Day 4 - LLM - Tool Calling
Tool Calling Asking some query to the LLM. LLM is like a phone book , it can't call on its own. With some tools only it will. Tools are nothing but FUNCTIONS. Tool calling is also called as FUNCTION Calling. Its a method , which models reliably connect and interact with external tools like API , database or knowledge base. LLM gets set of tools and it decides which tools needs to be invoked for a…
Day 4 of the LLM (large language model) development focuses on tool calling, a method that allows models to reliably interact with external tools such as APIs, databases, or knowledge bases. Tools are essentially functions that the model can invoke to complete a specific user query or task.
Tool calling is also referred to as function calling. The model receives a set of tools and determines which ones are necessary for a given query. This represents a significant advancement in LLM capabilities, often built upon a more foundational approach known as Multi-Modal Conversations (MCP).
In the Langchain framework, a tool is akin to a method decorated with a special annotation, denoted by the @tool decorator. This decorator signifies that the method can be called by the LLM. A tool_map is a dictionary that maps tools to their respective functions.
Langchain integrates with Groq, a platform that provides access to various large language models, such as llama3 or gpt-oss120 billion. To utilize these models, an API key must be created. The Groq client, a software component, facilitates communication between the Langchain LLM and these cloud-based language models.
Implementing tool calling offers several advantages, including real-time data access, reduced hallucinations (instances where the model generates incorrect or nonsensical information), and an expansion of the LLM's functionality. However, there are also drawbacks to consider, such as increased token consumption and costs, potential security vulnerabilities, and risks related to model safety.
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