### LangChainGo: Getting Started with Ollama

Source: https://tmc.github.io/langchaingo/docs/index

This guide provides instructions on how to get started with LangChainGo by setting up and integrating with Ollama, a local LLM runner. It's a quick setup for beginners to start building applications.

```Go
package main

import (
	"context"
	"fmt"
	"log"

	"github.com/langchain-ai/langchain-go/llms/ollama"
)

func main() {
	ctx := context.Background()

	// Initialize Ollama with a model (e.g., "llama2")
	llm, err := ollama.New(
		ollama.WithModel("llama2"),
	)

	if err != nil {
		log.Fatalf("Failed to create Ollama client: %v", err)
	}

	// Generate content
	resp, err := llm.GenerateContent(ctx, "What is LangChainGo?")
	if err != nil {
		log.Fatalf("Failed to generate content: %v", err)
	}

	fmt.Println(resp.Choices[0].Message.Content)
}

```

--------------------------------

### LangChainGo: Getting Started with OpenAI

Source: https://tmc.github.io/langchaingo/docs/index

This guide explains how to set up and begin using LangChainGo with OpenAI's language models. It covers the initial configuration for integrating with the OpenAI API.

```Go
package main

import (
	"context"
	"fmt"
	"log"

	"github.com/langchain-ai/langchain-go/llms/openai"
)

func main() {
	ctx := context.Background()

	// Initialize OpenAI with an API key and model (e.g., "gpt-3.5-turbo")
	// Ensure OPENAI_API_KEY environment variable is set
	llm, err := openai.New(
		openai.WithModel("gpt-3.5-turbo"),
	)

	if err != nil {
		log.Fatalf("Failed to create OpenAI client: %v", err)
	}

	// Generate content
	resp, err := llm.GenerateContent(ctx, "What is LangChainGo?")
	if err != nil {
		log.Fatalf("Failed to generate content: %v", err)
	}

	fmt.Println(resp.Choices[0].Message.Content)
}

```

--------------------------------

### LangChainGo How-to Guide Template

Source: https://tmc.github.io/langchaingo/docs/contributing/documentation

This is a template for writing how-to guides in LangChainGo, focusing on solving specific problems. It includes sections for the problem statement, solution overview, implementation with focused code examples, and considerations for performance, security, and alternatives.

```Go
# How to [Specific Task]  
  
## Problem  
  
[Clear description of the problem being solved]  
  
## Solution  
  
[Brief overview of the approach]  
  
## Implementation  
  
```go  
// Focused code example  

```

## Considerations​
  * [Performance implications]
  * [Security considerations]
  * [Alternative approaches]


## Related guides​
  * [Link to related how-tos]

```

--------------------------------

### LangChainGo Runnable Code Example

Source: https://tmc.github.io/langchaingo/docs/contributing/documentation

This Go code demonstrates how to initialize an LLM client using the LangChainGo library, specifically for OpenAI. It includes proper error handling and is a complete, runnable example as required for tutorials.

```Go
package main  
  
import (  
    "context"
    "fmt"
    "log"
  
    "github.com/tmc/langchaingo/llms/openai"
)
  
func main() {
    llm, err := openai.New()
    if err != nil {
        log.Fatal(err)
    }
    // ... rest of example
}

```

--------------------------------

### Generate Package Documentation Example (Go)

Source: https://tmc.github.io/langchaingo/docs/tutorials/smart-documentation

Generates a realistic Go code example for a given package, demonstrating import, basic usage, error handling, and a realistic use case. It uses a prompt template to guide the LLM.

```go
func(dg *DocGenerator)generatePackageExample(ctx context.Context, pkg *PackageInfo)(string,error){
    template := prompts.NewPromptTemplate(`
Create a realistic Go code example showing how to use this package:

Package: {{.name}}
Description: {{.description}}
Key Functions: {{range .functions}}{{if .is_exported}}{{.name}}, {{end}}{{end}}
Key Types: {{range .types}}{{if .is_exported}}{{.name}}, {{end}}{{end}}

Write a complete, runnable example that shows:
1. Import statement
2. Basic usage
3. Error handling
4. Realistic use case

Return only the Go code, no explanations.`, 
[]string{"name","description","functions","types"})
  
    prompt, err := template.Format(map[string]any{
"name":        pkg.Name,
"description": pkg.Description,
"functions":   pkg.Functions,
"types":       pkg.Types,
})
if err !=nil{
return"", err
}
  
    response, err := dg.llm.GenerateContent(ctx,[]llms.MessageContent{
        llms.TextParts(llms.ChatMessageTypeHuman, prompt),
})
if err !=nil{
return"", err
}
  
return strings.TrimSpace(response.Choices[0].Content),nil
}
```

--------------------------------

### LangChainGo Tutorial Template

Source: https://tmc.github.io/langchaingo/docs/contributing/documentation

This is a template for creating tutorials in LangChainGo. It includes sections for the title, a brief description, what the user will build, prerequisites, and step-by-step implementation guidance with runnable code examples.

```Go
# Building [What You're Building]  
  
[One sentence description of what the reader will build]  
  
## What you'll build  
  
A [type of application] that:  
- [Feature 1]  
- [Feature 2]  
- [Feature 3]  
  
## Prerequisites  
  
- Go 1.21+  
- [Required API keys]  
- [Other requirements]  
  
## Step 1: [First Task]  
  
[Brief explanation of what this step accomplishes]  
  
