balibabu
fix: Fixed an issue where the first message would be displayed when sending the second message #2625 (#2626)
809dc5c
# | |
# 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.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, ConversationService, chat, ask | |
from api.db.services.knowledgebase_service import KnowledgebaseService | |
from api.db.services.llm_service import LLMBundle, TenantService, TenantLLMService | |
from api.settings import RetCode, retrievaler | |
from api.utils import get_uuid | |
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 | |
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(retmsg="Conversation not found!") | |
e, conv = ConversationService.get_by_id(conv_id) | |
if not e: | |
return get_data_error_result( | |
retmsg="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(retmsg="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) | |
e, conv = ConversationService.get_by_id(conv["id"]) | |
if not e: | |
return get_data_error_result(retmsg="Fail to new a conversation!") | |
conv = conv.to_dict() | |
return get_json_result(data=conv) | |
except Exception as e: | |
return server_error_response(e) | |
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(retmsg="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, retmsg=f'Only owner of conversation authorized for this operation.', | |
retcode=RetCode.OPERATING_ERROR) | |
conv = conv.to_dict() | |
return get_json_result(data=conv) | |
except Exception as e: | |
return server_error_response(e) | |
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(retmsg="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, retmsg=f'Only owner of conversation authorized for this operation.', | |
retcode=RetCode.OPERATING_ERROR) | |
ConversationService.delete_by_id(cid) | |
return get_json_result(data=True) | |
except Exception as e: | |
return server_error_response(e) | |
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, retmsg=f'Only owner of dialog authorized for this operation.', | |
retcode=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) | |
def completion(): | |
req = request.json | |
# req = {"conversation_id": "9aaaca4c11d311efa461fa163e197198", "messages": [ | |
# {"role": "user", "content": "上海有吗?"} | |
# ]} | |
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(retmsg="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(retmsg="Dialog not found!") | |
del req["conversation_id"] | |
del req["messages"] | |
if not conv.reference: | |
conv.reference = [] | |
conv.message.append({"role": "assistant", "content": "", "id": message_id}) | |
conv.reference.append({"chunks": [], "doc_aggs": []}) | |
def fillin_conv(ans): | |
nonlocal conv, message_id | |
if not conv.reference: | |
conv.reference.append(ans["reference"]) | |
else: | |
conv.reference[-1] = ans["reference"] | |
conv.message[-1] = {"role": "assistant", "content": ans["answer"], | |
"id": message_id, "prompt": ans.get("prompt", "")} | |
ans["id"] = message_id | |
def stream(): | |
nonlocal dia, msg, req, conv | |
try: | |
for ans in chat(dia, msg, True, **req): | |
fillin_conv(ans) | |
yield "data:" + json.dumps({"retcode": 0, "retmsg": "", "data": ans}, ensure_ascii=False) + "\n\n" | |
ConversationService.update_by_id(conv.id, conv.to_dict()) | |
except Exception as e: | |
yield "data:" + json.dumps({"retcode": 500, "retmsg": str(e), | |
"data": {"answer": "**ERROR**: " + str(e), "reference": []}}, | |
ensure_ascii=False) + "\n\n" | |
yield "data:" + json.dumps({"retcode": 0, "retmsg": "", "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 = ans | |
fillin_conv(ans) | |
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) | |
def tts(): | |
req = request.json | |
text = req["text"] | |
tenants = TenantService.get_by_user_id(current_user.id) | |
if not tenants: | |
return get_data_error_result(retmsg="Tenant not found!") | |
tts_id = tenants[0]["tts_id"] | |
if not tts_id: | |
return get_data_error_result(retmsg="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({"retcode": 500, "retmsg": 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 | |
def delete_msg(): | |
req = request.json | |
e, conv = ConversationService.get_by_id(req["conversation_id"]) | |
if not e: | |
return get_data_error_result(retmsg="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) | |
def thumbup(): | |
req = request.json | |
e, conv = ConversationService.get_by_id(req["conversation_id"]) | |
if not e: | |
return get_data_error_result(retmsg="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) | |
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({"retcode": 0, "retmsg": "", "data": ans}, ensure_ascii=False) + "\n\n" | |
except Exception as e: | |
yield "data:" + json.dumps({"retcode": 500, "retmsg": str(e), | |
"data": {"answer": "**ERROR**: " + str(e), "reference": []}}, | |
ensure_ascii=False) + "\n\n" | |
yield "data:" + json.dumps({"retcode": 0, "retmsg": "", "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 | |
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(retmsg="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 = 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) | |
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)]) | |