forked from phoenix/litellm-mirror
Merge branch 'main' into litellm_end_user_spend_tracking
This commit is contained in:
Krish Dholakia
GitHub
commit
5f9cd5a4f6
41 changed files with 305 additions and 135 deletions
26
.github/workflows/ghcr_deploy.yml
vendored
26
.github/workflows/ghcr_deploy.yml
vendored
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@ -147,13 +147,19 @@ jobs:
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core.setFailed(error.message);
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}
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- name: Github Releases To Discord
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uses: SethCohen/github-releases-to-discord@v1.13.1
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with:
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webhook_url: ${{ secrets.WEBHOOK_URL }}
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color: "2105893"
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username: "Release Changelog"
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avatar_url: "https://cdn.discordapp.com/avatars/487431320314576937/bd64361e4ba6313d561d54e78c9e7171.png"
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content: "||@everyone||"
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footer_title: "Changelog"
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footer_icon_url: "https://cdn.discordapp.com/avatars/487431320314576937/bd64361e4ba6313d561d54e78c9e7171.png"
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footer_timestamp: true
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env:
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WEBHOOK_URL: ${{ secrets.WEBHOOK_URL }}
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run: |
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curl -H "Content-Type: application/json" -X POST -d '{
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"content": "||@everyone||",
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"username": "Release Changelog",
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"avatar_url": "https://cdn.discordapp.com/avatars/487431320314576937/bd64361e4ba6313d561d54e78c9e7171.png",
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"embeds": [
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{
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"title": "Changelog",
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"description": "This is the changelog for the latest release.",
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"color": 2105893
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}
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]
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}' $WEBHOOK_URL
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@ -1,6 +1,8 @@
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# Groq
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https://groq.com/
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**We support ALL Groq models, just set `groq/` as a prefix when sending completion requests**
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## API Key
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```python
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# env variable
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@ -47,3 +49,4 @@ We support ALL Groq models, just set `groq/` as a prefix when sending completion
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| Model Name | Function Call |
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|--------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| llama2-70b-4096 | `completion(model="groq/llama2-70b-4096", messages)` |
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| mixtral-8x7b-32768 | `completion(model="groq/mixtral-8x7b-32768", messages)` |
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|
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@ -152,8 +152,14 @@ LiteLLM Supports the following image types passed in `url`
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- Images with Cloud Storage URIs - gs://cloud-samples-data/generative-ai/image/boats.jpeg
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- Images with direct links - https://storage.googleapis.com/github-repo/img/gemini/intro/landmark3.jpg
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- Videos with Cloud Storage URIs - https://storage.googleapis.com/github-repo/img/gemini/multimodality_usecases_overview/pixel8.mp4
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- Base64 Encoded Local Images
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**Example Request - image url**
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<Tabs>
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<TabItem value="direct" label="Images with direct links">
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**Example Request**
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```python
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import litellm
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@ -179,6 +185,43 @@ response = litellm.completion(
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)
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print(response)
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```
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</TabItem>
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<TabItem value="base" label="Local Base64 Images">
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```python
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import litellm
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def encode_image(image_path):
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import base64
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with open(image_path, "rb") as image_file:
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return base64.b64encode(image_file.read()).decode("utf-8")
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image_path = "cached_logo.jpg"
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# Getting the base64 string
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base64_image = encode_image(image_path)
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response = litellm.completion(
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model="vertex_ai/gemini-pro-vision",
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messages=[
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{
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"role": "user",
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"content": [
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{"type": "text", "text": "Whats in this image?"},
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{
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"type": "image_url",
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"image_url": {
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"url": "data:image/jpeg;base64," + base64_image
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},
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},
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],
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}
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],
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)
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print(response)
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```
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</TabItem>
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</Tabs>
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## Chat Models
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@ -122,8 +122,6 @@ const sidebars = {
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"providers/openai_compatible",
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"providers/azure",
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"providers/azure_ai",
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"providers/huggingface",
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"providers/ollama",
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"providers/vertex",
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"providers/palm",
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"providers/gemini",
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@ -132,6 +130,8 @@ const sidebars = {
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"providers/aws_sagemaker",
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"providers/bedrock",
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"providers/anyscale",
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"providers/huggingface",
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"providers/ollama",
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"providers/perplexity",
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"providers/groq",
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"providers/vllm",
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@ -68,9 +68,9 @@ class OllamaConfig:
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repeat_last_n: Optional[int] = None
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repeat_penalty: Optional[float] = None
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temperature: Optional[float] = None
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stop: Optional[
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list
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] = None # stop is a list based on this - https://github.com/jmorganca/ollama/pull/442
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stop: Optional[list] = (
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None # stop is a list based on this - https://github.com/jmorganca/ollama/pull/442
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)
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tfs_z: Optional[float] = None
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num_predict: Optional[int] = None
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top_k: Optional[int] = None
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@ -147,6 +147,11 @@ def get_ollama_response(
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stream = optional_params.pop("stream", False)
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format = optional_params.pop("format", None)
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for m in messages:
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if "role" in m and m["role"] == "tool":
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m["role"] = "assistant"
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data = {
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"model": model,
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"messages": messages,
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|
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@ -334,10 +334,14 @@ class OpenAIChatCompletion(BaseLLM):
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model_response_object=model_response,
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)
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except Exception as e:
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if print_verbose is not None:
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print_verbose(f"openai.py: Received openai error - {str(e)}")
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if (
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"Conversation roles must alternate user/assistant" in str(e)
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or "user and assistant roles should be alternating" in str(e)
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) and messages is not None:
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if print_verbose is not None:
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print_verbose("openai.py: REFORMATS THE MESSAGE!")
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# reformat messages to ensure user/assistant are alternating, if there's either 2 consecutive 'user' messages or 2 consecutive 'assistant' message, add a blank 'user' or 'assistant' message to ensure compatibility
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new_messages = []
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for i in range(len(messages) - 1): # type: ignore
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|
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@ -225,6 +225,24 @@ def _gemini_vision_convert_messages(messages: list):
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part_mime = "video/mp4"
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google_clooud_part = Part.from_uri(img, mime_type=part_mime)
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processed_images.append(google_clooud_part)
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elif "base64" in img:
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# Case 4: Images with base64 encoding
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import base64, re
