Upload 3 files

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+HD_Brochure_2025_26.pdf filter=lfs diff=lfs merge=lfs -text
+os.pdf filter=lfs diff=lfs merge=lfs -text

HD_Brochure_2025_26.pdf

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+version https://git-lfs.github.com/spec/v1
+oid sha256:71dc91142fe5e1f30a2a8b4ccd5bea7d97448b0814cf767ef2438d455b63cda0
+size 1178289

os.pdf

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0e3c71200900a821ada81ebb20df8347d3fb5a49017a29b6d62962927e231bc2
+size 13070525

simplerag.py

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+import os
+from dotenv import load_dotenv
+from langchain_community.document_loaders import PyMuPDFLoader
+from pinecone import Pinecone
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from sentence_transformers import SentenceTransformer
+import ollama
+import numpy as np
+from tqdm import tqdm
+import hashlib
+
+# PDF_FILE = "DeepSeekR1.pdf"
+PDF_FILE = "os.pdf"
+# PDF_FILE = "HD_Brochure_2025_26.pdf"
+PINECONE_API_KEY = "pcsk_71Vjnk_5MBhhy4u8VajTtHBoJTCULFf2D2FxzssiGgsEqWheg9fAx1nxXFWCynRQtUx384"
+INDEX_NAME = "chatdatabase"
+
+# Initialize connections
+pc = Pinecone(api_key=PINECONE_API_KEY)
+index = pc.Index(INDEX_NAME)
+embedder = SentenceTransformer("thenlper/gte-large")
+
+def get_pdf_hash(file_path):
+    """Get MD5 hash of PDF file"""
+    with open(file_path, 'rb') as f:
+        return hashlib.md5(f.read()).hexdigest()[:12]  # Use first 12 chars
+
+def check_embeddings_exist(pdf_hash):
+    try:
+        dummy_vector = [0.0] * 1024  # gte-large dimension
+        results = index.query(
+            vector=dummy_vector,
+            filter={"pdf_hash": pdf_hash},
+            top_k=1
+        )
+        return len(results.matches) > 0
+    except:
+        return False
+
+def create_chunks(file_path):
+    """Load PDF and create chunks"""
+    print(f"📄 Loading PDF: {file_path}")
+    loader = PyMuPDFLoader(file_path)
+    docs = loader.load()
+    
+    splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)
+    chunks = splitter.split_documents(docs)
+    
+    print(f"Created {len(chunks)} chunks from {len(docs)} pages")
+    return chunks
+
+def create_and_store_embeddings(chunks, pdf_hash):
+    """Create embeddings and store in Pinecone"""
+    print("🔄 Creating embeddings...")
+    
+    vectors = []
+    for i, chunk in enumerate(tqdm(chunks, desc="Processing chunks")):
+        # Create embedding
+        embedding = embedder.encode(chunk.page_content)
+        
+        # Prepare vector for Pinecone
+        vectors.append({
+            "id": f"{pdf_hash}_{i}",
+            "values": embedding.tolist(),
+            "metadata": {
+                "text": chunk.page_content,
+                "pdf_hash": pdf_hash,
+                "chunk_id": i,
+                "page": chunk.metadata.get('page', 0)
+            }
+        })
+    
+    # Upload to Pinecone in batches
+    print("⬆️ Uploading to Pinecone...")
+    BATCH_SIZE = 100
+    for i in range(0, len(vectors), BATCH_SIZE):
+        batch = vectors[i:i+BATCH_SIZE]
+        index.upsert(vectors=batch)
+        print(f"Uploaded batch {i//BATCH_SIZE + 1}/{(len(vectors)-1)//BATCH_SIZE + 1}")
+    
+    print(f"✅ Stored {len(vectors)} embeddings")
+
+def query_system(question, top_k=5):
+    """Query the system and get answer"""
+    print(f"\n🔍 Question: {question}")
+    
+    # Create embedding for question
+    question_embedding = embedder.encode(question)
+    
+    # Search Pinecone
+    results = index.query(
+        vector=question_embedding.tolist(),
+        top_k=top_k,
+        include_metadata=True
+    )
+    
+    if not results.matches:
+        return "No relevant information found."
+    
+    context = "\n\n".join([match.metadata['text'] for match in results.matches])
+    
+    prompt = f"""Based on the following context, answer the question. If the answer is not in the context, say so.
+
+Context:
+{context}
+
+Question: {question}
+
+Answer:"""
+    
+    print("🤖 Generating answer...")
+    try:
+        response = ollama.generate(model='llama3:latest', prompt=prompt)
+        answer = response['response']
+        
+        print(f"🎯 Answer: {answer}")
+        
+        return answer
+    except Exception as e:
+        print(f"❌ Error generating answer: {e}")
+        return "Error generating answer. Check if Ollama is running."
+
+def main():
+    pdf_hash = get_pdf_hash(PDF_FILE)
+    print(f"📋 PDF Hash: {pdf_hash}")
+    
+    if check_embeddings_exist(pdf_hash):
+        print("✅ Embeddings already exist, skipping creation")
+    else:
+        print("🆕 Creating new embeddings...")
+        chunks = create_chunks(PDF_FILE)
+        create_and_store_embeddings(chunks, pdf_hash)
+    
+    print("\n🚀 System ready! Ask your questions (type 'quit' to exit)")
+    while True:
+        question = input("\n❓ Your question: ").strip()
+        
+        if question.lower() in ['quit', 'exit', 'q']:
+            break
+            
+        if question:
+            query_system(question)
+
+if __name__ == "__main__":
+    main()