github.com/teilomillet/raggo

Raggo - Retrieval Augmented Generation Library

A flexible RAG (Retrieval Augmented Generation) library for Go, designed to make document processing and context-aware AI interactions simple and efficient.

🔍 Smart Document Search • 💬 Context-Aware Responses • 🤖 Intelligent RAG

Quick Start

package main

import (
	"context"
	"fmt"
	"github.com/teilomillet/raggo"
)

func main() {
	// Initialize RAG with default settings
	rag, err := raggo.NewSimpleRAG(raggo.DefaultConfig())
	if err != nil {
		fmt.Printf("Error: %v\n", err)
		return
	}
	defer rag.Close()

	// Add documents from a directory
	err = rag.AddDocuments(context.Background(), "./docs")
	if err != nil {
		fmt.Printf("Error: %v\n", err)
		return
	}

	// Search with natural language
	response, _ := rag.Search(context.Background(), "What are the key features?")
	fmt.Printf("Answer: %s\n", response)
}

Configuration

Raggo provides a flexible configuration system that can be loaded from multiple sources (environment variables, JSON files, or programmatic defaults):

// Load configuration (automatically checks standard paths)
cfg, err := config.LoadConfig()
if err != nil {
    log.Fatal(err)
}

// Or create a custom configuration
cfg := &config.Config{
    Provider:   "milvus",           // Vector store provider
    Model:      "text-embedding-3-small",
    Collection: "my_documents",
    
    // Search settings
    DefaultTopK:     5,      // Number of similar chunks to retrieve
    DefaultMinScore: 0.7,    // Similarity threshold
    
    // Document processing
    DefaultChunkSize:    300,  // Size of text chunks
    DefaultChunkOverlap: 50,   // Overlap between chunks
}

// Create RAG instance with config
rag, err := raggo.NewSimpleRAG(cfg)

Configuration can be saved for reuse:

err := cfg.Save("~/.raggo/config.json")

Environment variables (take precedence over config files):

• RAGGO_PROVIDER: Service provider
• RAGGO_MODEL: Model identifier
• RAGGO_COLLECTION: Collection name
• RAGGO_API_KEY: Default API key

Table of Contents

Part 1: Core Components

1. Quick Start
2. Building Blocks
   • Document Loading
   • Text Parsing
   • Text Chunking
   • Embeddings
   • Vector Storage

Part 2: RAG Implementations

1. Simple RAG
   • Basic Usage
   • Document Q&A
   • Configuration
2. Contextual RAG
   • Advanced Features
   • Context Window
   • Hybrid Search
3. Memory Context
   • Chat Applications
   • Memory Management
   • Context Enhancement
4. Advanced Use Cases
   • Full Processing Pipeline
   • Concurrent Processing
   • Rate Limiting

Part 1: Core Components

Quick Start

Prerequisites

# Set API key
export OPENAI_API_KEY=your-api-key

# Install Raggo
go get github.com/teilomillet/raggo

Building Blocks

Document Loading

loader := raggo.NewLoader(raggo.SetTimeout(1*time.Minute))
doc, err := loader.LoadURL(context.Background(), "https://example.com/doc.pdf")

Text Parsing

parser := raggo.NewParser()
doc, err := parser.Parse("document.pdf")

Text Chunking

chunker := raggo.NewChunker(raggo.ChunkSize(100))
chunks := chunker.Chunk(doc.Content)

Embeddings

embedder := raggo.NewEmbedder(
    raggo.SetProvider("openai"),
    raggo.SetModel("text-embedding-3-small"),
)

Vector Storage

db := raggo.NewVectorDB(raggo.WithMilvus("collection"))

Part 2: RAG Implementations

Simple RAG

Best for straightforward document Q&A:

package main

import (
    "context"
    "log"
    "github.com/teilomillet/raggo"
)

func main() {
    // Initialize SimpleRAG
    rag, err := raggo.NewSimpleRAG(raggo.SimpleRAGConfig{
        Collection: "docs",
        Model:      "text-embedding-3-small",
        ChunkSize:  300,
        TopK:       3,
    })
    if err != nil {
        log.Fatal(err)
    }
    defer rag.Close()

    // Add documents
    err = rag.AddDocuments(context.Background(), "./documents")
    if err != nil {
        log.Fatal(err)
    }

    // Search with different strategies
    basicResponse, _ := rag.Search(context.Background(), "What is the main feature?")
    hybridResponse, _ := rag.SearchHybrid(context.Background(), "How does it work?", 0.7)
    
    log.Printf("Basic Search: %s\n", basicResponse)
    log.Printf("Hybrid Search: %s\n", hybridResponse)
}

Contextual RAG

For complex document understanding and context-aware responses:

package main

import (
	"context"
	"fmt"
	"os"
	"path/filepath"

	"github.com/teilomillet/raggo"
)

func main() {
	// Initialize RAG with default settings
	rag, err := raggo.NewDefaultContextualRAG("basic_contextual_docs")
	if err != nil {
		fmt.Printf("Failed to initialize RAG: %v\n", err)
		os.Exit(1)
	}
	defer rag.Close()

