mirror of
https://github.com/infiniflow/ragflow.git
synced 2025-12-08 12:32:30 +08:00
a82e9b3d91
### What problem does this PR solve? Fix: can't upload image in ollama model #10447 ### Type of change - [X] Bug Fix (non-breaking change which fixes an issue) ### Change all `image=[]` to `image = None` Changing `image=[]` to `images=None` avoids Python’s mutable default parameter issue. If you keep `images=[]`, all calls share the same list, so modifying it (e.g., images.append()) will affect later calls. Using images=None and creating a new list inside the function ensures each call is independent. This change does not affect current behavior — it simply makes the code safer and more predictable. 把 `images=[]` 改成 `images=None` 是为了避免 Python 默认参数的可变对象问题。 如果保留 `images=[]`，所有调用都会共用同一个列表，一旦修改就会影响后续调用。 改成 None 并在函数内部重新创建列表，可以确保每次调用都是独立的。 这个修改不会影响现有运行结果，只是让代码更安全、更可控。
279 lines
12 KiB
Python
279 lines
12 KiB
Python
#
|
|
# 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 inspect
|
|
import logging
|
|
import re
|
|
from functools import partial
|
|
from typing import Generator
|
|
from api.db.db_models import LLM
|
|
from api.db.services.common_service import CommonService
|
|
from api.db.services.tenant_llm_service import LLM4Tenant, TenantLLMService
|
|
|
|
|
|
class LLMService(CommonService):
|
|
model = LLM
|
|
|
|
|
|
def get_init_tenant_llm(user_id):
|
|
from api import settings
|
|
tenant_llm = []
|
|
|
|
seen = set()
|
|
factory_configs = []
|
|
for factory_config in [
|
|
settings.CHAT_CFG,
|
|
settings.EMBEDDING_CFG,
|
|
settings.ASR_CFG,
|
|
settings.IMAGE2TEXT_CFG,
|
|
settings.RERANK_CFG,
|
|
]:
|
|
factory_name = factory_config["factory"]
|
|
if factory_name not in seen:
|
|
seen.add(factory_name)
|
|
factory_configs.append(factory_config)
|
|
|
|
for factory_config in factory_configs:
|
|
for llm in LLMService.query(fid=factory_config["factory"]):
|
|
tenant_llm.append(
|
|
{
|
|
"tenant_id": user_id,
|
|
"llm_factory": factory_config["factory"],
|
|
"llm_name": llm.llm_name,
|
|
"model_type": llm.model_type,
|
|
"api_key": factory_config["api_key"],
|
|
"api_base": factory_config["base_url"],
|
|
"max_tokens": llm.max_tokens if llm.max_tokens else 8192,
|
|
}
|
|
)
|
|
|
|
if settings.LIGHTEN != 1:
|
|
for buildin_embedding_model in settings.BUILTIN_EMBEDDING_MODELS:
|
|
mdlnm, fid = TenantLLMService.split_model_name_and_factory(buildin_embedding_model)
|
|
tenant_llm.append(
|
|
{
|
|
"tenant_id": user_id,
|
|
"llm_factory": fid,
|
|
"llm_name": mdlnm,
|
|
"model_type": "embedding",
|
|
"api_key": "",
|
|
"api_base": "",
|
|
"max_tokens": 1024 if buildin_embedding_model == "BAAI/bge-large-zh-v1.5@BAAI" else 512,
|
|
}
|
|
)
|
|
|
|
unique = {}
|
|
for item in tenant_llm:
|
|
key = (item["tenant_id"], item["llm_factory"], item["llm_name"])
|
|
if key not in unique:
|
|
unique[key] = item
|
|
return list(unique.values())
|
|
|
|
|
|
class LLMBundle(LLM4Tenant):
|
|
def __init__(self, tenant_id, llm_type, llm_name=None, lang="Chinese", **kwargs):
|
|
super().__init__(tenant_id, llm_type, llm_name, lang, **kwargs)
|
|
|
|
def bind_tools(self, toolcall_session, tools):
|
|
if not self.is_tools:
|
|
logging.warning(f"Model {self.llm_name} does not support tool call, but you have assigned one or more tools to it!")
