feat(model): Add new LLMClient and new build tools (#967)

2025-09-02 09:37:03 +00:00 · 2023-12-23 16:33:01 +08:00
parent 12234ae258
commit 0c46c339ca
30 changed files with 1072 additions and 133 deletions
--- a/61
+++ b/61
@@ -0,0 +1,61 @@
+.DEFAULT_GOAL := help
+
+SHELL=/bin/bash
+VENV = venv
+
+# Detect the operating system and set the virtualenv bin directory
+ifeq ($(OS),Windows_NT)
+	VENV_BIN=$(VENV)/Scripts
+else
+	VENV_BIN=$(VENV)/bin
+endif
+
+setup: ## Set up the Python development environment
+	python3 -m venv $(VENV)
+	$(VENV_BIN)/pip install --upgrade pip
+	$(VENV_BIN)/pip install -r requirements/dev-requirements.txt
+	$(VENV_BIN)/pip install -r requirements/lint-requirements.txt
+
+testenv: setup ## Set up the Python test environment
+	$(VENV_BIN)/pip install -e ".[simple_framework]"
+
+.PHONY: fmt
+fmt: setup ## Format Python code
+	# TODO: Use isort to sort Python imports.
+	# https://github.com/PyCQA/isort
+	# $(VENV_BIN)/isort .
+	# https://github.com/psf/black
+	$(VENV_BIN)/black .
+	# TODO: Use blackdoc to format Python doctests.
+	# https://blackdoc.readthedocs.io/en/latest/
+	# $(VENV_BIN)/blackdoc .
+	# TODO: Type checking of Python code.
+	# https://github.com/python/mypy
+	# $(VENV_BIN)/mypy dbgpt
+	# TODO: uUse flake8 to enforce Python style guide.
+	# https://flake8.pycqa.org/en/latest/
+	# $(VENV_BIN)/flake8 dbgpt
+
+.PHONY: pre-commit
+pre-commit: fmt test ## Run formatting and unit tests before committing
+
+.PHONY: test
+test: testenv ## Run unit tests
+	$(VENV_BIN)/pytest dbgpt
+
+.PHONY: coverage
+coverage: setup ## Run tests and report coverage
+	$(VENV_BIN)/pytest dbgpt --cov=dbgpt
+
+.PHONY: clean
+clean: ## Clean up the environment
+	rm -rf $(VENV)
+	find . -type f -name '*.pyc' -delete
+	find . -type d -name '__pycache__' -delete
+	find . -type d -name '.pytest_cache' -delete
+	find . -type d -name '.coverage' -delete
+
+.PHONY: help
+help:  ## Display this help screen
+	@echo "Available commands:"
+	@grep -E '^[a-z.A-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | awk 'BEGIN {FS = ":.*?## "}; {printf "  \033[36m%-18s\033[0m %s\n", $$1, $$2}' | sort
--- a/dbgpt/core/init.py
+++ b/dbgpt/core/init.py
@@ -1,9 +1,12 @@
 from dbgpt.core.interface.llm import (
    ModelInferenceMetrics,
+    ModelRequest,
    ModelOutput,
-    OpenAILLM,
-    BaseLLMOperator,
+    LLMClient,
+    LLMOperator,
+    StreamingLLMOperator,
    RequestBuildOperator,
+    ModelMetadata,
 )
 from dbgpt.core.interface.message import (
    ModelMessage,
@@ -37,11 +40,15 @@ from dbgpt.core.interface.storage import (

 __ALL__ = [
    "ModelInferenceMetrics",
+    "ModelRequest",
    "ModelOutput",
-    "OpenAILLM",
-    "BaseLLMOperator",
+    "Operator",
    "RequestBuildOperator",
+    "ModelMetadata",
    "ModelMessage",
+    "LLMClient",
+    "LLMOperator",
+    "StreamingLLMOperator",
    "ModelMessageRoleType",
    "OnceConversation",
    "StorageConversation",
--- a/dbgpt/core/awel/operator/base.py
+++ b/dbgpt/core/awel/operator/base.py
@@ -211,7 +211,9 @@ class BaseOperator(DAGNode, ABC, Generic[OUT], metaclass=BaseOperatorMeta):
        Returns:
            AsyncIterator[OUT]: An asynchronous iterator over the output stream.
        """
-        out_ctx = await self._runner.execute_workflow(self, call_data)
+        out_ctx = await self._runner.execute_workflow(
+            self, call_data, streaming_call=True
+        )
        return out_ctx.current_task_context.task_output.output_stream

    def _blocking_call_stream(
--- a/dbgpt/core/awel/trigger/http_trigger.py
+++ b/dbgpt/core/awel/trigger/http_trigger.py
@@ -130,8 +130,9 @@ async def _trigger_dag(
                "Connection": "keep-alive",
                "Transfer-Encoding": "chunked",
            }
+        generator = await end_node.call_stream(call_data={"data": body})
        return StreamingResponse(
-            end_node.call_stream(call_data={"data": body}),
+            generator,
            headers=headers,
            media_type=media_type,
        )
--- a/dbgpt/core/interface/llm.py
+++ b/dbgpt/core/interface/llm.py
@@ -1,10 +1,10 @@
-from abc import ABC
+from abc import ABC, abstractmethod
 from typing import Optional, Dict, List, Any, Union, AsyncIterator
-
 import time
-from dataclasses import dataclass, asdict
+from dataclasses import dataclass, asdict, field
 import copy

+from dbgpt.util import BaseParameters
 from dbgpt.util.annotations import PublicAPI
 from dbgpt.util.model_utils import GPUInfo
 from dbgpt.core.interface.message import ModelMessage, ModelMessageRoleType
@@ -12,6 +12,7 @@ from dbgpt.core.awel import MapOperator, StreamifyAbsOperator


@dataclass
+@PublicAPI(stability="beta")
 class ModelInferenceMetrics:
    """A class to represent metrics for assessing the inference performance of a LLM."""

@@ -97,6 +98,7 @@ class ModelInferenceMetrics:


@dataclass
+@PublicAPI(stability="beta")
 class ModelOutput:
    """A class to represent the output of a LLM.""" ""

@@ -118,6 +120,7 @@ _ModelMessageType = Union[ModelMessage, Dict[str, Any]]


@dataclass
+@PublicAPI(stability="beta")
 class ModelRequest:
    model: str
    """The name of the model."""
@@ -142,7 +145,7 @@ class ModelRequest:
    span_id: Optional[str] = None
    """The span id of the model inference."""

-    def to_dict(self) -> Dict:
+    def to_dict(self) -> Dict[str, Any]:
        new_reqeust = copy.deepcopy(self)
        new_reqeust.messages = list(
            map(lambda m: m if isinstance(m, dict) else m.dict(), new_reqeust.messages)
@@ -166,6 +169,110 @@ class ModelRequest:
            **kwargs,
        )

