llama

This code defines an asynchronous function askLlamaMatchingFunction that searches for matching functions based on a given query by utilizing imported functions and caching cell and RPC function data. The function stores, filters, and queries LLM functions to return an array of matching functions.

This function, askLlamaAboutCategories, queries the Llama language model about categories related to a given query and returns a list of notebook filenames containing matching categories.

The askLlamaGeneralizeCategories function generalizes a list of categories by iteratively refining the list with the LLM until a satisfactory result is obtained. It returns a refined list of categories and updates the function cache if necessary.

The code defines two functions: askLlamaToGeneralize and askLlamaToGeneralizeAll, which use a large language model to generalize lists of categories into shorter lists. The askLlamaToGeneralizeAll function batches the categories and calls askLlamaToGeneralize for each batch, then filters out duplicates from the results.

The askLlamaAboutFunctions function is an asynchronous query that matches a given input with a function listed in an array, providing an optional description and categorization. It constructs a query string, sends it to a Large Language Model, and returns the matched function name.

The askLlamaAboutCode function takes a string of code, uses an LLM to provide a short breakdown, and logs the prompt and response. It limits input code to 2048 characters and trims the LLM response.

The code provides a module that exports a function askLlamaAboutNotebooks which asks the LLaMA language model about functions in notebook files within a directory and its subdirectories. The function breaks down the query into chunks of 20 and sends them to the model for response.

The provided code defines three asynchronous functions: askLlamaToSummerize, askLlamaToGeneralize, and askLlamaToImplement, which interact with a Large Language Model (LLM) to perform tasks such as summarizing queries and improving code snippets. These functions are exported as an object and can be used in other parts of the application.

The code imports functions from modules, defines a storeLlamaFunction to store metadata in a functionCache object, and generates and updates code in a cacheCell to export the metadata. The storeLlamaFunction is then exported as a module.

This JavaScript code imports functions from other modules to interact with a large language model (LLM) for code summarization and caching, then iterates through a cache of cells to retrieve and store the cached data using these LLM functions.

The code imports necessary modules, defines constants and variables, and creates LLaMA-related objects for initializing and interacting with a large language model. It includes two main functions: createSession for creating a new model instance and initSession for initializing a new chat session with the model.