diff --git a/docs/my-website/docs/providers/anthropic.md b/docs/my-website/docs/providers/anthropic.md
index 198a6a03d..21140f29b 100644
--- a/docs/my-website/docs/providers/anthropic.md
+++ b/docs/my-website/docs/providers/anthropic.md
@@ -1,3 +1,6 @@
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
# Anthropic
LiteLLM supports
@@ -45,7 +48,97 @@ for chunk in response:
print(chunk["choices"][0]["delta"]["content"]) # same as openai format
```
+## OpenAI Proxy Usage
+Here's how to call Anthropic with the LiteLLM Proxy Server
+
+### 1. Save key in your environment
+
+```bash
+export ANTHROPIC_API_KEY="your-api-key"
+```
+
+### 2. Start the proxy
+
+```bash
+$ litellm --model claude-3-opus-20240229
+
+# Server running on http://0.0.0.0:8000
+```
+
+### 3. Test it
+
+
+
+
+
+```shell
+curl --location 'http://0.0.0.0:8000/chat/completions' \
+--header 'Content-Type: application/json' \
+--data ' {
+ "model": "gpt-3.5-turbo",
+ "messages": [
+ {
+ "role": "user",
+ "content": "what llm are you"
+ }
+ ]
+ }
+'
+```
+
+
+
+```python
+import openai
+client = openai.OpenAI(
+ api_key="anything",
+ base_url="http://0.0.0.0:8000"
+)
+
+# request sent to model set on litellm proxy, `litellm --model`
+response = client.chat.completions.create(model="gpt-3.5-turbo", messages = [
+ {
+ "role": "user",
+ "content": "this is a test request, write a short poem"
+ }
+])
+
+print(response)
+
+```
+
+
+
+```python
+from langchain.chat_models import ChatOpenAI
+from langchain.prompts.chat import (
+ ChatPromptTemplate,
+ HumanMessagePromptTemplate,
+ SystemMessagePromptTemplate,
+)
+from langchain.schema import HumanMessage, SystemMessage
+
+chat = ChatOpenAI(
+ openai_api_base="http://0.0.0.0:8000", # set openai_api_base to the LiteLLM Proxy
+ model = "gpt-3.5-turbo",
+ temperature=0.1
+)
+
+messages = [
+ SystemMessage(
+ content="You are a helpful assistant that im using to make a test request to."
+ ),
+ HumanMessage(
+ content="test from litellm. tell me why it's amazing in 1 sentence"
+ ),
+]
+response = chat(messages)
+
+print(response)
+```
+
+
## Supported Models
@@ -56,6 +149,7 @@ for chunk in response:
| claude-2.1 | `completion('claude-2.1', messages)` | `os.environ['ANTHROPIC_API_KEY']` |
| claude-2 | `completion('claude-2', messages)` | `os.environ['ANTHROPIC_API_KEY']` |
| claude-instant-1.2 | `completion('claude-instant-1.2', messages)` | `os.environ['ANTHROPIC_API_KEY']` |
+| claude-instant-1 | `completion('claude-instant-1', messages)` | `os.environ['ANTHROPIC_API_KEY']` |
## Advanced
diff --git a/litellm/__init__.py b/litellm/__init__.py
index c64dc0ea8..017bd46ac 100644
--- a/litellm/__init__.py
+++ b/litellm/__init__.py
@@ -574,6 +574,7 @@ from .utils import (
)
from .llms.huggingface_restapi import HuggingfaceConfig
from .llms.anthropic import AnthropicConfig
+from .llms.anthropic_text import AnthropicTextConfig
from .llms.replicate import ReplicateConfig
from .llms.cohere import CohereConfig
from .llms.ai21 import AI21Config
diff --git a/litellm/llms/anthropic.py b/litellm/llms/anthropic.py
index 047616cfe..5e0887901 100644
--- a/litellm/llms/anthropic.py
+++ b/litellm/llms/anthropic.py
@@ -227,7 +227,7 @@ def completion(
else:
text_content = completion_response["content"][0].get("text", None)
## TOOL CALLING - OUTPUT PARSE
- if _is_function_call == True:
+ if text_content is not None and "invoke" in text_content:
function_name = extract_between_tags("tool_name", text_content)[0]
