1. Preprocessing & Chunking

• Text cleaning:

  Strip HTML tags, remove special characters, normalize whitespace.

• Chunking:

  Divide texts into coherent chunks of approximately 100-400 words. Overlap slightly (~50 words) for context continuity.

2. Vector Embedding Generation

• Use advanced embedding models such as:
  • OpenAI embeddings (e.g., text-embedding-3-large)
  • Cohere Embed v3
  • SentenceTransformers (open-source option) (e.g., all-MiniLM-L6-v2)
• Generate embeddings via API or locally using GPU inference:

# Example: OpenAI embeddings API call
import openai

response = openai.embeddings.create(
    input=["chunk of text"],
    model="text-embedding-3-large"
)
embedding_vector = response.data[0].embedding

3. Database Storage & Retrieval

• Use a dedicated vector database for storage and querying:
  • Weaviate
  • Pinecone
  • Chroma
  • Qdrant
  • FAISS (local/offline usage)
• Recommended storage schema:

{
  "id": "unique_chunk_id",
  "metadata": {
    "title": "Article Title",
    "url": "https://...",
    "date": "YYYY-MM-DD",
    "chunk_number": 3
  },
  "vector": [0.123, -0.456, ...],
  "text": "The actual chunk text for reference"
}

4. Semantic Search Implementation

• Queries converted into embeddings:

query_embedding = model.encode("demographic trends 2025")

• Vector database similarity search: