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58
README.md
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@ -225,37 +225,37 @@ curl 'http://0.0.0.0:4000/key/generate' \
## Supported Providers ([Docs](https://docs.litellm.ai/docs/providers))
| Provider | [Completion](https://docs.litellm.ai/docs/#basic-usage) | [Streaming](https://docs.litellm.ai/docs/completion/stream#streaming-responses) | [Async Completion](https://docs.litellm.ai/docs/completion/stream#async-completion) | [Async Streaming](https://docs.litellm.ai/docs/completion/stream#async-streaming) | [Async Embedding](https://docs.litellm.ai/docs/embedding/supported_embedding) | [Async Image Generation](https://docs.litellm.ai/docs/image_generation) |
| ----------------------------------------------------------------------------------- | ------------------------------------------------------- | ------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------- | --------------------------------------------------------------------------------- | ----------------------------------------------------------------------------- | ----------------------------------------------------------------------- |
|-------------------------------------------------------------------------------------|---------------------------------------------------------|---------------------------------------------------------------------------------|-------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------|-------------------------------------------------------------------------------|-------------------------------------------------------------------------|
| [openai](https://docs.litellm.ai/docs/providers/openai) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
| [azure](https://docs.litellm.ai/docs/providers/azure) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
| [aws - sagemaker](https://docs.litellm.ai/docs/providers/aws_sagemaker) | ✅ | ✅ | ✅ | ✅ | ✅ |
| [aws - bedrock](https://docs.litellm.ai/docs/providers/bedrock) | ✅ | ✅ | ✅ | ✅ | ✅ |
| [google - vertex_ai](https://docs.litellm.ai/docs/providers/vertex) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅
| [google - palm](https://docs.litellm.ai/docs/providers/palm) | ✅ | ✅ | ✅ | ✅ |
| [google AI Studio - gemini](https://docs.litellm.ai/docs/providers/gemini) | ✅ | ✅ | ✅ | ✅ | |
| [mistral ai api](https://docs.litellm.ai/docs/providers/mistral) | ✅ | ✅ | ✅ | ✅ | ✅ |
| [cloudflare AI Workers](https://docs.litellm.ai/docs/providers/cloudflare_workers) | ✅ | ✅ | ✅ | ✅ |
| [cohere](https://docs.litellm.ai/docs/providers/cohere) | ✅ | ✅ | ✅ | ✅ | ✅ |
| [anthropic](https://docs.litellm.ai/docs/providers/anthropic) | ✅ | ✅ | ✅ | ✅ |
| [huggingface](https://docs.litellm.ai/docs/providers/huggingface) | ✅ | ✅ | ✅ | ✅ | ✅ |
| [replicate](https://docs.litellm.ai/docs/providers/replicate) | ✅ | ✅ | ✅ | ✅ |
| [together_ai](https://docs.litellm.ai/docs/providers/togetherai) | ✅ | ✅ | ✅ | ✅ |
| [openrouter](https://docs.litellm.ai/docs/providers/openrouter) | ✅ | ✅ | ✅ | ✅ |
| [ai21](https://docs.litellm.ai/docs/providers/ai21) | ✅ | ✅ | ✅ | ✅ |
| [baseten](https://docs.litellm.ai/docs/providers/baseten) | ✅ | ✅ | ✅ | ✅ |
| [vllm](https://docs.litellm.ai/docs/providers/vllm) | ✅ | ✅ | ✅ | ✅ |
| [nlp_cloud](https://docs.litellm.ai/docs/providers/nlp_cloud) | ✅ | ✅ | ✅ | ✅ |
| [aleph alpha](https://docs.litellm.ai/docs/providers/aleph_alpha) | ✅ | ✅ | ✅ | ✅ |
| [petals](https://docs.litellm.ai/docs/providers/petals) | ✅ | ✅ | ✅ | ✅ |
| [ollama](https://docs.litellm.ai/docs/providers/ollama) | ✅ | ✅ | ✅ | ✅ | ✅ |
| [deepinfra](https://docs.litellm.ai/docs/providers/deepinfra) | ✅ | ✅ | ✅ | ✅ |
| [perplexity-ai](https://docs.litellm.ai/docs/providers/perplexity) | ✅ | ✅ | ✅ | ✅ |
