rag_server/routes/buleprint.py

import traceback
import uuid

from quart import Blueprint, jsonify, request

from applications.config import DEFAULT_MODEL, LOCAL_MODEL_CONFIG, ChunkerConfig, WEIGHT_MAP
from applications.api import get_basic_embedding
from applications.async_task import ChunkEmbeddingTask
from applications.utils.milvus import MilvusSearch

server_bp = Blueprint("api", __name__, url_prefix="/api")


def server_routes(mysql_db, vector_db):

    @server_bp.route("/embed", methods=["POST"])
    async def embed():
        body = await request.get_json()
        text = body.get("text")
        model_name = body.get("model", DEFAULT_MODEL)
        if not LOCAL_MODEL_CONFIG.get(model_name):
            return jsonify({"error": "error  model"})

        embedding = await get_basic_embedding(text, model_name)
        return jsonify({"embedding": embedding})

    @server_bp.route("/chunk", methods=["POST"])
    async def chunk():
        body = await request.get_json()
        text = body.get("text", "")
        text = text.strip()
        if not text:
            return jsonify({"error": "error  text"})
        doc_id = f"doc-{uuid.uuid4()}"
        chunk_task = ChunkEmbeddingTask(mysql_db, vector_db, cfg=ChunkerConfig(), doc_id=doc_id)
        doc_id = await chunk_task.deal(body)
        return jsonify({"doc_id": doc_id})

    @server_bp.route("/search", methods=["POST"])
    async def search():
        body = await request.get_json()
        search_type = body.get("search_type")
        if not search_type:
            return jsonify({"error": "missing search_type"}), 400

        searcher = MilvusSearch(vector_db)

        try:
            # 统一参数
            expr = body.get("expr")
            search_params = body.get("search_params") or {"metric_type": "COSINE", "params": {"ef": 64}}
            limit = body.get("limit", 50)
            query = body.get("query")

            async def by_vector():
                if not query:
                    return {"error": "missing query"}
                field = body.get("field", "vector_text")
                query_vec = await get_basic_embedding(text=query, model=DEFAULT_MODEL)
                return await searcher.vector_search(
                    query_vec=query_vec,
                    anns_field=field,
                    expr=expr,
                    search_params=search_params,
                    limit=limit,
                )

            async def hybrid():
                if not query:
                    return {"error": "missing query"}
                field = body.get("field", "vector_text")
                query_vec = await get_basic_embedding(text=query, model=DEFAULT_MODEL)
                return await searcher.hybrid_search(
                    query_vec=query_vec,
                    anns_field=field,
                    filters=body.get("filter_map"),
                    limit=limit,
                )

            async def strategy():
                if not query:
                    return {"error": "missing query"}
                query_vec = await get_basic_embedding(text=query, model=DEFAULT_MODEL)
                return await searcher.search_by_strategy(
                    query_vec=query_vec,
                    weight_map=body.get("weight_map", WEIGHT_MAP),
                    expr=expr,
                    limit=limit,
                )

            # dispatch table
            handlers = {
                "by_vector": by_vector,
                "hybrid": hybrid,
                "strategy": strategy,
            }

            if search_type not in handlers:
                return jsonify({"error": "invalid search_type"}), 400

            result = await handlers[search_type]()
            return jsonify(result)

        except Exception as e:
            return jsonify({"error": str(e), "traceback": traceback.format_exc()}), 500

    return server_bp