function_arguments_str = extract_between_tags("invoke", text_content)[
0
diff --git a/litellm/llms/anthropic_text.py b/litellm/llms/anthropic_text.py
new file mode 100644
index 000000000..bccc8c769
--- /dev/null
+++ b/litellm/llms/anthropic_text.py
@@ -0,0 +1,222 @@
+import os, types
+import json
+from enum import Enum
+import requests
+import time
+from typing import Callable, Optional
+from litellm.utils import ModelResponse, Usage
+import litellm
+from .prompt_templates.factory import prompt_factory, custom_prompt
+import httpx
+
+
+class AnthropicConstants(Enum):
+ HUMAN_PROMPT = "\n\nHuman: "
+ AI_PROMPT = "\n\nAssistant: "
+
+
+class AnthropicError(Exception):
+ def __init__(self, status_code, message):
+ self.status_code = status_code
+ self.message = message
+ self.request = httpx.Request(
+ method="POST", url="https://api.anthropic.com/v1/complete"
+ )
+ self.response = httpx.Response(status_code=status_code, request=self.request)
+ super().__init__(
+ self.message
+ ) # Call the base class constructor with the parameters it needs
+
+
+class AnthropicTextConfig:
+ """
+ Reference: https://docs.anthropic.com/claude/reference/complete_post
+
+ to pass metadata to anthropic, it's {"user_id": "any-relevant-information"}
+ """
+
+ max_tokens_to_sample: Optional[int] = (
+ litellm.max_tokens
+ ) # anthropic requires a default
+ stop_sequences: Optional[list] = None
+ temperature: Optional[int] = None
+ top_p: Optional[int] = None
+ top_k: Optional[int] = None
+ metadata: Optional[dict] = None
+
+ def __init__(
+ self,
+ max_tokens_to_sample: Optional[int] = 256, # anthropic requires a default
+ stop_sequences: Optional[list] = None,
+ temperature: Optional[int] = None,
+ top_p: Optional[int] = None,
+ top_k: Optional[int] = None,
+ metadata: Optional[dict] = 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
+ }
+
+
+# makes headers for API call
+def validate_environment(api_key, user_headers):
+ 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"
+ )
+ headers = {
+ "accept": "application/json",
+ "anthropic-version": "2023-06-01",
+ "content-type": "application/json",
+ "x-api-key": api_key,
+ }
+ if user_headers is not None and isinstance(user_headers, dict):
+ headers = {**headers, **user_headers}
+ return headers
+
+
+def completion(
+ model: str,
+ messages: list,
+ api_base: str,
+ custom_prompt_dict: dict,
+ model_response: ModelResponse,
+ print_verbose: Callable,
+ encoding,
+ api_key,
+ logging_obj,
+ optional_params=None,
+ litellm_params=None,
+ logger_fn=None,
+ headers={},
+):
+ headers = validate_environment(api_key, headers)
+ if model in custom_prompt_dict:
+ # check if the model has a registered custom prompt
+ model_prompt_details = custom_prompt_dict[model]
+ prompt = custom_prompt(
+ role_dict=model_prompt_details["roles"],
+ initial_prompt_value=model_prompt_details["initial_prompt_value"],
+ final_prompt_value=model_prompt_details["final_prompt_value"],
+ messages=messages,
+ )
+ else:
+ prompt = prompt_factory(
+ model=model, messages=messages, custom_llm_provider="anthropic"
+ )
+
+ ## Load Config
+ config = litellm.AnthropicTextConfig.get_config()
+ for k, v in config.items():
+ if (
+ k not in optional_params
+ ): # completion(top_k=3) > anthropic_config(top_k=3) <- allows for dynamic variables to be passed in
+ optional_params[k] = v
+
+ data = {
+ "model": model,
+ "prompt": prompt,
+ **optional_params,
+ }
+
