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feat(core): Multiple ways to run dbgpts (#1734)

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Fangyin Cheng 2024-07-18 17:50:40 +08:00 committed by GitHub
parent d389fddc2f
commit f889fa3775
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19 changed files with 1410 additions and 304 deletions
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2
dbgpt/cli/cli_scripts.py
View File

@ -201,7 +201,7 @@ except ImportError as e:
logging.warning(f"Integrating dbgpt dbgpts command line tool failed: {e}")
try:
from dbgpt.client._cli import run_flow
from dbgpt.client._cli import flow as run_flow
add_command_alias(run_flow, name="flow", parent_group=run)
except ImportError as e:

381
dbgpt/client/_cli.py
View File

@ -1,22 +1,55 @@
"""CLI for DB-GPT client."""
import asyncio
import functools
import json
import time
import uuid
from typing import Any, Dict
from typing import Any, AsyncIterator, Callable, Dict, Tuple, cast
import click
from dbgpt.component import SystemApp
from dbgpt.core.awel import DAG, BaseOperator, DAGVar
from dbgpt.core.awel.dag.dag_manager import DAGMetadata, _parse_metadata
from dbgpt.core.awel.flow.flow_factory import FlowFactory
from dbgpt.util import get_or_create_event_loop
from dbgpt.util.console import CliLogger
from dbgpt.util.i18n_utils import _
from .client import Client
from .flow import list_flow
from .flow import run_flow_cmd as client_run_flow_cmd
cl = CliLogger()
_LOCAL_MODE: bool | None = False
_FILE_PATH: str | None = None
@click.group()
@click.option(
"--local",
required=False,
type=bool,
default=False,
is_flag=True,
help="Whether use local mode(run local AWEL file)",
)
@click.option(
"-f",
"--file",
type=str,
default=None,
required=False,
help=_("The path of the AWEL flow"),
)
def flow(local: bool = False, file: str | None = None):
"""Run a AWEL flow."""
global _LOCAL_MODE, _FILE_PATH
_LOCAL_MODE = local
_FILE_PATH = file
def add_base_flow_options(func):
"""Add base flow options to the command."""
@ -124,32 +157,229 @@ def add_chat_options(func):
return _wrapper
@click.command(name="flow")
@flow.command(name="chat")
@add_base_flow_options
@add_chat_options
def run_flow(name: str, uid: str, data: str, interactive: bool, **kwargs):
def run_flow_chat(name: str, uid: str, data: str, interactive: bool, **kwargs):
"""Run a AWEL flow."""
json_data = _parse_chat_json_data(data, **kwargs)
stream = "stream" in json_data and str(json_data["stream"]).lower() in ["true", "1"]
loop = get_or_create_event_loop()
if _LOCAL_MODE:
_run_flow_chat_local(loop, name, interactive, json_data, stream)
return
client = Client()
loop = get_or_create_event_loop()
res = loop.run_until_complete(list_flow(client, name, uid))
# AWEL flow store the python module name now, so we need to replace "-" with "_"
new_name = name.replace("-", "_")
res = loop.run_until_complete(list_flow(client, new_name, uid))
if not res:
cl.error("Flow not found with the given name or uid", exit_code=1)
if len(res) > 1:
cl.error("More than one flow found", exit_code=1)
flow = res[0]
json_data = _parse_json_data(data, **kwargs)
json_data["chat_param"] = flow.uid
json_data["chat_mode"] = "chat_flow"
stream = "stream" in json_data and str(json_data["stream"]).lower() in ["true", "1"]
if stream:
loop.run_until_complete(_chat_stream(client, interactive, json_data))
_run_flow_chat_stream(loop, client, interactive, json_data)
else:
loop.run_until_complete(_chat(client, interactive, json_data))
_run_flow_chat(loop, client, interactive, json_data)
def _parse_json_data(data: str, **kwargs):
@flow.command(name="cmd")
@add_base_flow_options
@click.option(
"-d",
"--data",
type=str,
default=None,
required=False,
help=_("The json data to run AWEL flow, if set, will overwrite other options"),
)
@click.option(
"--output_key",
type=str,
default=None,
required=False,
help=_(
"The output key of the AWEL flow, if set, it will try to get the output by the "
"key"
),
)
def run_flow_cmd(
name: str, uid: str, data: str | None = None, output_key: str | None = None
):
"""Run a AWEL flow with command mode."""
json_data = _parse_json_data(data)
loop = get_or_create_event_loop()
if _LOCAL_MODE:
_run_flow_cmd_local(loop, name, json_data, output_key)
else:
_run_flow_cmd(loop, name, uid, json_data, output_key)
def _run_flow_cmd_local(
loop: asyncio.BaseEventLoop,
name: str,
data: Dict[str, Any] | None = None,
output_key: str | None = None,
):
from dbgpt.core.awel.util.chat_util import safe_chat_stream_with_dag_task
end_node, dag, dag_metadata, call_body = _parse_and_check_local_dag(
name, _FILE_PATH, data
)
async def _streaming_call():
start_time = time.time()
try:
cl.debug("[~info] Flow started")
cl.debug(f"[~info] JSON data: {json.dumps(data, ensure_ascii=False)}")
cl.debug("Command output: ")
async for out in safe_chat_stream_with_dag_task(
end_node, call_body, incremental=True, covert_to_str=True
):
if not out.success:
cl.error(out.text)
else:
cl.print(out.text, end="")
except Exception as e:
cl.error(f"Failed to run flow: {e}", exit_code=1)
finally:
time_cost = round(time.time() - start_time, 2)
cl.success(f"\n:tada: Flow finished, timecost: {time_cost} s")
loop.run_until_complete(_streaming_call())
def _run_flow_cmd(
loop: asyncio.BaseEventLoop,
name: str | None = None,
uid: str | None = None,
json_data: Dict[str, Any] | None = None,
output_key: str | None = None,
):
client = Client()
def _non_streaming_callback(text: str):
parsed_text: Any = None
if output_key:
try:
json_out = json.loads(text)
parsed_text = json_out.get(output_key)
except Exception as e:
cl.warning(f"Failed to parse output by key: {output_key}, {e}")
if not parsed_text:
parsed_text = text
cl.markdown(parsed_text)
def _streaming_callback(text: str):
cl.print(text, end="")
async def _client_run_cmd():
cl.debug("[~info] Flow started")
cl.debug(f"[~info] JSON data: {json.dumps(json_data, ensure_ascii=False)}")
cl.debug("Command output: ")
start_time = time.time()
# AWEL flow store the python module name now, so we need to replace "-" with "_"
new_name = name.replace("-", "_")
try:
await client_run_flow_cmd(
client,
new_name,
uid,
json_data,
non_streaming_callback=_non_streaming_callback,
streaming_callback=_streaming_callback,
)
except Exception as e:
cl.error(f"Failed to run flow: {e}", exit_code=1)
finally:
time_cost = round(time.time() - start_time, 2)
cl.success(f"\n:tada: Flow finished, timecost: {time_cost} s")
loop.run_until_complete(_client_run_cmd())
def _parse_and_check_local_dag(
name: str,
filepath: str | None = None,
data: Dict[str, Any] | None = None,
) -> Tuple[BaseOperator, DAG, DAGMetadata, Any]:
dag, dag_metadata = _parse_local_dag(name, filepath)
return _check_local_dag(dag, dag_metadata, data)
def _check_local_dag(