| [Groq AI](https://docs.litellm.ai/docs/providers/groq) | ✅ | ✅ | ✅ | ✅ |
| [Deepseek](https://docs.litellm.ai/docs/providers/deepseek) | ✅ | ✅ | ✅ | ✅ |
| [anyscale](https://docs.litellm.ai/docs/providers/anyscale) | ✅ | ✅ | ✅ | ✅ |
| [IBM - watsonx.ai](https://docs.litellm.ai/docs/providers/watsonx) | ✅ | ✅ | ✅ | ✅ | ✅
| [voyage ai](https://docs.litellm.ai/docs/providers/voyage) | | | | | ✅ |
| [xinference [Xorbits Inference]](https://docs.litellm.ai/docs/providers/xinference) | | | | | ✅ |
| [aws - sagemaker](https://docs.litellm.ai/docs/providers/aws_sagemaker) | ✅ | ✅ | ✅ | ✅ | ✅ | |
| [aws - bedrock](https://docs.litellm.ai/docs/providers/bedrock) | ✅ | ✅ | ✅ | ✅ | ✅ | |
| [google - vertex_ai](https://docs.litellm.ai/docs/providers/vertex) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
| [google - palm](https://docs.litellm.ai/docs/providers/palm) | ✅ | ✅ | ✅ | ✅ | | |
| [google AI Studio - gemini](https://docs.litellm.ai/docs/providers/gemini) | ✅ | ✅ | ✅ | ✅ | | |
| [mistral ai api](https://docs.litellm.ai/docs/providers/mistral) | ✅ | ✅ | ✅ | ✅ | ✅ | |
| [cloudflare AI Workers](https://docs.litellm.ai/docs/providers/cloudflare_workers) | ✅ | ✅ | ✅ | ✅ | | |
| [cohere](https://docs.litellm.ai/docs/providers/cohere) | ✅ | ✅ | ✅ | ✅ | ✅ | |
| [anthropic](https://docs.litellm.ai/docs/providers/anthropic) | ✅ | ✅ | ✅ | ✅ | | |
| [huggingface](https://docs.litellm.ai/docs/providers/huggingface) | ✅ | ✅ | ✅ | ✅ | ✅ | |
| [replicate](https://docs.litellm.ai/docs/providers/replicate) | ✅ | ✅ | ✅ | ✅ | | |
| [together_ai](https://docs.litellm.ai/docs/providers/togetherai) | ✅ | ✅ | ✅ | ✅ | | |
| [openrouter](https://docs.litellm.ai/docs/providers/openrouter) | ✅ | ✅ | ✅ | ✅ | | |
| [ai21](https://docs.litellm.ai/docs/providers/ai21) | ✅ | ✅ | ✅ | ✅ | | |
| [baseten](https://docs.litellm.ai/docs/providers/baseten) | ✅ | ✅ | ✅ | ✅ | | |
| [vllm](https://docs.litellm.ai/docs/providers/vllm) | ✅ | ✅ | ✅ | ✅ | | |
| [nlp_cloud](https://docs.litellm.ai/docs/providers/nlp_cloud) | ✅ | ✅ | ✅ | ✅ | | |
| [aleph alpha](https://docs.litellm.ai/docs/providers/aleph_alpha) | ✅ | ✅ | ✅ | ✅ | | |
| [petals](https://docs.litellm.ai/docs/providers/petals) | ✅ | ✅ | ✅ | ✅ | | |
| [ollama](https://docs.litellm.ai/docs/providers/ollama) | ✅ | ✅ | ✅ | ✅ | ✅ | |
| [deepinfra](https://docs.litellm.ai/docs/providers/deepinfra) | ✅ | ✅ | ✅ | ✅ | | |
| [perplexity-ai](https://docs.litellm.ai/docs/providers/perplexity) | ✅ | ✅ | ✅ | ✅ | | |
| [Groq AI](https://docs.litellm.ai/docs/providers/groq) | ✅ | ✅ | ✅ | ✅ | | |
| [Deepseek](https://docs.litellm.ai/docs/providers/deepseek) | ✅ | ✅ | ✅ | ✅ | | |
| [anyscale](https://docs.litellm.ai/docs/providers/anyscale) | ✅ | ✅ | ✅ | ✅ | | |
| [IBM - watsonx.ai](https://docs.litellm.ai/docs/providers/watsonx) | ✅ | ✅ | ✅ | ✅ | ✅ | |
| [voyage ai](https://docs.litellm.ai/docs/providers/voyage) | | | | | ✅ | |
| [xinference [Xorbits Inference]](https://docs.litellm.ai/docs/providers/xinference) | | | | | ✅ | |
[**Read the Docs**](https://docs.litellm.ai/docs/)

20
docs/my-website/docs/observability/langfuse_integration.md
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@ -144,6 +144,26 @@ print(response)
```
You can also pass `metadata` as part of the request header with a `langfuse_*` prefix:
```shell
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--header 'langfuse_trace_id: trace-id22' \
--header 'langfuse_trace_user_id: user-id2' \
--header 'langfuse_trace_metadata: {"key":"value"}' \
--data '{
"model": "gpt-3.5-turbo",
"messages": [
{
"role": "user",
"content": "what llm are you"
}
]
}'
```
### Trace & Generation Parameters
#### Trace Specific Parameters

2
docs/my-website/docs/providers/groq.md
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@ -47,7 +47,7 @@ for chunk in response:
