ragflow / api /apps /llm_app.py
Kevin Hu
Replace image2text model check with internal image. (#4250)
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#
# 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 logging
import json
import os
from flask import request
from flask_login import login_required, current_user
from api.db.services.llm_service import LLMFactoriesService, TenantLLMService, LLMService
from api import settings
from api.utils.api_utils import server_error_response, get_data_error_result, validate_request
from api.db import StatusEnum, LLMType
from api.db.db_models import TenantLLM
from api.utils.api_utils import get_json_result
from api.utils.file_utils import get_project_base_directory
from rag.llm import EmbeddingModel, ChatModel, RerankModel, CvModel, TTSModel
@manager.route('/factories', methods=['GET']) # noqa: F821
@login_required
def factories():
try:
fac = LLMFactoriesService.get_all()
fac = [f.to_dict() for f in fac if f.name not in ["Youdao", "FastEmbed", "BAAI"]]
llms = LLMService.get_all()
mdl_types = {}
for m in llms:
if m.status != StatusEnum.VALID.value:
continue
if m.fid not in mdl_types:
mdl_types[m.fid] = set([])
mdl_types[m.fid].add(m.model_type)
for f in fac:
f["model_types"] = list(mdl_types.get(f["name"], [LLMType.CHAT, LLMType.EMBEDDING, LLMType.RERANK,
LLMType.IMAGE2TEXT, LLMType.SPEECH2TEXT, LLMType.TTS]))
return get_json_result(data=fac)
except Exception as e:
return server_error_response(e)
@manager.route('/set_api_key', methods=['POST']) # noqa: F821
@login_required
@validate_request("llm_factory", "api_key")
def set_api_key():
req = request.json
# test if api key works
chat_passed, embd_passed, rerank_passed = False, False, False
factory = req["llm_factory"]
msg = ""
for llm in LLMService.query(fid=factory):
if not embd_passed and llm.model_type == LLMType.EMBEDDING.value:
mdl = EmbeddingModel[factory](
req["api_key"], llm.llm_name, base_url=req.get("base_url"))
try:
arr, tc = mdl.encode(["Test if the api key is available"])
if len(arr[0]) == 0:
raise Exception("Fail")
embd_passed = True
except Exception as e:
msg += f"\nFail to access embedding model({llm.llm_name}) using this api key." + str(e)
elif not chat_passed and llm.model_type == LLMType.CHAT.value:
mdl = ChatModel[factory](
req["api_key"], llm.llm_name, base_url=req.get("base_url"))
try:
m, tc = mdl.chat(None, [{"role": "user", "content": "Hello! How are you doing!"}],
{"temperature": 0.9, 'max_tokens': 50})
if m.find("**ERROR**") >= 0:
raise Exception(m)
chat_passed = True
except Exception as e:
msg += f"\nFail to access model({llm.llm_name}) using this api key." + str(
e)
elif not rerank_passed and llm.model_type == LLMType.RERANK:
mdl = RerankModel[factory](
req["api_key"], llm.llm_name, base_url=req.get("base_url"))
try:
arr, tc = mdl.similarity("What's the weather?", ["Is it sunny today?"])
