| Title: | Generative Artificial Intelligence |
|---|---|
| Description: | Utilizing Generative Artificial Intelligence models like 'GPT-4' and 'Gemini Pro' as coding and writing assistants for 'R' users. Through these models, 'GenAI' offers a variety of functions, encompassing text generation, code optimization, natural language processing, chat, and image interpretation. The goal is to aid 'R' users in streamlining laborious coding and language processing tasks. |
| Authors: | Li Yuan [aut, cre] |
| Maintainer: | Li Yuan <[email protected]> |
| License: | CC BY 4.0 |
| Version: | 0.2.0 |
| Built: | 2025-03-12 02:45:02 UTC |
| Source: | https://github.com/cran/GenAI |
This function sends a request to GenAI database API to retrieve information about available generative AI models.
available.models()available.models()
The function utilizes the GenAI database API to fetch the latest information about available Generative AI models. The retrieved data includes details about different models offered by various service providers.
If successful, the function returns a list containing generative AI service providers and their corresponding models. If the function encounters an error, it will halt execution and provide an error message.
GenAI - R Package "GenAI" Documentation
## Not run: # Assuming there is a GenAI object named 'genai.model' supporting this # function, please refer to the "Live Demo in Colab" above for real # examples. The following examples are just some basic guidelines. all.models = available.models() %>% print() ## End(Not run)## Not run: # Assuming there is a GenAI object named 'genai.model' supporting this # function, please refer to the "Live Demo in Colab" above for real # examples. The following examples are just some basic guidelines. all.models = available.models() %>% print() ## End(Not run)
This function establishes a connection to a generative AI model through a generative AI object. It generates a chat response based on the provided prompt and stores it in the chat history along with the generative AI object.
chat(genai.object, prompt, verbose = FALSE, config = list())chat(genai.object, prompt, verbose = FALSE, config = list())
genai.object |
A generative AI object containing necessary and correct information. |
prompt |
A character string representing the query for chat generation. |
verbose |
Optional. Default to |
config |
Optional. Default to |
Providing accurate and valid information for each argument is crucial for successful chat
generation by the generative AI model. If any parameter is incorrect, the function responds with an
error message based on the API feedback. To view all supported generative AI models, use the
function available.models.
In addition, this function modifies the chat history along with the generative AI object directly,
meaning the chat history is mutable. You can print out the chat history using the
function chat.history.print or simply use verbose = TRUE in this function. If you
want to edit a message, use the function chat.edit. To reset the chat history along with
the generative AI object, use the function chat.history.reset.
For Google Generative AI models, available configurations are as follows. For more detail,
please refer
to https://ai.google.dev/api/rest/v1/HarmCategory,
https://ai.google.dev/api/rest/v1/SafetySetting, and
https://ai.google.dev/api/rest/v1/GenerationConfig.
harm.category.dangerous.content
Optional. An integer, from 1 to 5 inclusive, representing the threshold for dangerous content, with a higher value representing a lower probability of being blocked.
harm.category.harassment
Optional. An integer, from 1 to 5 inclusive, representing the threshold for harasment content, with a higher value representing a lower probability of being blocked.
harm.category.hate.speech
Optional. An integer, from 1 to 5 inclusive, representing the threshold for hate speech and content, with a higher value representing a lower probability of being blocked.
harm.category.sexually.explicit
Optional. An integer, from 1 to 5 inclusive, representing the threshold for sexually explicit content, with a higher value representing a lower probability of being blocked.
stop.sequences
Optional. A list of character sequences (up to 5) that will stop output generation. If specified, the API will stop at the first appearance of a stop sequence. The stop sequence will not be included as part of the response.
max.output.tokens
Optional. An integer, value varies by model, representing maximum number of tokens to include in a candidate.
temperature
Optional. A number, from 0.0 to 1.0 inclusive, controlling the randomness of the output.
top.p
Optional. A number, value varies by model, representing maximum cumulative probability of tokens to consider when sampling.
top.k
Optional. A number, value varies by model, representing maximum number of tokens to consider when sampling.
For Moonshot AI models, available configurations are as follows. For more detail, please refer to
https://platform.moonshot.cn/api.html#chat-completion.
max.tokens
Optional. An integer. The maximum number of tokens that will be generated when the chat completes. If the chat is not finished by the maximum number of tokens generated, the finish reason will be "length", otherwise it will be "stop".
temperature
Optional. A number. What sampling temperature to use, between 0 and 1. Higher values (e.g. 0.7) will make the output more random, while lower values (e.g. 0.2) will make it more focused and deterministic.
top.p
Optional. A number. Another sampling temperature.
For OpenAI models, available configurations are as follows. For more detail, please refer to
https://platform.openai.com/docs/api-reference/chat/create.
frequency.penalty
Optional. A number from -2.0 to 2.0 inclusive. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.
logit.bias
Optional. A map. Modify the likelihood of specified tokens appearing in the completion. Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token.
logprobs
Optional. A boolean value. Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each output token returned in the content of message
top.logprobs
Optional. An integer between 0 and 5 specifying the number of most likely tokens to return at each token
position, each with an associated log probability. logprobs must be set to TRUE if this
parameter is used.
max.tokens
Optional. An integer. The maximum number of tokens that can be generated in the chat completion. The total length of input tokens and generated tokens is limited by the model's context length.
presence.penalty
Optional. A Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics.
response.format
Optional. An object specifying the format that the model must output. Compatible with GPT-4 Turbo and
all GPT-3.5 Turbo models newer than gpt-3.5-turbo-1106.
seed
Optional. An integer. If specified, our system will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result.
stop
Optional. A character string or list contains up to 4 sequences where the API will stop generating further tokens.
temperature
Optional. A number. What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.
top.p
Optional. A number. An alternative to sampling with temperature, called nucleus sampling, where the model considers
the results of the tokens with top.p probability mass. So 0.1 means only the tokens comprising the top
10
tools
Optional. A list of tools the model may call. Currently, only functions are supported as a tool. Use this to provide a list of functions the model may generate JSON inputs for.
tool.choice
Optional. A character string or object. Controls which (if any) function is called by the model. none means
the model will not call a function and instead generates a message. auto means the model can pick
between generating a message or calling a function.
user
Optional. A character string. A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse.
