import asyncio
import json
import logging
import os
import httpx
import time
from collections import defaultdict
from datetime import datetime, timedelta

# Set up proper logging
logger = logging.getLogger("search")
logger.setLevel(logging.INFO)  # Change to INFO to see more details

# Configuration for search service
SEARCH_ENABLED = bool(os.environ.get("SEARCH_ENABLED", "true").lower() in ["true", "1", "yes"])
TXTAI_SERVICE_URL = os.environ.get("TXTAI_SERVICE_URL", "none")
MAX_BATCH_SIZE = int(os.environ.get("SEARCH_MAX_BATCH_SIZE", "25"))

# Search cache configuration
SEARCH_CACHE_ENABLED = bool(os.environ.get("SEARCH_CACHE_ENABLED", "true").lower() in ["true", "1", "yes"])
SEARCH_CACHE_TTL_SECONDS = int(os.environ.get("SEARCH_CACHE_TTL_SECONDS", "900"))  # Default: 15 minutes
SEARCH_MIN_SCORE = float(os.environ.get("SEARCH_MIN_SCORE", "0.1"))
SEARCH_PREFETCH_SIZE = int(os.environ.get("SEARCH_PREFETCH_SIZE", "200"))
SEARCH_USE_REDIS = bool(os.environ.get("SEARCH_USE_REDIS", "true").lower() in ["true", "1", "yes"])

search_offset = 0

# Import Redis client if Redis caching is enabled
if SEARCH_USE_REDIS:
    try:
        from services.redis import redis
        logger.info("Redis client imported for search caching")
    except ImportError:
        logger.warning("Redis client import failed, falling back to memory cache")
        SEARCH_USE_REDIS = False

class SearchCache:
    """Cache for search results to enable efficient pagination"""
    
    def __init__(self, ttl_seconds=SEARCH_CACHE_TTL_SECONDS, max_items=100):
        self.cache = {}  # Maps search query to list of results
        self.last_accessed = {}  # Maps search query to last access timestamp
        self.ttl = ttl_seconds
        self.max_items = max_items
        self._redis_prefix = "search_cache:"
    
    async def store(self, query, results):
        """Store search results for a query"""
        normalized_query = self._normalize_query(query)
        
        if SEARCH_USE_REDIS:
            try:
                serialized_results = json.dumps(results)
                await redis.set(
                    f"{self._redis_prefix}{normalized_query}", 
                    serialized_results,
                    ex=self.ttl
                )
                logger.info(f"Stored {len(results)} search results for query '{query}' in Redis")
                return True
            except Exception as e:
                logger.error(f"Error storing search results in Redis: {e}")
                # Fall back to memory cache if Redis fails
        
        # First cleanup if needed for memory cache
        if len(self.cache) >= self.max_items:
            self._cleanup()
            
        # Store results and update timestamp
        self.cache[normalized_query] = results
        self.last_accessed[normalized_query] = time.time()
        logger.info(f"Cached {len(results)} search results for query '{query}' in memory")
        return True
    
    async def get(self, query, limit=10, offset=0):
        """Get paginated results for a query"""
        normalized_query = self._normalize_query(query)
        all_results = None

        # Try to get from Redis first
        if SEARCH_USE_REDIS:
            try:
                cached_data = await redis.get(f"{self._redis_prefix}{normalized_query}")
                if cached_data:
                    all_results = json.loads(cached_data)
                    logger.info(f"Retrieved search results for '{query}' from Redis")
            except Exception as e:
                logger.error(f"Error retrieving search results from Redis: {e}")
        
        # Fall back to memory cache if not in Redis
        if all_results is None and normalized_query in self.cache:
            all_results = self.cache[normalized_query]
            self.last_accessed[normalized_query] = time.time()
            logger.info(f"Retrieved search results for '{query}' from memory cache")
        
        # If not found in any cache
        if all_results is None:
            logger.info(f"Cache miss for query '{query}'")
            return None
        
        # Return paginated subset
        end_idx = min(offset + limit, len(all_results))
        if offset >= len(all_results):
            logger.warning(f"Requested offset {offset} exceeds result count {len(all_results)}")
            return []
            
        logger.info(f"Cache hit for '{query}': serving {offset}:{end_idx} of {len(all_results)} results")
        return all_results[offset:end_idx]
    
    async def has_query(self, query):
        """Check if query exists in cache"""
        normalized_query = self._normalize_query(query)
        
        # Check Redis first
        if SEARCH_USE_REDIS:
            try:
                exists = await redis.get(f"{self._redis_prefix}{normalized_query}")
                if exists:
                    return True
            except Exception as e:
                logger.error(f"Error checking Redis for query existence: {e}")
        
        # Fall back to memory cache
        return normalized_query in self.cache
    
    async def get_total_count(self, query):
        """Get total count of results for a query"""
        normalized_query = self._normalize_query(query)
        
        # Check Redis first
        if SEARCH_USE_REDIS:
            try:
                cached_data = await redis.get(f"{self._redis_prefix}{normalized_query}")
                if cached_data:
                    all_results = json.loads(cached_data)
                    return len(all_results)
            except Exception as e:
                logger.error(f"Error getting result count from Redis: {e}")
        
        # Fall back to memory cache
        if normalized_query in self.cache:
            return len(self.cache[normalized_query])
            
        return 0
    
    def _normalize_query(self, query):
        """Normalize query string for cache key"""
        if not query:
            return ""
        # Simple normalization - lowercase and strip whitespace
        return query.lower().strip()
    
    def _cleanup(self):
        """Remove oldest entries if memory cache is full"""
        now = time.time()
        # First remove expired entries
        expired_keys = [
            key for key, last_access in self.last_accessed.items() 
            if now - last_access > self.ttl
        ]
        
        for key in expired_keys:
            if key in self.cache:
                del self.cache[key]
            if key in self.last_accessed:
                del self.last_accessed[key]
        
        logger.info(f"Cleaned up {len(expired_keys)} expired search cache entries")
        
        # If still above max size, remove oldest entries
        if len(self.cache) >= self.max_items:
            # Sort by last access time
            sorted_items = sorted(self.last_accessed.items(), key=lambda x: x[1])
            # Remove oldest 20%
            remove_count = max(1, int(len(sorted_items) * 0.2))
            for key, _ in sorted_items[:remove_count]:
                if key in self.cache:
                    del self.cache[key]
                if key in self.last_accessed:
                    del self.last_accessed[key]
            logger.info(f"Removed {remove_count} oldest search cache entries")

class SearchService:
    def __init__(self):
        logger.info(f"Initializing search service with URL: {TXTAI_SERVICE_URL}")
        self.available = SEARCH_ENABLED
        # Use different timeout settings for indexing and search requests
        self.client = httpx.AsyncClient(timeout=30.0, base_url=TXTAI_SERVICE_URL)
        self.index_client = httpx.AsyncClient(timeout=120.0, base_url=TXTAI_SERVICE_URL)
        # Initialize search cache
        self.cache = SearchCache() if SEARCH_CACHE_ENABLED else None
        
        if not self.available:
            logger.info("Search disabled (SEARCH_ENABLED = False)")
        
        if SEARCH_CACHE_ENABLED:
            cache_location = "Redis" if SEARCH_USE_REDIS else "Memory"
            logger.info(f"Search caching enabled using {cache_location} cache with TTL={SEARCH_CACHE_TTL_SECONDS}s")
            logger.info(f"Minimum score filter: {SEARCH_MIN_SCORE}, prefetch size: {SEARCH_PREFETCH_SIZE}")
            
    async def info(self):
        """Return information about search service"""
        if not self.available:
            return {"status": "disabled"}
        try:
            response = await self.client.get("/info")
            response.raise_for_status()
            result = response.json()
            logger.info(f"Search service info: {result}")
            return result
        except Exception as e:
            logger.error(f"Failed to get search info: {e}")
            return {"status": "error", "message": str(e)}
    
    def is_ready(self):
        """Check if service is available"""
        return self.available
    
    
    async def verify_docs(self, doc_ids):
        """Verify which documents exist in the search index across all content types"""
        if not self.available:
            return {"status": "disabled"}
            
        try:
            logger.info(f"Verifying {len(doc_ids)} documents in search index")
            response = await self.client.post(
                "/verify-docs",
                json={"doc_ids": doc_ids},
                timeout=60.0  # Longer timeout for potentially large ID lists
            )
            response.raise_for_status()
            result = response.json()
            
            # Process the more detailed response format
            bodies_missing = set(result.get("bodies", {}).get("missing", []))
            titles_missing = set(result.get("titles", {}).get("missing", []))
            