dag: DAG, dag_metadata: DAGMetadata, data: Dict[str, Any] | None = None
) -> Tuple[BaseOperator, DAG, DAGMetadata, Any]:
from dbgpt.core.awel import HttpTrigger
leaf_nodes = dag.leaf_nodes
if not leaf_nodes:
cl.error("No leaf nodes found in the flow", exit_code=1)
if len(leaf_nodes) > 1:
cl.error("More than one leaf nodes found in the flow", exit_code=1)
if not isinstance(leaf_nodes[0], BaseOperator):
cl.error("Unsupported leaf node type", exit_code=1)
end_node = cast(BaseOperator, leaf_nodes[0])
call_body: Any = data
trigger_nodes = dag.trigger_nodes
if trigger_nodes:
if len(trigger_nodes) > 1:
cl.error("More than one trigger nodes found in the flow", exit_code=1)
trigger = trigger_nodes[0]
if isinstance(trigger, HttpTrigger):
http_trigger = trigger
if http_trigger._req_body and data:
call_body = http_trigger._req_body(**data)
else:
cl.error("Unsupported trigger type", exit_code=1)
return end_node, dag, dag_metadata, call_body
def _parse_local_dag(name: str, filepath: str | None = None) -> Tuple[DAG, DAGMetadata]:
system_app = SystemApp()
DAGVar.set_current_system_app(system_app)
if not filepath:
# Load DAG from installed package(dbgpts)
from dbgpt.util.dbgpts.loader import (
_flow_package_to_flow_panel,
_load_flow_package_from_path,
)
flow_panel = _flow_package_to_flow_panel(_load_flow_package_from_path(name))
if flow_panel.define_type == "json":
factory = FlowFactory()
factory.pre_load_requirements(flow_panel)
dag = factory.build(flow_panel)
else:
dag = flow_panel.flow_dag
return dag, _parse_metadata(dag)
else:
from dbgpt.core.awel.dag.loader import _process_file
dags = _process_file(filepath)
if not dags:
cl.error("No DAG found in the file", exit_code=1)
if len(dags) > 1:
dags = [dag for dag in dags if dag.dag_id == name]
# Filter by name
if len(dags) > 1:
cl.error("More than one DAG found in the file", exit_code=1)
if not dags:
cl.error("No DAG found with the given name", exit_code=1)
return dags[0], _parse_metadata(dags[0])
def _parse_chat_json_data(data: str, **kwargs):
json_data = {}
if data:
try:
@ -170,7 +400,100 @@ def _parse_json_data(data: str, **kwargs):
return json_data
async def _chat_stream(client: Client, interactive: bool, json_data: Dict[str, Any]):
def _parse_json_data(data: str | None) -> Dict[str, Any] | None:
if not data:
return None
try:
return json.loads(data)
except Exception as e:
cl.error(f"Invalid JSON data: {data}, {e}", exit_code=1)
# Should not reach here
return None
def _run_flow_chat_local(
loop: asyncio.BaseEventLoop,
name: str,
interactive: bool,
json_data: Dict[str, Any],
stream: bool,
):
from dbgpt.core.awel.util.chat_util import (
parse_single_output,
safe_chat_stream_with_dag_task,
)
dag, dag_metadata = _parse_local_dag(name, _FILE_PATH)
async def _streaming_call(_call_body: Dict[str, Any]):
nonlocal dag, dag_metadata
end_node, dag, dag_metadata, handled_call_body = _check_local_dag(
dag, dag_metadata, _call_body
)
async for out in safe_chat_stream_with_dag_task(
end_node, handled_call_body, incremental=True, covert_to_str=True
):
if not out.success:
cl.error(f"Error: {out.text}")
raise Exception(out.text)
else:
yield out.text
async def _call(_call_body: Dict[str, Any]):
nonlocal dag, dag_metadata
end_node, dag, dag_metadata, handled_call_body = _check_local_dag(
dag, dag_metadata, _call_body
)
res = await end_node.call(handled_call_body)
parsed_res = parse_single_output(res, is_sse=False, covert_to_str=True)
if not parsed_res.success:
raise Exception(parsed_res.text)
return parsed_res.text
if stream:
loop.run_until_complete(_chat_stream(_streaming_call, interactive, json_data))
else:
loop.run_until_complete(_chat(_call, interactive, json_data))
def _run_flow_chat_stream(
loop: asyncio.BaseEventLoop,
client: Client,
interactive: bool,
json_data: Dict[str, Any],
):
async def _streaming_call(_call_body: Dict[str, Any]):
async for out in client.chat_stream(**_call_body):
if out.choices:
text = out.choices[0].delta.content
if text:
yield text
loop.run_until_complete(_chat_stream(_streaming_call, interactive, json_data))
def _run_flow_chat(
loop: asyncio.BaseEventLoop,
client: Client,
interactive: bool,
json_data: Dict[str, Any],
):
async def _call(_call_body: Dict[str, Any]):
res = await client.chat(**_call_body)
if res.choices:
text = res.choices[0].message.content
return text
loop.run_until_complete(_chat(_call, interactive, json_data))
async def _chat_stream(
streaming_func: Callable[[Dict[str, Any]], AsyncIterator[str]],
interactive: bool,
json_data: Dict[str, Any],
):
user_input = json_data.get("messages", "")
if "conv_uid" not in json_data and interactive:
json_data["conv_uid"] = str(uuid.uuid4())
@ -187,16 +510,14 @@ async def _chat_stream(client: Client, interactive: bool, json_data: Dict[str, A
json_data["messages"] = user_input
if first_message:
cl.info("You: " + user_input)
cl.info("Chat stream started")
cl.debug(f"JSON data: {json.dumps(json_data, ensure_ascii=False)}")
cl.debug("[~info] Chat stream started")
cl.debug(f"[~info] JSON data: {json.dumps(json_data, ensure_ascii=False)}")
full_text = ""
cl.print("Bot: ")
async for out in client.chat_stream(**json_data):
if out.choices:
text = out.choices[0].delta.content
if text:
full_text += text
cl.print(text, end="")
async for text in streaming_func(json_data):
if text:
full_text += text
cl.print(text, end="")
end_time = time.time()
time_cost = round(end_time - start_time, 2)
cl.success(f"\n:tada: Chat stream finished, timecost: {time_cost} s")
@ -210,7 +531,11 @@ async def _chat_stream(client: Client, interactive: bool, json_data: Dict[str, A
break
async def _chat(client: Client, interactive: bool, json_data: Dict[str, Any]):
async def _chat(
func: Callable[[Dict[str, Any]], Any],
interactive: bool,
json_data: Dict[str, Any],
):
user_input = json_data.get("messages", "")
if "conv_uid" not in json_data and interactive:
json_data["conv_uid"] = str(uuid.uuid4())
@ -228,17 +553,19 @@ async def _chat(client: Client, interactive: bool, json_data: Dict[str, Any]):
if first_message:
cl.info("You: " + user_input)
cl.info("Chat started")
cl.debug(f"JSON data: {json.dumps(json_data, ensure_ascii=False)}")
res = await client.chat(**json_data)
cl.debug("[~info] Chat started")
cl.debug(f"[~info] JSON data: {json.dumps(json_data, ensure_ascii=False)}")
res = await func(json_data)
cl.print("Bot: ")
if res.choices:
text = res.choices[0].message.content
cl.markdown(text)
if res:
cl.markdown(res)
time_cost = round(time.time() - start_time, 2)
cl.success(f"\n:tada: Chat stream finished, timecost: {time_cost} s")
except Exception as e:
cl.error(f"Chat failed: {e}", exit_code=1)
import traceback
messages = traceback.format_exc()
cl.error(f"Chat failed: {e}\n, error detail: {messages}", exit_code=1)
finally:
first_message = False
if interactive:

10
dbgpt/client/client.py
View File

@ -1,4 +1,5 @@
"""This module contains the client for the DB-GPT API."""
import atexit
import json
import os
@ -102,6 +103,15 @@ class Client:
)
atexit.register(self.close)
def _base_url(self):
parsed_url = urlparse(self._api_url)
host = parsed_url.hostname
scheme = parsed_url.scheme
port = parsed_url.port
if port:
return f"{scheme}://{host}:{port}"
return f"{scheme}://{host}"
async def chat(
self,
model: str,

183
dbgpt/client/flow.py
View File

@ -1,5 +1,8 @@
"""this module contains the flow client functions."""
from typing import List
from typing import Any, Callable, Dict, List
from httpx import AsyncClient
from dbgpt.core.awel.flow.flow_factory import FlowPanel
from dbgpt.core.schema.api import Result
@ -117,3 +120,181 @@ async def list_flow(
raise ClientException(status=result["err_code"], reason=result)
except Exception as e:
raise ClientException(f"Failed to list flows: {e}")
async def run_flow_cmd(
client: Client,
name: str | None = None,
uid: str | None = None,
data: Dict[str, Any] | None = None,
non_streaming_callback: Callable[[str], None] | None = None,
streaming_callback: Callable[[str], None] | None = None,
) -> None:
"""
Run flows.
Args:
client (Client): The dbgpt client.
name (str): The name of the flow.
uid (str): The uid of the flow.
data (Dict[str, Any]): The data to run the flow.
non_streaming_callback (Callable[[str], None]): The non-streaming callback.
streaming_callback (Callable[[str], None]): The streaming callback.
Returns:
List[FlowPanel]: The list of flow panels.
Raises:
ClientException: If the request failed.
"""
try:
res = await client.get("/awel/flows", **{"name": name, "uid": uid})
result: Result = res.json()
if not result["success"]:
raise ClientException("Flow not found with the given name or uid")
flows = result["data"]["items"]
if not flows:
raise ClientException("Flow not found with the given name or uid")
if len(flows) > 1:
raise ClientException("More than one flow found")
flow = flows[0]
flow_panel = FlowPanel(**flow)
metadata = flow.get("metadata")
await _run_flow_trigger(
client,
flow_panel,
metadata,
data,
non_streaming_callback=non_streaming_callback,
streaming_callback=streaming_callback,
)
except Exception as e:
raise ClientException(f"Failed to run flows: {e}")
async def _run_flow_trigger(
client: Client,
flow: FlowPanel,
metadata: Dict[str, Any] | None = None,
data: Dict[str, Any] | None = None,
non_streaming_callback: Callable[[str], None] | None = None,
streaming_callback: Callable[[str], None] | None = None,
):
if not metadata:
raise ClientException("No AWEL flow metadata found")
if "triggers" not in metadata:
raise ClientException("No triggers found in AWEL flow metadata")
triggers = metadata["triggers"]
if len(triggers) > 1:
raise ClientException("More than one trigger found")
trigger = triggers[0]
sse_output = metadata.get("sse_output", False)
streaming_output = metadata.get("streaming_output", False)
trigger_type = trigger["trigger_type"]
if trigger_type == "http":
methods = trigger["methods"]
if not methods:
method = "GET"
else:
method = methods[0]
path = trigger["path"]
base_url = client._base_url()
req_url = f"{base_url}{path}"
if streaming_output:
await _call_stream_request(
client._http_client,
method,
req_url,
sse_output,
data,
streaming_callback,
)
elif non_streaming_callback:
await _call_non_stream_request(
client._http_client, method, req_url, data, non_streaming_callback
)
else:
raise ClientException(f"Invalid trigger type: {trigger_type}")
async def _call_non_stream_request(
http_client: AsyncClient,
method: str,
base_url: str,
data: Dict[str, Any] | None = None,
non_streaming_callback: Callable[[str], None] | None = None,
):
import httpx
kwargs: Dict[str, Any] = {"url": base_url, "method": method}
if method in ["POST", "PUT"]:
kwargs["json"] = data
else:
kwargs["params"] = data
response = await http_client.request(**kwargs)
bytes_response_content = await response.aread()
if response.status_code != 200:
str_error_message = ""
error_message = await response.aread()
if error_message:
str_error_message = error_message.decode("utf-8")
raise httpx.RequestError(
f"Request failed with status {response.status_code}, error_message: "
f"{str_error_message}",
request=response.request,
)
response_content = bytes_response_content.decode("utf-8")
if non_streaming_callback:
non_streaming_callback(response_content)
return response_content
async def _call_stream_request(
http_client: AsyncClient,
method: str,
base_url: str,
sse_output: bool,
data: Dict[str, Any] | None = None,
streaming_callback: Callable[[str], None] | None = None,
):
full_out = ""
async for out in _stream_request(http_client, method, base_url, sse_output, data):
if streaming_callback:
streaming_callback(out)
full_out += out
return full_out
async def _stream_request(
http_client: AsyncClient,
method: str,
base_url: str,
sse_output: bool,
data: Dict[str, Any] | None = None,
):
import json
from dbgpt.core.awel.util.chat_util import parse_openai_output
kwargs: Dict[str, Any] = {"url": base_url, "method": method}
if method in ["POST", "PUT"]:
kwargs["json"] = data
else:
kwargs["params"] = data
async with http_client.stream(**kwargs) as response:
if response.status_code == 200:
async for line in response.aiter_lines():
if not line:
continue
if sse_output:
out = parse_openai_output(line)
if not out.success:
raise ClientException(f"Failed to parse output: {out.text}")
yield out.text
else:
yield line
else:
try:
error = await response.aread()
yield json.loads(error)
except Exception as e:
raise e

23
dbgpt/core/awel/dag/base.py
View File

@ -2,6 +2,7 @@
DAG is the core component of AWEL, it is used to define the relationship between tasks.
"""
import asyncio
import contextvars
import logging
@ -613,10 +614,14 @@ class DAG:
"""
def __init__(
self, dag_id: str, resource_group: Optional[ResourceGroup] = None
self,
dag_id: str,
resource_group: Optional[ResourceGroup] = None,
tags: Optional[Dict[str, str]] = None,
) -> None:
"""Initialize a DAG."""
self._dag_id = dag_id
self._tags: Dict[str, str] = tags or {}
self.node_map: Dict[str, DAGNode] = {}
self.node_name_to_node: Dict[str, DAGNode] = {}
self._root_nodes: List[DAGNode] = []
@ -651,6 +656,22 @@ class DAG:
"""Return the dag id of current DAG."""
return self._dag_id
@property
def tags(self) -> Dict[str, str]:
"""Return the tags of current DAG."""
return self._tags
@property
def dev_mode(self) -> bool:
"""Whether the current DAG is in dev mode.
Returns:
bool: Whether the current DAG is in dev mode
"""
from ..operators.base import _dev_mode
return _dev_mode()
def _build(self) -> None:
from ..operators.common_operator import TriggerOperator

87
dbgpt/core/awel/dag/dag_manager.py
View File

@ -3,18 +3,49 @@
DAGManager will load DAGs from dag_dirs, and register the trigger nodes
to TriggerManager.
"""
import logging
import threading
from typing import Dict, List, Optional
from collections import defaultdict
from typing import Dict, List, Optional, Set
from dbgpt._private.pydantic import BaseModel, Field, model_to_dict
from dbgpt.component import BaseComponent, ComponentType, SystemApp
from .. import BaseOperator
from ..trigger.base import TriggerMetadata
from .base import DAG
from .loader import LocalFileDAGLoader
logger = logging.getLogger(__name__)
class DAGMetadata(BaseModel):
"""Metadata for the DAG."""
triggers: List[TriggerMetadata] = Field(
default_factory=list, description="The trigger metadata"
)
sse_output: bool = Field(
default=False, description="Whether the DAG is a server-sent event output"
)
streaming_output: bool = Field(
default=False, description="Whether the DAG is a streaming output"
)
tags: Optional[Dict[str, str]] = Field(
default=None, description="The tags of the DAG"
)
def to_dict(self):
"""Convert the metadata to dict."""
triggers_dict = []
for trigger in self.triggers:
triggers_dict.append(trigger.dict())
dict_value = model_to_dict(self, exclude={"triggers"})
dict_value["triggers"] = triggers_dict
return dict_value
class DAGManager(BaseComponent):
"""The component of DAGManager."""
@ -35,6 +66,8 @@ class DAGManager(BaseComponent):
self.system_app = system_app
self.dag_map: Dict[str, DAG] = {}
self.dag_alias_map: Dict[str, str] = {}
self._dag_metadata_map: Dict[str, DAGMetadata] = {}
self._tags_to_dag_ids: Dict[str, Dict[str, Set[str]]] = {}
self._trigger_manager: Optional["DefaultTriggerManager"] = None
def init_app(self, system_app: SystemApp):
@ -73,12 +106,26 @@ class DAGManager(BaseComponent):
if alias_name:
self.dag_alias_map[alias_name] = dag_id
trigger_metadata: List["TriggerMetadata"] = []
dag_metadata = _parse_metadata(dag)
if self._trigger_manager:
for trigger in dag.trigger_nodes:
self._trigger_manager.register_trigger(trigger, self.system_app)
tm = self._trigger_manager.register_trigger(
trigger, self.system_app
)
if tm:
trigger_metadata.append(tm)
self._trigger_manager.after_register()
else:
logger.warning("No trigger manager, not register dag trigger")
dag_metadata.triggers = trigger_metadata
self._dag_metadata_map[dag_id] = dag_metadata
tags = dag_metadata.tags
if tags:
for tag_key, tag_value in tags.items():
if tag_key not in self._tags_to_dag_ids:
self._tags_to_dag_ids[tag_key] = defaultdict(set)
self._tags_to_dag_ids[tag_key][tag_value].add(dag_id)
def unregister_dag(self, dag_id: str):
"""Unregister a DAG."""
@ -104,7 +151,13 @@ class DAGManager(BaseComponent):
for trigger in dag.trigger_nodes:
self._trigger_manager.unregister_trigger(trigger, self.system_app)
# Finally remove the DAG from the map
metadata = self._dag_metadata_map[dag_id]
del self.dag_map[dag_id]
del self._dag_metadata_map[dag_id]
if metadata.tags:
for tag_key, tag_value in metadata.tags.items():
if tag_key in self._tags_to_dag_ids:
self._tags_to_dag_ids[tag_key][tag_value].remove(dag_id)
def get_dag(
self, dag_id: Optional[str] = None, alias_name: Optional[str] = None
@ -116,3 +169,33 @@ class DAGManager(BaseComponent):
if alias_name in self.dag_alias_map:
return self.dag_map.get(self.dag_alias_map[alias_name])
return None
def get_dags_by_tag(self, tag_key: str, tag_value) -> List[DAG]:
"""Get all DAGs with the given tag."""
with self.lock:
dag_ids = self._tags_to_dag_ids.get(tag_key, {}).get(tag_value, set())
return [self.dag_map[dag_id] for dag_id in dag_ids]
def get_dag_metadata(
self, dag_id: Optional[str] = None, alias_name: Optional[str] = None
) -> Optional[DAGMetadata]:
"""Get a DAGMetadata by dag_id or alias_name."""
dag = self.get_dag(dag_id, alias_name)
if not dag:
return None
return self._dag_metadata_map.get(dag.dag_id)
def _parse_metadata(dag: DAG):
from ..util.chat_util import _is_sse_output
metadata = DAGMetadata()
metadata.tags = dag.tags
if not dag.leaf_nodes:
return metadata
end_node = dag.leaf_nodes[0]
if not isinstance(end_node, BaseOperator):
return metadata
metadata.sse_output = _is_sse_output(end_node)
metadata.streaming_output = end_node.streaming_operator
return metadata

