"application/vnd.google.colaboratory.module+javascript": "\n import \"https://ssl.gstatic.com/colaboratory/data_table/881c4a0d49046431/data_table.js\";\n\n const table = window.createDataTable({\n data: [[{\n 'v': 0,\n 'f': \"0\",\n },\n\"how do i call completion() using LiteLLM\",\n\"To call the `completion()` function using LiteLLM, you need to follow these steps:\\n\\n1. Install the `litellm` package by running `pip install litellm` in your terminal.\\n2. Import the `Completion` class from the `litellm` module.\\n3. Initialize an instance of the `Completion` class by providing the required parameters like the API endpoint URL and your API key.\\n4. Call the `complete()` method on the `Completion` instance and pass the text prompt as a string.\\n5. Retrieve the generated completion from the response object and use it as desired.\\n\\nHere's an example:\\n\\n```python\\nfrom litellm.completion import Completion\\n\\n# Initialize the Completion client\\ncompletion_client = Completion(\\n model_name='gpt-3.5-turbo',\\n api_key='your_api_key',\\n endpoint='https://your_endpoint_url'\\n)\\n\\n# Call the completion() method\\nresponse = completion_client.complete(\\\"Once upon a time\\\")\\n\\n# Retrieve the generated completion\\ncompletion = response['choices'][0]['text']\\n\\nprint(completion)\\n```\\n\\nMake sure to replace `'gpt-3.5-turbo'` with the desired model name, `'your_api_key'` with your actual API key, and `'https://your_endpoint_url'` with the correct API endpoint URL provided by your service provider.\\n\\nNote: The above example assumes you have a valid API key and endpoint URL for the OpenAI GPT-3.5-turbo model. Make sure to obtain the necessary credentials according to the API you are using.\",\n\" Here is how you can call the completion() method using LiteLLM:\\n\\nFirst, import LiteLLM:\\n\\n```python\\nimport litellm as lm\\n```\\n\\nThen create a LiteLLM object, specifying the API you want to use (e.g. \\\"openai\\\"):\\n\\n```python \\nai = lm.LiteLLM(\\\"openai\\\")\\n```\\n\\nNow you can call the completion() method on the ai object:\\n\\n```python\\nresponse = ai.completion(\\n prompt=\\\"Hello\\\", \\n model=\\\"text-davinci-003\\\",\\n max_tokens=100\\n)\\n```\\n\\nThe completion() method takes parameters like:\\n\\n- prompt (str): The prompt text to complete \\n- model (str): The AI model to use\\n- max_tokens (int): The maximum number of tokens to generate\\n\\nIt returns a Python dictionary with the AI's response.\\n\\nYou can then access the generated text using:\\n\\n```python\\nprint(response[\\\"choices\\\"][0][\\\"text\\\"]) \\n```\\n\\nSo LiteLLM provides a simple unified interface to call the underlying AI APIs. The completion() method works similarly for OpenAI\"],\n [{\n 'v': 1,\n 'f': \"1\",\n },\n\"does LiteLLM support VertexAI\",\n\"Yes, LiteLLM does support Google Cloud Vertex AI. It provides convenient wrappers and simplified functions to call Vertex AI API endpoints for natural language processing tasks such as text classification, entity extraction, sentiment analysis, etc. You can easily integrate LiteLLM with Vertex AI in your code to leverage its capabilities.\",\n\" Unfortunately, LiteLLM does not currently support VertexAI. LiteLLM focuses on providing a simple interface to call the APIs of services like OpenAI, Azure, Cohere, Anthropic, and Hugging Face. \\n\\nVertexAI is Google's managed machine learning platform. Integrating VertexAI would require additional development work to wrap the VertexAI SDK in a simple interface like LiteLLM provides for other services. \\n\\nHowever, LiteLLM is open source, so it is possible for someone to contribute support for VertexAI. The maintainers would likely welcome a pull request to add VertexAI as an option if implemented well. But out of the box, LiteLLM does not have built-in support for calling VertexAI APIs.\\n\\nThe key services LiteLLM supports are:\\n\\n- OpenAI (GPT, Codex, DALL-E)\\n- Azure Cognitive Services (Text Analytics,