            # Combine missing IDs from both bodies and titles
            # A document is considered missing if it's missing from either index
            all_missing = list(bodies_missing.union(titles_missing))
            
            # Log summary of verification results
            bodies_missing_count = len(bodies_missing)
            titles_missing_count = len(titles_missing)
            total_missing_count = len(all_missing)
            
            logger.info(f"Document verification complete: {bodies_missing_count} bodies missing, {titles_missing_count} titles missing")
            logger.info(f"Total unique missing documents: {total_missing_count} out of {len(doc_ids)} total")
            
            # Return in a backwards-compatible format plus the detailed breakdown
            return {
                "missing": all_missing,
                "details": {
                    "bodies_missing": list(bodies_missing),
                    "titles_missing": list(titles_missing),
                    "bodies_missing_count": bodies_missing_count,
                    "titles_missing_count": titles_missing_count
                }
            }
        except Exception as e:
            logger.error(f"Document verification error: {e}")
            return {"status": "error", "message": str(e)}

    
    def index(self, shout):
        """Index a single document"""
        if not self.available:
            return
        logger.info(f"Indexing post {shout.id}")
        # Start in background to not block
        asyncio.create_task(self.perform_index(shout))

    async def perform_index(self, shout):
        """Index a single document across multiple endpoints"""
        if not self.available:
            return
        
        try:
            logger.info(f"Indexing document {shout.id} to individual endpoints")
            indexing_tasks = []
            
            # 1. Index title if available
            if hasattr(shout, 'title') and shout.title and isinstance(shout.title, str):
                title_doc = {
                    "id": str(shout.id),
                    "title": shout.title.strip()
                }
                indexing_tasks.append(
                    self.index_client.post("/index-title", json=title_doc)
                )
            
            # 2. Index body content (subtitle, lead, body)
            body_text_parts = []
            for field_name in ['subtitle', 'lead', 'body']:
                field_value = getattr(shout, field_name, None)
                if field_value and isinstance(field_value, str) and field_value.strip():
                    body_text_parts.append(field_value.strip())
            
            # Process media content if available
            media = getattr(shout, 'media', None)
            if media:
                if isinstance(media, str):
                    try:
                        media_json = json.loads(media)
                        if isinstance(media_json, dict):
                            if 'title' in media_json:
                                body_text_parts.append(media_json['title'])
                            if 'body' in media_json:
                                body_text_parts.append(media_json['body'])
                    except json.JSONDecodeError:
                        body_text_parts.append(media)
                elif isinstance(media, dict):
                    if 'title' in media:
                        body_text_parts.append(media['title'])
                    if 'body' in media:
                        body_text_parts.append(media['body'])
            
            if body_text_parts:
                body_text = " ".join(body_text_parts)
                # Truncate if too long
                MAX_TEXT_LENGTH = 4000
                if len(body_text) > MAX_TEXT_LENGTH:
                    body_text = body_text[:MAX_TEXT_LENGTH]
                
                body_doc = {
                    "id": str(shout.id),
                    "body": body_text
                }
                indexing_tasks.append(
                    self.index_client.post("/index-body", json=body_doc)
                )
            
            # 3. Index authors
            authors = getattr(shout, 'authors', [])
            for author in authors:
                author_id = str(getattr(author, 'id', 0))
                if not author_id or author_id == '0':
                    continue
                
                name = getattr(author, 'name', '')
                
                # Combine bio and about fields
                bio_parts = []
                bio = getattr(author, 'bio', '')
                if bio and isinstance(bio, str):
                    bio_parts.append(bio.strip())
                    
                about = getattr(author, 'about', '')
                if about and isinstance(about, str):
                    bio_parts.append(about.strip())
                
                combined_bio = " ".join(bio_parts)
                
                if name:
                    author_doc = {
                        "id": author_id,
                        "name": name,
                        "bio": combined_bio
                    }
                    indexing_tasks.append(
                        self.index_client.post("/index-author", json=author_doc)
                    )
            
            # Run all indexing tasks in parallel
            if indexing_tasks:
                responses = await asyncio.gather(*indexing_tasks, return_exceptions=True)
                