```go  
// Complete, runnable code  

```

## Step 2: [next task]​
[Continue with progressive steps...]## Running the application​
```
# Clear commands to run  

```

## Next steps​
  * [Potential improvements]
  * [Related tutorials]

```

--------------------------------

### Run LangChainGo OpenAI Completion Example

Source: https://tmc.github.io/langchaingo/docs/getting-started/guide-openai

Executes a Go program that uses LangChainGo to get a text completion from OpenAI's models. This requires the OpenAI API key to be set as an environment variable.

```go
package main  
  
import(
"context"
"fmt"
"log"
  
"github.com/tmc/langchaingo/llms"
"github.com/tmc/langchaingo/llms/openai"
)
  
funcmain(){
	llm, err := openai.New()
if err !=nil{
		log.Fatal(err)
}
	ctx := context.Background()
	completion, err := llms.GenerateFromSinglePrompt(ctx,
		llm,
"The first man to walk on the moon",
		llms.WithTemperature(0.8),
		llms.WithStopWords([]string{"Armstrong"}),
)
if err !=nil{
		log.Fatal(err)
}

	fmt.Println("The first man to walk on the moon:")
	fmt.Println(completion)
}


```

--------------------------------

### Extract Code Examples from Documentation

Source: https://tmc.github.io/langchaingo/docs/tutorials/smart-documentation

Parses a documentation string to extract code examples. It identifies blocks starting with 'Example:', 'Usage:', or '```go' and captures indented lines until a closing '```' or an empty line is encountered.

```go
func(ca *CodeAnalyzer) extractExamples(doc string) []string {
	// Extract code examples from documentation
	var examples []string
	lines := strings.Split(doc, "\n")

	var inExample bool
	var currentExample strings.Builder

	for _, line := range lines {
		rimmed := strings.TrimSpace(line)

		if strings.HasPrefix(trimmed, "Example:") ||
			strings.HasPrefix(trimmed, "Usage:") ||
			strings.Contains(trimmed, "```go") {
			inExample = true
			currentExample.Reset()
			continue
		}

		if inExample {
			if strings.Contains(trimmed, "```") ||
				(trimmed == "" && currentExample.Len() > 0) {
				if currentExample.Len() > 0 {
					examples = append(examples, currentExample.String())
					currentExample.Reset()
				}
				inExample = false
				continue
			}

			if strings.HasPrefix(line, "    ") || strings.HasPrefix(line, "\t") {
				currentExample.WriteString(strings.TrimPrefix(strings.TrimPrefix(line, "    "), "\t"))
				currentExample.WriteString("\n")
			}
		}
	}

	return examples
}
```

--------------------------------

### Create and Run LangChainGo Agent Executor

Source: https://tmc.github.io/langchaingo/docs/modules/agents/executor/getting-started

This Go code demonstrates how to create and run an agent executor using LangChainGo. It initializes an OpenAI LLM, a SerpAPI tool, and a Calculator tool. It then creates a one-shot agent and an executor, and runs a question through the executor.

```go
package main

import(
"context"
"fmt"
"os"

"github.com/tmc/langchaingo/agents"
"github.com/tmc/langchaingo/chains"
"github.com/tmc/langchaingo/llms/openai"
"github.com/tmc/langchaingo/tools"
"github.com/tmc/langchaingo/tools/serpapi"
)

funcmain(){
if err :=run(); err !=nil{
		fmt.Fprintln(os.Stderr, err)
		os.Exit(1)
}
}

funcrun()error{
	llm, err := openai.New()
if err !=nil{
return err
}
	search, err := serpapi.New()
if err !=nil{
return err
}
	agentTools :=[]tools.Tool{
		tools.Calculator{},
		search,
}

	agent := agents.NewOneShotAgent(llm,
		agentTools,
		agents.WithMaxIterations(3))
	executor := agents.NewExecutor(agent)

	question :="Who is Olivia Wilde's boyfriend? What is his current age raised to the 0.23 power?"
	answer, err := chains.Run(context.Background(), executor, question)
	fmt.Println(answer)
return err
}

```

--------------------------------

### Build Documentation Locally

Source: https://tmc.github.io/langchaingo/docs/contributing/documentation

Commands to build and start a local development server for the documentation using npm. This allows for live previewing of changes.

```shell
cd docs
npminstall
npm run start
```

--------------------------------

### Manage Package Examples (Go)

Source: https://tmc.github.io/langchaingo/docs/tutorials/smart-documentation

Manages the generation and appending of examples for Go packages. If no examples exist, it calls `generatePackageExample`. It also iterates through functions to generate examples for exported functions that lack them.

```go
func(dg *DocGenerator)generateExamples(pkg *PackageInfo)error{
    ctx := context.Background()

// Generate package-level usage example
iflen(pkg.Examples)==0{
        example, err := dg.generatePackageExample(ctx, pkg)
if err ==nil&& example !=""{
            pkg.Examples =append(pkg.Examples, ExampleInfo{
                Name:"Basic Usage",
                Code: example,
                Doc:"Basic usage example",
})
}
}

// Generate function examples
for i :=range pkg.Functions {
iflen(pkg.Functions[i].Examples)==0&& pkg.Functions[i].IsExported {
            example, err := dg.generateFunctionExample(ctx,&pkg.Functions[i], pkg)
if err ==nil&& example !=""{
                pkg.Functions[i].Examples = append(pkg.Functions[i].Examples, example)
}
}
}

returnnil
}
```

