Embedding and Indexing Layer

This layer converts content into searchable vector representations.

Embedding Process

graph TD
    A[Clean Content] --> B{Content Type}
    B -->|Text| C[Text Embedding Model]
    B -->|Image| D[Vision Embedding Model]
    B -->|Both| E[Multimodal Embedding]
    
    C & D & E --> F[Vector Embeddings]
    F --> G[Vector Database]
    G --> H[Indexed & Searchable]

Embedding Models

Text Embeddings

# OpenAI Ada-002
embedding = openai.embed("This is a document about cats")
# Returns: [0.023, -0.104, 0.445, ...]  (1536 dimensions)

# Cohere Embed v3
embedding = cohere.embed(texts=["Document text"], model="embed-english-v3.0")

Image Embeddings

# CLIP
image_emb = clip.encode_image(image)
text_emb = clip.encode_text("a photo of a cat")
# Both in same vector space!

Indexing Strategy

# Conceptual vector DB indexing
from chromadb import Client

client = Client()
collection = client.create_collection("documents")

collection.add(
    embeddings=[embedding_vector],
    documents=[text_content],
    metadatas=[{'source': 'doc.pdf', 'page': 1}],
    ids=['doc-1-page-1']
)

Index Optimization

• Chunking: Split large docs into 500-1000 token chunks
• Overlap: 10-20% overlap between chunks
• Metadata: Store source, page, section for filtering
• IDs: Unique identifiers for tracking

Next: Retrieval and ranking.