# -*- coding:utf-8 -*-
"""Plugin registry - manages available RAG plugins and pipeline configs."""
import json
import os
import urllib.request
from typing import Dict, List, Optional

CONFIG_DIR = "/data/ymq/rag-pipeline/pipelines"

# Default pipeline config
DEFAULT_PIPELINE = {
    "name": "kg-rag-standard",
    "description": "标准知识图谱RAG",
    "plugins": {
        "embedding": {"type": "clip-vith14", "endpoint": "http://localhost:9086"},
        "vdb": {"type": "vdb-milvus", "endpoint": "http://localhost:8886"},
        "graph": {"type": "networkx", "endpoint": "http://localhost:9092"},
        "llm": {"type": "harnessed", "endpoint": "internal"},
        "reranker": {"type": "bge-reranker", "endpoint": "http://localhost:9090"},
        "chunker": {"type": "recursive", "chunk_size": 512, "overlap": 64},
        "extractor": {"type": "llm-structured"},
        "retriever": {"type": "hybrid", "vector_top_k": 20, "graph_hops": 2}
    }
}

LITE_PIPELINE = {
    "name": "kg-rag-lite",
    "description": "轻量版RAG（纯向量，无图谱）",
    "plugins": {
        "embedding": {"type": "clip-vith14", "endpoint": "http://localhost:9086"},
        "vdb": {"type": "vdb-milvus", "endpoint": "http://localhost:8886"},
        "graph": {"type": "none"},
        "llm": {"type": "harnessed", "endpoint": "internal"},
        "reranker": {"type": "bge-reranker", "endpoint": "http://localhost:9090"},
        "chunker": {"type": "recursive", "chunk_size": 512, "overlap": 64},
        "retriever": {"type": "vector_only", "top_k": 10}
    }
}


def _ensure_config_dir():
    os.makedirs(CONFIG_DIR, exist_ok=True)


def list_pipelines() -> List[Dict]:
    """List all available pipeline configs."""
    _ensure_config_dir()
    pipelines = [DEFAULT_PIPELINE, LITE_PIPELINE]
    
    # Load custom pipelines from disk
    for f in os.listdir(CONFIG_DIR):
        if f.endswith('.json'):
            try:
                with open(os.path.join(CONFIG_DIR, f), 'r') as fh:
                    pipelines.append(json.load(fh))
            except:
                pass
    
    return pipelines


def get_pipeline(name: str) -> Optional[Dict]:
    """Get a pipeline config by name."""
    for p in list_pipelines():
        if p["name"] == name:
            return p
    return DEFAULT_PIPELINE


def save_pipeline(config: Dict) -> Dict:
    """Save a custom pipeline config."""
    _ensure_config_dir()
    filepath = os.path.join(CONFIG_DIR, f"{config['name']}.json")
    with open(filepath, 'w') as f:
        json.dump(config, f, ensure_ascii=False, indent=2)
    return {"status": "ok", "filepath": filepath}


def list_plugins() -> Dict:
    """List all available plugins by capability."""
    return {
        "embedding": [
            {"type": "clip-vith14", "model": "BAAI/CLIP-ViT-H-14", "dim": 1024, "endpoint": "http://localhost:9086", "status": "available"},
            {"type": "bge-m3", "model": "BAAI/bge-m3", "dim": 1024, "status": "not_deployed"},
        ],
        "vdb": [
            {"type": "vdb-milvus", "backend": "Milvus", "endpoint": "http://localhost:8886", "status": "available"},
            {"type": "qdrant", "backend": "Qdrant", "status": "not_deployed"},
        ],
        "graph": [
            {"type": "networkx", "backend": "NetworkX", "endpoint": "http://localhost:9092", "status": "available"},
            {"type": "falkordb", "backend": "FalkorDB+Redis", "status": "blocked_redis_module"},
            {"type": "none", "description": "Disable graph"},
        ],
        "llm": [
            {"type": "harnessed", "description": "harnessed_agent", "status": "available"},
        ],
        "reranker": [
            {"type": "bge-reranker", "model": "BAAI/bge-reranker-v2-m3", "endpoint": "http://localhost:9090", "status": "available"},
            {"type": "none", "description": "Skip reranking"},
        ],
        "chunker": [
            {"type": "recursive", "description": "Recursive character splitter"},
            {"type": "sentence", "description": "Sentence-based splitter"},
        ],
        "extractor": [
            {"type": "llm-structured", "description": "LLM-based entity/relation extraction"},
            {"type": "none", "description": "Skip extraction"},
        ],
        "retriever": [
            {"type": "hybrid", "description": "Vector + Graph hybrid retrieval"},
            {"type": "vector_only", "description": "Pure vector retrieval"},
        ],
        "face": [
            {"type": "insightface", "model": "buffalo_l", "dim": 512, "status": "available", "endpoint": "http://localhost:9091"},
        ]
    }


def call_plugin(endpoint: str, path: str, data: Dict, timeout: int = 30) -> Dict:
    """Call a plugin endpoint via HTTP POST."""
    url = f"{endpoint}{path}"
    payload = json.dumps(data).encode('utf-8')
    req = urllib.request.Request(url, data=payload, headers={'Content-Type': 'application/json'})
    try:
        with urllib.request.urlopen(req, timeout=timeout) as resp:
            return json.loads(resp.read().decode('utf-8'))
    except Exception as e:
        return {"error": str(e)}