--------------------------------

### Run LangChainGo Ollama completion example

Source: https://tmc.github.io/langchaingo/docs/getting-started/guide-ollama

This command executes a Go program that uses LangChainGo to interact with a locally running Ollama instance for text completion.

```Shell
go run github.com/tmc/langchaingo/examples/ollama-completion-example@main  

```

--------------------------------

### Configure LLM Providers in LangChainGo

Source: https://tmc.github.io/langchaingo/docs/how-to

This guide explains how to configure different Language Model (LLM) providers within your LangChainGo projects. It covers the basic setup required to connect to various LLM services.

```Go
package main

import (
	"context"
	"fmt"
	"github.com/tmc/langchaingo/llms"
	"github.com/tmc/langchaingo/llms/openai"
)

func main() {
	ctx := context.Background()
	llm, err := openai.New(
		openai.WithModel("gpt-3.5-turbo"),
	)
	if err != nil {
		panic(err)
	}

	content, err := llm.Call(ctx, "What is LangChainGo?", nil)
	if err != nil {
		panic(err)
	}

	fmt.Println(content)
}
```

--------------------------------

### Load Documentation Templates

Source: https://tmc.github.io/langchaingo/docs/tutorials/smart-documentation

Loads various documentation templates, including a package documentation template with sections for name, description, installation, usage (with examples), and API reference (functions).

```go
func (dg *DocGenerator) loadTemplates() error {
	// Package documentation template
	packageTmpl := `# {{.Name}}  
  
{{.Description}}  
  
## Installation  
  
'''bash  
go get {{.Path}}  
'''  
  
## Usage  
  
{{if .Examples}}  
{{range .Examples}}  
'''go  
{{.Code}}  
'''  
{{end}}  
{{end}}  
  
## API Reference  
  
{{if .Functions}}  
### Functions  
  
{{range .Functions}}  
{{if .IsExported}}  

```

--------------------------------

### LangChainGo Project Setup

Source: https://tmc.github.io/langchaingo/docs/tutorials/smart-documentation

This snippet demonstrates the initial setup for a Go project using LangChainGo to build a smart documentation generator. It includes creating a project directory, initializing Go modules, and fetching necessary dependencies like LangChainGo and Go analysis tools.

```Go
mkdir smart-docs
cd smart-docs
go mod init smart-docs
go get github.com/tmc/langchaingo
go get golang.org/x/tools/go/packages
go get golang.org/x/tools/go/ast/astutil
```

--------------------------------

### Go Function Examples

Source: https://tmc.github.io/langchaingo/docs/tutorials/smart-documentation

Illustrates the usage of Go functions with practical code examples. This section is rendered only if examples are provided for the function.

```go
{{if .Examples}}  
**Example:**  
{{range .Examples}}  
'''go  
{{.}}  
'''  
{{end}}  
{{end}}
```

--------------------------------

### Configure SerpAPI Key

Source: https://tmc.github.io/langchaingo/docs/modules/agents/executor/getting-started

This snippet shows how to set the SerpAPI environment variable in a .env file, which is required for using the SerpAPI tool within LangChainGo.

```shell
SERPAPI_API_KEY="..."
```

--------------------------------

### Run LangChainGo Mistral Completion Example

Source: https://tmc.github.io/langchaingo/docs/getting-started/guide-mistral

Executes a Go program that uses LangChainGo to interact with the Mistral API for text completion. This command assumes the Go environment is set up and the Mistral API key is configured.

```bash
go run github.com/tmc/langchaingo/examples/mistral-completion-example@main
```

--------------------------------

### Generate Function Documentation Example (Go)

Source: https://tmc.github.io/langchaingo/docs/tutorials/smart-documentation

Generates a Go code example for a specific function, including its signature and parameters. The example demonstrates how to call the function realistically and includes error handling if necessary. It relies on a prompt template for LLM guidance.

```go
func(dg *DocGenerator)generateFunctionExample(ctx context.Context, fn *FunctionInfo, pkg *PackageInfo)(string,error){
    template := prompts.NewPromptTemplate(`
Create a Go code example for this function:

Function: {{.name}}
Signature: {{.signature}}
Package: {{.package}}
{{if .parameters}}Parameters: {{range .parameters}}{{.name}} {{.type}}, {{end}}{{end}}

Write a realistic example showing how to call this function.
Include proper error handling if needed.
Return only the Go code snippet.`, 
[]string{"name","signature","package","parameters"})
  
    prompt, err := template.Format(map[string]any{
"name":       fn.Name,
"signature":  fn.Signature,
"package":    pkg.Name,
"parameters": fn.Parameters,
})
if err !=nil{
return"", err
}
  
    response, err := dg.llm.GenerateContent(ctx,[]llms.MessageContent{
        llms.TextParts(llms.ChatMessageTypeHuman, prompt),
})
if err !=nil{
return"", err
}
  
return strings.TrimSpace(response.Choices[0].Content),nil
}
```

