mirror of
https://github.com/csunny/DB-GPT.git
synced 2025-09-11 05:49:22 +00:00
feat(core): More AWEL operators and new prompt manager API (#972)
Co-authored-by: csunny <cfqsunny@163.com>
This commit is contained in:
Fangyin Cheng
GitHub
@@ -6,7 +6,8 @@
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.. code-block:: shell
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curl -X POST http://127.0.0.1:5000/api/v1/awel/trigger/examples/simple_chat \
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DBGPT_SERVER="http://127.0.0.1:5000"
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curl -X POST $DBGPT_SERVER/api/v1/awel/trigger/examples/simple_chat \
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-H "Content-Type: application/json" -d '{
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"model": "proxyllm",
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"user_input": "hello"
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@@ -52,3 +53,14 @@ with DAG("dbgpt_awel_simple_dag_example") as dag:
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# type(out) == ModelOutput
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model_parse_task = MapOperator(lambda out: out.to_dict())
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trigger >> request_handle_task >> model_task >> model_parse_task
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if __name__ == "__main__":
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if dag.leaf_nodes[0].dev_mode:
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# Development mode, you can run the dag locally for debugging.
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from dbgpt.core.awel import setup_dev_environment
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setup_dev_environment([dag], port=5555)
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else:
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# Production mode, DB-GPT will automatically load and execute the current file after startup.
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pass
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186
examples/awel/simple_chat_history_example.py
Normal file
186
examples/awel/simple_chat_history_example.py
Normal file
@@ -0,0 +1,186 @@
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"""AWEL: Simple chat with history example
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DB-GPT will automatically load and execute the current file after startup.
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Examples:
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Call with non-streaming response.
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.. code-block:: shell
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DBGPT_SERVER="http://127.0.0.1:5000"
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MODEL="gpt-3.5-turbo"
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# Fist round
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curl -X POST $DBGPT_SERVER/api/v1/awel/trigger/examples/simple_history/multi_round/chat/completions \
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-H "Content-Type: application/json" -d '{
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"model": "gpt-3.5-turbo",
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"context": {
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"conv_uid": "uuid_conv_1234"
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},
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"messages": "Who is elon musk?"
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}'
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# Second round
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curl -X POST $DBGPT_SERVER/api/v1/awel/trigger/examples/simple_history/multi_round/chat/completions \
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-H "Content-Type: application/json" -d '{
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"model": "gpt-3.5-turbo",
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"context": {
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"conv_uid": "uuid_conv_1234"
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},
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"messages": "Is he rich?"
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}'
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Call with streaming response.
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.. code-block:: shell
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curl -X POST $DBGPT_SERVER/api/v1/awel/trigger/examples/simple_history/multi_round/chat/completions \
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-H "Content-Type: application/json" -d '{
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"model": "gpt-3.5-turbo",
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"context": {
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"conv_uid": "uuid_conv_stream_1234"
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},
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"stream": true,
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"messages": "Who is elon musk?"
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}'
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# Second round
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curl -X POST $DBGPT_SERVER/api/v1/awel/trigger/examples/simple_history/multi_round/chat/completions \
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-H "Content-Type: application/json" -d '{
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"model": "gpt-3.5-turbo",
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"context": {
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"conv_uid": "uuid_conv_stream_1234"
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},
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"stream": true,
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"messages": "Is he rich?"
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}'
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"""
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from typing import Dict, Any, Optional, Union, List
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import logging
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from dbgpt._private.pydantic import BaseModel, Field
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from dbgpt.core.awel import (
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DAG,
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HttpTrigger,
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MapOperator,
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JoinOperator,
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)
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from dbgpt.core import LLMClient, InMemoryStorage
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from dbgpt.core.operator import (
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LLMBranchOperator,
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LLMOperator,
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StreamingLLMOperator,
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RequestBuildOperator,
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PreConversationOperator,
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PostConversationOperator,
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PostStreamingConversationOperator,
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BufferedConversationMapperOperator,
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)
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from dbgpt.model import OpenAIStreamingOperator, MixinLLMOperator
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logger = logging.getLogger(__name__)
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class ReqContext(BaseModel):
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user_name: Optional[str] = Field(
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None, description="The user name of the model request."
