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import json |
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import re |
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import traceback |
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from copy import deepcopy |
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from api.db.services.user_service import UserTenantService |
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from flask import request, Response |
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from flask_login import login_required, current_user |
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from api.db import LLMType |
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from api.db.services.dialog_service import DialogService, ConversationService, chat, ask |
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from api.db.services.knowledgebase_service import KnowledgebaseService |
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from api.db.services.llm_service import LLMBundle, TenantService, TenantLLMService |
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from api.settings import RetCode, retrievaler |
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from api.utils.api_utils import get_json_result |
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from api.utils.api_utils import server_error_response, get_data_error_result, validate_request |
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from graphrag.mind_map_extractor import MindMapExtractor |
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@manager.route('/set', methods=['POST']) |
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@login_required |
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def set_conversation(): |
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req = request.json |
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conv_id = req.get("conversation_id") |
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is_new = req.get("is_new") |
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del req["is_new"] |
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if not is_new: |
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del req["conversation_id"] |
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try: |
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if not ConversationService.update_by_id(conv_id, req): |
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return get_data_error_result(retmsg="Conversation not found!") |
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e, conv = ConversationService.get_by_id(conv_id) |
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if not e: |
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return get_data_error_result( |
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retmsg="Fail to update a conversation!") |
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conv = conv.to_dict() |
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return get_json_result(data=conv) |
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except Exception as e: |
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return server_error_response(e) |
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try: |
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e, dia = DialogService.get_by_id(req["dialog_id"]) |
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if not e: |
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return get_data_error_result(retmsg="Dialog not found") |
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conv = { |
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"id": conv_id, |
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"dialog_id": req["dialog_id"], |
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"name": req.get("name", "New conversation"), |
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"message": [{"role": "assistant", "content": dia.prompt_config["prologue"]}] |
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} |
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ConversationService.save(**conv) |
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e, conv = ConversationService.get_by_id(conv["id"]) |
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if not e: |
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return get_data_error_result(retmsg="Fail to new a conversation!") |
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conv = conv.to_dict() |
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return get_json_result(data=conv) |
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except Exception as e: |
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return server_error_response(e) |
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@manager.route('/get', methods=['GET']) |
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@login_required |
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def get(): |
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conv_id = request.args["conversation_id"] |
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try: |
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e, conv = ConversationService.get_by_id(conv_id) |
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if not e: |
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return get_data_error_result(retmsg="Conversation not found!") |
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tenants = UserTenantService.query(user_id=current_user.id) |
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for tenant in tenants: |
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if DialogService.query(tenant_id=tenant.tenant_id, id=conv.dialog_id): |
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break |
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else: |
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return get_json_result( |
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data=False, retmsg=f'Only owner of conversation authorized for this operation.', |
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retcode=RetCode.OPERATING_ERROR) |
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conv = conv.to_dict() |
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return get_json_result(data=conv) |
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except Exception as e: |
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return server_error_response(e) |
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@manager.route('/rm', methods=['POST']) |
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@login_required |
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def rm(): |
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conv_ids = request.json["conversation_ids"] |
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try: |
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for cid in conv_ids: |
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exist, conv = ConversationService.get_by_id(cid) |
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if not exist: |
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return get_data_error_result(retmsg="Conversation not found!") |
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tenants = UserTenantService.query(user_id=current_user.id) |
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for tenant in tenants: |
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if DialogService.query(tenant_id=tenant.tenant_id, id=conv.dialog_id): |
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break |
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else: |
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return get_json_result( |
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data=False, retmsg=f'Only owner of conversation authorized for this operation.', |
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retcode=RetCode.OPERATING_ERROR) |
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ConversationService.delete_by_id(cid) |
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return get_json_result(data=True) |
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except Exception as e: |
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return server_error_response(e) |
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@manager.route('/list', methods=['GET']) |
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@login_required |
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def list_convsersation(): |
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dialog_id = request.args["dialog_id"] |
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try: |
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if not DialogService.query(tenant_id=current_user.id, id=dialog_id): |
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return get_json_result( |
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data=False, retmsg=f'Only owner of dialog authorized for this operation.', |
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retcode=RetCode.OPERATING_ERROR) |
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convs = ConversationService.query( |
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dialog_id=dialog_id, |
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order_by=ConversationService.model.create_time, |
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reverse=True) |
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convs = [d.to_dict() for d in convs] |
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return get_json_result(data=convs) |