+ ## LOGGING
+ logging_obj.pre_call(
+ input=prompt,
+ api_key=api_key,
+ additional_args={
+ "complete_input_dict": data,
+ "api_base": api_base,
+ "headers": headers,
+ },
+ )
+
+ ## COMPLETION CALL
+ if "stream" in optional_params and optional_params["stream"] == True:
+ response = requests.post(
+ api_base,
+ headers=headers,
+ data=json.dumps(data),
+ stream=optional_params["stream"],
+ )
+
+ if response.status_code != 200:
+ raise AnthropicError(
+ status_code=response.status_code, message=response.text
+ )
+
+ return response.iter_lines()
+ else:
+ response = requests.post(api_base, headers=headers, data=json.dumps(data))
+ if response.status_code != 200:
+ raise AnthropicError(
+ status_code=response.status_code, message=response.text
+ )
+
+ ## LOGGING
+ logging_obj.post_call(
+ input=prompt,
+ api_key=api_key,
+ original_response=response.text,
+ additional_args={"complete_input_dict": data},
+ )
+ print_verbose(f"raw model_response: {response.text}")
+ ## RESPONSE OBJECT
+ try:
+ completion_response = response.json()
+ except:
+ raise AnthropicError(
+ message=response.text, status_code=response.status_code
+ )
+ if "error" in completion_response:
+ raise AnthropicError(
+ message=str(completion_response["error"]),
+ status_code=response.status_code,
+ )
+ else:
+ if len(completion_response["completion"]) > 0:
+ model_response["choices"][0]["message"]["content"] = (
+ completion_response["completion"]
+ )
+ model_response.choices[0].finish_reason = completion_response["stop_reason"]
+
+ ## CALCULATING USAGE
+ prompt_tokens = len(
+ encoding.encode(prompt)
+ ) ##[TODO] use the anthropic tokenizer here
+ completion_tokens = len(
+ encoding.encode(model_response["choices"][0]["message"].get("content", ""))
+ ) ##[TODO] use the anthropic tokenizer here
+
+ model_response["created"] = int(time.time())
+ model_response["model"] = model
+ usage = Usage(
+ prompt_tokens=prompt_tokens,
+ completion_tokens=completion_tokens,
+ total_tokens=prompt_tokens + completion_tokens,
+ )
+ model_response.usage = usage
+ return model_response
+
+
+def embedding():
+ # logic for parsing in - calling - parsing out model embedding calls
+ pass
diff --git a/litellm/llms/huggingface_restapi.py b/litellm/llms/huggingface_restapi.py
index 61a6ac040..293773289 100644
--- a/litellm/llms/huggingface_restapi.py
+++ b/litellm/llms/huggingface_restapi.py
@@ -658,36 +658,26 @@ class Huggingface(BaseLLM):
message=first_chunk,
)
- return self.async_streaming_generator(
- first_chunk=first_chunk,
- response_iterator=response_iterator,
+ # Create a new async generator that begins with the first_chunk and includes the remaining items
+ async def custom_stream_with_first_chunk():
+ yield first_chunk # Yield back the first chunk
+ async for (
+ chunk
+ ) in response_iterator: # Continue yielding the rest of the chunks
+ yield chunk
+
+ # Creating a new completion stream that starts with the first chunk
+ completion_stream = custom_stream_with_first_chunk()
+
+ streamwrapper = CustomStreamWrapper(
+ completion_stream=completion_stream,
model=model,
+ custom_llm_provider="huggingface",
logging_obj=logging_obj,
)
- async def async_streaming_generator(
- self, first_chunk, response_iterator, model, logging_obj
- ):
- # Create a new async generator that begins with the first_chunk and includes the remaining items
- async def custom_stream_with_first_chunk():
- yield first_chunk # Yield back the first chunk
