"""
ChromaDB Client for storing and retrieving document embeddings
"""
import chromadb
from chromadb.config import Settings
from chromadb.utils import embedding_functions
import logging
import os
import time

class ChromaClient:
    """
    Client for interacting with ChromaDB vector database.
    Uses Ollama for generating embeddings if available, otherwise falls back to default.
    """
    
    def __init__(self, host, port, collection_name='munich_news_articles', ollama_base_url=None):
        """
        Initialize ChromaDB client
        
        Args:
            host: ChromaDB host (e.g. 'localhost' or 'chromadb')
            port: ChromaDB port (default 8000)
            collection_name: Name of the collection to use
            ollama_base_url: Optional URL for Ollama embedding function
        """
        self.host = host
        self.port = port
        self.collection_name = collection_name
        self.client = None
        self.collection = None
        
        # Setup embedding function
        # We prefer using a local embedding model compatible with Ollama or SentenceTransformers
        # For simplicity in this stack, we can use the default SentenceTransformer (all-MiniLM-L6-v2)
        # which is downloaded automatically by chromadb utils.
        # Alternatively, we could define a custom function using Ollama's /api/embeddings
        self.embedding_function = embedding_functions.DefaultEmbeddingFunction()
        
    def connect(self):
        """Establish connection to ChromaDB"""
        try:
            self.client = chromadb.HttpClient(
                host=self.host,
                port=self.port,
                settings=Settings(allow_reset=True, anonymized_telemetry=False)
            )
            
            # Create or get collection
            self.collection = self.client.get_or_create_collection(
                name=self.collection_name,
                embedding_function=self.embedding_function,
                metadata={"hnsw:space": "cosine"}
            )
            print(f"✓ Connected to ChromaDB at {self.host}:{self.port}")
            return True
        except Exception as e:
            print(f"⚠ Could not connect to ChromaDB: {e}")
            return False

    def add_articles(self, articles):
        """
        Add articles to the vector database
        
        Args:
            articles: List of dictionaries containing article data.
                     Must have 'link' (used as ID), 'title', 'content', etc.
        """
        if not self.client or not self.collection:
            if not self.connect():
                return False
                
        if not articles:
            return True
            
        ids = []
        documents = []
        metadatas = []
        
        for article in articles:
            # Skip if critical data missing
            if not article.get('link') or not article.get('content'):
                continue
                
            # Use link as unique ID
            article_id = article.get('link')
            
            # Prepare text for embedding (Title + Summary + Start of Content)
            # This gives semantic search a good overview
            # Use English title if available, otherwise original
            title = article.get('title_en') if article.get('title_en') else article.get('title', '')
            summary = article.get('summary') or ''
            content_snippet = article.get('content', '')[:1000]
            
            text_to_embed = f"{title}\n\n{summary}\n\n{content_snippet}"
            
            # robust metadata (flat dict, no nested objects)
            metadata = {
                "title": title[:100], # Truncate for metadata limits
                "url": article_id,
                "source": article.get('source', 'unknown'),
                "category": article.get('category', 'general'),
                "published_at": str(article.get('published_at', '')),
                "mongo_id": str(article.get('_id', ''))
            }
            
            ids.append(article_id)
            documents.append(text_to_embed)
            metadatas.append(metadata)
            
        if not ids:
            return True
            
        try:
            self.collection.upsert(
                ids=ids,
                documents=documents,
                metadatas=metadatas
            )
            print(f"✓ Indexed {len(ids)} articles in ChromaDB")
            return True
        except Exception as e:
            print(f"✗ Failed to index in ChromaDB: {e}")
            return False

    def search(self, query_text, n_results=5, where=None):
        """
        Search for relevant articles
        
        Args:
            query_text: The search query
            n_results: Number of results to return
            where: Metadata filter dict (e.g. {"category": "sports"})
        """
        if not self.client or not self.collection:
            if not self.connect():
                return []
                
        try:
            results = self.collection.query(
                query_texts=[query_text],
                n_results=n_results,
                where=where
            )
            
            # Format results into a nice list of dicts
            formatted_results = []
            if results and results['ids']:
                for i, id in enumerate(results['ids'][0]):
                    item = {
                        'id': id,
                        'document': results['documents'][0][i] if results['documents'] else None,
                        'metadata': results['metadatas'][0][i] if results['metadatas'] else {},
                        'distance': results['distances'][0][i] if results['distances'] else 0
                    }
                    formatted_results.append(item)
                    
            return formatted_results
        except Exception as e:
            print(f"✗ Search failed: {e}")
            return []

if __name__ == "__main__":
    # Test client
    client = ChromaClient(host='localhost', port=8000)
    client.connect()