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# base 64 is passed as data:image/jpeg;base64,<base-64-encoded-image>
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image_metadata, img_without_base_64 = img.split(",")
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# read mime_type from img_without_base_64=data:image/jpeg;base64
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# Extract MIME type using regular expression
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mime_type_match = re.match(r"data:(.*?);base64", image_metadata)
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if mime_type_match:
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mime_type = mime_type_match.group(1)
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else:
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mime_type = "image/jpeg"
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decoded_img = base64.b64decode(img_without_base_64)
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processed_image = Part.from_data(data=decoded_img, mime_type=mime_type)
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processed_images.append(processed_image)
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return prompt, processed_images
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except Exception as e:
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raise e
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|
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@ -580,6 +580,20 @@
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"litellm_provider": "mistral",
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"mode": "embedding"
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},
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"groq/llama2-70b-4096": {
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"max_tokens": 4096,
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"input_cost_per_token": 0.00000070,
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"output_cost_per_token": 0.00000080,
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"litellm_provider": "groq",
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"mode": "chat"
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},
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"groq/mixtral-8x7b-32768": {
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"max_tokens": 32768,
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"input_cost_per_token": 0.00000027,
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"output_cost_per_token": 0.00000027,
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"litellm_provider": "groq",
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"mode": "chat"
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},
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"claude-instant-1.2": {
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"max_tokens": 100000,
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"max_output_tokens": 8191,
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@ -1 +1 @@
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<!DOCTYPE html><html id="__next_error__"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="preload" as="script" fetchPriority="low" href="/ui/_next/static/chunks/webpack-12184ee6a95c1363.js" crossorigin=""/><script src="/ui/_next/static/chunks/fd9d1056-a85b2c176012d8e5.js" async="" crossorigin=""></script><script src="/ui/_next/static/chunks/69-e1b183dda365ec86.js" async="" crossorigin=""></script><script src="/ui/_next/static/chunks/main-app-096338c8e1915716.js" async="" crossorigin=""></script><title>🚅 LiteLLM</title><meta name="description" content="LiteLLM Proxy Admin UI"/><link rel="icon" href="/ui/favicon.ico" type="image/x-icon" sizes="16x16"/><meta name="next-size-adjust"/><script src="/ui/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js" crossorigin="" noModule=""></script></head><body><script src="/ui/_next/static/chunks/webpack-12184ee6a95c1363.js" crossorigin="" async=""></script><script>(self.__next_f=self.__next_f||[]).push([0]);self.__next_f.push([2,null])</script><script>self.__next_f.push([1,"1:HL[\"/ui/_next/static/media/c9a5bc6a7c948fb0-s.p.woff2\",\"font\",{\"crossOrigin\":\"\",\"type\":\"font/woff2\"}]\n2:HL[\"/ui/_next/static/css/a40ad0909dd7838e.css\",\"style\",{\"crossOrigin\":\"\"}]\n0:\"$L3\"\n"])</script><script>self.__next_f.push([1,"4:I[47690,[],\"\"]\n6:I[77831,[],\"\"]\n7:I[30280,[\"303\",\"static/chunks/303-d80f23087a9e6aec.js\",\"931\",\"static/chunks/app/page-d45e6da1a4600c67.js\"],\"\"]\n8:I[5613,[],\"\"]\n9:I[31778,[],\"\"]\nb:I[48955,[],\"\"]\nc:[]\n"])</script><script>self.__next_f.push([1,"3:[[[\"$\",\"link\",\"0\",{\"rel\":\"stylesheet\",\"href\":\"/ui/_next/static/css/a40ad0909dd7838e.css\",\"precedence\":\"next\",\"crossOrigin\":\"\"}]],[\"$\",\"$L4\",null,{\"buildId\":\"Vg95L0RZj1rXfAFdj_TYV\",\"assetPrefix\":\"/ui\",\"initialCanonicalUrl\":\"/\",\"initialTree\":[\"\",{\"children\":[\"__PAGE__\",{}]},\"$undefined\",\"$undefined\",true],\"initialSeedData\":[\"\",{\"children\":[\"__PAGE__\",{},[\"$L5\",[\"$\",\"$L6\",null,{\"propsForComponent\":{\"params\":{}},\"Component\":\"$7\",\"isStaticGeneration\":true}],null]]},[null,[\"$\",\"html\",null,{\"lang\":\"en\",\"children\":[\"$\",\"body\",null,{\"className\":\"__className_c23dc8\",\"children\":[\"$\",\"$L8\",null,{\"parallelRouterKey\":\"children\",\"segmentPath\":[\"children\"],\"loading\":\"$undefined\",\"loadingStyles\":\"$undefined\",\"loadingScripts\":\"$undefined\",\"hasLoading\":false,\"error\":\"$undefined\",\"errorStyles\":\"$undefined\",\"errorScripts\":\"$undefined\",\"template\":[\"$\",\"$L9\",null,{}],\"templateStyles\":\"$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]],\"initialHead\":[false,\"$La\"],\"globalErrorComponent\":\"$b\",\"missingSlots\":\"$Wc\"}]]\n"])</script><script>self.__next_f.push([1,"a:[[\"$\",\"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\"}]]\n5:null\n"])</script><script>self.__next_f.push([1,""])</script></body></html>
|
||||
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|
||||
1:null
|
||||
|
|
|
|||
|
|
@ -4069,7 +4069,12 @@ async def view_spend_logs(
|
|||
tags=["Budget & Spend Tracking"],
|
||||
dependencies=[Depends(user_api_key_auth)],
|
||||
)
|
||||
async def global_spend_logs():
|
||||
async def global_spend_logs(
|
||||
api_key: str = fastapi.Query(
|
||||
default=None,
|
||||
description="API Key to get global spend (spend per day for last 30d). Admin-only endpoint",
|
||||
)
|
||||
):
|
||||
"""
|
||||
[BETA] This is a beta endpoint. It will change.