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except Exception as e: |
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return server_error_response(e) |
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@manager.route('/completion', methods=['POST']) |
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@login_required |
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@validate_request("conversation_id", "messages") |
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def completion(): |
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req = request.json |
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msg = [] |
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for m in req["messages"]: |
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if m["role"] == "system": |
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continue |
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if m["role"] == "assistant" and not msg: |
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continue |
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msg.append(m) |
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message_id = msg[-1].get("id") |
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try: |
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e, conv = ConversationService.get_by_id(req["conversation_id"]) |
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if not e: |
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return get_data_error_result(retmsg="Conversation not found!") |
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conv.message = deepcopy(req["messages"]) |
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e, dia = DialogService.get_by_id(conv.dialog_id) |
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if not e: |
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return get_data_error_result(retmsg="Dialog not found!") |
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del req["conversation_id"] |
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del req["messages"] |
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if not conv.reference: |
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conv.reference = [] |
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conv.message.append({"role": "assistant", "content": "", "id": message_id}) |
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conv.reference.append({"chunks": [], "doc_aggs": []}) |
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def fillin_conv(ans): |
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nonlocal conv, message_id |
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if not conv.reference: |
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conv.reference.append(ans["reference"]) |
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else: |
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conv.reference[-1] = ans["reference"] |
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conv.message[-1] = {"role": "assistant", "content": ans["answer"], |
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"id": message_id, "prompt": ans.get("prompt", "")} |
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ans["id"] = message_id |
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def stream(): |
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nonlocal dia, msg, req, conv |
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try: |
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for ans in chat(dia, msg, True, **req): |
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fillin_conv(ans) |
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yield "data:" + json.dumps({"retcode": 0, "retmsg": "", "data": ans}, ensure_ascii=False) + "\n\n" |
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ConversationService.update_by_id(conv.id, conv.to_dict()) |
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except Exception as e: |
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traceback.print_exc() |
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yield "data:" + json.dumps({"retcode": 500, "retmsg": str(e), |
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"data": {"answer": "**ERROR**: " + str(e), "reference": []}}, |
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ensure_ascii=False) + "\n\n" |
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yield "data:" + json.dumps({"retcode": 0, "retmsg": "", "data": True}, ensure_ascii=False) + "\n\n" |
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if req.get("stream", True): |
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resp = Response(stream(), mimetype="text/event-stream") |
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resp.headers.add_header("Cache-control", "no-cache") |
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resp.headers.add_header("Connection", "keep-alive") |
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resp.headers.add_header("X-Accel-Buffering", "no") |
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resp.headers.add_header("Content-Type", "text/event-stream; charset=utf-8") |
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return resp |
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else: |
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answer = None |
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for ans in chat(dia, msg, **req): |
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answer = ans |
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fillin_conv(ans) |
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ConversationService.update_by_id(conv.id, conv.to_dict()) |
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break |
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return get_json_result(data=answer) |
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except Exception as e: |
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return server_error_response(e) |
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@manager.route('/tts', methods=['POST']) |
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@login_required |
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def tts(): |
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req = request.json |
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text = req["text"] |
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tenants = TenantService.get_info_by(current_user.id) |
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if not tenants: |
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return get_data_error_result(retmsg="Tenant not found!") |
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tts_id = tenants[0]["tts_id"] |
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if not tts_id: |
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return get_data_error_result(retmsg="No default TTS model is set") |
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tts_mdl = LLMBundle(tenants[0]["tenant_id"], LLMType.TTS, tts_id) |
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def stream_audio(): |
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try: |
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for txt in re.split(r"[,。/《》?;:!\n\r:;]+", text): |
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for chunk in tts_mdl.tts(txt): |
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yield chunk |
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except Exception as e: |
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yield ("data:" + json.dumps({"retcode": 500, "retmsg": str(e), |
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"data": {"answer": "**ERROR**: " + str(e)}}, |
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ensure_ascii=False)).encode('utf-8') |
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resp = Response(stream_audio(), mimetype="audio/mpeg") |
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resp.headers.add_header("Cache-Control", "no-cache") |
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resp.headers.add_header("Connection", "keep-alive") |
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resp.headers.add_header("X-Accel-Buffering", "no") |
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return resp |
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@manager.route('/delete_msg', methods=['POST']) |
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@login_required |
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@validate_request("conversation_id", "message_id") |
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def delete_msg(): |
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req = request.json |
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e, conv = ConversationService.get_by_id(req["conversation_id"]) |
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if not e: |
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return get_data_error_result(retmsg="Conversation not found!") |
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conv = conv.to_dict() |
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for i, msg in enumerate(conv["message"]): |
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if req["message_id"] != msg.get("id", ""): |
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continue |
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assert conv["message"][i + 1]["id"] == req["message_id"] |
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conv["message"].pop(i) |
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conv["message"].pop(i) |
