feat: Implement Pinecone vector store integration

- Update config.py with Pinecone settings and model configurations
- Implement VectorStore class with Pinecone backend
- Add comprehensive vector operations (add, search, delete)
- Set up proper error handling and metadata management
- Add .gitignore for Python project

test_model.py

@@ -0,0 +1,63 @@
+import argparse
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+def generate_text(model_path, prompt, max_length=100, num_return_sequences=1, temperature=0.7):
+    """Generate text using the finetuned model."""
+    # Load the finetuned model and tokenizer
+    print(f"Loading model from {model_path}")
+    tokenizer = AutoTokenizer.from_pretrained(model_path)
+    model = AutoModelForCausalLM.from_pretrained(model_path)
+    
+    # Format the prompt
+    formatted_prompt = f"Prompt: {prompt}\nCompletion:"
+    
+    # Generate text
+    print(f"Generating text for prompt: {prompt}")
+    inputs = tokenizer(formatted_prompt, return_tensors="pt")
+    outputs = model.generate(
+        **inputs,
+        max_length=max_length,
+        num_return_sequences=num_return_sequences,
+        temperature=temperature,
+        do_sample=True,
+        pad_token_id=tokenizer.eos_token_id
+    )
+    
+    # Decode and return the generated text
+    generated_texts = []
+    for output in outputs:
+        generated_text = tokenizer.decode(output, skip_special_tokens=True)
+        # Extract just the completion part
+        completion = generated_text.split("Completion:")[-1].strip()
+        generated_texts.append(completion)
+    
+    return generated_texts
+
+def main():
+    class Args:
+        def __init__(self):
+            self.model_path = "finetuned_model"  # Default path to the finetuned model
+            self.prompt = "Create a welcome message for new clients"  # Default prompt
+            self.max_length = 100
+            self.num_return_sequences = 1
+            self.temperature = 0.7
+    
+    args = Args()
+    
+    # Generate text
+    generated_texts = generate_text(
+        args.model_path,
+        args.prompt,
+        args.max_length,
+        args.num_return_sequences,
+        args.temperature
+    )
+    
+    # Print the generated text
+    print("\nGenerated text:")
+    for i, text in enumerate(generated_texts):
+        print(f"\n--- Generation {i+1} ---")
+        print(text)
+
+if __name__ == "__main__":
+    main()