                # Check for errors in responses
                for i, response in enumerate(responses):
                    if isinstance(response, Exception):
                        logger.error(f"Error in indexing task {i}: {response}")
                    elif hasattr(response, 'status_code') and response.status_code >= 400:
                        logger.error(f"Error response in indexing task {i}: {response.status_code}, {await response.text()}")
                
                logger.info(f"Document {shout.id} indexed across {len(indexing_tasks)} endpoints")
            else:
                logger.warning(f"No content to index for shout {shout.id}")
                
        except Exception as e:
            logger.error(f"Indexing error for shout {shout.id}: {e}")

    async def bulk_index(self, shouts):
        """Index multiple documents across three separate endpoints"""
        if not self.available or not shouts:
            logger.warning(f"Bulk indexing skipped: available={self.available}, shouts_count={len(shouts) if shouts else 0}")
            return
        
        start_time = time.time()
        logger.info(f"Starting multi-endpoint bulk indexing of {len(shouts)} documents")

        # Prepare documents for different endpoints
        title_docs = []
        body_docs = []
        author_docs = {}  # Use dict to prevent duplicate authors
        
        total_skipped = 0
        
        for shout in shouts:
            try:
                # 1. Process title documents
                if hasattr(shout, 'title') and shout.title and isinstance(shout.title, str):
                    title_docs.append({
                        "id": str(shout.id),
                        "title": shout.title.strip()
                    })
                
                # 2. Process body documents (subtitle, lead, body)
                body_text_parts = []
                for field_name in ['subtitle', 'lead', 'body']:
                    field_value = getattr(shout, field_name, None)
                    if field_value and isinstance(field_value, str) and field_value.strip():
                        body_text_parts.append(field_value.strip())
                
                # Process media content if available
                media = getattr(shout, 'media', None)
                if media:
                    if isinstance(media, str):
                        try:
                            media_json = json.loads(media)
                            if isinstance(media_json, dict):
                                if 'title' in media_json:
                                    body_text_parts.append(media_json['title'])
                                if 'body' in media_json:
                                    body_text_parts.append(media_json['body'])
                        except json.JSONDecodeError:
                            body_text_parts.append(media)
                    elif isinstance(media, dict):
                        if 'title' in media:
                            body_text_parts.append(media['title'])
                        if 'body' in media:
                            body_text_parts.append(media['body'])
                
                # Only add body document if we have body text
                if body_text_parts:
                    body_text = " ".join(body_text_parts)
                    # Truncate if too long
                    MAX_TEXT_LENGTH = 4000
                    if len(body_text) > MAX_TEXT_LENGTH:
                        body_text = body_text[:MAX_TEXT_LENGTH]
                    
                    body_docs.append({
                        "id": str(shout.id),
                        "body": body_text
                    })
                
                # 3. Process authors if available
                authors = getattr(shout, 'authors', [])
                for author in authors:
                    author_id = str(getattr(author, 'id', 0))
                    if not author_id or author_id == '0':
                        continue
                        
                    # Skip if we've already processed this author
                    if author_id in author_docs:
                        continue
                        
                    name = getattr(author, 'name', '')
                    
                    # Combine bio and about fields
                    bio_parts = []
                    bio = getattr(author, 'bio', '')
                    if bio and isinstance(bio, str):
                        bio_parts.append(bio.strip())
                        
                    about = getattr(author, 'about', '')
                    if about and isinstance(about, str):
                        bio_parts.append(about.strip())
                    
                    combined_bio = " ".join(bio_parts)
                    
                    # Only add if we have author data
                    if name:
                        author_docs[author_id] = {
                            "id": author_id,
                            "name": name,
                            "bio": combined_bio
                        }
            
            except Exception as e:
                logger.error(f"Error processing shout {getattr(shout, 'id', 'unknown')} for indexing: {e}")
                total_skipped += 1
        
        # Convert author dict to list
        author_docs_list = list(author_docs.values())
        
        # Process each endpoint in parallel
        indexing_tasks = [
            self._index_endpoint(title_docs, "/bulk-index-titles", "title"),
            self._index_endpoint(body_docs, "/bulk-index-bodies", "body"),
            self._index_endpoint(author_docs_list, "/bulk-index-authors", "author")
        ]
        
        await asyncio.gather(*indexing_tasks)
        
        elapsed = time.time() - start_time
        logger.info(
            f"Multi-endpoint indexing completed in {elapsed:.2f}s: "
            f"{len(title_docs)} titles, {len(body_docs)} bodies, {len(author_docs_list)} authors, "
            f"{total_skipped} shouts skipped"
        )
        
    async def _index_endpoint(self, documents, endpoint, doc_type):
        """Process and index documents to a specific endpoint"""
        if not documents:
            logger.info(f"No {doc_type} documents to index")
            return
            
        logger.info(f"Indexing {len(documents)} {doc_type} documents")
        
