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Versatile node leveraging local Large Language Models for AI tasks, enabling prompt-based responses without external servers.
AnyNodeLocal is a versatile node designed to leverage local Large Language Models (LLMs) for various AI-driven tasks. This node allows you to input a prompt and receive a response generated by a local LLM, making it ideal for scenarios where you prefer or require local processing over cloud-based solutions. The primary benefit of AnyNodeLocal is its ability to operate independently of external servers, ensuring data privacy and reducing latency. It is particularly useful for generating text, performing calculations, or any other task that can be handled by a local LLM. The node is designed to be user-friendly, allowing you to easily configure and utilize the local model for your specific needs.
The prompt
parameter is a string input that serves as the main instruction or query for the local LLM. This parameter supports multiline text, allowing you to provide detailed and complex prompts. The default value is "Take the input and multiply by 5". The prompt directly influences the output generated by the model, so crafting a clear and specific prompt is crucial for obtaining the desired results.
The model
parameter specifies the name of the local LLM to be used. The default value is "mistral". This parameter allows you to select from different available models, each with its own strengths and capabilities. Choosing the appropriate model can significantly impact the quality and relevance of the generated output.
The server
parameter is a string that defines the server address where the local LLM is hosted. The default value is "http://localhost:11434". This parameter is essential for directing the node to the correct server for processing the prompt. Ensure that the server address is correctly configured to avoid connectivity issues.
The any
parameter is an optional input that can accept any type of data. This flexibility allows you to pass additional context or data to the local LLM, which can be used to enhance the response. The specific impact of this parameter depends on the prompt and the model's capabilities.
Similar to the any
parameter, any2
is another optional input that can accept any type of data. It provides an additional layer of flexibility for passing multiple pieces of context or data to the local LLM. The use of this parameter can further refine and improve the generated output.
The api_key
parameter is an optional string input used for authentication purposes. The default value is "ollama". This parameter is necessary if the local LLM server requires an API key for access. Ensure that the correct API key is provided to avoid authentication errors.
The unique_id
parameter is a hidden input used internally by the node. It is not intended for manual configuration and is automatically managed by the system.
The extra_pnginfo
parameter is another hidden input used internally by the node. Similar to unique_id
, it is not intended for manual configuration and is automatically managed by the system.
The DICT
output parameter provides the response generated by the local LLM in a dictionary format. This output includes the processed prompt and any additional information or data generated by the model. The dictionary format allows for easy parsing and utilization of the response in subsequent nodes or processes.
server
parameter is correctly configured to point to the local LLM server to avoid connectivity issues.any
and any2
parameters to pass additional context or data that can enhance the quality of the generated output.model
based on the task requirements to leverage the strengths of different local LLMs.server
parameter is correctly configured and that the local LLM server is running and accessible.api_key
is provided and that it matches the requirements of the local LLM server.model
parameter to ensure that the correct model name is specified and that the model is available on the server.prompt
parameter for any syntax errors or unsupported characters and ensure it is correctly formatted.© Copyright 2024 RunComfy. All Rights Reserved.