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# from huggingface_hub import InferenceClient
# import os
# # Use a Hugging Face inference endpoint like "google/gemma-1.1-7b-it"
# # You must have access to this model (either public or via token)
# HUGGINGFACE_API_TOKEN = os.getenv("HF_TOKEN") # Add this in your HF Space's secret settings
# DEFAULT_MODEL = "google/gemma-1.1-7b-it"
# client = InferenceClient(DEFAULT_MODEL, token=HUGGINGFACE_API_TOKEN)
# def send_chat_prompt(prompt: str, model: str, system_prompt: str) -> str:
# full_prompt = f"<|start_of_turn|>system\n{system_prompt}<|end_of_turn|>\n" \
# f"<|start_of_turn|>user\n{prompt}<|end_of_turn|>\n" \
# f"<|start_of_turn|>assistant\n"
# response = client.text_generation(
# prompt=full_prompt,
# max_new_tokens=500,
# temperature=0.5,
# stop_sequences=["<|end_of_turn|>"]
# )
# return response.strip()
# def main_generate(prompt, model=DEFAULT_MODEL, system_prompt="You are a helpful assistant that generates SPARQL queries."):
# response = send_chat_prompt(prompt, model, system_prompt)
# response = response.replace('```', '').replace('json', '').strip()
# return response
# from sentence_transformers import SentenceTransformer
# model = SentenceTransformer("thenlper/gte-large") # downloaded from Hugging Face
# def get_embeddings(texts):
# if isinstance(texts, str):
# texts = [texts]
# embeddings = model.encode(texts, convert_to_numpy=True)
# return embeddings
import ollama
import openai
def get_embeddings(texts):
response = ollama.embed(model="mxbai-embed-large", input=texts)
embeddings = response["embeddings"]
return embeddings
openai_api_key = "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"
#generate
def send_chat_prompt(prompt, model, system_prompt ):
client = openai.OpenAI(
base_url="http://localhost:11434/v1" if not "gpt" in model else None,
api_key= "ollama" if not "gpt" in model else openai_api_key)
resp = client.chat.completions.create(
model=model,
temperature = 0.5 ,
messages=[
{"role": "system", "content": system_prompt},
{"role": "user", "content": prompt}])
response = resp.choices[0].message.content
return response
def main_generate(prompt,model, system_prompt):
response = send_chat_prompt(prompt,model, system_prompt)
response = response.replace('```','').replace('json','')
#print(f" {model} Response:", response)
return response
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