if len(arr) == 0 or tc == 0:
raise Exception("Fail")
rerank_passed = True
logging.debug(f'passed model rerank {llm.llm_name}')
except Exception as e:
msg += f"\nFail to access model({llm.llm_name}) using this api key." + str(
e)
if any([embd_passed, chat_passed, rerank_passed]):
msg = ''
break
if msg:
return get_data_error_result(message=msg)
llm_config = {
"api_key": req["api_key"],
"api_base": req.get("base_url", "")
}
for n in ["model_type", "llm_name"]:
if n in req:
llm_config[n] = req[n]
for llm in LLMService.query(fid=factory):
llm_config["max_tokens"]=llm.max_tokens
if not TenantLLMService.filter_update(
[TenantLLM.tenant_id == current_user.id,
TenantLLM.llm_factory == factory,
TenantLLM.llm_name == llm.llm_name],
llm_config):
TenantLLMService.save(
tenant_id=current_user.id,
llm_factory=factory,
llm_name=llm.llm_name,
model_type=llm.model_type,
api_key=llm_config["api_key"],
api_base=llm_config["api_base"],
max_tokens=llm_config["max_tokens"]
)
return get_json_result(data=True)
@manager.route('/add_llm', methods=['POST']) # noqa: F821
@login_required
@validate_request("llm_factory")
def add_llm():
req = request.json
factory = req["llm_factory"]
def apikey_json(keys):
nonlocal req
return json.dumps({k: req.get(k, "") for k in keys})
if factory == "VolcEngine":
# For VolcEngine, due to its special authentication method
# Assemble ark_api_key endpoint_id into api_key
llm_name = req["llm_name"]
api_key = apikey_json(["ark_api_key", "endpoint_id"])
elif factory == "Tencent Hunyuan":
req["api_key"] = apikey_json(["hunyuan_sid", "hunyuan_sk"])
return set_api_key()
elif factory == "Tencent Cloud":
req["api_key"] = apikey_json(["tencent_cloud_sid", "tencent_cloud_sk"])
elif factory == "Bedrock":
# For Bedrock, due to its special authentication method
# Assemble bedrock_ak, bedrock_sk, bedrock_region
llm_name = req["llm_name"]
api_key = apikey_json(["bedrock_ak", "bedrock_sk", "bedrock_region"])
elif factory == "LocalAI":
llm_name = req["llm_name"] + "___LocalAI"
api_key = "xxxxxxxxxxxxxxx"
elif factory == "HuggingFace":
llm_name = req["llm_name"] + "___HuggingFace"
api_key = "xxxxxxxxxxxxxxx"
elif factory == "OpenAI-API-Compatible":
llm_name = req["llm_name"] + "___OpenAI-API"
api_key = req.get("api_key", "xxxxxxxxxxxxxxx")
elif factory == "XunFei Spark":
llm_name = req["llm_name"]
if req["model_type"] == "chat":
api_key = req.get("spark_api_password", "xxxxxxxxxxxxxxx")
elif req["model_type"] == "tts":
api_key = apikey_json(["spark_app_id", "spark_api_secret", "spark_api_key"])
elif factory == "BaiduYiyan":
llm_name = req["llm_name"]
api_key = apikey_json(["yiyan_ak", "yiyan_sk"])
elif factory == "Fish Audio":
llm_name = req["llm_name"]
api_key = apikey_json(["fish_audio_ak", "fish_audio_refid"])
elif factory == "Google Cloud":
llm_name = req["llm_name"]
api_key = apikey_json(["google_project_id", "google_region", "google_service_account_key"])
elif factory == "Azure-OpenAI":
llm_name = req["llm_name"]
api_key = apikey_json(["api_key", "api_version"])
else:
llm_name = req["llm_name"]
api_key = req.get("api_key", "xxxxxxxxxxxxxxx")
llm = {
"tenant_id": current_user.id,
"llm_factory": factory,
"model_type": req["model_type"],
"llm_name": llm_name,
"api_base": req.get("api_base", ""),
"api_key": api_key,
"max_tokens": req.get("max_tokens")
}
msg = ""
if llm["model_type"] == LLMType.EMBEDDING.value:
mdl = EmbeddingModel[factory](
key=llm['api_key'],
model_name=llm["llm_name"],
base_url=llm["api_base"])
try:
arr, tc = mdl.encode(["Test if the api key is available"])
if len(arr[0]) == 0:
raise Exception("Fail")
except Exception as e:
msg += f"\nFail to access embedding model({llm['llm_name']})." + str(e)
elif llm["model_type"] == LLMType.CHAT.value:
mdl = ChatModel[factory](
key=llm['api_key'],
model_name=llm["llm_name"],
base_url=llm["api_base"]
)
try:
m, tc = mdl.chat(None, [{"role": "user", "content": "Hello! How are you doing!"}], {
"temperature": 0.9})
if not tc:
raise Exception(m)
except Exception as e:
msg += f"\nFail to access model({llm['llm_name']})." + str(
e)
elif llm["model_type"] == LLMType.RERANK:
mdl = RerankModel[factory](
key=llm["api_key"],
model_name=llm["llm_name"],
base_url=llm["api_base"]
)
try:
arr, tc = mdl.similarity("Hello~ Ragflower!", ["Hi, there!", "Ohh, my friend!"])