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)
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sys_code: Optional[str] = Field(
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None, description="The system code of the model request."
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)
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conv_uid: Optional[str] = Field(
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None, description="The conversation uid of the model request."
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)
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class TriggerReqBody(BaseModel):
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messages: Union[str, List[Dict[str, str]]] = Field(
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..., description="User input messages"
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)
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model: str = Field(..., description="Model name")
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stream: Optional[bool] = Field(default=False, description="Whether return stream")
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context: Optional[ReqContext] = Field(
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default=None, description="The context of the model request."
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)
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class MyLLMOperator(MixinLLMOperator, LLMOperator):
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def __init__(self, llm_client: Optional[LLMClient] = None, **kwargs):
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super().__init__(llm_client)
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LLMOperator.__init__(self, llm_client, **kwargs)
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class MyStreamingLLMOperator(MixinLLMOperator, StreamingLLMOperator):
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def __init__(self, llm_client: Optional[LLMClient] = None, **kwargs):
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super().__init__(llm_client)
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StreamingLLMOperator.__init__(self, llm_client, **kwargs)
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with DAG("dbgpt_awel_simple_chat_history") as multi_round_dag:
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# Receive http request and trigger dag to run.
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trigger = HttpTrigger(
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"/examples/simple_history/multi_round/chat/completions",
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methods="POST",
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request_body=TriggerReqBody,
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streaming_predict_func=lambda req: req.stream,
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)
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# Transform request body to model request.
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request_handle_task = RequestBuildOperator()
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# Pre-process conversation, use InMemoryStorage to store conversation.
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pre_conversation_task = PreConversationOperator(
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storage=InMemoryStorage(), message_storage=InMemoryStorage()
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)
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# Keep last k round conversation.
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history_conversation_task = BufferedConversationMapperOperator(last_k_round=5)
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# Save conversation to storage.
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post_conversation_task = PostConversationOperator()
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# Save streaming conversation to storage.
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post_streaming_conversation_task = PostStreamingConversationOperator()
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# Use LLMOperator to generate response.
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llm_task = MyLLMOperator(task_name="llm_task")
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streaming_llm_task = MyStreamingLLMOperator(task_name="streaming_llm_task")
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branch_task = LLMBranchOperator(
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stream_task_name="streaming_llm_task", no_stream_task_name="llm_task"
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)
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model_parse_task = MapOperator(lambda out: out.to_dict())
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openai_format_stream_task = OpenAIStreamingOperator()
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result_join_task = JoinOperator(
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combine_function=lambda not_stream_out, stream_out: not_stream_out or stream_out
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)
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(
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trigger
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>> request_handle_task
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>> pre_conversation_task
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>> history_conversation_task
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>> branch_task
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)
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# The branch of no streaming response.
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(
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branch_task
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>> llm_task
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>> post_conversation_task
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>> model_parse_task
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>> result_join_task
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)
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# The branch of streaming response.
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(
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branch_task
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>> streaming_llm_task
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>> post_streaming_conversation_task
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>> openai_format_stream_task
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>> result_join_task
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)
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if __name__ == "__main__":
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if multi_round_dag.leaf_nodes[0].dev_mode:
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# Development mode, you can run the dag locally for debugging.
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from dbgpt.core.awel import setup_dev_environment
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setup_dev_environment([multi_round_dag], port=5555)
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else:
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# Production mode, DB-GPT will automatically load and execute the current file after startup.
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pass
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@@ -2,45 +2,58 @@
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DB-GPT will automatically load and execute the current file after startup.
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Example:
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Examples:
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.. code-block:: shell
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Call with non-streaming response.
