Merge branch 'BerriAI:main' into fireworks-fix-cost-map
This commit is contained in:
Daniel Bichuetti
GitHub
commit
efb1ee66d7
2
.gitignore
vendored
2
.gitignore
vendored
@ -1,5 +1,7 @@
|
||||
.venv
|
||||
.env
|
||||
.newenv
|
||||
newenv/*
|
||||
litellm/proxy/myenv/*
|
||||
litellm_uuid.txt
|
||||
__pycache__/
|
||||
|
||||
@ -434,6 +434,33 @@ litellm_settings:
|
||||
|
||||
|
||||
|
||||
### Default Fallbacks
|
||||
|
||||
You can also set default_fallbacks, in case a specific model group is misconfigured / bad.
|
||||
|
||||
|
||||
```yaml
|
||||
model_list:
|
||||
- model_name: gpt-3.5-turbo-small
|
||||
litellm_params:
|
||||
model: azure/chatgpt-v-2
|
||||
api_base: os.environ/AZURE_API_BASE
|
||||
api_key: os.environ/AZURE_API_KEY
|
||||
api_version: "2023-07-01-preview"
|
||||
|
||||
- model_name: claude-opus
|
||||
litellm_params:
|
||||
model: claude-3-opus-20240229
|
||||
api_key: os.environ/ANTHROPIC_API_KEY
|
||||
|
||||
litellm_settings:
|
||||
default_fallbacks: ["claude-opus"]
|
||||
```
|
||||
|
||||
This will default to claude-opus in case any model fails.
|
||||
|
||||
A model-specific fallbacks (e.g. {"gpt-3.5-turbo-small": ["claude-opus"]}) overrides default fallback.
|
||||
|
||||
### Test Fallbacks!
|
||||
|
||||
Check if your fallbacks are working as expected.
|
||||
|
||||
@ -4,7 +4,7 @@ import TabItem from '@theme/TabItem';
|
||||
|
||||
# 🤗 UI - Self-Serve
|
||||
|
||||
Allow users to create their own keys on [Proxy UI](./ui.md).
|
||||
## Allow users to create their own keys on [Proxy UI](./ui.md).
|
||||
|
||||
1. Add user with permissions to a team on proxy
|
||||
|
||||
@ -125,6 +125,41 @@ LiteLLM Enterprise: Enable [SSO login](./ui.md#setup-ssoauth-for-ui)
|
||||
|
||||
<Image img={require('../../img/ui_self_serve_create_key.png')} style={{ width: '800px', height: 'auto' }} />
|
||||
|
||||
## Allow users to View Usage, Caching Analytics
|
||||
|
||||
1. Go to Internal Users -> +Invite User
|
||||
|
||||
Set their role to `Admin Viewer` - this means they can only view usage, caching analytics
|
||||
|
||||
<Image img={require('../../img/ui_invite_user.png')} style={{ width: '800px', height: 'auto' }} />
|
||||
<br />
|
||||
|
||||
2. Share invitation link with user
|
||||
|
||||
|
||||
<Image img={require('../../img/ui_invite_link.png')} style={{ width: '800px', height: 'auto' }} />
|
||||
<br />
|
||||
|
||||
3. User logs in via email + password auth
|
||||
|
||||
<Image img={require('../../img/ui_clean_login.png')} style={{ width: '500px', height: 'auto' }} />
|
||||
<br />
|
||||
|
||||
4. User can now view Usage, Caching Analytics
|
||||
|
||||
<Image img={require('../../img/ui_usage.png')} style={{ width: '800px', height: 'auto' }} />
|
||||
|
||||
|
||||
## Available Roles
|
||||
Here's the available UI roles for a LiteLLM Internal User:
|
||||
|
||||
**Admin Roles:**
|
||||
- `proxy_admin`: admin over the platform
|
||||
- `proxy_admin_viewer`: can login, view all keys, view all spend. **Cannot** create/delete keys, add new users.
|
||||
|
||||
**Internal User Roles:**
|
||||
- `internal_user`: can login, view/create/delete their own keys, view their spend. **Cannot** add new users.
|
||||
- `internal_user_viewer`: can login, view their own keys, view their own spend. **Cannot** create/delete keys, add new users.
|
||||
|
||||
## Advanced
|
||||
### Setting custom logout URLs
|
||||
|
||||
@ -347,6 +347,70 @@ curl --location 'http://localhost:4000/key/generate' \
|
||||
"max_budget": 0,}'
|
||||
```
|
||||
|
||||
## Advanced - Pass LiteLLM Key in custom header
|
||||
|
||||
Use this to make LiteLLM proxy look for the virtual key in a custom header instead of the default `"Authorization"` header
|
||||
|
||||
**Step 1** Define `litellm_key_header_name` name on litellm config.yaml
|
||||
|
||||
```yaml
|
||||
model_list:
|
||||
- model_name: fake-openai-endpoint
|
||||
litellm_params:
|
||||
model: openai/fake
|
||||
api_key: fake-key
|
||||
api_base: https://exampleopenaiendpoint-production.up.railway.app/
|
||||
|
||||
general_settings:
|
||||
master_key: sk-1234
|
||||
litellm_key_header_name: "X-Litellm-Key" # 👈 Key Change
|
||||
|
||||
```
|
||||
|
||||
**Step 2** Test it
|
||||
|
||||
In this request, litellm will use the Virtual key in the `X-Litellm-Key` header
|
||||
|
||||
<Tabs>
|
||||
<TabItem value="curl" label="curl">
|
||||
|
||||
```shell
|
||||
curl http://localhost:4000/v1/chat/completions \
|
||||
-H "Content-Type: application/json" \
|
||||
-H "X-Litellm-Key: Bearer sk-1234" \
|
||||
-H "Authorization: Bearer bad-key" \
|
||||
-d '{
|
||||
"model": "fake-openai-endpoint",
|
||||
"messages": [
|
||||
{"role": "user", "content": "Hello, Claude gm!"}
|
||||
]
|
||||
}'
|
||||
```
|
||||
|
||||
**Expected Response**
|
||||
|
||||
Expect to see a successfull response from the litellm proxy since the key passed in `X-Litellm-Key` is valid
|
||||
```shell
|
||||
{"id":"chatcmpl-f9b2b79a7c30477ab93cd0e717d1773e","choices":[{"finish_reason":"stop","index":0,"message":{"content":"\n\nHello there, how may I assist you today?","role":"assistant","tool_calls":null,"function_call":null}}],"created":1677652288,"model":"gpt-3.5-turbo-0125","object":"chat.completion","system_fingerprint":"fp_44709d6fcb","usage":{"completion_tokens":12,"prompt_tokens":9,"total_tokens":21}
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
|
||||
<TabItem value="python" label="OpenAI Python SDK">
|
||||
|
||||
```python
|
||||
client = openai.OpenAI(
|
||||
api_key="not-used",
|
||||
base_url="https://api-gateway-url.com/llmservc/api/litellmp",
|
||||
default_headers={
|
||||
"Authorization": f"Bearer {API_GATEWAY_TOKEN}", # (optional) For your API Gateway
|
||||
"X-Litellm-Key": f"Bearer sk-1234" # For LiteLLM Proxy
|
||||
}
|
||||
)
|
||||
```
|
||||
</TabItem>
|
||||
</Tabs>
|
||||
|
||||
## Advanced - Custom Auth
|
||||
|
||||
You can now override the default api key auth.