4
dbgpt/core/awel/flow/flow_factory.py
View File

@ -18,6 +18,7 @@ from dbgpt._private.pydantic import (
model_validator,
)
from dbgpt.core.awel.dag.base import DAG, DAGNode
from dbgpt.core.awel.dag.dag_manager import DAGMetadata
from .base import (
OperatorType,
@ -352,6 +353,9 @@ class FlowPanel(BaseModel):
description="The flow panel modified time.",
examples=["2021-08-01 12:00:00", "2021-08-01 12:00:01", "2021-08-01 12:00:02"],
)
metadata: Optional[Union[DAGMetadata, Dict[str, Any]]] = Field(
default=None, description="The metadata of the flow"
)
@model_validator(mode="before")
@classmethod

18
dbgpt/core/awel/operators/base.py
View File

@ -69,6 +69,15 @@ class WorkflowRunner(ABC, Generic[T]):
default_runner: Optional[WorkflowRunner] = None
def _dev_mode() -> bool:
"""Check if the operator is in dev mode.
In production mode, the default runner is not None, and the operator will run in
the same process with the DB-GPT webserver.
"""
return default_runner is None
class BaseOperatorMeta(ABCMeta):
"""Metaclass of BaseOperator."""
@ -86,7 +95,9 @@ class BaseOperatorMeta(ABCMeta):
if not executor:
if system_app:
executor = system_app.get_component(
ComponentType.EXECUTOR_DEFAULT, DefaultExecutorFactory
ComponentType.EXECUTOR_DEFAULT,
DefaultExecutorFactory,
default_component=DefaultExecutorFactory(),
).create() # type: ignore
else:
executor = DefaultExecutorFactory().create()
@ -173,13 +184,14 @@ class BaseOperator(DAGNode, ABC, Generic[OUT], metaclass=BaseOperatorMeta):
def dev_mode(self) -> bool:
"""Whether the operator is in dev mode.
In production mode, the default runner is not None.
In production mode, the default runner is not None, and the operator will run in
the same process with the DB-GPT webserver.
Returns:
bool: Whether the operator is in dev mode. True if the
default runner is None.
"""
return default_runner is None
return _dev_mode()
async def _run(self, dag_ctx: DAGContext, task_log_id: str) -> TaskOutput[OUT]:
if not self.node_id:

13
dbgpt/core/awel/trigger/base.py
View File

@ -1,13 +1,22 @@
"""Base class for all trigger classes."""
from __future__ import annotations
from abc import ABC, abstractmethod
from typing import Any, Generic
from typing import Any, Generic, Optional
from dbgpt._private.pydantic import BaseModel, Field
from ..operators.common_operator import TriggerOperator
from ..task.base import OUT
class TriggerMetadata(BaseModel):
"""Metadata for the trigger."""
trigger_type: Optional[str] = Field(
default=None, description="The type of the trigger"
)
class Trigger(TriggerOperator[OUT], ABC, Generic[OUT]):
"""Base class for all trigger classes.

21
dbgpt/core/awel/trigger/http_trigger.py
View File

@ -43,7 +43,7 @@ from ..operators.base import BaseOperator
from ..operators.common_operator import MapOperator
from ..util._typing_util import _parse_bool
from ..util.http_util import join_paths
from .base import Trigger
from .base import Trigger, TriggerMetadata
if TYPE_CHECKING:
from fastapi import APIRouter, FastAPI
@ -82,6 +82,17 @@ def _default_streaming_predict_func(body: "CommonRequestType") -> bool:
return _parse_bool(streaming)
class HttpTriggerMetadata(TriggerMetadata):
"""Trigger metadata."""
path: str = Field(..., description="The path of the trigger")
methods: List[str] = Field(..., description="The methods of the trigger")
trigger_type: Optional[str] = Field(
default="http", description="The type of the trigger"
)
class BaseHttpBody(BaseModel):
"""Http body.
@ -444,7 +455,7 @@ class HttpTrigger(Trigger):
def mount_to_router(
self, router: "APIRouter", global_prefix: Optional[str] = None
) -> None:
) -> HttpTriggerMetadata:
"""Mount the trigger to a router.
Args:
@ -466,8 +477,11 @@ class HttpTrigger(Trigger):
)(dynamic_route_function)
logger.info(f"Mount http trigger success, path: {path}")
return HttpTriggerMetadata(path=path, methods=self._methods)
def mount_to_app(self, app: "FastAPI", global_prefix: Optional[str] = None) -> None:
def mount_to_app(
self, app: "FastAPI", global_prefix: Optional[str] = None
) -> HttpTriggerMetadata:
"""Mount the trigger to a FastAPI app.
TODO: The performance of this method is not good, need to be optimized.
@ -498,6 +512,7 @@ class HttpTrigger(Trigger):
app.openapi_schema = None
app.middleware_stack = None
logger.info(f"Mount http trigger success, path: {path}")
return HttpTriggerMetadata(path=path, methods=self._methods)
def remove_from_app(
self, app: "FastAPI", global_prefix: Optional[str] = None