- async for (
- chunk
- ) in response_iterator: # Continue yielding the rest of the chunks
- yield chunk
-
- # Creating a new completion stream that starts with the first chunk
- completion_stream = custom_stream_with_first_chunk()
-
- streamwrapper = CustomStreamWrapper(
- completion_stream=completion_stream,
- model=model,
- custom_llm_provider="huggingface",
- logging_obj=logging_obj,
- )
-
- async for transformed_chunk in streamwrapper:
- yield transformed_chunk
+ async for transformed_chunk in streamwrapper:
+ yield transformed_chunk
def embedding(
self,
diff --git a/litellm/main.py b/litellm/main.py
index b7707b722..60effd96f 100644
--- a/litellm/main.py
+++ b/litellm/main.py
@@ -39,6 +39,7 @@ from litellm.utils import (
)
from .llms import (
anthropic,
+ anthropic_text,
together_ai,
ai21,
sagemaker,
@@ -1018,28 +1019,55 @@ def completion(
or litellm.api_key
or os.environ.get("ANTHROPIC_API_KEY")
)
- api_base = (
- api_base
- or litellm.api_base
- or get_secret("ANTHROPIC_API_BASE")
- or "https://api.anthropic.com/v1/messages"
- )
custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict
- response = anthropic.completion(
- model=model,
- messages=messages,
- api_base=api_base,
- custom_prompt_dict=litellm.custom_prompt_dict,
- model_response=model_response,
- print_verbose=print_verbose,
- optional_params=optional_params,
- litellm_params=litellm_params,
- logger_fn=logger_fn,
- encoding=encoding, # for calculating input/output tokens
- api_key=api_key,
- logging_obj=logging,
- headers=headers,
- )
+
+ if (model == "claude-2") or (model == "claude-instant-1"):
+ # call anthropic /completion, only use this route for claude-2, claude-instant-1
+ api_base = (
+ api_base
+ or litellm.api_base
+ or get_secret("ANTHROPIC_API_BASE")
+ or "https://api.anthropic.com/v1/complete"
+ )
+ response = anthropic_text.completion(
+ model=model,
+ messages=messages,
+ api_base=api_base,
+ custom_prompt_dict=litellm.custom_prompt_dict,
+ model_response=model_response,
+ print_verbose=print_verbose,
+ optional_params=optional_params,
+ litellm_params=litellm_params,
+ logger_fn=logger_fn,
+ encoding=encoding, # for calculating input/output tokens
+ api_key=api_key,
+ logging_obj=logging,
+ headers=headers,
+ )
+ else:
+ # call /messages
+ # default route for all anthropic models
+ api_base = (
+ api_base
+ or litellm.api_base
+ or get_secret("ANTHROPIC_API_BASE")
+ or "https://api.anthropic.com/v1/messages"
+ )
+ response = anthropic.completion(
+ model=model,
+ messages=messages,
+ api_base=api_base,
+ custom_prompt_dict=litellm.custom_prompt_dict,
+ model_response=model_response,
+ print_verbose=print_verbose,
+ optional_params=optional_params,
+ litellm_params=litellm_params,
+ logger_fn=logger_fn,
+ encoding=encoding, # for calculating input/output tokens
+ api_key=api_key,
+ logging_obj=logging,
+ headers=headers,
+ )
if "stream" in optional_params and optional_params["stream"] == True:
# don't try to access stream object,
response = CustomStreamWrapper(
diff --git a/litellm/proxy/_experimental/out/404.html b/litellm/proxy/_experimental/out/404.html
index 064bb2163..eb5cf4286 100644
--- a/litellm/proxy/_experimental/out/404.html
+++ b/litellm/proxy/_experimental/out/404.html
@@ -1 +1 @@
-404: This page could not be found.🚅 LiteLLM
404
This page could not be found.
\ No newline at end of file
+404: This page could not be found.🚅 LiteLLM
404
This page could not be found.
\ No newline at end of file
diff --git a/litellm/proxy/_experimental/out/_next/static/p1zjZBLDqxGf-NaFvZkeF/_buildManifest.js b/litellm/proxy/_experimental/out/_next/static/ZF-EluyKCEJoZptE3dOXT/_buildManifest.js
similarity index 100%
rename from litellm/proxy/_experimental/out/_next/static/p1zjZBLDqxGf-NaFvZkeF/_buildManifest.js
rename to litellm/proxy/_experimental/out/_next/static/ZF-EluyKCEJoZptE3dOXT/_buildManifest.js
diff --git a/litellm/proxy/_experimental/out/_next/static/p1zjZBLDqxGf-NaFvZkeF/_ssgManifest.js b/litellm/proxy/_experimental/out/_next/static/ZF-EluyKCEJoZptE3dOXT/_ssgManifest.js
similarity index 100%
rename from litellm/proxy/_experimental/out/_next/static/p1zjZBLDqxGf-NaFvZkeF/_ssgManifest.js
rename to litellm/proxy/_experimental/out/_next/static/ZF-EluyKCEJoZptE3dOXT/_ssgManifest.js
diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/app/page-2ed0bc91ffef505b.js b/litellm/proxy/_experimental/out/_next/static/chunks/app/page-2ed0bc91ffef505b.js
new file mode 100644
index 000000000..a0bd09f21
--- /dev/null
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/app/page-2ed0bc91ffef505b.js
@@ -0,0 +1 @@
+(self.webpackChunk_N_E=self.webpackChunk_N_E||[]).push([[931],{20661:function(e,t,s){Promise.resolve().then(s.bind(s,57492))},57492:function(e,t,s){"use strict";s.r(t),s.d(t,{default:function(){return eM}});var l=s(3827),n=s(64090),r=s(47907),a=s(8792),o=s(2179),i=e=>{let{userID:t,userRole:s,userEmail:n,showSSOBanner:r}=e;return console.log("User ID:",t),console.log("userEmail:",n),(0,l.jsxs)("nav",{className:"left-0 right-0 top-0 flex justify-between items-center h-12 mb-4",children:[(0,l.jsx)("div",{className:"text-left my-2 absolute top-0 left-0",children:(0,l.jsx)("div",{className:"flex flex-col items-center",children:(0,l.jsx)(a.default,{href:"/",children:(0,l.jsx)("button",{className:"text-gray-800 text-2xl py-1 rounded text-center",children:(0,l.jsx)("img",{src:"/get_image",width:200,height:200,alt:"LiteLLM Brand",className:"mr-2"})})})})}),(0,l.jsxs)("div",{className:"text-right mx-4 my-2 absolute top-0 right-0 flex items-center justify-end space-x-2",children:[r?(0,l.jsx)("a",{href:"https://docs.litellm.ai/docs/proxy/ui#setup-ssoauth-for-ui",target:"_blank",className:"mr-2",children:(0,l.jsx)(o.Z,{variant:"primary",size:"lg",children:"Enable SSO"})}):null,(0,l.jsxs)(o.Z,{variant:"secondary",size:"lg",children:[n,(0,l.jsxs)("p",{children:["Role: ",s]}),(0,l.jsxs)("p",{children:["ID: ",t]})]})]})]})},c=s(80588);let d=async(e,t,s)=>{try{if(console.log("Form Values in keyCreateCall:",s),s.description&&(s.metadata||(s.metadata={}),s.metadata.description=s.description,delete s.description,s.metadata=JSON.stringify(s.metadata)),s.metadata){console.log("formValues.metadata:",s.metadata);try{s.metadata=JSON.parse(s.metadata)}catch(e){throw c.ZP.error("Failed to parse metadata: "+e),Error("Failed to parse metadata: "+e)}}console.log("Form Values after check:",s);let l=await fetch("/key/generate",{method:"POST",headers:{Authorization:"Bearer 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diff --git a/litellm/proxy/_experimental/out/index.html b/litellm/proxy/_experimental/out/index.html
index aaaaa698a..437d90aac 100644
--- a/litellm/proxy/_experimental/out/index.html
+++ b/litellm/proxy/_experimental/out/index.html
@@ -1 +1 @@
-🚅 LiteLLM
\ No newline at end of file
+🚅 LiteLLM
\ No newline at end of file
diff --git a/litellm/proxy/_experimental/out/index.txt b/litellm/proxy/_experimental/out/index.txt
index 72b67997a..28c9282be 100644
--- a/litellm/proxy/_experimental/out/index.txt
+++ b/litellm/proxy/_experimental/out/index.txt
@@ -1,7 +1,7 @@
2:I[77831,[],""]
-3:I[56239,["730","static/chunks/730-1411b729a1c79695.js","931","static/chunks/app/page-37bd7c3d0bb898a3.js"],""]
+3:I[57492,["730","static/chunks/730-1411b729a1c79695.js","931","static/chunks/app/page-2ed0bc91ffef505b.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"}],["$","title","2",{"children":"🚅 LiteLLM"}],["$","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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diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py
index 7dbb068bc..47b8c1535 100644
--- a/litellm/proxy/proxy_server.py
+++ b/litellm/proxy/proxy_server.py
@@ -5811,6 +5811,58 @@ async def model_info_v2(
return {"data": all_models}
+@router.get(
+ "/model/metrics",
+ description="View number of requests & avg latency per model on config.yaml",
+ tags=["model management"],
+ dependencies=[Depends(user_api_key_auth)],
+)
+async def model_metrics(
+ user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
+):
+ global prisma_client
+ if prisma_client is None:
+ raise ProxyException(
+ message="Prisma Client is not initialized",
+ type="internal_error",
+ param="None",
+ code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+ )
+
+ sql_query = """
+ SELECT
+ CASE WHEN api_base = '' THEN model ELSE CONCAT(model, '-', api_base) END AS combined_model_api_base,
+ COUNT(*) AS num_requests,
+ AVG(EXTRACT(epoch FROM ("endTime" - "startTime"))) AS avg_latency_seconds
+ FROM
+ "LiteLLM_SpendLogs"
+ WHERE
+ "startTime" >= NOW() - INTERVAL '10000 hours'
+ GROUP BY
+ CASE WHEN api_base = '' THEN model ELSE CONCAT(model, '-', api_base) END
+ ORDER BY
+ num_requests DESC
+ LIMIT 50;
+ """
+
+ db_response = await prisma_client.db.query_raw(query=sql_query)
+ response: List[dict] = []
+ if response is not None:
+ # loop through all models
+ for model_data in db_response:
+ model = model_data.get("combined_model_api_base", "")
+ num_requests = model_data.get("num_requests", 0)
+ avg_latency_seconds = model_data.get("avg_latency_seconds", 0)
+ response.append(
+ {
+ "model": model,
+ "num_requests": num_requests,
+ "avg_latency_seconds": avg_latency_seconds,
+ }
+ )
+ return response
+
+
@router.get(
"/model/info",
description="Provides more info about each model in /models, including config.yaml descriptions (except api key and api base)",
diff --git a/litellm/tests/test_completion.py b/litellm/tests/test_completion.py
index 9c7796c50..57b0e436f 100644
--- a/litellm/tests/test_completion.py
+++ b/litellm/tests/test_completion.py
@@ -56,7 +56,7 @@ def test_completion_custom_provider_model_name():
def test_completion_claude():
litellm.set_verbose = True
litellm.cache = None
- litellm.AnthropicConfig(max_tokens=200, metadata={"user_id": "1224"})
+ litellm.AnthropicTextConfig(max_tokens_to_sample=200, metadata={"user_id": "1224"})
messages = [
{
"role": "system",
@@ -67,10 +67,7 @@ def test_completion_claude():
try:
# test without max tokens
response = completion(
- model="claude-instant-1.2",
- messages=messages,
- request_timeout=10,
- max_tokens=10,
+ model="claude-instant-1", messages=messages, request_timeout=10
)
# Add any assertions, here to check response args
print(response)
diff --git a/litellm/utils.py b/litellm/utils.py
index e42ab836c..d4d85cad1 100644
--- a/litellm/utils.py
+++ b/litellm/utils.py
@@ -4216,6 +4216,11 @@ def get_optional_params(
if top_p is not None:
optional_params["top_p"] = top_p
if max_tokens is not None:
+ if (model == "claude-2") or (model == "claude-instant-1"):
+ # these models use antropic_text.py which only accepts max_tokens_to_sample
+ optional_params["max_tokens_to_sample"] = max_tokens
+ else:
+ optional_params["max_tokens"] = max_tokens
optional_params["max_tokens"] = max_tokens
if tools is not None:
optional_params["tools"] = tools
diff --git a/pyproject.toml b/pyproject.toml
index 78d4b5cea..8de9a9f10 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
[tool.poetry]
name = "litellm"
-version = "1.28.14"
+version = "1.29.2"
description = "Library to easily interface with LLM API providers"
authors = ["BerriAI"]
license = "MIT"
@@ -74,7 +74,7 @@ requires = ["poetry-core", "wheel"]
build-backend = "poetry.core.masonry.api"
[tool.commitizen]
-version = "1.28.14"
+version = "1.29.2"
version_files = [
"pyproject.toml:^version"
]
diff --git a/ui/litellm-dashboard/out/404.html b/ui/litellm-dashboard/out/404.html
index 064bb2163..eb5cf4286 100644
--- a/ui/litellm-dashboard/out/404.html
+++ b/ui/litellm-dashboard/out/404.html
@@ -1 +1 @@
-404: This page could not be found.🚅 LiteLLM
404
This page could not be found.
\ No newline at end of file
+404: This page could not be found.🚅 LiteLLM
404
This page could not be found.
\ No newline at end of file
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similarity index 100%
rename from ui/litellm-dashboard/out/_next/static/p1zjZBLDqxGf-NaFvZkeF/_buildManifest.js
rename to ui/litellm-dashboard/out/_next/static/ZF-EluyKCEJoZptE3dOXT/_buildManifest.js
diff --git a/ui/litellm-dashboard/out/_next/static/p1zjZBLDqxGf-NaFvZkeF/_ssgManifest.js b/ui/litellm-dashboard/out/_next/static/ZF-EluyKCEJoZptE3dOXT/_ssgManifest.js
similarity index 100%
rename from ui/litellm-dashboard/out/_next/static/p1zjZBLDqxGf-NaFvZkeF/_ssgManifest.js
rename to ui/litellm-dashboard/out/_next/static/ZF-EluyKCEJoZptE3dOXT/_ssgManifest.js
diff --git a/ui/litellm-dashboard/out/_next/static/chunks/app/page-2ed0bc91ffef505b.js b/ui/litellm-dashboard/out/_next/static/chunks/app/page-2ed0bc91ffef505b.js
new file mode 100644
index 000000000..a0bd09f21
--- /dev/null
+++ b/ui/litellm-dashboard/out/_next/static/chunks/app/page-2ed0bc91ffef505b.js
@@ -0,0 +1 @@
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diff --git a/ui/litellm-dashboard/out/index.html b/ui/litellm-dashboard/out/index.html
index aaaaa698a..437d90aac 100644
--- a/ui/litellm-dashboard/out/index.html
+++ b/ui/litellm-dashboard/out/index.html
@@ -1 +1 @@
-🚅 LiteLLM
\ No newline at end of file
+🚅 LiteLLM
\ No newline at end of file
diff --git a/ui/litellm-dashboard/out/index.txt b/ui/litellm-dashboard/out/index.txt
index 72b67997a..28c9282be 100644
--- a/ui/litellm-dashboard/out/index.txt
+++ b/ui/litellm-dashboard/out/index.txt
@@ -1,7 +1,7 @@
2:I[77831,[],""]
-3:I[56239,["730","static/chunks/730-1411b729a1c79695.js","931","static/chunks/app/page-37bd7c3d0bb898a3.js"],""]
+3:I[57492,["730","static/chunks/730-1411b729a1c79695.js","931","static/chunks/app/page-2ed0bc91ffef505b.js"],""]
4:I[5613,[],""]
5:I[31778,[],""]
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diff --git a/ui/litellm-dashboard/src/components/model_dashboard.tsx b/ui/litellm-dashboard/src/components/model_dashboard.tsx
index ce042c9aa..f18d76e6a 100644
--- a/ui/litellm-dashboard/src/components/model_dashboard.tsx
+++ b/ui/litellm-dashboard/src/components/model_dashboard.tsx
@@ -11,7 +11,8 @@ import {
Metric,
Grid,
} from "@tremor/react";
-import { modelInfoCall, userGetRequesedtModelsCall } from "./networking";
+import { modelInfoCall, userGetRequesedtModelsCall, modelMetricsCall } from "./networking";
+import { BarChart } from "@tremor/react";
import { Badge, BadgeDelta, Button } from "@tremor/react";
import RequestAccess from "./request_model_access";
import { Typography } from "antd";
@@ -30,6 +31,7 @@ const ModelDashboard: React.FC = ({
userID,
}) => {
const [modelData, setModelData] = useState({ data: [] });
+ const [modelMetrics, setModelMetrics] = useState([]);
const [pendingRequests, setPendingRequests] = useState([]);
useEffect(() => {
@@ -47,6 +49,15 @@ const ModelDashboard: React.FC = ({
console.log("Model data response:", modelDataResponse.data);
setModelData(modelDataResponse);
+ const modelMetricsResponse = await modelMetricsCall(
+ accessToken,
+ userID,
+ userRole
+ );
+
+ console.log("Model metrics response:", modelMetricsResponse);
+ setModelMetrics(modelMetricsResponse);
+
// if userRole is Admin, show the pending requests
if (userRole === "Admin" && accessToken) {
const user_requests = await userGetRequesedtModelsCall(accessToken);
@@ -75,8 +86,7 @@ const ModelDashboard: React.FC = ({
// loop through model data and edit each row
for (let i = 0; i < modelData.data.length; i++) {
let curr_model = modelData.data[i];
- let litellm_model_name = curr_model?.litellm_params?.model;
-
+ let litellm_model_name = curr_model?.litellm_params?.mode
let model_info = curr_model?.model_info;
let defaultProvider = "openai";
@@ -109,6 +119,7 @@ const ModelDashboard: React.FC = ({
modelData.data[i].input_cost = input_cost;
modelData.data[i].output_cost = output_cost;
modelData.data[i].max_tokens = max_tokens;
+ modelData.data[i].api_base = curr_model?.litellm_params?.api_base;
all_models_on_proxy.push(curr_model.model_name);
@@ -141,6 +152,14 @@ const ModelDashboard: React.FC = ({
Provider
+ {
+ userRole === "Admin" && (
+
+ API Base
+
+ )
+ }
+
Access
@@ -162,6 +181,11 @@ const ModelDashboard: React.FC = ({
{model.model_name}{model.provider}
+ {
+ userRole === "Admin" && (
+ {model.api_base}
+ )
+ }
{model.user_access ? (
@@ -183,7 +207,18 @@ const ModelDashboard: React.FC = ({
- {userRole === "Admin" &&
+
+ Model Statistics (Number Requests, Latency)
+
+
+ {/* {userRole === "Admin" &&
pendingRequests &&
pendingRequests.length > 0 ? (
@@ -229,7 +264,7 @@ const ModelDashboard: React.FC = ({
- ) : null}
+ ) : null} */}
);
diff --git a/ui/litellm-dashboard/src/components/networking.tsx b/ui/litellm-dashboard/src/components/networking.tsx
index 5993df34c..01cc61f3b 100644
--- a/ui/litellm-dashboard/src/components/networking.tsx
+++ b/ui/litellm-dashboard/src/components/networking.tsx
@@ -242,6 +242,41 @@ export const modelInfoCall = async (
}
};
+
+export const modelMetricsCall = async (
+ accessToken: String,
+ userID: String,
+ userRole: String
+) => {
+ /**
+ * Get all models on proxy
+ */
+ try {
+ let url = proxyBaseUrl ? `${proxyBaseUrl}/model/metrics` : `/model/metrics`;
+ // message.info("Requesting model data");
+ const response = await fetch(url, {
+ method: "GET",
+ headers: {
+ Authorization: `Bearer ${accessToken}`,
+ "Content-Type": "application/json",
+ },
+ });
+
+ if (!response.ok) {
+ const errorData = await response.text();
+ message.error(errorData);
+ throw new Error("Network response was not ok");
+ }
+ const data = await response.json();
+ // message.info("Received model data");
+ return data;
+ // Handle success - you might want to update some state or UI based on the created key
+ } catch (error) {
+ console.error("Failed to create key:", error);
+ throw error;
+ }
+};
+
export const modelAvailableCall = async (
accessToken: String,
userID: String,