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.. code-block:: shell
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DBGPT_SERVER="http://127.0.0.1:5000"
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curl -X POST $DBGPT_SERVER/api/v1/awel/trigger/examples/simple_client/generate \
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-H "Content-Type: application/json" -d '{
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"model": "proxyllm",
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"messages": "hello"
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}'
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DBGPT_SERVER="http://127.0.0.1:5000"
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curl -X POST $DBGPT_SERVER/api/v1/awel/trigger/examples/simple_client/chat/completions \
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-H "Content-Type: application/json" -d '{
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"model": "proxyllm",
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"messages": "hello"
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}'
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curl -X POST $DBGPT_SERVER/api/v1/awel/trigger/examples/simple_client/generate_stream \
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-H "Content-Type: application/json" -d '{
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"model": "proxyllm",
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"messages": "hello",
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"stream": true
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}'
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Call with streaming response.
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.. code-block:: shell
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curl -X POST $DBGPT_SERVER/api/v1/awel/trigger/examples/simple_client/count_token \
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-H "Content-Type: application/json" -d '{
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"model": "proxyllm",
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"messages": "hello"
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}'
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curl -X POST $DBGPT_SERVER/api/v1/awel/trigger/examples/simple_client/chat/completions \
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-H "Content-Type: application/json" -d '{
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"model": "proxyllm",
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"messages": "hello",
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"stream": true
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}'
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Call model and count token.
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.. code-block:: shell
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curl -X POST $DBGPT_SERVER/api/v1/awel/trigger/examples/simple_client/count_token \
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-H "Content-Type: application/json" -d '{
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"model": "proxyllm",
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"messages": "hello"
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}'
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"""
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from typing import Dict, Any, AsyncIterator, Optional, Union, List
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from typing import Dict, Any, Optional, Union, List
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import logging
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from dbgpt._private.pydantic import BaseModel, Field
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from dbgpt.component import ComponentType
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from dbgpt.core.awel import DAG, HttpTrigger, MapOperator, TransformStreamAbsOperator
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from dbgpt.core import (
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ModelMessage,
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LLMClient,
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from dbgpt.core.awel import (
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DAG,
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HttpTrigger,
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MapOperator,
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JoinOperator,
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)
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from dbgpt.core import LLMClient
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from dbgpt.core.operator import (
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LLMBranchOperator,
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LLMOperator,
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StreamingLLMOperator,
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ModelOutput,
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ModelRequest,
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RequestBuildOperator,
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)
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from dbgpt.model import DefaultLLMClient
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from dbgpt.model.cluster import WorkerManagerFactory
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from dbgpt.model import OpenAIStreamingOperator, MixinLLMOperator
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logger = logging.getLogger(__name__)
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class TriggerReqBody(BaseModel):
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@@ -51,58 +64,24 @@ class TriggerReqBody(BaseModel):
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stream: Optional[bool] = Field(default=False, description="Whether return stream")
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class RequestHandleOperator(MapOperator[TriggerReqBody, ModelRequest]):
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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async def map(self, input_value: TriggerReqBody) -> ModelRequest:
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messages = [ModelMessage.build_human_message(input_value.messages)]
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await self.current_dag_context.save_to_share_data(
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"request_model_name", input_value.model
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)
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return ModelRequest(
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model=input_value.model,
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messages=messages,
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echo=False,
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)
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class MyLLMOperator(MixinLLMOperator, LLMOperator):
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def __init__(self, llm_client: Optional[LLMClient] = None, **kwargs):
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super().__init__(llm_client)
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LLMOperator.__init__(self, llm_client, **kwargs)
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class LLMMixin:
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@property
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def llm_client(self) -> LLMClient:
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if not self._llm_client:
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worker_manager = self.system_app.get_component(
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ComponentType.WORKER_MANAGER_FACTORY, WorkerManagerFactory
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).create()
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self._llm_client = DefaultLLMClient(worker_manager)
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return self._llm_client
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class MyStreamingLLMOperator(MixinLLMOperator, StreamingLLMOperator):