|
||||
|
||||
|
|
@ -4078,12 +4083,34 @@ async def global_spend_logs():
|
|||
More efficient implementation of /spend/logs, by creating a view over the spend logs table.
|
||||
"""
|
||||
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,
|
||||
)
|
||||
if api_key is None:
|
||||
sql_query = """SELECT * FROM "MonthlyGlobalSpend";"""
|
||||
|
||||
sql_query = """SELECT * FROM "MonthlyGlobalSpend";"""
|
||||
response = await prisma_client.db.query_raw(query=sql_query)
|
||||
|
||||
response = await prisma_client.db.query_raw(query=sql_query)
|
||||
return response
|
||||
else:
|
||||
sql_query = (
|
||||
"""
|
||||
SELECT * FROM "MonthlyGlobalSpendPerKey"
|
||||
WHERE "api_key" = '"""
|
||||
+ api_key
|
||||
+ """'
|
||||
ORDER BY "date";
|
||||
"""
|
||||
)
|
||||
|
||||
return response
|
||||
response = await prisma_client.db.query_raw(query=sql_query)
|
||||
|
||||
return response
|
||||
return
|
||||
|
||||
|
||||
@router.get(
|
||||
|
|
|
|||
|
|
@ -97,7 +97,7 @@ model LiteLLM_SpendLogs {
|
|||
cache_hit String @default("")
|
||||
cache_key String @default("")
|
||||
request_tags Json @default("[]")
|
||||
team_id String?
|
||||
team_id String?
|
||||
end_user String?
|
||||
}
|
||||
// Beta - allow team members to request access to a model
|
||||
|
|
|
|||
|
|
@ -592,6 +592,29 @@ class PrismaClient:
|
|||
await self.db.execute_raw(query=sql_query)
|
||||
|
||||
print("Last30dModelsBySpend Created!") # noqa
|
||||
try:
|
||||
await self.db.query_raw(
|
||||
"""SELECT 1 FROM "MonthlyGlobalSpendPerKey" LIMIT 1"""
|
||||
)
|
||||
print("MonthlyGlobalSpendPerKey Exists!") # noqa
|
||||
except Exception as e:
|
||||
sql_query = """
|
||||
CREATE OR REPLACE VIEW "MonthlyGlobalSpendPerKey" AS
|
||||
SELECT
|
||||
DATE("startTime") AS date,
|
||||
SUM("spend") AS spend,
|
||||
api_key as api_key
|
||||
FROM
|
||||
"LiteLLM_SpendLogs"
|
||||
WHERE
|
||||
"startTime" >= (CURRENT_DATE - INTERVAL '30 days')
|
||||
GROUP BY
|
||||
DATE("startTime"),
|
||||
api_key;
|
||||
"""
|
||||
await self.db.execute_raw(query=sql_query)
|
||||
|
||||
print("MonthlyGlobalSpendPerKey Created!") # noqa
|
||||
|
||||
try:
|
||||
await self.db.query_raw(
|
||||
|
|
|
|||
|
|
@ -336,6 +336,52 @@ def test_gemini_pro_vision():
|
|||
# test_gemini_pro_vision()
|
||||
|
||||
|
||||
def encode_image(image_path):
|
||||
import base64
|
||||
|
||||
with open(image_path, "rb") as image_file:
|
||||
return base64.b64encode(image_file.read()).decode("utf-8")
|
||||
|
||||
|
||||
@pytest.mark.skip(
|
||||
reason="we already test gemini-pro-vision, this is just another way to pass images"
