │  Chat  │ │ Embedding │ │ Rerank │                          │
│  └────────┘ └───────────┘ └────────┘                          │
│  openai, cohere, anthropic, ollama, azure...                  │
├───────────────────────────────────────────────────────────────┤
│  Storage (PostgreSQL + pgvector)                              │
│  vectors | metadata | tables | keywords | full-text search    │
├───────────────────────────────────────────────────────────────┤
│  Retrieval Pipelines (plugin choice controls features)        │
│  dense (no rerank) | dense_rerank (with rerank)               │
├───────────────────────────────────────────────────────────────┤
│  Enrichment (baked in via ChunkEnricher)                      │
│  summaries | keywords | entities | hierarchical summaries     │
├───────────────────────────────────────────────────────────────┤
│  Constraints (epistemic safety)                               │
│  ConflictAware | InsufficientEvidence | CausalAttribution     │
└───────────────────────────────────────────────────────────────┘

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📦 CLI Reference → Full CLI Guide

fitz quickstart [PATH] [QUESTION]    # Zero-config RAG (start here)
fitz init                            # Interactive setup wizard
fitz ingest                          # Interactive ingestion
fitz query                           # Single question with sources
fitz chat                            # Multi-turn conversation with your knowledge base
fitz collections                     # List and delete knowledge collections
fitz keywords                        # Manage keyword vocabulary for exact matching
fitz plugin                          # Generate plugins with AI
fitz serve                           # Start REST API server
fitz config                          # View/edit configuration
fitz doctor                          # System diagnostics

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📦 Python SDK Reference → Full SDK Guide

Simple usage (module-level, matches CLI):

import fitz_ai

fitz_ai.ingest("./docs")
answer = fitz_ai.query("What is the refund policy?")
print(answer.text)

Advanced usage (multiple collections):

from fitz_ai import fitz

# Create separate instances for different collections
physics = fitz(collection="physics")
physics.ingest("./physics_papers")

legal = fitz(collection="legal")
legal.ingest("./contracts")

# Query each collection
physics_answer = physics.query("Explain entanglement")
legal_answer = legal.query("What are the payment terms?")

Working with answers:

answer = fitz_ai.query("What is the refund policy?")

print(answer.text)
print(answer.mode)  # CONFIDENT, QUALIFIED, DISPUTED, or ABSTAIN

for source in answer.provenance:
    print(f"Source: {source.source_id}")
    print(f"Excerpt: {source.excerpt}")

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📦 REST API Reference → Full API Guide

Start the server:

pip install fitz-ai[api]

fitz serve                    # localhost:8000
fitz serve -p 3000            # custom port
fitz serve --host 0.0.0.0     # all interfaces

Interactive docs: Visit http://localhost:8000/docs for Swagger UI.

Endpoints:

Method	Endpoint	Description
POST	/query	Query knowledge base
POST	/chat	Multi-turn chat (stateless)
POST	/ingest	Ingest documents from path
GET	/collections	List all collections
GET	/collections/{name}	Get collection stats
DELETE	/collections/{name}	Delete a collection
GET	/health	Health check

Example requests:

# Query
curl -X POST http://localhost:8000/query \
  -H "Content-Type: application/json" \
  -d '{"question": "What is the refund policy?", "collection": "default"}'

# Ingest
curl -X POST http://localhost:8000/ingest \
  -H "Content-Type: application/json" \
  -d '{"source": "./docs", "collection": "mydata"}'

# Chat (stateless - client manages history)
curl -X POST http://localhost:8000/chat \
  -H "Content-Type: application/json" \
  -d '{
    "message": "What about returns?",
    "history": [
      {"role": "user", "content": "What is the refund policy?"},
      {"role": "assistant", "content": "The refund policy allows..."}
    ],
    "collection": "default"
  }'

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License

MIT

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Links

• GitHub
• PyPI
• Changelog

Documentation:

• CLI Reference
• Python SDK
• REST API
• Configuration Guide
• Architecture
• Unified Storage (PostgreSQL + pgvector)
• Ingestion Pipeline
• Enrichment (Hierarchies, Entities)
• Epistemic Constraints
• Plugin Development
• Feature Control
• Custom Engines
• Engine Comparison
• Troubleshooting