If successful, the most recent chat response will be returned. If the API response indicates an error, the function halts execution and provides an error message.
GenAI - R Package "GenAI" Documentation
## Not run: # Assuming there is a GenAI object named 'genai.model' supporting this # function, please refer to the "Live Demo in Colab" above for real # examples. The following examples are just some basic guidelines. # Method 1 (recommended): use the pipe operator "%>%" genai.model %>% chat(prompt = "Write a story about Mars in 50 words.") %>% cat() # Method 2: use the reference operator "$" cat(genai.model$chat(prompt = "Write a story about Jupiter in 50 words.")) # Method 3: use the function chat() directly cat(chat(genai.object = genai.model, prompt = "Summarize the chat.")) ## End(Not run)## Not run: # Assuming there is a GenAI object named 'genai.model' supporting this # function, please refer to the "Live Demo in Colab" above for real # examples. The following examples are just some basic guidelines. # Method 1 (recommended): use the pipe operator "%>%" genai.model %>% chat(prompt = "Write a story about Mars in 50 words.") %>% cat() # Method 2: use the reference operator "$" cat(genai.model$chat(prompt = "Write a story about Jupiter in 50 words.")) # Method 3: use the function chat() directly cat(chat(genai.object = genai.model, prompt = "Summarize the chat.")) ## End(Not run)
This function establishes a connection to a generative AI model through a generative AI object. It generates a chat response based on the new prompt and stores it in the chat history along with the generative AI object.
chat.edit( genai.object, prompt, message.to.edit, verbose = FALSE, config = list() )chat.edit( genai.object, prompt, message.to.edit, verbose = FALSE, config = list() )
genai.object |
A generative AI object containing necessary and correct information. |
prompt |
A character string representing the query for chat generation. |
message.to.edit |
An integer representing the index of the message to be edited. |
verbose |
Optional. Default to |
config |
Optional. Default to |
Providing accurate and valid information for each argument is crucial for successful chat
generation by the generative AI model. If any parameter is incorrect, the function responds with an
error message based on the API feedback. To view all supported generative AI models, use the
function available.models.
In addition, this function modifies the chat history along with the generative AI object directly,
meaning the chat history is mutable. You can print out the chat history using the
function chat.history.print or simply use verbose = TRUE in this function. To reset the chat history along with
the chat history along with the generative AI object, use the function chat.history.reset.
For Google Generative AI models, available configurations are as follows. For more detail,
please refer
to https://ai.google.dev/api/rest/v1/HarmCategory,
https://ai.google.dev/api/rest/v1/SafetySetting, and
https://ai.google.dev/api/rest/v1/GenerationConfig.
harm.category.dangerous.content
Optional. An integer, from 1 to 5 inclusive, representing the threshold for dangerous content, with a higher value representing a lower probability of being blocked.
harm.category.harassment
Optional. An integer, from 1 to 5 inclusive, representing the threshold for harasment content, with a higher value representing a lower probability of being blocked.
harm.category.hate.speech
Optional. An integer, from 1 to 5 inclusive, representing the threshold for hate speech and content, with a higher value representing a lower probability of being blocked.
harm.category.sexually.explicit
Optional. An integer, from 1 to 5 inclusive, representing the threshold for sexually explicit content, with a higher value representing a lower probability of being blocked.
stop.sequences
Optional. A list of character sequences (up to 5) that will stop output generation. If specified, the API will stop at the first appearance of a stop sequence. The stop sequence will not be included as part of the response.
max.output.tokens
Optional. An integer, value varies by model, representing maximum number of tokens to include in a candidate.
temperature
Optional. A number, from 0.0 to 1.0 inclusive, controlling the randomness of the output.
top.p
Optional. A number, value varies by model, representing maximum cumulative probability of tokens to consider when sampling.
top.k
Optional. A number, value varies by model, representing maximum number of tokens to consider when sampling.
For Moonshot AI models, available configurations are as follows. For more detail, please refer to
https://platform.moonshot.cn/api.html#chat-completion.
max.tokens
Optional. An integer. The maximum number of tokens that will be generated when the chat completes. If the chat is not finished by the maximum number of tokens generated, the finish reason will be "length", otherwise it will be "stop".
temperature
Optional. A number. What sampling temperature to use, between 0 and 1. Higher values (e.g. 0.7) will make the output more random, while lower values (e.g. 0.2) will make it more focused and deterministic.
top.p
Optional. A number. Another sampling temperature.
