Files
DB-GPT/examples/awel/simple_rag_example.py
2023-12-28 14:14:20 +08:00

73 lines
2.2 KiB
Python

"""AWEL: Simple rag example
DB-GPT will automatically load and execute the current file after startup.
Example:
.. code-block:: shell
curl -X POST http://127.0.0.1:5000/api/v1/awel/trigger/examples/simple_rag \
-H "Content-Type: application/json" -d '{
"conv_uid": "36f0e992-8825-11ee-8638-0242ac150003",
"model_name": "proxyllm",
"chat_mode": "chat_knowledge",
"user_input": "What is DB-GPT?",
"select_param": "default"
}'
"""
from dbgpt.app.openapi.api_view_model import ConversationVo
from dbgpt.app.scene import ChatScene
from dbgpt.app.scene.operator._experimental import (
BaseChatOperator,
ChatContext,
ChatHistoryOperator,
ChatHistoryStorageOperator,
EmbeddingEngingOperator,
PromptManagerOperator,
)
from dbgpt.core.awel import DAG, HttpTrigger, MapOperator
from dbgpt.model.operator.model_operator import ModelOperator
class RequestParseOperator(MapOperator[ConversationVo, ChatContext]):
def __init__(self, **kwargs):
super().__init__(**kwargs)
async def map(self, input_value: ConversationVo) -> ChatContext:
return ChatContext(
current_user_input=input_value.user_input,
model_name=input_value.model_name,
chat_session_id=input_value.conv_uid,
select_param=input_value.select_param,
chat_scene=ChatScene.ChatKnowledge,
)
with DAG("simple_rag_example") as dag:
trigger_task = HttpTrigger(
"/examples/simple_rag", methods="POST", request_body=ConversationVo
)
req_parse_task = RequestParseOperator()
# TODO should register prompt template first
prompt_task = PromptManagerOperator()
history_storage_task = ChatHistoryStorageOperator()
history_task = ChatHistoryOperator()
embedding_task = EmbeddingEngingOperator()
chat_task = BaseChatOperator()
model_task = ModelOperator()
output_parser_task = MapOperator(lambda out: out.to_dict()["text"])
(
trigger_task
>> req_parse_task
>> prompt_task
>> history_storage_task
>> history_task
>> embedding_task
>> chat_task
>> model_task
>> output_parser_task
)