33
dbgpt/core/awel/trigger/trigger_manager.py
View File

@ -2,15 +2,16 @@
After DB-GPT started, the trigger manager will be initialized and register all triggers
"""
import logging
from abc import ABC, abstractmethod
from collections import defaultdict
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Set, Union
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Set, Tuple, Union
from dbgpt.component import BaseComponent, ComponentType, SystemApp
from ..util.http_util import join_paths
from .base import Trigger
from .base import Trigger, TriggerMetadata
if TYPE_CHECKING:
from fastapi import APIRouter
@ -23,7 +24,9 @@ class TriggerManager(ABC):
"""Base class for trigger manager."""
@abstractmethod
def register_trigger(self, trigger: Any, system_app: SystemApp) -> None:
def register_trigger(
self, trigger: Any, system_app: SystemApp
) -> Optional[TriggerMetadata]:
"""Register a trigger to current manager."""
@abstractmethod
@ -65,10 +68,12 @@ class HttpTriggerManager(TriggerManager):
self._inited = False
self._router_prefix = router_prefix
self._router = router
self._trigger_map: Dict[str, Trigger] = {}
self._trigger_map: Dict[str, Tuple[Trigger, TriggerMetadata]] = {}
self._router_tables: Dict[str, Set[str]] = defaultdict(set)
def register_trigger(self, trigger: Any, system_app: SystemApp) -> None:
def register_trigger(
self, trigger: Any, system_app: SystemApp
) -> Optional[TriggerMetadata]:
"""Register a trigger to current manager."""
from .http_trigger import HttpTrigger
@ -86,13 +91,17 @@ class HttpTriggerManager(TriggerManager):
if not app:
raise ValueError("System app not initialized")
# Mount to app, support dynamic route.
trigger.mount_to_app(app, self._router_prefix)
trigger_metadata = trigger.mount_to_app(app, self._router_prefix)
else:
trigger.mount_to_router(self._router, self._router_prefix)
self._trigger_map[trigger_id] = trigger
trigger_metadata = trigger.mount_to_router(
self._router, self._router_prefix
)
self._trigger_map[trigger_id] = (trigger, trigger_metadata)
return trigger_metadata
except Exception as e:
self._unregister_route_tables(path, methods)
raise e
return None
def unregister_trigger(self, trigger: Any, system_app: SystemApp) -> None:
"""Unregister a trigger to current manager."""
@ -183,7 +192,9 @@ class DefaultTriggerManager(TriggerManager, BaseComponent):
if system_app and self.system_app.app:
self._http_trigger = HttpTriggerManager()
def register_trigger(self, trigger: Any, system_app: SystemApp) -> None:
def register_trigger(
self, trigger: Any, system_app: SystemApp
) -> Optional[TriggerMetadata]:
"""Register a trigger to current manager."""
from .http_trigger import HttpTrigger
@ -191,7 +202,9 @@ class DefaultTriggerManager(TriggerManager, BaseComponent):
logger.info(f"Register trigger {trigger}")
if not self._http_trigger:
raise ValueError("Http trigger manager not initialized")
self._http_trigger.register_trigger(trigger, system_app)
return self._http_trigger.register_trigger(trigger, system_app)
else:
return None
def unregister_trigger(self, trigger: Any, system_app: SystemApp) -> None:
"""Unregister a trigger to current manager."""

323
dbgpt/core/awel/util/chat_util.py Normal file
View File

@ -0,0 +1,323 @@
"""The utility functions for chatting with the DAG task."""
import json
import traceback
from typing import Any, AsyncIterator, Dict, Optional
from ...interface.llm import ModelInferenceMetrics, ModelOutput
from ...schema.api import ChatCompletionResponseStreamChoice
from ..operators.base import BaseOperator
from ..trigger.http_trigger import CommonLLMHttpResponseBody
def is_chat_flow_type(output_obj: Any, is_class: bool = False) -> bool:
"""Check whether the output object is a chat flow type."""
if is_class:
return output_obj in (str, CommonLLMHttpResponseBody, ModelOutput)
else:
chat_types = (str, CommonLLMHttpResponseBody)
return isinstance(output_obj, chat_types)
async def safe_chat_with_dag_task(
task: BaseOperator, request: Any, covert_to_str: bool = False
) -> ModelOutput:
"""Chat with the DAG task.
Args:
task (BaseOperator): The DAG task to be executed.
request (Any): The request to be passed to the DAG task.
covert_to_str (bool, optional): Whether to convert the output to string.
Returns:
ModelOutput: The model output, the result is not incremental.
"""
try:
finish_reason = None
usage = None
metrics = None
error_code = 0
text = ""
async for output in safe_chat_stream_with_dag_task(
task, request, False, covert_to_str=covert_to_str
):
finish_reason = output.finish_reason
usage = output.usage
metrics = output.metrics
error_code = output.error_code
text = output.text
return ModelOutput(
error_code=error_code,
text=text,
metrics=metrics,
usage=usage,
finish_reason=finish_reason,
)
except Exception as e:
return ModelOutput(error_code=1, text=str(e), incremental=False)
async def safe_chat_stream_with_dag_task(
task: BaseOperator, request: Any, incremental: bool, covert_to_str: bool = False
) -> AsyncIterator[ModelOutput]:
"""Chat with the DAG task.
This function is similar to `chat_stream_with_dag_task`, but it will catch the
exception and return the error message.
Args:
task (BaseOperator): The DAG task to be executed.
request (Any): The request to be passed to the DAG task.
incremental (bool): Whether the output is incremental.
covert_to_str (bool, optional): Whether to convert the output to string.
Yields:
ModelOutput: The model output.
"""
try:
async for output in chat_stream_with_dag_task(
task, request, incremental, covert_to_str=covert_to_str
):
yield output
except Exception as e:
simple_error_msg = str(e)
if not simple_error_msg:
simple_error_msg = traceback.format_exc()
yield ModelOutput(error_code=1, text=simple_error_msg, incremental=incremental)
finally:
if task.streaming_operator and task.dag:
await task.dag._after_dag_end(task.current_event_loop_task_id)
def _is_sse_output(task: BaseOperator) -> bool:
"""Check whether the DAG task is a server-sent event output.
Args:
task (BaseOperator): The DAG task.
Returns:
bool: Whether the DAG task is a server-sent event output.
"""
return task.output_format is not None and task.output_format.upper() == "SSE"
async def chat_stream_with_dag_task(
task: BaseOperator, request: Any, incremental: bool, covert_to_str: bool = False
) -> AsyncIterator[ModelOutput]:
"""Chat with the DAG task.
Args:
task (BaseOperator): The DAG task to be executed.
request (Any): The request to be passed to the DAG task.
incremental (bool): Whether the output is incremental.
covert_to_str (bool, optional): Whether to convert the output to string.
Yields:
ModelOutput: The model output.
"""
is_sse = _is_sse_output(task)
if not task.streaming_operator:
try:
result = await task.call(request)
model_output = parse_single_output(
result, is_sse, covert_to_str=covert_to_str
)
model_output.incremental = incremental
yield model_output
except Exception as e:
simple_error_msg = str(e)
if not simple_error_msg:
simple_error_msg = traceback.format_exc()
yield ModelOutput(
error_code=1, text=simple_error_msg, incremental=incremental
)
else:
from dbgpt.model.utils.chatgpt_utils import OpenAIStreamingOutputOperator
if OpenAIStreamingOutputOperator and isinstance(
task, OpenAIStreamingOutputOperator
):
full_text = ""
async for output in await task.call_stream(request):
model_output = parse_openai_output(output)
# The output of the OpenAI streaming API is incremental
full_text += model_output.text
model_output.incremental = incremental
model_output.text = model_output.text if incremental else full_text
yield model_output
if not model_output.success:
break
else:
full_text = ""
previous_text = ""
async for output in await task.call_stream(request):
model_output = parse_single_output(
output, is_sse, covert_to_str=covert_to_str
)
model_output.incremental = incremental
if task.incremental_output:
# Output is incremental, append the text
full_text += model_output.text
else:
# Output is not incremental, last output is the full text
full_text = model_output.text
if not incremental:
# Return the full text
model_output.text = full_text
else:
# Return the incremental text
delta_text = full_text[len(previous_text) :]
previous_text = (
full_text
if len(full_text) > len(previous_text)
else previous_text
)
model_output.text = delta_text
yield model_output
if not model_output.success:
break
def parse_single_output(
output: Any, is_sse: bool, covert_to_str: bool = False
) -> ModelOutput:
"""Parse the single output.
Args:
output (Any): The output to parse.
is_sse (bool): Whether the output is in SSE format.
covert_to_str (bool, optional): Whether to convert the output to string.
Defaults to False.
Returns:
ModelOutput: The parsed output.
"""
finish_reason: Optional[str] = None
usage: Optional[Dict[str, Any]] = None
metrics: Optional[ModelInferenceMetrics] = None
if output is None:
error_code = 1
text = "The output is None!"
elif isinstance(output, str):
if is_sse:
sse_output = parse_sse_data(output)
if sse_output is None:
error_code = 1
text = "The output is not a SSE format"
else:
error_code = 0
text = sse_output
else:
error_code = 0
text = output
elif isinstance(output, ModelOutput):
error_code = output.error_code
text = output.text
finish_reason = output.finish_reason
usage = output.usage
metrics = output.metrics
elif isinstance(output, CommonLLMHttpResponseBody):
error_code = output.error_code
text = output.text
elif isinstance(output, dict):
error_code = 0
text = json.dumps(output, ensure_ascii=False)
elif covert_to_str:
error_code = 0
text = str(output)
else:
error_code = 1
text = f"The output is not a valid format({type(output)})"
return ModelOutput(
error_code=error_code,
text=text,
finish_reason=finish_reason,
usage=usage,
metrics=metrics,
)
def parse_openai_output(output: Any) -> ModelOutput:
"""Parse the OpenAI output.
Args:
output (Any): The output to parse. It must be a stream format.
Returns:
ModelOutput: The parsed output.
"""
text = ""
if not isinstance(output, str):
return ModelOutput(
error_code=1,
text="The output is not a stream format",
)
if output.strip() == "data: [DONE]" or output.strip() == "data:[DONE]":
return ModelOutput(error_code=0, text="")
if not output.startswith("data:"):
return ModelOutput(
error_code=1,
text="The output is not a stream format",
)
sse_output = parse_sse_data(output)
if sse_output is None:
return ModelOutput(error_code=1, text="The output is not a SSE format")
json_data = sse_output.strip()
try:
dict_data = json.loads(json_data)
except Exception as e:
return ModelOutput(
error_code=1,
text=f"Invalid JSON data: {json_data}, {e}",
)
if "choices" not in dict_data:
return ModelOutput(
error_code=1,
text=dict_data.get("text", "Unknown error"),
)
choices = dict_data["choices"]
finish_reason: Optional[str] = None
if choices:
choice = choices[0]
delta_data = ChatCompletionResponseStreamChoice(**choice)
if delta_data.delta.content:
text = delta_data.delta.content
finish_reason = delta_data.finish_reason
return ModelOutput(error_code=0, text=text, finish_reason=finish_reason)
def parse_sse_data(output: str) -> Optional[str]:
r"""Parse the SSE data.
Just keep the data part.
Examples:
.. code-block:: python
from dbgpt.core.awel.util.chat_util import parse_sse_data
assert parse_sse_data("data: [DONE]") == "[DONE]"
assert parse_sse_data("data:[DONE]") == "[DONE]"
assert parse_sse_data("data: Hello") == "Hello"
assert parse_sse_data("data: Hello\n") == "Hello"
assert parse_sse_data("data: Hello\r\n") == "Hello"
assert parse_sse_data("data: Hi, what's up?") == "Hi, what's up?"
Args:
output (str): The output.
Returns:
Optional[str]: The parsed data.
"""
if output.startswith("data:"):
output = output.strip()
if output.startswith("data: "):
output = output[6:]
else:
output = output[5:]
return output
else:
return None