|
||||
|
||||
BIN
docs/my-website/img/ui_invite_link.png
Normal file
BIN
docs/my-website/img/ui_invite_link.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 88 KiB |
BIN
docs/my-website/img/ui_invite_user.png
Normal file
BIN
docs/my-website/img/ui_invite_user.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 207 KiB |
BIN
docs/my-website/img/ui_usage.png
Normal file
BIN
docs/my-website/img/ui_usage.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 181 KiB |
@ -757,6 +757,7 @@ openai_image_generation_models = ["dall-e-2", "dall-e-3"]
|
||||
from .timeout import timeout
|
||||
from .cost_calculator import completion_cost
|
||||
from litellm.litellm_core_utils.litellm_logging import Logging
|
||||
from litellm.litellm_core_utils.core_helpers import remove_index_from_tool_calls
|
||||
from litellm.litellm_core_utils.token_counter import get_modified_max_tokens
|
||||
from .utils import (
|
||||
client,
|
||||
|
||||
@ -39,3 +39,18 @@ def map_finish_reason(
|
||||
elif finish_reason == "content_filtered":
|
||||
return "content_filter"
|
||||
return finish_reason
|
||||
|
||||
|
||||
def remove_index_from_tool_calls(messages, tool_calls):
|
||||
for tool_call in tool_calls:
|
||||
if "index" in tool_call:
|
||||
tool_call.pop("index")
|
||||
|
||||
for message in messages:
|
||||
if "tool_calls" in message:
|
||||
tool_calls = message["tool_calls"]
|
||||
for tool_call in tool_calls:
|
||||
if "index" in tool_call:
|
||||
tool_call.pop("index")
|
||||
|
||||
return
|
||||
|
||||
@ -475,10 +475,12 @@ class AnthropicConfig:
|
||||
|
||||
|
||||
# makes headers for API call
|
||||
def validate_environment(api_key, user_headers):
|
||||
def validate_environment(api_key, user_headers, model):
|
||||
if api_key is None:
|
||||
raise ValueError(
|
||||
"Missing Anthropic API Key - A call is being made to anthropic but no key is set either in the environment variables or via params"
|
||||
raise litellm.AuthenticationError(
|
||||
message="Missing Anthropic API Key - A call is being made to anthropic but no key is set either in the environment variables or via params. Please set `ANTHROPIC_API_KEY` in your environment vars",
|
||||
llm_provider="anthropic",
|
||||
model=model,
|
||||
)
|
||||
headers = {
|
||||
"accept": "application/json",
|
||||
@ -734,7 +736,7 @@ class AnthropicChatCompletion(BaseLLM):
|
||||
logger_fn=None,
|
||||
headers={},
|
||||
):
|
||||
headers = validate_environment(api_key, headers)
|
||||
headers = validate_environment(api_key, headers, model)
|
||||
_is_function_call = False
|
||||
messages = copy.deepcopy(messages)
|
||||
optional_params = copy.deepcopy(optional_params)
|
||||
|
||||
@ -953,6 +953,12 @@ class OpenAIChatCompletion(BaseLLM):
|
||||
new_messages = messages
|
||||
new_messages.append({"role": "user", "content": ""})
|
||||
messages = new_messages
|
||||
elif (
|
||||
"unknown field: parameter index is not a valid field" in str(e)
|
||||
) and "tools" in data:
|
||||
litellm.remove_index_from_tool_calls(
|
||||
tool_calls=data["tools"], messages=messages
|
||||
)
|
||||
else:
|
||||
raise e
|
||||
except OpenAIError as e:
|
||||
|
||||
@ -1081,7 +1081,7 @@ def convert_to_gemini_tool_call_result(
|
||||
return _part
|
||||
|
||||
|
||||
def convert_to_anthropic_tool_result(message: dict) -> dict:
|
||||
def convert_to_anthropic_tool_result(message: dict) -> AnthropicMessagesToolResultParam:
|
||||
"""
|
||||
OpenAI message with a tool result looks like:
|
||||
{
|
||||
@ -1114,44 +1114,50 @@ def convert_to_anthropic_tool_result(message: dict) -> dict:
|
||||
}
|
||||
"""
|
||||
if message["role"] == "tool":
|
||||
tool_call_id = message.get("tool_call_id")
|
||||
content = message.get("content")
|
||||
tool_call_id: str = message.get("tool_call_id") # type: ignore
|
||||
content: str = message.get("content") # type: ignore
|
||||
|
||||
# We can't determine from openai message format whether it's a successful or
|
||||
# error call result so default to the successful result template
|
||||
anthropic_tool_result = {
|
||||
"type": "tool_result",
|
||||
"tool_use_id": tool_call_id,
|
||||
"content": content,
|
||||
}
|
||||
anthropic_tool_result = AnthropicMessagesToolResultParam(
|
||||
type="tool_result", tool_use_id=tool_call_id, content=content
|
||||
)
|
||||
return anthropic_tool_result
|
||||
elif message["role"] == "function":
|
||||
content = message.get("content")
|
||||
anthropic_tool_result = {
|
||||
"type": "tool_result",
|
||||
"tool_use_id": str(uuid.uuid4()),
|
||||
"content": content,
|
||||
}
|
||||
if message["role"] == "function":
|
||||
content = message.get("content") # type: ignore
|
||||
anthropic_tool_result = AnthropicMessagesToolResultParam(
|
||||
type="tool_result", tool_use_id=str(uuid.uuid4()), content=content
|
||||
)
|
||||
|
||||
return anthropic_tool_result
|
||||
return {}
|
||||
else:
|
||||
raise Exception(
|
||||
"Invalid role={}. Only 'tool' or 'function' are accepted for tool result blocks.".format(
|
||||
message.get("content")
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
def convert_function_to_anthropic_tool_invoke(function_call):
|
||||
def convert_function_to_anthropic_tool_invoke(
|
||||
function_call,
|
||||
) -> List[AnthropicMessagesToolUseParam]:
|
||||
try:
|
||||
anthropic_tool_invoke = [
|
||||
{
|
||||
"type": "tool_use",
|
||||
"id": str(uuid.uuid4()),
|
||||
"name": get_attribute_or_key(function_call, "name"),
|
||||
"input": json.loads(get_attribute_or_key(function_call, "arguments")),
|
||||
}
|
||||
AnthropicMessagesToolUseParam(
|
||||
type="tool_use",
|
||||
id=str(uuid.uuid4()),
|
||||
name=get_attribute_or_key(function_call, "name"),
|
||||
input=json.loads(get_attribute_or_key(function_call, "arguments")),
|
||||
)
|
||||
]
|
||||
return anthropic_tool_invoke
|
||||
except Exception as e:
|
||||
raise e
|
||||
|
||||
|
||||
def convert_to_anthropic_tool_invoke(tool_calls: list) -> list:
|
||||
def convert_to_anthropic_tool_invoke(
|
||||
tool_calls: list,
|
||||
) -> List[AnthropicMessagesToolUseParam]:
|
||||
"""
|
||||
OpenAI tool invokes:
|
||||
{
|
||||
@ -1189,18 +1195,16 @@ def convert_to_anthropic_tool_invoke(tool_calls: list) -> list:
|
||||
}
|
||||
"""
|
||||
anthropic_tool_invoke = [
|
||||
{
|
||||
"type": "tool_use",
|
||||
"id": get_attribute_or_key(tool, "id"),
|
||||
"name": get_attribute_or_key(
|
||||
get_attribute_or_key(tool, "function"), "name"
|
||||
),
|
||||
"input": json.loads(
|
||||
AnthropicMessagesToolUseParam(
|
||||
type="tool_use",
|
||||
id=get_attribute_or_key(tool, "id"),
|
||||
name=get_attribute_or_key(get_attribute_or_key(tool, "function"), "name"),
|
||||
input=json.loads(
|
||||
get_attribute_or_key(
|
||||
get_attribute_or_key(tool, "function"), "arguments"
|
||||
)
|
||||
),
|
||||
}
|
||||
)
|
||||
for tool in tool_calls
|
||||
if get_attribute_or_key(tool, "type") == "function"
|
||||
]
|
||||
@ -1212,7 +1216,12 @@ def anthropic_messages_pt(
|
||||
messages: list,
|
||||
model: str,
|
||||
llm_provider: str,
|
||||
):
|
||||
) -> List[
|
||||
Union[
|
||||
AnthropicMessagesUserMessageParam,
|
||||
AnthopicMessagesAssistantMessageParam,
|
||||
]
|
||||
]:
|
||||
"""
|
||||
format messages for anthropic
|
||||
1. Anthropic supports roles like "user" and "assistant" (system prompt sent separately)
|
||||
@ -1225,24 +1234,33 @@ def anthropic_messages_pt(
|
||||
# add role=tool support to allow function call result/error submission
|
||||
user_message_types = {"user", "tool", "function"}
|
||||
# reformat messages to ensure user/assistant are alternating, if there's either 2 consecutive 'user' messages or 2 consecutive 'assistant' message, merge them.
