From d0f11e7a13a5f8342653e2adccb4b6495fab948c Mon Sep 17 00:00:00 2001 From: ishaan-jaff Date: Wed, 22 Nov 2023 14:40:52 -0800 Subject: [PATCH] (docs) input params for litellm.embedding() --- .../docs/embedding/supported_embedding.md | 28 +++++++++++++++++-- 1 file changed, 26 insertions(+), 2 deletions(-) diff --git a/docs/my-website/docs/embedding/supported_embedding.md b/docs/my-website/docs/embedding/supported_embedding.md index 04fb42c99c..8c122770a7 100644 --- a/docs/my-website/docs/embedding/supported_embedding.md +++ b/docs/my-website/docs/embedding/supported_embedding.md @@ -5,10 +5,34 @@ from litellm import embedding import os os.environ['OPENAI_API_KEY'] = "" -response = embedding('text-embedding-ada-002', input=["good morning from litellm"]) +response = embedding(model='text-embedding-ada-002', input=["good morning from litellm"]) ``` -### Expected Output from litellm.embedding() +### Input Params for `litellm.embedding()` +### Required Fields + +- `model`: *string* - ID of the model to use. `model='text-embedding-ada-002'` + +- `input`: *array* - Input text to embed, encoded as a string or array of tokens. To embed multiple inputs in a single request, pass an array of strings or array of token arrays. The input must not exceed the max input tokens for the model (8192 tokens for text-embedding-ada-002), cannot be an empty string, and any array must be 2048 dimensions or less. +``` +input=["good morning from litellm"] +``` + +### Optional LiteLLM Fields + +- `user`: *string (optional)* A unique identifier representing your end-user, + +- `timeout`: *integer* - The maximum time, in seconds, to wait for the API to respond. Defaults to 600 seconds (10 minutes). + +- `api_base`: *string (optional)* - The api endpoint you want to call the model with + +- `api_version`: *string (optional)* - (Azure-specific) the api version for the call + +- `api_key`: *string (optional)* - The API key to authenticate and authorize requests. If not provided, the default API key is used. + +- `api_type`: *string (optional)* - The type of API to use. + +### Output from `litellm.embedding()` ```json {