import os
import sys
import asyncio
import json
from typing import List, Dict, Any, Optional
import chromadb
from chromadb.config import Settings
from src import models
from src import mcp_client

try:
 from sentence_transformers import SentenceTransformer
except ImportError:
 SentenceTransformer = None

from google import genai
from google.genai import types
from src import ai_client

class BaseEmbeddingProvider:
 def embed(self, texts: List[str]) -> List[List[float]]:
  raise NotImplementedError()

class LocalEmbeddingProvider(BaseEmbeddingProvider):
 def __init__(self, model_name: str = 'all-MiniLM-L6-v2'):
  if SentenceTransformer is None:
   raise ImportError("sentence-transformers is not installed")
  self.model = SentenceTransformer(model_name)

 def embed(self, texts: List[str]) -> List[List[float]]:
  embeddings = self.model.encode(texts)
  return embeddings.tolist()

class GeminiEmbeddingProvider(BaseEmbeddingProvider):
 def __init__(self, model_name: str = 'text-embedding-004'):
  self.model_name = model_name

 def embed(self, texts: List[str]) -> List[List[float]]:
  ai_client._ensure_gemini_client()
  client = ai_client._gemini_client
  if not client:
   raise ValueError("Gemini client not initialized")
  
  # For text-embedding-004, we can embed a batch
  res = client.models.embed_content(
   model=self.model_name,
   contents=texts,
   config=types.EmbedContentConfig(task_type="RETRIEVAL_DOCUMENT")
  )
  return [e.values for e in res.embeddings]

class RAGEngine:
 def __init__(self, config: models.RAGConfig, base_dir: str = "."):
  self.config = config
  self.base_dir = base_dir
  self.client = None
  self.collection = None
  self.embedding_provider = None
  
  if not self.config.enabled:
   return
   
  self._init_embedding_provider()
  self._init_vector_store()

 def _init_embedding_provider(self):
  if self.config.embedding_provider == 'gemini':
   self.embedding_provider = GeminiEmbeddingProvider()
  elif self.config.embedding_provider == 'local':
   self.embedding_provider = LocalEmbeddingProvider()
  else:
   raise ValueError(f"Unknown embedding provider: {self.config.embedding_provider}")

 def _init_vector_store(self):
  vs_config = self.config.vector_store
  if vs_config.provider == 'chroma':
   db_path = os.path.join(self.base_dir, ".slop_cache", "chroma_db")
   os.makedirs(db_path, exist_ok=True)
   self.client = chromadb.PersistentClient(path=db_path)
   self.collection = self.client.get_or_create_collection(name=vs_config.collection_name)
  elif vs_config.provider == 'mock':
   self.client = "mock"
   self.collection = "mock"
  else:
   raise ValueError(f"Unknown vector store provider: {vs_config.provider}")

 def is_empty(self) -> bool:
  if not self.config.enabled:
   return True
  if self.config.vector_store.provider == 'mock' or self.collection == "mock":
   return True
  if self.collection is None:
   return True
  return self.collection.count() == 0

 def add_documents(self, ids: List[str], texts: List[str], metadatas: Optional[List[Dict[str, Any]]] = None):
  """
  [C: tests/test_rag_engine.py:test_rag_engine_chroma]
  """
  if not self.config.enabled or self.collection == "mock":
   return
  embeddings = self.embedding_provider.embed(texts)
  self.collection.upsert(
   ids=ids,
   embeddings=embeddings,
   documents=texts,
   metadatas=metadatas
  )

 def _chunk_text(self, content: str) -> List[str]:
  """Character-based chunking with overlap."""
  chunks = []
  if not content:
   return chunks
  chunk_size = self.config.chunk_size
  overlap = self.config.chunk_overlap
  start = 0
  while start < len(content):
   end = start + chunk_size
   chunks.append(content[start:end])
   if end >= len(content):
    break
   start += (chunk_size - overlap)
  return chunks

 def _chunk_code(self, content: str, file_path: str) -> List[str]:
  """AST-aware chunking for Python code."""
  try:
   from src.file_cache import ASTParser
   parser = ASTParser("python")
   tree = parser.parse(content)
   chunks = []
   