|
||||
new_messages: list = []
|
||||
new_messages: List[
|
||||
Union[
|
||||
AnthropicMessagesUserMessageParam,
|
||||
AnthopicMessagesAssistantMessageParam,
|
||||
]
|
||||
] = []
|
||||
msg_i = 0
|
||||
tool_use_param = False
|
||||
while msg_i < len(messages):
|
||||
user_content = []
|
||||
user_content: List[AnthropicMessagesUserMessageValues] = []
|
||||
init_msg_i = msg_i
|
||||
## MERGE CONSECUTIVE USER CONTENT ##
|
||||
while msg_i < len(messages) and messages[msg_i]["role"] in user_message_types:
|
||||
if isinstance(messages[msg_i]["content"], list):
|
||||
for m in messages[msg_i]["content"]:
|
||||
if m.get("type", "") == "image_url":
|
||||
image_chunk = convert_to_anthropic_image_obj(
|
||||
m["image_url"]["url"]
|
||||
)
|
||||
user_content.append(
|
||||
{
|
||||
"type": "image",
|
||||
"source": convert_to_anthropic_image_obj(
|
||||
m["image_url"]["url"]
|
||||
AnthropicMessagesImageParam(
|
||||
type="image",
|
||||
source=AnthropicImageParamSource(
|
||||
type="base64",
|
||||
media_type=image_chunk["media_type"],
|
||||
data=image_chunk["data"],
|
||||
),
|
||||
}
|
||||
)
|
||||
)
|
||||
elif m.get("type", "") == "text":
|
||||
user_content.append({"type": "text", "text": m["text"]})
|
||||
@ -1262,14 +1280,25 @@ def anthropic_messages_pt(
|
||||
if user_content:
|
||||
new_messages.append({"role": "user", "content": user_content})
|
||||
|
||||
assistant_content = []
|
||||
assistant_content: List[AnthropicMessagesAssistantMessageValues] = []
|
||||
## MERGE CONSECUTIVE ASSISTANT CONTENT ##
|
||||
while msg_i < len(messages) and messages[msg_i]["role"] == "assistant":
|
||||
assistant_text = (
|
||||
messages[msg_i].get("content") or ""
|
||||
) # either string or none
|
||||
if assistant_text:
|
||||
assistant_content.append({"type": "text", "text": assistant_text})
|
||||
if "content" in messages[msg_i] and isinstance(
|
||||
messages[msg_i]["content"], list
|
||||
):
|
||||
for m in messages[msg_i]["content"]:
|
||||
# handle text
|
||||
if m.get("type", "") == "text":
|
||||
anthropic_message = AnthropicMessagesTextParam(
|
||||
type="text", text=m.get("text")
|
||||
)
|
||||
assistant_content.append(anthropic_message)
|
||||
elif "content" in messages[msg_i] and isinstance(
|
||||
messages[msg_i]["content"], str
|
||||
):
|
||||
assistant_content.append(
|
||||
{"type": "text", "text": messages[msg_i]["content"]}
|
||||
)
|
||||
|
||||
if messages[msg_i].get(
|
||||
"tool_calls", []
|
||||
|
||||
@ -1,14 +1,19 @@
|
||||
import os, types
|
||||
import copy
|
||||
import json
|
||||
from enum import Enum
|
||||
import requests, copy # type: ignore
|
||||
import os
|
||||
import time
|
||||
from typing import Callable, Optional, List
|
||||
import litellm
|
||||
from .prompt_templates.factory import prompt_factory, custom_prompt
|
||||
from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
|
||||
from .base import BaseLLM
|
||||
import types
|
||||
from enum import Enum
|
||||
from typing import Callable, List, Optional
|
||||
|
||||
import httpx # type: ignore
|
||||
import requests # type: ignore
|
||||
|
||||
import litellm
|
||||
from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
|
||||
|
||||
from .base import BaseLLM
|
||||
from .prompt_templates.factory import custom_prompt, prompt_factory
|
||||
|
||||
|
||||
class TritonError(Exception):
|
||||
@ -52,17 +57,25 @@ class TritonChatCompletion(BaseLLM):
|
||||
logging_obj.post_call(original_response=_text_response)
|
||||
|
||||
_json_response = response.json()
|
||||
_embedding_output = []
|
||||
|
||||
_outputs = _json_response["outputs"]
|
||||
_output_data = _outputs[0]["data"]
|
||||
_embedding_output = {
|
||||
"object": "embedding",
|
||||
"index": 0,
|
||||
"embedding": _output_data,
|
||||
}
|
||||
for output in _outputs:
|
||||
_shape = output["shape"]
|
||||
_data = output["data"]
|
||||
_split_output_data = self.split_embedding_by_shape(_data, _shape)
|
||||
|
||||
for idx, embedding in enumerate(_split_output_data):
|
||||
_embedding_output.append(
|
||||
{
|
||||
"object": "embedding",
|
||||
"index": idx,
|
||||
"embedding": embedding,
|
||||
}
|
||||
)
|
||||
|
||||
model_response.model = _json_response.get("model_name", "None")
|
||||
model_response.data = [_embedding_output]
|
||||
model_response.data = _embedding_output
|
||||
|
||||
return model_response
|
||||
|
||||
@ -83,7 +96,7 @@ class TritonChatCompletion(BaseLLM):
|
||||
"inputs": [
|
||||
{
|
||||
"name": "input_text",
|
||||
"shape": [1],
|
||||
"shape": [len(input)],
|
||||
"datatype": "BYTES",
|
||||
"data": input,
|
||||
}
|
||||
@ -116,3 +129,14 @@ class TritonChatCompletion(BaseLLM):
|
||||
raise Exception(
|
||||
"Only async embedding supported for triton, please use litellm.aembedding() for now"
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def split_embedding_by_shape(
|
||||
data: List[float], shape: List[int]
|
||||
) -> List[List[float]]:
|
||||
if len(shape) != 2:
|
||||
raise ValueError("Shape must be of length 2.")