250
dbgpt/serve/flow/service/service.py
View File

@ -1,19 +1,13 @@
import json
import logging
import traceback
from typing import Any, AsyncIterator, List, Optional, cast
from typing import AsyncIterator, List, Optional, cast
import schedule
from fastapi import HTTPException
from dbgpt._private.pydantic import model_to_json
from dbgpt.component import SystemApp
from dbgpt.core.awel import (
DAG,
BaseOperator,
CommonLLMHttpRequestBody,
CommonLLMHttpResponseBody,
)
from dbgpt.core.awel import DAG, BaseOperator, CommonLLMHttpRequestBody
from dbgpt.core.awel.dag.dag_manager import DAGManager
from dbgpt.core.awel.flow.flow_factory import (
FlowCategory,
@ -22,10 +16,13 @@ from dbgpt.core.awel.flow.flow_factory import (
fill_flow_panel,
)
from dbgpt.core.awel.trigger.http_trigger import CommonLLMHttpTrigger
from dbgpt.core.awel.util.chat_util import (
is_chat_flow_type,
safe_chat_stream_with_dag_task,
safe_chat_with_dag_task,
)
from dbgpt.core.interface.llm import ModelOutput
from dbgpt.core.schema.api import (
ChatCompletionResponse,
ChatCompletionResponseChoice,
ChatCompletionResponseStreamChoice,
ChatCompletionStreamResponse,
DeltaMessage,
@ -333,6 +330,11 @@ class Service(BaseService[ServeEntity, ServeRequest, ServerResponse]):
flow = self.dao.get_one(query_request)
if flow:
fill_flow_panel(flow)
metadata = self.dag_manager.get_dag_metadata(
flow.dag_id, alias_name=flow.uid
)
if metadata:
flow.metadata = metadata.to_dict()
return flow
def delete(self, uid: str) -> Optional[ServerResponse]:
@ -390,7 +392,14 @@ class Service(BaseService[ServeEntity, ServeRequest, ServerResponse]):
Returns:
List[ServerResponse]: The response
"""
return self.dao.get_list_page(request, page, page_size)
page_result = self.dao.get_list_page(request, page, page_size)
for item in page_result.items:
metadata = self.dag_manager.get_dag_metadata(
item.dag_id, alias_name=item.uid
)
if metadata:
item.metadata = metadata.to_dict()
return page_result
async def chat_stream_flow_str(
self, flow_uid: str, request: CommonLLMHttpRequestBody
@ -463,7 +472,7 @@ class Service(BaseService[ServeEntity, ServeRequest, ServerResponse]):
incremental = request.incremental
try:
task = await self._get_callable_task(flow_uid)
return await _safe_chat_with_dag_task(task, request)
return await safe_chat_with_dag_task(task, request)
except HTTPException as e:
return ModelOutput(error_code=1, text=e.detail, incremental=incremental)
except Exception as e:
@ -484,7 +493,7 @@ class Service(BaseService[ServeEntity, ServeRequest, ServerResponse]):
incremental = request.incremental
try:
task = await self._get_callable_task(flow_uid)
async for output in _safe_chat_stream_with_dag_task(
async for output in safe_chat_stream_with_dag_task(
task, request, incremental
):
yield output
@ -556,220 +565,7 @@ class Service(BaseService[ServeEntity, ServeRequest, ServerResponse]):
output = leaf_node.metadata.outputs[0]
try:
real_class = _get_type_cls(output.type_cls)
if common_http_trigger and _is_chat_flow_type(real_class, is_class=True):
if common_http_trigger and is_chat_flow_type(real_class, is_class=True):
return FlowCategory.CHAT_FLOW
except Exception:
return FlowCategory.COMMON
def _is_chat_flow_type(output_obj: Any, is_class: bool = False) -> bool:
if is_class:
return (
output_obj == str
or output_obj == CommonLLMHttpResponseBody
or output_obj == ModelOutput
)
else:
chat_types = (str, CommonLLMHttpResponseBody)
return isinstance(output_obj, chat_types)
async def _safe_chat_with_dag_task(task: BaseOperator, request: Any) -> ModelOutput:
"""Chat with the DAG task."""
try:
finish_reason = None
usage = None
metrics = None
error_code = 0
text = ""
async for output in _safe_chat_stream_with_dag_task(task, request, False):
finish_reason = output.finish_reason
usage = output.usage
metrics = output.metrics
error_code = output.error_code
text = output.text
return ModelOutput(
error_code=error_code,
text=text,
metrics=metrics,
usage=usage,
finish_reason=finish_reason,
)
except Exception as e:
return ModelOutput(error_code=1, text=str(e), incremental=False)
async def _safe_chat_stream_with_dag_task(
task: BaseOperator,
request: Any,
incremental: bool,
) -> AsyncIterator[ModelOutput]:
"""Chat with the DAG task."""
try:
async for output in _chat_stream_with_dag_task(task, request, incremental):
yield output
except Exception as e:
yield ModelOutput(error_code=1, text=str(e), incremental=incremental)
finally:
if task.streaming_operator:
if task.dag:
await task.dag._after_dag_end(task.current_event_loop_task_id)
async def _chat_stream_with_dag_task(
task: BaseOperator,
request: Any,
incremental: bool,
) -> AsyncIterator[ModelOutput]:
"""Chat with the DAG task."""
is_sse = task.output_format and task.output_format.upper() == "SSE"
if not task.streaming_operator:
try:
result = await task.call(request)
model_output = _parse_single_output(result, is_sse)
model_output.incremental = incremental
yield model_output
except Exception as e:
yield ModelOutput(error_code=1, text=str(e), incremental=incremental)
else:
from dbgpt.model.utils.chatgpt_utils import OpenAIStreamingOutputOperator
if OpenAIStreamingOutputOperator and isinstance(
task, OpenAIStreamingOutputOperator
):
full_text = ""
async for output in await task.call_stream(request):
model_output = _parse_openai_output(output)
# The output of the OpenAI streaming API is incremental
full_text += model_output.text
model_output.incremental = incremental
model_output.text = model_output.text if incremental else full_text
yield model_output
if not model_output.success:
break
else:
full_text = ""
previous_text = ""
async for output in await task.call_stream(request):
model_output = _parse_single_output(output, is_sse)
model_output.incremental = incremental
if task.incremental_output:
# Output is incremental, append the text
full_text += model_output.text
else:
# Output is not incremental, last output is the full text
full_text = model_output.text
if not incremental:
# Return the full text
model_output.text = full_text
else:
# Return the incremental text
delta_text = full_text[len(previous_text) :]
previous_text = (
full_text
if len(full_text) > len(previous_text)
else previous_text
)
model_output.text = delta_text
yield model_output
if not model_output.success:
break
def _parse_single_output(output: Any, is_sse: bool) -> ModelOutput:
"""Parse the single output."""
finish_reason = None
usage = None
metrics = None
if output is None:
error_code = 1
text = "The output is None!"
elif isinstance(output, str):
if is_sse:
sse_output = _parse_sse_data(output)
if sse_output is None:
error_code = 1
text = "The output is not a SSE format"
else:
error_code = 0
text = sse_output
else:
error_code = 0
text = output
elif isinstance(output, ModelOutput):
error_code = output.error_code
text = output.text
finish_reason = output.finish_reason
usage = output.usage
metrics = output.metrics
elif isinstance(output, CommonLLMHttpResponseBody):
error_code = output.error_code
text = output.text
elif isinstance(output, dict):
error_code = 0
text = json.dumps(output, ensure_ascii=False)
else:
error_code = 1
text = f"The output is not a valid format({type(output)})"
return ModelOutput(
error_code=error_code,
text=text,
finish_reason=finish_reason,
usage=usage,
metrics=metrics,
)
def _parse_openai_output(output: Any) -> ModelOutput:
"""Parse the OpenAI output."""
text = ""
if not isinstance(output, str):
return ModelOutput(
error_code=1,
text="The output is not a stream format",
)
if output.strip() == "data: [DONE]" or output.strip() == "data:[DONE]":
return ModelOutput(error_code=0, text="")
if not output.startswith("data:"):
return ModelOutput(
error_code=1,
text="The output is not a stream format",
)
sse_output = _parse_sse_data(output)
if sse_output is None:
return ModelOutput(error_code=1, text="The output is not a SSE format")
json_data = sse_output.strip()
try:
dict_data = json.loads(json_data)
except Exception as e:
return ModelOutput(
error_code=1,
text=f"Invalid JSON data: {json_data}, {e}",
)
if "choices" not in dict_data:
return ModelOutput(
error_code=1,
text=dict_data.get("text", "Unknown error"),
)
choices = dict_data["choices"]
finish_reason: Optional[str] = None
if choices:
choice = choices[0]
delta_data = ChatCompletionResponseStreamChoice(**choice)
if delta_data.delta.content:
text = delta_data.delta.content
finish_reason = delta_data.finish_reason
return ModelOutput(error_code=0, text=text, finish_reason=finish_reason)
def _parse_sse_data(output: str) -> Optional[str]:
if output.startswith("data:"):
if output.startswith("data: "):
output = output[6:]
else:
output = output[5:]
return output
else:
return None

