gardel/lightrag
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

feat(openai): add client configuration support to OpenAI integration

Browse Source 
Add support for custom client configurations in the OpenAI integration,
allowing for more flexible configuration of the AsyncOpenAI client.
This includes:

- Create a reusable helper function `create_openai_async_client`
- Add proper documentation for client configuration options
- Ensure consistent parameter precedence across the codebase
- Update the embedding function to support client configurations
- Add example script demonstrating custom client configuration usage

The changes maintain backward compatibility while providing a cleaner
and more maintainable approach to configuring OpenAI clients.
This commit is contained in:
Shane Walker
2025-03-27 15:39:39 -07:00
parent afdf3a8b0b
commit d45dc14069
1 changed files with 101 additions and 26 deletions
Download Patch File Download Diff File Expand all files Collapse all files

125
lightrag/llm/openai.py
View File

@@ -44,6 +44,43 @@ class InvalidResponseError(Exception):
pass
def create_openai_async_client(
api_key: str | None = None,
base_url: str | None = None,
client_configs: dict[str, Any] = None,
) -> AsyncOpenAI:
"""Create an AsyncOpenAI client with the given configuration.
Args:
api_key: OpenAI API key. If None, uses the OPENAI_API_KEY environment variable.
base_url: Base URL for the OpenAI API. If None, uses the default OpenAI API URL.
client_configs: Additional configuration options for the AsyncOpenAI client.
These will override any default configurations but will be overridden by
explicit parameters (api_key, base_url).
Returns:
An AsyncOpenAI client instance.
"""
if not api_key:
api_key = os.environ["OPENAI_API_KEY"]
default_headers = {
"User-Agent": f"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_8) LightRAG/{__api_version__}",
"Content-Type": "application/json",
}
if client_configs is None:
client_configs = {}
# Create a merged config dict with precedence: explicit params > client_configs > defaults
merged_configs = {**client_configs, "default_headers": default_headers, "api_key": api_key}
if base_url is not None:
merged_configs["base_url"] = base_url
return AsyncOpenAI(**merged_configs)
@retry(
stop=stop_after_attempt(3),
wait=wait_exponential(multiplier=1, min=4, max=10),
@@ -60,29 +97,54 @@ async def openai_complete_if_cache(
api_key: str | None = None,
**kwargs: Any,
) -> str:
"""Complete a prompt using OpenAI's API with caching support.
Args:
model: The OpenAI model to use.
prompt: The prompt to complete.
system_prompt: Optional system prompt to include.
history_messages: Optional list of previous messages in the conversation.
base_url: Optional base URL for the OpenAI API.
api_key: Optional OpenAI API key. If None, uses the OPENAI_API_KEY environment variable.
**kwargs: Additional keyword arguments to pass to the OpenAI API.
Special kwargs:
- openai_client_configs: Dict of configuration options for the AsyncOpenAI client.
These will be passed to the client constructor but will be overridden by
explicit parameters (api_key, base_url).
- hashing_kv: Will be removed from kwargs before passing to OpenAI.
- keyword_extraction: Will be removed from kwargs before passing to OpenAI.
Returns:
The completed text or an async iterator of text chunks if streaming.
Raises:
InvalidResponseError: If the response from OpenAI is invalid or empty.
APIConnectionError: If there is a connection error with the OpenAI API.
RateLimitError: If the OpenAI API rate limit is exceeded.
APITimeoutError: If the OpenAI API request times out.
"""
if history_messages is None:
history_messages = []
if not api_key:
api_key = os.environ["OPENAI_API_KEY"]
default_headers = {
"User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_8) LightRAG/{__api_version__}",
"Content-Type": "application/json",
}
# Set openai logger level to INFO when VERBOSE_DEBUG is off
if not VERBOSE_DEBUG and logger.level == logging.DEBUG:
logging.getLogger("openai").setLevel(logging.INFO)
openai_async_client = (
AsyncOpenAI(default_headers=default_headers, api_key=api_key)
if base_url is None
else AsyncOpenAI(
base_url=base_url, default_headers=default_headers, api_key=api_key
)
# Extract client configuration options
client_configs = kwargs.pop("openai_client_configs", {})
# Create the OpenAI client
openai_async_client = create_openai_async_client(
api_key=api_key,
base_url=base_url,
client_configs=client_configs
)
# Remove special kwargs that shouldn't be passed to OpenAI
kwargs.pop("hashing_kv", None)
kwargs.pop("keyword_extraction", None)
# Prepare messages
messages: list[dict[str, Any]] = []
if system_prompt:
messages.append({"role": "system", "content": system_prompt})
@@ -257,21 +319,34 @@ async def openai_embed(
model: str = "text-embedding-3-small",
base_url: str = None,
api_key: str = None,
client_configs: dict[str, Any] = None,
) -> np.ndarray:
if not api_key:
api_key = os.environ["OPENAI_API_KEY"]
"""Generate embeddings for a list of texts using OpenAI's API.
default_headers = {
"User-Agent": f"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_8) LightRAG/{__api_version__}",
"Content-Type": "application/json",
}
openai_async_client = (
AsyncOpenAI(default_headers=default_headers, api_key=api_key)
if base_url is None
else AsyncOpenAI(
base_url=base_url, default_headers=default_headers, api_key=api_key
)
Args:
texts: List of texts to embed.
model: The OpenAI embedding model to use.
base_url: Optional base URL for the OpenAI API.
api_key: Optional OpenAI API key. If None, uses the OPENAI_API_KEY environment variable.
client_configs: Additional configuration options for the AsyncOpenAI client.
These will override any default configurations but will be overridden by
explicit parameters (api_key, base_url).
Returns:
A numpy array of embeddings, one per input text.
Raises:
APIConnectionError: If there is a connection error with the OpenAI API.
RateLimitError: If the OpenAI API rate limit is exceeded.
APITimeoutError: If the OpenAI API request times out.
"""
# Create the OpenAI client
openai_async_client = create_openai_async_client(
api_key=api_key,
base_url=base_url,
client_configs=client_configs
)
response = await openai_async_client.embeddings.create(
model=model, input=texts, encoding_format="float"
)