We support ALL Groq models, just set `groq/` as a prefix when sending completion requests
| Model Name | Function Call |
|--------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|--------------------|---------------------------------------------------------|
| llama3-8b-8192 | `completion(model="groq/llama3-8b-8192", messages)` |
| llama3-70b-8192 | `completion(model="groq/llama3-70b-8192", messages)` |
| llama2-70b-4096 | `completion(model="groq/llama2-70b-4096", messages)` |

14
docs/my-website/docs/providers/togetherai.md
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@ -27,12 +27,12 @@ Example TogetherAI Usage - Note: liteLLM supports all models deployed on Togethe
### Llama LLMs - Chat
| Model Name | Function Call | Required OS Variables |
|-----------------------------------|------------------------------------------------------------------------|---------------------------------|
|-----------------------------------|-------------------------------------------------------------------------|------------------------------------|
| togethercomputer/llama-2-70b-chat | `completion('together_ai/togethercomputer/llama-2-70b-chat', messages)` | `os.environ['TOGETHERAI_API_KEY']` |
### Llama LLMs - Language / Instruct
| Model Name | Function Call | Required OS Variables |
|-----------------------------------|------------------------------------------------------------------------|---------------------------------|
|------------------------------------------|--------------------------------------------------------------------------------|------------------------------------|
| togethercomputer/llama-2-70b | `completion('together_ai/togethercomputer/llama-2-70b', messages)` | `os.environ['TOGETHERAI_API_KEY']` |
| togethercomputer/LLaMA-2-7B-32K | `completion('together_ai/togethercomputer/LLaMA-2-7B-32K', messages)` | `os.environ['TOGETHERAI_API_KEY']` |
| togethercomputer/Llama-2-7B-32K-Instruct | `completion('together_ai/togethercomputer/Llama-2-7B-32K-Instruct', messages)` | `os.environ['TOGETHERAI_API_KEY']` |
@ -40,23 +40,23 @@ Example TogetherAI Usage - Note: liteLLM supports all models deployed on Togethe
### Falcon LLMs
| Model Name | Function Call | Required OS Variables |
|-----------------------------------|------------------------------------------------------------------------|---------------------------------|
|--------------------------------------|----------------------------------------------------------------------------|------------------------------------|
| togethercomputer/falcon-40b-instruct | `completion('together_ai/togethercomputer/falcon-40b-instruct', messages)` | `os.environ['TOGETHERAI_API_KEY']` |
| togethercomputer/falcon-7b-instruct | `completion('together_ai/togethercomputer/falcon-7b-instruct', messages)` | `os.environ['TOGETHERAI_API_KEY']` |
### Alpaca LLMs
| Model Name | Function Call | Required OS Variables |
|-----------------------------------|------------------------------------------------------------------------|---------------------------------|
|----------------------------|------------------------------------------------------------------|------------------------------------|
| togethercomputer/alpaca-7b | `completion('together_ai/togethercomputer/alpaca-7b', messages)` | `os.environ['TOGETHERAI_API_KEY']` |
### Other Chat LLMs
| Model Name | Function Call | Required OS Variables |
|-----------------------------------|------------------------------------------------------------------------|---------------------------------|
|------------------------------|--------------------------------------------------------------------|------------------------------------|
| HuggingFaceH4/starchat-alpha | `completion('together_ai/HuggingFaceH4/starchat-alpha', messages)` | `os.environ['TOGETHERAI_API_KEY']` |
