mirror of
https://github.com/BerriAI/litellm.git
synced 2025-04-26 19:24:27 +00:00
Merge branch 'main' into litellm_vertex_completion_httpx
This commit is contained in:
Krish Dholakia
GitHub
commit
05e21441a6
56 changed files with 568 additions and 145 deletions
|
|
@ -202,6 +202,7 @@ jobs:
|
|||
-e REDIS_PORT=$REDIS_PORT \
|
||||
-e AZURE_FRANCE_API_KEY=$AZURE_FRANCE_API_KEY \
|
||||
-e AZURE_EUROPE_API_KEY=$AZURE_EUROPE_API_KEY \
|
||||
-e MISTRAL_API_KEY=$MISTRAL_API_KEY \
|
||||
-e AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID \
|
||||
-e AWS_SECRET_ACCESS_KEY=$AWS_SECRET_ACCESS_KEY \
|
||||
-e AWS_REGION_NAME=$AWS_REGION_NAME \
|
||||
|
|
|
|||
|
|
@ -1,10 +1,13 @@
|
|||
# Clarifai
|
||||
Anthropic, OpenAI, Mistral, Llama and Gemini LLMs are Supported on Clarifai.
|
||||
|
||||
:::warning
|
||||
|
||||
Streaming is not yet supported on using clarifai and litellm. Tracking support here: https://github.com/BerriAI/litellm/issues/4162
|
||||
|
||||
:::
|
||||
|
||||
## Pre-Requisites
|
||||
|
||||
`pip install clarifai`
|
||||
|
||||
`pip install litellm`
|
||||
|
||||
## Required Environment Variables
|
||||
|
|
@ -12,6 +15,7 @@ To obtain your Clarifai Personal access token follow this [link](https://docs.cl
|
|||
|
||||
```python
|
||||
os.environ["CLARIFAI_API_KEY"] = "YOUR_CLARIFAI_PAT" # CLARIFAI_PAT
|
||||
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
|
@ -68,7 +72,7 @@ Example Usage - Note: liteLLM supports all models deployed on Clarifai
|
|||
| clarifai/meta.Llama-2.codeLlama-70b-Python | `completion('clarifai/meta.Llama-2.codeLlama-70b-Python', messages)`|
|
||||
| clarifai/meta.Llama-2.codeLlama-70b-Instruct | `completion('clarifai/meta.Llama-2.codeLlama-70b-Instruct', messages)` |
|
||||
|
||||
## Mistal LLMs
|
||||
## Mistral LLMs
|
||||
| Model Name | Function Call |
|
||||
|---------------------------------------------|------------------------------------------------------------------------|
|
||||
| clarifai/mistralai.completion.mixtral-8x22B | `completion('clarifai/mistralai.completion.mixtral-8x22B', messages)` |
|
||||
|
|
|
|||
|
|
@ -558,6 +558,29 @@ All models listed [here](https://github.com/BerriAI/litellm/blob/57f37f743886a02
|
|||
| text-embedding-preview-0409 | `embedding(model="vertex_ai/text-embedding-preview-0409", input)` |
|
||||
| text-multilingual-embedding-preview-0409 | `embedding(model="vertex_ai/text-multilingual-embedding-preview-0409", input)` |
|
||||
|
||||
### Advanced Use `task_type` and `title` (Vertex Specific Params)
|
||||
|
||||
👉 `task_type` and `title` are vertex specific params
|
||||
|
||||
LiteLLM Supported Vertex Specific Params
|
||||
|
||||
```python
|
||||
auto_truncate: Optional[bool] = None
|
||||
task_type: Optional[Literal["RETRIEVAL_QUERY","RETRIEVAL_DOCUMENT", "SEMANTIC_SIMILARITY", "CLASSIFICATION", "CLUSTERING", "QUESTION_ANSWERING", "FACT_VERIFICATION"]] = None
|
||||
title: Optional[str] = None # The title of the document to be embedded. (only valid with task_type=RETRIEVAL_DOCUMENT).
|
||||
```
|
||||
|
||||
**Example Usage with LiteLLM**
|
||||
```python
|
||||
response = litellm.embedding(
|
||||
model="vertex_ai/text-embedding-004",
|
||||
input=["good morning from litellm", "gm"]
|
||||
task_type = "RETRIEVAL_DOCUMENT",
|
||||
dimensions=1,
|
||||
auto_truncate=True,
|
||||
)
|
||||
```
|
||||
|
||||
## Image Generation Models
|
||||
|
||||
Usage
|
||||
|
|
|
|||
|
|
@ -1,5 +1,6 @@
|
|||
import Tabs from '@theme/Tabs';
|
||||
import TabItem from '@theme/TabItem';
|
||||
import Image from '@theme/IdealImage';
|
||||
|
||||
# 🐳 Docker, Deploying LiteLLM Proxy
|
||||
|
||||
|
|
@ -537,7 +538,9 @@ ghcr.io/berriai/litellm-database:main-latest --config your_config.yaml
|
|||
|
||||
## Advanced Deployment Settings
|
||||
|
||||
### Customization of the server root path
|
||||
### 1. Customization of the server root path (custom Proxy base url)
|
||||
|
||||
💥 Use this when you want to serve LiteLLM on a custom base url path like `https://localhost:4000/api/v1`
|
||||
|
||||
:::info
|
||||
|
||||
|
|
@ -548,9 +551,29 @@ In a Kubernetes deployment, it's possible to utilize a shared DNS to host multip
|
|||
Customize the root path to eliminate the need for employing multiple DNS configurations during deployment.
