Core Concepts
Retrieval-Augmented Generation (RAG) Systems
Understanding RAG systems and how Simba enhances them
Retrieval-Augmented Generation (RAG) Systems
Retrieval-Augmented Generation (RAG) combines large language models (LLMs) with external knowledge retrieval to provide more accurate and contextually relevant responses.
What is RAG?
RAG systems combine two key components:
- Retriever: Fetches relevant information from a knowledge base
- Generator: Uses the retrieved information to generate accurate responses
Advantages:
- Reduces hallucinations in LLM outputs
- Enables access to domain-specific knowledge
- Provides up-to-date information
- Improves factual accuracy
How RAG Systems Work
-
Ingestion Pipeline:
- Documents are parsed into manageable chunks
- Chunks are converted into embeddings (vector representations)
- Embeddings are stored in a vector database
-
Query Processing:
- User query is converted into an embedding
- Vector database retrieves relevant chunks based on similarity
- Retrieved chunks provide context for the LLM
-
Response Generation:
- LLM generates a response using both the query and retrieved context
How Simba Enhances RAG Systems
Simba provides a complete knowledge management system designed specifically for RAG applications:
1. Flexible Ingestion Pipeline
- Support for multiple document formats
- Configurable chunking strategies
- Multiple embedding model options
2. Vector Store Abstraction
- Unified API across different vector store backends
- Easy switching between vector stores
- Metadata filtering capabilities
3. Retrieval Optimization
- Query preprocessing
- Contextual retrieval
- Result filtering and reranking
4. Developer Experience
- Simple Python SDK
- Intuitive API design
Example: Basic RAG with Simba
Best Practices for RAG with Simba
- Optimize chunk size for your specific use case
- Choose appropriate embedding models for your domain
- Use metadata filtering to narrow down search space
- Monitor and evaluate retrieval quality
Common RAG Challenges and Simba Solutions
Challenge | Simba Solution |
---|---|
Document format variety | Multi-format parser support |
Chunking strategy optimization | Configurable chunking parameters |
Vector store selection | Pluggable vector store backends |
Embedding model choice | Multiple embedding models support |
Query-document mismatch | Query transformation options |
Context window limitations | Smart context assembly |
Relevance scoring | Configurable similarity metrics |
Advanced RAG Techniques
- Multi-stage Retrieval: Iterative refinement of search results
- Query Decomposition: Breaking complex queries into simpler sub-queries
- Hypothetical Document Embeddings: Generating synthetic queries for better retrieval
- Self-RAG: Having the LLM critique and improve its own retrievals
Next Steps
- Learn about Vector Stores in Simba
- Explore Embedding Models options
- Understand Chunking Strategies
- Try our Document Ingestion Example