LLM Function Calling API
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A LLM Function Calling API is an application programming interface (API) that enables stateless communication between LLM systems and external functions (to support tool operation and data retrieval capabilities).
- Context:
- It can (typically) perform Integration Functions through API interfaces:
- It can (typically) enable Tool Access through predefined function schemas.
- It can (typically) support External Execution through JSON parameter passing.
- It can (typically) implement Request-Response Pattern through HTTP communication.
- It can (typically) require Function Declaration through function schema definitions.
- It can (typically) facilitate Stateless Integration through discrete requests.
- It can (typically) process Function Selection through model reasoning.
- It can (typically) define a Function Calling API Component through:
- Schema Component for defining Function Signatures and Parameter Types
- Invocation Component for handling Function Selection and Parameter Extraction
- Response Component for processing Function Results and Error Handling
- It can (typically) implement Function Schema Definition through:
- Function Name Field for identifying API Operation
- Function Description Field for guiding Model Selection Process
- Parameter Object for specifying Input Requirements
- It can (typically) structure Parameter Objects through:
- Parameter Name for labeling Input Fields
- Parameter Type for enforcing Data Type Constraints
- Parameter Description for explaining Input Purpose
- Required Flag for indicating Mandatory Parameters
- It can (typically) support Data Types through:
- It can (typically) implement API Security through:
- It can (typically) handle Error Conditions through:
- It can (often) utilize Application Context through conversation history.
- It can (often) support Function Chaining through sequential invocations.
- It can (often) provide Developer SDKs through language-specific librarys.
- It can (often) implement Fallback Mechanisms through error recovery patterns.
- It can (often) generate Implementation Examples through code snippets.
- It can (often) request User Permission through consent interfaces.
- It can (often) facilitate Function Discovery through function library browsers.
- It can (often) manage Version Compatibility through API versioning.
- ...
- It can range from being a Simple Function Calling API to being a Complex Function Calling API, depending on its functional complexity.
- Simple Function Calling APIs typically support basic operations with limited parameters.
- Complex Function Calling APIs typically enable sophisticated workflows with nested parameter structures.
- It can range from being a Model-Selective Function Calling API to being a Developer-Selective Function Calling API, depending on its selection mechanism.
- Model-Selective Function Calling APIs typically allow LLM systems to choose appropriate functions based on user intent.
- Developer-Selective Function Calling APIs typically require application developers to route requests to specific functions.
- It can range from being a Single-Turn Function Calling API to being a Multi-Turn Function Calling API, depending on its interaction pattern.
- Single-Turn Function Calling APIs typically complete function execution within one request-response cycle.
- Multi-Turn Function Calling APIs typically support conversation continuation after function result presentation.
- ...
- It can provide Function Type capabilities through:
- Data Retrieval Functions, such as:
- Computational Functions, such as:
- Action Functions, such as:
- It can implement Integration Patterns through:
- It can maintain State Management Approaches through:
- ...
- Examples:
- Function Calling API Providers, such as:
- OpenAI Function Calling API for ChatGPT integration, featuring:
- Google AI Function Calling API for Gemini models, featuring:
- Anthropic Function Calling API for Claude models, featuring:
- Function Calling API Implementations, such as:
- Function Calling API Use Cases, such as:
- ...
- Function Calling API Providers, such as:
- Counter-Example(s):
- Anthropic Model Context Protocol (MCP), which provides stateful awareness and dynamic tool discovery.
- Prompt Engineering Techniques, which lack standardized function interfaces.
- Raw API Integrations, which require custom parsing logic.
- Hardcoded Response Patterns, which lack dynamic execution capability.
- WebSocket Protocols, which implement persistent connections rather than stateless requests.
- See: Integration Protocol, LLM System, API Standard, Tool Integration, Parameter Schema, JSON Schema, Anthropic Model Context Protocol (MCP), LLM Tool Use, Stateless Protocol.