+    def to_openai_messages(self) -> List[Dict[str, Any]]:
+        """Convert the messages to the format of OpenAI API.
+
+        This function will move last user message to the end of the list.
+
+        Returns:
+            List[Dict[str, Any]]: The messages in the format of OpenAI API.
+
+        Examples:
+            .. code-block:: python
+                messages = [
+                    ModelMessage(role=ModelMessageRoleType.HUMAN, content="Hi"),
+                    ModelMessage(role=ModelMessageRoleType.AI, content="Hi, I'm a robot.")
+                    ModelMessage(role=ModelMessageRoleType.HUMAN, content="Who are your"),
+                ]
+                openai_messages = ModelRequest.to_openai_messages(messages)
+                assert openai_messages == [
+                    {"role": "user", "content": "Hi"},
+                    {"role": "assistant", "content": "Hi, I'm a robot."},
+                    {"role": "user", "content": "Who are your"},
+                ]
+        """
+        messages = [
+            m if isinstance(m, ModelMessage) else ModelMessage(**m)
+            for m in self.messages
+        ]
+        return ModelMessage.to_openai_messages(messages)
+
+
+@dataclass
+@PublicAPI(stability="beta")
+class ModelMetadata(BaseParameters):
+    """A class to represent a LLM model."""
+
+    model: str = field(
+        metadata={"help": "Model name"},
+    )
+    context_length: Optional[int] = field(
+        default=4096,
+        metadata={"help": "Context length of model"},
+    )
+    chat_model: Optional[bool] = field(
+        default=True,
+        metadata={"help": "Whether the model is a chat model"},
+    )
+    is_function_calling_model: Optional[bool] = field(
+        default=False,
+        metadata={"help": "Whether the model is a function calling model"},
+    )
+    metadata: Optional[Dict[str, Any]] = field(
+        default_factory=dict,
+        metadata={"help": "Model metadata"},
+    )
+
+
+@PublicAPI(stability="beta")
+class LLMClient(ABC):
+    """An abstract class for LLM client."""
+
+    @abstractmethod
+    async def generate(self, request: ModelRequest) -> ModelOutput:
+        """Generate a response for a given model request.
+
+        Args:
+            request(ModelRequest): The model request.
+
+        Returns:
+            ModelOutput: The model output.
+
+        """
+
+    @abstractmethod
+    async def generate_stream(
+        self, request: ModelRequest
+    ) -> AsyncIterator[ModelOutput]:
+        """Generate a stream of responses for a given model request.
+
+        Args:
+            request(ModelRequest): The model request.
+
+        Returns:
+            AsyncIterator[ModelOutput]: The model output stream.
+        """
+
+    @abstractmethod
+    async def models(self) -> List[ModelMetadata]:
+        """Get all the models.
+
+        Returns:
+            List[ModelMetadata]: A list of model metadata.
+        """
+
+    @abstractmethod
+    async def count_token(self, model: str, prompt: str) -> int:
+        """Count the number of tokens in a given prompt.
+
+        Args:
+            model(str): The model name.
+            prompt(str): The prompt.
+
+        Returns:
+            int: The number of tokens.
+        """
+

 class RequestBuildOperator(MapOperator[str, ModelRequest], ABC):
    def __init__(self, model: str, **kwargs):
@@ -176,85 +283,52 @@ class RequestBuildOperator(MapOperator[str, ModelRequest], ABC):
        return ModelRequest._build(self._model, input_value)


-class BaseLLMOperator(
-    MapOperator[ModelRequest, ModelOutput],
-    StreamifyAbsOperator[ModelRequest, ModelOutput],
-    ABC,
-):
+class BaseLLM:
    """The abstract operator for a LLM."""

+    def __init__(self, llm_client: Optional[LLMClient] = None):
+        self._llm_client = llm_client

-@PublicAPI(stability="beta")
-class OpenAILLM(BaseLLMOperator):
-    """The operator for OpenAI LLM.
+    @property
+    def llm_client(self) -> LLMClient:
+        """Return the LLM client."""
+        if not self._llm_client:
+            raise ValueError("llm_client is not set")
+        return self._llm_client

-    Examples:

-        .. code-block:: python
-            llm = OpenAILLM()
-            model_request = ModelRequest(model="gpt-3.5-turbo", messages=[ModelMessage(role=ModelMessageRoleType.HUMAN, content="Hello")])
-            model_output = await llm.map(model_request)
+class LLMOperator(BaseLLM, MapOperator[ModelRequest, ModelOutput], ABC):
+    """The operator for a LLM.
+
+    Args:
+        llm_client (LLMClient, optional): The LLM client. Defaults to None.
+
+    This operator will generate a no streaming response.
    """

-    def __int__(self):
-        try:
-            import openai
-        except ImportError as e:
-            raise ImportError("Please install openai package to use OpenAILLM") from e
-        import importlib.metadata as metadata
+    def __init__(self, llm_client: Optional[LLMClient] = None, **kwargs):
+        super().__init__(llm_client=llm_client)
+        MapOperator.__init__(self, **kwargs)

-        if not metadata.version("openai") >= "1.0.0":
-            raise ImportError("Please upgrade openai package to version 1.0.0 or above")
+    async def map(self, request: ModelRequest) -> ModelOutput:
+        return await self.llm_client.generate(request)

-    async def _send_request(
-        self, model_request: ModelRequest, stream: Optional[bool] = False
-    ):
-        import os
-        from openai import AsyncOpenAI

-        client = AsyncOpenAI(
-            api_key=os.environ.get("OPENAI_API_KEY"),
-            base_url=os.environ.get("OPENAI_API_BASE"),
-        )
-        messages = ModelMessage.to_openai_messages(model_request._get_messages())
-        payloads = {
-            "model": model_request.model,
-            "stream": stream,
-        }
-        if model_request.temperature is not None:
-            payloads["temperature"] = model_request.temperature
-        if model_request.max_new_tokens:
-            payloads["max_tokens"] = model_request.max_new_tokens
+class StreamingLLMOperator(
+    BaseLLM, StreamifyAbsOperator[ModelRequest, ModelOutput], ABC
+):
+    """The streaming operator for a LLM.

-        return await client.chat.completions.create(messages=messages, **payloads)
+    Args:
+        llm_client (LLMClient, optional): The LLM client. Defaults to None.

-    async def map(self, model_request: ModelRequest) -> ModelOutput:
-        try:
-            chat_completion = await self._send_request(model_request, stream=False)
-            text = chat_completion.choices[0].message.content
-            usage = chat_completion.usage.dict()
-            return ModelOutput(text=text, error_code=0, usage=usage)
-        except Exception as e:
-            return ModelOutput(
-                text=f"**LLMServer Generate Error, Please CheckErrorInfo.**: {e}",
-                error_code=1,
-            )
+    This operator will generate streaming response.
+    """

-    async def streamify(
-        self, model_request: ModelRequest
-    ) -> AsyncIterator[ModelOutput]:
-        try:
-            chat_completion = await self._send_request(model_request, stream=True)
-            text = ""
-            for r in chat_completion:
-                if len(r.choices) == 0:
-                    continue
-                if r.choices[0].delta.content is not None:
-                    content = r.choices[0].delta.content
-                    text += content
-                    yield ModelOutput(text=text, error_code=0)
-        except Exception as e:
-            yield ModelOutput(
-                text=f"**LLMServer Generate Error, Please CheckErrorInfo.**: {e}",
-                error_code=1,
-            )
+    def __init__(self, llm_client: Optional[LLMClient] = None, **kwargs):
+        super().__init__(llm_client=llm_client)
+        StreamifyAbsOperator.__init__(self, **kwargs)
+
+    async def streamify(self, request: ModelRequest) -> AsyncIterator[ModelOutput]:
+        async for output in self.llm_client.generate_stream(request):
+            yield output
--- a/dbgpt/datasource/rdbms/base.py
+++ b/dbgpt/datasource/rdbms/base.py
@@ -1,7 +1,6 @@
 from __future__ import annotations
 import sqlparse
 import regex as re
-import pandas as pd
 from urllib.parse import quote
 from urllib.parse import quote_plus as urlquote
 from typing import Any, Iterable, List, Optional, Dict
@@ -383,6 +382,10 @@ class RDBMSDatabase(BaseConnect):
                return self.get_simple_fields(table_name)

