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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']}")
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