|
||||
embedding_size = shape[1]
|
||||
return [
|
||||
data[i * embedding_size : (i + 1) * embedding_size] for i in range(shape[0])
|
||||
]
|
||||
|
||||
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(self.webpackChunk_N_E=self.webpackChunk_N_E||[]).push([[185],{13993:function(n,e,t){Promise.resolve().then(t.t.bind(t,63385,23)),Promise.resolve().then(t.t.bind(t,99646,23))},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=13993)}),_N_E=n.O()}]);
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@ -0,0 +1 @@
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(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()}]);
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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"}]]
|
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1:null
|
||||
|
||||
@ -6,19 +6,21 @@ Currently only supports admin.
|
||||
JWT token must have 'litellm_proxy_admin' in scope.
|
||||
"""
|
||||
|
||||
import jwt
|
||||
import json
|
||||
import os
|
||||
from litellm.caching import DualCache
|
||||
from litellm._logging import verbose_proxy_logger
|
||||
from litellm.proxy._types import LiteLLM_JWTAuth, LiteLLM_UserTable
|
||||
from litellm.proxy.utils import PrismaClient
|
||||
from litellm.llms.custom_httpx.httpx_handler import HTTPHandler
|
||||
from typing import Optional
|
||||
|
||||
import jwt
|
||||
from cryptography import x509
|
||||
from cryptography.hazmat.backends import default_backend
|
||||
from cryptography.hazmat.primitives import serialization
|
||||
|
||||
from litellm._logging import verbose_proxy_logger
|
||||
from litellm.caching import DualCache
|
||||
from litellm.llms.custom_httpx.httpx_handler import HTTPHandler
|
||||
from litellm.proxy._types import LiteLLM_JWTAuth, LiteLLM_UserTable
|
||||
from litellm.proxy.utils import PrismaClient
|
||||
|
||||
|
||||
class JWTHandler:
|
||||
"""
|
||||
|
||||
@ -123,13 +123,10 @@ async def user_api_key_auth(
|
||||
if isinstance(api_key, str):
|
||||
passed_in_key = api_key
|
||||
api_key = _get_bearer_token(api_key=api_key)
|
||||
|
||||
elif isinstance(azure_api_key_header, str):
|
||||
api_key = azure_api_key_header
|
||||
|
||||
elif isinstance(anthropic_api_key_header, str):
|
||||
api_key = anthropic_api_key_header
|
||||
|
||||
elif pass_through_endpoints is not None:
|
||||
for endpoint in pass_through_endpoints:
|
||||
if endpoint.get("path", "") == route:
|
||||
@ -138,6 +135,15 @@ async def user_api_key_auth(
|
||||
header_key: str = headers.get("litellm_user_api_key", "")
|
||||
if request.headers.get(key=header_key) is not None:
|
||||
api_key = request.headers.get(key=header_key)
|
||||
|
||||
# if user wants to pass LiteLLM_Master_Key as a custom header, example pass litellm keys as X-LiteLLM-Key: Bearer sk-1234
|
||||
custom_litellm_key_header_name = general_settings.get("litellm_key_header_name")
|
||||
if custom_litellm_key_header_name is not None:
|
||||
api_key = get_api_key_from_custom_header(
|
||||
request=request,
|
||||
custom_litellm_key_header_name=custom_litellm_key_header_name,
|
||||
)
|
||||
|
||||
parent_otel_span: Optional[Span] = None
|
||||
if open_telemetry_logger is not None:
|
||||
parent_otel_span = open_telemetry_logger.tracer.start_span(
|
||||
@ -1267,3 +1273,27 @@ def _check_valid_ip(allowed_ips: Optional[List[str]], request: Request) -> bool:
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
|
||||
def get_api_key_from_custom_header(
|
||||
request: Request, custom_litellm_key_header_name: str
|
||||
):
|
||||
# use this as the virtual key passed to litellm proxy
|
||||
custom_litellm_key_header_name = custom_litellm_key_header_name.lower()
|
||||
verbose_proxy_logger.debug(
|
||||
"searching for custom_litellm_key_header_name= %s",
|
||||
custom_litellm_key_header_name,
|
||||
)
|
||||
custom_api_key = request.headers.get(custom_litellm_key_header_name)
|
||||
if custom_api_key:
|
||||
api_key = _get_bearer_token(api_key=custom_api_key)
|
||||
verbose_proxy_logger.debug(
|
||||
"Found custom API key using header: {}, setting api_key={}".format(
|
||||
custom_litellm_key_header_name, api_key
|
||||
)
|
||||
)
|
||||
else:
|
||||
raise ValueError(
|
||||
f"No LiteLLM Virtual Key pass. Please set header={custom_litellm_key_header_name}: Bearer <api_key>"
|
||||
)
|
||||
return api_key
|
||||
|
||||
@ -56,6 +56,7 @@ def check_file_size_under_limit(
|
||||
|
||||
file_contents_size = file.size or 0
|
||||
file_content_size_in_mb = file_contents_size / (1024 * 1024)
|
||||
max_file_size_mb = None
|
||||
|
||||
if llm_router is not None and request_data["model"] in router_model_names:
|
||||
try:
|
||||
|
||||
@ -778,6 +778,11 @@ async def delete_user(
|
||||
where={"user_id": {"in": data.user_ids}}
|
||||
)
|
||||
|
||||
## DELETE ASSOCIATED INVITATION LINKS
|
||||
await prisma_client.db.litellm_invitationlink.delete_many(
|
||||
where={"user_id": {"in": data.user_ids}}
|
||||
)
|
||||
|
||||
## DELETE USERS
|
||||
deleted_users = await prisma_client.db.litellm_usertable.delete_many(
|
||||
where={"user_id": {"in": data.user_ids}}
|
||||
|
||||
@ -713,6 +713,7 @@ async def team_info(
|
||||
team_id: str = fastapi.Query(
|
||||
default=None, description="Team ID in the request parameters"
|
||||
),
|
||||
user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
|
||||
):
|
||||
"""
|
||||
get info on team + related keys
|
||||
@ -747,6 +748,18 @@ async def team_info(
|
||||
detail={"message": "Malformed request. No team id passed in."},
|
||||
)
|
||||
|
||||
if user_api_key_dict.user_role == LitellmUserRoles.PROXY_ADMIN.value:
|
||||
pass
|
||||
elif user_api_key_dict.team_id is None or (
|
||||
team_id != user_api_key_dict.team_id
|
||||
):
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_403_FORBIDDEN,
|
||||
detail="key not allowed to access this team's info. Key team_id={}, Requested team_id={}".format(
|
||||
user_api_key_dict.team_id, team_id
|
||||
),
|
||||
)
|
||||
|
||||
team_info = await prisma_client.get_data(
|
||||
team_id=team_id, table_name="team", query_type="find_unique"
|
||||
)
|
||||
|
||||
@ -17,6 +17,7 @@ model_list:
|
||||
|
||||
general_settings:
|
||||
master_key: sk-1234
|
||||
litellm_key_header_name: "X-Litellm-Key"
|
||||
|
||||
litellm_settings:
|
||||
cache: true
|
||||
|
||||
@ -284,11 +284,18 @@ class ProxyLogging:
|
||||
if self.alerting is None:
|
||||
return
|
||||
|
||||
# current alerting threshold
|
||||
alerting_threshold: float = self.alerting_threshold
|
||||
|
||||
# add a 100 second buffer to the alerting threshold
|
||||
# ensures we don't send errant hanging request slack alerts
|
||||
alerting_threshold += 100
|
||||
|
||||
await self.internal_usage_cache.async_set_cache(
|
||||
key="request_status:{}".format(litellm_call_id),
|
||||
value=status,
|
||||
local_only=True,
|
||||