    def run_to_df(self, command: str, fetch: str = "all"):
+        import pandas as pd
+
+        # Pandas has too much dependence and the import time is too long
+        # TODO: Remove the dependency on pandas
        result_lst = self.run(command, fetch)
        colunms = result_lst[0]
        values = result_lst[1:]
--- a/dbgpt/datasource/rdbms/conn_clickhouse.py
+++ b/dbgpt/datasource/rdbms/conn_clickhouse.py
@@ -1,13 +1,8 @@
 import re
 import sqlparse
-import clickhouse_connect
 from typing import List, Optional, Any, Iterable, Dict
 from sqlalchemy import text
-from urllib.parse import quote
-from sqlalchemy.schema import CreateTable
-from urllib.parse import quote_plus as urlquote
 from dbgpt.datasource.rdbms.base import RDBMSDatabase
-from clickhouse_connect.driver import httputil
 from dbgpt.storage.schema import DBType
 from sqlalchemy import (
    MetaData,
@@ -56,6 +51,11 @@ class ClickhouseConnect(RDBMSDatabase):
        engine_args: Optional[dict] = None,
        **kwargs: Any,
    ) -> RDBMSDatabase:
+        import clickhouse_connect
+        from clickhouse_connect.driver import httputil
+
+        # Lazy import
+
        big_pool_mgr = httputil.get_pool_manager(maxsize=16, num_pools=12)
        client = clickhouse_connect.get_client(
            host=host,
--- a/dbgpt/datasource/rdbms/conn_sqlite.py
+++ b/dbgpt/datasource/rdbms/conn_sqlite.py
@@ -37,7 +37,14 @@ class SQLiteConnect(RDBMSDatabase):
        """Get table indexes about specified table."""
        cursor = self.session.execute(text(f"PRAGMA index_list({table_name})"))
        indexes = cursor.fetchall()
-        return [(index[1], index[3]) for index in indexes]
+        result = []
+        for idx in indexes:
+            index_name = idx[1]
+            cursor = self.session.execute(text(f"PRAGMA index_info({index_name})"))
+            index_infos = cursor.fetchall()
+            column_names = [index_info[2] for index_info in index_infos]
+            result.append({"name": index_name, "column_names": column_names})
+        return result

    def get_show_create_table(self, table_name):
        """Get table show create table about specified table."""
--- a/dbgpt/datasource/rdbms/tests/test_conn_sqlite.py
+++ b/dbgpt/datasource/rdbms/tests/test_conn_sqlite.py
@@ -47,7 +47,8 @@ def test_run_no_throw(db):
 def test_get_indexes(db):
    db.run("CREATE TABLE test (name TEXT);")
    db.run("CREATE INDEX idx_name ON test(name);")
-    assert db.get_indexes("test") == [("idx_name", "c")]
+    indexes = db.get_indexes("test")
+    assert indexes == [{"name": "idx_name", "column_names": ["name"]}]


 def test_get_indexes_empty(db):
--- a/dbgpt/model/init.py
+++ b/dbgpt/model/init.py
@@ -0,0 +1,4 @@
+from dbgpt.model.cluster.client import DefaultLLMClient
+from dbgpt.model.utils.chatgpt_utils import OpenAILLMClient
+
+__ALL__ = ["DefaultLLMClient", "OpenAILLMClient"]
--- a/dbgpt/model/cluster/base.py
+++ b/dbgpt/model/cluster/base.py
@@ -30,6 +30,15 @@ class EmbeddingsRequest(BaseModel):
    span_id: str = None


+class CountTokenRequest(BaseModel):
+    model: str
+    prompt: str
+
+
+class ModelMetadataRequest(BaseModel):
+    model: str
+
+
 class WorkerApplyRequest(BaseModel):
    model: str
    apply_type: WorkerApplyType
--- a/dbgpt/model/cluster/client.py
+++ b/dbgpt/model/cluster/client.py
@@ -0,0 +1,40 @@
+from typing import AsyncIterator, List
+import asyncio
+from dbgpt.core.interface.llm import LLMClient, ModelRequest, ModelOutput, ModelMetadata
+from dbgpt.model.parameter import WorkerType
+from dbgpt.model.cluster.manager_base import WorkerManager
+
+
+class DefaultLLMClient(LLMClient):
+    def __init__(self, worker_manager: WorkerManager):
+        self._worker_manager = worker_manager
+
+    async def generate(self, request: ModelRequest) -> ModelOutput:
+        return await self._worker_manager.generate(request.to_dict())
+
+    async def generate_stream(
+        self, request: ModelRequest
+    ) -> AsyncIterator[ModelOutput]:
+        async for output in self._worker_manager.generate_stream(request.to_dict()):
+            yield output
+
+    async def models(self) -> List[ModelMetadata]:
+        instances = await self._worker_manager.get_all_model_instances(
+            WorkerType.LLM.value, healthy_only=True
+        )
+        query_metadata_task = []
+        for instance in instances:
+            worker_name, _ = WorkerType.parse_worker_key(instance.worker_key)
+            query_metadata_task.append(
+                self._worker_manager.get_model_metadata({"model": worker_name})
+            )
+        models: List[ModelMetadata] = await asyncio.gather(*query_metadata_task)
+        model_map = {}
+        for single_model in models:
+            model_map[single_model.model] = single_model
+        return [model_map[model_name] for model_name in sorted(model_map.keys())]
+
+    async def count_token(self, model: str, prompt: str) -> int:
+        return await self._worker_manager.count_token(
+            {"model": model, "prompt": prompt}
+        )
--- a/dbgpt/model/cluster/manager_base.py
+++ b/dbgpt/model/cluster/manager_base.py
@@ -5,7 +5,7 @@ from abc import ABC, abstractmethod
 from datetime import datetime
 from concurrent.futures import Future
 from dbgpt.component import BaseComponent, ComponentType, SystemApp
-from dbgpt.core import ModelOutput
+from dbgpt.core import ModelOutput, ModelMetadata
 from dbgpt.model.base import WorkerSupportedModel, WorkerApplyOutput
 from dbgpt.model.cluster.worker_base import ModelWorker
 from dbgpt.model.cluster.base import WorkerStartupRequest, WorkerApplyRequest
@@ -38,6 +38,11 @@ class WorkerRunData:
        port = self.port
        return f"model {model_name}@{model_type}({host}:{port})"

+    @property
+    def stopped(self):
+        """Check if the worker is stopped""" ""
+        return self.stop_event.is_set()
+

 class WorkerManager(ABC):
    @abstractmethod
@@ -62,6 +67,20 @@ class WorkerManager(ABC):
    ) -> List[WorkerRunData]:
        """Asynchronous get model instances by worker type and model name"""

+    @abstractmethod
+    async def get_all_model_instances(
+        self, worker_type: str, healthy_only: bool = True
+    ) -> List[WorkerRunData]:
+        """Asynchronous get all model instances
+
+        Args:
+            worker_type (str): worker type
+            healthy_only (bool, optional): only return healthy instances. Defaults to True.
+
+        Returns:
+            List[WorkerRunData]: worker run data list
+        """
+
    @abstractmethod
    def sync_get_model_instances(
        self, worker_type: str, model_name: str, healthy_only: bool = True
@@ -112,6 +131,25 @@ class WorkerManager(ABC):
        We must provide a synchronous version.
        """

+    @abstractmethod
+    async def count_token(self, params: Dict) -> int:
+        """Count token of prompt
+
+        Args:
+            params (Dict): parameters, eg. {"prompt": "hello", "model": "vicuna-13b-v1.5"}
+
+        Returns:
+            int: token count
+        """
+
+    @abstractmethod
+    async def get_model_metadata(self, params: Dict) -> ModelMetadata:
+        """Get model metadata
+
+        Args:
+            params (Dict): parameters, eg. {"model": "vicuna-13b-v1.5"}
+        """
+
    @abstractmethod
    async def worker_apply(self, apply_req: WorkerApplyRequest) -> WorkerApplyOutput:
        """Worker apply"""
--- a/dbgpt/model/cluster/tests/conftest.py
+++ b/dbgpt/model/cluster/tests/conftest.py
@@ -3,7 +3,7 @@ import pytest_asyncio
 from contextlib import contextmanager, asynccontextmanager
 from typing import List, Iterator, Dict, Tuple
 from dbgpt.model.parameter import ModelParameters, ModelWorkerParameters, WorkerType
-from dbgpt.core import ModelOutput
+from dbgpt.core import ModelOutput, ModelMetadata
 from dbgpt.model.cluster.worker_base import ModelWorker
 from dbgpt.model.cluster.worker.manager import (
    WorkerManager,
@@ -80,6 +80,14 @@ class MockModelWorker(ModelWorker):
            output = out
        return output