### Code LLMs
| Model Name | Function Call | Required OS Variables |
|-----------------------------------|------------------------------------------------------------------------|---------------------------------|
|-----------------------------------------|-------------------------------------------------------------------------------|------------------------------------|
| togethercomputer/CodeLlama-34b | `completion('together_ai/togethercomputer/CodeLlama-34b', messages)` | `os.environ['TOGETHERAI_API_KEY']` |
| togethercomputer/CodeLlama-34b-Instruct | `completion('together_ai/togethercomputer/CodeLlama-34b-Instruct', messages)` | `os.environ['TOGETHERAI_API_KEY']` |
| togethercomputer/CodeLlama-34b-Python | `completion('together_ai/togethercomputer/CodeLlama-34b-Python', messages)` | `os.environ['TOGETHERAI_API_KEY']` |
@ -67,7 +67,7 @@ Example TogetherAI Usage - Note: liteLLM supports all models deployed on Togethe
### Language LLMs
| Model Name | Function Call | Required OS Variables |
|-----------------------------------|------------------------------------------------------------------------|---------------------------------|
|-------------------------------------|---------------------------------------------------------------------------|------------------------------------|
| NousResearch/Nous-Hermes-Llama2-13b | `completion('together_ai/NousResearch/Nous-Hermes-Llama2-13b', messages)` | `os.environ['TOGETHERAI_API_KEY']` |
| Austism/chronos-hermes-13b | `completion('together_ai/Austism/chronos-hermes-13b', messages)` | `os.environ['TOGETHERAI_API_KEY']` |
| upstage/SOLAR-0-70b-16bit | `completion('together_ai/upstage/SOLAR-0-70b-16bit', messages)` | `os.environ['TOGETHERAI_API_KEY']` |

4
docs/my-website/docs/providers/vllm.md
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@ -156,8 +156,8 @@ def default_pt(messages):
#### Models we already have Prompt Templates for
| Model Name | Works for Models | Function Call |
| -------- | -------- | -------- |
| meta-llama/Llama-2-7b-chat | All meta-llama llama2 chat models| `completion(model='vllm/meta-llama/Llama-2-7b', messages=messages, api_base="your_api_endpoint")` |
|--------------------------------------|-----------------------------------|------------------------------------------------------------------------------------------------------------------|
| meta-llama/Llama-2-7b-chat | All meta-llama llama2 chat models | `completion(model='vllm/meta-llama/Llama-2-7b', messages=messages, api_base="your_api_endpoint")` |
| tiiuae/falcon-7b-instruct | All falcon instruct models | `completion(model='vllm/tiiuae/falcon-7b-instruct', messages=messages, api_base="your_api_endpoint")` |
| mosaicml/mpt-7b-chat | All mpt chat models | `completion(model='vllm/mosaicml/mpt-7b-chat', messages=messages, api_base="your_api_endpoint")` |
| codellama/CodeLlama-34b-Instruct-hf | All codellama instruct models | `completion(model='vllm/codellama/CodeLlama-34b-Instruct-hf', messages=messages, api_base="your_api_endpoint")` |

4
docs/my-website/docs/providers/watsonx.md
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@ -252,7 +252,7 @@ response = completion(
Here are some examples of models available in IBM watsonx.ai that you can use with LiteLLM:
| Mode Name | Command |
| ---------- | --------- |
|------------------------------------|------------------------------------------------------------------------------------------|
| Flan T5 XXL | `completion(model=watsonx/google/flan-t5-xxl, messages=messages)` |
| Flan Ul2 | `completion(model=watsonx/google/flan-ul2, messages=messages)` |
| Mt0 XXL | `completion(model=watsonx/bigscience/mt0-xxl, messages=messages)` |