|
||||
|
||||
👉 Set `SERVER_ROOT_PATH` in your .env and this will be set as your server root path
|
||||
```
|
||||
export SERVER_ROOT_PATH="/api/v1"
|
||||
```
|
||||
|
||||
**Step 1. Run Proxy with `SERVER_ROOT_PATH` set in your env **
|
||||
|
||||
### Setting SSL Certification
|
||||
```shell
|
||||
docker run --name litellm-proxy \
|
||||
-e DATABASE_URL=postgresql://<user>:<password>@<host>:<port>/<dbname> \
|
||||
-e SERVER_ROOT_PATH="/api/v1" \
|
||||
-p 4000:4000 \
|
||||
ghcr.io/berriai/litellm-database:main-latest --config your_config.yaml
|
||||
```
|
||||
|
||||
After running the proxy you can access it on `http://0.0.0.0:4000/api/v1/` (since we set `SERVER_ROOT_PATH="/api/v1"`)
|
||||
|
||||
**Step 2. Verify Running on correct path**
|
||||
|
||||
<Image img={require('../../img/custom_root_path.png')} />
|
||||
|
||||
**That's it**, that's all you need to run the proxy on a custom root path
|
||||
|
||||
### 2. Setting SSL Certification
|
||||
|
||||
Use this, If you need to set ssl certificates for your on prem litellm proxy
|
||||
|
||||
|
|
|
|||
BIN
docs/my-website/img/custom_root_path.png
Normal file
BIN
docs/my-website/img/custom_root_path.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 151 KiB |
|
|
@ -766,8 +766,8 @@ from .llms.gemini import GeminiConfig
|
|||
from .llms.nlp_cloud import NLPCloudConfig
|
||||
from .llms.aleph_alpha import AlephAlphaConfig
|
||||
from .llms.petals import PetalsConfig
|
||||
from .llms.vertex_ai import VertexAIConfig
|
||||
from .llms.vertex_httpx import VertexGeminiConfig
|
||||
from .llms.vertex_ai import VertexAIConfig, VertexAITextEmbeddingConfig
|
||||
from .llms.vertex_ai_anthropic import VertexAIAnthropicConfig
|
||||
from .llms.sagemaker import SagemakerConfig
|
||||
from .llms.ollama import OllamaConfig
|
||||
|
|
@ -789,7 +789,9 @@ from .llms.openai import (
|
|||
OpenAIConfig,
|
||||
OpenAITextCompletionConfig,
|
||||
MistralConfig,
|
||||
MistralEmbeddingConfig,
|
||||
DeepInfraConfig,
|
||||
AzureAIStudioConfig,
|
||||
)
|
||||
from .llms.azure import (
|
||||
AzureOpenAIConfig,
|
||||
|
|
|
|||
|
|
@ -36,6 +36,9 @@ from ..types.llms.openai import (
|
|||
AsyncAssistantStreamManager,
|
||||
AssistantStreamManager,
|
||||
)
|
||||
from litellm.caching import DualCache
|
||||
|
||||
azure_ad_cache = DualCache()
|
||||
|
||||
|
||||
class AzureOpenAIError(Exception):
|
||||
|
|
@ -309,9 +312,10 @@ def select_azure_base_url_or_endpoint(azure_client_params: dict):
|
|||
|
||||
def get_azure_ad_token_from_oidc(azure_ad_token: str):
|
||||
azure_client_id = os.getenv("AZURE_CLIENT_ID", None)
|
||||
azure_tenant = os.getenv("AZURE_TENANT_ID", None)
|
||||
azure_tenant_id = os.getenv("AZURE_TENANT_ID", None)
|
||||
azure_authority_host = os.getenv("AZURE_AUTHORITY_HOST", "https://login.microsoftonline.com")
|
||||
|
||||
if azure_client_id is None or azure_tenant is None:
|
||||
if azure_client_id is None or azure_tenant_id is None:
|
||||
raise AzureOpenAIError(
|
||||
status_code=422,
|
||||
message="AZURE_CLIENT_ID and AZURE_TENANT_ID must be set",
|
||||
|
|
@ -325,8 +329,19 @@ def get_azure_ad_token_from_oidc(azure_ad_token: str):
|
|||
message="OIDC token could not be retrieved from secret manager.",
|
||||
)
|
||||
|
||||
azure_ad_token_cache_key = json.dumps({
|
||||
"azure_client_id": azure_client_id,
|
||||
"azure_tenant_id": azure_tenant_id,
|
||||
"azure_authority_host": azure_authority_host,
|
||||
"oidc_token": oidc_token,
|
||||
})
|
||||
|
||||
azure_ad_token_access_token = azure_ad_cache.get_cache(azure_ad_token_cache_key)
|
||||
if azure_ad_token_access_token is not None:
|
||||
return azure_ad_token_access_token
|
||||
|
||||
req_token = httpx.post(
|
||||
f"https://login.microsoftonline.com/{azure_tenant}/oauth2/v2.0/token",
|
||||
f"{azure_authority_host}/{azure_tenant_id}/oauth2/v2.0/token",
|
||||
data={
|
||||
"client_id": azure_client_id,
|
||||
"grant_type": "client_credentials",
|
||||
|
|
@ -342,12 +357,23 @@ def get_azure_ad_token_from_oidc(azure_ad_token: str):
|
|||
message=req_token.text,
|
||||
)
|
||||
|
||||
possible_azure_ad_token = req_token.json().get("access_token", None)
|
||||
azure_ad_token_json = req_token.json()
|
||||
azure_ad_token_access_token = azure_ad_token_json.get("access_token", None)
|
||||
azure_ad_token_expires_in = azure_ad_token_json.get("expires_in", None)
|
||||
|
||||
if possible_azure_ad_token is None:
|
||||
raise AzureOpenAIError(status_code=422, message="Azure AD Token not returned")
|
||||
if azure_ad_token_access_token is None:
|
||||
raise AzureOpenAIError(
|
||||
status_code=422, message="Azure AD Token access_token not returned"
|
||||
)
|
||||
|
||||
return possible_azure_ad_token
|
||||
if azure_ad_token_expires_in is None:
|
||||
raise AzureOpenAIError(
|
||||
status_code=422, message="Azure AD Token expires_in not returned"
|
||||
)
|
||||
|
||||
azure_ad_cache.set_cache(key=azure_ad_token_cache_key, value=azure_ad_token_access_token, ttl=azure_ad_token_expires_in)
|
||||
|
||||
return azure_ad_token_access_token
|
||||
|
||||
|
||||
class AzureChatCompletion(BaseLLM):
|
||||
|
|
|
|||
|
|
@ -53,7 +53,9 @@ from litellm.types.llms.openai import (
|
|||
ChatCompletionToolCallFunctionChunk,
|
||||
ChatCompletionDeltaChunk,
|
||||
)
|
||||
from litellm.caching import DualCache
|
||||
|
||||
iam_cache = DualCache()
|
||||
|
||||
class AmazonCohereChatConfig:
|
||||
"""
|
||||
|
|
@ -325,11 +327,16 @@ class BedrockLLM(BaseLLM):
|
|||
) = params_to_check
|
||||
|
||||
### CHECK STS ###
|
||||
if (
|
||||
aws_web_identity_token is not None
|
||||
and aws_role_name is not None
|
||||
and aws_session_name is not None
|
||||
):
|
||||
if aws_web_identity_token is not None and aws_role_name is not None and aws_session_name is not None:
|
||||
iam_creds_cache_key = json.dumps({
|
||||
"aws_web_identity_token": aws_web_identity_token,
|
||||
"aws_role_name": aws_role_name,
|
||||
"aws_session_name": aws_session_name,
|
||||
"aws_region_name": aws_region_name,
|
||||
})
|
||||
|
||||
iam_creds_dict = iam_cache.get_cache(iam_creds_cache_key)
|
||||
if iam_creds_dict is None:
|
||||
oidc_token = get_secret(aws_web_identity_token)
|
||||
|
||||
if oidc_token is None:
|
||||
|
|
@ -338,7 +345,11 @@ class BedrockLLM(BaseLLM):
|
|||
status_code=401,
|
||||
)
|
||||
|
||||
sts_client = boto3.client("sts")
|
||||
sts_client = boto3.client(
|
||||
"sts",
|
||||
region_name=aws_region_name,