73
dbgpt/util/dbgpts/loader.py
View File

@ -320,6 +320,51 @@ def _load_package_from_path(path: str):
return parsed_packages
def _load_flow_package_from_path(name: str, path: str = INSTALL_DIR) -> FlowPackage:
raw_packages = _load_installed_package(path)
new_name = name.replace("_", "-")
packages = [p for p in raw_packages if p.package == name or p.name == name]
if not packages:
packages = [
p for p in raw_packages if p.package == new_name or p.name == new_name
]
if not packages:
raise ValueError(f"Can't find the package {name} or {new_name}")
flow_package = _parse_package_metadata(packages[0])
if flow_package.package_type != "flow":
raise ValueError(f"Unsupported package type: {flow_package.package_type}")
return cast(FlowPackage, flow_package)
def _flow_package_to_flow_panel(package: FlowPackage) -> FlowPanel:
dict_value = {
"name": package.name,
"label": package.label,
"version": package.version,
"editable": False,
"description": package.description,
"source": package.repo,
"define_type": "json",
}
if isinstance(package, FlowJsonPackage):
dict_value["flow_data"] = package.read_definition_json()
elif isinstance(package, FlowPythonPackage):
dict_value["flow_data"] = {
"nodes": [],
"edges": [],
"viewport": {
"x": 213,
"y": 269,
"zoom": 0,
},
}
dict_value["flow_dag"] = package.dag
dict_value["define_type"] = "python"
else:
raise ValueError(f"Unsupported package type: {package}")
return FlowPanel(**dict_value)
class DBGPTsLoader(BaseComponent):
"""The loader of the dbgpts packages"""
@ -373,32 +418,8 @@ class DBGPTsLoader(BaseComponent):
if package.package_type != "flow":
continue
package = cast(FlowPackage, package)
dict_value = {
"name": package.name,
"label": package.label,
"version": package.version,
"editable": False,
"description": package.description,
"source": package.repo,
"define_type": "json",
}
if isinstance(package, FlowJsonPackage):
dict_value["flow_data"] = package.read_definition_json()
elif isinstance(package, FlowPythonPackage):
dict_value["flow_data"] = {
"nodes": [],
"edges": [],
"viewport": {
"x": 213,
"y": 269,
"zoom": 0,
},
}
dict_value["flow_dag"] = package.dag
dict_value["define_type"] = "python"
else:
raise ValueError(f"Unsupported package type: {package}")
panels.append(FlowPanel(**dict_value))
flow_panel = _flow_package_to_flow_panel(package)
panels.append(flow_panel)
return panels
def _register_packages(self, package: BasePackage):