+    def count_token(self, prompt: str) -> int:
+        return len(prompt)
+
+    def get_model_metadata(self, params: Dict) -> ModelMetadata:
+        return ModelMetadata(
+            model=self.model_parameters.model_name,
+        )
+
    def embeddings(self, params: Dict) -> List[List[float]]:
        return self._embeddings

--- a/dbgpt/model/cluster/worker/default_worker.py
+++ b/dbgpt/model/cluster/worker/default_worker.py
@@ -8,7 +8,7 @@ import traceback
 from dbgpt.configs.model_config import get_device
 from dbgpt.model.adapter.base import LLMModelAdapter
 from dbgpt.model.adapter.model_adapter import get_llm_model_adapter
-from dbgpt.core import ModelOutput, ModelInferenceMetrics
+from dbgpt.core import ModelOutput, ModelInferenceMetrics, ModelMetadata
 from dbgpt.model.loader import ModelLoader, _get_model_real_path
 from dbgpt.model.parameter import ModelParameters
 from dbgpt.model.cluster.worker_base import ModelWorker
@@ -118,6 +118,8 @@ class DefaultModelWorker(ModelWorker):
                    f"Parse model max length {model_max_length} from model {self.model_name}."
                )
                self.context_len = model_max_length
+            elif hasattr(model_params, "max_context_size"):
+                self.context_len = model_params.max_context_size

    def stop(self) -> None:
        if not self.model:
@@ -186,6 +188,22 @@ class DefaultModelWorker(ModelWorker):
            output = out
        return output

+    def count_token(self, prompt: str) -> int:
+        return _try_to_count_token(prompt, self.tokenizer)
+
+    async def async_count_token(self, prompt: str) -> int:
+        # TODO if we deploy the model by vllm, it can't work, we should run transformer _try_to_count_token to async
+        raise NotImplementedError
+
+    def get_model_metadata(self, params: Dict) -> ModelMetadata:
+        return ModelMetadata(
+            model=self.model_name,
+            context_length=self.context_len,
+        )
+
+    async def async_get_model_metadata(self, params: Dict) -> ModelMetadata:
+        return self.get_model_metadata(params)
+
    def embeddings(self, params: Dict) -> List[List[float]]:
        raise NotImplementedError

@@ -436,6 +454,25 @@ def _new_metrics_from_model_output(
    return metrics


+def _try_to_count_token(prompt: str, tokenizer) -> int:
+    """Try to count token of prompt
+
+    Args:
+        prompt (str): prompt
+        tokenizer ([type]): tokenizer
+
+    Returns:
+        int: token count, if error return -1
+
+    TODO: More implementation
+    """
+    try:
+        return len(tokenizer(prompt).input_ids[0])
+    except Exception as e:
+        logger.warning(f"Count token error, detail: {e}, return -1")
+        return -1
+
+
 def _try_import_torch():
    global torch
    global _torch_imported
--- a/dbgpt/model/cluster/worker/embedding_worker.py
+++ b/dbgpt/model/cluster/worker/embedding_worker.py
@@ -2,6 +2,7 @@ import logging
 from typing import Dict, List, Type, Optional

 from dbgpt.configs.model_config import get_device
+from dbgpt.core import ModelMetadata
 from dbgpt.model.loader import _get_model_real_path
 from dbgpt.model.parameter import (
    EmbeddingModelParameters,
@@ -89,6 +90,14 @@ class EmbeddingsModelWorker(ModelWorker):
        """Generate non stream result"""
        raise NotImplementedError("Not supported generate for embeddings model")

+    def count_token(self, prompt: str) -> int:
+        raise NotImplementedError("Not supported count_token for embeddings model")
+
+    def get_model_metadata(self, params: Dict) -> ModelMetadata:
+        raise NotImplementedError(
+            "Not supported get_model_metadata for embeddings model"
+        )
+
    def embeddings(self, params: Dict) -> List[List[float]]:
        model = params.get("model")
        logger.info(f"Receive embeddings request, model: {model}")
--- a/dbgpt/model/cluster/worker/manager.py
+++ b/dbgpt/model/cluster/worker/manager.py
@@ -15,7 +15,7 @@ from fastapi.responses import StreamingResponse

 from dbgpt.component import SystemApp
 from dbgpt.configs.model_config import LOGDIR
-from dbgpt.core import ModelOutput
+from dbgpt.core import ModelOutput, ModelMetadata
 from dbgpt.model.base import (
    ModelInstance,
    WorkerApplyOutput,
@@ -271,6 +271,18 @@ class LocalWorkerManager(WorkerManager):
    ) -> List[WorkerRunData]:
        return self.sync_get_model_instances(worker_type, model_name, healthy_only)

+    async def get_all_model_instances(
+        self, worker_type: str, healthy_only: bool = True
+    ) -> List[WorkerRunData]:
+        instances = list(itertools.chain(*self.workers.values()))
+        result = []
+        for instance in instances:
+            name, wt = WorkerType.parse_worker_key(instance.worker_key)
+            if wt != worker_type or (healthy_only and instance.stopped):
+                continue
+            result.append(instance)
+        return result
+
    def sync_get_model_instances(
        self, worker_type: str, model_name: str, healthy_only: bool = True
    ) -> List[WorkerRunData]:
@@ -390,6 +402,43 @@ class LocalWorkerManager(WorkerManager):
        worker_run_data = self._sync_get_model(params, worker_type="text2vec")
        return worker_run_data.worker.embeddings(params)

+    async def count_token(self, params: Dict) -> int:
+        """Count token of prompt"""
+        with root_tracer.start_span(
+            "WorkerManager.count_token", params.get("span_id")
+        ) as span:
+            params["span_id"] = span.span_id
+            try:
+                worker_run_data = await self._get_model(params)
+            except Exception as e:
+                raise e
+            prompt = params.get("prompt")
+            async with worker_run_data.semaphore:
+                if worker_run_data.worker.support_async():
+                    return await worker_run_data.worker.async_count_token(prompt)
+                else:
+                    return await self.run_blocking_func(
+                        worker_run_data.worker.count_token, prompt
+                    )
+
+    async def get_model_metadata(self, params: Dict) -> ModelMetadata:
+        """Get model metadata"""
+        with root_tracer.start_span(
+            "WorkerManager.get_model_metadata", params.get("span_id")
+        ) as span:
+            params["span_id"] = span.span_id
+            try:
+                worker_run_data = await self._get_model(params)
+            except Exception as e:
+                raise e
+            async with worker_run_data.semaphore:
+                if worker_run_data.worker.support_async():
+                    return await worker_run_data.worker.async_get_model_metadata(params)
+                else:
+                    return await self.run_blocking_func(
+                        worker_run_data.worker.get_model_metadata, params
+                    )
+
    async def worker_apply(self, apply_req: WorkerApplyRequest) -> WorkerApplyOutput:
        apply_func: Callable[[WorkerApplyRequest], Awaitable[str]] = None
        if apply_req.apply_type == WorkerApplyType.START:
@@ -601,6 +650,13 @@ class WorkerManagerAdapter(WorkerManager):
            worker_type, model_name, healthy_only
        )