|
||||
endpoint_url=f"https://sts.{aws_region_name}.amazonaws.com"
|
||||
)
|
||||
|
||||
# https://docs.aws.amazon.com/STS/latest/APIReference/API_AssumeRoleWithWebIdentity.html
|
||||
# https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sts/client/assume_role_with_web_identity.html
|
||||
|
|
@ -349,14 +360,20 @@ class BedrockLLM(BaseLLM):
|
|||
DurationSeconds=3600,
|
||||
)
|
||||
|
||||
session = boto3.Session(
|
||||
aws_access_key_id=sts_response["Credentials"]["AccessKeyId"],
|
||||
aws_secret_access_key=sts_response["Credentials"]["SecretAccessKey"],
|
||||
aws_session_token=sts_response["Credentials"]["SessionToken"],
|
||||
region_name=aws_region_name,
|
||||
)
|
||||
iam_creds_dict = {
|
||||
"aws_access_key_id": sts_response["Credentials"]["AccessKeyId"],
|
||||
"aws_secret_access_key": sts_response["Credentials"]["SecretAccessKey"],
|
||||
"aws_session_token": sts_response["Credentials"]["SessionToken"],
|
||||
"region_name": aws_region_name,
|
||||
}
|
||||
|
||||
return session.get_credentials()
|
||||
iam_cache.set_cache(key=iam_creds_cache_key, value=json.dumps(iam_creds_dict), ttl=3600 - 60)
|
||||
|
||||
session = boto3.Session(**iam_creds_dict)
|
||||
|
||||
iam_creds = session.get_credentials()
|
||||
|
||||
return iam_creds
|
||||
elif aws_role_name is not None and aws_session_name is not None:
|
||||
sts_client = boto3.client(
|
||||
"sts",
|
||||
|
|
@ -1416,11 +1433,16 @@ class BedrockConverseLLM(BaseLLM):
|
|||
) = params_to_check
|
||||
|
||||
### CHECK STS ###
|
||||
if (
|
||||
aws_web_identity_token is not None
|
||||
and aws_role_name is not None
|
||||
and aws_session_name is not None
|
||||
):
|
||||
if aws_web_identity_token is not None and aws_role_name is not None and aws_session_name is not None:
|
||||
iam_creds_cache_key = json.dumps({
|
||||
"aws_web_identity_token": aws_web_identity_token,
|
||||
"aws_role_name": aws_role_name,
|
||||
"aws_session_name": aws_session_name,
|
||||
"aws_region_name": aws_region_name,
|
||||
})
|
||||
|
||||
iam_creds_dict = iam_cache.get_cache(iam_creds_cache_key)
|
||||
if iam_creds_dict is None:
|
||||
oidc_token = get_secret(aws_web_identity_token)
|
||||
|
||||
if oidc_token is None:
|
||||
|
|
@ -1429,7 +1451,11 @@ class BedrockConverseLLM(BaseLLM):
|
|||
status_code=401,
|
||||
)
|
||||
|
||||
sts_client = boto3.client("sts")
|
||||
sts_client = boto3.client(
|
||||
"sts",
|
||||
region_name=aws_region_name,
|
||||
endpoint_url=f"https://sts.{aws_region_name}.amazonaws.com"
|
||||
)
|
||||
|
||||
# https://docs.aws.amazon.com/STS/latest/APIReference/API_AssumeRoleWithWebIdentity.html
|
||||
# https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sts/client/assume_role_with_web_identity.html
|
||||
|
|
@ -1440,14 +1466,20 @@ class BedrockConverseLLM(BaseLLM):
|
|||
DurationSeconds=3600,
|
||||
)
|
||||
|
||||
session = boto3.Session(
|
||||
aws_access_key_id=sts_response["Credentials"]["AccessKeyId"],
|
||||
aws_secret_access_key=sts_response["Credentials"]["SecretAccessKey"],
|
||||
aws_session_token=sts_response["Credentials"]["SessionToken"],
|
||||
region_name=aws_region_name,
|
||||
)
|
||||
iam_creds_dict = {
|
||||
"aws_access_key_id": sts_response["Credentials"]["AccessKeyId"],
|
||||
"aws_secret_access_key": sts_response["Credentials"]["SecretAccessKey"],
|
||||
"aws_session_token": sts_response["Credentials"]["SessionToken"],
|
||||
"region_name": aws_region_name,
|
||||
}
|
||||
|
||||
return session.get_credentials()
|
||||
iam_cache.set_cache(key=iam_creds_cache_key, value=json.dumps(iam_creds_dict), ttl=3600 - 60)
|
||||
|
||||
session = boto3.Session(**iam_creds_dict)
|
||||
|
||||
iam_creds = session.get_credentials()
|
||||
|
||||
return iam_creds
|
||||
elif aws_role_name is not None and aws_session_name is not None:
|
||||
sts_client = boto3.client(
|
||||
"sts",
|
||||
|
|
|
|||
|
|
@ -139,6 +139,7 @@ def process_response(
|
|||
|
||||
def convert_model_to_url(model: str, api_base: str):
|
||||
user_id, app_id, model_id = model.split(".")
|
||||
model_id = model_id.lower()
|
||||
return f"{api_base}/users/{user_id}/apps/{app_id}/models/{model_id}/outputs"
|
||||
|
||||
|
||||
|
|
@ -171,19 +172,55 @@ async def async_completion(
|
|||
|
||||
async_handler = AsyncHTTPHandler(timeout=httpx.Timeout(timeout=600.0, connect=5.0))
|
||||
response = await async_handler.post(
|
||||
api_base, headers=headers, data=json.dumps(data)
|
||||
url=model, headers=headers, data=json.dumps(data)
|
||||
)
|
||||
|
||||
return process_response(
|
||||
model=model,
|
||||
prompt=prompt,
|
||||
response=response,
|
||||
model_response=model_response,
|
||||
logging_obj.post_call(
|
||||
input=prompt,
|
||||
api_key=api_key,
|
||||
data=data,
|
||||
encoding=encoding,
|
||||
logging_obj=logging_obj,
|
||||
original_response=response.text,
|
||||
additional_args={"complete_input_dict": data},
|
||||
)
|
||||
## RESPONSE OBJECT
|
||||
try:
|
||||
completion_response = response.json()
|
||||
except Exception:
|
||||
raise ClarifaiError(
|
||||
message=response.text, status_code=response.status_code, url=model
|
||||
)
|
||||
# print(completion_response)
|
||||
try:
|
||||
choices_list = []
|
||||
for idx, item in enumerate(completion_response["outputs"]):
|
||||
if len(item["data"]["text"]["raw"]) > 0:
|
||||
message_obj = Message(content=item["data"]["text"]["raw"])
|
||||
else:
|
||||
message_obj = Message(content=None)
|
||||
choice_obj = Choices(
|
||||
finish_reason="stop",
|
||||
index=idx + 1, # check
|
||||
message=message_obj,
|
||||
)
|
||||
choices_list.append(choice_obj)
|
||||
model_response["choices"] = choices_list
|
||||
|
||||
except Exception as e:
|
||||
raise ClarifaiError(
|
||||
message=traceback.format_exc(), status_code=response.status_code, url=model
|
||||
)
|
||||
|
||||
# Calculate Usage
|
||||
prompt_tokens = len(encoding.encode(prompt))
|
||||
completion_tokens = len(
|
||||
encoding.encode(model_response["choices"][0]["message"].get("content"))
|
||||
)
|
||||
model_response["model"] = model
|
||||
model_response["usage"] = Usage(
|
||||
prompt_tokens=prompt_tokens,
|
||||
completion_tokens=completion_tokens,
|
||||
total_tokens=prompt_tokens + completion_tokens,
|
||||
)
|
||||
return model_response
|
||||
|
||||
|
||||
def completion(
|
||||
|
|
@ -241,7 +278,7 @@ def completion(
|
|||
additional_args={
|
||||
"complete_input_dict": data,
|
||||
"headers": headers,
|
||||
