### `_setNextModelsPageToken(nextModelsPageToken)` Sets the token used to fetch the next page of models in the AI session. #### Parameters | Name | Type | Description | |-----------------------|----------|-------------------------------------------------------| | `nextModelsPageToken` | `string` | The token representing the next page of model results. | #### Returns - **`void`** – This method does not return a value. #### Behavior - Updates the internal `_nextModelsPageToken` property with the provided token if it is a string. - If the value is not a string, it sets `_nextModelsPageToken` to `null`. #### Example ```js session._setNextModelsPageToken("eyJwYWdlIjoxfQ=="); ``` > **Note:** This token is used automatically in `getModels()` if no token is explicitly passed. --- ### `_setGetModels(getModels)` This method sets a custom function to fetch the list of models from an API. The function you provide will handle the process of fetching models and returning the necessary data. #### Parameters | Name | Type | Description | |------------|-------------|-------------------------------------------------------------------| | `getModels`| `Function` | The function that will fetch model data. It should return a promise with model information. | #### Example of Implementation ```js // Example of how to use _setGetModels aiSession._setGetModels( (apiKey, pageSize, pageToken) => { return new Promise((resolve, reject) => { // Simulate an API request to fetch model data fetch(`https://api.example.com/models?key=${apiKey}&pageSize=${pageSize}&pageToken=${pageToken}`) .then(response => response.json()) .then(result => { if (result.error) { reject("Error fetching models"); } else { // Simulate filtering and categorizing models const models = result.models.map(model => ({ id: model.id, name: model.name, category: model.category, version: model.version, })); resolve(models); } }) .catch(reject); }); } ); ``` #### Explanation: - **`getModels` function**: This function you define will handle fetching model data from an external source (like an API). It should accept parameters such as `apiKey`, `pageSize`, and `pageToken`, and return a promise with the result. - **Promise**: The `getModels` function uses a promise (`new Promise()`) to handle the asynchronous nature of an API call. It resolves with the model data or rejects with an error. #### What the function does: 1. **Sets the `getModels` function**: This lets you define the custom logic for retrieving the list of available models. You can fetch this data from an API and format it as needed. 2. **Simulate fetching model data**: The example shows how the `fetch` function can be used to get model data from an API and process it into a simpler format. 3. **Returns model data**: After fetching and processing the models, the function returns the formatted data, including details like `id`, `name`, `category`, and `version`. #### Returns | Type | Description | |-----------|-------------------------------------------------------------------| | `void` | This method does not return a value directly. It sets the function for fetching models. | --- ### `_insertNewModel(model)` Inserts a new model into the AI session's models list. If the model already exists, it will not be inserted again. #### Parameters | Name | Type | Description | |------------------------------------|---------------|--------------------------------------------------------------------------------------| | `model` | `Object` | The model to insert. | | `model.id` | `string` | The unique identifier for the model. | | `model.name` | `string` | The name of the model (optional). | | `model.displayName` | `string` | The display name of the model (optional). | | `model.version` | `string` | The version of the model (optional). | | `model.description` | `string` | A description of the model (optional). | | `model.inputTokenLimit` | `number` | The input token limit for the model (optional). | | `model.outputTokenLimit` | `number` | The output token limit for the model (optional). | | `model.temperature` | `number` | The temperature setting for the model (optional). | | `model.maxTemperature` | `number` | The maximum temperature setting for the model (optional). | | `model.topP` | `number` | The top P setting for the model (optional). | | `model.topK` | `number` | The top K setting for the model (optional). | | `model.supportedGenerationMethods` | `Array`| The generation methods supported by the model (optional). | | `model.category` | `Object` | The category of the model (optional). | | `model.category.id` | `string` | The unique identifier for the category. | | `model.category.displayName` | `string` | The display name of the category. | | `model.category.index` | `number` | The index of the category. | #### Returns | Type | Description | |----------|---------------------------------------------------------------------------| | `Object` | The inserted model data if the model was successfully added, or `null` if the model already exists. | #### Example ```js const newModel = { id: 'modelId123', name: 'AI Model 1', displayName: 'AI Model One', version: 'v1.0', description: 'An AI model designed for specific tasks.', inputTokenLimit: 1000, outputTokenLimit: 500, temperature: 0.7, maxTemperature: 1.0, topP: 0.9, topK: 50, supportedGenerationMethods: ['text', 'image'], category: { id: 'categoryId1', displayName: 'Category One', index: 0, }, }; const insertedModel = session._insertNewModel(newModel); console.log(insertedModel); ``` --- ### `_setCountTokens(countTokens)` Sets