@ -276,7 +276,7 @@ For a list of all available models in watsonx.ai, see [here](https://dataplatfor
## Supported IBM watsonx.ai Embedding Models
| Model Name | Function Call |
|----------------------|---------------------------------------------|
|------------|------------------------------------------------------------------------|
| Slate 30m | `embedding(model="watsonx/ibm/slate-30m-english-rtrvr", input=input)` |
| Slate 125m | `embedding(model="watsonx/ibm/slate-125m-english-rtrvr", input=input)` |

2
docs/my-website/docs/providers/xinference.md
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@ -38,7 +38,7 @@ print(response)
All models listed here https://inference.readthedocs.io/en/latest/models/builtin/embedding/index.html are supported
| Model Name | Function Call |
|------------------------------|--------------------------------------------------------|
|-----------------------------|--------------------------------------------------------------------|
| bge-base-en | `embedding(model="xinference/bge-base-en", input)` |
| bge-base-en-v1.5 | `embedding(model="xinference/bge-base-en-v1.5", input)` |
| bge-base-zh | `embedding(model="xinference/bge-base-zh", input)` |

2
docs/my-website/docs/proxy/deploy.md
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@ -260,7 +260,7 @@ Requirements:
<TabItem value="docker-deploy" label="Dockerfile">
We maintain a [seperate Dockerfile](https://github.com/BerriAI/litellm/pkgs/container/litellm-database) for reducing build time when running LiteLLM proxy with a connected Postgres Database
We maintain a [separate Dockerfile](https://github.com/BerriAI/litellm/pkgs/container/litellm-database) for reducing build time when running LiteLLM proxy with a connected Postgres Database
```shell
docker pull ghcr.io/berriai/litellm-database:main-latest

2
docs/my-website/docs/proxy/enterprise.md
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@ -459,7 +459,7 @@ Step 1 Set a `LAKERA_API_KEY` in your env
LAKERA_API_KEY="7a91a1a6059da*******"
```
Step 2. Add `lakera_prompt_injection` to your calbacks
Step 2. Add `lakera_prompt_injection` to your callbacks
```yaml
litellm_settings:

4
docs/my-website/docs/proxy/logging.md
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@ -41,7 +41,9 @@ litellm_settings:
**Step 3**: Set required env variables for logging to langfuse
```shell
export LANGFUSE_PUBLIC_KEY="pk_kk"
export LANGFUSE_SECRET_KEY="sk_ss
export LANGFUSE_SECRET_KEY="sk_ss"
# Optional, defaults to https://cloud.langfuse.com
export LANGFUSE_HOST="https://xxx.langfuse.com"
```
**Step 4**: Start the proxy, make a test request

4
docs/my-website/docs/tutorials/finetuned_chat_gpt.md
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@ -1,8 +1,8 @@
# Using Fine-Tuned gpt-3.5-turbo
LiteLLM allows you to call `completion` with your fine-tuned gpt-3.5-turbo models
If you're trying to create your custom finetuned gpt-3.5-turbo model following along on this tutorial: https://platform.openai.com/docs/guides/fine-tuning/preparing-your-dataset
If you're trying to create your custom fine-tuned gpt-3.5-turbo model following along on this tutorial: https://platform.openai.com/docs/guides/fine-tuning/preparing-your-dataset
Once you've created your fine tuned model, you can call it with `litellm.completion()`
Once you've created your fine-tuned model, you can call it with `litellm.completion()`
## Usage
```python

38
litellm/integrations/langfuse.py
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@ -69,6 +69,43 @@ class LangFuseLogger:
else:
self.upstream_langfuse = None
@staticmethod
def add_metadata_from_header(litellm_params: dict, metadata: dict) -> dict:
"""
Adds metadata from proxy request headers to Langfuse logging if keys start with "langfuse_"
and overwrites litellm_params.metadata if already included.