"api_base": api_base,
|
||||
"api_base": model,
|
||||
},
|
||||
)
|
||||
if acompletion == True:
|
||||
|
|
|
|||
|
|
@ -28,6 +28,7 @@ from .prompt_templates.factory import prompt_factory, custom_prompt
|
|||
from openai import OpenAI, AsyncOpenAI
|
||||
from ..types.llms.openai import *
|
||||
import openai
|
||||
from litellm.types.utils import ProviderField
|
||||
|
||||
|
||||
class OpenAIError(Exception):
|
||||
|
|
@ -164,6 +165,68 @@ class MistralConfig:
|
|||
return optional_params
|
||||
|
||||
|
||||
class MistralEmbeddingConfig:
|
||||
"""
|
||||
Reference: https://docs.mistral.ai/api/#operation/createEmbedding
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
) -> None:
|
||||
locals_ = locals().copy()
|
||||
for key, value in locals_.items():
|
||||
if key != "self" and value is not None:
|
||||
setattr(self.__class__, key, value)
|
||||
|
||||
@classmethod
|
||||
def get_config(cls):
|
||||
return {
|
||||
k: v
|
||||
for k, v in cls.__dict__.items()
|
||||
if not k.startswith("__")
|
||||
and not isinstance(
|
||||
v,
|
||||
(
|
||||
types.FunctionType,
|
||||
types.BuiltinFunctionType,
|
||||
classmethod,
|
||||
staticmethod,
|
||||
),
|
||||
)
|
||||
and v is not None
|
||||
}
|
||||
|
||||
def get_supported_openai_params(self):
|
||||
return [
|
||||
"encoding_format",
|
||||
]
|
||||
|
||||
def map_openai_params(self, non_default_params: dict, optional_params: dict):
|
||||
for param, value in non_default_params.items():
|
||||
if param == "encoding_format":
|
||||
optional_params["encoding_format"] = value
|
||||
return optional_params
|
||||
|
||||
|
||||
class AzureAIStudioConfig:
|
||||
def get_required_params(self) -> List[ProviderField]:
|
||||
"""For a given provider, return it's required fields with a description"""
|
||||
return [
|
||||
ProviderField(
|
||||
field_name="api_key",
|
||||
field_type="string",
|
||||
field_description="Your Azure AI Studio API Key.",
|
||||
field_value="zEJ...",
|
||||
),
|
||||
ProviderField(
|
||||
field_name="api_base",
|
||||
field_type="string",
|
||||
field_description="Your Azure AI Studio API Base.",
|
||||
field_value="https://Mistral-serverless.",
|
||||
),
|
||||
]
|
||||
|
||||
|
||||
class DeepInfraConfig:
|
||||
"""
|
||||
Reference: https://deepinfra.com/docs/advanced/openai_api
|
||||
|
|
|
|||
|
|
@ -4,6 +4,7 @@ from enum import Enum
|
|||
import requests # type: ignore
|
||||
import time
|
||||
from typing import Callable, Optional, Union, List, Literal, Any
|
||||
from pydantic import BaseModel
|
||||
from litellm.utils import ModelResponse, Usage, CustomStreamWrapper, map_finish_reason
|
||||
import litellm, uuid
|
||||
import httpx, inspect # type: ignore
|
||||
|
|
@ -1298,6 +1299,95 @@ async def async_streaming(
|
|||
return streamwrapper
|
||||
|
||||
|
||||
class VertexAITextEmbeddingConfig(BaseModel):
|
||||
"""
|
||||
Reference: https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/text-embeddings-api#TextEmbeddingInput
|
||||
|
||||
Args:
|
||||
auto_truncate: Optional(bool) If True, will truncate input text to fit within the model's max input length.
|
||||
task_type: Optional(str) The type of task to be performed. The default is "RETRIEVAL_QUERY".
|
||||
title: Optional(str) The title of the document to be embedded. (only valid with task_type=RETRIEVAL_DOCUMENT).
|
||||
"""
|
||||
|
||||
auto_truncate: Optional[bool] = None
|
||||
task_type: Optional[
|
||||
Literal[
|
||||
"RETRIEVAL_QUERY",
|
||||
"RETRIEVAL_DOCUMENT",
|
||||
"SEMANTIC_SIMILARITY",
|
||||
"CLASSIFICATION",
|
||||
"CLUSTERING",
|
||||
"QUESTION_ANSWERING",
|
||||
"FACT_VERIFICATION",
|
||||
]
|
||||
] = None
|
||||
title: Optional[str] = None
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
auto_truncate: Optional[bool] = None,
|
||||
task_type: Optional[
|
||||
Literal[
|
||||
"RETRIEVAL_QUERY",
|
||||
"RETRIEVAL_DOCUMENT",
|
||||
"SEMANTIC_SIMILARITY",
|
||||
"CLASSIFICATION",
|
||||
"CLUSTERING",
|
||||
"QUESTION_ANSWERING",
|
||||
"FACT_VERIFICATION",
|
||||
]
|
||||
] = None,
|
||||
title: Optional[str] = None,
|
||||
) -> None:
|
||||
locals_ = locals()
|
||||
for key, value in locals_.items():
|
||||
if key != "self" and value is not None:
|
||||
setattr(self.__class__, key, value)
|
||||
|
||||
@classmethod
|
||||
def get_config(cls):
|
||||
return {
|
||||
k: v
|
||||
for k, v in cls.__dict__.items()
|
||||
if not k.startswith("__")
|
||||
and not isinstance(
|
||||
v,
|
||||
(
|
||||
types.FunctionType,
|
||||
types.BuiltinFunctionType,
|
||||
classmethod,
|
||||
staticmethod,
|
||||
),
|
||||
)
|
||||
and v is not None
|
||||
}
|
||||
|
||||
def get_supported_openai_params(self):
|
||||
return [
|
||||
"dimensions",
|
||||
]
|
||||
|
||||
def map_openai_params(self, non_default_params: dict, optional_params: dict):
|
||||
for param, value in non_default_params.items():
|
||||
if param == "dimensions":
|
||||
optional_params["output_dimensionality"] = value
|
||||
return optional_params
|
||||
|
||||
def get_mapped_special_auth_params(self) -> dict:
|
||||
"""
|
||||
Common auth params across bedrock/vertex_ai/azure/watsonx
|
||||
"""
|
||||
return {"project": "vertex_project", "region_name": "vertex_location"}
|
||||
|
||||
def map_special_auth_params(self, non_default_params: dict, optional_params: dict):
|
||||
mapped_params = self.get_mapped_special_auth_params()
|
||||
|
||||
for param, value in non_default_params.items():
|
||||
if param in mapped_params:
|
||||
optional_params[mapped_params[param]] = value
|
||||
return optional_params
|
||||
|
||||
|
||||
def embedding(
|
||||
model: str,
|
||||
input: Union[list, str],
|
||||
|
|
@ -1321,7 +1411,7 @@ def embedding(
|
|||
message="vertexai import failed please run `pip install google-cloud-aiplatform`",
|
||||
)
|
||||
|
||||
from vertexai.language_models import TextEmbeddingModel
|
||||
from vertexai.language_models import TextEmbeddingModel, TextEmbeddingInput
|
||||
import google.auth # type: ignore
|
||||
|
||||
## Load credentials with the correct quota project ref: https://github.com/googleapis/python-aiplatform/issues/2557#issuecomment-1709284744
|
||||
|
|
@ -1352,6 +1442,16 @@ def embedding(
|
|||
if isinstance(input, str):
|
||||
input = [input]
|
||||
|
||||
if optional_params is not None and isinstance(optional_params, dict):
|
||||
if optional_params.get("task_type") or optional_params.get("title"):
|
||||
# if user passed task_type or title, cast to TextEmbeddingInput
|