a function to handle the count of tokens in the AI session. If a valid function is provided, it will be used to count tokens. #### Parameters | Name | Type | Description | |--------------|-------------|----------------------------------------------------------------------------------------| | `countTokens`| `Function` | The function that will handle the token count. This function will be called to count tokens. | #### Throws | Error Type | Description | |------------|-----------------------------------------------------------------------------| | `Error` | Throws an error if the provided value is not a function. | #### Returns | Type | Description | |----------|-------------------------------------------------------------------------| | `void` | This function does not return a value. | #### Example ```js // Example of a token counting function const countTokens = (text) => { return text.split(' ').length; // Simple token count based on spaces }; // Set the function to count tokens session._setCountTokens(countTokens); // Now you can use the function to count tokens const tokens = session.#_countTokens("This is a sample sentence."); console.log(tokens); // Output: 5 ``` --- ### `_setCountTokens(countTokens)` This method sets a custom function to handle token counting for an AI session. The function you provide will be responsible for counting tokens, and it must return a promise that resolves with the token count data. #### Parameters | Name | Type | Description | |--------------|-------------|------------------------------------------------------------------------| | `countTokens`| `Function` | The function that will count the tokens. It should return a promise resolving with token count information. | #### Example of Implementation ```js // Example of how to use _setCountTokens aiSession._setCountTokens( (apiKey, model, requestData) => { return new Promise((resolve, reject) => { // Simulate an API request to count tokens const tokenData = { totalTokens: 1000, promptTokens: 500 }; // Example response data // If the API call is successful, resolve the data resolve(tokenData); // If something goes wrong, reject the promise with an error // reject("Error counting tokens"); }); } ); ``` #### Explanation: - **`countTokens` function**: This is a function that you define. It will count the tokens used in an AI session. It should take necessary parameters like `apiKey`, `model`, and `requestData` (or any other data needed) and return a promise. The promise should resolve with an object containing token count information. - **Promise**: The function you provide will use a promise (`new Promise()`) to simulate asynchronous behavior. This allows the AI session to wait for the token count result before continuing. #### What the function does: 1. **Sets the `countTokens` function**: It allows you to define how the token count is performed for the AI session. 2. **Asynchronous behavior**: The `countTokens` function can perform tasks such as making API calls to get token data, and it needs to return a promise that resolves with the result. 3. **Token Count Data**: The returned data can include things like the total number of tokens, prompt tokens, and any other token-related information. #### Returns | Type | Description | |----------|------------------------------------------------------------------------| | `void` | This method does not return any value directly. It just sets the function for token counting. | --- ### `_setErrorCodes(errors)` This method allows you to define custom error codes for a session. Error codes help in identifying the reasons behind specific failures or issues during the session. You can set a list of predefined error codes with descriptions, so the program knows how to handle or display them when needed. #### Parameters | Name | Type | Description | |--------|----------|-----------------------------------------------------------------------------| | `errors`| `Object` | An object containing key-value pairs where keys are error codes and values are error descriptions. | #### Example of Execution ```js // Set custom error codes for the session tinyAi._setErrorCodes({ FINISH_REASON_UNSPECIFIED: { text: 'Default value. This value is unused.' }, STOP: { text: 'Natural stop point of the model or provided stop sequence.', hide: true }, MAX_TOKENS: { text: 'The maximum number of tokens as specified in the request was reached.' }, SAFETY: { text: 'The response candidate content was flagged for safety reasons.' }, RECITATION: { text: 'The response candidate content was flagged for recitation reasons.' }, LANGUAGE: { text: 'The response candidate content was flagged for using an unsupported language.' }, OTHER: { text: 'Unknown reason.' }, BLOCKLIST: { text: 'Token generation stopped because the content contains forbidden terms.' }, PROHIBITED_CONTENT: { text: 'Token generation stopped for potentially containing prohibited content.' }, SPII: { text: 'Token generation stopped because the content potentially contains Sensitive Personally Identifiable Information (SPII).' }, MALFORMED_FUNCTION_CALL: { text: 'The function call generated by the model is invalid.' }, IMAGE_SAFETY: { text: 'Token generation stopped because generated images contain safety violations.' }, }); ``` #### Explanation: - **Error Codes Object**: This object contains multiple error codes (e.g., `STOP`, `MAX_TOKENS`, etc.) and their associated descriptions. These descriptions help explain the reason for the error. - **Setting Error Codes**: When you call `_setErrorCodes`, you're telling the system how to handle specific errors by defining them in a structured way. - **Error Details**: Each error code has a `text` field, which explains the reason for the error. Some error codes might also include other attributes, like `hide`, which can control whether the error is shown or not. #### Example Scenario: - If the model hits the maximum token limit, the error code `MAX_TOKENS` will be set, and the corresponding message `The maximum number of tokens as specified in the request was reached.