A list of previous messages between the user and the model, meant to give the model conversational context for responding to the user's message.
list of relevant documents that the model can cite to generate a more accurate reply. Some suggested keys are "text", "author", and "date". For better generation quality, it is recommended to keep the total word count of the strings in the dictionary to under 300 words.
To reduce repetitiveness of generated tokens, this number penalizes new tokens based on their frequency in the generated text so far. Greater numbers encourage the model to use new tokens, while lower numbers encourage the model to repeat the tokens. Set to 0 to disable. Note: Numbers greater than Number.MAX_SAFE_INTEGER will result in rounding issues.
When true, the response will only contain a list of generated search queries, but no search will take place, and no reply from the model to the user's message will be generated.
Whether to stream back partial progress. If set, tokens are sent as data-only server-sent events as they become available.
The maximum number of tokens to predict for each response. Includes input plus output tokens. Note: Numbers greater than Number.MAX_SAFE_INTEGER will result in rounding issues.
Text input for the model to respond to.
When specified, the default Cohere preamble will be replaced with the provided one. Preambles are a part of the prompt used to adjust the model's overall behavior and conversation style. Default preambles vary for different models.
To reduce repetitiveness of generated tokens, this number penalizes new tokens based on whether they've appeared in the generated text so far. Greater numbers encourage the model to use new tokens, while lower numbers encourage the model to repeat the tokens.
Similar to frequency penalty, a penalty is applied to previously present tokens, except that this penalty is applied equally to all tokens that have already appeared, regardless of how many times they've appeared. Set to 0 to disable. Note: Numbers greater than Number.MAX_SAFE_INTEGER will result in rounding issues.
A number that sets the randomness of the generated output. A lower temperature means a less random generations. Use lower numbers for tasks with a correct answer such as question answering or summarizing. High temperatures can generate hallucinations or factually incorrect information. Start with temperatures lower than 1.0 and increase the temperature for more creative outputs, as you regenerate the prompts to refine the outputs. Note: Numbers greater than Number.MAX_SAFE_INTEGER will result in rounding issues.
An integer that sets up the model to use only the top k most likely tokens in the generated output. A higher k introduces more randomness into the output making the output text sound more natural. Default value is 0 which disables this method and considers all tokens. To set a number for the likely tokens, choose an integer between 1 and 500.
If also using top p, then the model considers only the top tokens whose probabilities add up to p percent and ignores the rest of the k tokens. For example, if k is 20, but the probabilities of the top 10 add up to .75, then only the top 10 tokens are chosen. Note: Numbers greater than Number.MAX_SAFE_INTEGER will result in rounding issues.
If set to a probability 0.0 < p < 1.0, it ensures that only the most likely tokens, with total probability mass of p, are considered for generation at each step.
To eliminate tokens with low likelihood, assign p a minimum percentage for the next token's likelihood. For example, when p is set to 0.75, the model eliminates the bottom 25 percent for the next token. Set to 1.0 to consider all tokens and set to 0 to disable. If both k and p are enabled, p acts after k. Note: Numbers greater than Number.MAX_SAFE_INTEGER will result in rounding issues.
Details for the chat request for Cohere models.