Pinecone Integration

# Traditional approach with Pinecone
import pinecone
import openai
from sentence_transformers import SentenceTransformer
import pandas as pd

# Initialize Pinecone
pinecone.init(api_key="your-api-key", environment="us-west1-gcp")
index = pinecone.Index("your-index")

# Separate data processing
def process_documents(docs):
    processed = []
    model = SentenceTransformer('all-MiniLM-L6-v2')
    
    for doc in docs:
        # Custom text extraction
        text = extract_text(doc)
        
        # Generate embeddings  
        embedding = model.encode(text)
        
        # Store in Pinecone
        index.upsert([(doc['id'], embedding, doc)])
        processed.append(doc)
    
    return processed

# Custom search function
def search_docs(query, top_k=10):
    model = SentenceTransformer('all-MiniLM-L6-v2')
    query_embedding = model.encode(query)
    
    results = index.query(
        vector=query_embedding.tolist(),
        top_k=top_k,
        include_metadata=True
    )
    
    return results['matches']

# Separate orchestration needed for updates, versioning, etc.