For OpenAI models, available configurations are as follows. For more detail, please refer to
https://platform.openai.com/docs/api-reference/chat/create.
frequency.penalty
Optional. A number from -2.0 to 2.0 inclusive. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.
logit.bias
Optional. A map. Modify the likelihood of specified tokens appearing in the completion. Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token.
logprobs
Optional. A boolean value. Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each output token returned in the content of message
top.logprobs
Optional. An integer between 0 and 5 specifying the number of most likely tokens to return at each token
position, each with an associated log probability. logprobs must be set to TRUE if this
parameter is used.
max.tokens
Optional. An integer. The maximum number of tokens that can be generated in the chat completion. The total length of input tokens and generated tokens is limited by the model's context length.
presence.penalty
Optional. A Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics.
response.format
Optional. An object specifying the format that the model must output. Compatible with GPT-4 Turbo and
all GPT-3.5 Turbo models newer than gpt-3.5-turbo-1106.
seed
Optional. An integer. If specified, our system will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result.
stop
Optional. A character string or list contains up to 4 sequences where the API will stop generating further tokens.
temperature
Optional. A number. What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.
top.p
Optional. A number. An alternative to sampling with temperature, called nucleus sampling, where the model considers
the results of the tokens with top.p probability mass. So 0.1 means only the tokens comprising the top
10
tools
Optional. A list of tools the model may call. Currently, only functions are supported as a tool. Use this to provide a list of functions the model may generate JSON inputs for.
tool.choice
Optional. A character string or object. Controls which (if any) function is called by the model. none means
the model will not call a function and instead generates a message. auto means the model can pick
between generating a message or calling a function.
user
Optional. A character string. A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse.
If successful, the most recent chat response will be returned. If the API response indicates an error, the function halts execution and provides an error message.
GenAI - R Package "GenAI" Documentation
## Not run: # Assuming there is a GenAI object named 'genai.model' supporting this # function, please refer to the "Live Demo in Colab" above for real # examples. The following examples are just some basic guidelines. # Method 1 (recommended): use the pipe operator "%>%" genai.model %>% chat.edit(prompt = "What is XGBoost?", message.to.edit = 5, verbose = TRUE, config = parameters) %>% cat() # Method 2: use the reference operator "$" cat(genai.model$chat.edit(prompt = "What is CatBoost?", message.to.edit = 3)) # Method 3: use the function chat.edit() directly cat(chat.edit(genai.object = genai.model, prompt = "What is LightGBM?", message.to.edit = 1)) ## End(Not run)## Not run: # Assuming there is a GenAI object named 'genai.model' supporting this # function, please refer to the "Live Demo in Colab" above for real # examples. The following examples are just some basic guidelines. # Method 1 (recommended): use the pipe operator "%>%" genai.model %>% chat.edit(prompt = "What is XGBoost?", message.to.edit = 5, verbose = TRUE, config = parameters) %>% cat() # Method 2: use the reference operator "$" cat(genai.model$chat.edit(prompt = "What is CatBoost?", message.to.edit = 3)) # Method 3: use the function chat.edit() directly cat(chat.edit(genai.object = genai.model, prompt = "What is LightGBM?", message.to.edit = 1)) ## End(Not run)
This function converts the chat history along with a generative AI object to a valid format for another generative AI object.
chat.history.convert(from.genai.object, to.genai.object)chat.history.convert(from.genai.object, to.genai.object)
from.genai.object |
A source generative AI object containing necessary and correct information. |
to.genai.object |
A target generative AI object containing necessary and correct information. |
Providing accurate and valid information for each argument is crucial for successful chat
generation by the generative AI model. If any parameter is incorrect, the function responds with an
error message based on the API feedback. To view all supported generative AI models, use the
function available.models. Moreover, you can print out the chat history using the
function chat.history.print or simply use verbose = TRUE during the chat.
If successful, the converted chat history list will be returned.
GenAI - R Package "GenAI" Documentation
## Not run: # Assuming there are two GenAI objects named 'genai.model' and 'another.genai.model' # supporting this function, please refer to the "Live Demo in Colab" above for # real examples. The following examples are just some basic guidelines. # Method 1 (recommended): use the pipe operator "%>%" converted.history = genai.model %>% chat.history.convert(to.genai.object = another.genai.model) # Method 2: use the reference operator "$" converted.history = genai.model$chat.history.convert(to.genai.object = another.genai.model) # Method 3: use the function chat.history.convert() directly converted.history = chat.history.convert(from.genai.object = genai.model, to.genai.object = another.genai.model) ## End(Not run)## Not run: # Assuming there are two GenAI objects named 'genai.model' and 'another.genai.model' # supporting this function, please refer to the "Live Demo in Colab" above for # real examples. The following examples are just some basic guidelines. # Method 1 (recommended): use the pipe operator "%>%" converted.history = genai.model %>% chat.history.convert(to.genai.object = another.genai.model) # Method 2: use the reference operator "$" converted.history = genai.model$chat.history.convert(to.genai.object = another.genai.model) # Method 3: use the function chat.history.convert() directly converted.history = chat.history.convert(from.genai.object = genai.model, to.genai.object = another.genai.model) ## End(Not run)
This function exports the chat history along with a generative AI object as a list.
chat.history.export(genai.object)chat.history.export(genai.object)
genai.object |
A generative AI object containing necessary and correct information. |
Providing accurate and valid information for each argument is crucial for successful chat
generation by the generative AI model. If any parameter is incorrect, the function responds with an
error message based on the API feedback. To view all supported generative AI models, use the
function available.models.
If successful, the chat history list will be returned.
GenAI - R Package "GenAI" Documentation
## Not run: # Assuming there is a GenAI object named 'genai.model' supporting this # function, please refer to the "Live Demo in Colab" above for real # examples. The following examples are just some basic guidelines. # Method 1 (recommended): use the pipe operator "%>%" exported.history = genai.model %>% chat.history.export() # Method 2: use the reference operator "$" exported.history = genai.model$chat.history.export() # Method 3: use the function chat.history.export() directly exported.history = chat.history.export(genai.object = genai.model) ## End(Not run)## Not run: # Assuming there is a GenAI object named 'genai.model' supporting this # function, please refer to the "Live Demo in Colab" above for real # examples. The following examples are just some basic guidelines. # Method 1 (recommended): use the pipe operator "%>%" exported.history = genai.model %>% chat.history.export() # Method 2: use the reference operator "$" exported.history = genai.model$chat.history.export() # Method 3: use the function chat.history.export() directly exported.history = chat.history.export(genai.object = genai.model) ## End(Not run)
This function imports a chat history in list format to a generative AI object.
chat.history.import(genai.object, new.chat.history)chat.history.import(genai.object, new.chat.history)
genai.object |
A generative AI object containing necessary and correct information. |
new.chat.history |
A list containing a chat history in correct format. |
Providing accurate and valid information for each argument is crucial for successful chat
generation by the generative AI model. If any parameter is incorrect, the function responds with an
error message based on the API feedback. To view all supported generative AI models, use the
function available.models.
GenAI - R Package "GenAI" Documentation
## Not run: # Assuming there is a GenAI object named 'genai.model' supporting this # function and a valid chat history list named 'new.history', please # refer to the "Live Demo in Colab" above for real examples. The # following examples are just some basic guidelines. # Method 1 (recommended): use the pipe operator "%>%" genai.model %>% chat.history.import(new.chat.history = new.history) # Method 2: use the reference operator "$" genai.model$chat.history.import(new.chat.history = new.history) # Method 3: use the function chat.history.import() directly chat.history.import(genai.object = genai.model, new.chat.history = new.history) ## End(Not run)## Not run: # Assuming there is a GenAI object named 'genai.model' supporting this # function and a valid chat history list named 'new.history', please # refer to the "Live Demo in Colab" above for real examples. The # following examples are just some basic guidelines. # Method 1 (recommended): use the pipe operator "%>%" genai.model %>% chat.history.import(new.chat.history = new.history) # Method 2: use the reference operator "$" genai.model$chat.history.import(new.chat.history = new.history) # Method 3: use the function chat.history.import() directly chat.history.import(genai.object = genai.model, new.chat.history = new.history) ## End(Not run)
This function prints out the chat history along with a generative AI object.
chat.history.print(genai.object, from = 1, to = NULL)chat.history.print(genai.object, from = 1, to = NULL)
genai.object |
A generative AI object containing necessary and correct information. |
from |
Optional. Default to 1. An integer representing the first message in the chat history that needs to be printed. |
to |
Optional. Default to |
Providing accurate and valid information for each argument is crucial for successful chat
generation by the generative AI model. If any parameter is incorrect, the function responds with an
error message based on the API feedback. To view all supported generative AI models, use the
function available.models.
GenAI - R Package "GenAI" Documentation
## Not run: # Assuming there is a GenAI object named 'genai.model' supporting this # function, please refer to the "Live Demo in Colab" above for real # examples. The following examples are just some basic guidelines. # Method 1 (recommended): use the pipe operator "%>%" genai.model %>% chat.history.print() # Method 2: use the reference operator "$" genai.model$chat.history.print(from = 3) # Method 3: use the function chat.history.print() directly chat.history.print(genai.object = genai.model, from = 3, to = 5) ## End(Not run)## Not run: # Assuming there is a GenAI object named 'genai.model' supporting this # function, please refer to the "Live Demo in Colab" above for real # examples. The following examples are just some basic guidelines. # Method 1 (recommended): use the pipe operator "%>%" genai.model %>% chat.history.print() # Method 2: use the reference operator "$" genai.model$chat.history.print(from = 3) # Method 3: use the function chat.history.print() directly chat.history.print(genai.object = genai.model, from = 3, to = 5) ## End(Not run)
This function resets the chat history along with a generative AI object.
chat.history.reset(genai.object)chat.history.reset(genai.object)
genai.object |
A generative AI object containing necessary and correct information. |
Providing accurate and valid information for each argument is crucial for successful chat
generation by the generative AI model. If any parameter is incorrect, the function responds with an
error message based on the API feedback. To view all supported generative AI models, use the
function available.models.
GenAI - R Package "GenAI" Documentation
## Not run: # Assuming there is a GenAI object named 'genai.model' supporting this # function, please refer to the "Live Demo in Colab" above for real # examples. The following examples are just some basic guidelines. # Method 1 (recommended): use the pipe operator "%>%" genai.model %>% chat.history.reset() # Method 2: use the reference operator "$" genai.model$chat.history.reset() # Method 3: use the function chat.history.reset() directly chat.history.reset(genai.object = genai.model) ## End(Not run)## Not run: # Assuming there is a GenAI object named 'genai.model' supporting this # function, please refer to the "Live Demo in Colab" above for real # examples. The following examples are just some basic guidelines. # Method 1 (recommended): use the pipe operator "%>%" genai.model %>% chat.history.reset() # Method 2: use the reference operator "$" genai.model$chat.history.reset() # Method 3: use the function chat.history.reset() directly chat.history.reset(genai.object = genai.model) ## End(Not run)
This function saves a chat history along with a generative AI object as a JSON file.
chat.history.save(genai.object, file.name)chat.history.save(genai.object, file.name)
genai.object |
A generative AI object containing necessary and correct information. |
file.name |
A character string representing the name of the JSON file for the chat history. |
Providing accurate and valid information for each argument is crucial for successful chat
generation by the generative AI model. If any parameter is incorrect, the function responds with an
error message based on the API feedback. To view all supported generative AI models, use the
function available.models.