   # Capture classes and top-level functions
   for node in tree.root_node.children:
    if node.type in ("function_definition", "class_definition"):
     chunks.append(content[node.start_byte:node.end_byte])
   
   # Fallback if no structural chunks found or if file is small
   if not chunks or len(content) < self.config.chunk_size:
    return self._chunk_text(content)
   return chunks
  except Exception:
   return self._chunk_text(content)

 def index_file(self, file_path: str):
  """Reads, chunks, and indexes a file into the vector store."""
  if not self.config.enabled or self.collection == "mock":
   return
  
  full_path = os.path.join(self.base_dir, file_path)
  if not os.path.exists(full_path):
   return
   
  try:
   mtime = os.path.getmtime(full_path)
  except Exception:
   return

  # Incremental check: see if we already have this file with the same mtime
  try:
   res = self.collection.get(where={"path": file_path}, limit=1, include=["metadatas"])
   if res and res["metadatas"] and res["metadatas"][0]:
    if res["metadatas"][0].get("mtime") == mtime:
     return
  except Exception:
   pass

  try:
   with open(full_path, "r", encoding="utf-8", errors="ignore") as f:
    content = f.read()
  except Exception:
   return

  # Remove old entries for this file
  self.collection.delete(where={"path": file_path})
  
  if file_path.lower().endswith(".py"):
   chunks = self._chunk_code(content, file_path)
  else:
   chunks = self._chunk_text(content)
   
  if not chunks:
   return

  ids = [f"{file_path}_{i}" for i in range(len(chunks))]
  metadatas = [{"path": file_path, "chunk": i, "mtime": mtime} for i in range(len(chunks))]
  self.add_documents(ids, chunks, metadatas)

 def _search_mcp(self, query: str, top_k: int = 5) -> List[Dict[str, Any]]:
  async def _async_search_mcp():
   tool_name = self.config.vector_store.mcp_tool or "rag_search"
   args = {"query": query, "top_k": top_k}
   res_str = await mcp_client.async_dispatch(tool_name, args)
   try:
    data = json.loads(res_str)
    if isinstance(data, list):
     return data
    elif isinstance(data, dict) and "results" in data:
     return data["results"]
    return []
   except:
    return []

  return asyncio.run(_async_search_mcp())

 def search(self, query: str, top_k: int = 5) -> List[Dict[str, Any]]:
  """
  [C: tests/mock_concurrent_mma.py:main, tests/test_rag_engine.py:test_rag_engine_chroma]
  """
  if not self.config.enabled:
   return []
  if self.config.vector_store.provider == 'mcp':
   return self._search_mcp(query, top_k)
  if self.collection == "mock":
   return []
  
  query_embedding = self.embedding_provider.embed([query])[0]
  results = self.collection.query(
   query_embeddings=[query_embedding],
   n_results=top_k
  )
  
  ret = []
  if results and results["ids"] and results["ids"][0]:
   for i in range(len(results["ids"][0])):
    ret.append({
     "id": results["ids"][0][i],
     "document": results["documents"][0][i],
     "metadata": results["metadatas"][0][i] if results["metadatas"] else {},
     "distance": results["distances"][0][i] if "distances" in results and results["distances"] else 0.0
    })
  return ret

 def delete_documents(self, ids: List[str]):
  """
  [C: tests/test_rag_engine.py:test_rag_engine_chroma]
  """
  if not self.config.enabled or self.collection == "mock":
   return
  self.collection.delete(ids=ids)

 def get_all_indexed_paths(self) -> List[str]:
  if not self.config.enabled or self.collection == "mock":
   return []
  res = self.collection.get(include=["metadatas"])
  if not res or not res["metadatas"]:
   return []
  return list(set(m.get("path") for m in res["metadatas"] if m.get("path")))

 def delete_documents_by_path(self, file_paths: List[str]):
  if not self.config.enabled or self.collection == "mock":
   return
  for path in file_paths:
   self.collection.delete(where={"path": path})