|
||||
)
|
||||
def test_gemini_pro_vision_base64():
|
||||
try:
|
||||
load_vertex_ai_credentials()
|
||||
litellm.set_verbose = True
|
||||
litellm.num_retries = 3
|
||||
image_path = "cached_logo.jpg"
|
||||
# Getting the base64 string
|
||||
base64_image = encode_image(image_path)
|
||||
resp = litellm.completion(
|
||||
model="vertex_ai/gemini-pro-vision",
|
||||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{"type": "text", "text": "Whats in this image?"},
|
||||
{
|
||||
"type": "image_url",
|
||||
"image_url": {
|
||||
"url": "data:image/jpeg;base64," + base64_image
|
||||
},
|
||||
},
|
||||
],
|
||||
}
|
||||
],
|
||||
)
|
||||
print(resp)
|
||||
|
||||
prompt_tokens = resp.usage.prompt_tokens
|
||||
|
||||
except Exception as e:
|
||||
if "500 Internal error encountered.'" in str(e):
|
||||
pass
|
||||
else:
|
||||
pytest.fail(f"An exception occurred - {str(e)}")
|
||||
|
||||
|
||||
def test_gemini_pro_function_calling():
|
||||
load_vertex_ai_credentials()
|
||||
tools = [
|
||||
|
|
|
|||
|
|
@ -292,7 +292,7 @@ def test_completion_gpt4_vision():
|
|||
|
||||
|
||||
def test_completion_azure_gpt4_vision():
|
||||
# azure/gpt-4, vision takes 5seconds to respond
|
||||
# azure/gpt-4, vision takes 5 seconds to respond
|
||||
try:
|
||||
litellm.set_verbose = True
|
||||
response = completion(
|
||||
|
|
|
|||
|
|
@ -580,6 +580,20 @@
|
|||
"litellm_provider": "mistral",
|
||||
"mode": "embedding"
|
||||
},
|
||||
"groq/llama2-70b-4096": {
|
||||
"max_tokens": 4096,
|
||||
"input_cost_per_token": 0.00000070,
|
||||
"output_cost_per_token": 0.00000080,
|
||||
"litellm_provider": "groq",
|
||||
"mode": "chat"
|
||||
},
|
||||
"groq/mixtral-8x7b-32768": {
|
||||
"max_tokens": 32768,
|
||||
"input_cost_per_token": 0.00000027,
|
||||
"output_cost_per_token": 0.00000027,
|
||||
"litellm_provider": "groq",
|
||||
"mode": "chat"
|
||||
},
|
||||
"claude-instant-1.2": {
|
||||
"max_tokens": 100000,
|
||||
"max_output_tokens": 8191,
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
[tool.poetry]
|
||||
name = "litellm"
|
||||
version = "1.28.1"
|
||||
version = "1.28.3"
|
||||
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 = "1.28.1"
|
||||
version = "1.28.3"
|
||||
version_files = [
|
||||
"pyproject.toml:^version"
|
||||
]
|
||||
|
|
|
|||
|
|
@ -97,7 +97,7 @@ model LiteLLM_SpendLogs {
|
|||
cache_hit String @default("")
|
||||
cache_key String @default("")
|
||||
request_tags Json @default("[]")
|
||||
team_id String?
|
||||
team_id String?
|
||||
end_user String?