||||
_task_type = optional_params.pop("task_type", None)
|
||||
_title = optional_params.pop("title", None)
|
||||
input = [
|
||||
TextEmbeddingInput(text=x, task_type=_task_type, title=_title)
|
||||
for x in input
|
||||
]
|
||||
|
||||
try:
|
||||
llm_model = TextEmbeddingModel.from_pretrained(model)
|
||||
except Exception as e:
|
||||
|
|
@ -1368,7 +1468,8 @@ def embedding(
|
|||
encoding=encoding,
|
||||
)
|
||||
|
||||
request_str = f"""embeddings = llm_model.get_embeddings({input})"""
|
||||
_input_dict = {"texts": input, **optional_params}
|
||||
request_str = f"""embeddings = llm_model.get_embeddings({_input_dict})"""
|
||||
## LOGGING PRE-CALL
|
||||
logging_obj.pre_call(
|
||||
input=input,
|
||||
|
|
@ -1380,7 +1481,7 @@ def embedding(
|
|||
)
|
||||
|
||||
try:
|
||||
embeddings = llm_model.get_embeddings(input)
|
||||
embeddings = llm_model.get_embeddings(**_input_dict)
|
||||
except Exception as e:
|
||||
raise VertexAIError(status_code=500, message=str(e))
|
||||
|
||||
|
|
@ -1388,6 +1489,7 @@ def embedding(
|
|||
logging_obj.post_call(input=input, api_key=None, original_response=embeddings)
|
||||
## Populate OpenAI compliant dictionary
|
||||
embedding_response = []
|
||||
input_tokens: int = 0
|
||||
for idx, embedding in enumerate(embeddings):
|
||||
embedding_response.append(
|
||||
{
|
||||
|
|
@ -1396,14 +1498,10 @@ def embedding(
|
|||
"embedding": embedding.values,
|
||||
}
|
||||
)
|
||||
input_tokens += embedding.statistics.token_count
|
||||
model_response["object"] = "list"
|
||||
model_response["data"] = embedding_response
|
||||
model_response["model"] = model
|
||||
input_tokens = 0
|
||||
|
||||
input_str = "".join(input)
|
||||
|
||||
input_tokens += len(encoding.encode(input_str))
|
||||
|
||||
usage = Usage(
|
||||
prompt_tokens=input_tokens, completion_tokens=0, total_tokens=input_tokens
|
||||
|
|
@ -1425,7 +1523,8 @@ async def async_embedding(
|
|||
"""
|
||||
Async embedding implementation
|
||||
"""
|
||||
request_str = f"""embeddings = llm_model.get_embeddings({input})"""
|
||||
_input_dict = {"texts": input, **optional_params}
|
||||
request_str = f"""embeddings = llm_model.get_embeddings({_input_dict})"""
|
||||
## LOGGING PRE-CALL
|
||||
logging_obj.pre_call(
|
||||
input=input,
|
||||
|
|
@ -1437,7 +1536,7 @@ async def async_embedding(
|
|||
)
|
||||
|
||||
try:
|
||||
embeddings = await client.get_embeddings_async(input)
|
||||
embeddings = await client.get_embeddings_async(**_input_dict)
|
||||
except Exception as e:
|
||||
raise VertexAIError(status_code=500, message=str(e))
|
||||
|
||||
|
|
@ -1445,6 +1544,7 @@ async def async_embedding(
|
|||
logging_obj.post_call(input=input, api_key=None, original_response=embeddings)
|
||||
## Populate OpenAI compliant dictionary
|
||||
embedding_response = []
|
||||
input_tokens: int = 0
|
||||
for idx, embedding in enumerate(embeddings):
|
||||
embedding_response.append(
|
||||
{
|
||||
|
|
@ -1453,18 +1553,13 @@ async def async_embedding(
|
|||
"embedding": embedding.values,
|
||||
}
|
||||
)
|
||||
input_tokens += embedding.statistics.token_count
|
||||
|
||||
model_response["object"] = "list"
|
||||
model_response["data"] = embedding_response
|
||||
model_response["model"] = model
|
||||
input_tokens = 0
|
||||
|
||||
input_str = "".join(input)
|
||||
|
||||
input_tokens += len(encoding.encode(input_str))
|
||||
|
||||
usage = Usage(
|
||||
prompt_tokens=input_tokens, completion_tokens=0, total_tokens=input_tokens
|
||||
)
|
||||
model_response.usage = usage
|
||||
|
||||
return model_response
|
||||
|
|
|
|||
|
|
@ -336,6 +336,7 @@ async def acompletion(
|
|||
or custom_llm_provider == "predibase"
|
||||
or custom_llm_provider == "bedrock"
|
||||
or custom_llm_provider == "databricks"
|
||||
or custom_llm_provider == "clarifai"
|
||||
or custom_llm_provider in litellm.openai_compatible_providers
|
||||
): # currently implemented aiohttp calls for just azure, openai, hf, ollama, vertex ai soon all.
|
||||
init_response = await loop.run_in_executor(None, func_with_context)
|
||||
|
|
|
|||
1
litellm/proxy/_experimental/out/404.html
Normal file
1
litellm/proxy/_experimental/out/404.html
Normal file
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
|
|
@ -1 +1 @@
|
|||
(self.webpackChunk_N_E=self.webpackChunk_N_E||[]).push([[185],{87421:function(n,e,t){Promise.resolve().then(t.t.bind(t,99646,23)),Promise.resolve().then(t.t.bind(t,63385,23))},63385:function(){},99646:function(n){n.exports={style:{fontFamily:"'__Inter_c23dc8', '__Inter_Fallback_c23dc8'",fontStyle:"normal"},className:"__className_c23dc8"}}},function(n){n.O(0,[971,69,744],function(){return n(n.s=87421)}),_N_E=n.O()}]);
|
||||
(self.webpackChunk_N_E=self.webpackChunk_N_E||[]).push([[185],{87421:function(n,e,t){Promise.resolve().then(t.t.bind(t,99646,23)),Promise.resolve().then(t.t.bind(t,63385,23))},63385:function(){},99646:function(n){n.exports={style:{fontFamily:"'__Inter_12bbc4', '__Inter_Fallback_12bbc4'",fontStyle:"normal"},className:"__className_12bbc4"}}},function(n){n.O(0,[971,69,744],function(){return n(n.s=87421)}),_N_E=n.O()}]);
|
||||
File diff suppressed because one or more lines are too long
|
|
@ -1 +1 @@
|
|||
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|
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4:I[5613,[],""]
|
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5:I[31778,[],""]
|
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6:[["$","meta","0",{"name":"viewport","content":"width=device-width, initial-scale=1"}],["$","meta","1",{"charSet":"utf-8"}],["$","title","2",{"children":"LiteLLM Dashboard"}],["$","meta","3",{"name":"description","content":"LiteLLM Proxy Admin UI"}],["$","link","4",{"rel":"icon","href":"/ui/favicon.ico","type":"image/x-icon","sizes":"16x16"}],["$","meta","5",{"name":"next-size-adjust"}]]
|
||||
1:null
|
||||
|
|
|
|||
1
litellm/proxy/_experimental/out/model_hub.html
Normal file
1
litellm/proxy/_experimental/out/model_hub.html
Normal file
File diff suppressed because one or more lines are too long
|
|
@ -1,7 +1,7 @@
|
|||
2:I[77831,[],""]
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3:I[87494,["294","static/chunks/294-0e35509d5ca95267.js","131","static/chunks/131-6a03368053f9d26d.js","777","static/chunks/777-17b0c91edd3a24fe.js","418","static/chunks/app/model_hub/page-4cb65c32467214b5.js"],""]
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3:I[87494,["294","static/chunks/294-0e35509d5ca95267.js","131","static/chunks/131-6a03368053f9d26d.js","777","static/chunks/777-71fb78fdb4897cc3.js","418","static/chunks/app/model_hub/page-4cb65c32467214b5.js"],""]
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4:I[5613,[],""]
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5:I[31778,[],""]
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|
||||
6:[["$","meta","0",{"name":"viewport","content":"width=device-width, initial-scale=1"}],["$","meta","1",{"charSet":"utf-8"}],["$","title","2",{"children":"LiteLLM Dashboard"}],["$","meta","3",{"name":"description","content":"LiteLLM Proxy Admin UI"}],["$","link","4",{"rel":"icon","href":"/ui/favicon.ico","type":"image/x-icon","sizes":"16x16"}],["$","meta","5",{"name":"next-size-adjust"}]]
|
||||
1:null
|
||||
|
|
|
|||
1
litellm/proxy/_experimental/out/onboarding.html
Normal file
1
litellm/proxy/_experimental/out/onboarding.html
Normal file
File diff suppressed because one or more lines are too long
|
|
@ -1,7 +1,7 @@
|
|||
2:I[77831,[],""]
|
||||
3:I[667,["665","static/chunks/3014691f-589a5f4865c3822f.js","294","static/chunks/294-0e35509d5ca95267.js","684","static/chunks/684-bb2d2f93d92acb0b.js","777","static/chunks/777-17b0c91edd3a24fe.js","461","static/chunks/app/onboarding/page-664c7288e11fff5a.js"],""]
|
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3:I[667,["665","static/chunks/3014691f-589a5f4865c3822f.js","294","static/chunks/294-0e35509d5ca95267.js","684","static/chunks/684-bb2d2f93d92acb0b.js","777","static/chunks/777-71fb78fdb4897cc3.js","461","static/chunks/app/onboarding/page-664c7288e11fff5a.js"],""]
|
||||
4:I[5613,[],""]
|
||||
5:I[31778,[],""]
|
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|
||||
6:[["$","meta","0",{"name":"viewport","content":"width=device-width, initial-scale=1"}],["$","meta","1",{"charSet":"utf-8"}],["$","title","2",{"children":"LiteLLM Dashboard"}],["$","meta","3",{"name":"description","content":"LiteLLM Proxy Admin UI"}],["$","link","4",{"rel":"icon","href":"/ui/favicon.ico","type":"image/x-icon","sizes":"16x16"}],["$","meta","5",{"name":"next-size-adjust"}]]
|
||||
1:null
|
||||
|
|
|
|||
|
|
@ -224,6 +224,7 @@ class LiteLLMRoutes(enum.Enum):
|
|||
"/key/delete",
|
||||
"/global/spend/logs",
|
||||
"/global/predict/spend/logs",
|
||||
"/sso/get/logout_url",
|
||||
]
|
||||
|
||||
management_routes: List = [ # key
|
||||
|
|
|
|||
|
|
@ -79,10 +79,6 @@ async def add_litellm_data_to_request(
|
|||
data["cache"][k] = v
|
||||
|
||||
verbose_proxy_logger.debug("receiving data: %s", data)
|
||||
# users can pass in 'user' param to /chat/completions. Don't override it
|
||||
if data.get("user", None) is None and user_api_key_dict.user_id is not None:
|
||||
# if users are using user_api_key_auth, set `user` in `data`
|
||||
data["user"] = user_api_key_dict.user_id
|
||||
|
||||
if "metadata" not in data:
|
||||
data["metadata"] = {}
|
||||
|
|
|
|||
|
|
@ -14,10 +14,9 @@ model_list:
|
|||
litellm_params:
|
||||
model: openai/*
|
||||
api_key: os.environ/OPENAI_API_KEY
|
||||
- model_name: my-triton-model
|
||||
- model_name: mistral-embed
|
||||
litellm_params:
|
||||
model: triton/any"
|
||||
api_base: https://exampleopenaiendpoint-production.up.railway.app/triton/embeddings
|
||||
model: mistral/mistral-embed
|
||||
|
||||
general_settings:
|
||||
master_key: sk-1234
|
||||
|
|
|
|||
|
|
@ -879,6 +879,7 @@ async def user_api_key_auth(
|
|||
|
||||
## check for cache hit (In-Memory Cache)
|
||||
original_api_key = api_key # (Patch: For DynamoDB Backwards Compatibility)
|
||||
_user_role = None
|
||||
if api_key.startswith("sk-"):
|
||||
api_key = hash_token(token=api_key)
|
||||
valid_token: Optional[UserAPIKeyAuth] = user_api_key_cache.get_cache( # type: ignore
|
||||
|
|
@ -1512,7 +1513,7 @@ async def user_api_key_auth(
|
|||
):
|
||||
return UserAPIKeyAuth(
|
||||
api_key=api_key,
|
||||
user_role="app_owner",
|
||||
user_role=_user_role,
|
||||
parent_otel_span=parent_otel_span,
|
||||
**valid_token_dict,
|
||||
)
|
||||
|
|
@ -8857,7 +8858,7 @@ async def new_user(data: NewUserRequest):
|
|||
- organization_id: Optional[str] - specify the org a user belongs to.
|
||||
- user_email: Optional[str] - Specify a user email.
|
||||
- send_invite_email: Optional[bool] - Specify if an invite email should be sent.
|
||||
- user_role: Optional[str] - Specify a user role - "admin", "app_owner", "app_user"
|
||||
- user_role: Optional[str] - Specify a user role - "proxy_admin", "proxy_admin_viewer", "internal_user", "internal_user_viewer", "team", "customer". Info about each role here: `https://github.com/BerriAI/litellm/litellm/proxy/_types.py#L20`
|
||||
- max_budget: Optional[float] - Specify max budget for a given user.
|
||||
- models: Optional[list] - Model_name's a user is allowed to call. (if empty, key is allowed to call all models)
|
||||
- tpm_limit: Optional[int] - Specify tpm limit for a given user (Tokens per minute)
|
||||
|
|
@ -14737,6 +14738,22 @@ async def cache_flushall():
|
|||
)
|
||||
|
||||
|
||||
@router.get(
|
||||
"/get/litellm_model_cost_map",
|
||||
include_in_schema=False,
|
||||
dependencies=[Depends(user_api_key_auth)],
|
||||
)
|
||||
async def get_litellm_model_cost_map():
|
||||
try:
|
||||
_model_cost_map = litellm.model_cost
|
||||
return _model_cost_map
|
||||
except Exception as e:
|
||||
raise HTTPException(
|
||||
status_code=500,
|
||||
detail=f"Internal Server Error ({str(e)})",
|
||||
)
|
||||
|
||||
|
||||
@router.get("/", dependencies=[Depends(user_api_key_auth)])
|
||||
async def home(request: Request):
|
||||
return "LiteLLM: RUNNING"
|
||||
|
|
|
|||
|
|
@ -3618,6 +3618,7 @@ class Router:
|
|||
except Exception:
|
||||
model_info = None
|
||||
# get llm provider
|
||||
model, llm_provider = "", ""
|
||||
try:
|
||||
model, llm_provider, _, _ = litellm.get_llm_provider(
|
||||
model=litellm_params.model,
|
||||
|
|
|
|||
|
|
@ -905,10 +905,17 @@ def test_vertexai_embedding_embedding_latest():
|
|||
try:
|
||||
load_vertex_ai_credentials()
|
||||
litellm.set_verbose = True
|
||||
|
||||
response = embedding(
|
||||
model="vertex_ai/text-embedding-004",
|
||||
input=["good morning from litellm", "this is another item"],
|
||||
input=["hi"],
|
||||
dimensions=1,
|
||||
auto_truncate=True,
|
||||
task_type="RETRIEVAL_QUERY",
|
||||
)
|
||||
|
||||
assert len(response.data[0]["embedding"]) == 1
