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bfde3e7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 | # import requests
# import json
# import os
# from dotenv import load_dotenv
# load_dotenv()
# def get_answer(model_name, context, question):
# llm_key = os.getenv("llm_key")
# url = os.getenv("main_url")
# # Construct the prompt for the model
# prompt = f"You are a Question Answering Model. Can you help me answer the question: {question} from the context: {context}? Just return the answer only. The document may contain some Arabic text; please translate that to English if needed."
# # Prepare payload for API request
# payload = {
# "model": model_name,
# "messages": [
# {
# "role": "user",
# "content": prompt
# }
# ],
# "max_tokens": 300,
# "temperature": 0.2
# }
# headers = {
# 'Authorization': f'Bearer {llm_key}',
# 'Content-Type': 'application/json'
# }
# # Convert payload to JSON string
# json_payload = json.dumps(payload)
# try:
# # Send POST request to the API
# response = requests.post(url, headers=headers, data=json_payload)
# # Check if request was successful
# if response.status_code == 200:
# response_data = response.json() # Parse response JSON
# answer = response_data['choices'][0]['message']['content'] # Extract model's answer from response
# return answer
# else:
# print(f"Request failed with status code: {response.status_code}")
# return None
# except requests.exceptions.RequestException as e:
# print(f"Error occurred: {e}")
# return None
# from huggingface_hub import InferenceClient
# def get_hugging_face_answer(model_name, context, question):
# client = InferenceClient(model_name, token=os.getenv("HF_TOKEN"))
# prompt = f"You are a Question Answering Model. Can you help me answer the question: {question} from the context: {context}? Just return the answer only. The document may contain some Arabic text; please translate that to English if needed."
# output = client.text_generation(prompt , max_new_tokens = 200, stream=True, temperature=0.1)
# return output
import os
from groq import Groq
from dotenv import load_dotenv
load_dotenv()
GROQ_API_KEY = os.getenv('GROQ_API')
def get_answer_from_context(model_name, context, question):
client = Groq(api_key=GROQ_API_KEY)
chat_completion = client.chat.completions.create(
model=model_name,
messages=[
{
"role": "system",
"content": f"You are a Question Answering LLM that uses context provided: {context} to answer user's query. Just return the answer only. The document may contain some Arabic text; please translate that to English if needed."
},
{
"role": "user",
"content": question,
}
],
temperature=0.2,
max_tokens=200,
top_p=1,
stop=None,
stream=False,
# response_format={"type": "json_object"}
)
return chat_completion.choices[0].message.content |