ttl=120,
|
||||
ttl=alerting_threshold,
|
||||
)
|
||||
|
||||
# The actual implementation of the function
|
||||
@ -593,12 +600,41 @@ class ProxyLogging:
|
||||
)
|
||||
|
||||
if litellm_logging_obj is not None:
|
||||
## UPDATE LOGGING INPUT
|
||||
_optional_params = {}
|
||||
for k, v in request_data.items():
|
||||
if k != "model" and k != "user" and k != "litellm_params":
|
||||
_optional_params[k] = v
|
||||
litellm_logging_obj.update_environment_variables(
|
||||
model=request_data.get("model", ""),
|
||||
user=request_data.get("user", ""),
|
||||
optional_params=_optional_params,
|
||||
litellm_params=request_data.get("litellm_params", {}),
|
||||
)
|
||||
|
||||
input: Union[list, str, dict] = ""
|
||||
if "messages" in request_data and isinstance(
|
||||
request_data["messages"], list
|
||||
):
|
||||
input = request_data["messages"]
|
||||
elif "prompt" in request_data and isinstance(
|
||||
request_data["prompt"], str
|
||||
):
|
||||
input = request_data["prompt"]
|
||||
elif "input" in request_data and isinstance(
|
||||
request_data["input"], list
|
||||
):
|
||||
input = request_data["input"]
|
||||
|
||||
litellm_logging_obj.pre_call(
|
||||
input=input,
|
||||
api_key="",
|
||||
)
|
||||
|
||||
# log the custom exception
|
||||
await litellm_logging_obj.async_failure_handler(
|
||||
exception=original_exception,
|
||||
traceback_exception=traceback.format_exc(),
|
||||
start_time=time.time(),
|
||||
end_time=time.time(),
|
||||
)
|
||||
|
||||
threading.Thread(
|
||||
@ -606,8 +642,6 @@ class ProxyLogging:
|
||||
args=(
|
||||
original_exception,
|
||||
traceback.format_exc(),
|
||||
time.time(),
|
||||
time.time(),
|
||||
),
|
||||
).start()
|
||||
|
||||
|
||||
@ -388,6 +388,33 @@ def test_completion_openai_exception():
|
||||
# test_completion_openai_exception()
|
||||
|
||||
|
||||
def test_anthropic_openai_exception():
|
||||
# test if anthropic raises litellm.AuthenticationError
|
||||
try:
|
||||
litellm.set_verbose = True
|
||||
## Test azure call
|
||||
old_azure_key = os.environ["ANTHROPIC_API_KEY"]
|
||||
os.environ.pop("ANTHROPIC_API_KEY")
|
||||
response = completion(
|
||||
model="anthropic/claude-3-sonnet-20240229",
|
||||
messages=[{"role": "user", "content": "hello"}],
|
||||
)
|
||||
print(f"response: {response}")
|
||||
print(response)
|
||||
except litellm.AuthenticationError as e:
|
||||
os.environ["ANTHROPIC_API_KEY"] = old_azure_key
|
||||
print("Exception vars=", vars(e))
|
||||
assert (
|
||||
"Missing Anthropic API Key - A call is being made to anthropic but no key is set either in the environment variables or via params"
|
||||
in e.message
|
||||
)
|
||||
print(
|
||||
"ANTHROPIC_API_KEY: good job got the correct error for ANTHROPIC_API_KEY when key not set"
|
||||
)
|
||||
except Exception as e:
|
||||
pytest.fail(f"Error occurred: {e}")
|
||||
|
||||
|
||||
def test_completion_mistral_exception():
|
||||
# test if mistral/mistral-tiny raises openai.AuthenticationError
|
||||
try:
|
||||
|
||||
@ -2545,3 +2545,53 @@ async def test_update_user_role(prisma_client):
|
||||
# use generated key to auth in
|
||||
result = await user_api_key_auth(request=request, api_key=api_key)
|
||||
print("result from user auth with new key", result)
|
||||
|
||||
|
||||
@pytest.mark.asyncio()
|
||||
async def test_custom_api_key_header_name(prisma_client):
|
||||
""" """
|
||||
setattr(litellm.proxy.proxy_server, "prisma_client", prisma_client)
|
||||
setattr(litellm.proxy.proxy_server, "master_key", "sk-1234")
|
||||
setattr(
|
||||
litellm.proxy.proxy_server,
|
||||
"general_settings",
|
||||
{"litellm_key_header_name": "x-litellm-key"},
|
||||
)
|
||||
await litellm.proxy.proxy_server.prisma_client.connect()
|
||||
|
||||
api_route = APIRoute(path="/chat/completions", endpoint=chat_completion)
|
||||
request = Request(
|
||||
{
|
||||
"type": "http",
|
||||
"route": api_route,
|
||||
"path": api_route.path,
|
||||
"headers": [
|
||||
(b"x-litellm-key", b"Bearer sk-1234"),
|
||||
],
|
||||
}
|
||||
)
|
||||
|
||||
# this should pass because we pass the master key as X-Litellm-Key and litellm_key_header_name="X-Litellm-Key" in general settings
|
||||
result = await user_api_key_auth(request=request, api_key="Bearer invalid-key")
|
||||
|
||||
# this should fail because X-Litellm-Key is invalid
|
||||
request = Request(
|
||||
{
|
||||
"type": "http",
|
||||
"route": api_route,
|
||||
"path": api_route.path,
|
||||
"headers": [],
|
||||
}
|
||||
)
|
||||
try:
|
||||
result = await user_api_key_auth(request=request, api_key="Bearer sk-1234")
|
||||
pytest.fail(f"This should have failed!. invalid Auth on this request")
|
||||
except Exception as e:
|
||||
print("failed with error", e)
|
||||
assert (
|
||||
"No LiteLLM Virtual Key pass. Please set header=x-litellm-key: Bearer <api_key>"
|
||||
in e.message
|
||||
)
|
||||
pass
|
||||
|
||||
# this should pass because X-Litellm-Key is valid
|
||||
|
||||
@ -7,6 +7,8 @@ import pytest
|
||||
|
||||
sys.path.insert(0, os.path.abspath("../.."))
|
||||
|
||||
from typing import Union
|
||||
|
||||
# from litellm.llms.prompt_templates.factory import prompt_factory
|
||||
import litellm
|
||||
from litellm import completion
|
||||
@ -138,6 +140,38 @@ def test_anthropic_messages_pt():
|
||||
assert "Invalid first message" in str(err.value)
|
||||
|
||||
|
||||
def test_anthropic_messages_nested_pt():
|
||||
from litellm.types.llms.anthropic import (
|
||||
AnthopicMessagesAssistantMessageParam,
|
||||
AnthropicMessagesUserMessageParam,
|
||||
)
|
||||
|
||||
messages = [
|
||||
{"content": [{"text": "here is a task", "type": "text"}], "role": "user"},
|
||||
{
|
||||
"content": [{"text": "sure happy to help", "type": "text"}],
|
||||
"role": "assistant",
|
||||
},
|
||||
{
|
||||
"content": [
|
||||
{
|
||||
"text": "Here is a screenshot of the current desktop with the "
|
||||
"mouse coordinates (500, 350). Please select an action "
|
||||
"from the provided schema.",
|
||||
"type": "text",
|
||||
}
|
||||
],
|
||||
"role": "user",
|
||||
},
|
||||
]
|
||||
|
||||
new_messages = anthropic_messages_pt(
|
||||
messages, model="claude-3-sonnet-20240229", llm_provider="anthropic"
|
||||
)
|
||||
|
||||
assert isinstance(new_messages[1]["content"][0]["text"], str)
|
||||
|
||||
|
||||
# codellama_prompt_format()
|
||||
def test_bedrock_tool_calling_pt():
|
||||
tools = [
|
||||
|
||||
@ -1,20 +1,29 @@
|
||||
import sys, os
|
||||
import os
|
||||
import sys
|
||||
import traceback
|
||||
from unittest import mock
|
||||
|
||||
from dotenv import load_dotenv
|
||||
|
||||
import litellm.proxy
|
||||
import litellm.proxy.proxy_server
|
||||
|
||||
load_dotenv()
|
||||
import os, io
|
||||
import io
|
||||
import os
|
||||
|
||||
# this file is to test litellm/proxy
|
||||
|
||||
sys.path.insert(
|
||||
0, os.path.abspath("../..")