For example if you want to append your trace to an existing `trace_id` via header, send
`headers: { ..., langfuse_existing_trace_id: your-existing-trace-id }` via proxy request.
"""
if litellm_params is None:
return metadata
if litellm_params.get("proxy_server_request") is None:
return metadata
if metadata is None:
metadata = {}
proxy_headers = (
litellm_params.get("proxy_server_request", {}).get("headers", {}) or {}
)
for metadata_param_key in proxy_headers:
if metadata_param_key.startswith("langfuse_"):
trace_param_key = metadata_param_key.replace("langfuse_", "", 1)
if trace_param_key in metadata:
verbose_logger.warning(
f"Overwriting Langfuse `{trace_param_key}` from request header"
)
else:
verbose_logger.debug(
f"Found Langfuse `{trace_param_key}` in request header"
)
metadata[trace_param_key] = proxy_headers.get(metadata_param_key)
return metadata
# def log_error(kwargs, response_obj, start_time, end_time):
# generation = trace.generation(
# level ="ERROR" # can be any of DEBUG, DEFAULT, WARNING or ERROR
@ -97,6 +134,7 @@ class LangFuseLogger:
metadata = (
litellm_params.get("metadata", {}) or {}
) # if litellm_params['metadata'] == None
metadata = self.add_metadata_from_header(litellm_params, metadata)
optional_params = copy.deepcopy(kwargs.get("optional_params", {}))
prompt = {"messages": kwargs.get("messages")}

15
litellm/llms/ollama.py
View file

@ -2,8 +2,9 @@ from itertools import chain
import requests, types, time # type: ignore
import json, uuid
import traceback
from typing import Optional
from typing import Optional, List
import litellm
from litellm.types.utils import ProviderField
import httpx, aiohttp, asyncio # type: ignore
from .prompt_templates.factory import prompt_factory, custom_prompt
@ -124,6 +125,18 @@ class OllamaConfig:
)
and v is not None
}
def get_required_params(self) -> List[ProviderField]:
"""For a given provider, return it's required fields with a description"""
return [
ProviderField(
field_name="base_url",
field_type="string",
field_description="Your Ollama API Base",
field_value="http://10.10.11.249:11434",
)
]
def get_supported_openai_params(
self,
):

4
litellm/llms/prompt_templates/factory.py
View file

@ -833,7 +833,7 @@ def anthropic_messages_pt_xml(messages: list):
) # either string or none
if messages[msg_i].get(
"tool_calls", []
): # support assistant tool invoke convertion
): # support assistant tool invoke conversion
assistant_text += convert_to_anthropic_tool_invoke_xml( # type: ignore
messages[msg_i]["tool_calls"]
)
@ -1224,7 +1224,7 @@ def anthropic_messages_pt(messages: list):
if messages[msg_i].get(
"tool_calls", []
): # support assistant tool invoke convertion
): # support assistant tool invoke conversion
assistant_content.extend(
convert_to_anthropic_tool_invoke(messages[msg_i]["tool_calls"])
)

25
litellm/llms/vertex_ai.py
View file

@ -297,24 +297,29 @@ def _convert_gemini_role(role: str) -> Literal["user", "model"]:
def _process_gemini_image(image_url: str) -> PartType:
try:
if "gs://" in image_url:
# Case 1: Images with Cloud Storage URIs
if ".mp4" in image_url and "gs://" in image_url:
# Case 1: Videos with Cloud Storage URIs
part_mime = "video/mp4"
_file_data = FileDataType(mime_type=part_mime, file_uri=image_url)
return PartType(file_data=_file_data)
elif ".pdf" in image_url and "gs://" in image_url:
# Case 2: PDF's with Cloud Storage URIs
part_mime = "application/pdf"
_file_data = FileDataType(mime_type=part_mime, file_uri=image_url)
return PartType(file_data=_file_data)
elif "gs://" in image_url:
# Case 3: Images with Cloud Storage URIs
# The supported MIME types for images include image/png and image/jpeg.
part_mime = "image/png" if "png" in image_url else "image/jpeg"
_file_data = FileDataType(mime_type=part_mime, file_uri=image_url)
return PartType(file_data=_file_data)
elif "https:/" in image_url:
# Case 2: Images with direct links
# Case 4: Images with direct links
image = _load_image_from_url(image_url)
_blob = BlobType(data=image.data, mime_type=image._mime_type)
return PartType(inline_data=_blob)
elif ".mp4" in image_url and "gs://" in image_url:
# Case 3: Videos with Cloud Storage URIs
part_mime = "video/mp4"
_file_data = FileDataType(mime_type=part_mime, file_uri=image_url)
return PartType(file_data=_file_data)
elif "base64" in image_url:
# Case 4: Images with base64 encoding
# Case 5: Images with base64 encoding
import base64, re
# base 64 is passed as data:image/jpeg;base64,<base-64-encoded-image>
@ -390,7 +395,7 @@ def _gemini_convert_messages_with_history(messages: list) -> List[ContentType]:
assistant_content.extend(_parts)
elif messages[msg_i].get(
"tool_calls", []
): # support assistant tool invoke convertion
): # support assistant tool invoke conversion
assistant_content.extend(
convert_to_gemini_tool_call_invoke(messages[msg_i]["tool_calls"])
)

4
litellm/utils.py
View file

@ -7352,6 +7352,10 @@ def get_provider_fields(custom_llm_provider: str) -> List[ProviderField]:
if custom_llm_provider == "databricks":
return litellm.DatabricksConfig().get_required_params()
elif custom_llm_provider == "ollama":
return litellm.OllamaConfig().get_required_params()
else:
return []

14
poetry.lock generated
View file

@ -1,4 +1,4 @@
# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
[[package]]
name = "aiohttp"
@ -2114,6 +2114,7 @@ files = [
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@ -2121,8 +2122,15 @@ files = [
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{file = "PyYAML-6.0.1-cp311-cp311-win_amd64.whl", hash = "sha256:bf07ee2fef7014951eeb99f56f39c9bb4af143d8aa3c21b1677805985307da34"},
{file = "PyYAML-6.0.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:855fb52b0dc35af121542a76b9a84f8d1cd886ea97c84703eaa6d88e37a2ad28"},
{file = "PyYAML-6.0.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:40df9b996c2b73138957fe23a16a4f0ba614f4c0efce1e9406a184b6d07fa3a9"},
{file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6c22bec3fbe2524cde73d7ada88f6566758a8f7227bfbf93a408a9d86bcc12a0"},
{file = "PyYAML-6.0.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:8d4e9c88387b0f5c7d5f281e55304de64cf7f9c0021a3525bd3b1c542da3b0e4"},
{file = "PyYAML-6.0.1-cp312-cp312-win32.whl", hash = "sha256:d483d2cdf104e7c9fa60c544d92981f12ad66a457afae824d146093b8c294c54"},
{file = "PyYAML-6.0.1-cp312-cp312-win_amd64.whl", hash = "sha256:0d3304d8c0adc42be59c5f8a4d9e3d7379e6955ad754aa9d6ab7a398b59dd1df"},
{file = "PyYAML-6.0.1-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:50550eb667afee136e9a77d6dc71ae76a44df8b3e51e41b77f6de2932bfe0f47"},
{file = "PyYAML-6.0.1-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1fe35611261b29bd1de0070f0b2f47cb6ff71fa6595c077e42bd0c419fa27b98"},
{file = "PyYAML-6.0.1-cp36-cp36m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:704219a11b772aea0d8ecd7058d0082713c3562b4e271b849ad7dc4a5c90c13c"},
@ -2139,6 +2147,7 @@ files = [
{file = "PyYAML-6.0.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a0cd17c15d3bb3fa06978b4e8958dcdc6e0174ccea823003a106c7d4d7899ac5"},
{file = "PyYAML-6.0.1-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:28c119d996beec18c05208a8bd78cbe4007878c6dd15091efb73a30e90539696"},
{file = "PyYAML-6.0.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7e07cbde391ba96ab58e532ff4803f79c4129397514e1413a7dc761ccd755735"},
{file = "PyYAML-6.0.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:49a183be227561de579b4a36efbb21b3eab9651dd81b1858589f796549873dd6"},
{file = "PyYAML-6.0.1-cp38-cp38-win32.whl", hash = "sha256:184c5108a2aca3c5b3d3bf9395d50893a7ab82a38004c8f61c258d4428e80206"},
{file = "PyYAML-6.0.1-cp38-cp38-win_amd64.whl", hash = "sha256:1e2722cc9fbb45d9b87631ac70924c11d3a401b2d7f410cc0e3bbf249f2dca62"},
{file = "PyYAML-6.0.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:9eb6caa9a297fc2c2fb8862bc5370d0303ddba53ba97e71f08023b6cd73d16a8"},
@ -2146,6 +2155,7 @@ files = [
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{file = "PyYAML-6.0.1-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b786eecbdf8499b9ca1d697215862083bd6d2a99965554781d0d8d1ad31e13a0"},
{file = "PyYAML-6.0.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bc1bf2925a1ecd43da378f4db9e4f799775d6367bdb94671027b73b393a7c42c"},
{file = "PyYAML-6.0.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:04ac92ad1925b2cff1db0cfebffb6ffc43457495c9b3c39d3fcae417d7125dc5"},
{file = "PyYAML-6.0.1-cp39-cp39-win32.whl", hash = "sha256:faca3bdcf85b2fc05d06ff3fbc1f83e1391b3e724afa3feba7d13eeab355484c"},
{file = "PyYAML-6.0.1-cp39-cp39-win_amd64.whl", hash = "sha256:510c9deebc5c0225e8c96813043e62b680ba2f9c50a08d3724c7f28a747d1486"},
{file = "PyYAML-6.0.1.tar.gz", hash = "sha256:bfdf460b1736c775f2ba9f6a92bca30bc2095067b8a9d77876d1fad6cc3b4a43"},
@ -3140,4 +3150,4 @@ proxy = ["PyJWT", "apscheduler", "backoff", "cryptography", "fastapi", "fastapi-
[metadata]
lock-version = "2.0"
python-versions = ">=3.8.1,<4.0, !=3.9.7"
content-hash = "a54d969a1a707413e7cd3ce869d14ef73dd41bb9d36ebf0fb878d9e929bc15b3"
content-hash = "6a37992b63b11d254f5f40687bd96898b1d9515728f663f30dcc81c4ef8df7b7"

3
pyproject.toml
View file

@ -62,7 +62,8 @@ extra_proxy = [
"azure-identity",
"azure-keyvault-secrets",
"google-cloud-kms",
"resend"
"resend",
"pynacl"
]
[tool.poetry.scripts]

3
ui/litellm-dashboard/src/components/model_dashboard.tsx
View file

@ -145,6 +145,7 @@ enum Providers {
OpenAI_Compatible = "OpenAI-Compatible Endpoints (Groq, Together AI, Mistral AI, etc.)",
Vertex_AI = "Vertex AI (Anthropic, Gemini, etc.)",
Databricks = "Databricks",
Ollama = "Ollama",
}
const provider_map: Record<string, string> = {
@ -156,6 +157,7 @@ const provider_map: Record<string, string> = {
OpenAI_Compatible: "openai",
Vertex_AI: "vertex_ai",
Databricks: "databricks",
Ollama: "ollama",
};
const retry_policy_map: Record<string, string> = {
@ -1747,6 +1749,7 @@ const ModelDashboard: React.FC<ModelDashboardProps> = ({
)}
{selectedProvider != Providers.Bedrock &&
selectedProvider != Providers.Vertex_AI &&
selectedProvider != Providers.Ollama &&
(dynamicProviderForm === undefined ||
dynamicProviderForm.fields.length == 0) && (
<Form.Item