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"""
Hugging Face API integration for Norwegian RAG chatbot.
Provides functions to interact with Hugging Face Inference API for both LLM and embedding models.
"""
import os
import json
import time
import requests
from typing import Dict, List, Optional, Union, Any
from .config import (
LLM_MODELS,
DEFAULT_LLM_MODEL,
EMBEDDING_MODELS,
DEFAULT_EMBEDDING_MODEL,
HF_API_ENDPOINTS,
API_PARAMS
)
class HuggingFaceAPI:
"""
Client for interacting with Hugging Face Inference API.
Supports both text generation (LLM) and embedding generation.
"""
def __init__(
self,
api_key: Optional[str] = None,
llm_model: str = DEFAULT_LLM_MODEL,
embedding_model: str = DEFAULT_EMBEDDING_MODEL
):
"""
Initialize the Hugging Face API client.
Args:
api_key: Hugging Face API key (optional, can use HF_API_KEY env var)
llm_model: LLM model identifier from config
embedding_model: Embedding model identifier from config
"""
self.api_key = api_key or os.environ.get("HF_API_KEY", "")
# Set up model IDs
self.llm_model_id = LLM_MODELS[llm_model]["model_id"] if llm_model in LLM_MODELS else LLM_MODELS[DEFAULT_LLM_MODEL]["model_id"]
self.embedding_model_id = EMBEDDING_MODELS[embedding_model]["model_id"] if embedding_model in EMBEDDING_MODELS else EMBEDDING_MODELS[DEFAULT_EMBEDDING_MODEL]["model_id"]
# Set up headers
self.headers = {"Authorization": f"Bearer {self.api_key}"}
if not self.api_key:
print("Warning: No API key provided. API calls may be rate limited.")
self.headers = {}
def generate_text(
self,
prompt: str,
max_length: int = API_PARAMS["max_length"],
temperature: float = API_PARAMS["temperature"],
top_p: float = API_PARAMS["top_p"],
top_k: int = API_PARAMS["top_k"],
repetition_penalty: float = API_PARAMS["repetition_penalty"],
wait_for_model: bool = True
) -> str:
"""
Generate text using the LLM model.
Args:
prompt: Input text prompt
max_length: Maximum length of generated text
temperature: Sampling temperature
top_p: Top-p sampling parameter
top_k: Top-k sampling parameter
repetition_penalty: Penalty for repetition
wait_for_model: Whether to wait for model to load
Returns:
Generated text response
"""
payload = {
"inputs": prompt,
"parameters": {
"max_length": max_length,
"temperature": temperature,
"top_p": top_p,
"top_k": top_k,
"repetition_penalty": repetition_penalty
}
}
api_url = f"{HF_API_ENDPOINTS['inference']}{self.llm_model_id}"
# Make API request
response = self._make_api_request(api_url, payload, wait_for_model)
# Parse response
if isinstance(response, list) and len(response) > 0:
if "generated_text" in response[0]:
return response[0]["generated_text"]
return response[0].get("text", "")
elif isinstance(response, dict):
return response.get("generated_text", "")
# Fallback
return str(response)
def generate_embeddings(
self,
texts: Union[str, List[str]],
wait_for_model: bool = True
) -> List[List[float]]:
"""
Generate embeddings for text using the embedding model.
Args:
texts: Single text or list of texts to embed
wait_for_model: Whether to wait for model to load
Returns:
List of embedding vectors
"""
# Ensure texts is a list
if isinstance(texts, str):
texts = [texts]
payload = {
"inputs": texts,
}
api_url = f"{HF_API_ENDPOINTS['feature-extraction']}{self.embedding_model_id}"
# Make API request
response = self._make_api_request(api_url, payload, wait_for_model)
# Return embeddings
return response
def _make_api_request(
self,
api_url: str,
payload: Dict[str, Any],
wait_for_model: bool = True,
max_retries: int = 5,
retry_delay: int = 1
) -> Any:
"""
Make a request to the Hugging Face API with retry logic.
Args:
api_url: API endpoint URL
payload: Request payload
wait_for_model: Whether to wait for model to load
max_retries: Maximum number of retries
retry_delay: Delay between retries in seconds
Returns:
API response
"""
for attempt in range(max_retries):
try:
response = requests.post(api_url, headers=self.headers, json=payload)
# Check if model is still loading
if response.status_code == 503 and wait_for_model:
# Model is loading, wait and retry
estimated_time = json.loads(response.content.decode("utf-8")).get("estimated_time", 20)
print(f"Model is loading. Waiting {estimated_time} seconds...")
time.sleep(estimated_time)
continue
# Check for other errors
if response.status_code != 200:
print(f"API request failed with status code {response.status_code}: {response.text}")
if attempt < max_retries - 1:
time.sleep(retry_delay * (2 ** attempt)) # Exponential backoff
continue
return {"error": response.text}
return response.json()
except Exception as e:
print(f"API request failed: {str(e)}")
if attempt < max_retries - 1:
time.sleep(retry_delay * (2 ** attempt)) # Exponential backoff
continue
return {"error": str(e)}
return {"error": "Max retries exceeded"}
# Example RAG prompt template for Norwegian
def create_rag_prompt(query: str, context: List[str]) -> str:
"""
Create a RAG prompt with retrieved context for the LLM.
Args:
query: User query
context: List of retrieved document chunks
Returns:
Formatted prompt with context
"""
context_text = "\n\n".join([f"Dokument {i+1}:\n{chunk}" for i, chunk in enumerate(context)])
prompt = f"""Du er en hjelpsom assistent som svarer på norsk. Bruk følgende kontekst for å svare på spørsmålet.
KONTEKST:
{context_text}
SPØRSMÅL:
{query}
SVAR:
"""
return prompt
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