import os import openai def plan_course(messages, sources): """ Generate a structured course outline as a JSON object using the conversation and collected sources. This function reads a JSON schema from the repository (``course_outline_schema.json``) and instructs the language model to produce an output that strictly follows the schema. The conversation history (``messages``) and list of resources (``sources``) are provided to the model as context. Args: messages (list[dict]): Conversation history with roles and content. sources (list[dict]): List of source dictionaries with "title" and "url" keys. Returns: str: A JSON string representing the course outline that matches the schema. Raises: RuntimeError: If the OpenAI API call fails. ValueError: If an API key is not provided via environment variables. """ # Ensure API key is available (support COURSECREATOR_API_KEY as fallback) api_key = os.getenv("OPENAI_API_KEY") or os.getenv("COURSECREATOR_API_KEY") if not api_key: raise ValueError( "An OpenAI API key is required to plan the course (set OPENAI_API_KEY or COURSECREATOR_API_KEY)" ) # Load the JSON schema from the local file to guide the model schema_path = os.path.join(os.path.dirname(__file__) or ".", "course_outline_schema.json") try: with open(schema_path, "r") as f: schema_content = f.read().strip() except Exception: # If the schema is not found, define a minimal fallback structure schema_content = ( '{"title":"","description":"","course_plan":[]}' ) # Compose system prompt: instruct the model to output JSON matching the schema and to use # information from the conversation and the provided sources. system_prompt = ( "You are an expert course planner. Use the conversation and sources provided to produce a " "detailed course outline. Your response MUST be a valid JSON object that strictly follows " "this schema:\n\n" f"{schema_content}\n\n" "Do not wrap your answer in markdown or include any additional commentary. Only output the JSON." ) # Build messages array for the model: include system prompt, conversation, and a description of sources formatted_messages = [ {"role": "system", "content": system_prompt}, ] # Include the conversation history for msg in messages: formatted_messages.append(msg) # Append sources description if present if sources: # Format sources as a numbered list for the model to reference source_lines = [] for i, src in enumerate(sources, start=1): if isinstance(src, dict): t = src.get("title", "") u = src.get("url", "") source_lines.append(f"[{i}] {t} - {u}") source_text = "\n".join(source_lines) formatted_messages.append({"role": "system", "content": f"Sources:\n{source_text}"}) # Model configuration model = os.getenv("OPENAI_MODEL", "gpt-3.5-turbo") temperature = float(os.getenv("TEMPERATURE", "0.3")) # Lower temperature for more deterministic JSON max_tokens = int(os.getenv("MAX_OUTPUT_TOKENS", "4096")) try: # Use new OpenAI client if available if hasattr(openai, "OpenAI"): client = openai.OpenAI(api_key=api_key) try: resp = client.chat.completions.create( model=model, messages=formatted_messages, temperature=temperature, max_tokens=max_tokens, ) except Exception: # Fallback to max_completion_tokens if model requires it resp = client.chat.completions.create( model=model, messages=formatted_messages, temperature=temperature, max_completion_tokens=max_tokens, ) content = resp.choices[0].message.content else: # Legacy OpenAI SDK (<1.0) openai.api_key = api_key try: resp = openai.ChatCompletion.create( model=model, messages=formatted_messages, temperature=temperature, max_tokens=max_tokens, ) except Exception: resp = openai.ChatCompletion.create( model=model, messages=formatted_messages, temperature=temperature, max_completion_tokens=max_tokens, ) content = resp["choices"][0]["message"]["content"] except Exception as e: raise RuntimeError(f"OpenAI API error: {e}") # The content should be valid JSON. Return as string so the caller can write to file or parse. return content