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http_client and http_async_client bugfix (#1454)

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This commit is contained in:
Pranav Puranik
2024-07-02 18:13:33 -05:00
committed by GitHub
parent b305d674de
commit 5258fd91ea
5 changed files with 124 additions and 14 deletions
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9
docs/api-reference/advanced/configuration.mdx
View File

@@ -30,6 +30,7 @@ llm:
response_format:
type: json_object
api_version: 2024-02-01
http_client_proxies: http://testproxy.mem0.net:8000
prompt: |
Use the following pieces of context to answer the query at the end.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
@@ -89,7 +90,8 @@ cache:
"system_prompt": "Act as William Shakespeare. Answer the following questions in the style of William Shakespeare.",
"api_key": "sk-xxx",
"model_kwargs": {"response_format": {"type": "json_object"}},
"api_version": "2024-02-01"
"api_version": "2024-02-01",
"http_client_proxies": "http://testproxy.mem0.net:8000",
}
},
"vectordb": {
@@ -150,7 +152,8 @@ config = {
"Act as William Shakespeare. Answer the following questions in the style of William Shakespeare."
),
'api_key': 'sk-xxx',
"model_kwargs": {"response_format": {"type": "json_object"}}
"model_kwargs": {"response_format": {"type": "json_object"}},
"http_client_proxies": "http://testproxy.mem0.net:8000",
}
},
'vectordb': {
@@ -211,6 +214,8 @@ Alright, let's dive into what each key means in the yaml config above:
- `number_documents` (Integer): Number of documents to pull from the vectordb as context, defaults to 1
- `api_key` (String): The API key for the language model.
- `model_kwargs` (Dict): Keyword arguments to pass to the language model. Used for `aws_bedrock` provider, since it requires different arguments for each model.
- `http_client_proxies` (Dict | String): The proxy server settings used to create `self.http_client` using `httpx.Client(proxies=http_client_proxies)`
- `http_async_client_proxies` (Dict | String): The proxy server settings for async calls used to create `self.http_async_client` using `httpx.AsyncClient(proxies=http_async_client_proxies)`
3. `vectordb` Section:
- `provider` (String): The provider for the vector database, set to 'chroma'. You can find the full list of vector database providers in [our docs](/components/vector-databases).
- `config`:

19
embedchain/config/llm/base.py
View File

@@ -1,7 +1,9 @@
import logging
import re
from string import Template
from typing import Any, Mapping, Optional
from typing import Any, Mapping, Optional, Dict, Union
import httpx
from embedchain.config.base_config import BaseConfig
from embedchain.helpers.json_serializable import register_deserializable
@@ -99,8 +101,8 @@ class BaseLlmConfig(BaseConfig):
base_url: Optional[str] = None,
endpoint: Optional[str] = None,
model_kwargs: Optional[dict[str, Any]] = None,
http_client: Optional[Any] = None,
http_async_client: Optional[Any] = None,
http_client_proxies: Optional[Union[Dict, str]] = None,
http_async_client_proxies: Optional[Union[Dict, str]] = None,
local: Optional[bool] = False,
default_headers: Optional[Mapping[str, str]] = None,
api_version: Optional[str] = None,
@@ -149,6 +151,11 @@ class BaseLlmConfig(BaseConfig):
:type callbacks: Optional[list], optional
:param query_type: The type of query to use, defaults to None
:type query_type: Optional[str], optional
:param http_client_proxies: The proxy server settings used to create self.http_client, defaults to None
:type http_client_proxies: Optional[Dict | str], optional
:param http_async_client_proxies: The proxy server settings for async calls used to create
self.http_async_client, defaults to None
:type http_async_client_proxies: Optional[Dict | str], optional
:param local: If True, the model will be run locally, defaults to False (for huggingface provider)
:type local: Optional[bool], optional
:param default_headers: Set additional HTTP headers to be sent with requests to OpenAI
@@ -181,8 +188,10 @@ class BaseLlmConfig(BaseConfig):
self.base_url = base_url
self.endpoint = endpoint
self.model_kwargs = model_kwargs
self.http_client = http_client
self.http_async_client = http_async_client
self.http_client = httpx.Client(proxies=http_client_proxies) if http_client_proxies else None
self.http_async_client = (
httpx.AsyncClient(proxies=http_async_client_proxies) if http_async_client_proxies else None
)
self.local = local
self.default_headers = default_headers
self.online = online

11
embedchain/llm/openai.py
View File

@@ -56,7 +56,13 @@ class OpenAILlm(BaseLlm):
http_async_client=config.http_async_client,
)
else:
chat = ChatOpenAI(**kwargs, api_key=api_key, base_url=base_url)
chat = ChatOpenAI(
**kwargs,
api_key=api_key,
base_url=base_url,
http_client=config.http_client,
http_async_client=config.http_async_client,
)
if self.tools:
return self._query_function_call(chat, self.tools, messages)
@@ -69,8 +75,7 @@ class OpenAILlm(BaseLlm):
messages: list[BaseMessage],
) -> str:
from langchain.output_parsers.openai_tools import JsonOutputToolsParser
from langchain_core.utils.function_calling import \
convert_to_openai_tool
from langchain_core.utils.function_calling import convert_to_openai_tool
openai_tools = [convert_to_openai_tool(tools)]
chat = chat.bind(tools=openai_tools).pipe(JsonOutputToolsParser())

