Fake LLM
Overview
This documentation provides an overview of the fake
package, which offers a simulated implementation of a Language Learning Model (LLM) for testing purposes in Go applications.
Installation
To use the fake
package, import it into your Go project:
go get "github.com/tmc/langchaingo"
Prerequisites
Ensure you have Go programming language installed on your machine (version 1.15 or higher recommended).
Example Usage
Here is an example demonstrating how to use the fake package:
package main
import (
"context"
"fmt"
"log"
"github.com/tmc/langchaingo/llms/fake"
)
func main() {
// Creating a fake LLM with initial responses.
responses := []string{
"Hello!",
"How are you?",
"I'm fine, thanks.",
}
llm := fake.NewFakeLLM(responses)
// Calling the fake LLM with a prompt.
ctx := context.Background()
response, err := llm.Call(ctx, "Hi there!")
if err != nil {
log.Printf("Error calling LLM: %v\n", err)
} else {
fmt.Println("LLM Response:", response)
}
// Adding a new response and testing again.
llm.AddResponse("Goodbye!")
response, err = llm.Call(ctx, "See you later!")
if err != nil {
log.Printf("Error calling LLM: %v\n", err)
} else {
fmt.Println("LLM Response:", response)
}
}
API Reference
Constructor
func NewFakeLLM(responses []string) *LLM
Creates a new instance of the fake LLM with the provided responses.
Methods
Call
func (f *LLM) Call(ctx context.Context, prompt string, options ...llms.CallOption) (string, error)
Simulates calling the model with a specific prompt and returns a predefined response. Supports all standard LLM options like WithTemperature
, WithMaxTokens
, etc.
GenerateContent
func (f *LLM) GenerateContent(ctx context.Context, messages []llms.MessageContent, options ...llms.CallOption) (*llms.ContentResponse, error)
Simulates generating content from message sequences. This is the modern interface that supports multi-modal inputs.
Reset
func (f *LLM) Reset()
Resets the internal response index, allowing responses to cycle through from the beginning again.
AddResponse
func (f *LLM) AddResponse(response string)
Adds a new response to the list of possible responses of the fake LLM.
Purpose
The fake package is designed to facilitate testing of applications that interact with language learning models, without relying on real model implementations. It helps validate application logic and behavior in a controlled environment.