+    async def get_all_model_instances(
+        self, worker_type: str, healthy_only: bool = True
+    ) -> List[WorkerRunData]:
+        return await self.worker_manager.get_all_model_instances(
+            worker_type, healthy_only
+        )
+
    def sync_get_model_instances(
        self, worker_type: str, model_name: str, healthy_only: bool = True
    ) -> List[WorkerRunData]:
@@ -635,6 +691,12 @@ class WorkerManagerAdapter(WorkerManager):
    def sync_embeddings(self, params: Dict) -> List[List[float]]:
        return self.worker_manager.sync_embeddings(params)

+    async def count_token(self, params: Dict) -> int:
+        return await self.worker_manager.count_token(params)
+
+    async def get_model_metadata(self, params: Dict) -> ModelMetadata:
+        return await self.worker_manager.get_model_metadata(params)
+
    async def worker_apply(self, apply_req: WorkerApplyRequest) -> WorkerApplyOutput:
        return await self.worker_manager.worker_apply(apply_req)

@@ -696,6 +758,24 @@ async def api_embeddings(request: EmbeddingsRequest):
    return await worker_manager.embeddings(params)


+@router.post("/worker/count_token")
+async def api_count_token(request: CountTokenRequest):
+    params = request.dict(exclude_none=True)
+    span_id = root_tracer.get_current_span_id()
+    if "span_id" not in params and span_id:
+        params["span_id"] = span_id
+    return await worker_manager.count_token(params)
+
+
+@router.post("/worker/model_metadata")
+async def api_get_model_metadata(request: ModelMetadataRequest):
+    params = request.dict(exclude_none=True)
+    span_id = root_tracer.get_current_span_id()
+    if "span_id" not in params and span_id:
+        params["span_id"] = span_id
+    return await worker_manager.get_model_metadata(params)
+
+
@router.post("/worker/apply")
 async def api_worker_apply(request: WorkerApplyRequest):
    return await worker_manager.worker_apply(request)
--- a/dbgpt/model/cluster/worker/remote_manager.py
+++ b/dbgpt/model/cluster/worker/remote_manager.py
@@ -133,22 +133,29 @@ class RemoteWorkerManager(LocalWorkerManager):
        self, model_name: str, instances: List[ModelInstance]
    ) -> List[WorkerRunData]:
        worker_instances = []
-        for ins in instances:
-            worker = RemoteModelWorker()
-            worker.load_worker(model_name, model_name, host=ins.host, port=ins.port)
-            wr = WorkerRunData(
-                host=ins.host,
-                port=ins.port,
-                worker_key=ins.model_name,
-                worker=worker,
-                worker_params=None,
-                model_params=None,
-                stop_event=asyncio.Event(),
-                semaphore=asyncio.Semaphore(100),  # Not limit in client
+        for instance in instances:
+            worker_instances.append(
+                self._build_single_worker_instance(model_name, instance)
            )
-            worker_instances.append(wr)
        return worker_instances

+    def _build_single_worker_instance(self, model_name: str, instance: ModelInstance):
+        worker = RemoteModelWorker()
+        worker.load_worker(
+            model_name, model_name, host=instance.host, port=instance.port
+        )
+        wr = WorkerRunData(
+            host=instance.host,
+            port=instance.port,
+            worker_key=instance.model_name,
+            worker=worker,
+            worker_params=None,
+            model_params=None,
+            stop_event=asyncio.Event(),
+            semaphore=asyncio.Semaphore(100),  # Not limit in client
+        )
+        return wr
+
    async def get_model_instances(
        self, worker_type: str, model_name: str, healthy_only: bool = True
    ) -> List[WorkerRunData]:
@@ -158,6 +165,20 @@ class RemoteWorkerManager(LocalWorkerManager):
        )
        return self._build_worker_instances(model_name, instances)

+    async def get_all_model_instances(
+        self, worker_type: str, healthy_only: bool = True
+    ) -> List[WorkerRunData]:
+        instances: List[
+            ModelInstance
+        ] = await self.model_registry.get_all_model_instances(healthy_only=healthy_only)
+        result = []
+        for instance in instances:
+            name, wt = WorkerType.parse_worker_key(instance.model_name)
+            if wt != worker_type:
+                continue
+            result.append(self._build_single_worker_instance(name, instance))
+        return result
+
    def sync_get_model_instances(
        self, worker_type: str, model_name: str, healthy_only: bool = True
    ) -> List[WorkerRunData]:
--- a/dbgpt/model/cluster/worker/remote_worker.py
+++ b/dbgpt/model/cluster/worker/remote_worker.py
@@ -1,7 +1,7 @@
 import json
 from typing import Dict, Iterator, List
 import logging
-from dbgpt.core import ModelOutput
+from dbgpt.core import ModelOutput, ModelMetadata
 from dbgpt.model.parameter import ModelParameters
 from dbgpt.model.cluster.worker_base import ModelWorker

@@ -90,6 +90,44 @@ class RemoteModelWorker(ModelWorker):
            )
            return ModelOutput(**response.json())

+    def count_token(self, prompt: str) -> int:
+        raise NotImplementedError
+
+    async def async_count_token(self, prompt: str) -> int:
+        import httpx
+
+        async with httpx.AsyncClient() as client:
+            url = self.worker_addr + "/count_token"
+            logger.debug(f"Send async_count_token to url {url}, params: {prompt}")
+            response = await client.post(
+                url,
+                headers=self.headers,
+                json={"prompt": prompt},
+                timeout=self.timeout,
+            )
+            return response.json()
+
+    async def async_get_model_metadata(self, params: Dict) -> ModelMetadata:
+        """Asynchronously get model metadata"""
+        import httpx
+
+        async with httpx.AsyncClient() as client:
+            url = self.worker_addr + "/model_metadata"
+            logger.debug(
+                f"Send async_get_model_metadata to url {url}, params: {params}"
+            )
+            response = await client.post(
+                url,
+                headers=self.headers,
+                json=params,
+                timeout=self.timeout,
+            )
+            return ModelMetadata(**response.json())
+
+    def get_model_metadata(self, params: Dict) -> ModelMetadata:
+        """Get model metadata"""
+        raise NotImplementedError
+
    def embeddings(self, params: Dict) -> List[List[float]]:
        """Get embeddings for input"""
        import requests
--- a/dbgpt/model/cluster/worker_base.py
+++ b/dbgpt/model/cluster/worker_base.py
@@ -1,7 +1,7 @@
 from abc import ABC, abstractmethod
 from typing import Dict, Iterator, List, Type

-from dbgpt.core import ModelOutput
+from dbgpt.core import ModelOutput, ModelMetadata
 from dbgpt.model.parameter import ModelParameters, WorkerType
 from dbgpt.util.parameter_utils import (
    ParameterDescription,
@@ -92,6 +92,42 @@ class ModelWorker(ABC):
        """Asynchronously generate output (non-stream) based on provided parameters."""
        raise NotImplementedError

