src/api.py

import httpx
from typing import Optional, Iterator, List, Dict, Union
import logging

class InferenceApi:
    def __init__(self, config: dict):
        """Initialize the Inference API with configuration."""
        self.logger = logging.getLogger(__name__)
        self.logger.info("Initializing Inference API")

        # Get base URL from config
        self.base_url = config["llm_server"]["base_url"]
        self.timeout = config["llm_server"].get("timeout", 60)

        # Initialize HTTP client
        self.client = httpx.AsyncClient(
            base_url=self.base_url,
            timeout=self.timeout
        )

        self.logger.info("Inference API initialized successfully")

    async def generate_response(
            self,
            prompt: str,
            system_message: Optional[str] = None,
            max_new_tokens: Optional[int] = None
    ) -> str:
        """
        Generate a complete response by forwarding the request to the LLM Server.
        """
        self.logger.debug(f"Forwarding generation request for prompt: {prompt[:50]}...")

        try:
            response = await self.client.post(
                "/api/v1/generate",
                json={
                    "prompt": prompt,
                    "system_message": system_message,
                    "max_new_tokens": max_new_tokens
                }
            )
            response.raise_for_status()
            data = response.json()
            return data["generated_text"]

        except Exception as e:
            self.logger.error(f"Error in generate_response: {str(e)}")
            raise

    async def generate_stream(
            self,
            prompt: str,
            system_message: Optional[str] = None,
            max_new_tokens: Optional[int] = None
    ) -> Iterator[str]:
        """
        Generate a streaming response by forwarding the request to the LLM Server.
        """
        self.logger.debug(f"Forwarding streaming request for prompt: {prompt[:50]}...")

        try:
            async with self.client.stream(
                    "POST",
                    "/api/v1/generate/stream",
                    json={
                        "prompt": prompt,
                        "system_message": system_message,
                        "max_new_tokens": max_new_tokens
                    }
            ) as response:
                response.raise_for_status()
                async for chunk in response.aiter_text():
                    yield chunk

        except Exception as e:
            self.logger.error(f"Error in generate_stream: {str(e)}")
            raise

    async def generate_embedding(self, text: str) -> List[float]:
        """
        Generate embedding by forwarding the request to the LLM Server.
        """
        self.logger.debug(f"Forwarding embedding request for text: {text[:50]}...")

        try:
            response = await self.client.post(
                "/api/v1/embedding",
                json={"text": text}
            )
            response.raise_for_status()
            data = response.json()
            return data["embedding"]

        except Exception as e:
            self.logger.error(f"Error in generate_embedding: {str(e)}")
            raise

    async def check_system_status(self) -> Dict[str, Union[Dict, str]]:
        """
        Get system status from the LLM Server.
        """
        try:
            response = await self.client.get("/api/v1/system/status")
            response.raise_for_status()
            return response.json()

        except Exception as e:
            self.logger.error(f"Error getting system status: {str(e)}")
            raise

    async def validate_system(self) -> Dict[str, Union[Dict, str, List[str]]]:
        """
        Get system validation status from the LLM Server.
        """
        try:
            response = await self.client.get("/api/v1/system/validate")
            response.raise_for_status()
            return response.json()

        except Exception as e:
            self.logger.error(f"Error validating system: {str(e)}")
            raise

    async def initialize_model(self, model_name: Optional[str] = None) -> Dict[str, str]:
        """
        Initialize a model on the LLM Server.
        """
        try:
            response = await self.client.post(
                "/api/v1/model/initialize",
                params={"model_name": model_name} if model_name else None
            )
            response.raise_for_status()
            return response.json()

        except Exception as e:
            self.logger.error(f"Error initializing model: {str(e)}")
            raise

    async def initialize_embedding_model(self, model_name: Optional[str] = None) -> Dict[str, str]:
        """
        Initialize an embedding model on the LLM Server.
        """
        try:
            response = await self.client.post(
                "/api/v1/model/initialize/embedding",
                params={"model_name": model_name} if model_name else None
            )
            response.raise_for_status()
            return response.json()

        except Exception as e:
            self.logger.error(f"Error initializing embedding model: {str(e)}")
            raise

    async def close(self):
        """Close the HTTP client session."""
        await self.client.aclose()