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RAG Implementation

FluidGrids provides a sophisticated Retrieval Augmented Generation (RAG) implementation that enables you to build context-aware AI applications. Our platform combines advanced retrieval mechanisms with state-of-the-art language models to deliver accurate, contextually relevant responses.

Core Components​

Document Processing Intelligent document handling:

Our document processing pipeline includes:

  • Multi-format support (PDF, Word, HTML)
  • Smart text extraction
  • Metadata preservation
  • Structure recognition
  • Content normalization

Chunking Strategies Advanced text segmentation:

Implement various chunking approaches:

  • Semantic chunking
  • Sliding window
  • Fixed-size chunks
  • Overlap control
  • Boundary preservation

Implementation Guide​

Basic Setup Configure RAG system:

from fluidgrids.rag import RAGSystem

# Initialize RAG
rag = RAGSystem(
    embedding_model="openai",
    llm_model="gpt-4",
    vector_store="pinecone"
)

# Process documents
rag.process_documents(
    documents=["doc1.pdf", "doc2.docx"],
    chunk_size=500,
    overlap=50
)

Query Processing Handle user queries:

# Simple query
response = rag.query(
    "What is the project timeline?",
    max_tokens=200
)

# Advanced query
response = rag.query(
    "Explain the technical architecture",
    context_window=5,
    temperature=0.7,
    filters={"domain": "technical"}
)

Advanced Features​

Context Management Sophisticated context handling:

from fluidgrids.rag import ContextManager

# Configure context
context = ContextManager(
    window_size=3,
    relevance_threshold=0.8,
    deduplication=True
)

# Query with context
response = rag.query(
    "Follow-up question",
    context=context,
    conversation_history=history
)

Hybrid Search Combined search strategies:

# Configure hybrid search
results = rag.hybrid_search(
    query="technical requirements",
    weights={
        "semantic": 0.7,
        "keyword": 0.3,
        "metadata": 0.2
    },
    filters={"category": "technical"}
)

Performance Optimization​

Caching System Implement response caching:

from fluidgrids.rag import ResponseCache

# Configure cache
cache = ResponseCache(
    ttl=3600,
    max_size=1000,
    strategy="lru"
)

# Query with cache
response = rag.query(
    "Cached query",
    cache=cache
)

Batch Processing Handle multiple queries:

# Batch processing
responses = rag.batch_query(
    queries=["query1", "query2"],
    max_concurrent=5,
    batch_size=10
)

Quality Controls​

Response Validation Ensure response quality:

from fluidgrids.rag import QualityChecker

# Configure validation
checker = QualityChecker(
    checks=[
        "relevance",
        "factuality",
        "coherence"
    ]
)

# Validate response
quality_score = checker.validate(
    query="What is the timeline?",
    response=response,
    context=retrieved_context
)

Source Attribution Track information sources:

# Get source attribution
sources = rag.get_sources(
    response,
    include_metadata=True,
    confidence_threshold=0.8
)

Best Practices​

Implementation Guidelines Follow these practices:

  • Optimize chunk sizes
  • Configure proper overlap
  • Implement caching
  • Monitor performance
  • Regular index updates

Query Optimization Enhance query quality:

  • Use appropriate context
  • Implement hybrid search
  • Configure filters
  • Validate responses
  • Track performance

Getting Started​

Begin implementing RAG:

For RAG implementation support, contact our AI Team.