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src/api/openai_api.py

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+"""
+OpenAI API integration for Norwegian RAG chatbot.
+Provides functions to interact with OpenAI API for both GPT-4o and embedding models.
+"""
+
+import os
+import json
+import time
+import requests
+from typing import Dict, List, Optional, Union, Any
+import openai
+
+class OpenAIAPI:
+    """
+    Client for interacting with OpenAI API.
+    Supports both text generation (GPT-4o) and embedding generation.
+    """
+    
+    def __init__(
+        self, 
+        api_key: Optional[str] = None,
+        model: str = "gpt-4o",
+        embedding_model: str = "text-embedding-3-small"
+    ):
+        """
+        Initialize the OpenAI API client.
+        
+        Args:
+            api_key: OpenAI API key (optional, can use OPENAI_API_KEY env var)
+            model: GPT model to use (default: gpt-4o)
+            embedding_model: Embedding model to use (default: text-embedding-3-small)
+        """
+        self.api_key = api_key or os.environ.get("OPENAI_API_KEY", "")
+        if not self.api_key:
+            raise ValueError("OpenAI API key is required. Set it as OPENAI_API_KEY environment variable or pass it to the constructor.")
+        
+        self.client = openai.OpenAI(api_key=self.api_key)
+        self.model = model
+        self.embedding_model = embedding_model
+    
+    def generate_text(
+        self, 
+        prompt: str,
+        max_tokens: int = 512,
+        temperature: float = 0.7,
+        top_p: float = 0.9,
+        stream: bool = False
+    ) -> Union[str, Any]:
+        """
+        Generate text using the GPT-4o model.
+        
+        Args:
+            prompt: Input text prompt
+            max_tokens: Maximum number of tokens to generate
+            temperature: Sampling temperature
+            top_p: Top-p sampling parameter
+            stream: Whether to stream the response
+            
+        Returns:
+            Generated text response or stream
+        """
+        try:
+            messages = [{"role": "user", "content": prompt}]
+            
+            response = self.client.chat.completions.create(
+                model=self.model,
+                messages=messages,
+                max_tokens=max_tokens,
+                temperature=temperature,
+                top_p=top_p,
+                stream=stream
+            )
+            
+            if stream:
+                return response
+            else:
+                return response.choices[0].message.content
+                
+        except Exception as e:
+            print(f"Error generating text: {str(e)}")
+            return f"Error: {str(e)}"
+    
+    def generate_embeddings(
+        self, 
+        texts: Union[str, List[str]]
+    ) -> List[List[float]]:
+        """
+        Generate embeddings for text using the embedding model.
+        
+        Args:
+            texts: Single text or list of texts to embed
+            
+        Returns:
+            List of embedding vectors
+        """
+        # Ensure texts is a list
+        if isinstance(texts, str):
+            texts = [texts]
+        
+        try:
+            response = self.client.embeddings.create(
+                model=self.embedding_model,
+                input=texts
+            )
+            
+            # Extract embeddings from response
+            embeddings = [item.embedding for item in response.data]
+            return embeddings
+            
+        except Exception as e:
+            print(f"Error generating embeddings: {str(e)}")
+            # Return empty embeddings as fallback
+            return [[0.0] * 1536] * len(texts)
+
+
+# Example RAG prompt template for Norwegian
+def create_rag_prompt(query: str, context: List[str]) -> str:
+    """
+    Create a RAG prompt with retrieved context for GPT-4o.
+    
+    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

src/api/openai_config.py

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+"""
+Updated configuration for Norwegian RAG chatbot with GPT-4o integration.
+Contains model IDs, API endpoints, and other configuration parameters.
+"""
+
+# OpenAI GPT-4o configuration
+OPENAI_CONFIG = {
+    "model": "gpt-4o",
+    "embedding_model": "text-embedding-3-small",
+    "max_tokens": 512,
+    "temperature": 0.7,
+    "top_p": 0.9
+}
+
+# Document processing parameters
+CHUNK_SIZE = 512
+CHUNK_OVERLAP = 100
+
+# RAG parameters
+MAX_CHUNKS_TO_RETRIEVE = 5
+SIMILARITY_THRESHOLD = 0.75
+
+# Requirements for OpenAI integration
+REQUIRED_PACKAGES = [
+    "openai>=1.0.0",
+    "numpy>=1.24.0",
+    "gradio>=4.0.0",
+    "PyPDF2>=3.0.0",
+    "beautifulsoup4>=4.12.0",
+    "requests>=2.31.0"
+]