import os
import asyncio
import time
from typing import List, Optional
from fastapi import FastAPI, HTTPException, Depends, Header
from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
from fastapi.middleware.cors import CORSMiddleware
from prisma import Prisma
from dotenv import load_dotenv

from models import (
    VectorStoreCreateRequest,
    VectorStoreResponse,
    VectorStoreSearchRequest,
    VectorStoreSearchResponse,
    SearchResult,
    EmbeddingCreateRequest,
    EmbeddingResponse,
    EmbeddingBatchCreateRequest,
    EmbeddingBatchCreateResponse,
    VectorStoreListResponse,
    ContentChunk
)
from config import settings
from embedding_service import embedding_service

load_dotenv()

app = FastAPI(
    title="OpenAI Vector Stores API",
    description="OpenAI-compatible Vector Stores API using PGVector",
    version="1.0.0"
)

# CORS middleware
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Global Prisma client
db = Prisma()

security = HTTPBearer()


async def get_api_key(credentials: HTTPAuthorizationCredentials = Depends(security)):
    """Validate API key from Authorization header"""
    expected_key = settings.server_api_key
    if credentials.credentials != expected_key:
        raise HTTPException(status_code=401, detail="Invalid API key")
    return credentials.credentials


@app.on_event("startup")
async def startup():
    """Connect to database on startup"""
    await db.connect()


@app.on_event("shutdown")
async def shutdown():
    """Disconnect from database on shutdown"""
    await db.disconnect()


async def generate_query_embedding(query: str) -> List[float]:
    """
    Generate an embedding for the query using LiteLLM
    """
    return await embedding_service.generate_embedding(query)


@app.post("/v1/vector_stores", response_model=VectorStoreResponse)
async def create_vector_store(
    request: VectorStoreCreateRequest,
    api_key: str = Depends(get_api_key)
):
    """
    Create a new vector store.
    """
    try:
        # Use raw SQL to insert the vector store with configurable table/field names
        vector_store_table = settings.table_names["vector_stores"]

        # ponytail: honor a caller-supplied id (request.id) instead of
        # always minting one — litellm's vector_store_registry addresses
        # this store by a fixed id (see litellm-config.yaml), which never
        # matched anything when this always generated a random UUID.
        import uuid as _uuid
        vector_store_id = request.id or str(_uuid.uuid4())

        result = await db.query_raw(
            f"""
            INSERT INTO {vector_store_table} (id, name, file_counts, status, usage_bytes, expires_after, metadata, created_at)
            VALUES ($1, $2, $3, $4, $5, $6, $7, NOW())
            RETURNING id, name, file_counts, status, usage_bytes, expires_after, expires_at, last_active_at, metadata,
                     EXTRACT(EPOCH FROM created_at)::bigint as created_at_timestamp
            """,
            vector_store_id,
            request.name,
            {"in_progress": 0, "completed": 0, "failed": 0, "cancelled": 0, "total": 0},
            "completed",
            0,
            request.expires_after,
            request.metadata or {}
        )
        
        if not result:
            raise HTTPException(status_code=500, detail="Failed to create vector store")
            
        vector_store = result[0]
        
        # Convert to response format
        created_at = int(vector_store["created_at_timestamp"])
        expires_at = int(vector_store["expires_at"].timestamp()) if vector_store.get("expires_at") else None
        last_active_at = int(vector_store["last_active_at"].timestamp()) if vector_store.get("last_active_at") else None
        
        return VectorStoreResponse(
            id=vector_store["id"],
            created_at=created_at,
            name=vector_store["name"],
            usage_bytes=vector_store["usage_bytes"] or 0,
            file_counts=vector_store["file_counts"] or {"in_progress": 0, "completed": 0, "failed": 0, "cancelled": 0, "total": 0},
            status=vector_store["status"],
            expires_after=vector_store["expires_after"],
            expires_at=expires_at,
            last_active_at=last_active_at,
            metadata=vector_store["metadata"]
        )
        
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Failed to create vector store: {str(e)}")


@app.get("/v1/vector_stores", response_model=VectorStoreListResponse)
async def list_vector_stores(
    limit: Optional[int] = 20,
    after: Optional[str] = None,
    before: Optional[str] = None,
    api_key: str = Depends(get_api_key)
):
    """
    List vector stores with optional pagination.
    """
    try:
        limit = min(limit or 20, 100)  # Cap at 100 results
        
        vector_store_table = settings.table_names["vector_stores"]
        
