Create nl_converter.py

nl_converter.py

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+import logging
+import requests
+import json
+from typing import Dict, Any, List
+import spacy
+from spacy.cli import download
+
+class NLConverter:
+    def __init__(self, api_key: str):
+        self.api_key = api_key
+        self.api_url = "https://api.groq.com/openai/v1/chat/completions"
+        self.headers = {
+            "Authorization": f"Bearer {api_key}",
+            "Content-Type": "application/json"
+        }
+
+        # Attempt to load spaCy model, and install if missing
+        try:
+            self.nlp = spacy.load("en_core_web_sm")
+        except OSError:
+            logging.info("Model 'en_core_web_sm' not found. Downloading...")
+            download("en_core_web_sm")
+            self.nlp = spacy.load("en_core_web_sm")
+        
+        logging.basicConfig(level=logging.INFO)
+        self.logger = logging.getLogger(__name__)
+
+    def extract_entities(self, text: str) -> List[Dict[str, Any]]:
+        """Use spaCy to extract named entities from text."""
+        doc = self.nlp(text)
+        entities = [{"text": ent.text, "label": ent.label_} for ent in doc.ents]
+        self.logger.info(f"Extracted entities: {entities}")
+        
+        # Print extracted entities
+        for entity in entities:
+            print(f"Entity: {entity['text']} - Label: {entity['label']}")
+        
+        return entities
+
+    def convert_to_natural_language(self, query_result: Dict[str, Any], original_query: str) -> Dict[str, Any]:
+        """Convert query results to natural language using LLaMA-3 and spaCy for NER."""
+        if not query_result["success"]:
+            self.logger.error("Query execution failed; no results to process.")
+            return {"success": False, "error": "No results to process"}
+
+        # Format the query result data
+        formatted_data = [dict(zip(query_result["columns"], row)) for row in query_result["results"]]
+        
+        # Convert formatted data to a string for entity extraction
+        formatted_text = "\n".join([str(row) for row in formatted_data])
+
+        # Extract named entities from the query result
+        entities = self.extract_entities(formatted_text)
+
+        # Prepare system and user prompts
+        system_prompt = (
+            "You are a data interpreter that uses named entities to create a clear, natural language explanation. "
+            "Your job is to make sense of the given entities, summarize key insights, and answer the original question."
+        )
+
+        # Include the original query and the extracted entities in the user prompt
+        user_prompt = f"""
+        Original question: {original_query}
+        Extracted Entities:
+        {json.dumps(entities, indent=2)}
+        Data Summary:
+        {formatted_text}
+        Based on this information, generate a natural language explanation of the query results.
+        """
+
+        try:
+            # Prepare the payload to send to the API
+            payload = {
+                "model": "llama3-8b-8192",  # Adjust model name if necessary
+                "messages": [
+                    {"role": "system", "content": system_prompt},
+                    {"role": "user", "content": user_prompt}
+                ],
+                "max_tokens": 500,
+                "temperature": 0.3
+            }
+
+            # Send the request to the API
+            response = requests.post(self.api_url, headers=self.headers, json=payload, timeout=30)
+            response.raise_for_status()
+            
+            result = response.json()
+            if 'choices' in result and result['choices']:
+                explanation = result['choices'][0]['message']['content'].strip()
+                self.logger.info(f"Generated natural language explanation: {explanation}")
+                return {"success": True, "explanation": explanation}
+            
+            return {"success": False, "error": "Failed to generate explanation"}
+
+        except requests.exceptions.RequestException as e:
+            self.logger.error(f"API request error: {str(e)}")
+            return {"success": False, "error": f"API request failed: {str(e)}"}
+
+# Example Usage
+if __name__ == "__main__":
+    api_key = "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"
+    query_result = {
+        "success": True,
+        "columns": ["order_id", "total_price", "order_date"],
+        "results": [
+            [1001, 150, "2024-10-01"],
+            [1002, 200, "2024-10-02"]
+        ]
+    }
+    original_query = "Show me the orders with total price greater than 100"
+    
+    nl_converter = NLConverter(api_key)
+    result = nl_converter.convert_to_natural_language(query_result, original_query)
+    if result["success"]:
+        print(result["explanation"])
+    else:
+        print(f"Error: {result['error']}")