2
embedchain/utils/misc.py
View File

@@ -442,6 +442,8 @@ def validate_config(config_data):
Optional("base_url"): str,
Optional("default_headers"): dict,
Optional("api_version"): Or(str, datetime.date),
Optional("http_client_proxies"): Or(str, dict),
Optional("http_async_client_proxies"): Or(str, dict),
},
},
Optional("vectordb"): {

97
tests/llm/test_openai.py
View File

@@ -1,5 +1,6 @@
import os
import httpx
import pytest
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
@@ -7,15 +8,27 @@ from embedchain.config import BaseLlmConfig
from embedchain.llm.openai import OpenAILlm
@pytest.fixture
def config():
@pytest.fixture()
def env_config():
os.environ["OPENAI_API_KEY"] = "test_api_key"
os.environ["OPENAI_API_BASE"] = "https://api.openai.com/v1/engines/"
yield
os.environ.pop("OPENAI_API_KEY")
@pytest.fixture
def config(env_config):
config = BaseLlmConfig(
temperature=0.7, max_tokens=50, top_p=0.8, stream=False, system_prompt="System prompt", model="gpt-3.5-turbo"
temperature=0.7,
max_tokens=50,
top_p=0.8,
stream=False,
system_prompt="System prompt",
model="gpt-3.5-turbo",
http_client_proxies=None,
http_async_client_proxies=None,
)
yield config
os.environ.pop("OPENAI_API_KEY")
def test_get_llm_model_answer(config, mocker):
@@ -75,6 +88,8 @@ def test_get_llm_model_answer_without_system_prompt(config, mocker):
model_kwargs={"top_p": config.top_p},
api_key=os.environ["OPENAI_API_KEY"],
base_url=os.environ["OPENAI_API_BASE"],
http_client=None,
http_async_client=None,
)
@@ -93,6 +108,8 @@ def test_get_llm_model_answer_with_special_headers(config, mocker):
api_key=os.environ["OPENAI_API_KEY"],
base_url=os.environ["OPENAI_API_BASE"],
default_headers={"test": "test"},
http_client=None,
http_async_client=None,
)
@@ -110,6 +127,8 @@ def test_get_llm_model_answer_with_model_kwargs(config, mocker):
model_kwargs={"top_p": config.top_p, "response_format": {"type": "json_object"}},
api_key=os.environ["OPENAI_API_KEY"],
base_url=os.environ["OPENAI_API_BASE"],
http_client=None,
http_async_client=None,
)
@@ -136,8 +155,78 @@ def test_get_llm_model_answer_with_tools(config, mocker, mock_return, expected):
model_kwargs={"top_p": config.top_p},
api_key=os.environ["OPENAI_API_KEY"],
base_url=os.environ["OPENAI_API_BASE"],
http_client=None,
http_async_client=None,
)
mocked_convert_to_openai_tool.assert_called_once_with({"test": "test"})
mocked_json_output_tools_parser.assert_called_once()
assert answer == expected
def test_get_llm_model_answer_with_http_client_proxies(env_config, mocker):
mocked_openai_chat = mocker.patch("embedchain.llm.openai.ChatOpenAI")
mock_http_client = mocker.Mock(spec=httpx.Client)
mock_http_client_instance = mocker.Mock(spec=httpx.Client)
mock_http_client.return_value = mock_http_client_instance
mocker.patch("httpx.Client", new=mock_http_client)
config = BaseLlmConfig(
temperature=0.7,
max_tokens=50,
top_p=0.8,
stream=False,
system_prompt="System prompt",
model="gpt-3.5-turbo",
http_client_proxies="http://testproxy.mem0.net:8000",
)
llm = OpenAILlm(config)
llm.get_llm_model_answer("Test query")
mocked_openai_chat.assert_called_once_with(
model=config.model,
temperature=config.temperature,
max_tokens=config.max_tokens,
model_kwargs={"top_p": config.top_p},
api_key=os.environ["OPENAI_API_KEY"],
base_url=os.environ["OPENAI_API_BASE"],
http_client=mock_http_client_instance,
http_async_client=None,
)
mock_http_client.assert_called_once_with(proxies="http://testproxy.mem0.net:8000")
def test_get_llm_model_answer_with_http_async_client_proxies(env_config, mocker):
mocked_openai_chat = mocker.patch("embedchain.llm.openai.ChatOpenAI")
mock_http_async_client = mocker.Mock(spec=httpx.AsyncClient)
mock_http_async_client_instance = mocker.Mock(spec=httpx.AsyncClient)
mock_http_async_client.return_value = mock_http_async_client_instance
mocker.patch("httpx.AsyncClient", new=mock_http_async_client)
config = BaseLlmConfig(
temperature=0.7,
max_tokens=50,
top_p=0.8,
stream=False,
system_prompt="System prompt",
model="gpt-3.5-turbo",
http_async_client_proxies={"http://": "http://testproxy.mem0.net:8000"},
)
llm = OpenAILlm(config)
llm.get_llm_model_answer("Test query")
mocked_openai_chat.assert_called_once_with(
model=config.model,
temperature=config.temperature,
max_tokens=config.max_tokens,
model_kwargs={"top_p": config.top_p},
api_key=os.environ["OPENAI_API_KEY"],
base_url=os.environ["OPENAI_API_BASE"],
http_client=None,
http_async_client=mock_http_async_client_instance,
)
mock_http_async_client.assert_called_once_with(proxies={"http://": "http://testproxy.mem0.net:8000"})