        # Build base query
        base_query = f"""
        SELECT id, name, file_counts, status, usage_bytes, expires_after, expires_at, last_active_at, metadata,
               EXTRACT(EPOCH FROM created_at)::bigint as created_at_timestamp
        FROM {vector_store_table}
        """
        
        # Add pagination conditions
        conditions = []
        params = []
        param_count = 1
        
        if after:
            conditions.append(f"id > ${param_count}")
            params.append(after)
            param_count += 1
            
        if before:
            conditions.append(f"id < ${param_count}")
            params.append(before)
            param_count += 1
        
        if conditions:
            base_query += " WHERE " + " AND ".join(conditions)
        
        # Add ordering and limit
        final_query = base_query + f" ORDER BY created_at DESC LIMIT {limit + 1}"
        
        # Execute query
        results = await db.query_raw(final_query, *params)
        
        # Check if there are more results
        has_more = len(results) > limit
        if has_more:
            results = results[:limit]  # Remove extra result
        
        # Convert to response format
        vector_stores = []
        for row in results:
            created_at = int(row["created_at_timestamp"])
            expires_at = int(row["expires_at"].timestamp()) if row.get("expires_at") else None
            last_active_at = int(row["last_active_at"].timestamp()) if row.get("last_active_at") else None
            
            vector_store = VectorStoreResponse(
                id=row["id"],
                created_at=created_at,
                name=row["name"],
                usage_bytes=row["usage_bytes"] or 0,
                file_counts=row["file_counts"] or {"in_progress": 0, "completed": 0, "failed": 0, "cancelled": 0, "total": 0},
                status=row["status"],
                expires_after=row["expires_after"],
                expires_at=expires_at,
                last_active_at=last_active_at,
                metadata=row["metadata"]
            )
            vector_stores.append(vector_store)
        
        # Determine first_id and last_id
        first_id = vector_stores[0].id if vector_stores else None
        last_id = vector_stores[-1].id if vector_stores else None
        
        return VectorStoreListResponse(
            data=vector_stores,
            first_id=first_id,
            last_id=last_id,
            has_more=has_more
        )
        
    except Exception as e:
        import traceback
        traceback.print_exc()
        raise HTTPException(status_code=500, detail=f"Failed to list vector stores: {str(e)}")


@app.post("/v1/vector_stores/{vector_store_id}/search", response_model=VectorStoreSearchResponse)
@app.post("/vector_stores/{vector_store_id}/search", response_model=VectorStoreSearchResponse)
async def search_vector_store(
    vector_store_id: str,
    request: VectorStoreSearchRequest,
    api_key: str = Depends(get_api_key)
):
    """
    Search a vector store for similar content.
    """
    try:
        # Check if vector store exists
        vector_store_table = settings.table_names["vector_stores"]
        vector_store_result = await db.query_raw(
            f"SELECT id FROM {vector_store_table} WHERE id = $1",
            vector_store_id
        )
        if not vector_store_result:
            raise HTTPException(status_code=404, detail="Vector store not found")
        
        # Generate embedding for query
        query_embedding = await generate_query_embedding(request.query)
        query_vector_str = "[" + ",".join(map(str, query_embedding)) + "]"
        
        # Build the raw SQL query for vector similarity search
        limit = min(request.limit or 20, 100)  # Cap at 100 results
        
        # Base query with vector similarity using cosine distance
        # Use configurable field names
        fields = settings.db_fields
        table_name = settings.table_names["embeddings"]
        
        # Build query with proper parameter placeholders for Prisma
        param_count = 1
        query_params = [query_vector_str, vector_store_id]
        
        base_query = f"""
        SELECT 
            {fields.id_field},
            {fields.content_field},
            {fields.metadata_field},
            ({fields.embedding_field} <=> ${param_count}::vector) as distance
        FROM {table_name} 
        WHERE {fields.vector_store_id_field} = ${param_count + 1}
        """
        param_count += 2
        
