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Mahdi Bazrafshan 754da323ff docs: add comprehensive project documentation

Why:
- Need documentation for team onboarding and reference
- Need technical details for strategy implementations
- Need API reference for developers

Changes:
- README.md: Documentation index and quick start guide
- api-reference.md: All 10 endpoints with examples
- architecture.md: System structure and design decisions
- configuration.md: All environment variables and parameters
- data-flow.md: How data moves through the system
- evaluation-metrics.md: How scoring works with weights
- strategy-technical-details.md: Deep dive into each strategy's implementation

2026-07-27 14:15:28 +03:30

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Configuration Guide

All settings and environment variables for the RAG Chunking Benchmarker.

Environment Variables

Create a .env file in the project root:

# ── Database ──────────────────────────────────────────────────────

# SQLite database path
DATABASE_URL=sqlite:///./data/chunking_benchmark.db

# Qdrant vector database
QDRANT_URL=http://localhost:6333
QDRANT_API_KEY=

# ── OpenAI ────────────────────────────────────────────────────────

# API key for embeddings and LLM
OPENAI_API_KEY=sk-your-key-here

# ── Chunking Parameters ───────────────────────────────────────────

# Fixed-size strategy
CHUNK_SIZE=512
CHUNK_OVERLAP=50

# Semantic strategy
SEMANTIC_THRESHOLD=0.5
SEMANTIC_MIN_CHUNK_SIZE=5

# ── Models ────────────────────────────────────────────────────────

# Embedding model (used for all strategies)
EMBEDDING_MODEL=text-embedding-3-small

# LLM model (used for contextual retrieval and evaluation)
LLM_MODEL=gpt-4o-mini

Parameter Details

Chunking Parameters

Parameter	Type	Default	Range	Description
`CHUNK_SIZE`	int	512	100-2000	Target tokens per chunk
`CHUNK_OVERLAP`	int	50	0-200	Token overlap between chunks
`SEMANTIC_THRESHOLD`	float	0.5	0.0-1.0	Cosine similarity threshold for chunk boundaries
`SEMANTIC_MIN_CHUNK_SIZE`	int	5	1-20	Minimum sentences per semantic chunk

Model Parameters

Parameter	Type	Default	Options	Description
`EMBEDDING_MODEL`	str	text-embedding-3-small	text-embedding-3-small, text-embedding-3-large	OpenAI embedding model
`LLM_MODEL`	str	gpt-4o-mini	gpt-4o-mini, gpt-4o	Model for contextual retrieval and evaluation

Default Values in Code

# src/core/config.py
class Settings(BaseSettings):
    # Database
    database_url: str = "sqlite:///./data/chunking_benchmark.db"
    qdrant_url: str = "http://localhost:6333"
    qdrant_api_key: str = ""
    
    # OpenAI
    openai_api_key: str = ""
    
    # Chunking
    chunk_size: int = 512
    chunk_overlap: int = 50
    semantic_threshold: float = 0.5
    semantic_min_chunk_size: int = 5
    
    # Models
    embedding_model: str = "text-embedding-3-small"
    llm_model: str = "gpt-4o-mini"

Effect of Parameters

chunk_size

Value	Chunks	Quality	Speed
256	More	Higher context loss	Faster
512	Balanced	Balanced	Balanced
1024	Fewer	Lower context loss	Slower

chunk_overlap

Value	Context Preservation	Redundancy
0	None	None
50	Moderate	Low
100	High	High

semantic_threshold

Value	Chunk Size	Topic Sensitivity
0.3	Larger	Less sensitive
0.5	Balanced	Balanced
0.7	Smaller	More sensitive

Environment-Specific Configs

Development

DATABASE_URL=sqlite:///./data/dev.db
QDRANT_URL=http://localhost:6333
LOG_LEVEL=DEBUG

Production

DATABASE_URL=postgresql://user:pass@localhost/dbname
QDRANT_URL=http://qdrant:6333
QDRANT_API_KEY=your-api-key
LOG_LEVEL=WARNING

Testing

DATABASE_URL=sqlite:///./data/test.db
QDRANT_URL=http://localhost:6333
CHUNK_SIZE=100

Validation

Settings are validated on startup. Invalid values will cause:

pydantic.ValidationError: 1 validation error for Settings

Common errors:

Error	Cause	Fix
Missing OPENAI_API_KEY	No API key	Add to .env
Invalid QDRANT_URL	Wrong URL format	Check URL
CHUNK_SIZE < 1	Too small	Increase value

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