--------------------------------

### Implement Few-Shot Prompting in LangChainGo

Source: https://tmc.github.io/langchaingo/docs/how-to

Learn how to implement few-shot prompting techniques in LangChainGo to improve LLM performance by providing examples within the prompt.

```Go
package main

import (
	"fmt"

	"github.com/tmc/langchaingo/prompts"
)

func main() {
	// Define a template with examples (few-shot)
	template := """
Convert the following movie titles into emoji.

Back to the Future: 👨‍🚀🚗💨
Batman: The Dark Knight: 🦇🌃
{{.movie_title}}: ""
	"

	// Create a PromptTemplate object
	promptTemplate, err := prompts.NewPromptTemplate(template, []string{"movie_title"})
	if err != nil {
		panic(err)
	}

	// Format the prompt with a new movie title
	formattedPrompt, err := promptTemplate.Format(map[string]interface{}{"movie_title": "The Matrix"})
	if err != nil {
		panic(err)
	}

	fmt.Println(formattedPrompt)
}
```

--------------------------------

### LangChainGo Ollama completion example in Go

Source: https://tmc.github.io/langchaingo/docs/getting-started/guide-ollama

This Go program demonstrates how to use LangChainGo with Ollama to generate text completions. It initializes the Ollama LLM with a specified model and uses a streaming function to print the output.

```Go
package main  
  
import(
"context"
"fmt"
"log"

"github.com/tmc/langchaingo/llms"
"github.com/tmc/langchaingo/llms/ollama"
)

funcmain(){
	llm, err := ollama.New(ollama.WithModel("llama2"))  
if err !=nil{
		log.Fatal(err)
}
	ctx := context.Background()
	completion, err := llms.GenerateFromSinglePrompt(
		ctx,
		llm,
"Human: Who was the first man to walk on the moon?\nAssistant:",
		llms.WithTemperature(0.8),
		llms.WithStreamingFunc(func(ctx context.Context, chunk []byte)error{
			fmt.Print(string(chunk))
returnnil
}),
)
if err !=nil{
		log.Fatal(err)
}
  
_= completion  
}


```

--------------------------------

### Project Setup for AI Code Reviewer in Go

Source: https://tmc.github.io/langchaingo/docs/tutorials/code-reviewer

This snippet demonstrates the initial project setup for building an AI code reviewer using Go and LangChainGo. It includes creating a project directory, initializing Go modules, and fetching the LangChainGo library.

```Go
mkdir ai-code-reviewer
cd ai-code-reviewer
go mod init code-reviewer
go get github.com/tmc/langchaingo

```

--------------------------------

### LangChainGo Project Setup

Source: https://tmc.github.io/langchaingo/docs/tutorials/basic-chat-app

Commands to set up a new Go project and add LangChainGo as a dependency for building a chat application.

```bash
mkdir langchain-chat-app
cd langchain-chat-app
go mod init chat-app

go get github.com/tmc/langchaingo
```

--------------------------------

### Install LangChainGo

Source: https://tmc.github.io/langchaingo/docs/modules/model_io/models/llms/Integrations/groq

Command to install the LangChainGo library into your Go project. This is the first step to using LangChainGo functionalities.

```bash
go get github.com/tmc/langchaingo
```

--------------------------------

### LangChainGo Agents Conceptual Guide and How-to

Source: https://tmc.github.io/langchaingo/docs/modules/agents/agents

This entry covers the conceptual aspects of Agents in LangChainGo, along with practical guidance on their implementation, as found in the How-to Guides.

```Go
## 📄️ Agents
Conceptual Guide • How-to Guides

```

--------------------------------

### Go Text Splitting Example

Source: https://tmc.github.io/langchaingo/docs/modules/data_connection/text_splitters/examples

Demonstrates how to use LangChainGo's text splitting capabilities. This example loads a text file, configures a RecursiveCharacter splitter with specified chunk size and overlap, and then splits the document accordingly. It handles file opening and potential errors during loading and splitting.

```Go
func main() {
	func textToSplit() []schema.Document {

		f, err := os.Open("./splitters/docs/transcript.txt")
		if err != nil {
			fmt.Println("Error opening file: ", err)
		}

		p := documentloaders.NewText(f)

		split := textsplitter.NewRecursiveCharacter()
		split.ChunkSize = 300 // size of the chunk is number of characters
		split.ChunkOverlap = 30 // overlap is the number of characters that the chunks overlap
		docs, err := p.LoadAndSplit(context.Background(), split)

		if err != nil {
			fmt.Println("Error loading document: ", err)
		}

		log.Println("Document loaded: ", len(docs))
	}

}
```

--------------------------------

### Create Dynamic Prompt Templates in LangChainGo

Source: https://tmc.github.io/langchaingo/docs/how-to

This guide explains how to create dynamic prompt templates in LangChainGo, allowing for flexible and context-aware prompt generation for LLM interactions.

```Go
package main

import (
	"fmt"

	"github.com/tmc/langchaingo/prompts"
)

func main() {
	// Define a template string with variables
	template := "Translate the following text from {{.source_lang}} to {{.target_lang}}: {{.text_to_translate}}"

	// Create a PromptTemplate object
	promptTemplate, err := prompts.NewPromptTemplate(template, []string{"source_lang", "target_lang", "text_to_translate"})
	if err != nil {
		panic(err)
	}

	// Define the variables to fill the template
	sinputVariables := map[string]interface{}{
		"source_lang":      "English",
		"target_lang":      "French",
		"text_to_translate": "Hello, how are you?",
	}

	// Format the prompt using the variables
	formattedPrompt, err := promptTemplate.Format(**inputVariables)
	if err != nil {
		panic(err)
	}

	fmt.Println(formattedPrompt)
}
```