        # Categorize documents by size
        small_docs, medium_docs, large_docs = self._categorize_by_size(documents, doc_type)
        
        # Process each category with appropriate batch sizes
        batch_sizes = {
            "small": min(MAX_BATCH_SIZE, 15),
            "medium": min(MAX_BATCH_SIZE, 10), 
            "large": min(MAX_BATCH_SIZE, 3)
        }
        
        for category, docs in [("small", small_docs), ("medium", medium_docs), ("large", large_docs)]:
            if docs:
                batch_size = batch_sizes[category]
                await self._process_batches(docs, batch_size, endpoint, f"{doc_type}-{category}")
    
    def _categorize_by_size(self, documents, doc_type):
        """Categorize documents by size for optimized batch processing"""
        small_docs = []
        medium_docs = []
        large_docs = []
        
        for doc in documents:
            # Extract relevant text based on document type
            if doc_type == "title":
                text = doc.get("title", "")
            elif doc_type == "body":
                text = doc.get("body", "")
            else:  # author
                # For authors, consider both name and bio length
                text = doc.get("name", "") + " " + doc.get("bio", "")
                    
            text_len = len(text)
            
            if text_len > 5000:
                large_docs.append(doc)
            elif text_len > 2000:
                medium_docs.append(doc)
            else:
                small_docs.append(doc)
        
        logger.info(f"{doc_type.capitalize()} documents categorized: {len(small_docs)} small, {len(medium_docs)} medium, {len(large_docs)} large")
        return small_docs, medium_docs, large_docs
    
    async def _process_batches(self, documents, batch_size, endpoint, batch_prefix):
        """Process document batches with retry logic"""
        for i in range(0, len(documents), batch_size):
            batch = documents[i:i+batch_size]
            batch_id = f"{batch_prefix}-{i//batch_size + 1}"
            
            retry_count = 0
            max_retries = 3
            success = False
            
            while not success and retry_count < max_retries:
                try:
                    logger.info(f"Sending batch {batch_id} ({len(batch)} docs) to {endpoint}")
                    response = await self.index_client.post(
                        endpoint,
                        json=batch,
                        timeout=90.0
                    )
                    
                    if response.status_code == 422:
                        error_detail = response.json()
                        logger.error(f"Validation error from search service for batch {batch_id}: {self._truncate_error_detail(error_detail)}")
                        break
                        
                    response.raise_for_status()
                    success = True
                    logger.info(f"Successfully indexed batch {batch_id}")
                    
                except Exception as e:
                    retry_count += 1
                    if retry_count >= max_retries:
                        if len(batch) > 1:
                            mid = len(batch) // 2
                            logger.warning(f"Splitting batch {batch_id} into smaller batches for retry")
                            await self._process_batches(batch[:mid], batch_size // 2, endpoint, f"{batch_prefix}-{i//batch_size}-A")
                            await self._process_batches(batch[mid:], batch_size // 2, endpoint, f"{batch_prefix}-{i//batch_size}-B")
                        else:
                            logger.error(f"Failed to index single document in batch {batch_id} after {max_retries} attempts: {str(e)}")
                        break
                    
                    wait_time = (2 ** retry_count) + (random.random() * 0.5)
                    logger.warning(f"Retrying batch {batch_id} in {wait_time:.1f}s... (attempt {retry_count+1}/{max_retries})")
                    await asyncio.sleep(wait_time)

    def _truncate_error_detail(self, error_detail):
        """Truncate error details for logging"""
        truncated_detail = error_detail.copy() if isinstance(error_detail, dict) else error_detail
        
        if isinstance(truncated_detail, dict) and 'detail' in truncated_detail and isinstance(truncated_detail['detail'], list):
            for i, item in enumerate(truncated_detail['detail']):
                if isinstance(item, dict) and 'input' in item:
                    if isinstance(item['input'], dict) and any(k in item['input'] for k in ['documents', 'text']):
                        if 'documents' in item['input'] and isinstance(item['input']['documents'], list):
                            for j, doc in enumerate(item['input']['documents']):
                                if 'text' in doc and isinstance(doc['text'], str) and len(doc['text']) > 100:
                                    item['input']['documents'][j]['text'] = f"{doc['text'][:100]}... [truncated, total {len(doc['text'])} chars]"
                        