|
||||
assert response.usage.prompt_tokens > 0
|
||||
print(f"response:", response)
|
||||
except litellm.RateLimitError as e:
|
||||
pass
|
||||
|
|
|
|||
|
|
@ -220,13 +220,13 @@ def test_completion_bedrock_claude_sts_oidc_auth():
|
|||
aws_web_identity_token = "oidc/circleci_v2/"
|
||||
aws_region_name = os.environ["AWS_REGION_NAME"]
|
||||
# aws_role_name = os.environ["AWS_TEMP_ROLE_NAME"]
|
||||
# TODO: This is using David's IAM role, we should use Litellm's IAM role eventually
|
||||
# TODO: This is using ai.moda's IAM role, we should use LiteLLM's IAM role eventually
|
||||
aws_role_name = "arn:aws:iam::335785316107:role/litellm-github-unit-tests-circleci"
|
||||
|
||||
try:
|
||||
litellm.set_verbose = True
|
||||
|
||||
response = completion(
|
||||
response_1 = completion(
|
||||
model="bedrock/anthropic.claude-3-haiku-20240307-v1:0",
|
||||
messages=messages,
|
||||
max_tokens=10,
|
||||
|
|
@ -236,8 +236,40 @@ def test_completion_bedrock_claude_sts_oidc_auth():
|
|||
aws_role_name=aws_role_name,
|
||||
aws_session_name="my-test-session",
|
||||
)
|
||||
# Add any assertions here to check the response
|
||||
print(response)
|
||||
print(response_1)
|
||||
assert len(response_1.choices) > 0
|
||||
assert len(response_1.choices[0].message.content) > 0
|
||||
|
||||
# This second call is to verify that the cache isn't breaking anything
|
||||
response_2 = completion(
|
||||
model="bedrock/anthropic.claude-3-haiku-20240307-v1:0",
|
||||
messages=messages,
|
||||
max_tokens=5,
|
||||
temperature=0.2,
|
||||
aws_region_name=aws_region_name,
|
||||
aws_web_identity_token=aws_web_identity_token,
|
||||
aws_role_name=aws_role_name,
|
||||
aws_session_name="my-test-session",
|
||||
)
|
||||
print(response_2)
|
||||
assert len(response_2.choices) > 0
|
||||
assert len(response_2.choices[0].message.content) > 0
|
||||
|
||||
# This third call is to verify that the cache isn't used for a different region
|
||||
response_3 = completion(
|
||||
model="bedrock/anthropic.claude-3-haiku-20240307-v1:0",
|
||||
messages=messages,
|
||||
max_tokens=6,
|
||||
temperature=0.3,
|
||||
aws_region_name="us-east-1",
|
||||
aws_web_identity_token=aws_web_identity_token,
|
||||
aws_role_name=aws_role_name,
|
||||
aws_session_name="my-test-session",
|
||||
)
|
||||
print(response_3)
|
||||
assert len(response_3.choices) > 0
|
||||
assert len(response_3.choices[0].message.content) > 0
|
||||
|
||||
except RateLimitError:
|
||||
pass
|
||||
except Exception as e:
|
||||
|
|
@ -255,7 +287,7 @@ def test_completion_bedrock_httpx_command_r_sts_oidc_auth():
|
|||
aws_web_identity_token = "oidc/circleci_v2/"
|
||||
aws_region_name = os.environ["AWS_REGION_NAME"]
|
||||
# aws_role_name = os.environ["AWS_TEMP_ROLE_NAME"]
|
||||
# TODO: This is using David's IAM role, we should use Litellm's IAM role eventually
|
||||
# TODO: This is using ai.moda's IAM role, we should use LiteLLM's IAM role eventually
|
||||
aws_role_name = "arn:aws:iam::335785316107:role/litellm-github-unit-tests-circleci"
|
||||
|
||||
try:
|
||||
|
|
|
|||
|
|
@ -557,7 +557,13 @@ async def test_completion_predibase_streaming(sync_mode):
|
|||
print(f"complete_response: {complete_response}")
|
||||
except litellm.Timeout as e:
|
||||
pass
|
||||
except litellm.InternalServerError as e:
|
||||
pass
|
||||
except Exception as e:
|
||||
print("ERROR class", e.__class__)
|
||||
print("ERROR message", e)
|
||||
print("ERROR traceback", traceback.format_exc())
|
||||
|
||||
pytest.fail(f"Error occurred: {e}")
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -4903,6 +4903,18 @@ def get_optional_params_embeddings(
|
|||
)
|
||||
final_params = {**optional_params, **kwargs}
|
||||
return final_params
|
||||
if custom_llm_provider == "vertex_ai":
|
||||
supported_params = get_supported_openai_params(
|
||||
model=model,
|
||||
custom_llm_provider="vertex_ai",
|
||||
request_type="embeddings",
|
||||
)
|
||||
_check_valid_arg(supported_params=supported_params)
|
||||
optional_params = litellm.VertexAITextEmbeddingConfig().map_openai_params(
|
||||
non_default_params=non_default_params, optional_params={}
|
||||
)
|
||||
final_params = {**optional_params, **kwargs}
|
||||
return final_params
|
||||
if custom_llm_provider == "vertex_ai":
|
||||
if len(non_default_params.keys()) > 0:
|
||||
if litellm.drop_params is True: # drop the unsupported non-default values
|
||||
|
|
@ -4936,7 +4948,18 @@ def get_optional_params_embeddings(
|
|||
message=f"Setting user/encoding format is not supported by {custom_llm_provider}. To drop it from the call, set `litellm.drop_params = True`.",
|
||||
)
|
||||
return {**non_default_params, **kwargs}
|
||||
|
||||
if custom_llm_provider == "mistral":
|
||||
supported_params = get_supported_openai_params(
|
||||
model=model,
|
||||
custom_llm_provider="mistral",
|
||||
request_type="embeddings",
|
||||
)
|
||||
_check_valid_arg(supported_params=supported_params)
|
||||
optional_params = litellm.MistralEmbeddingConfig().map_openai_params(
|
||||
non_default_params=non_default_params, optional_params={}
|
||||
)
|
||||
final_params = {**optional_params, **kwargs}
|
||||
return final_params
|
||||
if (
|
||||
custom_llm_provider != "openai"
|
||||
and custom_llm_provider != "azure"
|
||||
|
|
@ -6355,7 +6378,10 @@ def get_supported_openai_params(
|
|||
"max_retries",
|
||||
]
|
||||
elif custom_llm_provider == "mistral":
|
||||
if request_type == "chat_completion":
|
||||
return litellm.MistralConfig().get_supported_openai_params()
|
||||
elif request_type == "embeddings":
|
||||
return litellm.MistralEmbeddingConfig().get_supported_openai_params()
|
||||
elif custom_llm_provider == "replicate":
|
||||
return [
|
||||
"stream",
|
||||
|
|
@ -6397,7 +6423,10 @@ def get_supported_openai_params(
|
|||
elif custom_llm_provider == "palm" or custom_llm_provider == "gemini":
|
||||
return ["temperature", "top_p", "stream", "n", "stop", "max_tokens"]
|
||||
elif custom_llm_provider == "vertex_ai":
|
||||
if request_type == "chat_completion":
|
||||
return litellm.VertexAIConfig().get_supported_openai_params()
|
||||
elif request_type == "embeddings":
|
||||
return litellm.VertexAITextEmbeddingConfig().get_supported_openai_params()
|
||||
elif custom_llm_provider == "sagemaker":
|
||||
return ["stream", "temperature", "max_tokens", "top_p", "stop", "n"]
|
||||
elif custom_llm_provider == "aleph_alpha":
|
||||
|
|