|
||||
) # Adds the parent directory to the system path
|
||||
import pytest, logging, asyncio
|
||||
import asyncio
|
||||
import logging
|
||||
|
||||
import pytest
|
||||
|
||||
import litellm
|
||||
from litellm import embedding, completion, completion_cost, Timeout
|
||||
from litellm import RateLimitError
|
||||
from litellm import RateLimitError, Timeout, completion, completion_cost, embedding
|
||||
|
||||
# Configure logging
|
||||
logging.basicConfig(
|
||||
@ -22,14 +31,20 @@ logging.basicConfig(
|
||||
format="%(asctime)s - %(levelname)s - %(message)s",
|
||||
)
|
||||
|
||||
from unittest.mock import AsyncMock, MagicMock, patch
|
||||
|
||||
from fastapi import FastAPI
|
||||
|
||||
# test /chat/completion request to the proxy
|
||||
from fastapi.testclient import TestClient
|
||||
from fastapi import FastAPI
|
||||
from litellm.proxy.proxy_server import (
|
||||
|
||||
from litellm.integrations.custom_logger import CustomLogger
|
||||
from litellm.proxy.proxy_server import ( # Replace with the actual module where your FastAPI router is defined
|
||||
app,
|
||||
save_worker_config,
|
||||
initialize,
|
||||
) # Replace with the actual module where your FastAPI router is defined
|
||||
save_worker_config,
|
||||
)
|
||||
from litellm.proxy.utils import ProxyLogging
|
||||
|
||||
# Your bearer token
|
||||
token = "sk-1234"
|
||||
@ -158,6 +173,61 @@ def test_chat_completion(mock_acompletion, client_no_auth):
|
||||
pytest.fail(f"LiteLLM Proxy test failed. Exception - {str(e)}")
|
||||
|
||||
|
||||
from litellm.tests.test_custom_callback_input import CompletionCustomHandler
|
||||
|
||||
|
||||
@mock_patch_acompletion()
|
||||
def test_custom_logger_failure_handler(mock_acompletion, client_no_auth):
|
||||
from litellm.proxy._types import UserAPIKeyAuth
|
||||
from litellm.proxy.proxy_server import hash_token, user_api_key_cache
|
||||
|
||||
rpm_limit = 0
|
||||
|
||||
mock_api_key = "sk-my-test-key"
|
||||
cache_value = UserAPIKeyAuth(token=hash_token(mock_api_key), rpm_limit=rpm_limit)
|
||||
|
||||
user_api_key_cache.set_cache(key=hash_token(mock_api_key), value=cache_value)
|
||||
|
||||
mock_logger = CustomLogger()
|
||||
mock_logger_unit_tests = CompletionCustomHandler()
|
||||
proxy_logging_obj: ProxyLogging = getattr(
|
||||
litellm.proxy.proxy_server, "proxy_logging_obj"
|
||||
)
|
||||
|
||||
litellm.callbacks = [mock_logger, mock_logger_unit_tests]
|
||||
proxy_logging_obj._init_litellm_callbacks(llm_router=None)
|
||||
|
||||
setattr(litellm.proxy.proxy_server, "user_api_key_cache", user_api_key_cache)
|
||||
setattr(litellm.proxy.proxy_server, "master_key", "sk-1234")
|
||||
setattr(litellm.proxy.proxy_server, "prisma_client", "FAKE-VAR")
|
||||
setattr(litellm.proxy.proxy_server, "proxy_logging_obj", proxy_logging_obj)
|
||||
|
||||
with patch.object(
|
||||
mock_logger, "async_log_failure_event", new=AsyncMock()
|
||||
) as mock_failed_alert:
|
||||
# Your test data
|
||||
test_data = {
|
||||
"model": "gpt-3.5-turbo",
|
||||
"messages": [
|
||||
{"role": "user", "content": "hi"},
|
||||
],
|
||||
"max_tokens": 10,
|
||||
}
|
||||
|
||||
print("testing proxy server with chat completions")
|
||||
response = client_no_auth.post(
|
||||
"/v1/chat/completions",
|
||||
json=test_data,
|
||||
headers={"Authorization": "Bearer {}".format(mock_api_key)},
|
||||
)
|
||||
assert response.status_code == 429
|
||||
|
||||
# confirm async_log_failure_event is called
|
||||
mock_failed_alert.assert_called()
|
||||
|
||||
assert len(mock_logger_unit_tests.errors) == 0
|
||||
|
||||
|
||||
@mock_patch_acompletion()
|
||||
def test_engines_model_chat_completions(mock_acompletion, client_no_auth):
|
||||
global headers
|
||||
@ -422,9 +492,10 @@ def test_add_new_model(client_no_auth):
|
||||
|
||||
def test_health(client_no_auth):
|
||||
global headers
|
||||
import time
|
||||
from litellm._logging import verbose_logger, verbose_proxy_logger
|
||||
import logging
|
||||
import time
|
||||
|
||||
from litellm._logging import verbose_logger, verbose_proxy_logger
|
||||
|
||||
verbose_proxy_logger.setLevel(logging.DEBUG)
|
||||
|
||||
|
||||
29
litellm/tests/test_triton.py
Normal file
29
litellm/tests/test_triton.py
Normal file
@ -0,0 +1,29 @@
|
||||
import pytest
|
||||
from litellm.llms.triton import TritonChatCompletion
|
||||
|
||||
|
||||
def test_split_embedding_by_shape_passes():
|
||||
try:
|
||||
triton = TritonChatCompletion()
|
||||
data = [
|
||||
{
|
||||
"shape": [2, 3],
|
||||
"data": [1, 2, 3, 4, 5, 6],
|
||||
}
|
||||
]
|
||||
split_output_data = triton.split_embedding_by_shape(data[0]["data"], data[0]["shape"])
|
||||
assert split_output_data == [[1, 2, 3], [4, 5, 6]]
|
||||
except Exception as e:
|
||||
pytest.fail(f"An exception occured: {e}")
|
||||
|
||||
|
||||
def test_split_embedding_by_shape_fails_with_shape_value_error():
|
||||
triton = TritonChatCompletion()
|
||||
data = [
|
||||
{
|
||||
"shape": [2],
|
||||
"data": [1, 2, 3, 4, 5, 6],
|
||||
}
|
||||
]
|
||||
with pytest.raises(ValueError):
|
||||
triton.split_embedding_by_shape(data[0]["data"], data[0]["shape"])
|
||||
@ -60,6 +60,10 @@ from litellm.litellm_core_utils.redact_messages import (
|
||||
)
|
||||
from litellm.litellm_core_utils.token_counter import get_modified_max_tokens
|
||||
from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
|
||||
from litellm.types.llms.openai import (
|
||||
ChatCompletionNamedToolChoiceParam,
|
||||
ChatCompletionToolParam,
|
||||
)
|
||||
from litellm.types.utils import (
|
||||
CallTypes,
|
||||
ChatCompletionDeltaToolCall,
|
||||
@ -79,7 +83,6 @@ from litellm.types.utils import (
|
||||
TranscriptionResponse,
|
||||
Usage,
|
||||
)
|
||||
from litellm.types.llms.openai import ChatCompletionToolParam, ChatCompletionNamedToolChoiceParam
|
||||
|
||||
oidc_cache = DualCache()
|
||||
|
||||
@ -1572,8 +1575,8 @@ def openai_token_counter(
|
||||
model="gpt-3.5-turbo-0613",
|
||||
text: Optional[str] = None,
|
||||
is_tool_call: Optional[bool] = False,
|
||||
tools: list[ChatCompletionToolParam] | None = None,
|
||||
tool_choice: ChatCompletionNamedToolChoiceParam | None = None,
|
||||