                        if 'text' in item['input'] and isinstance(item['input']['text'], str) and len(item['input']['text']) > 100:
                            item['input']['text'] = f"{item['input']['text'][:100]}... [truncated, total {len(item['input']['text'])} chars]"
        
        return truncated_detail


    #*******************
    # Specialized search methods for shouts and authors
    #*******************
    
    async def search(self, text, limit, offset):
        """Search documents"""
        if not self.available:
            return []
        
        if not isinstance(text, str) or not text.strip():
            return []
            
        logger.info(f"Searching for: '{text}' (limit={limit}, offset={offset})")
        
        # Check if we can serve from cache
        if SEARCH_CACHE_ENABLED:
            has_cache = await self.cache.has_query(text)
            if has_cache:
                cached_results = await self.cache.get(text, limit, offset)
                if cached_results is not None:
                    return cached_results
        
        # Not in cache or cache disabled, perform new search
        try:
            search_limit = limit
            search_offset = offset

            if SEARCH_CACHE_ENABLED:
                search_limit = SEARCH_PREFETCH_SIZE
                search_offset = 0
            else:
                search_limit = limit
                search_offset = offset
                
            response = await self.client.post(
                "/search-combined",
                json={"text": text, "limit": search_limit, "offset": search_offset}
            )
            response.raise_for_status()
            
            result = response.json()
            
            formatted_results = result.get("results", [])
            
            valid_results = []
            for item in formatted_results:
                doc_id = item.get("id")
                if doc_id and doc_id.isdigit():
                    valid_results.append(item)
            
            if len(valid_results) != len(formatted_results):
                formatted_results = valid_results
                
            if SEARCH_MIN_SCORE > 0:
                initial_count = len(formatted_results)
                formatted_results = [r for r in formatted_results if r.get("score", 0) >= SEARCH_MIN_SCORE]
            
            if SEARCH_CACHE_ENABLED:
                await self.cache.store(text, formatted_results)
                end_idx = offset + limit
                page_results = formatted_results[offset:end_idx]
                return page_results
                    
            return formatted_results
        except Exception as e:
            logger.error(f"Search error for '{text}': {e}", exc_info=True)
            return []
    
    async def search_authors(self, text, limit=10, offset=0):
        """Search only for authors using the specialized endpoint"""
        if not self.available or not text.strip():
            return []
            
        cache_key = f"author:{text}"
        
        # Try cache first if enabled
        if SEARCH_CACHE_ENABLED:
            if await self.cache.has_query(cache_key):
                return await self.cache.get(cache_key, limit, offset)
        
        try:
            logger.info(f"Searching authors for: '{text}' (limit={limit}, offset={offset})")
            response = await self.client.post(
                "/search-author",
                json={"text": text, "limit": limit + offset}
            )
            response.raise_for_status()
            
            result = response.json()
            author_results = result.get("results", [])
            
            # Apply score filtering if needed
            if SEARCH_MIN_SCORE > 0:
                author_results = [r for r in author_results if r.get("score", 0) >= SEARCH_MIN_SCORE]
                
            # Store in cache if enabled
            if SEARCH_CACHE_ENABLED:
                await self.cache.store(cache_key, author_results)
                
            # Apply offset/limit
            return author_results[offset:offset+limit]
            
        except Exception as e:
            logger.error(f"Error searching authors for '{text}': {e}")
            return []
    
    async def search(self, text, limit, offset):
        """
        Legacy search method that searches only bodies for backward compatibility.
        Consider using the specialized search methods instead.
        """
        logger.warning("Using deprecated search() method - consider using search_bodies(), search_titles(), or search_authors()")
        return await self.search_bodies(text, limit, offset)
    
    async def check_index_status(self):
        """Get detailed statistics about the search index health"""
        if not self.available:
            return {"status": "disabled"}
            
        try:
            response = await self.client.get("/index-status")
            response.raise_for_status()
            result = response.json()
            
            if result.get("consistency", {}).get("status") != "ok":
                null_count = result.get("consistency", {}).get("null_embeddings_count", 0)
                if null_count > 0:
                    logger.warning(f"Found {null_count} documents with NULL embeddings")
                    
            return result
        except Exception as e:
            logger.error(f"Failed to check index status: {e}")
            return {"status": "error", "message": str(e)}

