"""API routes.

Define your API endpoints here. All routes are prefixed with /api.

Built-in AI endpoints (via Druppie SDK):
    POST /api/ai/chat    — LLM chat completion  (body: {prompt, system?})  [module-llm]
    POST /api/ai/ocr     — OCR text extraction   (body: {image_url})       [module-vision]
    POST /api/ai/search  — Web search            (body: {query})           [module-web]

RAG endpoints (vectors stored in THIS app's own database):
    POST /api/rag/index  — embed + store documents (body: {documents: [...]})
    POST /api/rag/search — semantic similarity search (body: {query})

Example adding your own:

    @api.route('/items', methods=['GET'])
    def list_items():
        db = next(get_db())
        items = db.query(Item).all()
        return jsonify([{'id': str(i.id), 'name': i.name} for i in items])
"""

from flask import Blueprint, jsonify, request
from druppie_sdk import DruppieClient

from app.database import get_db
from app.rag import RAG

api = Blueprint("api", __name__)

druppie = DruppieClient()

RAG_INDEX = "knowledge-base"


@api.route("/info")
def info():
    from app.config import settings

    return jsonify(app_name=settings.app_name)


# ---------------------------------------------------------------------------
# AI endpoints — via Druppie SDK (calls module-llm and module-vision)
# ---------------------------------------------------------------------------


@api.route("/ai/chat", methods=["POST"])
def ai_chat_endpoint():
    """LLM chat completion. Body: {"prompt": "...", "system": "..."}"""
    data = request.get_json(silent=True)
    if not data or "prompt" not in data:
        return jsonify(error="Missing required field: prompt"), 400
    result = druppie.call("llm", "chat", {
        "prompt": data["prompt"],
        "system": data.get("system", "You are a helpful assistant."),
    })
    return jsonify(answer=result.get("answer", ""))


@api.route("/ai/ocr", methods=["POST"])
def ai_ocr_endpoint():
    """OCR text extraction. Body: {"image_url": "https://..."}"""
    data = request.get_json(silent=True)
    if not data or "image_url" not in data:
        return jsonify(error="Missing required field: image_url"), 400
    result = druppie.call("vision", "ocr", {"image_source": data["image_url"]})
    return jsonify(text=result.get("text", ""))


@api.route("/ai/search", methods=["POST"])
def ai_search_endpoint():
    """Web search. Body: {"query": "search terms"}"""
    data = request.get_json(silent=True)
    if not data or "query" not in data:
        return jsonify(error="Missing required field: query"), 400
    result = druppie.call("web", "search_web", {"query": data["query"]})
    return jsonify(result)


# ---------------------------------------------------------------------------
# RAG endpoints — worked example of the embed → store → search loop
#
# Vectors live in THIS app's own Postgres (pgvector); embeddings are
# generated by the stateless module-llm `embed` tool via the SDK. There is
# no shared vectorstore — each app owns its own vectors. The `RAG` helper
# (app/rag.py) handles chunking, the embed call, storage, and search.
# ---------------------------------------------------------------------------


@api.route("/rag/index", methods=["POST"])
def rag_index_endpoint():
    """Embed and store documents in the app's own database.

    Body: {"documents": [{"content": "...", "source_name": "...",
                          "source_page": 1}, ...]}

    For each document the RAG helper chunks the text, calls
    module-llm `embed` to turn each chunk into a vector, and stores the
    chunk + vector in this app's `vector_chunks` table (pgvector).
    """
    data = request.get_json(silent=True)
    if not data or not data.get("documents"):
        return jsonify(error="Missing required field: documents"), 400

    db = next(get_db())
    rag = RAG(db, druppie)
    rag.create_index(RAG_INDEX)
    result = rag.index_documents(RAG_INDEX, data["documents"])
    return jsonify(result)


@api.route("/rag/search", methods=["POST"])
def rag_search_endpoint():
    """Semantic similarity search over the stored documents.

    Body: {"query": "natural-language question", "top_k": 5}

    The query is embedded with the same module-llm `embed` tool, then
    matched against the stored chunks with pgvector's cosine distance
    (`embedding <=> :qvec`). Returns the top-k chunks with their source
    metadata so the caller can build a cited answer.
    """
    data = request.get_json(silent=True)
    if not data or "query" not in data:
        return jsonify(error="Missing required field: query"), 400

    db = next(get_db())
    rag = RAG(db, druppie)
    results = rag.search(RAG_INDEX, data["query"], top_k=data.get("top_k", 5))
    return jsonify(results=results)