+    @abstractmethod
+    def count_token(self, prompt: str) -> int:
+        """Count token of prompt
+        Args:
+            prompt (str): prompt
+
+        Returns:
+            int: token count
+        """
+
+    async def async_count_token(self, prompt: str) -> int:
+        """Asynchronously count token of prompt
+        Args:
+            prompt (str): prompt
+
+        Returns:
+            int: token count
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def get_model_metadata(self, params: Dict) -> ModelMetadata:
+        """Get model metadata
+
+        Args:
+            params (Dict): parameters, eg. {"model": "vicuna-13b-v1.5"}
+        """
+
+    async def async_get_model_metadata(self, params: Dict) -> ModelMetadata:
+        """Asynchronously get model metadata
+
+        Args:
+            params (Dict): parameters, eg. {"model": "vicuna-13b-v1.5"}
+        """
+        raise NotImplementedError
+
    @abstractmethod
    def embeddings(self, params: Dict) -> List[List[float]]:
        """
--- a/dbgpt/model/proxy/llms/chatgpt.py
+++ b/dbgpt/model/proxy/llms/chatgpt.py
@@ -70,7 +70,7 @@ def _initialize_openai_v1(params: ProxyModelParameters):
    api_type = params.proxy_api_type or os.getenv("OPENAI_API_TYPE", "open_ai")

    base_url = params.proxy_api_base or os.getenv(
-        "OPENAI_API_TYPE",
+        "OPENAI_API_BASE",
        os.getenv("AZURE_OPENAI_ENDPOINT") if api_type == "azure" else None,
    )
    api_key = params.proxy_api_key or os.getenv(
--- a/dbgpt/model/utils/init.py
+++ b/dbgpt/model/utils/init.py
--- a/dbgpt/model/utils/chatgpt_utils.py
+++ b/dbgpt/model/utils/chatgpt_utils.py
@@ -0,0 +1,282 @@
+from __future__ import annotations
+
+import os
+import logging
+from dataclasses import dataclass
+import importlib.metadata as metadata
+from typing import List, Dict, Any, Optional, TYPE_CHECKING, Union, AsyncIterator
+
+from dbgpt.core.interface.llm import ModelMetadata, LLMClient
+from dbgpt.core.interface.llm import ModelOutput, ModelRequest
+
+if TYPE_CHECKING:
+    import httpx
+    from httpx._types import ProxiesTypes
+    from openai import AsyncAzureOpenAI
+    from openai import AsyncOpenAI
+
+    ClientType = Union[AsyncAzureOpenAI, AsyncOpenAI]
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class OpenAIParameters:
+    """A class to represent a LLM model."""
+
+    api_type: str = "open_ai"
+    api_base: Optional[str] = None
+    api_key: Optional[str] = None
+    api_version: Optional[str] = None
+    full_url: Optional[str] = None
+    proxies: Optional["ProxiesTypes"] = None
+
+
+def _initialize_openai_v1(init_params: OpenAIParameters):
+    try:
+        from openai import OpenAI
+    except ImportError as exc:
+        raise ValueError(
+            "Could not import python package: openai "
+            "Please install openai by command `pip install openai"
+        ) from exc
+
+    if not metadata.version("openai") >= "1.0.0":
+        raise ImportError("Please upgrade openai package to version 1.0.0 or above")
+
+    api_type: Optional[str] = init_params.api_type
+    api_base: Optional[str] = init_params.api_base
+    api_key: Optional[str] = init_params.api_key
+    api_version: Optional[str] = init_params.api_version
+    full_url: Optional[str] = init_params.full_url
+
+    api_type = api_type or os.getenv("OPENAI_API_TYPE", "open_ai")
+
+    base_url = api_base or os.getenv(
+        "OPENAI_API_BASE",
+        os.getenv("AZURE_OPENAI_ENDPOINT") if api_type == "azure" else None,
+    )
+    api_key = api_key or os.getenv(
+        "OPENAI_API_KEY",
+        os.getenv("AZURE_OPENAI_KEY") if api_type == "azure" else None,
+    )
+    api_version = api_version or os.getenv("OPENAI_API_VERSION")
+
+    if not base_url and full_url:
+        base_url = full_url.split("/chat/completions")[0]
+
+    if api_key is None:
+        raise ValueError("api_key is required, please set OPENAI_API_KEY environment")
+    if base_url is None:
+        raise ValueError("base_url is required, please set OPENAI_BASE_URL environment")
+    if base_url.endswith("/"):
+        base_url = base_url[:-1]
+
+    openai_params = {
+        "api_key": api_key,
+        "base_url": base_url,
+    }
+    return openai_params, api_type, api_version
+
+
+def _build_openai_client(init_params: OpenAIParameters):
+    import httpx
+
+    openai_params, api_type, api_version = _initialize_openai_v1(init_params)
+    if api_type == "azure":
+        from openai import AsyncAzureOpenAI
+
+        return AsyncAzureOpenAI(
+            api_key=openai_params["api_key"],
+            api_version=api_version,
+            azure_endpoint=openai_params["base_url"],
+            http_client=httpx.AsyncClient(proxies=init_params.proxies),
+        )
+    else:
+        from openai import AsyncOpenAI
+
+        return AsyncOpenAI(
+            **openai_params, http_client=httpx.AsyncClient(proxies=init_params.proxies)
+        )
+
+
+class OpenAILLMClient(LLMClient):
+    """An implementation of LLMClient using OpenAI API.
+
+    In order to have as few dependencies as possible, we directly use the http API.
+    """
+
+    def __init__(
+        self,
+        api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
+        api_type: Optional[str] = None,
+        api_version: Optional[str] = None,
+        model: Optional[str] = "gpt-3.5-turbo",
+        proxies: Optional["ProxiesTypes"] = None,
+        timeout: Optional[int] = 240,
+        model_alias: Optional[str] = "chatgpt_proxyllm",
+        context_length: Optional[int] = 8192,
+        openai_client: Optional["ClientType"] = None,
+        openai_kwargs: Optional[Dict[str, Any]] = None,
+    ):
+        self._init_params = OpenAIParameters(
+            api_type=api_type,
+            api_base=api_base,
+            api_key=api_key,
+            api_version=api_version,
+            proxies=proxies,
+        )
+
+        self._model = model
+        self._proxies = proxies
+        self._timeout = timeout
+        self._model_alias = model_alias
+        self._context_length = context_length
+        self._client = openai_client
+        self._openai_kwargs = openai_kwargs or {}
+
+    @property
+    def client(self) -> ClientType:
+        if self._client is None:
+            self._client = _build_openai_client(init_params=self._init_params)
+        return self._client
+
+    def _build_request(
+        self, request: ModelRequest, stream: Optional[bool] = False
+    ) -> Dict[str, Any]:
+        payload = {"model": request.model or self._model, "stream": stream}
+
+        # Apply openai kwargs
+        for k, v in self._openai_kwargs.items():
+            payload[k] = v
+        if request.temperature:
+            payload["temperature"] = request.temperature
+        if request.max_new_tokens:
+            payload["max_tokens"] = request.max_new_tokens
+        return payload
+
+    async def generate(self, request: ModelRequest) -> ModelOutput:
+        messages = request.to_openai_messages()
+        payload = self._build_request(request)
+        try:
+            chat_completion = await self.client.chat.completions.create(
+                messages=messages, **payload
+            )
+            text = chat_completion.choices[0].message.content
+            usage = chat_completion.usage.dict()
+            return ModelOutput(text=text, error_code=0, usage=usage)
+        except Exception as e:
+            return ModelOutput(
+                text=f"**LLMServer Generate Error, Please CheckErrorInfo.**: {e}",
+                error_code=1,
+            )
+
+    async def generate_stream(
+        self, request: ModelRequest
+    ) -> AsyncIterator[ModelOutput]:
+        messages = request.to_openai_messages()
+        payload = self._build_request(request)
+        try:
+            chat_completion = await self.client.chat.completions.create(
+                messages=messages, **payload
+            )
+            text = ""
+            for r in chat_completion:
+                if len(r.choices) == 0:
+                    continue
+                if r.choices[0].delta.content is not None:
+                    content = r.choices[0].delta.content
+                    text += content
+                    yield ModelOutput(text=text, error_code=0)
+        except Exception as e:
+            yield ModelOutput(
+                text=f"**LLMServer Generate Error, Please CheckErrorInfo.**: {e}",
+                error_code=1,
+            )
+
+    async def models(self) -> List[ModelMetadata]:
+        model_metadata = ModelMetadata(
+            model=self._model_alias,
+            context_length=await self.get_context_length(),
+        )
+        return [model_metadata]
+
+    async def get_context_length(self) -> int:
+        """Get the context length of the model.
+
+        Returns:
+            int: The context length.
+        # TODO: This is a temporary solution. We should have a better way to get the context length.
+            eg. get real context length from the openai api.
+        """
+        return self._context_length
+
+    async def count_token(self, model: str, prompt: str) -> int:
+        """Count the number of tokens in a given prompt.
+
+        TODO: Get the real number of tokens from the openai api or tiktoken package
+        """
+
+        raise NotImplementedError()
+
+
+async def _to_openai_stream(
+    model: str, output_iter: AsyncIterator[ModelOutput]
+) -> AsyncIterator[str]:
+    """Convert the output_iter to openai stream format.
+
+    Args:
+        model (str): The model name.
+        output_iter (AsyncIterator[ModelOutput]): The output iterator.
+    """
+    import json
+    import shortuuid
+    from fastchat.protocol.openai_api_protocol import (
+        ChatCompletionResponseStreamChoice,
+        ChatCompletionStreamResponse,
+        DeltaMessage,
+    )
+
+    id = f"chatcmpl-{shortuuid.random()}"
+
+    choice_data = ChatCompletionResponseStreamChoice(
+        index=0,
+        delta=DeltaMessage(role="assistant"),
+        finish_reason=None,
+    )
+    chunk = ChatCompletionStreamResponse(id=id, choices=[choice_data], model=model)
+    yield f"data: {chunk.json(exclude_unset=True, ensure_ascii=False)}\n\n"
+
+    previous_text = ""
+    finish_stream_events = []
+    async for model_output in output_iter:
+        model_output: ModelOutput = model_output
+        if model_output.error_code != 0:
+            yield f"data: {json.dumps(model_output.to_dict(), ensure_ascii=False)}\n\n"
+            yield "data: [DONE]\n\n"
+            return
+        decoded_unicode = model_output.text.replace("\ufffd", "")
+        delta_text = decoded_unicode[len(previous_text) :]
+        previous_text = (
+            decoded_unicode
+            if len(decoded_unicode) > len(previous_text)
+            else previous_text
+        )
+
+        if len(delta_text) == 0:
+            delta_text = None
+        choice_data = ChatCompletionResponseStreamChoice(
+            index=0,
+            delta=DeltaMessage(content=delta_text),
+            finish_reason=model_output.finish_reason,
+        )
+        chunk = ChatCompletionStreamResponse(id=id, choices=[choice_data], model=model)
+        if delta_text is None:
+            if model_output.finish_reason is not None:
+                finish_stream_events.append(chunk)
+            continue
+        yield f"data: {chunk.json(exclude_unset=True, ensure_ascii=False)}\n\n"
+    for finish_chunk in finish_stream_events:
+        yield f"data: {finish_chunk.json(exclude_none=True, ensure_ascii=False)}\n\n"
+    yield "data: [DONE]\n\n"
--- a/dbgpt/rag/summary/db_summary_client.py
+++ b/dbgpt/rag/summary/db_summary_client.py
@@ -1,5 +1,6 @@
 import logging

