| import os | |
| import asyncio | |
| from llama_index.core import Document, VectorStoreIndex, Settings | |
| from llama_index.embeddings.huggingface import HuggingFaceEmbedding | |
| from pymongo import MongoClient | |
| from anthropic import AsyncAnthropic | |
| import requests | |
| import base64 | |
| from PIL import Image | |
| from io import BytesIO | |
| Settings.embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5") | |
| Settings.chunk_size = 512 | |
| async_anthropic_client = AsyncAnthropic() | |
| client = MongoClient(os.getenv("MONGODB_URI")) | |
| db = client.get_database() | |
| collection = db.get_collection("travelrecords") | |
| async def get_image_b64_data(image_url: str): | |
| def blocking_io_and_compress(): | |
| try: | |
| response = requests.get(image_url, timeout=10) | |
| response.raise_for_status() | |
| content = response.content | |
| if len(content) <= 5 * 1024 * 1024: | |
| return response.headers.get('Content-Type','image/jpeg'), base64.b64encode(content).decode('utf-8') | |
| print(f"⚠️ Image >5MB, compressing: {image_url}") | |
| img = Image.open(BytesIO(content)) | |
| buffer = BytesIO() | |
| img.save(buffer, format="JPEG", quality=70, optimize=True) | |
| compressed_data = buffer.getvalue() | |
| if len(compressed_data) > 5 * 1024 * 1024: | |
| print(f"❌ Compression failed, still too large.") | |
| return None, None | |
| return "image/jpeg", base64.b64encode(compressed_data).decode('utf-8') | |
| except Exception as e: | |
| print(f"❌ Image processing error: {e}") | |
| return None, None | |
| return await asyncio.to_thread(blocking_io_and_compress) | |
| async def describe_image(image_data: str, media_type: str) -> str: | |
| if not image_data: return "" | |
| try: | |
| response = await async_anthropic_client.messages.create( | |
| model="claude-3-haiku-20240307", max_tokens=75, | |
| messages=[{"role":"user", "content":[ | |
| {"type":"image", "source":{"type":"base64", "media_type":media_type, "data":image_data}}, | |
| {"type":"text", "text":"Briefly describe this travel photo's key elements and atmosphere in one sentence."} | |
| ]}] | |
| ) | |
| return response.content[0].text | |
| except Exception as e: | |
| print(f"ERROR calling vision model: {e}") | |
| return "" | |
| async def describe_all_images(image_urls: list) -> str: | |
| if not image_urls: return "No images provided." | |
| tasks = [get_image_b64_data(url) for url in image_urls] | |
| results = await asyncio.gather(*tasks) | |
| desc_tasks = [describe_image(img_data, media_type) for media_type, img_data in results if img_data] | |
| descriptions = await asyncio.gather(*desc_tasks) | |
| return "\n".join(descriptions) | |
| async def create_personalized_plan(user_name: str, new_destination: str, trip_duration_days: int, user_request: str) -> str: | |
| print(f"--- [Corrected Async] Starting Personalized Planner for {user_name} to {new_destination} ---") | |
| try: | |
| user_records = await asyncio.to_thread(list, collection.find({"name": {"$regex": user_name, "$options": "i"}})) | |
| if not user_records: return f"I couldn't find any past travel records for {user_name}." | |
| print(f"Found {len(user_records)} past trips for {user_name}.") | |
| async def create_doc(record): | |
| image_descriptions = await describe_all_images(record.get('uploadedImages', [])) | |
| text_content = (f"Trip to {record.get('destinationName', 'N/A')}: Highlights: {record.get('highlights', 'N/A')}\nImage Summary: {image_descriptions}") | |
| return Document(text=text_content) | |
| documents = await asyncio.gather(*[create_doc(r) for r in user_records]) | |
| print(f"Successfully created {len(documents)} documents for RAG.") | |
| def build_and_retrieve(docs): | |
| print("Building RAG index... You should see a progress bar now.") | |
| index = VectorStoreIndex.from_documents(docs, show_progress=True) | |
| return index.as_retriever(similarity_top_k=3).retrieve(f"Preferences for {new_destination}: {user_request}") | |
| retrieved_nodes = await asyncio.to_thread(build_and_retrieve, documents) | |
| retrieved_context = "\n\n---\n\n".join([node.get_content() for node in retrieved_nodes]) | |
| print(f"\n--- Retrieved Context for Persona ---\n{retrieved_context}\n-----------------------------------\n") | |
| system_prompt = "You are an expert travel agent and persona analyst. Your core function is to synthesize a user's past travel preferences with their current request to generate a truly personalized and actionable travel itinerary." | |
| final_prompt = f""" | |
| **Mission:** Generate a hyper-personalized travel plan. | |
| **1. Input Data:** | |
| * **User Name:** {user_name} | |
| * **Destination:** {new_destination} | |
| * **Trip Duration:** {trip_duration_days} days | |
| * **Specific Request:** "{user_request}" | |
| * **User's Historical Travel Context (for Persona Analysis):** | |
| --- | |
| {retrieved_context} | |
| --- | |
| **2. Your Task (A mandatory two-step process):** | |
| * **Step A: Define the User's Travel Persona.** | |
| Based *only* on their historical preferences provided above, build a detailed understanding of this user's core travel style, values, and preferences. | |
| * **Step B: Craft the Custom Itinerary.** | |
| Using your deep understanding of the user's persona from Step A, create a day-by-day travel plan for their trip to {new_destination}. Every recommendation must align with their inferred preferences. | |
| **3. Required Output Format (Crucial for user connection):** | |
| 1. **Greeting and Persona Summary:** | |
| Start with a detailed summary of the user's travel persona, beginning with the phrase "Based on your past travel experiences, I've discovered you are a traveler who...". This summary should be rich with insights. For example: "Based on your past travel experiences, I've discovered you are a traveler who seeks out spectacular, awe-inspiring moments and deep cultural immersion. You appreciate both iconic, grand-scale views (like the fireworks in Tokyo and the Valley of the Kings in Luxor) and have a keen sense for authentic cuisine, while actively avoiding overrated experiences (like the cocktails in Helsinki). You balance thrilling adventures (hot air ballooning) with quiet cultural exploration and maintain a savvy, cautious approach to new environments." | |
| 2. **Introduction to the Plan:** | |
| After the persona summary, add a transitional sentence like: "With this understanding of your unique style, I've crafted this tailored itinerary for your Paris adventure:" | |
| 3. **Personalized Itinerary:** | |
| Finally, present the day-by-day itinerary in a clear, easy-to-read format. | |
| """ | |
| print("--- Calling Final LLM with direct RAG context... ---") | |
| response_message = await async_anthropic_client.messages.create( | |
| model="claude-3-5-sonnet-20240620", | |
| max_tokens=4096, system=system_prompt, | |
| messages=[{"role":"user", "content":final_prompt}] | |
| ) | |
| return response_message.content[0].text | |
| except Exception as e: | |
| error_message = f"FATAL TOOL ERROR: {type(e).__name__}: {str(e)}" | |
| print("\n\n---" + error_message + "---\n\n") | |
| return error_message |