CSV-Based Retrieval

Lightweight retrieval approach using CSV files to store pre-processed document chunks with TF-IDF based similarity search. Particularly effective for small to medium-sized document corpora where vector database overhead is unnecessary.

Core Architecture

Data Structure

• CSV format: Document chunks stored as rows with metadata columns
• Standardized schema: Consistent chunk representation across different corpora
• Source tracking: Metadata linking chunks back to original documents
• Pre-processing: Chunks already cleaned, segmented, and formatted for search

Search Implementation

• TF-IDF tokenization: Term frequency analysis for relevance scoring
• Similarity calculation: Fast text-based matching without vector embeddings
• Ranked results: Sorted by relevance score with configurable result limits
• Chunk objects: Standardized return format compatible with vector retrievers

Performance Characteristics

Caching Strategy

The assistant-rh implementation demonstrates effective caching patterns:

• Single CSV read: File loaded once per session and cached in memory
• Tokenization caching: Pre-computed TF-IDF weights stored for reuse
• Corpus statistics: Overview metrics cached separately from search index
• Session persistence: Retriever instance reused across multiple queries

Scalability Considerations

• Memory footprint: Entire corpus held in memory for fast access
• Search latency: Sub-second response times for typical document sizes
• Corpus size limits: Practical upper bound around 1000-10000 chunks
• Startup time: Initial CSV parsing adds session initialization delay

Integration Patterns

Streamlit UI Integration

Effective patterns for web application integration:

• Sidebar configuration: Corpus selection and statistics display
• Fallback handling: Graceful degradation when other retrievers unavailable
• Result formatting: Consistent chunk display across retrieval methods
• Error recovery: Robust handling of CSV parsing and search failures

Interface Consistency

Key architectural principle ensuring interchangeable retrievers:

• Standardized return types: All retrievers return Chunk objects
• Common parameters: Consistent search API across backends
• Metadata preservation: Source information maintained through search pipeline
• Error handling: Uniform exception patterns for UI error display

Use Cases

Lightweight RAG Applications

Ideal scenarios for CSV-based retrieval:

• Small document corpora: 100-1000 pre-processed chunks
• Rapid prototyping: Quick setup without vector database infrastructure
• Demo applications: Simplified deployment with file-based storage
• Development testing: Local development without external dependencies

Hybrid Architectures

CSV retrieval as part of larger systems:

• Fallback mechanism: Backup when vector databases unavailable
• Development phase: Initial implementation before vector migration
• A/B testing: Comparison baseline for vector search performance
• Edge deployment: Resource-constrained environments

Implementation Examples

French Legal Corpus

The assistant-rh project used CSV retrieval for:

• 130 decree chunks: French legal documents from corpus_130.csv
• TF-IDF search: Term-based relevance without embeddings
• Streamlit chat: Interactive Q&A with retrieved context
• Performance optimization: Cached loading with session persistence

Advantages and Limitations

Advantages

• Simple deployment: No database setup or vector infrastructure required
• Fast development: Rapid iteration with file-based storage
• Transparent debugging: CSV contents easily inspected and modified
• Low resource usage: Minimal memory and compute requirements
• Portable: Easy backup, version control, and distribution

Limitations

• Scalability ceiling: Performance degrades with large corpora
• Memory constraints: Entire dataset must fit in application memory
• Limited search sophistication: No semantic similarity or neural ranking
• Update complexity: Modifying corpus requires file regeneration
• Concurrent access: File-based storage not suitable for multi-user systems

See also

• lightweight-rag
• cached-retrieval
• streamlit-ui
• interface-consistency
• tf-idf
• assistant-rh