+import traceback
 from dbgpt.component import SystemApp
 from dbgpt._private.config import Config
 from dbgpt.configs.model_config import (
@@ -67,8 +68,9 @@ class DBSummaryClient:
            try:
                self.db_summary_embedding(item["db_name"], item["db_type"])
            except Exception as e:
+                message = traceback.format_exc()
                logger.warn(
-                    f'{item["db_name"]}, {item["db_type"]} summary error!{str(e)}', e
+                    f'{item["db_name"]}, {item["db_type"]} summary error!{str(e)}, detail: {message}'
                )

    def init_db_profile(self, db_summary_client, dbname, embeddings):
--- a/examples/awel/simple_llm_client_example.py
+++ b/examples/awel/simple_llm_client_example.py
@@ -0,0 +1,149 @@
+"""AWEL: Simple llm client example
+
+    DB-GPT will automatically load and execute the current file after startup.
+
+    Example:
+
+    .. code-block:: shell
+
+        DBGPT_SERVER="http://127.0.0.1:5000"
+        curl -X POST $DBGPT_SERVER/api/v1/awel/trigger/examples/simple_client/generate \
+        -H "Content-Type: application/json" -d '{
+            "model": "proxyllm",
+            "messages": "hello"
+        }'
+
+        curl -X POST $DBGPT_SERVER/api/v1/awel/trigger/examples/simple_client/generate_stream \
+        -H "Content-Type: application/json" -d '{
+            "model": "proxyllm",
+            "messages": "hello",
+            "stream": true
+        }'
+
+        curl -X POST $DBGPT_SERVER/api/v1/awel/trigger/examples/simple_client/count_token \
+        -H "Content-Type: application/json" -d '{
+            "model": "proxyllm",
+            "messages": "hello"
+        }'
+
+"""
+from typing import Dict, Any, AsyncIterator, Optional, Union, List
+from dbgpt._private.pydantic import BaseModel, Field
+from dbgpt.component import ComponentType
+from dbgpt.core.awel import DAG, HttpTrigger, MapOperator, TransformStreamAbsOperator
+from dbgpt.core import (
+    ModelMessage,
+    LLMClient,
+    LLMOperator,
+    StreamingLLMOperator,
+    ModelOutput,
+    ModelRequest,
+)
+from dbgpt.model import DefaultLLMClient
+from dbgpt.model.cluster import WorkerManagerFactory
+
+
+class TriggerReqBody(BaseModel):
+    messages: Union[str, List[Dict[str, str]]] = Field(
+        ..., description="User input messages"
+    )
+    model: str = Field(..., description="Model name")
+    stream: Optional[bool] = Field(default=False, description="Whether return stream")
+
+
+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)]
+        await self.current_dag_context.save_to_share_data(
+            "request_model_name", input_value.model
+        )
+        return ModelRequest(
+            model=input_value.model,
+            messages=messages,
+            echo=False,
+        )
+
+
+class LLMMixin:
+    @property
+    def llm_client(self) -> LLMClient:
+        if not self._llm_client:
+            worker_manager = self.system_app.get_component(
+                ComponentType.WORKER_MANAGER_FACTORY, WorkerManagerFactory
+            ).create()
+            self._llm_client = DefaultLLMClient(worker_manager)
+        return self._llm_client
+
+
+class MyLLMOperator(LLMMixin, LLMOperator):
+    def __init__(self, llm_client: LLMClient = None, **kwargs):
+        super().__init__(llm_client, **kwargs)
+
+
+class MyStreamingLLMOperator(LLMMixin, StreamingLLMOperator):
+    def __init__(self, llm_client: LLMClient = None, **kwargs):
+        super().__init__(llm_client, **kwargs)
+
+
+class MyLLMStreamingOperator(TransformStreamAbsOperator[ModelOutput, str]):
+    async def transform_stream(
+        self, input_value: AsyncIterator[ModelOutput]
+    ) -> AsyncIterator[str]:
+        from dbgpt.model.utils.chatgpt_utils import _to_openai_stream
+
+        model = await self.current_dag_context.get_share_data("request_model_name")
+        async for output in _to_openai_stream(model, input_value):
+            yield output
+
+
+class MyModelToolOperator(LLMMixin, MapOperator[TriggerReqBody, Dict[str, Any]]):
+    def __init__(self, llm_client: LLMClient = None, **kwargs):
+        self._llm_client = llm_client
+        MapOperator.__init__(self, **kwargs)
+
+    async def map(self, input_value: TriggerReqBody) -> Dict[str, Any]:
+        prompt_tokens = await self.llm_client.count_token(
+            input_value.model, input_value.messages
+        )
+        available_models = await self.llm_client.models()
+        return {
+            "prompt_tokens": prompt_tokens,
+            "available_models": available_models,
+        }
+
+
+with DAG("dbgpt_awel_simple_llm_client_generate") as client_generate_dag:
+    # Receive http request and trigger dag to run.
+    trigger = HttpTrigger(
+        "/examples/simple_client/generate", methods="POST", request_body=TriggerReqBody
+    )
+    request_handle_task = RequestHandleOperator()
+    model_task = MyLLMOperator()
+    model_parse_task = MapOperator(lambda out: out.to_dict())
+    trigger >> request_handle_task >> model_task >> model_parse_task
+
+with DAG("dbgpt_awel_simple_llm_client_generate_stream") as client_generate_stream_dag:
+    # Receive http request and trigger dag to run.
+    trigger = HttpTrigger(
+        "/examples/simple_client/generate_stream",
+        methods="POST",
+        request_body=TriggerReqBody,
+        streaming_response=True,
+    )
+    request_handle_task = RequestHandleOperator()
+    model_task = MyStreamingLLMOperator()
+    openai_format_stream_task = MyLLMStreamingOperator()
+    trigger >> request_handle_task >> model_task >> openai_format_stream_task
+
+with DAG("dbgpt_awel_simple_llm_client_count_token") as client_count_token_dag:
+    # Receive http request and trigger dag to run.
+    trigger = HttpTrigger(
+        "/examples/simple_client/count_token",
+        methods="POST",
+        request_body=TriggerReqBody,
+    )
+    model_task = MyModelToolOperator()
+    trigger >> model_task
--- a/examples/sdk/simple_sdk_llm_example.py
+++ b/examples/sdk/simple_sdk_llm_example.py
@@ -1,13 +1,19 @@
 import asyncio
 from dbgpt.core.awel import DAG
-from dbgpt.core import BaseOutputParser, OpenAILLM, RequestBuildOperator, PromptTemplate
+from dbgpt.core import (
+    BaseOutputParser,
+    RequestBuildOperator,
+    PromptTemplate,
+    LLMOperator,
+)
+from dbgpt.model import OpenAILLMClient