--------------------------------

### LangChainGo Text Splitter Examples

Source: https://tmc.github.io/langchaingo/docs/modules/data_connection/text_splitters

This section provides examples of how to use Text Splitters in LangChainGo. Text Splitters are essential for dividing large texts into smaller, manageable chunks, which is crucial for processing with language models and enhancing vector store search performance. The examples demonstrate practical applications of these utilities.

```Go
# This section is intended to contain code examples for Text Splitters.
# Actual code snippets would be provided here to demonstrate usage.
# For instance, initializing a splitter and splitting a document:

# import (
# 	"github.com/tmc/langchaingo/textsplitter"
# )
# 
# func main() {
# 	ssplitter := textsplitter.NewRecursiveCharacterTextSplitter(
# 		textsplitter.WithChunkSize(1000),
# 		textsplitter.WithChunkOverlap(200),
# 	)
# 	text := "Your long document text here..."
# 	chunks := splitter.SplitText(text)
# 	// Process the chunks...
# }

```

--------------------------------

### Basic Agent Setup in LangChainGo

Source: https://tmc.github.io/langchaingo/docs/modules/agents

Demonstrates how to set up a basic agent in LangChainGo. This involves creating a slice of tools (e.g., Calculator, WebSearch), initializing a one-shot agent with an LLM and the tools, and then creating an executor to call the agent with a sample input.

```Go
package main

import (
	"context"
	"fmt"

	"github.com/langchain-ai/langchain-go/agents"
	"github.com/langchain-ai/langchain-go/llms"
	"github.com/langchain-ai/langchain-go/tools"
)

func main() {
	ctx := context.Background()

	// Initialize LLM (replace with your actual LLM initialization)
	llm := llms.NewOpenAI(llms.WithAPIKey("YOUR_OPENAI_API_KEY"))

	// Create tools
	tools := []tools.Tool{
		t	tools.Calculator{},
		tools.WebSearch{APIKey: "your-api-key"},
	}

	// Create agent
	agent := agents.NewOneShotAgent(llm, tools)

	// Create executor
	executor := agents.NewExecutor(agent)

	// Execute
	result, err := executor.Call(ctx, map[string]any{
		"input": "What is 25 * 4 and what's the weather like today?",
	})

	if err != nil {
		fmt.Printf("Error executing agent: %v\n", err)
		return
	}

	fmt.Printf("Agent result: %v\n", result["output"])
}

```

--------------------------------

### Install LangChainGo Fake LLM

Source: https://tmc.github.io/langchaingo/docs/modules/model_io/models/llms/Integrations/fake

This snippet shows how to install the LangChainGo fake LLM package using the go get command. It's a prerequisite for using the fake LLM in your Go projects.

```bash
go get "github.com/tmc/langchaingo"

```

--------------------------------

### Pull llama2 model with Ollama

Source: https://tmc.github.io/langchaingo/docs/getting-started/guide-ollama

This command downloads the llama2 model from Ollama, which is required for the LangChainGo example.

```Shell
ollama pull llama2  

```

--------------------------------

### LangChainGo Project Setup for Log Analyzer

Source: https://tmc.github.io/langchaingo/docs/tutorials/log-analyzer

Sets up a new Go project for a log analyzer using LangChainGo. It initializes the Go module and fetches necessary dependencies, including LangChainGo and a logging library.

```Go
mkdir log-analyzer  
cd log-analyzer  
go mod init log-analyzer  
go get github.com/tmc/langchaingo  
go get github.com/sirupsen/logrus  # For structured logging examples  

```

--------------------------------

### Create Sample Go Package for Documentation

Source: https://tmc.github.io/langchaingo/docs/tutorials/smart-documentation

This Go code defines a User struct and a UserService with methods to create and retrieve users. It's used as an example package to be documented by the generator.

```Go
// Package user provides user management functionality.
package user

import (
    "errors"
    "time"
)

// User represents a system user.
type User struct {
    ID       int       `json:"id"`
    Name     string    `json:"name"`
    Email    string    `json:"email"`
    Created  time.Time `json:"created"`
}

// UserService handles user operations.
type UserService struct {
    users map[int]*User
}

// NewUserService creates a new user service.
func NewUserService() *UserService {
    return &UserService{
        users: make(map[int]*User),
    }
}

// CreateUser creates a new user.
func (s *UserService) CreateUser(name, email string) (*User, error) {
    if name == "" {
        return nil, errors.New("name cannot be empty")
    }

    user := &User{
        ID:      len(s.users) + 1,
        Name:    name,
        Email:   email,
        Created: time.Now(),
    }

    s.users[user.ID] = user
    return user, nil
}

// GetUser retrieves a user by ID.
func (s *UserService) GetUser(id int) (*User, error) {
    user, exists := s.users[id]
    if !exists {
        return nil, errors.New("user not found")
    }
    return user, nil
}

