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集成openai plugins插件

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glide-the 2024-01-18 17:56:37 +08:00 committed by liunux4odoo
parent f1b71e5629
commit cce2b55719
3 changed files with 43 additions and 1 deletions
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6
server/chat/file_chat.py
View File

@ -99,6 +99,9 @@ async def file_chat(query: str = Body(..., description="用户输入", examples=
"content": "虎头虎脑"}]]
),
stream: bool = Body(False, description="流式输出"),
endpoint_host: str = Body(False, description="接入点地址"),
endpoint_host_key: str = Body(False, description="接入点key"),
endpoint_host_proxy: str = Body(False, description="接入点代理地址"),
model_name: str = Body(None, description="LLM 模型名称。"),
temperature: float = Body(0.01, description="LLM 采样温度", ge=0.0, le=1.0),
max_tokens: Optional[int] = Body(None, description="限制LLM生成Token数量，默认None代表模型最大值"),
@ -116,6 +119,9 @@ async def file_chat(query: str = Body(..., description="用户输入", examples=
max_tokens = None
model = get_ChatOpenAI(
endpoint_host=endpoint_host,
endpoint_host_key=endpoint_host_key,
endpoint_host_proxy=endpoint_host_proxy,
model_name=model_name,
temperature=temperature,
max_tokens=max_tokens,

37
server/knowledge_base/kb_summary_api.py
View File

@ -12,12 +12,16 @@ from server.knowledge_base.kb_summary.summary_chunk import SummaryAdapter
from server.utils import wrap_done, get_ChatOpenAI, BaseResponse
from server.knowledge_base.model.kb_document_model import DocumentWithVSId
def recreate_summary_vector_store(
knowledge_base_name: str = Body(..., examples=["samples"]),
allow_empty_kb: bool = Body(True),
vs_type: str = Body(DEFAULT_VS_TYPE),
embed_model: str = Body(EMBEDDING_MODEL),
file_description: str = Body(''),
endpoint_host: str = Body(False, description="接入点地址"),
endpoint_host_key: str = Body(False, description="接入点key"),
endpoint_host_proxy: str = Body(False, description="接入点代理地址"),
model_name: str = Body(None, description="LLM 模型名称。"),
temperature: float = Body(0.01, description="LLM 采样温度", ge=0.0, le=1.0),
max_tokens: Optional[int] = Body(None, description="限制LLM生成Token数量，默认None代表模型最大值"),
@ -25,6 +29,9 @@ def recreate_summary_vector_store(
"""
重建单个知识库文件摘要
:param max_tokens:
:param endpoint_host:
:param endpoint_host_key:
:param endpoint_host_proxy:
:param model_name:
:param temperature:
:param file_description:
@ -47,11 +54,17 @@ def recreate_summary_vector_store(
kb_summary.create_kb_summary()
llm = get_ChatOpenAI(
endpoint_host=endpoint_host,
endpoint_host_key=endpoint_host_key,
endpoint_host_proxy=endpoint_host_proxy,
model_name=model_name,
temperature=temperature,
max_tokens=max_tokens,
)
reduce_llm = get_ChatOpenAI(
endpoint_host=endpoint_host,
endpoint_host_key=endpoint_host_key,
endpoint_host_proxy=endpoint_host_proxy,
model_name=model_name,
temperature=temperature,
max_tokens=max_tokens,
@ -99,12 +112,18 @@ def summary_file_to_vector_store(
vs_type: str = Body(DEFAULT_VS_TYPE),
embed_model: str = Body(EMBEDDING_MODEL),
file_description: str = Body(''),
endpoint_host: str = Body(False, description="接入点地址"),
endpoint_host_key: str = Body(False, description="接入点key"),
endpoint_host_proxy: str = Body(False, description="接入点代理地址"),
model_name: str = Body(None, description="LLM 模型名称。"),
temperature: float = Body(0.01, description="LLM 采样温度", ge=0.0, le=1.0),
max_tokens: Optional[int] = Body(None, description="限制LLM生成Token数量，默认None代表模型最大值"),
):
"""
单个知识库根据文件名称摘要
:param endpoint_host:
:param endpoint_host_key:
:param endpoint_host_proxy:
:param model_name:
:param max_tokens:
:param temperature:
@ -127,11 +146,17 @@ def summary_file_to_vector_store(
kb_summary.create_kb_summary()
llm = get_ChatOpenAI(
endpoint_host=endpoint_host,
endpoint_host_key=endpoint_host_key,
endpoint_host_proxy=endpoint_host_proxy,
model_name=model_name,
temperature=temperature,
max_tokens=max_tokens,
)
reduce_llm = get_ChatOpenAI(
endpoint_host=endpoint_host,
endpoint_host_key=endpoint_host_key,
endpoint_host_proxy=endpoint_host_proxy,
model_name=model_name,
temperature=temperature,
max_tokens=max_tokens,
@ -171,6 +196,9 @@ def summary_doc_ids_to_vector_store(
vs_type: str = Body(DEFAULT_VS_TYPE),
embed_model: str = Body(EMBEDDING_MODEL),
file_description: str = Body(''),
endpoint_host: str = Body(False, description="接入点地址"),
endpoint_host_key: str = Body(False, description="接入点key"),
endpoint_host_proxy: str = Body(False, description="接入点代理地址"),
model_name: str = Body(None, description="LLM 模型名称。"),
temperature: float = Body(0.01, description="LLM 采样温度", ge=0.0, le=1.0),
max_tokens: Optional[int] = Body(None, description="限制LLM生成Token数量，默认None代表模型最大值"),
@ -178,6 +206,9 @@ def summary_doc_ids_to_vector_store(
"""
单个知识库根据doc_ids摘要
:param knowledge_base_name:
:param endpoint_host:
:param endpoint_host_key:
:param endpoint_host_proxy:
:param doc_ids:
:param model_name:
:param max_tokens:
@ -192,11 +223,17 @@ def summary_doc_ids_to_vector_store(
return BaseResponse(code=404, msg=f"未找到知识库 {knowledge_base_name}", data={})
else:
llm = get_ChatOpenAI(
endpoint_host=endpoint_host,
endpoint_host_key=endpoint_host_key,
endpoint_host_proxy=endpoint_host_proxy,
model_name=model_name,
temperature=temperature,
max_tokens=max_tokens,
)
reduce_llm = get_ChatOpenAI(
endpoint_host=endpoint_host,
endpoint_host_key=endpoint_host_key,
endpoint_host_proxy=endpoint_host_proxy,
model_name=model_name,
temperature=temperature,
max_tokens=max_tokens,

1
tests/test_qwen_agent.py
View File

@ -8,7 +8,6 @@ from pprint import pprint
from langchain.agents import AgentExecutor
from langchain_openai.chat_models import ChatOpenAI
# from langchain.chat_models.openai import ChatOpenAI
from server.utils import get_ChatOpenAI
from server.agent.tools_factory.tools_registry import all_tools
from server.agent.agent_factory.qwen_agent import create_structured_qwen_chat_agent
from server.callback_handler.agent_callback_handler import AgentExecutorAsyncIteratorCallbackHandler