@ -7207,6 +7236,9 @@ def get_provider_fields(custom_llm_provider: str) -> List[ProviderField]:
|
|||
elif custom_llm_provider == "ollama":
|
||||
return litellm.OllamaConfig().get_required_params()
|
||||
|
||||
elif custom_llm_provider == "azure_ai":
|
||||
return litellm.AzureAIStudioConfig().get_required_params()
|
||||
|
||||
else:
|
||||
return []
|
||||
|
||||
|
|
@ -10081,6 +10113,14 @@ def get_secret(
|
|||
return oidc_token
|
||||
else:
|
||||
raise ValueError("Github OIDC provider failed")
|
||||
elif oidc_provider == "azure":
|
||||
# https://azure.github.io/azure-workload-identity/docs/quick-start.html
|
||||
azure_federated_token_file = os.getenv("AZURE_FEDERATED_TOKEN_FILE")
|
||||
if azure_federated_token_file is None:
|
||||
raise ValueError("AZURE_FEDERATED_TOKEN_FILE not found in environment")
|
||||
with open(azure_federated_token_file, "r") as f:
|
||||
oidc_token = f.read()
|
||||
return oidc_token
|
||||
else:
|
||||
raise ValueError("Unsupported OIDC provider")
|
||||
|
||||
|
|
|
|||
|
|
@ -85,6 +85,9 @@ model_list:
|
|||
litellm_params:
|
||||
model: openai/*
|
||||
api_key: os.environ/OPENAI_API_KEY
|
||||
- model_name: mistral-embed
|
||||
litellm_params:
|
||||
model: mistral/mistral-embed
|
||||
- model_name: gpt-instruct # [PROD TEST] - tests if `/health` automatically infers this to be a text completion model
|
||||
litellm_params:
|
||||
model: text-completion-openai/gpt-3.5-turbo-instruct
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
[tool.poetry]
|
||||
name = "litellm"
|
||||
version = "1.40.9"
|
||||
version = "1.40.10"
|
||||
description = "Library to easily interface with LLM API providers"
|
||||
authors = ["BerriAI"]
|
||||
license = "MIT"
|
||||
|
|
@ -85,7 +85,7 @@ requires = ["poetry-core", "wheel"]
|
|||
build-backend = "poetry.core.masonry.api"
|
||||
|
||||
[tool.commitizen]
|
||||
version = "1.40.9"
|
||||
version = "1.40.10"
|
||||
version_files = [
|
||||
"pyproject.toml:^version"
|
||||
]
|
||||
|
|
|
|||
|
|
@ -22,6 +22,7 @@ async def generate_key(
|
|||
"text-embedding-ada-002",
|
||||
"dall-e-2",
|
||||
"fake-openai-endpoint-2",
|
||||
"mistral-embed",
|
||||
],
|
||||
):
|
||||
url = "http://0.0.0.0:4000/key/generate"
|
||||
|
|
@ -197,14 +198,14 @@ async def completion(session, key):
|
|||
return response
|
||||
|
||||
|
||||
async def embeddings(session, key):
|
||||
async def embeddings(session, key, model="text-embedding-ada-002"):
|
||||
url = "http://0.0.0.0:4000/embeddings"
|
||||
headers = {
|
||||
"Authorization": f"Bearer {key}",
|
||||
"Content-Type": "application/json",
|
||||
}
|
||||
data = {
|
||||
"model": "text-embedding-ada-002",
|
||||
"model": model,
|
||||
"input": ["hello world"],
|
||||
}
|
||||
|
||||
|
|
@ -408,6 +409,9 @@ async def test_embeddings():
|
|||
key_2 = key_gen["key"]
|
||||
await embeddings(session=session, key=key_2)
|
||||
|
||||
# embedding request with non OpenAI model
|
||||
await embeddings(session=session, key=key, model="mistral-embed")
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_image_generation():
|
||||
|
|
|
|||
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|
|
@ -1 +1 @@
|
|||
(self.webpackChunk_N_E=self.webpackChunk_N_E||[]).push([[185],{87421:function(n,e,t){Promise.resolve().then(t.t.bind(t,99646,23)),Promise.resolve().then(t.t.bind(t,63385,23))},63385:function(){},99646:function(n){n.exports={style:{fontFamily:"'__Inter_c23dc8', '__Inter_Fallback_c23dc8'",fontStyle:"normal"},className:"__className_c23dc8"}}},function(n){n.O(0,[971,69,744],function(){return n(n.s=87421)}),_N_E=n.O()}]);
|
||||
(self.webpackChunk_N_E=self.webpackChunk_N_E||[]).push([[185],{87421:function(n,e,t){Promise.resolve().then(t.t.bind(t,99646,23)),Promise.resolve().then(t.t.bind(t,63385,23))},63385:function(){},99646:function(n){n.exports={style:{fontFamily:"'__Inter_12bbc4', '__Inter_Fallback_12bbc4'",fontStyle:"normal"},className:"__className_12bbc4"}}},function(n){n.O(0,[971,69,744],function(){return n(n.s=87421)}),_N_E=n.O()}]);
|
||||
File diff suppressed because one or more lines are too long
|
|
@ -1 +1 @@
|
|||
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|
||||
1:null
|
||||
|
|
|
|||
|
|
@ -139,6 +139,7 @@ interface ProviderSettings {
|
|||
enum Providers {
|
||||
OpenAI = "OpenAI",
|
||||
Azure = "Azure",
|
||||
Azure_AI_Studio = "Azure AI Studio",
|
||||
Anthropic = "Anthropic",
|
||||
Google_AI_Studio = "Google AI Studio",
|
||||
Bedrock = "Amazon Bedrock",
|
||||
|
|
@ -151,6 +152,7 @@ enum Providers {
|
|||
const provider_map: Record<string, string> = {
|
||||
OpenAI: "openai",
|
||||
Azure: "azure",
|
||||
Azure_AI_Studio: "azure_ai",
|
||||
Anthropic: "anthropic",
|
||||
Google_AI_Studio: "gemini",
|
||||
Bedrock: "bedrock",
|
||||
|
|
@ -158,6 +160,7 @@ const provider_map: Record<string, string> = {
|
|||
Vertex_AI: "vertex_ai",
|
||||
Databricks: "databricks",
|
||||
Ollama: "ollama",
|
||||
|
||||
};
|
||||
|
||||
const retry_policy_map: Record<string, string> = {
|
||||
|
|
@ -1245,6 +1248,10 @@ const ModelDashboard: React.FC<ModelDashboardProps> = ({
|
|||
return "claude-3-opus";
|
||||
} else if (selectedProvider == Providers.Google_AI_Studio) {
|
||||
return "gemini-pro";
|
||||
} else if (selectedProvider == Providers.Azure_AI_Studio) {
|
||||
return "azure_ai/command-r-plus";
|
||||
} else if (selectedProvider == Providers.Azure) {
|
||||
return "azure/my-deployment";
|
||||
} else {
|
||||
return "gpt-3.5-turbo";
|
||||
}
|
||||
|
|
|
|||
|
|
@ -14,11 +14,12 @@ export interface Model {
|
|||
|
||||
export const modelCostMap = async () => {
|
||||
try {
|
||||
const url = proxyBaseUrl ? `${proxyBaseUrl}/get/litellm_model_cost_map` : `/get/litellm_model_cost_map`;
|
||||
const response = await fetch(
|
||||
"https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json"
|
||||
url
|
||||
);
|
||||
const jsonData = await response.json();
|
||||
console.log(`received data: ${jsonData}`);
|
||||
console.log(`received litellm model cost data: ${jsonData}`);
|
||||
return jsonData;
|
||||
} catch (error) {
|
||||
console.error("Failed to get model cost map:", error);
|
||||
|
|
@ -2405,7 +2406,6 @@ export const getProxyBaseUrlAndLogoutUrl = async (
|
|||
|
||||
if (!response.ok) {
|
||||
const errorData = await response.text();
|
||||
message.error(errorData, 10);
|
||||
throw new Error("Network response was not ok");
|
||||
}
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue