Tapsage serves as a platform for delivering generative AI solutions. This document provides a few examples of the services offered by OpenAI. For a comprehensive understanding of all the services and their functionalities, please refer to OpenAI’s official documentation. To utilize OpenAI’s APIs via Tapsage, simply replace the URL https://api.openai.com with https://api.tapsage.com and authenticate using your Tapsage Key instead of OpenAI key.
An embedding is a vector (list) of floating point numbers. The distance between two vectors measures their relatedness. Small distances suggest high relatedness and large distances suggest low relatedness.
Get a vector representation of a given input that can be easily consumed by machine learning models and algorithms.
POST https://api.tapsage.com/v1/embeddings
Creates an embedding vector representing the input text.
curl https://api.tapsage.com/v1/embeddings \
-H "Authorization: Bearer $TAPSAGE_KEY" \
-H "Content-Type: application/json" \
-d '{
"input": "The food was delicious and the waiter...",
"model": "text-embedding-ada-002",
"encoding_format": "float"
}'- input string or array | Required: 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. - model string | Required: ID of the model to use. You can use the List models API to see all of your available models, or see our Model overview for descriptions of them.
- encoding_format string | Optional | Defaults to float: The format to return the embeddings in. Can be either
floatorbase64. - user string | Optional: A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse.
{
"object": "list",
"data": [
{
"object": "embedding",
"embedding": [
0.0023064255,
-0.009327292,
.... (1536 floats total for ada-002)
-0.0028842222,
],
"index": 0
}
],
"model": "text-embedding-ada-002",
"usage": {
"prompt_tokens": 8,
"total_tokens": 8
}
}For more information visit this link.
POST https://api.openai.com/v1/chat/completions
Creates a model response for the given chat conversation.
curl https://api.tapsage.com/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $TAPSAGE_KEY" \
-d '{
"model": "gpt-3.5-turbo",
"messages": [
{
"role": "system",
"content": "You are a helpful assistant."
},
{
"role": "user",
"content": "Hello!"
}
],
"stream": true
}'{
"id": "chatcmpl-123",
"object": "chat.completion",
"created": 1677652288,
"model": "gpt-3.5-turbo-0613",
"system_fingerprint": "fp_44709d6fcb",
"choices": [{
"index": 0,
"message": {
"role": "assistant",
"content": "\n\nHello there, how may I assist you today?",
},
"logprobs": null,
"finish_reason": "stop"
}],
"usage": {
"prompt_tokens": 9,
"completion_tokens": 12,
"total_tokens": 21
}
}For more information visit this link.
POST https://api.openai.com/v1/chat/completions
Creates a model response in streaming mode for the given chat conversation.
curl https://api.tapsage.com/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $TAPSAGE_KEY" \
-d '{
"model": "gpt-3.5-turbo",
"messages": [
{
"role": "system",
"content": "You are a helpful assistant."
},
{
"role": "user",
"content": "Hello!"
}
],
"stream": true
}'{"id":"chatcmpl-123","object":"chat.completion.chunk","created":1694268190,"model":"gpt-3.5-turbo-0613", "system_fingerprint": "fp_44709d6fcb", "choices":[{"index":0,"delta":{"role":"assistant","content":""},"logprobs":null,"finish_reason":null}]}
{"id":"chatcmpl-123","object":"chat.completion.chunk","created":1694268190,"model":"gpt-3.5-turbo-0613", "system_fingerprint": "fp_44709d6fcb", "choices":[{"index":0,"delta":{"content":"Hello"},"logprobs":null,"finish_reason":null}]}
{"id":"chatcmpl-123","object":"chat.completion.chunk","created":1694268190,"model":"gpt-3.5-turbo-0613", "system_fingerprint": "fp_44709d6fcb", "choices":[{"index":0,"delta":{"content":"!"},"logprobs":null,"finish_reason":null}]}
....
{"id":"chatcmpl-123","object":"chat.completion.chunk","created":1694268190,"model":"gpt-3.5-turbo-0613", "system_fingerprint": "fp_44709d6fcb", "choices":[{"index":0,"delta":{"content":" today"},"logprobs":null,"finish_reason":null}]}
{"id":"chatcmpl-123","object":"chat.completion.chunk","created":1694268190,"model":"gpt-3.5-turbo-0613", "system_fingerprint": "fp_44709d6fcb", "choices":[{"index":0,"delta":{"content":"?"},"logprobs":null,"finish_reason":null}]}
{"id":"chatcmpl-123","object":"chat.completion.chunk","created":1694268190,"model":"gpt-3.5-turbo-0613", "system_fingerprint": "fp_44709d6fcb", "choices":[{"index":0,"delta":{},"logprobs":null,"finish_reason":"stop"}]}For more information visit this link.
Fine-tuning lets you get more out of the models available through the API by providing:
- Higher quality results than prompting
- Ability to train on more examples than can fit in a prompt
- Token savings due to shorter prompts
- Lower latency requests
OpenAI's text generation models have been pre-trained on a vast amount of text. To use the models effectively, we include instructions and sometimes several examples in a prompt. Using demonstrations to show how to perform a task is often called "few-shot learning."
Fine-tuning improves on few-shot learning by training on many more examples than can fit in the prompt, letting you achieve better results on a wide number of tasks. Once a model has been fine-tuned, you won't need to provide as many examples in the prompt. This saves costs and enables lower-latency requests.
At a high level, fine-tuning involves the following steps:
- Prepare and upload training data
- Train a new fine-tuned model
- Evaluate results and go back to step 1 if needed
- Use your fine-tuned model
For more ifnormation visit this link.