        # Add metadata filters if provided
        filter_conditions = []
        
        if request.filters:
            for key, value in request.filters.items():
                filter_conditions.append(f"{fields.metadata_field}->>${param_count} = ${param_count + 1}")
                query_params.extend([key, str(value)])
                param_count += 2
        
        if filter_conditions:
            base_query += " AND " + " AND ".join(filter_conditions)
        
        # Add ordering and limit
        final_query = base_query + f" ORDER BY distance ASC LIMIT {limit}"
        
        # Execute the query
        results = await db.query_raw(final_query, *query_params)
        
        # Convert results to SearchResult objects
        search_results = []
        for row in results:
            # Convert distance to similarity score (1 - normalized_distance)
            # Cosine distance ranges from 0 (identical) to 2 (opposite)
            similarity_score = max(0, 1 - (row['distance'] / 2))
            
            # Extract filename from metadata or use a default
            metadata = row[fields.metadata_field] or {}
            filename = metadata.get('filename', 'document.txt')
            
            content_chunks = [ContentChunk(type="text", text=row[fields.content_field])]
            
            result = SearchResult(
                file_id=row[fields.id_field],
                filename=filename,
                score=similarity_score,
                attributes=metadata if request.return_metadata else None,
                content=content_chunks
            )
            search_results.append(result)
        
        return VectorStoreSearchResponse(
            search_query=request.query,
            data=search_results,
            has_more=False,  # TODO: Implement pagination
            next_page=None
        )
        
    except HTTPException:
        raise
    except Exception as e:
        import traceback
        traceback.print_exc()
        raise HTTPException(status_code=500, detail=f"Search failed: {str(e)}")


@app.post("/v1/vector_stores/{vector_store_id}/embeddings", response_model=EmbeddingResponse)
async def create_embedding(
    vector_store_id: str,
    request: EmbeddingCreateRequest,
    api_key: str = Depends(get_api_key)
):
    """
    Add a single embedding to a vector store.
    """
    try:
        # Check if vector store exists
        vector_store_table = settings.table_names["vector_stores"]
        vector_store_result = await db.query_raw(
            f"SELECT id FROM {vector_store_table} WHERE id = $1",
            vector_store_id
        )
        if not vector_store_result:
            raise HTTPException(status_code=404, detail="Vector store not found")
        
        # Convert embedding to vector string format
        embedding_vector_str = "[" + ",".join(map(str, request.embedding)) + "]"
        
        # Insert embedding using configurable field names
        fields = settings.db_fields
        table_name = settings.table_names["embeddings"]
        
        result = await db.query_raw(
            f"""
            INSERT INTO {table_name} ({fields.id_field}, {fields.vector_store_id_field}, {fields.content_field}, 
                                     {fields.embedding_field}, {fields.metadata_field}, {fields.created_at_field})
            VALUES (gen_random_uuid(), $1, $2, $3::vector, $4, NOW())
            RETURNING {fields.id_field}, {fields.vector_store_id_field}, {fields.content_field}, 
                     {fields.metadata_field}, EXTRACT(EPOCH FROM {fields.created_at_field})::bigint as created_at_timestamp
            """,
            vector_store_id,
            request.content,
            embedding_vector_str,
            request.metadata or {}
        )
        
        if not result:
            raise HTTPException(status_code=500, detail="Failed to create embedding")
            
        embedding = result[0]
        
        # Update vector store statistics
        await db.query_raw(
            f"""
            UPDATE {vector_store_table} 
            SET 
                file_counts = jsonb_set(
                    jsonb_set(
                        COALESCE(file_counts, '{{"in_progress": 0, "completed": 0, "failed": 0, "cancelled": 0, "total": 0}}'::jsonb),
                        '{{completed}}',
                        (COALESCE(file_counts->>'completed', '0')::int + 1)::text::jsonb
                    ),
                    '{{total}}',
                    (COALESCE(file_counts->>'total', '0')::int + 1)::text::jsonb
                ),
                usage_bytes = COALESCE(usage_bytes, 0) + LENGTH($2),
                last_active_at = NOW()
            WHERE id = $1
            """,
            vector_store_id,
            request.content
        )
        
        return EmbeddingResponse(
            id=embedding[fields.id_field],
            vector_store_id=embedding[fields.vector_store_id_field],
            content=embedding[fields.content_field],
            metadata=embedding[fields.metadata_field],
            created_at=int(embedding["created_at_timestamp"])
        )
        
    except HTTPException:
        raise
    except Exception as e:
        import traceback
        traceback.print_exc()
        raise HTTPException(status_code=500, detail=f"Failed to create embedding: {str(e)}")