If successful, the chat history will be saved as a JSON file in your current or specified directory.
GenAI - R Package "GenAI" Documentation
## Not run: # Assuming there is a GenAI object named 'genai.model' supporting this # function, please refer to the "Live Demo in Colab" above for real # examples. The following examples are just some basic guidelines. # Method 1 (recommended): use the pipe operator "%>%" genai.model %>% chat.history.save(file.name = "saved_history") # Method 2: use the reference operator "$" genai.model$chat.history.save(file.name = "saved_history") # Method 3: use the function chat.history.save() directly chat.history.save(genai.object = genai.model, file.name = "saved_history") ## End(Not run)## Not run: # Assuming there is a GenAI object named 'genai.model' supporting this # function, please refer to the "Live Demo in Colab" above for real # examples. The following examples are just some basic guidelines. # Method 1 (recommended): use the pipe operator "%>%" genai.model %>% chat.history.save(file.name = "saved_history") # Method 2: use the reference operator "$" genai.model$chat.history.save(file.name = "saved_history") # Method 3: use the function chat.history.save() directly chat.history.save(genai.object = genai.model, file.name = "saved_history") ## End(Not run)
This function establishes a connection to a Google generative AI model by providing essential parameters.
genai.google(api, model, version, proxy = FALSE)genai.google(api, model, version, proxy = FALSE)
api |
A character string representing the API key required for accessing the model. |
model |
A character string representing the specific model. |
version |
A character string representing the version of the chosen model. |
proxy |
Optional. Default to |
Providing accurate and valid information for each argument is crucial for successful text
generation by the generative AI model. If any parameter is incorrect, the function responds with an
error message based on the API feedback. To view all supported generative AI models, use the
function available.models.
Please refer to https://ai.google.dev/tutorials/setup for the API key.
The API proxy service is designed to address the needs of users who hold a valid API key but find themselves outside their home countries or regions due to reasons such as travel, work, or study in locations that may not be covered by certain Generative AI service providers.
Please be aware that although GenAI and its affiliated organization - GitData - do not gather user information through this service, the server providers for GenAI API proxy service and the Generative AI service providers may engage in such data collection. Furthermore, the proxy service cannot guarantee a consistent connection speed. Users are strongly encouraged to utilize this service with caution and at their own discretion.
If successful, the function returns a Google generative AI object. If the API response indicates an error, the function halts execution and provides an error message.
GenAI - R Package "GenAI" Documentation
GenAI - Generative Artificial Intelligence API Proxy Service
## Not run: # Please change YOUR_GOOGLE_API to your own API key of Google Generative AI Sys.setenv(GOOGLE_API = "YOUR_GOOGLE_API") all.models = available.models() %>% print() # Create a Google Generative AI object google = genai.google(api = Sys.getenv("GOOGLE_API"), model = all.models$google$model[1], version = all.models$google$version[1], proxy = FALSE) ## End(Not run)## Not run: # Please change YOUR_GOOGLE_API to your own API key of Google Generative AI Sys.setenv(GOOGLE_API = "YOUR_GOOGLE_API") all.models = available.models() %>% print() # Create a Google Generative AI object google = genai.google(api = Sys.getenv("GOOGLE_API"), model = all.models$google$model[1], version = all.models$google$version[1], proxy = FALSE) ## End(Not run)
This function establishes a connection to a Moonshot AI model by providing essential parameters.
genai.moonshot(api, model, version, proxy = FALSE)genai.moonshot(api, model, version, proxy = FALSE)
api |
A character string representing the API key required for accessing the model. |
model |
A character string representing the specific model. |
version |
A character string representing the version of the chosen model. |
proxy |
Optional. Default to |
Providing accurate and valid information for each argument is crucial for successful text
generation by the generative AI model. If any parameter is incorrect, the function responds with an
error message based on the API feedback. To view all supported generative AI models, use the
function available.models.
Please refer to https://platform.moonshot.cn/console/api-keys for the API key.
The API proxy service is designed to address the needs of users who hold a valid API key but find themselves outside their home countries or regions due to reasons such as travel, work, or study in locations that may not be covered by certain Generative AI service providers.
Please be aware that although GenAI and its affiliated organization - GitData - do not gather user information through this service, the server providers for GenAI API proxy service and the Generative AI service providers may engage in such data collection. Furthermore, the proxy service cannot guarantee a consistent connection speed. Users are strongly encouraged to utilize this service with caution and at their own discretion.
If successful, the function returns an moonshot object. If the API response indicates an error, the function halts execution and provides an error message.
GenAI - R Package "GenAI" Documentation
GenAI - Generative Artificial Intelligence API Proxy Service
## Not run: # Please change YOUR_MOONSHOT_API to your own API key of Moonshot AI Sys.setenv(MOONSHOT_API = "YOUR_MOONSHOT_API") all.models = available.models() %>% print() # Create an moonshot object moonshot = genai.moonshot(api = Sys.getenv("MOONSHOT_API"), model = all.models$moonshot$model[1], version = all.models$moonshot$version[1], proxy = FALSE) ## End(Not run)## Not run: # Please change YOUR_MOONSHOT_API to your own API key of Moonshot AI Sys.setenv(MOONSHOT_API = "YOUR_MOONSHOT_API") all.models = available.models() %>% print() # Create an moonshot object moonshot = genai.moonshot(api = Sys.getenv("MOONSHOT_API"), model = all.models$moonshot$model[1], version = all.models$moonshot$version[1], proxy = FALSE) ## End(Not run)
This function establishes a connection to an OpenAI model by providing essential parameters.
genai.openai(api, model, version, proxy = FALSE, organization.id = NULL)genai.openai(api, model, version, proxy = FALSE, organization.id = NULL)
api |
A character string representing the API key required for accessing the model. |
model |
A character string representing the specific model. |
version |
A character string representing the version of the chosen model. |
proxy |
Optional. Default to |
organization.id |
Optional. Default to |
Providing accurate and valid information for each argument is crucial for successful text
generation by the generative AI model. If any parameter is incorrect, the function responds with an
error message based on the API feedback. To view all supported generative AI models, use the
function available.models.
Please refer to https://platform.openai.com/api-keys for the API key. Moreover, please refer
to https://platform.openai.com/account/organization for the optional organization ID.
The API proxy service is designed to address the needs of users who hold a valid API key but find themselves outside their home countries or regions due to reasons such as travel, work, or study in locations that may not be covered by certain Generative AI service providers.
Please be aware that although GenAI and its affiliated organization - GitData - do not gather user information through this service, the server providers for GenAI API proxy service and the Generative AI service providers may engage in such data collection. Furthermore, the proxy service cannot guarantee a consistent connection speed. Users are strongly encouraged to utilize this service with caution and at their own discretion.
If successful, the function returns an OpenAI object. If the API response indicates an error, the function halts execution and provides an error message.
GenAI - R Package "GenAI" Documentation
GenAI - Generative Artificial Intelligence API Proxy Service
## Not run: # Please change YOUR_OPENAI_API to your own API key of OpenAI Sys.setenv(OPENAI_API = "YOUR_OPENAI_API") # Oprional. Please change YOUR_OPENAI_ORG to your own organization ID for OpenAI Sys.setenv(OPENAI_ORG = "YOUR_OPENAI_ORG") all.models = available.models() %>% print() # Create an OpenAI object openai = genai.openai(api = Sys.getenv("OPENAI_API"), model = all.models$openai$model[1], version = all.models$openai$version[1], proxy = FALSE, organization.id = Sys.getenv("OPENAI_ORG")) ## End(Not run)## Not run: # Please change YOUR_OPENAI_API to your own API key of OpenAI Sys.setenv(OPENAI_API = "YOUR_OPENAI_API") # Oprional. Please change YOUR_OPENAI_ORG to your own organization ID for OpenAI Sys.setenv(OPENAI_ORG = "YOUR_OPENAI_ORG") all.models = available.models() %>% print() # Create an OpenAI object openai = genai.openai(api = Sys.getenv("OPENAI_API"), model = all.models$openai$model[1], version = all.models$openai$version[1], proxy = FALSE, organization.id = Sys.getenv("OPENAI_ORG")) ## End(Not run)
This function establishes a connection to a generative AI model through a generative AI object. It generates an image response based on the provided prompt.
img(genai.object, prompt, verbose = FALSE, config = list())img(genai.object, prompt, verbose = FALSE, config = list())
genai.object |
A generative AI object containing necessary and correct information. |
prompt |
A character string representing the query for image generation. |
verbose |
Optional. Default to |
config |
Optional. Default to |
Providing accurate and valid information for each argument is crucial for successful image
generation by the generative AI model. If any parameter is incorrect, the function responds with an
error message based on the API feedback. To view all supported generative AI models, use the
function available.models.
This function is only available when using OpenAI's models.
For OpenAI models, available configurations are as follows. For more detail, please refer to
https://platform.openai.com/docs/api-reference/images/create.
quality
Optional. A character string. The quality of the image that will be generated. hd creates
images with finer details and greater consistency across the image.
size
Optional. A character string. The size of the generated images. Must be one of 256x256,
512x512, or 1024x1024 for dall-e-2. Must be one of 1024x1024, 1792x1024, or
1024x1792 for dall-e-3 models.
style
Optional. The style of the generated images. Must be one of vivid or natural. Vivid causes
the model to lean towards generating hyper-real and dramatic images. Natural causes the model to produce
more natural, less hyper-real looking images.
user
Optional. A character string. A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse.
If successful, a image in ggplot format will be returned. If the API response indicates
an error, the function halts execution and provides an error message.
GenAI - R Package "GenAI" Documentation
## Not run: # Assuming there is a GenAI object named 'genai.model' supporting this # function, please refer to the "Live Demo in Colab" above for real # examples. The following examples are just some basic guidelines. # Method 1 (recommended): use the pipe operator "%>%" generated.image = genai.model %>% img(prompt = "A very cute panda eating banboo.") generated.image # Method 2: use the reference operator "$" generated.image = genai.model$img(prompt = "A very cute sea otter on a rock.") generated.image # Method 3: use the function img() directly generated.image = img(genai.object = genai.model, prompt = "A very cute bear.") generated.image ## End(Not run)## Not run: # Assuming there is a GenAI object named 'genai.model' supporting this # function, please refer to the "Live Demo in Colab" above for real # examples. The following examples are just some basic guidelines. # Method 1 (recommended): use the pipe operator "%>%" generated.image = genai.model %>% img(prompt = "A very cute panda eating banboo.") generated.image # Method 2: use the reference operator "$" generated.image = genai.model$img(prompt = "A very cute sea otter on a rock.") generated.image # Method 3: use the function img() directly generated.image = img(genai.object = genai.model, prompt = "A very cute bear.") generated.image ## End(Not run)
This function establishes a connection to a generative AI model through a generative AI object. It generates a text response based on the provided prompt.
txt(genai.object, prompt, verbose = FALSE, config = list())txt(genai.object, prompt, verbose = FALSE, config = list())
genai.object |
A generative AI object containing necessary and correct information. |
prompt |
A character string representing the query for text generation. |
verbose |
Optional. Default to |
config |
Optional. Default to |
Providing accurate and valid information for each argument is crucial for successful text
generation by the generative AI model. If any parameter is incorrect, the function responds with an
error message based on the API feedback. To view all supported generative AI models, use the
function available.models.
