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| import json |
| import re |
| import traceback |
| from copy import deepcopy |
| from api.db.db_models import APIToken |
|
|
| from api.db.services.conversation_service import ConversationService, structure_answer |
| from api.db.services.user_service import UserTenantService |
| from flask import request, Response |
| from flask_login import login_required, current_user |
|
|
| from api.db import LLMType |
| from api.db.services.dialog_service import DialogService, chat, ask |
| from api.db.services.knowledgebase_service import KnowledgebaseService |
| from api.db.services.llm_service import LLMBundle, TenantService, TenantLLMService |
| from api import settings |
| from api.utils.api_utils import get_json_result |
| from api.utils.api_utils import server_error_response, get_data_error_result, validate_request |
| from graphrag.mind_map_extractor import MindMapExtractor |
|
|
| @manager.route('/set', methods=['POST']) |
| @login_required |
| def set_conversation(): |
| req = request.json |
| conv_id = req.get("conversation_id") |
| is_new = req.get("is_new") |
| del req["is_new"] |
| if not is_new: |
| del req["conversation_id"] |
| try: |
| if not ConversationService.update_by_id(conv_id, req): |
| return get_data_error_result(message="Conversation not found!") |
| e, conv = ConversationService.get_by_id(conv_id) |
| if not e: |
| return get_data_error_result( |
| message="Fail to update a conversation!") |
| conv = conv.to_dict() |
| return get_json_result(data=conv) |
| except Exception as e: |
| return server_error_response(e) |
|
|
| try: |
| e, dia = DialogService.get_by_id(req["dialog_id"]) |
| if not e: |
| return get_data_error_result(message="Dialog not found") |
| conv = { |
| "id": conv_id, |
| "dialog_id": req["dialog_id"], |
| "name": req.get("name", "New conversation"), |
| "message": [{"role": "assistant", "content": dia.prompt_config["prologue"]}] |
| } |
| ConversationService.save(**conv) |
| return get_json_result(data=conv) |
| except Exception as e: |
| return server_error_response(e) |
|
|
|
|
| @manager.route('/get', methods=['GET']) |
| @login_required |
| def get(): |
| conv_id = request.args["conversation_id"] |
| try: |
| |
| e, conv = ConversationService.get_by_id(conv_id) |
| if not e: |
| return get_data_error_result(message="Conversation not found!") |
| tenants = UserTenantService.query(user_id=current_user.id) |
| avatar =None |
| for tenant in tenants: |
| dialog = DialogService.query(tenant_id=tenant.tenant_id, id=conv.dialog_id) |
| if dialog and len(dialog)>0: |
| avatar = dialog[0].icon |
| break |
| else: |
| return get_json_result( |
| data=False, message='Only owner of conversation authorized for this operation.', |
| code=settings.RetCode.OPERATING_ERROR) |
|
|
| def get_value(d, k1, k2): |
| return d.get(k1, d.get(k2)) |
|
|
| for ref in conv.reference: |
| if isinstance(ref, list): |
| continue |
| ref["chunks"] = [{ |
| "id": get_value(ck, "chunk_id", "id"), |
| "content": get_value(ck, "content", "content_with_weight"), |
| "document_id": get_value(ck, "doc_id", "document_id"), |
| "document_name": get_value(ck, "docnm_kwd", "document_name"), |
| "dataset_id": get_value(ck, "kb_id", "dataset_id"), |
| "image_id": get_value(ck, "image_id", "img_id"), |
| "positions": get_value(ck, "positions", "position_int"), |
| } for ck in ref.get("chunks", [])] |
|
|
| conv = conv.to_dict() |
| conv["avatar"]=avatar |
| return get_json_result(data=conv) |
| except Exception as e: |
| return server_error_response(e) |
|
|
| @manager.route('/getsse/<dialog_id>', methods=['GET']) |
| def getsse(dialog_id): |
| |
| token = request.headers.get('Authorization').split() |
| if len(token) != 2: |
| return get_data_error_result(message='Authorization is not valid!"') |
| token = token[1] |
| objs = APIToken.query(beta=token) |
| if not objs: |
| return get_data_error_result(message='Authentication error: API key is invalid!"') |
| try: |
| e, conv = DialogService.get_by_id(dialog_id) |
| if not e: |
| return get_data_error_result(message="Dialog not found!") |
| conv = conv.to_dict() |
| conv["avatar"]= conv["icon"] |
| del conv["icon"] |
| return get_json_result(data=conv) |
| except Exception as e: |
| return server_error_response(e) |
|
|
| @manager.route('/rm', methods=['POST']) |
| @login_required |
| def rm(): |
| conv_ids = request.json["conversation_ids"] |
| try: |
| for cid in conv_ids: |
| exist, conv = ConversationService.get_by_id(cid) |
| if not exist: |
| return get_data_error_result(message="Conversation not found!") |
| tenants = UserTenantService.query(user_id=current_user.id) |
| for tenant in tenants: |
| if DialogService.query(tenant_id=tenant.tenant_id, id=conv.dialog_id): |
| break |
| else: |
| return get_json_result( |
| data=False, message='Only owner of conversation authorized for this operation.', |
| code=settings.RetCode.OPERATING_ERROR) |
| ConversationService.delete_by_id(cid) |
| return get_json_result(data=True) |
| except Exception as e: |
| return server_error_response(e) |
|
|
|
|
| @manager.route('/list', methods=['GET']) |
| @login_required |
| def list_convsersation(): |
| dialog_id = request.args["dialog_id"] |
| try: |
| if not DialogService.query(tenant_id=current_user.id, id=dialog_id): |
| return get_json_result( |
| data=False, message='Only owner of dialog authorized for this operation.', |
| code=settings.RetCode.OPERATING_ERROR) |
| convs = ConversationService.query( |
| dialog_id=dialog_id, |
| order_by=ConversationService.model.create_time, |
| reverse=True) |
|
|
| convs = [d.to_dict() for d in convs] |
| return get_json_result(data=convs) |
| except Exception as e: |
| return server_error_response(e) |
|
|
|
|
| @manager.route('/completion', methods=['POST']) |
| @login_required |
| @validate_request("conversation_id", "messages") |
| def completion(): |
| req = request.json |
| msg = [] |
| for m in req["messages"]: |
| if m["role"] == "system": |
| continue |
| if m["role"] == "assistant" and not msg: |
| continue |
| msg.append(m) |
| message_id = msg[-1].get("id") |
| try: |
| e, conv = ConversationService.get_by_id(req["conversation_id"]) |
| if not e: |
| return get_data_error_result(message="Conversation not found!") |
| conv.message = deepcopy(req["messages"]) |
| e, dia = DialogService.get_by_id(conv.dialog_id) |
| if not e: |
| return get_data_error_result(message="Dialog not found!") |
| del req["conversation_id"] |
| del req["messages"] |
|
|
| if not conv.reference: |
| conv.reference = [] |
| else: |
| def get_value(d, k1, k2): |
| return d.get(k1, d.get(k2)) |
|
|
| for ref in conv.reference: |
| if isinstance(ref, list): |
| continue |
| ref["chunks"] = [{ |
| "id": get_value(ck, "chunk_id", "id"), |
| "content": get_value(ck, "content", "content_with_weight"), |
| "document_id": get_value(ck, "doc_id", "document_id"), |
| "document_name": get_value(ck, "docnm_kwd", "document_name"), |
| "dataset_id": get_value(ck, "kb_id", "dataset_id"), |
| "image_id": get_value(ck, "image_id", "img_id"), |
| "positions": get_value(ck, "positions", "position_int"), |
| } for ck in ref.get("chunks", [])] |
|
|
| if not conv.reference: |
| conv.reference = [] |
| conv.reference.append({"chunks": [], "doc_aggs": []}) |
| def stream(): |
| nonlocal dia, msg, req, conv |
| try: |
| for ans in chat(dia, msg, True, **req): |
| ans = structure_answer(conv, ans, message_id, conv.id) |
| yield "data:" + json.dumps({"code": 0, "message": "", "data": ans}, ensure_ascii=False) + "\n\n" |
| ConversationService.update_by_id(conv.id, conv.to_dict()) |
| except Exception as e: |
| traceback.print_exc() |
| yield "data:" + json.dumps({"code": 500, "message": str(e), |
| "data": {"answer": "**ERROR**: " + str(e), "reference": []}}, |
| ensure_ascii=False) + "\n\n" |
| yield "data:" + json.dumps({"code": 0, "message": "", "data": True}, ensure_ascii=False) + "\n\n" |
|
|
| if req.get("stream", True): |
| resp = Response(stream(), mimetype="text/event-stream") |
| resp.headers.add_header("Cache-control", "no-cache") |
| resp.headers.add_header("Connection", "keep-alive") |
| resp.headers.add_header("X-Accel-Buffering", "no") |
| resp.headers.add_header("Content-Type", "text/event-stream; charset=utf-8") |
| return resp |
|
|
| else: |
| answer = None |
| for ans in chat(dia, msg, **req): |
| answer = structure_answer(conv, ans, message_id, req["conversation_id"]) |
| ConversationService.update_by_id(conv.id, conv.to_dict()) |
| break |
| return get_json_result(data=answer) |
| except Exception as e: |
| return server_error_response(e) |
|
|
|
|
| @manager.route('/tts', methods=['POST']) |
| @login_required |
| def tts(): |
| req = request.json |
| text = req["text"] |
|
|
| tenants = TenantService.get_info_by(current_user.id) |
| if not tenants: |
| return get_data_error_result(message="Tenant not found!") |
|
|
| tts_id = tenants[0]["tts_id"] |
| if not tts_id: |
| return get_data_error_result(message="No default TTS model is set") |
|
|
| tts_mdl = LLMBundle(tenants[0]["tenant_id"], LLMType.TTS, tts_id) |
|
|
| def stream_audio(): |
| try: |
| for txt in re.split(r"[,。/《》?;:!\n\r:;]+", text): |
| for chunk in tts_mdl.tts(txt): |
| yield chunk |
| except Exception as e: |
| yield ("data:" + json.dumps({"code": 500, "message": str(e), |
| "data": {"answer": "**ERROR**: " + str(e)}}, |
| ensure_ascii=False)).encode('utf-8') |
|
|
| resp = Response(stream_audio(), mimetype="audio/mpeg") |
| resp.headers.add_header("Cache-Control", "no-cache") |
| resp.headers.add_header("Connection", "keep-alive") |
| resp.headers.add_header("X-Accel-Buffering", "no") |
|
|
| return resp |
|
|
|
|
| @manager.route('/delete_msg', methods=['POST']) |
| @login_required |
| @validate_request("conversation_id", "message_id") |
| def delete_msg(): |
| req = request.json |
| e, conv = ConversationService.get_by_id(req["conversation_id"]) |
| if not e: |
| return get_data_error_result(message="Conversation not found!") |
|
|
| conv = conv.to_dict() |
| for i, msg in enumerate(conv["message"]): |
| if req["message_id"] != msg.get("id", ""): |
| continue |
| assert conv["message"][i + 1]["id"] == req["message_id"] |
| conv["message"].pop(i) |
| conv["message"].pop(i) |
| conv["reference"].pop(max(0, i // 2 - 1)) |
| break |
|
|
| ConversationService.update_by_id(conv["id"], conv) |
| return get_json_result(data=conv) |
|
|
|
|
| @manager.route('/thumbup', methods=['POST']) |
| @login_required |
| @validate_request("conversation_id", "message_id") |
| def thumbup(): |
| req = request.json |
| e, conv = ConversationService.get_by_id(req["conversation_id"]) |
| if not e: |
| return get_data_error_result(message="Conversation not found!") |
| up_down = req.get("set") |
| feedback = req.get("feedback", "") |
| conv = conv.to_dict() |
| for i, msg in enumerate(conv["message"]): |
| if req["message_id"] == msg.get("id", "") and msg.get("role", "") == "assistant": |
| if up_down: |
| msg["thumbup"] = True |
| if "feedback" in msg: |
| del msg["feedback"] |
| else: |
| msg["thumbup"] = False |
| if feedback: |
| msg["feedback"] = feedback |
| break |
|
|
| ConversationService.update_by_id(conv["id"], conv) |
| return get_json_result(data=conv) |
|
|
|
|
| @manager.route('/ask', methods=['POST']) |
| @login_required |
| @validate_request("question", "kb_ids") |
| def ask_about(): |
| req = request.json |
| uid = current_user.id |
|
|
| def stream(): |
| nonlocal req, uid |
| try: |
| for ans in ask(req["question"], req["kb_ids"], uid): |
| yield "data:" + json.dumps({"code": 0, "message": "", "data": ans}, ensure_ascii=False) + "\n\n" |
| except Exception as e: |
| yield "data:" + json.dumps({"code": 500, "message": str(e), |
| "data": {"answer": "**ERROR**: " + str(e), "reference": []}}, |
| ensure_ascii=False) + "\n\n" |
| yield "data:" + json.dumps({"code": 0, "message": "", "data": True}, ensure_ascii=False) + "\n\n" |
|
|
| resp = Response(stream(), mimetype="text/event-stream") |
| resp.headers.add_header("Cache-control", "no-cache") |
| resp.headers.add_header("Connection", "keep-alive") |
| resp.headers.add_header("X-Accel-Buffering", "no") |
| resp.headers.add_header("Content-Type", "text/event-stream; charset=utf-8") |
| return resp |
|
|
|
|
| @manager.route('/mindmap', methods=['POST']) |
| @login_required |
| @validate_request("question", "kb_ids") |
| def mindmap(): |
| req = request.json |
| kb_ids = req["kb_ids"] |
| e, kb = KnowledgebaseService.get_by_id(kb_ids[0]) |
| if not e: |
| return get_data_error_result(message="Knowledgebase not found!") |
|
|
| embd_mdl = TenantLLMService.model_instance( |
| kb.tenant_id, LLMType.EMBEDDING.value, llm_name=kb.embd_id) |
| chat_mdl = LLMBundle(current_user.id, LLMType.CHAT) |
| ranks = settings.retrievaler.retrieval(req["question"], embd_mdl, kb.tenant_id, kb_ids, 1, 12, |
| 0.3, 0.3, aggs=False) |
| mindmap = MindMapExtractor(chat_mdl) |
| mind_map = mindmap([c["content_with_weight"] for c in ranks["chunks"]]).output |
| if "error" in mind_map: |
| return server_error_response(Exception(mind_map["error"])) |
| return get_json_result(data=mind_map) |
|
|
|
|
| @manager.route('/related_questions', methods=['POST']) |
| @login_required |
| @validate_request("question") |
| def related_questions(): |
| req = request.json |
| question = req["question"] |
| chat_mdl = LLMBundle(current_user.id, LLMType.CHAT) |
| prompt = """ |
| Objective: To generate search terms related to the user's search keywords, helping users find more valuable information. |
| Instructions: |
| - Based on the keywords provided by the user, generate 5-10 related search terms. |
| - Each search term should be directly or indirectly related to the keyword, guiding the user to find more valuable information. |
| - Use common, general terms as much as possible, avoiding obscure words or technical jargon. |
| - Keep the term length between 2-4 words, concise and clear. |
| - DO NOT translate, use the language of the original keywords. |
| |
| ### Example: |
| Keywords: Chinese football |
| Related search terms: |
| 1. Current status of Chinese football |
| 2. Reform of Chinese football |
| 3. Youth training of Chinese football |
| 4. Chinese football in the Asian Cup |
| 5. Chinese football in the World Cup |
| |
| Reason: |
| - When searching, users often only use one or two keywords, making it difficult to fully express their information needs. |
| - Generating related search terms can help users dig deeper into relevant information and improve search efficiency. |
| - At the same time, related terms can also help search engines better understand user needs and return more accurate search results. |
| |
| """ |
| ans = chat_mdl.chat(prompt, [{"role": "user", "content": f""" |
| Keywords: {question} |
| Related search terms: |
| """}], {"temperature": 0.9}) |
| return get_json_result(data=[re.sub(r"^[0-9]\. ", "", a) for a in ans.split("\n") if re.match(r"^[0-9]\. ", a)]) |
|
|