```

--------------------------------

### Run Vale Linting for Documentation

Source: https://tmc.github.io/langchaingo/docs/contributing/documentation

Commands to install Vale dependencies, lint all documentation, or run Vale directly on specific files. Vale is used to check documentation style and consistency.

```shell
# Install Vale (on macOS)
make lint-deps

# Lint all documentation
make lint-docs

# Or run Vale directly
vale docs
```

--------------------------------

### Generate Go Project Documentation

Source: https://tmc.github.io/langchaingo/docs/tutorials/smart-documentation

The main function parses command-line arguments for project directory, output directory, configuration file, watch mode, and package name. It then initializes the code analyzer and documentation generator. Based on the 'watch' flag, it either generates documentation once or enters a loop to watch for changes and regenerate.

```go
package main

import (
	"encoding/json"
	"flag"
	"fmt"
	"log"
	"os"
	"path/filepath"
)

func main() {
	var (
		projectDir = flag.String("dir", ".", "Project directory to analyze")
		outputDir  = flag.String("output", "./docs", "Output directory for documentation")
		configFile = flag.String("config", "", "Configuration file")
		watch      = flag.Bool("watch", false, "Watch for changes and regenerate")
		packageName = flag.String("package", "", "Specific package to document")
	)
	flag.Parse()

	// Load configuration
	config := DocConfig{
		OutputDir: *outputDir,
		IncludePrivate: false,
		GenerateExamples: true,
		Style: "markdown",
	}

	if *configFile != "" {
		if err := loadConfig(*configFile, &config); err != nil {
			log.Printf("Warning: Could not load config file: %v", err)
		}
	}

	// Initialize components
	analyzer := NewCodeAnalyzer()
	generator, err := NewDocGenerator()
	if err != nil {
		log.Fatal(err)
	}

	if *watch {
		if err := watchAndGenerate(analyzer, generator, *projectDir, config); err != nil {
			log.Fatal(err)
		}
	} else {
		if err := generateDocs(analyzer, generator, *projectDir, config, *packageName); err != nil {
			log.Fatal(err)
		}
	}
}

func generateDocs(analyzer *CodeAnalyzer, generator *DocGenerator, projectDir string, config DocConfig, packageName string) error {
	if packageName != "" {
		// Document specific package
		return generatePackageDocs(analyzer, generator, filepath.Join(projectDir, packageName), config)
	}

	// Document all packages
	return filepath.Walk(projectDir, func(path string, info os.FileInfo, err error) error {
		if err != nil {
			return err
		}

		if !info.IsDir() {
			return nil
		}

		// Skip vendor, .git, and test directories
		if shouldSkipDir(path) {
			return filepath.SkipDir
		}

		// Check if directory contains Go files
		hasGoFiles, err := hasGoSourceFiles(path)
		if err != nil {
			return err
		}

		if hasGoFiles {
			if err := generatePackageDocs(analyzer, generator, path, config); err != nil {
				log.Printf("Error documenting package %s: %v", path, err)
			}
		}

		return nil
	})
}

func generatePackageDocs(analyzer *CodeAnalyzer, generator *DocGenerator, packageDir string, config DocConfig) error {
	fmt.Printf("Analyzing package: %s\n", packageDir)

	// Analyze package
	pkg, err := analyzer.AnalyzePackage(packageDir)
	if err != nil {
		return fmt.Errorf("analyzing package: %w", err)
	}

	// Generate documentation
	doc, err := generator.GeneratePackageDoc(pkg, config)
	if err != nil {
		return fmt.Errorf("generating documentation: %w", err)
	}

	// Write to file
	outputPath := filepath.Join(config.OutputDir, pkg.Name+".md")
	if err := os.MkdirAll(filepath.Dir(outputPath), 0755); err != nil {
		return fmt.Errorf("creating output directory: %w", err)
	}

	if err := os.WriteFile(outputPath, []byte(doc), 0644); err != nil {
		return fmt.Errorf("writing documentation: %w", err)
	}

	fmt.Printf("Generated documentation: %s\n", outputPath)
	return nil
}

func watchAndGenerate(analyzer *CodeAnalyzer, generator *DocGenerator, projectDir string, config DocConfig) error {
	// Simplified file watching - you'd want to use fsnotify for production
	fmt.Printf("Watching %s for changes...\n", projectDir)

	for {
		if err := generateDocs(analyzer, generator, projectDir, config, ""); err != nil {
			log.Printf("Error generating docs: %v", err)
		}
		time.Sleep(30 * time.Second)
	}
}

func shouldSkipDir(path string) bool {
	base := filepath.Base(path)
	return base == "vendor" ||
		base == ".git" ||
		base == "testdata" ||
		strings.HasSuffix(base, "_test")
}

func hasGoSourceFiles(dir string) (bool, error) {
	files, err := os.ReadDir(dir)
	if err != nil {
		return false, err
	}

	for _, file := range files {
		if !file.IsDir() && strings.HasSuffix(file.Name(), ".go") &&
			!strings.HasSuffix(file.Name(), "_test.go") {
			return true, nil
		}
	}

	return false, nil
}

func loadConfig(filename string, config *DocConfig) error {
	data, err := os.ReadFile(filename)
	if err != nil {
		return err
	}

	return json.Unmarshal(data, config)
}

```

--------------------------------

### Mistral Streaming and Non-Streaming Completions (Go)

Source: https://tmc.github.io/langchaingo/docs/modules/model_io/models/llms/Integrations/mistral

Provides a complete Go example using LangChainGo to interact with Mistral models. It shows how to get both streaming and non-streaming text completions from the Mistral API, including setting temperature and model parameters.

```Go
package main

import(
"context"
"fmt"
"log"

"github.com/tmc/langchaingo/llms"
"github.com/tmc/langchaingo/llms/mistral"
)

funcmain(){
	llm, err := mistral.New(mistral.WithModel("open-mistral-7b"))
if err !=nil{
		log.Fatal(err)
}
	ctx := context.Background()
	completionWithStreaming, err := llms.GenerateFromSinglePrompt(ctx, llm,"Who was the first man to walk on the moon?",
		llms.WithTemperature(0.8),
		llms.WithStreamingFunc(func(ctx context.Context, chunk []byte)error{
			fmt.Print(string(chunk))
returnnil
}),
)
if err !=nil{
		log.Fatal(err)
}
// The full string response will be available in completionWithStreaming after the streaming is complete.
// (The Go compiler mandates declared variables be used at least once, hence the `_` assignment. https://go.dev/ref/spec#Blank_identifier)
	_ = completionWithStreaming

	completionWithoutStreaming, err := llms.GenerateFromSinglePrompt(ctx, llm,"Who was the first man to go to space?",
		llms.WithTemperature(0.2),
		llms.WithModel("mistral-small-latest"),
)
if err !=nil{
		log.Fatal(err)
}
	fmt.Println("\n"+ completionWithoutStreaming)
}

```

--------------------------------

### Go Code Analysis Engine Setup

Source: https://tmc.github.io/langchaingo/docs/tutorials/smart-documentation

Defines the `CodeAnalyzer` struct and related data structures for capturing Go code information. It includes types for `PackageInfo`, `FunctionInfo`, `TypeInfo`, `FieldInfo`, `ParamInfo`, `ReturnInfo`, `ConstantInfo`, `VariableInfo`, and `ExampleInfo`. The `NewCodeAnalyzer` function initializes the analyzer with a file set.

```go
package main

import (
	"fmt"
	"go/ast"
	"go/doc"
	"go/parser"
	"go/token"
	"path/filepath"
	"strings"
	"unicode"
)

type CodeAnalyzer struct{
    fset *token.FileSet
}

type PackageInfo struct{
    Name        string`json:"name"`
    Path        string`json:"path"`
    Description string`json:"description"`
    Functions   []FunctionInfo  `json:"functions"`
    Types       []TypeInfo      `json:"types"`
    Constants   []ConstantInfo  `json:"constants"`
    Variables   []VariableInfo  `json:"variables"`
    Examples    []ExampleInfo   `json:"examples"`
    Imports     []string`json:"imports"`
}

type FunctionInfo struct{
    Name        string`json:"name"`
    Signature   string`json:"signature"`
    Description string`json:"description"`
    Parameters  []ParamInfo  `json:"parameters"`
    Returns     []ReturnInfo `json:"returns"`
    Examples    []string`json:"examples"`
    IsExported  bool`json:"is_exported"`
    IsMethod    bool`json:"is_method"`
    Receiver    string`json:"receiver,omitempty"`
}

type TypeInfo struct{
    Name        string`json:"name"`
    Kind        string`json:"kind"`// struct, interface, alias, etc.
    Description string`json:"description"`
    Fields      []FieldInfo `json:"fields,omitempty"`
    Methods     []string`json:"methods,omitempty"`
    IsExported  bool`json:"is_exported"`
}

type FieldInfo struct{
    Name        string`json:"name"`
    Type        string`json:"type"`
    Tag         string`json:"tag,omitempty"`
    Description string`json:"description"`
}

type ParamInfo struct{
    Name string`json:"name"`
    Type string`json:"type"`
}

type ReturnInfo struct{
    Type        string`json:"type"`
    Description string`json:"description"`
}

type ConstantInfo struct{
    Name        string`json:"name"`
    Type        string`json:"type"`
    Value       string`json:"value"`
    Description string`json:"description"`
    IsExported  bool`json:"is_exported"`
}

type VariableInfo struct{
    Name        string`json:"name"`
    Type        string`json:"type"`
    Description string`json:"description"`
    IsExported  bool`json:"is_exported"`
}

type ExampleInfo struct{
    Name string`json:"name"`
    Code string`json:"code"`
    Doc  string`json:"doc"`
}

funcNewCodeAnalyzer()*CodeAnalyzer {
return&CodeAnalyzer{
        fset: token.NewFileSet(),
}
}

```

--------------------------------

### Basic PromptTemplate Usage

Source: https://tmc.github.io/langchaingo/docs/modules/model_io/prompts/prompt_templates

Provides an example of creating a `PromptTemplate` and rendering it with specific input variables. It includes error handling for the rendering process.

```Go
import"github.com/tmc/langchaingo/prompts"
import"log"
import"fmt"

funcmain(){
// Create a prompt template
    prompt := prompts.NewPromptTemplate(
"What is a good name for a company that makes {{.product}}?",
[]string{"product"},
)

// Render the template with data
    result, err := prompt.Format(map[string]any{
"product":"colorful socks",
})
if err !=nil{
        log.Fatal(err)
}

    fmt.Println(result)
// Output: What is a good name for a company that makes colorful socks?
}

```

--------------------------------

### Generate Package Documentation Go

Source: https://tmc.github.io/langchaingo/docs/tutorials/smart-documentation

Generates documentation for a Go package, optionally enhancing descriptions with AI and generating usage examples. It executes the 'package' template to produce the final documentation string.

```go
func(dg *DocGenerator)GeneratePackageDoc(pkg *PackageInfo, config DocConfig)(string,error){
// Enhance descriptions with AI
if err := dg.enhanceDescriptions(pkg); err !=nil{
return "", fmt.Errorf("enhancing descriptions: %w", err)
}

// Generate usage examples
if config.GenerateExamples {
if err := dg.generateExamples(pkg); err !=nil{
return "", fmt.Errorf("generating examples: %w", err)
}
}

// Apply template
var result strings.Builder
if err := dg.templates["package"].Execute(&result, pkg); err !=nil{
return "", fmt.Errorf("executing template: %w", err)
}

return result.String(),nil
}
```

--------------------------------

### F-string Template Syntax Example

Source: https://tmc.github.io/langchaingo/docs/modules/model_io/prompts

Provides an example of using Python-style f-strings for simple variable substitution within prompts.

```Go
template :="Process {document} using {method} with {params}"  
```

--------------------------------

### Example Go Commit Messages

Source: https://tmc.github.io/langchaingo/docs/contributing

Provides examples of well-formatted commit messages for Go projects, emphasizing clarity and adherence to package-prefixed conventions.

```Go
llms/anthropic: implement function calling support
memory: fix buffer overflow in conversation memory
tools: add calculator tool with error handling
all: update dependencies and organize go.mod file
```

--------------------------------

### Sequential Chain Orchestration in Go

Source: https://tmc.github.io/langchaingo/docs/concepts/architecture

Illustrates setting up sequential chains in LangChainGo. It covers both simple sequential chains where the output of one directly feeds into the next, and more complex ones with defined input and output keys.

```Go
// Sequential chain example
chain1 := chains.NewLLMChain(llm, template1)
chain2 := chains.NewLLMChain(llm, template2)

// For simple sequential chains where output of one feeds to next
sequential := chains.NewSimpleSequentialChain([]chains.Chain{chain1, chain2})

// Or for complex sequential chains with specific input/output keys
sequential, err := chains.NewSequentialChain(
[]chains.Chain{chain1, chain2},
[]string{"input"},// input keys
[]string{"final_output"},// output keys
)

```

--------------------------------

### Install LangChainGo

Source: https://tmc.github.io/langchaingo/docs/modules/model_io/models/llms/Integrations/huggingface

Command to install the LangChainGo library using the Go package manager. This is a prerequisite for using LangChainGo functionalities.

```bash
go get github.com/tmc/langchaingo
```

--------------------------------

### Jinja2 Template Syntax Example

Source: https://tmc.github.io/langchaingo/docs/modules/model_io/prompts

Shows an example of using Jinja2 templating syntax, which supports filters, conditionals, and loops, for constructing prompts.

```Go
template :=`  
Dear {{ customer_name | title }},  
{% if order_status == "shipped" %}  
    Your order has been shipped!  
{% else %}  
    Your order is being processed.  
{% endif %}  
`  
```

--------------------------------

### Groq LLM Integration Example in Go

Source: https://tmc.github.io/langchaingo/docs/modules/model_io/models/llms/Integrations/groq

A Go program demonstrating how to initialize and use Groq's LLM models via LangChainGo. It includes loading the API key from environment variables, setting model parameters, and generating text from a prompt with streaming output.

```Go
package main

import(
"context"
"fmt"
"log"
"os"

"github.com/joho/godotenv"
"github.com/tmc/langchaingo/llms"
"github.com/tmc/langchaingo/llms/openai"
)

func main(){
	err := godotenv.Load()
if err != nil{
		log.Fatalf("Error loading .env file")
}

	apiKey := os.Getenv("GROQ_API_KEY")

	llm, err := openai.New(
		openai.WithModel("llama3-8b-8192"),
		openai.WithBaseURL("https://api.groq.com/openai/v1"),
		openai.WithToken(apiKey),
)
if err != nil{
		log.Fatal(err)
}
	ctx := context.Background()
	_, err = llms.GenerateFromSinglePrompt(ctx,
		llm,
"Write a long poem about how golang is a fantastic language.",
		llms.WithTemperature(0.8),
		llms.WithMaxTokens(4096),
		llms.WithStreamingFunc(func(ctx context.Context, chunk []byte)error{
			fmt.Print(string(chunk))
return nil
}),
)
	fmt.Println()
if err != nil{
		log.Fatal(err)
}
}
```

--------------------------------

### Create and Format a Go Prompt Template

Source: https://tmc.github.io/langchaingo/docs/modules/model_io/prompts

Demonstrates how to create a prompt template using Go's text/template syntax and format it with provided values. This is the recommended approach for Go applications.

```Go
import"github.com/tmc/langchaingo/prompts"
  
// Create a prompt template  
template := prompts.NewPromptTemplate(  
"Write a {{.style}} summary of: {{.content}}",  
[]string{"style","content"},
)
  
// Format with values  
result, err := template.Format(map[string]any{  
"style":"technical",  
"content":"recent AI advances",  
})
// Result: "Write a technical summary of: recent AI advances"  
```