|
||||
}
|
||||
|
||||
|
|
|
|||
File diff suppressed because one or more lines are too long
|
|
@ -0,0 +1 @@
|
|||
self.__BUILD_MANIFEST={__rewrites:{afterFiles:[],beforeFiles:[],fallback:[]},"/_error":["static/chunks/pages/_error-d6107f1aac0c574c.js"],sortedPages:["/_app","/_error"]},self.__BUILD_MANIFEST_CB&&self.__BUILD_MANIFEST_CB();
|
||||
|
|
@ -0,0 +1 @@
|
|||
self.__SSG_MANIFEST=new Set([]);self.__SSG_MANIFEST_CB&&self.__SSG_MANIFEST_CB()
|
||||
File diff suppressed because one or more lines are too long
|
|
@ -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_c23dc8', '__Inter_Fallback_c23dc8'",fontStyle:"normal"},className:"__className_c23dc8"}}},function(n){n.O(0,[971,69,744],function(){return n(n.s=87421)}),_N_E=n.O()}]);
|
||||
File diff suppressed because one or more lines are too long
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|
|
@ -0,0 +1 @@
|
|||
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|
||||
|
|
@ -0,0 +1 @@
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@ -1 +1 @@
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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"}],["$","meta","5",{"name":"next-size-adjust"}]]
|
||||
1:null
|
||||
|
|
|
|||
|
|
@ -280,7 +280,7 @@ export const modelAvailableCall = async (
|
|||
|
||||
export const keySpendLogsCall = async (accessToken: String, token: String) => {
|
||||
try {
|
||||
const url = proxyBaseUrl ? `${proxyBaseUrl}/spend/logs` : `/spend/logs`;
|
||||
const url = proxyBaseUrl ? `${proxyBaseUrl}/global/spend/logs` : `/global/spend/logs`;
|
||||
console.log("in keySpendLogsCall:", url);
|
||||
const response = await fetch(`${url}/?api_key=${token}`, {
|
||||
method: "GET",
|
||||
|
|
|
|||
|
|
@ -78,85 +78,12 @@ const ViewKeySpendReport: React.FC<ViewKeySpendReportProps> = ({
|
|||
(token = token)
|
||||
);
|
||||
console.log("Response:", response);
|
||||
// loop through response
|
||||
// get spend, startTime for each element, place in new array
|
||||
|
||||
const pricePerDay: Record<string, number> = (
|
||||
Object.values(response) as ResponseValueType[]
|
||||
).reduce((acc: Record<string, number>, value) => {
|
||||
const startTime = new Date(value.startTime);
|
||||
const day = new Intl.DateTimeFormat("en-US", {
|
||||
day: "2-digit",
|
||||
month: "short",
|
||||
}).format(startTime);
|
||||
|
||||
acc[day] = (acc[day] || 0) + value.spend;
|
||||
|
||||
return acc;
|
||||
}, {});
|
||||
|
||||
// sort pricePerDay by day
|
||||
// Convert object to array of key-value pairs
|
||||
const pricePerDayArray = Object.entries(pricePerDay);
|
||||
|
||||
// Sort the array based on the date (key)
|
||||
pricePerDayArray.sort(([aKey], [bKey]) => {
|
||||
const dateA = new Date(aKey);
|
||||
const dateB = new Date(bKey);
|
||||
return dateA.getTime() - dateB.getTime();
|
||||
});
|
||||
|
||||
// Convert the sorted array back to an object
|
||||
const sortedPricePerDay = Object.fromEntries(pricePerDayArray);
|
||||
|
||||
console.log(sortedPricePerDay);
|
||||
|
||||
const pricePerUser: Record<string, number> = (
|
||||
Object.values(response) as ResponseValueType[]
|
||||
).reduce((acc: Record<string, number>, value) => {
|
||||
const user = value.user;
|
||||
acc[user] = (acc[user] || 0) + value.spend;
|
||||
|
||||
return acc;
|
||||
}, {});
|
||||
|
||||
console.log(pricePerDay);
|
||||
console.log(pricePerUser);
|
||||
|
||||
