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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, label_question |
| | from api.db.services.knowledgebase_service import KnowledgebaseService |
| | from api.db.services.llm_service import LLMBundle, TenantService |
| | 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.general.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 = LLMBundle(kb.tenant_id, LLMType.EMBEDDING, llm_name=kb.embd_id) |
| | chat_mdl = LLMBundle(current_user.id, LLMType.CHAT) |
| | question = req["question"] |
| | ranks = settings.retrievaler.retrieval(question, embd_mdl, kb.tenant_id, kb_ids, 1, 12, |
| | 0.3, 0.3, aggs=False, |
| | rank_feature=label_question(question, [kb]) |
| | ) |
| | 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)]) |
| |
|