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from fastapi import FastAPI
from pydantic import BaseModel
from llama_cpp import Llama
# Model loading with specified path and configuration
llm = Llama(
model_path="phi-3-mini-4k-instruct.Q4_K.gguf", # Update the path as necessary
n_ctx=4096,
n_threads=2,
)
# Pydantic object for validation
class Validation(BaseModel):
user_prompt: str
system_prompt: str
max_tokens: int = 1024
temperature: float = 0.01
# FastAPI application initialization
app = FastAPI()
# Endpoint for generating responses
@app.post("/generate_response")
async def generate_response(item: Validation):
# Construct the complete prompt using the given system and user prompts in the required format
prompt = f"<|user|>\n{item.system_prompt}\n<|end|>\n<|user|>\n{item.user_prompt}\n<|end|>\n<|assistant|>"
# Call the Llama model to generate a response
output = llm(prompt, max_tokens=item.max_tokens, temperature=item.temperature, echo=True)
# Extract and return the text from the response
return output['choices'][0]['text']
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