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conv["reference"].pop(max(0, i // 2 - 1)) |
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break |
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ConversationService.update_by_id(conv["id"], conv) |
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return get_json_result(data=conv) |
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@manager.route('/thumbup', methods=['POST']) |
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@login_required |
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@validate_request("conversation_id", "message_id") |
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def thumbup(): |
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req = request.json |
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e, conv = ConversationService.get_by_id(req["conversation_id"]) |
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if not e: |
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return get_data_error_result(retmsg="Conversation not found!") |
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up_down = req.get("set") |
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feedback = req.get("feedback", "") |
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conv = conv.to_dict() |
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for i, msg in enumerate(conv["message"]): |
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if req["message_id"] == msg.get("id", "") and msg.get("role", "") == "assistant": |
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if up_down: |
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msg["thumbup"] = True |
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if "feedback" in msg: del msg["feedback"] |
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else: |
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msg["thumbup"] = False |
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if feedback: msg["feedback"] = feedback |
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break |
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ConversationService.update_by_id(conv["id"], conv) |
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return get_json_result(data=conv) |
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@manager.route('/ask', methods=['POST']) |
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@login_required |
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@validate_request("question", "kb_ids") |
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def ask_about(): |
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req = request.json |
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uid = current_user.id |
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def stream(): |
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nonlocal req, uid |
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try: |
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for ans in ask(req["question"], req["kb_ids"], uid): |
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yield "data:" + json.dumps({"retcode": 0, "retmsg": "", "data": ans}, ensure_ascii=False) + "\n\n" |
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except Exception as e: |
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yield "data:" + json.dumps({"retcode": 500, "retmsg": str(e), |
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"data": {"answer": "**ERROR**: " + str(e), "reference": []}}, |
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ensure_ascii=False) + "\n\n" |
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yield "data:" + json.dumps({"retcode": 0, "retmsg": "", "data": True}, ensure_ascii=False) + "\n\n" |
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resp = Response(stream(), mimetype="text/event-stream") |
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resp.headers.add_header("Cache-control", "no-cache") |
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resp.headers.add_header("Connection", "keep-alive") |
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resp.headers.add_header("X-Accel-Buffering", "no") |
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resp.headers.add_header("Content-Type", "text/event-stream; charset=utf-8") |
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return resp |
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@manager.route('/mindmap', methods=['POST']) |
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@login_required |
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@validate_request("question", "kb_ids") |
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def mindmap(): |
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req = request.json |
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kb_ids = req["kb_ids"] |
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e, kb = KnowledgebaseService.get_by_id(kb_ids[0]) |
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if not e: |
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return get_data_error_result(retmsg="Knowledgebase not found!") |
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embd_mdl = TenantLLMService.model_instance( |
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kb.tenant_id, LLMType.EMBEDDING.value, llm_name=kb.embd_id) |
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chat_mdl = LLMBundle(current_user.id, LLMType.CHAT) |
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ranks = retrievaler.retrieval(req["question"], embd_mdl, kb.tenant_id, kb_ids, 1, 12, |
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0.3, 0.3, aggs=False) |
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mindmap = MindMapExtractor(chat_mdl) |
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mind_map = mindmap([c["content_with_weight"] for c in ranks["chunks"]]).output |
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if "error" in mind_map: |
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return server_error_response(Exception(mind_map["error"])) |
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return get_json_result(data=mind_map) |
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@manager.route('/related_questions', methods=['POST']) |
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@login_required |
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@validate_request("question") |
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def related_questions(): |
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req = request.json |
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question = req["question"] |
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chat_mdl = LLMBundle(current_user.id, LLMType.CHAT) |
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prompt = """ |
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Objective: To generate search terms related to the user's search keywords, helping users find more valuable information. |
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Instructions: |
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- Based on the keywords provided by the user, generate 5-10 related search terms. |
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- Each search term should be directly or indirectly related to the keyword, guiding the user to find more valuable information. |
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- Use common, general terms as much as possible, avoiding obscure words or technical jargon. |
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- Keep the term length between 2-4 words, concise and clear. |
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- DO NOT translate, use the language of the original keywords. |
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### Example: |
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Keywords: Chinese football |
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Related search terms: |
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1. Current status of Chinese football |
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2. Reform of Chinese football |
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3. Youth training of Chinese football |
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4. Chinese football in the Asian Cup |
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5. Chinese football in the World Cup |
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Reason: |
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- When searching, users often only use one or two keywords, making it difficult to fully express their information needs. |
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- Generating related search terms can help users dig deeper into relevant information and improve search efficiency. |
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- At the same time, related terms can also help search engines better understand user needs and return more accurate search results. |
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""" |
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ans = chat_mdl.chat(prompt, [{"role": "user", "content": f""" |
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Keywords: {question} |
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Related search terms: |
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"""}], {"temperature": 0.9}) |
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return get_json_result(data=[re.sub(r"^[0-9]\. ", "", a) for a in ans.split("\n") if re.match(r"^[0-9]\. ", a)]) |
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