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📌 Workflow:
Query → Search Mode → Ranking & Relevance → LLM Processing → Output
Understanding Your Search Modes:
- Full-Text Search: Exact text match using traditional databases/search engines.
SELECT FROM documents WHERE content LIKE '%keyword%';
- Keyword Search: Matches specific terms/tags.
from sklearn.feature_extraction.text import TfidfVectorizer vectorizer = TfidfVectorizer() X = vectorizer.fit_transform(documents)
- Semantic Search: Uses embeddings (e.g., BERT).
from sentence_transformers import SentenceTransformer model = SentenceTransformer('all-MiniLM-L6-v2') embeddings = model.encode(["your query"]) - Vector Search: Finds similar meanings using vector DBs (e.g., FAISS, Pinecone).
import faiss index = faiss.IndexFlatL2(embeddings.shape[bash]) index.add(embeddings)
Ranking & Relevance
- Results scored by relevance, recency, and user context.
- Re-ranking using LLMs (e.g., OpenAI’s GPT-4o).
response = openai.ChatCompletion.create( model="gpt-4", messages=[{"role": "user", "content": "Rank these results..."}] )
GenAI Layer → Application Output
- LLM generates refined answers.
- Example: Summarizing retrieved data.
summary = openai.ChatCompletion.create( model="gpt-4", messages=[{"role": "user", "content": "Summarize this..."}] )
You Should Know:
1. Implementing Semantic Search with Elasticsearch
PUT /semantic_index
{
"mappings": {
"properties": {
"embedding": { "type": "dense_vector", "dims": 768 }
}
}
}
2. Vector Similarity Search with FAISS
import numpy as np
d = 64 Dimension
nb = 100000 Database size
nq = 10000 Queries
xb = np.random.random((nb, d)).astype('float32')
index = faiss.IndexFlatL2(d)
index.add(xb)
D, I = index.search(xb[:5], k=5) Search top 5
3. Fine-Tuning BERT for Semantic Search
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased')
inputs = tokenizer("your query", return_tensors="pt")
outputs = model(inputs)
4. Using Pinecone for Vector Storage
import pinecone
pinecone.init(api_key="YOUR_API_KEY")
pinecone.create_index("genai-search", dimension=512)
index = pinecone.Index("genai-search")
index.upsert([("id1", [0.1, 0.2, ...])])
What Undercode Say:
GenAI-powered search is evolving rapidly, integrating vector databases, semantic embeddings, and LLM post-processing. Key takeaways:
– Hybrid search (keyword + vector) improves accuracy.
– Re-ranking with GPT-4 enhances relevance.
– Open-source tools (FAISS, Sentence-BERT) make AI search accessible.
Expected Output:
A scalable GenAI search pipeline combining Elasticsearch + FAISS + GPT-4 for high-accuracy results.
Prediction:
AI-powered search will replace traditional keyword-based engines by 2027, with real-time semantic understanding becoming standard.
URLs:
IT/Security Reporter URL:
Reported By: Tech In – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
