RAG Pipelines for Context-Aware Agents

Retrieval-Augmented Generation (RAG) has become essential for production AI agents. LLMs alone are limited to their training data—they can't access proprietary documents, real-time data, or information that didn't exist when they were trained.

RAG solves this by dynamically retrieving relevant information at inference time and providing it as context to the LLM.

Why Agents Need RAG

Knowledge Coverage: No LLM is trained on your company's internal documents, customer data, or domain-specific knowledge bases.

Freshness: LLM training data has a cutoff date. RAG provides access to current information.

Accuracy: Grounding responses in retrieved documents reduces hallucination.

Transparency: RAG enables citation—agents can point to specific source documents for their claims.

Cost Efficiency: Cheaper to retrieve relevant docs than fine-tune LLMs on all your data.

RAG Architecture Components

1. Ingestion Pipeline

Transform raw documents into searchable embeddings:

Document Loading: Parse PDFs, Word docs, HTML, databases
Chunking: Split documents into semantic units (paragraphs, sections)
Embedding Generation: Convert chunks to vector representations
Storage: Index vectors in vector database

2. Retrieval Pipeline

Find relevant information for a given query:

Query Processing: Convert user query to embedding
Vector Search: Find most similar chunks (cosine similarity, approximate nearest neighbors)
Ranking: Rerank results by relevance using cross-encoders
Filtering: Apply metadata filters (date ranges, document types, access controls)

3. Generation Pipeline

Use retrieved context to generate responses:

Context Assembly: Construct prompt with retrieved chunks
LLM Generation: Generate response grounded in retrieved context
Citation Extraction: Track which sources contributed to response
Quality Checks: Verify claims against source material

Advanced RAG Techniques

Hybrid Search: Combining Vector + Keyword Search

Vector search excels at semantic similarity but misses exact keyword matches. Hybrid search combines:

Dense Retrieval: Semantic similarity via embeddings
Sparse Retrieval: Exact keyword matching (BM25)
Fusion Ranking: Combine scores using reciprocal rank fusion

Hierarchical Retrieval

For long documents:

Level 1: Retrieve relevant sections (chapter, topic)
Level 2: Retrieve specific chunks within relevant sections
Improves precision by narrowing context before detailed retrieval

Query Rewriting

LLM rewrites user query into multiple search-optimized queries:

Original: "How do our competitors price their products?"
Rewritten: ["competitor pricing strategies", "market pricing comparison", "pricing models in [industry]"]
Retrieve for each query, combine results

Metadata Filtering

Combine semantic search with structured filters:

retrieval_query = {
  "vector_query": embedding(user_query),
  "filters": {
    "document_type": ["financial_report", "earnings_call"],
    "date_range": {"start": "2024-01-01", "end": "2024-12-31"},
    "company": ["Competitor_A", "Competitor_B"]
  },
  "limit": 10
}

Production RAG at Boston Agent House

Document Intelligence RAG

Challenge: Analyze 200+ documents monthly, each 50-200 pages.

Solution:

Hierarchical chunking (section → paragraph → sentence)
Domain-specific embedding models (fine-tuned on technical documents)
Graph-augmented RAG: Vector DB + knowledge graph of concept relationships
Multi-query retrieval: For complex questions, generate 3-5 sub-queries

Results:

95% citation accuracy (verified by legal team)
3x faster than manual analysis
Found cross-document patterns humans missed

Competitive Intelligence RAG

Challenge: Real-time retrieval from 18 months of competitive data (news, filings, social media).

Solution:

Hybrid search (semantic + keyword)
Temporal decay: Recent information weighted higher
Source diversity: Retrieve from multiple source types
Streaming retrieval: Fetch additional context as analysis proceeds

Results:

87% of stakeholders rate alerts "actionable"
Average 47 minutes from event to insight
40% reduction in noise vs. keyword-only approach

Vector Database Selection

Pinecone

Pros: Managed service, fast, easy to use
Cons: Cloud-only, limited metadata filtering
Best for: Rapid prototyping, cloud-first deployments

Weaviate

Pros: Hybrid search, rich metadata, open-source
Cons: More complex setup
Best for: Complex filtering requirements, on-prem deployments

Qdrant

Pros: High performance, rich filtering, open-source
Cons: Smaller ecosystem
Best for: Performance-critical applications

Chroma

Pros: Simple, embedded mode, open-source
Cons: Limited scalability
Best for: Prototyping, small-scale deployments

Conclusion

RAG transforms agents from general-purpose chatbots into domain experts grounded in your specific knowledge base. The key is designing retrieval strategies that balance precision (finding exactly what matters) with recall (not missing important context) while maintaining low latency.

Production RAG isn't just vector search—it's hybrid retrieval, intelligent chunking, metadata filtering, and quality assurance working together to provide agents with exactly the context they need.