Download ragflow-main/api/apps/conversation_app.py from pandaall/ragflow: direct link, hf CLI and curl.
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https://huggingface.co/datasets/pandaall/ragflow/resolve/main/ragflow-main/api/apps/conversation_app.py
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hf download hf://datasets/pandaall/ragflow/ragflow-main/api/apps/conversation_app.py
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curl -L -o conversation_app.py https://huggingface.co/datasets/pandaall/ragflow/resolve/main/ragflow-main/api/apps/conversation_app.py
17.1 kB
| # | |
| # Copyright 2024 The InfiniFlow Authors. All Rights Reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| # | |
| 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 | |
| # noqa: F821 | |
| 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) | |
| # noqa: F821 | |
| 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) | |
| # type: ignore # noqa: F821 | |
| 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) | |
| # noqa: F821 | |
| 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) | |
| # noqa: F821 | |
| 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) | |
| # noqa: F821 | |
| 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) | |
| # noqa: F821 | |
| 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 | |
| # noqa: F821 | |
| 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) | |
| # noqa: F821 | |
| 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) | |
| # noqa: F821 | |
| 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 | |
| # noqa: F821 | |
| 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) | |
| # noqa: F821 | |
| 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)]) | |