` will be used to explain the issue. - The `STOP` error might be triggered when the model naturally reaches the end of its generation, or a stop sequence is provided. #### Returns | Type | Description | |-----------|-------------------------------------------------------------------| | `void` | This method does not return anything. It simply sets the error codes for use in the session. | --- ### Why It’s Useful: - **Clear Error Management**: This method ensures that all possible errors have clear definitions, so the program knows how to respond or display appropriate error messages. - **Easier Debugging**: By defining error codes with descriptions, developers can easily understand why an error occurred and how to handle it. --- ### `_setGenContent(callback)` This method allows you to define a custom callback function that will handle content generation. It's used to set up how the AI should generate or stream content when requested. #### Parameters | Name | Type | Description | |-----------|------------|-------------| | `callback`| `Function` | A function that will handle the content generation process. It will be passed several parameters and should return a `Promise`. | #### Simplified Explanation - This method accepts a function (callback) that takes care of generating content for the AI. - The function will be called during the AI session whenever content needs to be generated, and it should handle the logic for making a request to the API, processing the response, and sending the result back. #### Example of Usage: Here's a simplified example that shows how you can use `_setGenContent`: ```js tinyAi._setGenContent( (apiKey, isStream, data, model, streamingCallback, controller) => new Promise((resolve, reject) => { // Build the request body using the provided data const requestBody = requestBuilder(data); // Handle the response from the API const handleResponse = (result) => { const finalData = { _response: result }; if (!result.error) { finalData.contents = []; finalData.modelVersion = result.modelVersion || null; // Token Usage metadata const [tokenUsage] = buildUsageMetadata(result); finalData.tokenUsage = tokenUsage; // Process the content if (result.candidates) { for (const item of result.candidates) { if (item.content) { tinyAi.buildContents(finalData.contents, item.content, item.content.role); } } } } return finalData; }; // Handle streaming responses const streamingResponse = async (stream) => { try { const reader = stream.getReader(); const decoder = new TextDecoder('utf-8'); let done = false; while (!done) { const readerData = await reader.read(); const { value, done: streamDone } = readerData; done = streamDone; if (value) { const chunk = decoder.decode(value, { stream: true }); const cleanedJson = jsonrepair(chunk.trim()); const jsonChunk = JSON.parse(cleanedJson); for (const result of jsonChunk) { const tinyData = { contents: [] }; buildContent(result, tinyData); streamingCallback({ contents: tinyData.contents, done: false }); } } } // Complete the streaming streamingCallback({ done: true }); resolve(handleResponse(streamResult)); } catch (err) { reject(err); } }; // Make the API request fetch( `${apiUrl}/models/${model}:${!isStream ? 'generateContent' : 'streamGenerateContent'}?key=${encodeURIComponent(apiKey)}`, { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: JSON.stringify(requestBody), signal: controller ? controller.signal : undefined, } ) .then((res) => { if (!isStream) { res.json().then((result) => resolve(handleResponse(result))).catch(reject); } else { if (!res.body) reject(new Error('No AI streaming value found.')); else streamingResponse(res.body); } }) .catch(reject); }) ); ``` #### Key Parts of the Example: 1. **Request Body**: - The function begins by creating the request body using the provided `data` with a helper function (`requestBuilder`). 2. **Handling API Response**: - The `handleResponse` function processes the result from the API, building content and collecting metadata. 3. **Streaming**: - If the content is being streamed (e.g., large responses), the function handles the streaming process and sends data chunks to `streamingCallback` as they arrive. 4. **Error Handling**: - If something goes wrong (like a network error), it gets caught and handled in the `.catch()` block. #### What Happens After `_setGenContent` Is Called? - The AI system will use the custom callback you provided whenever it needs to generate or stream content. - Your callback is in charge of making the request to the AI API, processing the response, and passing the result back. --- ### Simplified Overview - **What It Does**: This method lets you define how the AI will generate or stream content. You set up a function (`callback`) to handle that process, from requesting data to processing the result. - **When to Use**: Use this method when you need to define the content generation behavior for your AI system, especially if you're dealing with custom API calls or need to handle streaming data.