const arrayBarChart = [];
|
||||
// [
|
||||
// {
|
||||
// "day": "02 Feb",
|
||||
// "spend": pricePerDay["02 Feb"],
|
||||
// }
|
||||
// ]
|
||||
for (const [key, value] of Object.entries(sortedPricePerDay)) {
|
||||
arrayBarChart.push({ day: key, spend: value });
|
||||
}
|
||||
|
||||
// get 5 most expensive users
|
||||
const sortedUsers = Object.entries(pricePerUser).sort(
|
||||
(a, b) => b[1] - a[1]
|
||||
);
|
||||
const top5Users = sortedUsers.slice(0, 5);
|
||||
const userChart = top5Users.map(([key, value]) => ({
|
||||
name: key,
|
||||
value: value,
|
||||
}));
|
||||
|
||||
setData(arrayBarChart);
|
||||
setUserData(userChart);
|
||||
console.log("arrayBarChart:", arrayBarChart);
|
||||
setData(response);
|
||||
} catch (error) {
|
||||
console.error("There was an error fetching the data", error);
|
||||
// Optionally, update your UI to reflect the error state here as well
|
||||
}
|
||||
};
|
||||
|
||||
// useEffect(() => {
|
||||
// // Fetch data only when the token changes
|
||||
// fetchData();
|
||||
// }, [token]); // Dependency array containing the 'token' variable
|
||||
|
||||
if (!token) {
|
||||
return null;
|
||||
|
|
@ -164,7 +91,7 @@ const ViewKeySpendReport: React.FC<ViewKeySpendReportProps> = ({
|
|||
|
||||
return (
|
||||
<div>
|
||||
<Button className="mx-auto" onClick={showModal}>
|
||||
<Button size = "xs" onClick={showModal}>
|
||||
View Spend Report
|
||||
</Button>
|
||||
<Modal
|
||||
|
|
@ -183,19 +110,14 @@ const ViewKeySpendReport: React.FC<ViewKeySpendReportProps> = ({
|
|||
<BarChart
|
||||
className="mt-6"
|
||||
data={data}
|
||||
colors={["green"]}
|
||||
index="day"
|
||||
colors={["blue"]}
|
||||
index="date"
|
||||
categories={["spend"]}
|
||||
yAxisWidth={48}
|
||||
/>
|
||||
)}
|
||||
</Card>
|
||||
<Title className="mt-6">Top 5 Users Spend (USD)</Title>
|
||||
<Card className="mb-6">
|
||||
{userData && (
|
||||
<BarList className="mt-6" data={userData} color="teal" />
|
||||
)}
|
||||
</Card>
|
||||
|
||||
</Modal>
|
||||
</div>
|
||||
);
|
||||
|
|
|
|||
|
|
@ -86,6 +86,7 @@ const ViewKeyTable: React.FC<ViewKeyTableProps> = ({
|
|||
<TableHeaderCell>Secret Key</TableHeaderCell>
|
||||
<TableHeaderCell>Spend (USD)</TableHeaderCell>
|
||||
<TableHeaderCell>Key Budget (USD)</TableHeaderCell>
|
||||
<TableHeaderCell>Spend Report</TableHeaderCell>
|
||||
<TableHeaderCell>Team ID</TableHeaderCell>
|
||||
<TableHeaderCell>Metadata</TableHeaderCell>
|
||||
<TableHeaderCell>Models</TableHeaderCell>
|
||||
|
|
@ -122,6 +123,15 @@ const ViewKeyTable: React.FC<ViewKeyTableProps> = ({
|
|||
<Text>Unlimited Budget</Text>
|
||||
)}
|
||||
</TableCell>
|
||||
<TableCell>
|
||||
<ViewKeySpendReport
|
||||
token={item.token}
|
||||
accessToken={accessToken}
|
||||
keySpend={item.spend}
|
||||
keyBudget={item.max_budget}
|
||||
keyName={item.key_name}
|
||||
/>
|
||||
</TableCell>
|
||||
<TableCell>
|
||||
<Text>{item.team_id}</Text>
|
||||
</TableCell>
|
||||
|
|
@ -152,15 +162,6 @@ const ViewKeyTable: React.FC<ViewKeyTableProps> = ({
|
|||
size="sm"
|
||||
/>
|
||||
</TableCell>
|
||||
<TableCell>
|
||||
<ViewKeySpendReport
|
||||
token={item.token}
|
||||
accessToken={accessToken}
|
||||
keySpend={item.spend}
|
||||
keyBudget={item.max_budget}
|
||||
keyName={item.key_name}
|
||||
/>
|
||||
</TableCell>
|
||||
</TableRow>
|
||||
);
|
||||
})}
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue