langchain

Summary for handler.go

This code package provides a set of functions and methods for handling various aspects of a language model (LLM) chain, including agent actions, chain completion, and LLM errors. It also includes a ChainCallbackHandler struct that implements callbacks for different events in the chain, such as the start and end of a chain, as well as the generation of content by the LLM.

The package utilizes a database to store user information and usage statistics. It also includes a function to log the content of an LLM response, including the choice made by the model, the reason for stopping, and the generation information. This information is used to update the user's usage statistics and store them in the database.

In addition to the ChainCallbackHandler, the package provides methods for handling various events related to the LLM, such as the start and end of a tool, as well as the generation of content by the LLM. These methods allow for customization and control over the behavior of the LLM chain.

Overall, this code package provides a comprehensive set of tools for managing and interacting with an LLM chain, including handling callbacks, logging responses, and updating user usage statistics.

Summary for langchain.go

This code package provides a set of functions for interacting with language models, specifically OpenAI's API and a local AI model. It includes functions for initializing new conversations, continuing existing conversations, and generating content from single prompts.

The package utilizes the LangChain library to manage conversations and memory, allowing for context-aware interactions. It also provides a function to generate content from a single prompt, which can be used for tasks such as text generation or code completion.

The code package supports both OpenAI's API and a local AI model, allowing users to choose their preferred method of interaction. It also includes error handling and logging to ensure smooth operation.

In summary, this code package offers a comprehensive set of tools for interacting with language models, enabling users to create and manage conversations, generate content, and leverage the power of AI for various tasks.

Summary for setupSequenceWithKey.go

This code package provides a framework for managing user interactions with an AI model, specifically focusing on language-based tasks. It utilizes a database to store user information, including their AI session details and dialog status. The package also includes functions for setting up a sequence of interactions with the AI, handling language selection, and managing the dialog thread.

The package uses a mutex to ensure thread safety when accessing shared resources. It also defines a context key for storing user information within the context. The SetupSequenceWithKey function initializes a new interaction sequence, taking into account the user's language preference, AI session details, and the AI endpoint.

The tryLanguage function handles language selection and initiates a conversation with the AI model. It takes the user's language preference, language code, context, and AI endpoint as input. Based on the language code, it constructs a language prompt and initiates a new chat session with the AI model. The function returns the AI's response, the dialog thread, and any errors encountered during the process.

In summary, this code package provides a comprehensive solution for managing user interactions with an AI model, including language selection, dialog management, and error handling. It leverages a database to store user information and ensures thread safety through the use of a mutex. The package also includes functions for setting up a sequence of interactions with the AI and handling language selection.

Summary for startDialogSequence.go

This code package provides a function called StartDialogSequence that initiates a conversation with an AI model. It takes a Telegram bot, chat ID, prompt, context, and AI endpoint as input. The function retrieves the user's AI session data from the database, including the GPT model and dialog thread. It then uses the provided prompt to continue the conversation with the AI model, obtaining a response and updating the dialog thread. The response is sent to the user via Telegram, and the user's dialog status and usage are updated in the database.

In addition to the StartDialogSequence function, there's an error handling function called errorMessage that handles errors during the process. It logs the error, sends an error message to the user, and removes the user from the database. The package also includes a helper function to send a random video from a media directory to the user in case of an error.

The code package is designed to handle user interactions with an AI model, manage dialog threads, and update user data in a database. It utilizes a mutex to ensure thread safety when accessing shared resources. The package also includes error handling and recovery mechanisms to ensure smooth operation.

Package Summary

This code package provides a comprehensive set of tools for managing and interacting with an LLM chain, including handling callbacks, logging responses, and updating user usage statistics. It also includes functions for initializing new conversations, continuing existing conversations, and generating content from single prompts. The package supports both OpenAI's API and a local AI model, allowing users to choose their preferred method of interaction.

The package utilizes a database to store user information, including their AI session details and dialog status. It also includes functions for setting up a sequence of interactions with the AI, handling language selection, and managing the dialog thread. The package uses a mutex to ensure thread safety when accessing shared resources.

In addition to the ChainCallbackHandler, the package provides methods for handling various events related to the LLM, such as the start and end of a tool, as well as the generation of content by the LLM. These methods allow for customization and control over the behavior of the LLM chain.

The code package also includes a function called StartDialogSequence that initiates a conversation with an AI model. It takes a Telegram bot, chat ID, prompt, context, and AI endpoint as input. The function retrieves the user's AI session data from the database, including the GPT model and dialog thread. It then uses the provided prompt to continue the conversation with the AI model, obtaining a response and updating the dialog thread. The response is sent to the user via Telegram, and the user's dialog status and usage are updated in the database.

In summary, this code package provides a comprehensive solution for managing user interactions with an AI model, including language selection, dialog management, and error handling. It leverages a database to store user information and ensures thread safety through the use of a mutex. The package also includes functions for setting up a sequence of interactions with the AI and handling language selection.