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def __init__(self, llm_client: Optional[LLMClient] = None, **kwargs):
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super().__init__(llm_client)
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StreamingLLMOperator.__init__(self, llm_client, **kwargs)
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class MyLLMOperator(LLMMixin, LLMOperator):
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def __init__(self, llm_client: LLMClient = None, **kwargs):
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super().__init__(llm_client, **kwargs)
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class MyStreamingLLMOperator(LLMMixin, StreamingLLMOperator):
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def __init__(self, llm_client: LLMClient = None, **kwargs):
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super().__init__(llm_client, **kwargs)
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class MyLLMStreamingOperator(TransformStreamAbsOperator[ModelOutput, str]):
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async def transform_stream(
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self, input_value: AsyncIterator[ModelOutput]
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) -> AsyncIterator[str]:
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from dbgpt.model.utils.chatgpt_utils import _to_openai_stream
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model = await self.current_dag_context.get_share_data("request_model_name")
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async for output in _to_openai_stream(model, input_value):
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yield output
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class MyModelToolOperator(LLMMixin, MapOperator[TriggerReqBody, Dict[str, Any]]):
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def __init__(self, llm_client: LLMClient = None, **kwargs):
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self._llm_client = llm_client
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MapOperator.__init__(self, **kwargs)
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class MyModelToolOperator(
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MixinLLMOperator, MapOperator[TriggerReqBody, Dict[str, Any]]
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):
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def __init__(self, llm_client: Optional[LLMClient] = None, **kwargs):
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super().__init__(llm_client)
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MapOperator.__init__(self, llm_client, **kwargs)
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async def map(self, input_value: TriggerReqBody) -> Dict[str, Any]:
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prompt_tokens = await self.llm_client.count_token(
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@@ -118,25 +97,27 @@ class MyModelToolOperator(LLMMixin, MapOperator[TriggerReqBody, Dict[str, Any]])
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with DAG("dbgpt_awel_simple_llm_client_generate") as client_generate_dag:
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# Receive http request and trigger dag to run.
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trigger = HttpTrigger(
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"/examples/simple_client/generate", methods="POST", request_body=TriggerReqBody
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)
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request_handle_task = RequestHandleOperator()
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model_task = MyLLMOperator()
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model_parse_task = MapOperator(lambda out: out.to_dict())
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trigger >> request_handle_task >> model_task >> model_parse_task
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with DAG("dbgpt_awel_simple_llm_client_generate_stream") as client_generate_stream_dag:
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# Receive http request and trigger dag to run.
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trigger = HttpTrigger(
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"/examples/simple_client/generate_stream",
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"/examples/simple_client/chat/completions",
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methods="POST",
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request_body=TriggerReqBody,
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streaming_response=True,
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streaming_predict_func=lambda req: req.stream,
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)
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request_handle_task = RequestHandleOperator()
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model_task = MyStreamingLLMOperator()
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openai_format_stream_task = MyLLMStreamingOperator()
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trigger >> request_handle_task >> model_task >> openai_format_stream_task
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request_handle_task = RequestBuildOperator()
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llm_task = MyLLMOperator(task_name="llm_task")
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streaming_llm_task = MyStreamingLLMOperator(task_name="streaming_llm_task")
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branch_task = LLMBranchOperator(
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stream_task_name="streaming_llm_task", no_stream_task_name="llm_task"
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)
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model_parse_task = MapOperator(lambda out: out.to_dict())
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openai_format_stream_task = OpenAIStreamingOperator()
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result_join_task = JoinOperator(
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combine_function=lambda not_stream_out, stream_out: not_stream_out or stream_out
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)
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trigger >> request_handle_task >> branch_task
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branch_task >> llm_task >> model_parse_task >> result_join_task
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branch_task >> streaming_llm_task >> openai_format_stream_task >> result_join_task
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with DAG("dbgpt_awel_simple_llm_client_count_token") as client_count_token_dag:
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# Receive http request and trigger dag to run.
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@@ -147,3 +128,15 @@ with DAG("dbgpt_awel_simple_llm_client_count_token") as client_count_token_dag:
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)
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model_task = MyModelToolOperator()
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trigger >> model_task
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if __name__ == "__main__":
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if client_generate_dag.leaf_nodes[0].dev_mode:
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# Development mode, you can run the dag locally for debugging.
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from dbgpt.core.awel import setup_dev_environment
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dags = [client_generate_dag, client_count_token_dag]
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setup_dev_environment(dags, port=5555)
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else:
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# Production mode, DB-GPT will automatically load and execute the current file after startup.
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pass
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Block a user