For Google Generative AI models, available configurations are as follows. For more detail,
please refer
to https://ai.google.dev/api/rest/v1/HarmCategory,
https://ai.google.dev/api/rest/v1/SafetySetting, and
https://ai.google.dev/api/rest/v1/GenerationConfig.
harm.category.dangerous.content
Optional. An integer, from 1 to 5 inclusive, representing the threshold for dangerous content, with a higher value representing a lower probability of being blocked.
harm.category.harassment
Optional. An integer, from 1 to 5 inclusive, representing the threshold for harasment content, with a higher value representing a lower probability of being blocked.
harm.category.hate.speech
Optional. An integer, from 1 to 5 inclusive, representing the threshold for hate speech and content, with a higher value representing a lower probability of being blocked.
harm.category.sexually.explicit
Optional. An integer, from 1 to 5 inclusive, representing the threshold for sexually explicit content, with a higher value representing a lower probability of being blocked.
stop.sequences
Optional. A list of character sequences (up to 5) that will stop output generation. If specified, the API will stop at the first appearance of a stop sequence. The stop sequence will not be included as part of the response.
max.output.tokens
Optional. An integer, value varies by model, representing maximum number of tokens to include in a candidate.
temperature
Optional. A number, from 0.0 to 1.0 inclusive, controlling the randomness of the output.
top.p
Optional. A number, value varies by model, representing maximum cumulative probability of tokens to consider when sampling.
top.k
Optional. A number, value varies by model, representing maximum number of tokens to consider when sampling.
For Moonshot AI models, available configurations are as follows. For more detail, please refer to
https://platform.moonshot.cn/api.html#chat-completion.
max.tokens
Optional. An integer. The maximum number of tokens that will be generated when the chat completes. If the chat is not finished by the maximum number of tokens generated, the finish reason will be "length", otherwise it will be "stop".
temperature
Optional. A number. What sampling temperature to use, between 0 and 1. Higher values (e.g. 0.7) will make the output more random, while lower values (e.g. 0.2) will make it more focused and deterministic.
top.p
Optional. A number. Another sampling temperature.
For OpenAI models, available configurations are as follows. For more detail, please refer to
https://platform.openai.com/docs/api-reference/chat/create.
frequency.penalty
Optional. A number from -2.0 to 2.0 inclusive. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.
logit.bias
Optional. A map. Modify the likelihood of specified tokens appearing in the completion. Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token.
logprobs
Optional. A boolean value. Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each output token returned in the content of message
top.logprobs
Optional. An integer between 0 and 5 specifying the number of most likely tokens to return at each token
position, each with an associated log probability. logprobs must be set to TRUE if this
parameter is used.
max.tokens
Optional. An integer. The maximum number of tokens that can be generated in the chat completion. The total length of input tokens and generated tokens is limited by the model's context length.
presence.penalty
Optional. A Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics.
response.format
Optional. An object specifying the format that the model must output. Compatible with GPT-4 Turbo and
all GPT-3.5 Turbo models newer than gpt-3.5-turbo-1106.
seed
Optional. An integer. If specified, our system will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result.
stop
Optional. A character string or list contains up to 4 sequences where the API will stop generating further tokens.
temperature
Optional. A number. What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.
top.p
Optional. A number. An alternative to sampling with temperature, called nucleus sampling, where the model considers
the results of the tokens with top.p probability mass. So 0.1 means only the tokens comprising the top
10
tools
Optional. A list of tools the model may call. Currently, only functions are supported as a tool. Use this to provide a list of functions the model may generate JSON inputs for.
tool.choice
Optional. A character string or object. Controls which (if any) function is called by the model. none means
the model will not call a function and instead generates a message. auto means the model can pick
between generating a message or calling a function.
user
Optional. A character string. A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse.
If successful, a text response will be returned. If the API response indicates an error, the function halts execution and provides an error message.
GenAI - R Package "GenAI" Documentation
## Not run: # Assuming there is a GenAI object named 'genai.model' supporting this # function, please refer to the "Live Demo in Colab" above for real # examples. The following examples are just some basic guidelines. # Method 1 (recommended): use the pipe operator "%>%" genai.model %>% txt(prompt = "Write a story about Mars in 50 words.") %>% cat() # Method 2: use the reference operator "$" cat(genai.model$txt(prompt = "Write a story about Jupiter in 50 words.")) # Method 3: use the function txt() directly # Set verbose to TRUE to see the detail cat(txt(genai.object = genai.model, prompt = "Write a story about Earth in 50 words.")) ## End(Not run)## Not run: # Assuming there is a GenAI object named 'genai.model' supporting this # function, please refer to the "Live Demo in Colab" above for real # examples. The following examples are just some basic guidelines. # Method 1 (recommended): use the pipe operator "%>%" genai.model %>% txt(prompt = "Write a story about Mars in 50 words.") %>% cat() # Method 2: use the reference operator "$" cat(genai.model$txt(prompt = "Write a story about Jupiter in 50 words.")) # Method 3: use the function txt() directly # Set verbose to TRUE to see the detail cat(txt(genai.object = genai.model, prompt = "Write a story about Earth in 50 words.")) ## End(Not run)
This function establishes a connection to a generative AI model through a generative AI object. It generates a text response based on the provided prompt.
txt.image(genai.object, prompt, image.path, verbose = FALSE, config = list())txt.image(genai.object, prompt, image.path, verbose = FALSE, config = list())
genai.object |
A generative AI object containing necessary and correct information. |
prompt |
A character string representing the query for text generation. |
image.path |
A character string representing the path to the image. It should be a link
starting with |
verbose |
Optional. Default to |
config |
Optional. Default to |
Providing accurate and valid information for each argument is crucial for successful text
generation by the generative AI model. If any parameter is incorrect, the function responds with an
error message based on the API feedback. To view all supported generative AI models, use the
function available.models.
For Google Generative AI models, available configurations are as follows. For more detail,
please refer
to https://ai.google.dev/api/rest/v1/HarmCategory,
https://ai.google.dev/api/rest/v1/SafetySetting, and
https://ai.google.dev/api/rest/v1/GenerationConfig.
harm.category.dangerous.content
Optional. An integer, from 1 to 5 inclusive, representing the threshold for dangerous content, with a higher value representing a lower probability of being blocked.
harm.category.harassment
Optional. An integer, from 1 to 5 inclusive, representing the threshold for harasment content, with a higher value representing a lower probability of being blocked.
harm.category.hate.speech
Optional. An integer, from 1 to 5 inclusive, representing the threshold for hate speech and content, with a higher value representing a lower probability of being blocked.
harm.category.sexually.explicit
Optional. An integer, from 1 to 5 inclusive, representing the threshold for sexually explicit content, with a higher value representing a lower probability of being blocked.
stop.sequences
Optional. A list of character sequences (up to 5) that will stop output generation. If specified, the API will stop at the first appearance of a stop sequence. The stop sequence will not be included as part of the response.
max.output.tokens
Optional. An integer, value varies by model, representing maximum number of tokens to include in a candidate.
temperature
Optional. A number, from 0.0 to 1.0 inclusive, controlling the randomness of the output.
top.p
Optional. A number, value varies by model, representing maximum cumulative probability of tokens to consider when sampling.
top.k
Optional. A number, value varies by model, representing maximum number of tokens to consider when sampling.
For OpenAI models, available configurations are as follows. For more detail, please refer to
https://platform.openai.com/docs/api-reference/chat/create.
frequency.penalty
Optional. A number from -2.0 to 2.0 inclusive. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.
logit.bias
Optional. A map. Modify the likelihood of specified tokens appearing in the completion. Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token.
logprobs
Optional. A boolean value. Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each output token returned in the content of message
top.logprobs
Optional. An integer between 0 and 5 specifying the number of most likely tokens to return at each token
position, each with an associated log probability. logprobs must be set to TRUE if this
parameter is used.
max.tokens
Optional. An integer. The maximum number of tokens that can be generated in the chat completion. The total length of input tokens and generated tokens is limited by the model's context length.
presence.penalty
Optional. A Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics.
response.format
Optional. An object specifying the format that the model must output. Compatible with GPT-4 Turbo and
all GPT-3.5 Turbo models newer than gpt-3.5-turbo-1106.
seed
Optional. An integer. If specified, our system will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result.
stop
Optional. A character string or list contains up to 4 sequences where the API will stop generating further tokens.
temperature
Optional. A number. What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.
top.p
Optional. A number. An alternative to sampling with temperature, called nucleus sampling, where the model considers
the results of the tokens with top.p probability mass. So 0.1 means only the tokens comprising the top
10
tools
Optional. A list of tools the model may call. Currently, only functions are supported as a tool. Use this to provide a list of functions the model may generate JSON inputs for.
tool.choice
Optional. A character string or object. Controls which (if any) function is called by the model. none means
the model will not call a function and instead generates a message. auto means the model can pick
between generating a message or calling a function.
user
Optional. A character string. A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse.
If successful, a text response will be returned. If the API response indicates an error, the function halts execution and provides an error message.
GenAI - R Package "GenAI" Documentation
## Not run: # Assuming there is a GenAI object named 'genai.model' supporting this # function, an image in your current directory named 'example.png', and # an online image 'https://example.com/example.png/', please refer to # the "Live Demo in Colab" above for real examples. The following examples # are just some basic guidelines. # Method 1 (recommended): use the pipe operator "%>%" genai.model %>% txt.image(prompt = "Please describe the following image.", image.path = "https://example.com/example.png/") %>% cat() # Method 2: use the reference operator "$" cat(genai.model$txt.image(prompt = "Please describe the following image.", image.path = "https://example.com/example.png/")) # Method 3: use the function txt.image() directly cat(txt.image(genai.object = genai.model, prompt = "Please describe the following image.", image.path = "example.png")) ## End(Not run)## Not run: # Assuming there is a GenAI object named 'genai.model' supporting this # function, an image in your current directory named 'example.png', and # an online image 'https://example.com/example.png/', please refer to # the "Live Demo in Colab" above for real examples. The following examples # are just some basic guidelines. # Method 1 (recommended): use the pipe operator "%>%" genai.model %>% txt.image(prompt = "Please describe the following image.", image.path = "https://example.com/example.png/") %>% cat() # Method 2: use the reference operator "$" cat(genai.model$txt.image(prompt = "Please describe the following image.", image.path = "https://example.com/example.png/")) # Method 3: use the function txt.image() directly cat(txt.image(genai.object = genai.model, prompt = "Please describe the following image.", image.path = "example.png")) ## End(Not run)