|
|
return
|
|
self.mdl.bind_tools(toolcall_session, tools)
|
|
|
|
def encode(self, texts: list):
|
|
if self.langfuse:
|
|
generation = self.langfuse.start_generation(trace_context=self.trace_context, name="encode", model=self.llm_name, input={"texts": texts})
|
|
|
|
embeddings, used_tokens = self.mdl.encode(texts)
|
|
llm_name = getattr(self, "llm_name", None)
|
|
if not TenantLLMService.increase_usage(self.tenant_id, self.llm_type, used_tokens, llm_name):
|
|
logging.error("LLMBundle.encode can't update token usage for {}/EMBEDDING used_tokens: {}".format(self.tenant_id, used_tokens))
|
|
|
|
if self.langfuse:
|
|
generation.update(usage_details={"total_tokens": used_tokens})
|
|
generation.end()
|
|
|
|
return embeddings, used_tokens
|
|
|
|
def encode_queries(self, query: str):
|
|
if self.langfuse:
|
|
generation = self.langfuse.start_generation(trace_context=self.trace_context, name="encode_queries", model=self.llm_name, input={"query": query})
|
|
|
|
emd, used_tokens = self.mdl.encode_queries(query)
|
|
llm_name = getattr(self, "llm_name", None)
|
|
if not TenantLLMService.increase_usage(self.tenant_id, self.llm_type, used_tokens, llm_name):
|
|
logging.error("LLMBundle.encode_queries can't update token usage for {}/EMBEDDING used_tokens: {}".format(self.tenant_id, used_tokens))
|
|
|
|
if self.langfuse:
|
|
generation.update(usage_details={"total_tokens": used_tokens})
|
|
generation.end()
|
|
|
|
return emd, used_tokens
|
|
|
|
def similarity(self, query: str, texts: list):
|
|
if self.langfuse:
|
|
generation = self.langfuse.start_generation(trace_context=self.trace_context, name="similarity", model=self.llm_name, input={"query": query, "texts": texts})
|
|
|
|
sim, used_tokens = self.mdl.similarity(query, texts)
|
|
if not TenantLLMService.increase_usage(self.tenant_id, self.llm_type, used_tokens):
|
|
logging.error("LLMBundle.similarity can't update token usage for {}/RERANK used_tokens: {}".format(self.tenant_id, used_tokens))
|
|
|
|
if self.langfuse:
|
|
generation.update(usage_details={"total_tokens": used_tokens})
|
|
generation.end()
|
|
|
|
return sim, used_tokens
|
|
|
|
def describe(self, image, max_tokens=300):
|
|
if self.langfuse:
|
|
generation = self.langfuse.start_generation(trace_context=self.trace_context, name="describe", metadata={"model": self.llm_name})
|
|
|
|
txt, used_tokens = self.mdl.describe(image)
|
|
if not TenantLLMService.increase_usage(self.tenant_id, self.llm_type, used_tokens):
|
|
logging.error("LLMBundle.describe can't update token usage for {}/IMAGE2TEXT used_tokens: {}".format(self.tenant_id, used_tokens))
|
|
|
|
if self.langfuse:
|
|
generation.update(output={"output": txt}, usage_details={"total_tokens": used_tokens})
|
|
generation.end()
|
|
|
|
return txt
|
|
|
|
def describe_with_prompt(self, image, prompt):
|
|
if self.langfuse:
|
|
generation = self.langfuse.start_generation(trace_context=self.trace_context, name="describe_with_prompt", metadata={"model": self.llm_name, "prompt": prompt})
|
|
|
|
txt, used_tokens = self.mdl.describe_with_prompt(image, prompt)
|
|
if not TenantLLMService.increase_usage(self.tenant_id, self.llm_type, used_tokens):
|
|
logging.error("LLMBundle.describe can't update token usage for {}/IMAGE2TEXT used_tokens: {}".format(self.tenant_id, used_tokens))
|
|
|
|
if self.langfuse:
|
|
generation.update(output={"output": txt}, usage_details={"total_tokens": used_tokens})
|
|
generation.end()
|
|
|
|
return txt
|
|
|
|
def transcription(self, audio):
|
|
if self.langfuse:
|
|
generation = self.langfuse.start_generation(trace_context=self.trace_context, name="transcription", metadata={"model": self.llm_name})
|
|
|
|
txt, used_tokens = self.mdl.transcription(audio)
|
|
if not TenantLLMService.increase_usage(self.tenant_id, self.llm_type, used_tokens):
|
|
logging.error("LLMBundle.transcription can't update token usage for {}/SEQUENCE2TXT used_tokens: {}".format(self.tenant_id, used_tokens))
|
|
|
|
if self.langfuse:
|
|
generation.update(output={"output": txt}, usage_details={"total_tokens": used_tokens})