280
docs/docs/dbgpts/introduction.md Normal file
View File

@ -0,0 +1,280 @@
# dbgpts
[dbgpts](https://github.com/eosphoros-ai/dbgpts) contains some data apps, AWEL operators, AWEL workflows, agents and resources
which build upon the DB-GPT.
## Introduction
### Why We Need `dbgpts`
In a production-level LLM's application, there are many components that need to be
integrated, and you want to start your research and creativity quickly by using the
existing components.
At the same time, we hope that the core components of DB-GPT keep simple and easy to
maintain, and some complex components can be developed in the form of plugins.
So, we need a plugin system to extend the capabilities of DB-GPT, and `dbgpts` is the
plugin system or a part of ecosystem of DB-GPT.
### What Is `dbgpts`
There are some concepts in `dbgpts`:
- `app`: It includes data apps, AWEL operators, AWEL workflows, agents and resources, sometimes, we
call it `dbgpts` app or `dbgpts` package.
- `repo`: It is a repository of `dbgpts` apps, you can install a `dbgpts` app from a `dbgpts` repo,
the default `dbgpts` repo is [eosphoros-ai/dbgpts](https://github.com/eosphoros-ai/dbgpts), you can
also create your own `dbgpts` repo or use other's `dbgpts` repo.
### How To Run `dbgpts`
1. When you install a `dbgpts` app, it will be loaded to your DB-GPT webserver automatically,
and you can use it in the DB-GPT webserver or trigger it by command line `dbgpt run ...`.
2. You can also run a `dbgpts` app as a command line tool, you can use it in your terminal by
`dbgpt app run ...` with `--local` option, it will run the app in your local environment.
## Quick Start
Let's install a `dbgpts` package named [awel-flow-simple-streaming-chat](https://github.com/eosphoros-ai/dbgpts/tree/main/workflow/awel-flow-simple-streaming-chat)
```bash
dbgpt app install awel-flow-simple-streaming-chat -U
```
### Run The App Locally
Then, you can run the app in your terminal:
```bash
dbgpt run flow --local chat \
--name awel-flow-simple-streaming-chat \
--model "gpt-3.5-turbo" \
--messages "hello" \
--stream
```
- `dbgpt run flow`: Means you want to run a AWEL workflow.
- `--local`: Means you want to run the workflow in your local environment without
starting the DB-GPT webserver, it will find the `app` installed in your local
environment, then run it, also, you can use `--file` to specify the python file.
- `--name`: The name of the app.
- `--model`: The LLM model you want to use, `awel-flow-simple-streaming-chat` will
use OpenAI LLM by default if you run it with `--local`.
- `--messages`: The messages you want to send to the LLM.
- `--stream`: Means you want to run the workflow in streaming mode.
The output will be like this:
```bash
You: hello
[~info] Chat stream started
[~info] JSON data: {"model": "gpt-3.5-turbo", "messages": "hello", "stream": true}
Bot:
Hello! How can I assist you today?
🎉 Chat stream finished, timecost: 1.12 s
```
### Run The App In DB-GPT Webserver
After you install the `awel-flow-simple-streaming-chat` app, you can run it in the DB-GPT webserver.
Also, you can use the `dbgpt` command line tool to trigger the app.
```bash
dbgpt run flow chat \
--name awel-flow-simple-streaming-chat \
--model "chatgpt_proxyllm" \
--messages "hello" \
--stream
```
You just remove the `--local` option, then the command will connect to the DB-GPT webserver and run the app.
And you should modify the `--model` option to your model name in the DB-GPT webserver.
The output will be like this:
```bash
You: hello
[~info] Chat stream started
[~info] JSON data: {"model": "chatgpt_proxyllm", "messages": "hello", "stream": true, "chat_param": "1ecd35d4-a60a-420b-8943-8fc44f7f054a", "chat_mode": "chat_flow"}
Bot:
Hello! How can I assist you today?
🎉 Chat stream finished, timecost: 0.98 s
```
## Run The App With `command` Mode
In previous examples, we run the app in `chat` mode, but not all `dbgpts` apps support `chat` mode,
some apps support `command` mode, you can run the app with `dbgpt run flow cmd` command.
### Run The App Locally
```bash
dbgpt run flow --local cmd \
--name awel-flow-simple-streaming-chat \
-d '
{
"model": "gpt-3.5-turbo",
"messages": "hello",
"stream": true
}
'
```
We replace the `chat` mode with `cmd` mode, and use `-d` option to specify the data in JSON format.
The output will be like this:
```bash
[~info] Flow started
[~info] JSON data: {"model": "gpt-3.5-turbo", "messages": "hello", "stream": true}
Command output:
Hello! How can I assist you today?
🎉 Flow finished, timecost: 1.35 s
```
### Run The App In DB-GPT Webserver
Just remove the `--local` option, then the command will connect to the DB-GPT webserver and run the app.
```bash
dbgpt run flow cmd \
--name awel-flow-simple-streaming-chat \
-d '
{
"model": "chatgpt_proxyllm",
"messages": "hello",
"stream": true
}
'
```
The output will be like this:
```bash
[~info] Flow started
[~info] JSON data: {"model": "chatgpt_proxyllm", "messages": "hello", "stream": true}
Command output:
Hello! How can I assist you today?
🎉 Flow finished, timecost: 1.09 s
```
## `chat` Mode vs `command` Mode
In short, `chat` mode is used for chat applications, and `command` mode is used to
trigger the app with a command.
For example, you want to load your documents to the DB-GPT, you can use `command` mode
to trigger the app to load the documents, it always runs once and the result will be
returned.
And `chat` mode is a special case of `command` mode, it provides a chat interface to
the user, and you can chat with the LLM in an interactive way.
## Run You App With Python Script
If you run app locally, it will find the app which is installed in your local environment,
also, you can run the app by providing the python file.
Let's create a python file named `simple_chat_app.py`:
```python
import os
from dbgpt._private.pydantic import BaseModel, Field
from dbgpt.core import ModelMessage, ModelRequest
from dbgpt.core.awel import DAG, HttpTrigger, MapOperator
from dbgpt.model.proxy import OpenAILLMClient
from dbgpt.model.operators import LLMOperator
class TriggerReqBody(BaseModel):
model: str = Field(..., description="Model name")
messages: str = Field(..., description="User input")
class RequestHandleOperator(MapOperator[TriggerReqBody, ModelRequest]):
def __init__(self, **kwargs):
super().__init__(**kwargs)
async def map(self, input_value: TriggerReqBody) -> ModelRequest:
messages = [ModelMessage.build_human_message(input_value.messages)]
return ModelRequest.build_request(input_value.model, messages)
with DAG("dbgpts_simple_chat_app") as dag:
# Receive http request and trigger dag to run.
trigger = HttpTrigger(
"/dbgpts/simple_chat_app", methods="POST", request_body=TriggerReqBody
)
llm_client = OpenAILLMClient(
model_alias="gpt-3.5-turbo", # or other models, eg. "gpt-4o"
api_base=os.getenv("OPENAI_API_BASE"),
api_key=os.getenv("OPENAI_API_KEY"),
)
request_handle_task = RequestHandleOperator()
llm_task = LLMOperator(llm_client=llm_client)
model_parse_task = MapOperator(lambda out: out.text)
trigger >> request_handle_task >> llm_task >> model_parse_task
```
Then you can run the app by providing the python file:
```bash
dbgpt run flow --local --file simple_chat_app.py \
chat \
--name dbgpts_simple_chat_app \
--model "gpt-3.5-turbo" \
--messages "hello"
```
The output will be like this:
```bash
You: hello
[~info] Chat started
[~info] JSON data: {"model": "gpt-3.5-turbo", "messages": "hello", "stream": false}
Bot:
Hello! How can I assist you today?
🎉 Chat stream finished, timecost: 1.06 s
```
And you can run previous examples with `command` mode.
```bash
dbgpt run flow --local --file simple_chat_app.py \
cmd \
--name dbgpts_simple_chat_app \
-d '
{
"model": "gpt-3.5-turbo",
"messages": "hello"
}'
```
The output will be like this:
```bash
[~info] Flow started
[~info] JSON data: {"model": "gpt-3.5-turbo", "messages": "hello"}
Command output:
Hello! How can I assist you today?
🎉 Flow finished, timecost: 1.04 s
```
## Show Your App In DB-GPT Webserver
When you install the workflow, you can see the workflow in the DB-GPT webserver, you can open
the **AWEL Flow** page, then you can see the workflow named `awel_flow_simple_streaming_chat`.
<p align="left">
<img src={'/img/dbgpts/awel_flow_simple_streaming_chat_1.png'} width="720px" />
</p>
Then you can click the `edit` button to see the details of the workflow.
<p align="left">
<img src={'/img/dbgpts/awel_flow_simple_streaming_chat_2.png'} width="720px" />
</p>
Note: Not all workflows support editing, there are two types of workflows according to the
definition type: `json` and `python`, the `json` type workflow can be edited in the DB-GPT,
We will show you more details in the next sections.

10
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@ -621,6 +621,16 @@ const sidebars = {
}
],
},
{
type: "category",
label: "dbgpts",
items: [
{
type: "doc",
id: 'dbgpts/introduction',
}
]
}
],
},

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3
examples/awel/simple_chat_dag_example.py
View File

@ -14,6 +14,7 @@
"user_input": "hello"
}'
"""
from dbgpt._private.pydantic import BaseModel, Field
from dbgpt.core import ModelMessage, ModelRequest
from dbgpt.core.awel import DAG, HttpTrigger, MapOperator
@ -35,7 +36,7 @@ class RequestHandleOperator(MapOperator[TriggerReqBody, ModelRequest]):
return ModelRequest.build_request(input_value.model, messages)
with DAG("dbgpt_awel_simple_dag_example") as dag:
with DAG("dbgpt_awel_simple_dag_example", tags={"label": "example"}) as dag:
# Receive http request and trigger dag to run.
trigger = HttpTrigger(
"/examples/simple_chat", methods="POST", request_body=TriggerReqBody