tools: Optional[List[ChatCompletionToolParam]] = None,
|
||||
tool_choice: Optional[ChatCompletionNamedToolChoiceParam] = None,
|
||||
count_response_tokens: Optional[
|
||||
bool
|
||||
] = False, # Flag passed from litellm.stream_chunk_builder, to indicate counting tokens for LLM Response. We need this because for LLM input we add +3 tokens per message - based on OpenAI's token counter
|
||||
@ -1617,7 +1620,7 @@ def openai_token_counter(
|
||||
for message in messages:
|
||||
num_tokens += tokens_per_message
|
||||
if message.get("role", None) == "system":
|
||||
includes_system_message = True
|
||||
includes_system_message = True
|
||||
for key, value in message.items():
|
||||
if isinstance(value, str):
|
||||
num_tokens += len(encoding.encode(value, disallowed_special=()))
|
||||
@ -1868,14 +1871,15 @@ def _format_type(props, indent):
|
||||
# This is a guess, as an empty string doesn't yield the expected token count
|
||||
return "any"
|
||||
|
||||
|
||||
def token_counter(
|
||||
model="",
|
||||
custom_tokenizer: Optional[dict] = None,
|
||||
text: Optional[Union[str, List[str]]] = None,
|
||||
messages: Optional[List] = None,
|
||||
count_response_tokens: Optional[bool] = False,
|
||||
tools: list[ChatCompletionToolParam] | None = None,
|
||||
tool_choice: ChatCompletionNamedToolChoiceParam | None = None,
|
||||
tools: Optional[List[ChatCompletionToolParam]] = None,
|
||||
tool_choice: Optional[ChatCompletionNamedToolChoiceParam] = None,
|
||||
) -> int:
|
||||
"""
|
||||
Count the number of tokens in a given text using a specified model.
|
||||
@ -1955,7 +1959,7 @@ def token_counter(
|
||||
is_tool_call=is_tool_call,
|
||||
count_response_tokens=count_response_tokens,
|
||||
tools=tools,
|
||||
tool_choice=tool_choice
|
||||
tool_choice=tool_choice,
|
||||
)
|
||||
else:
|
||||
print_verbose(
|
||||
@ -1968,7 +1972,7 @@ def token_counter(
|
||||
is_tool_call=is_tool_call,
|
||||
count_response_tokens=count_response_tokens,
|
||||
tools=tools,
|
||||
tool_choice=tool_choice
|
||||
tool_choice=tool_choice,
|
||||
)
|
||||
else:
|
||||
num_tokens = len(encoding.encode(text, disallowed_special=())) # type: ignore
|
||||
@ -3927,25 +3931,51 @@ def get_api_base(
|
||||
if dynamic_api_base is not None:
|
||||
return dynamic_api_base
|
||||
|
||||
stream: bool = getattr(optional_params, "stream", False)
|
||||
|
||||
if (
|
||||
_optional_params.vertex_location is not None
|
||||
and _optional_params.vertex_project is not None
|
||||
):
|
||||
_api_base = "{}-aiplatform.googleapis.com/v1/projects/{}/locations/{}/publishers/google/models/{}:streamGenerateContent".format(
|
||||
_optional_params.vertex_location,
|
||||
_optional_params.vertex_project,
|
||||
_optional_params.vertex_location,
|
||||
model,
|
||||
)
|
||||
from litellm.llms.vertex_ai_anthropic import create_vertex_anthropic_url
|
||||
|
||||
if "claude" in model:
|
||||
_api_base = create_vertex_anthropic_url(
|
||||
vertex_location=_optional_params.vertex_location,
|
||||
vertex_project=_optional_params.vertex_project,
|
||||
model=model,
|
||||
stream=stream,
|
||||
)
|
||||
else:
|
||||
|
||||
if stream:
|
||||
_api_base = "{}-aiplatform.googleapis.com/v1/projects/{}/locations/{}/publishers/google/models/{}:streamGenerateContent".format(
|
||||
_optional_params.vertex_location,
|
||||
_optional_params.vertex_project,
|
||||
_optional_params.vertex_location,
|
||||
model,
|
||||
)
|
||||
else:
|
||||
_api_base = "{}-aiplatform.googleapis.com/v1/projects/{}/locations/{}/publishers/google/models/{}:generateContent".format(
|
||||
_optional_params.vertex_location,
|
||||
_optional_params.vertex_project,
|
||||
_optional_params.vertex_location,
|
||||
model,
|
||||
)
|
||||
return _api_base
|
||||
|
||||
if custom_llm_provider is None:
|
||||
return None
|
||||
|
||||
if custom_llm_provider == "gemini":
|
||||
_api_base = "https://generativelanguage.googleapis.com/v1beta/models/{}:generateContent".format(
|
||||
model
|
||||
)
|
||||
if stream:
|
||||
_api_base = "https://generativelanguage.googleapis.com/v1beta/models/{}:streamGenerateContent".format(
|
||||
model
|
||||
)
|
||||
else:
|
||||
_api_base = "https://generativelanguage.googleapis.com/v1beta/models/{}:generateContent".format(
|
||||
model
|
||||
)
|
||||
return _api_base
|
||||
elif custom_llm_provider == "openai":
|
||||
_api_base = "https://api.openai.com"
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
[tool.poetry]
|
||||
name = "litellm"
|
||||
version = "1.41.23"
|
||||
version = "1.41.24"
|
||||
description = "Library to easily interface with LLM API providers"
|
||||
authors = ["BerriAI"]
|
||||
license = "MIT"
|
||||
@ -91,7 +91,7 @@ requires = ["poetry-core", "wheel"]
|
||||
build-backend = "poetry.core.masonry.api"
|
||||
|
||||
[tool.commitizen]
|
||||
version = "1.41.23"
|
||||
version = "1.41.24"
|
||||
version_files = [
|
||||
"pyproject.toml:^version"
|
||||
]
|
||||
|
||||
@ -107,7 +107,8 @@ async def test_chat_completion_check_otel_spans():
|
||||
|
||||
print("Parent trace spans: ", parent_trace_spans)
|
||||
|
||||
assert len(parent_trace_spans) == 4
|
||||
# either 4 or 5 traces depending on how many redis calls were made
|
||||
assert len(parent_trace_spans) == 5 or len(parent_trace_spans) == 4
|
||||
|
||||
# 'postgres', 'redis', 'raw_gen_ai_request', 'litellm_request', 'Received Proxy Server Request' in the span
|
||||
assert "postgres" in parent_trace_spans
|
||||
|
||||
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File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
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@ -1 +0,0 @@
|
||||
(self.webpackChunk_N_E=self.webpackChunk_N_E||[]).push([[185],{13993:function(n,e,t){Promise.resolve().then(t.t.bind(t,63385,23)),Promise.resolve().then(t.t.bind(t,99646,23))},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=13993)}),_N_E=n.O()}]);
|
||||
@ -0,0 +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_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()}]);
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||||
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@ -1 +1 @@