# Create the search service singleton
search_service = SearchService()

# API-compatible function to perform a search

async def search_title_text(text: str, limit: int = 10, offset: int = 0):
    """Search titles API helper function"""
    if search_service.available:
        return await search_service.search_titles(text, limit, offset)
    return []

async def search_body_text(text: str, limit: int = 10, offset: int = 0):
    """Search bodies API helper function"""
    if search_service.available:
        return await search_service.search_bodies(text, limit, offset)
    return []

async def search_author_text(text: str, limit: int = 10, offset: int = 0):
    """Search authors API helper function"""
    if search_service.available:
        return await search_service.search_authors(text, limit, offset)
    return []

async def get_title_search_count(text: str):
    """Get count of title search results"""
    if not search_service.available:
        return 0
        
    if SEARCH_CACHE_ENABLED:
        cache_key = f"title:{text}"
        if await search_service.cache.has_query(cache_key):
            return await search_service.cache.get_total_count(cache_key)
    
    # If not found in cache, fetch from endpoint
    return len(await search_title_text(text, SEARCH_PREFETCH_SIZE, 0))

async def get_body_search_count(text: str):
    """Get count of body search results"""
    if not search_service.available:
        return 0
        
    if SEARCH_CACHE_ENABLED:
        cache_key = f"body:{text}"
        if await search_service.cache.has_query(cache_key):
            return await search_service.cache.get_total_count(cache_key)
    
    # If not found in cache, fetch from endpoint
    return len(await search_body_text(text, SEARCH_PREFETCH_SIZE, 0))

async def get_author_search_count(text: str):
    """Get count of author search results"""
    if not search_service.available:
        return 0
        
    if SEARCH_CACHE_ENABLED:
        cache_key = f"author:{text}"
        if await search_service.cache.has_query(cache_key):
            return await search_service.cache.get_total_count(cache_key)
    
    # If not found in cache, fetch from endpoint
    return len(await search_author_text(text, SEARCH_PREFETCH_SIZE, 0))

async def initialize_search_index(shouts_data):
    """Initialize search index with existing data during application startup"""
    if not SEARCH_ENABLED:
        return
        
    if not shouts_data:
        return
        
    info = await search_service.info()
    if info.get("status") in ["error", "unavailable", "disabled"]:
        return
    
    index_stats = info.get("index_stats", {})
    indexed_doc_count = index_stats.get("total_count", 0)

    index_status = await search_service.check_index_status()
    if index_status.get("status") == "inconsistent":
        problem_ids = index_status.get("consistency", {}).get("null_embeddings_sample", [])
                
        if problem_ids:
            problem_docs = [shout for shout in shouts_data if str(shout.id) in problem_ids]
            if problem_docs:
                await search_service.bulk_index(problem_docs)
    
    db_ids = [str(shout.id) for shout in shouts_data]
    
    try:
        numeric_ids = [int(sid) for sid in db_ids if sid.isdigit()]
        if numeric_ids:
            min_id = min(numeric_ids)
            max_id = max(numeric_ids)
            id_range = max_id - min_id + 1
    except Exception as e:
        pass
    
    if abs(indexed_doc_count - len(shouts_data)) > 10:
        doc_ids = [str(shout.id) for shout in shouts_data]
        
        verification = await search_service.verify_docs(doc_ids)
        
        if verification.get("status") == "error":
            return
            
        missing_ids = verification.get("missing", [])
        if missing_ids:
            missing_docs = [shout for shout in shouts_data if str(shout.id) in missing_ids]
            await search_service.bulk_index(missing_docs)
    else:
        pass
        
    try:
        test_query = "test"
        # Use body search since that's most likely to return results
        test_results = await search_body_text(test_query, 5)
        
        if test_results:
            categories = set()
            for result in test_results:
                result_id = result.get("id")
                matching_shouts = [s for s in shouts_data if str(s.id) == result_id]
                if matching_shouts and hasattr(matching_shouts[0], 'category'):
                    categories.add(getattr(matching_shouts[0], 'category', 'unknown'))
    except Exception as e:
        pass