Retrieval

Retrieval-Augmented Generation (RAG) enhances AI responses by integrating external knowledge retrieval. The retrieval phase is crucial as it fetches the most relevant documents from a vector database, enabling more informed and accurate responses.

How Retrieval Works

Retrieval in RAG operates by:

1. Receiving a Query: The input is transformed into an embedding vector.

2. Searching the Vector Database: The embedding is used to find the most similar stored vectors.

3. Ranking & Filtering: Retrieved documents are ranked based on relevance.

4. Passing to the Model: The top documents are appended to the context and sent to the LLM.

Implementing Retrieval with FAISS

FAISS (Facebook AI Similarity Search) is a popular vector database for efficient similarity searches. Below is an implementation using OpenAI embeddings:

from sentence_transformers import SentenceTransformer
import faiss
import numpy as np

# Initialize embedding model
model = SentenceTransformer("all-MiniLM-L6-v2")

# Sample documents
documents = [
    "Blockchain security is crucial for decentralized finance.",
    "AI-driven audits can detect vulnerabilities in smart contracts.",
    "Privacy DEX ensures anonymity in crypto transactions."
]

# Convert documents to embeddings
embeddings = np.array([model.encode(doc) for doc in documents])

# Initialize FAISS index
dimension = embeddings.shape[1]
index = faiss.IndexFlatL2(dimension)
index.add(embeddings)

# Query retrieval
query = "How to improve smart contract security?"
query_embedding = model.encode(query).reshape(1, -1)
D, I = index.search(query_embedding, k=2)  # Retrieve top-2 results

# Display results
for i in I[0]:
    print("Retrieved Document:", documents[i])

Using Pinecone for Scalable Retrieval

Pinecone is a managed vector database suitable for large-scale deployments. Below is an example using OpenAI:

import openai
import pinecone

# Initialize Pinecone
pinecone.init(api_key="YOUR_PINECONE_API_KEY", environment="us-west1-gcp")
index = pinecone.Index("rag-retrieval")

# Function to generate embeddings
def get_embedding(text):
    response = openai.Embedding.create(input=[text], model="text-embedding-ada-002")
    return response["data"][0]["embedding"]

# Insert documents
documents = ["AI enhances blockchain security.", "Smart contracts require regular audits."]
for i, doc in enumerate(documents):
    index.upsert([(str(i), get_embedding(doc), {"text": doc})])

# Query retrieval
query = "How do AI audits improve security?"
query_embedding = get_embedding(query)
results = index.query(query_embedding, top_k=2, include_metadata=True)

# Display results
for match in results["matches"]:
    print("Retrieved:", match["metadata"]["text"])

Best Practices for Efficient Retrieval

• Use High-Quality Embeddings: Models like OpenAI’s text-embedding-ada-002 or Sentence Transformers improve accuracy.

• Optimize Indexing Strategy: FAISS works well for in-memory searches, while Pinecone scales for production use.

• Fine-Tune Similarity Metrics: Experiment with cosine similarity or L2 distance for better relevance.