	// Add documents - the system will automatically:
	// - Split documents into semantic chunks
	// - Generate rich context for each chunk
	// - Store embeddings with contextual information
	docsPath := filepath.Join("examples", "docs")
	if err := rag.AddDocuments(context.Background(), docsPath); err != nil {
		fmt.Printf("Failed to add documents: %v\n", err)
		os.Exit(1)
	}

	// Simple search with automatic context enhancement
	query := "What are the key features of the product?"
	response, err := rag.Search(context.Background(), query)
	if err != nil {
		fmt.Printf("Failed to search: %v\n", err)
		os.Exit(1)
	}

	fmt.Printf("\nQuery: %s\nResponse: %s\n", query, response)
}

Advanced Configuration

// Create a custom configuration
config := &raggo.ContextualRAGConfig{
	Collection:   "advanced_contextual_docs",
	Model:        "text-embedding-3-small", // Embedding model
	LLMModel:     "gpt-4o-mini",           // Model for context generation
	ChunkSize:    300,                      // Larger chunks for more context
	ChunkOverlap: 75,                       // 25% overlap for better continuity
	TopK:         5,                        // Number of similar chunks to retrieve
	MinScore:     0.7,                      // Higher threshold for better relevance
}

// Initialize RAG with custom configuration
rag, err := raggo.NewContextualRAG(config)
if err != nil {
	log.Fatalf("Failed to initialize RAG: %v", err)
}
defer rag.Close()

Memory Context

For chat applications and long-term context retention:

package main

import (
    "context"
    "log"
    "github.com/teilomillet/raggo"
    "github.com/teilomillet/gollm"
)

func main() {
    // Initialize Memory Context
    memoryCtx, err := raggo.NewMemoryContext(
        os.Getenv("OPENAI_API_KEY"),
        raggo.MemoryTopK(5),
        raggo.MemoryCollection("chat"),
        raggo.MemoryStoreLastN(100),
        raggo.MemoryMinScore(0.7),
    )
    if err != nil {
        log.Fatal(err)
    }
    defer memoryCtx.Close()

    // Initialize Contextual RAG
    rag, err := raggo.NewContextualRAG(&raggo.ContextualRAGConfig{
        Collection: "docs",
        Model:     "text-embedding-3-small",
    })
    if err != nil {
        log.Fatal(err)
    }
    defer rag.Close()

    // Example chat interaction
    messages := []gollm.MemoryMessage{
        {Role: "user", Content: "How does the authentication system work?"},
    }
    
    // Store conversation
    err = memoryCtx.StoreMemory(context.Background(), messages)
    if err != nil {
        log.Fatal(err)
    }
    
    // Get enhanced response with context
    prompt := &gollm.Prompt{Messages: messages}
    enhanced, _ := memoryCtx.EnhancePrompt(context.Background(), prompt, messages)
    response, _ := rag.Search(context.Background(), enhanced.Messages[0].Content)
    
    log.Printf("Response: %s\n", response)
}

Advanced Use Cases

Full Processing Pipeline

Process large document sets with rate limiting and concurrent processing:

package main

import (
    "context"
    "log"
    "sync"
    "time"
    "github.com/teilomillet/raggo"
    "golang.org/x/time/rate"
)

const (
    GPT_RPM_LIMIT   = 5000    // Requests per minute
    GPT_TPM_LIMIT   = 4000000 // Tokens per minute
    MAX_CONCURRENT  = 10      // Max concurrent goroutines
)

func main() {
    // Initialize components
    parser := raggo.NewParser()
    chunker := raggo.NewChunker(raggo.ChunkSize(500))
    embedder := raggo.NewEmbedder(
        raggo.SetProvider("openai"),
        raggo.SetModel("text-embedding-3-small"),
    )

    // Create rate limiters
    limiter := rate.NewLimiter(rate.Limit(GPT_RPM_LIMIT/60), GPT_RPM_LIMIT)
    
    // Process documents concurrently
    var wg sync.WaitGroup
    semaphore := make(chan struct{}, MAX_CONCURRENT)

    files, _ := filepath.Glob("./documents/*.pdf")
    for _, file := range files {
        wg.Add(1)
        semaphore <- struct{}{} // Acquire semaphore
        
        go func(file string) {
            defer wg.Done()
            defer func() { <-semaphore }() // Release semaphore
            
            // Wait for rate limit
            limiter.Wait(context.Background())
            
            // Process document
            doc, _ := parser.Parse(file)
            chunks := chunker.Chunk(doc.Content)
            embeddings, _ := embedder.CreateEmbeddings(chunks)
            
            log.Printf("Processed %s: %d chunks\n", file, len(chunks))
        }(file)
    }
    
    wg.Wait()
}

Best Practices

Resource Management

• Always use defer Close()
• Monitor memory usage
• Clean up old data

Performance

• Use concurrent processing for large datasets
• Configure appropriate chunk sizes
• Enable hybrid search when needed

Context Management

• Use Memory Context for chat applications
• Configure context window size
• Clean up old memories periodically

Examples

Check /examples for more:

• Basic usage: /examples/simple/
• Context-aware: /examples/contextual/
• Chat applications: /examples/chat/
• Memory usage: /examples/memory_enhancer_example.go
• Full pipeline: /examples/full_process.go
• Benchmarks: /examples/process_embedding_benchmark.go

License

MIT License - see LICENSE file