|
|
generation.end()
|
|
|
|
return txt
|
|
|
|
def tts(self, text: str) -> Generator[bytes, None, None]:
|
|
if self.langfuse:
|
|
generation = self.langfuse.start_generation(trace_context=self.trace_context, name="tts", input={"text": text})
|
|
|
|
for chunk in self.mdl.tts(text):
|
|
if isinstance(chunk, int):
|
|
if not TenantLLMService.increase_usage(self.tenant_id, self.llm_type, chunk, self.llm_name):
|
|
logging.error("LLMBundle.tts can't update token usage for {}/TTS".format(self.tenant_id))
|
|
return
|
|
yield chunk
|
|
|
|
if self.langfuse:
|
|
generation.end()
|
|
|
|
def _remove_reasoning_content(self, txt: str) -> str:
|
|
first_think_start = txt.find("<think>")
|
|
if first_think_start == -1:
|
|
return txt
|
|
|
|
last_think_end = txt.rfind("</think>")
|
|
if last_think_end == -1:
|
|
return txt
|
|
|
|
if last_think_end < first_think_start:
|
|
return txt
|
|
|
|
return txt[last_think_end + len("</think>") :]
|
|
|
|
@staticmethod
|
|
def _clean_param(chat_partial, **kwargs):
|
|
func = chat_partial.func
|
|
sig = inspect.signature(func)
|
|
support_var_args = False
|
|
allowed_params = set()
|
|
|
|
for param in sig.parameters.values():
|
|
if param.kind == inspect.Parameter.VAR_KEYWORD:
|
|
support_var_args = True
|
|
elif param.kind in (inspect.Parameter.POSITIONAL_OR_KEYWORD, inspect.Parameter.KEYWORD_ONLY):
|
|
allowed_params.add(param.name)
|
|
if support_var_args:
|
|
return kwargs
|
|
else:
|
|
return {k: v for k, v in kwargs.items() if k in allowed_params}
|
|
def chat(self, system: str, history: list, gen_conf: dict = {}, **kwargs) -> str:
|
|
if self.langfuse:
|
|
generation = self.langfuse.start_generation(trace_context=self.trace_context, name="chat", model=self.llm_name, input={"system": system, "history": history})
|
|
|
|
chat_partial = partial(self.mdl.chat, system, history, gen_conf, **kwargs)
|
|
if self.is_tools and self.mdl.is_tools:
|
|
chat_partial = partial(self.mdl.chat_with_tools, system, history, gen_conf, **kwargs)
|
|
|
|
use_kwargs = self._clean_param(chat_partial, **kwargs)
|
|
txt, used_tokens = chat_partial(**use_kwargs)
|
|
txt = self._remove_reasoning_content(txt)
|
|
|
|
if not self.verbose_tool_use:
|
|
txt = re.sub(r"<tool_call>.*?</tool_call>", "", txt, flags=re.DOTALL)
|
|
|
|
if isinstance(txt, int) and not TenantLLMService.increase_usage(self.tenant_id, self.llm_type, used_tokens, self.llm_name):
|
|
logging.error("LLMBundle.chat can't update token usage for {}/CHAT llm_name: {}, used_tokens: {}".format(self.tenant_id, self.llm_name, used_tokens))
|
|
|
|
if self.langfuse:
|
|
generation.update(output={"output": txt}, usage_details={"total_tokens": used_tokens})
|
|
generation.end()
|
|
|
|
return txt
|
|
|
|
def chat_streamly(self, system: str, history: list, gen_conf: dict = {}, **kwargs):
|
|
if self.langfuse:
|
|
generation = self.langfuse.start_generation(trace_context=self.trace_context, name="chat_streamly", model=self.llm_name, input={"system": system, "history": history})
|
|
|
|
ans = ""
|
|
chat_partial = partial(self.mdl.chat_streamly, system, history, gen_conf)
|
|
total_tokens = 0
|
|
if self.is_tools and self.mdl.is_tools:
|
|
chat_partial = partial(self.mdl.chat_streamly_with_tools, system, history, gen_conf)
|
|
use_kwargs = self._clean_param(chat_partial, **kwargs)
|
|
for txt in chat_partial(**use_kwargs):
|
|
if isinstance(txt, int):
|
|
total_tokens = txt
|
|
if self.langfuse:
|
|
generation.update(output={"output": ans})
|
|
generation.end()
|
|
break
|
|
|
|
if txt.endswith("</think>"):
|
|
ans = ans[: -len("</think>")]
|
|
|
|
if not self.verbose_tool_use:
|
|
txt = re.sub(r"<tool_call>.*?</tool_call>", "", txt, flags=re.DOTALL)
|
|
|
|
ans += txt
|
|
yield ans
|
|
|
|
if total_tokens > 0:
|
|
if not TenantLLMService.increase_usage(self.tenant_id, self.llm_type, txt, self.llm_name):
|
|
logging.error("LLMBundle.chat_streamly can't update token usage for {}/CHAT llm_name: {}, content: {}".format(self.tenant_id, self.llm_name, txt))
|