if len(arr) == 0:
raise Exception("Not known.")
except Exception as e:
msg += f"\nFail to access model({llm['llm_name']})." + str(
e)
elif llm["model_type"] == LLMType.IMAGE2TEXT.value:
mdl = CvModel[factory](
key=llm["api_key"],
model_name=llm["llm_name"],
base_url=llm["api_base"]
)
try:
with open(os.path.join(get_project_base_directory(), "web/src/assets/yay.jpg"), "rb") as f:
m, tc = mdl.describe(f.read())
if not tc:
raise Exception(m)
except Exception as e:
msg += f"\nFail to access model({llm['llm_name']})." + str(e)
elif llm["model_type"] == LLMType.TTS:
mdl = TTSModel[factory](
key=llm["api_key"], model_name=llm["llm_name"], base_url=llm["api_base"]
)
try:
for resp in mdl.tts("Hello~ Ragflower!"):
pass
except RuntimeError as e:
msg += f"\nFail to access model({llm['llm_name']})." + str(e)
else:
# TODO: check other type of models
pass
if msg:
return get_data_error_result(message=msg)
if not TenantLLMService.filter_update(
[TenantLLM.tenant_id == current_user.id, TenantLLM.llm_factory == factory,
TenantLLM.llm_name == llm["llm_name"]], llm):
TenantLLMService.save(**llm)
return get_json_result(data=True)
@manager.route('/delete_llm', methods=['POST']) # noqa: F821
@login_required
@validate_request("llm_factory", "llm_name")
def delete_llm():
req = request.json
TenantLLMService.filter_delete(
[TenantLLM.tenant_id == current_user.id, TenantLLM.llm_factory == req["llm_factory"],
TenantLLM.llm_name == req["llm_name"]])
return get_json_result(data=True)
@manager.route('/delete_factory', methods=['POST']) # noqa: F821
@login_required
@validate_request("llm_factory")
def delete_factory():
req = request.json
TenantLLMService.filter_delete(
[TenantLLM.tenant_id == current_user.id, TenantLLM.llm_factory == req["llm_factory"]])
return get_json_result(data=True)
@manager.route('/my_llms', methods=['GET']) # noqa: F821
@login_required
def my_llms():
try:
res = {}
for o in TenantLLMService.get_my_llms(current_user.id):
if o["llm_factory"] not in res:
res[o["llm_factory"]] = {
"tags": o["tags"],
"llm": []
}
res[o["llm_factory"]]["llm"].append({
"type": o["model_type"],
"name": o["llm_name"],
"used_token": o["used_tokens"]
})
return get_json_result(data=res)
except Exception as e:
return server_error_response(e)
@manager.route('/list', methods=['GET']) # noqa: F821
@login_required
def list_app():
self_deploied = ["Youdao", "FastEmbed", "BAAI", "Ollama", "Xinference", "LocalAI", "LM-Studio"]
weighted = ["Youdao", "FastEmbed", "BAAI"] if settings.LIGHTEN != 0 else []
model_type = request.args.get("model_type")
try:
objs = TenantLLMService.query(tenant_id=current_user.id)
facts = set([o.to_dict()["llm_factory"] for o in objs if o.api_key])
llms = LLMService.get_all()
llms = [m.to_dict()
for m in llms if m.status == StatusEnum.VALID.value and m.fid not in weighted]
for m in llms:
m["available"] = m["fid"] in facts or m["llm_name"].lower() == "flag-embedding" or m["fid"] in self_deploied
llm_set = set([m["llm_name"] + "@" + m["fid"] for m in llms])
for o in objs:
if not o.api_key:
continue
if o.llm_name + "@" + o.llm_factory in llm_set:
continue
llms.append({"llm_name": o.llm_name, "model_type": o.model_type, "fid": o.llm_factory, "available": True})
res = {}
for m in llms:
if model_type and m["model_type"].find(model_type) < 0:
continue
if m["fid"] not in res:
res[m["fid"]] = []
res[m["fid"]].append(m)
return get_json_result(data=res)
except Exception as e:
return server_error_response(e)