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0:["PNleJDTsrtpYs8s3jQHZY",[[["",{"children":["onboarding",{"children":["__PAGE__",{}]}]},"$undefined","$undefined",true],["",{"children":["onboarding",{"children":["__PAGE__",{},["$L1",["$","$L2",null,{"propsForComponent":{"params":{}},"Component":"$3","isStaticGeneration":true}],null]]},["$","$L4",null,{"parallelRouterKey":"children","segmentPath":["children","onboarding","children"],"loading":"$undefined","loadingStyles":"$undefined","loadingScripts":"$undefined","hasLoading":false,"error":"$undefined","errorStyles":"$undefined","errorScripts":"$undefined","template":["$","$L5",null,{}],"templateStyles":"$undefined","templateScripts":"$undefined","notFound":"$undefined","notFoundStyles":"$undefined","styles":null}]]},[null,["$","html",null,{"lang":"en","children":["$","body",null,{"className":"__className_12bbc4","children":["$","$L4",null,{"parallelRouterKey":"children","segmentPath":["children"],"loading":"$undefined","loadingStyles":"$undefined","loadingScripts":"$undefined","hasLoading":false,"error":"$undefined","errorStyles":"$undefined","errorScripts":"$undefined","template":["$","$L5",null,{}],"templateStyles":"$undefined","templateScripts":"$undefined","notFound":[["$","title",null,{"children":"404: This page could not be found."}],["$","div",null,{"style":{"fontFamily":"system-ui,\"Segoe UI\",Roboto,Helvetica,Arial,sans-serif,\"Apple Color Emoji\",\"Segoe UI Emoji\"","height":"100vh","textAlign":"center","display":"flex","flexDirection":"column","alignItems":"center","justifyContent":"center"},"children":["$","div",null,{"children":[["$","style",null,{"dangerouslySetInnerHTML":{"__html":"body{color:#000;background:#fff;margin:0}.next-error-h1{border-right:1px solid rgba(0,0,0,.3)}@media (prefers-color-scheme:dark){body{color:#fff;background:#000}.next-error-h1{border-right:1px solid rgba(255,255,255,.3)}}"}}],["$","h1",null,{"className":"next-error-h1","style":{"display":"inline-block","margin":"0 20px 0 0","padding":"0 23px 0 0","fontSize":24,"fontWeight":500,"verticalAlign":"top","lineHeight":"49px"},"children":"404"}],["$","div",null,{"style":{"display":"inline-block"},"children":["$","h2",null,{"style":{"fontSize":14,"fontWeight":400,"lineHeight":"49px","margin":0},"children":"This page could not be found."}]}]]}]}]],"notFoundStyles":[],"styles":null}]}]}],null]],[[["$","link","0",{"rel":"stylesheet","href":"/ui/_next/static/css/443c634cc4763603.css","precedence":"next","crossOrigin":""}]],"$L6"]]]]
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0:["wCB0BxS4e85YTevNhQvz2",[[["",{"children":["onboarding",{"children":["__PAGE__",{}]}]},"$undefined","$undefined",true],["",{"children":["onboarding",{"children":["__PAGE__",{},["$L1",["$","$L2",null,{"propsForComponent":{"params":{}},"Component":"$3","isStaticGeneration":true}],null]]},["$","$L4",null,{"parallelRouterKey":"children","segmentPath":["children","onboarding","children"],"loading":"$undefined","loadingStyles":"$undefined","loadingScripts":"$undefined","hasLoading":false,"error":"$undefined","errorStyles":"$undefined","errorScripts":"$undefined","template":["$","$L5",null,{}],"templateStyles":"$undefined","templateScripts":"$undefined","notFound":"$undefined","notFoundStyles":"$undefined","styles":null}]]},[null,["$","html",null,{"lang":"en","children":["$","body",null,{"className":"__className_12bbc4","children":["$","$L4",null,{"parallelRouterKey":"children","segmentPath":["children"],"loading":"$undefined","loadingStyles":"$undefined","loadingScripts":"$undefined","hasLoading":false,"error":"$undefined","errorStyles":"$undefined","errorScripts":"$undefined","template":["$","$L5",null,{}],"templateStyles":"$undefined","templateScripts":"$undefined","notFound":[["$","title",null,{"children":"404: This page could not be found."}],["$","div",null,{"style":{"fontFamily":"system-ui,\"Segoe UI\",Roboto,Helvetica,Arial,sans-serif,\"Apple Color Emoji\",\"Segoe UI Emoji\"","height":"100vh","textAlign":"center","display":"flex","flexDirection":"column","alignItems":"center","justifyContent":"center"},"children":["$","div",null,{"children":[["$","style",null,{"dangerouslySetInnerHTML":{"__html":"body{color:#000;background:#fff;margin:0}.next-error-h1{border-right:1px solid rgba(0,0,0,.3)}@media (prefers-color-scheme:dark){body{color:#fff;background:#000}.next-error-h1{border-right:1px solid rgba(255,255,255,.3)}}"}}],["$","h1",null,{"className":"next-error-h1","style":{"display":"inline-block","margin":"0 20px 0 0","padding":"0 23px 0 0","fontSize":24,"fontWeight":500,"verticalAlign":"top","lineHeight":"49px"},"children":"404"}],["$","div",null,{"style":{"display":"inline-block"},"children":["$","h2",null,{"style":{"fontSize":14,"fontWeight":400,"lineHeight":"49px","margin":0},"children":"This page could not be found."}]}]]}]}]],"notFoundStyles":[],"styles":null}]}]}],null]],[[["$","link","0",{"rel":"stylesheet","href":"/ui/_next/static/css/275ab6ee150b4fea.css","precedence":"next","crossOrigin":""}]],"$L6"]]]]
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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
|
||||
|
||||
@ -31,6 +31,9 @@ const Sidebar: React.FC<SidebarProps> = ({
|
||||
<Menu.Item key="1" onClick={() => setPage("usage")}>
|
||||
Usage
|
||||
</Menu.Item>
|
||||
<Menu.Item key="9" onClick={() => setPage("caching")}>
|
||||
<Text>Caching</Text>
|
||||
</Menu.Item>
|
||||
</Menu>
|
||||
</Sider>
|
||||
</Layout>
|
||||
|
||||
@ -57,7 +57,12 @@ const Navbar: React.FC<NavbarProps> = ({
|
||||
|
||||
console.log("logoutUrl=", logoutUrl);
|
||||
|
||||
|
||||
const handleLogout = () => {
|
||||
// Clear cookies
|
||||
document.cookie = "token=; expires=Thu, 01 Jan 1970 00:00:00 UTC; path=/;";
|
||||
window.location.href = logoutUrl;
|
||||
}
|
||||
|
||||
|
||||
const items: MenuProps["items"] = [
|
||||
{
|
||||
@ -72,11 +77,7 @@ const Navbar: React.FC<NavbarProps> = ({
|
||||
},
|
||||
{
|
||||
key: "2",
|
||||
label: (
|
||||
<a href={logoutUrl}>
|
||||
<p>Logout</p>
|
||||
</a>
|
||||
),
|
||||
label: <p onClick={handleLogout}>Logout</p>,
|
||||
}
|
||||
];
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user