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Retrieval-Augmented Generation (RAG) has revolutionized how we build AI applications that can reason over custom documents and knowledge bases. In this post, I'll walk you through a complete RAG architecture that combines Google's Gemini model with PostgreSQL's vector capabilities to create a powerful document Q&A system.
Before diving into implementation, let's understand why PostgreSQL makes an excellent choice for vector databases:
Our RAG system follows a clean, six-phase workflow:
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The journey begins with raw documents that need to be processed:
This is where the magic happens:
Efficient storage is crucial for performance:
When users ask questions:
Find the most relevant information:
Bring it all together:
Using Gemini for both embedding and generation ensures:
While specialized vector databases exist, PostgreSQL + pgvector offers:
This architecture handles growth gracefully:
The quality of your chunks directly impacts RAG performance:
Choose the right distance function:
Key areas to monitor and optimize:
This RAG architecture delivers tangible value:
For Developers:
For Organizations:
For End Users:
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CREATE EXTENSION IF NOT EXISTS vector;
CREATE TABLE document_chunks (
id SERIAL PRIMARY KEY,
document_name VARCHAR(500) NOT NULL,
content TEXT NOT NULL,
embedding VECTOR(768),
created_at TIMESTAMP DEFAULT NOW()
);
CREATE INDEX ON document_chunks
USING hnsw (embedding vector_cosine_ops);
import google.generativeai as genai
import psycopg2
# Generate embedding
def get_embedding(text):
response = genai.embed_content(
model="models/embedding-001",
content=text
)
return response['embedding']
# Store in database
def store_chunk(content, doc_name):
embedding = get_embedding(content)
cursor.execute("""
INSERT INTO document_chunks (document_name, content, embedding)
VALUES (%s, %s, %s)
""", (doc_name, content, embedding))
SELECT content, document_name
FROM document_chunks
ORDER BY embedding <-> %s::vector
LIMIT 5;
def query_rag(question):
# Get query embedding
query_vector = get_embedding(question)
# Find similar chunks
cursor.execute("""
SELECT content FROM document_chunks
ORDER BY embedding <-> %s::vector
LIMIT 5
""", (query_vector,))
chunks = [row[0] for row in cursor.fetchall()]
context = "\n".join(chunks)
# Generate answer
prompt = f"Context: {context}\nQuestion: {question}\nAnswer:"
response = genai.GenerativeModel('gemini-pro').generate_content(prompt)
return response.text
To implement this architecture:
This RAG architecture represents a practical, production-ready approach to building intelligent document Q&A systems. By combining Google's powerful Gemini models with PostgreSQL's reliability and vector capabilities, you get the best of both worlds: cutting-edge AI performance with enterprise-grade data management.
The beauty of this system lies in its simplicity and power. With just six clear phases, you can transform static documents into an interactive knowledge base that provides accurate, contextual answers to user questions.
Ready to build your own RAG system? The combination of proven technologies and modern AI capabilities makes this the perfect time to start building intelligent applications that truly understand your data.
Retrieval-Augmented Generation (RAG) has revolutionized how we build AI applications that can reason over custom documents and knowledge bases.
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Retrieval-Augmented Generation (RAG) is an advanced AI architecture that combines two key components:
1. A retriever to fetch relevant information from an external knowledge source.
2. A generator (a large language model) to create human-like responses using that information.
Unlike traditional language models that rely only on internal training, RAG can pull in fresh, factual data from outside sources — making it smarter, more accurate, and less prone to hallucinations.
RAG is ideal for any task that requires current, factual, or domain-specific information.
Examples include:
- Customer Support Chatbots
- Search Engines
- Enterprise Knowledge Assistants
- Scientific/Medical Question Answering
- Document Summarization with References
- Legal Document Analysis
1. User Query
- User asks a question (e.g., "What causes global warming?")
2. Retriever
- Converts the query into an embedding
- Searches a document store (e.g., vector database)
- Retrieves the Top-K relevant texts
3. Fused Input
- The system combines the original question with the retrieved texts
- Creates a new prompt with richer context
4. Generator (LLM)
- A language model (e.g., GPT, T5, BART) uses this fused input
- Generates a natural, fact-based answer
5. Final Output
- The answer is returned to the user, grounded in actual documents
- Reduces hallucinations
- Allows up-to-date, dynamic knowledge injection
- More scalable than retraining LLMs
- Great for specialized, high-trust domains
- If retrieval quality is low, output suffers
- Context length limits in LLMs
- High compute cost for large-scale implementations
Retrieval-Augmented Generation (RAG) is an advanced AI architecture that combines two key components, a retriever to fetch relevant information from an external knowledge source and a generator (a large language model) to create human-like responses using that information.
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