@app.post("/v1/vector_stores/{vector_store_id}/embeddings/batch", response_model=EmbeddingBatchCreateResponse)
async def create_embeddings_batch(
    vector_store_id: str,
    request: EmbeddingBatchCreateRequest,
    api_key: str = Depends(get_api_key)
):
    """
    Add multiple embeddings to a vector store in batch.
    """
    try:
        # Check if vector store exists
        vector_store_table = settings.table_names["vector_stores"]
        vector_store_result = await db.query_raw(
            f"SELECT id FROM {vector_store_table} WHERE id = $1",
            vector_store_id
        )
        if not vector_store_result:
            raise HTTPException(status_code=404, detail="Vector store not found")
        
        if not request.embeddings:
            raise HTTPException(status_code=400, detail="No embeddings provided")
        
        # Prepare batch insert
        fields = settings.db_fields
        table_name = settings.table_names["embeddings"]
        
        # Build VALUES clause for batch insert
        values_clauses = []
        params = []
        param_count = 1
        
        for embedding_req in request.embeddings:
            embedding_vector_str = "[" + ",".join(map(str, embedding_req.embedding)) + "]"
            values_clauses.append(f"(gen_random_uuid(), ${param_count}, ${param_count + 1}, ${param_count + 2}::vector, ${param_count + 3}, NOW())")
            params.extend([
                vector_store_id,
                embedding_req.content,
                embedding_vector_str,
                embedding_req.metadata or {}
            ])
            param_count += 4
        
        values_clause = ", ".join(values_clauses)
        
        # Execute batch insert
        result = await db.query_raw(
            f"""
            INSERT INTO {table_name} ({fields.id_field}, {fields.vector_store_id_field}, {fields.content_field}, 
                                     {fields.embedding_field}, {fields.metadata_field}, {fields.created_at_field})
            VALUES {values_clause}
            RETURNING {fields.id_field}, {fields.vector_store_id_field}, {fields.content_field}, 
                     {fields.metadata_field}, EXTRACT(EPOCH FROM {fields.created_at_field})::bigint as created_at_timestamp
            """,
            *params
        )
        
        if not result:
            raise HTTPException(status_code=500, detail="Failed to create embeddings")
        
        # Calculate total content length for usage bytes update
        total_content_length = sum(len(emb.content) for emb in request.embeddings)
        
        # Update vector store statistics
        await db.query_raw(
            f"""
            UPDATE {vector_store_table} 
            SET 
                file_counts = jsonb_set(
                    jsonb_set(
                        COALESCE(file_counts, '{{"in_progress": 0, "completed": 0, "failed": 0, "cancelled": 0, "total": 0}}'::jsonb),
                        '{{completed}}',
                        (COALESCE(file_counts->>'completed', '0')::int + $2)::text::jsonb
                    ),
                    '{{total}}',
                    (COALESCE(file_counts->>'total', '0')::int + $2)::text::jsonb
                ),
                usage_bytes = COALESCE(usage_bytes, 0) + $3,
                last_active_at = NOW()
            WHERE id = $1
            """,
            vector_store_id,
            len(request.embeddings),
            total_content_length
        )
        
        # Convert results to response format
        embeddings = []
        for row in result:
            embeddings.append(EmbeddingResponse(
                id=row[fields.id_field],
                vector_store_id=row[fields.vector_store_id_field],
                content=row[fields.content_field],
                metadata=row[fields.metadata_field],
                created_at=int(row["created_at_timestamp"])
            ))
        
        return EmbeddingBatchCreateResponse(
            data=embeddings,
            created=int(time.time())
        )
        
    except HTTPException:
        raise
    except Exception as e:
        import traceback
        traceback.print_exc()
        raise HTTPException(status_code=500, detail=f"Failed to create embeddings batch: {str(e)}")


@app.get("/health")
async def health_check():
    """Health check endpoint"""
    return {"status": "healthy", "timestamp": int(time.time())}


if __name__ == "__main__":
    import uvicorn
    uvicorn.run("main:app", host=settings.host, port=settings.port, reload=True)