 with DAG("simple_sdk_llm_example_dag") as dag:
    prompt_task = PromptTemplate.from_template(
        "Write a SQL of {dialect} to query all data of {table_name}."
    )
    model_pre_handle_task = RequestBuildOperator(model="gpt-3.5-turbo")
-    llm_task = OpenAILLM()
+    llm_task = LLMOperator(OpenAILLMClient())
    out_parse_task = BaseOutputParser()
    prompt_task >> model_pre_handle_task >> llm_task >> out_parse_task

--- a/examples/sdk/simple_sdk_llm_sql_example.py
+++ b/examples/sdk/simple_sdk_llm_sql_example.py
@@ -8,10 +8,16 @@ from dbgpt.core.awel import (
    JoinOperator,
    MapOperator,
 )
-from dbgpt.core import SQLOutputParser, OpenAILLM, RequestBuildOperator, PromptTemplate
+from dbgpt.core import (
+    SQLOutputParser,
+    LLMOperator,
+    RequestBuildOperator,
+    PromptTemplate,
+)
 from dbgpt.datasource.rdbms.conn_sqlite import SQLiteTempConnect
 from dbgpt.datasource.operator.datasource_operator import DatasourceOperator
 from dbgpt.rag.operator.datasource import DatasourceRetrieverOperator
+from dbgpt.model import OpenAILLMClient


 def _create_temporary_connection():
@@ -115,7 +121,7 @@ with DAG("simple_sdk_llm_sql_example") as dag:
    prompt_input_task = JoinOperator(combine_function=_join_func)
    prompt_task = PromptTemplate.from_template(_sql_prompt())
    model_pre_handle_task = RequestBuildOperator(model="gpt-3.5-turbo")
-    llm_task = OpenAILLM()
+    llm_task = LLMOperator(OpenAILLMClient())
    out_parse_task = SQLOutputParser()
    sql_parse_task = MapOperator(map_function=lambda x: x["sql"])
    db_query_task = DatasourceOperator(connection=db_connection)
--- a/requirements/dev-requirements.txt
+++ b/requirements/dev-requirements.txt
@@ -10,7 +10,7 @@ pytest-mock
 pytest-recording
 pytesseract==0.3.10
 aioresponses
-# python code format, usage `black .`
-black
 # for git hooks
 pre-commit
+# Type checking
+mypy==0.991
--- a/requirements/lint-requirements.txt
+++ b/requirements/lint-requirements.txt
@@ -0,0 +1,11 @@
+# python code format, usage `black .`
+black==22.8.0
+blackdoc==0.3.7
+flake8==5.0.4
+flake8-bugbear==22.10.25
+flake8-comprehensions==3.10.0
+flake8-docstrings==1.6.0
+flake8-simplify==0.19.3
+flake8-tidy-imports==4.8.0
+isort==5.10.1
+pyupgrade==3.1.0
--- a/setup.py
+++ b/setup.py
@@ -364,23 +364,40 @@ def core_requires():
        "prettytable",
        "cachetools",
    ]
-
-    setup_spec.extras["framework"] = [
-        "coloredlogs",
+    # Just use by DB-GPT internal, we should find the smallest dependency set for run we core unit test.
+    # The dependency "framework" is too large for now.
+    setup_spec.extras["simple_framework"] = setup_spec.extras["core"] + [
+        "pydantic<2,>=1",
        "httpx",
+        "fastapi==0.98.0",
+        "shortuuid",
+        # change from fixed version 2.0.22 to variable version, because other dependencies are >=1.4, such as pydoris is <2
+        "SQLAlchemy>=1.4,<3",
+        # for cache
+        "msgpack",
+        # for cache, TODO pympler has not been updated for a long time and needs to find a new toolkit.
+        "pympler",
        "sqlparse==0.4.4",
+        "duckdb==0.8.1",
+        "duckdb-engine",
+    ]
+    # TODO: remove fschat from simple_framework
+    if BUILD_FROM_SOURCE:
+        setup_spec.extras["simple_framework"].append(
+            f"fschat @ {BUILD_FROM_SOURCE_URL_FAST_CHAT}"
+        )
+    else:
+        setup_spec.extras["simple_framework"].append("fschat")
+
+    setup_spec.extras["framework"] = setup_spec.extras["simple_framework"] + [
+        "coloredlogs",
        "seaborn",
        # https://github.com/eosphoros-ai/DB-GPT/issues/551
        "pandas==2.0.3",
        "auto-gpt-plugin-template",
        "gTTS==2.3.1",
        "langchain>=0.0.286",
-        # change from fixed version 2.0.22 to variable version, because other dependencies are >=1.4, such as pydoris is <2
-        "SQLAlchemy>=1.4,<3",
-        "fastapi==0.98.0",
        "pymysql",
-        "duckdb==0.8.1",
-        "duckdb-engine",
        "jsonschema",
        # TODO move transformers to default
        # "transformers>=4.31.0",
@@ -390,20 +407,10 @@ def core_requires():
        "openpyxl==3.1.2",
        "chardet==5.1.0",
        "xlrd==2.0.1",
-        # for cache, TODO pympler has not been updated for a long time and needs to find a new toolkit.
-        "pympler",
        "aiofiles",
-        # for cache
-        "msgpack",
        # for agent
        "GitPython",
    ]
-    if BUILD_FROM_SOURCE:
-        setup_spec.extras["framework"].append(
-            f"fschat @ {BUILD_FROM_SOURCE_URL_FAST_CHAT}"
-        )
-    else:
-        setup_spec.extras["framework"].append("fschat")


 def knowledge_requires():