Building RAG System with Golang: From OpenAI API to Vector Database Complete Guide

📖 Introduction

RAG (Retrieval-Augmented Generation) has become one of the hottest AI application architectures in 2024-2025. By combining external knowledge base retrieval with large language model generation capabilities, it effectively addresses LLM hallucination issues and knowledge timeliness limitations.

This article will detail how to build a complete RAG system using Golang, including:

• 🔍 Document processing and chunking strategies
• 🧠 Embedding implementation
• 📊 Vector database integration (Qdrant)
• 🔎 Semantic search optimization
• 💬 Integration with OpenAI API for answer generation
• 🚀 Production environment best practices

🎯 What is RAG?

RAG Workflow

User Question → Vectorization → Semantic Search → Retrieve Relevant Documents → Build Prompt → LLM Generate Answer

RAG vs Traditional LLM

Feature	Traditional LLM	RAG System
Knowledge Source	Training data (static)	External knowledge base (dynamic)
Timeliness	Poor (training time point)	Good (real-time updates)
Hallucination	Severe	Significantly reduced
Cost	High (requires many tokens)	Medium (retrieves relevant content only)
Traceability	None	Yes (can cite sources)

🛠️ Technology Stack Selection

Core Components

// We will use the following technology stack
- Go 1.21+               // Main development language
- OpenAI API (gpt-4)     // LLM service
- text-embedding-ada-002 // Embedding model
- Qdrant                 // Vector database
- gin                    // Web framework
- go-openai              // OpenAI Go SDK

Why Choose Golang?

1. High Performance: Concurrent processing of large volumes of documents
2. Simple Deployment: Single binary file
3. Excellent Concurrency Model: Goroutines for parallel tasks
4. Rich Ecosystem: AI-related libraries maturing rapidly

📦 Environment Setup

1. Install Dependencies

# Initialize project
mkdir golang-rag-system
cd golang-rag-system
go mod init github.com/yourusername/golang-rag

# Install core dependencies
go get github.com/sashabaranov/go-openai
go get github.com/qdrant/go-client
go get github.com/gin-gonic/gin
go get github.com/joho/godotenv

2. Configure Environment Variables

# .env
OPENAI_API_KEY=sk-xxxxxxxxxx
QDRANT_URL=http://localhost:6333
QDRANT_API_KEY=your-qdrant-key
COLLECTION_NAME=documents

3. Start Qdrant (using Docker)

docker run -p 6333:6333 -p 6334:6334 \
  -v $(pwd)/qdrant_storage:/qdrant/storage:z \
  qdrant/qdrant

💻 Core Implementation

1. Project Structure

golang-rag/
├── cmd/
│   └── main.go           # Entry file
├── internal/
│   ├── embedding/        # Embedding generation
│   │   └── openai.go
│   ├── vectordb/         # Vector database operations
│   │   └── qdrant.go
│   ├── chunker/          # Document chunking
│   │   └── text_splitter.go
│   ├── retriever/        # Retriever
│   │   └── retriever.go
│   └── rag/              # RAG core logic
│       └── pipeline.go
├── pkg/
│   └── models/           # Data models
│       └── document.go
├── .env
├── go.mod
└── go.sum

2. Data Model Definition

// pkg/models/document.go
package models

type Document struct {
    ID        string                 `json:"id"`
    Content   string                 `json:"content"`
    Metadata  map[string]interface{} `json:"metadata"`
    Embedding []float32              `json:"embedding,omitempty"`
}

type SearchResult struct {
    Document Document  `json:"document"`
    Score    float64   `json:"score"`
}

type RAGResponse struct {
    Answer    string         `json:"answer"`
    Sources   []SearchResult `json:"sources"`
    TokenUsed int            `json:"token_used"`
}

3. Embedding Generator

// internal/embedding/openai.go
package embedding

import (
    "context"
    "fmt"
    "github.com/sashabaranov/go-openai"
)

type EmbeddingService struct {
    client *openai.Client
    model  string
}

func NewEmbeddingService(apiKey string) *EmbeddingService {
    return &EmbeddingService{
        client: openai.NewClient(apiKey),
        model:  openai.AdaEmbeddingV2,
    }
}

// GenerateEmbedding generates embedding for a single text
func (s *EmbeddingService) GenerateEmbedding(ctx context.Context, text string) ([]float32, error) {
    req := openai.EmbeddingRequest{
        Input: []string{text},
        Model: s.model,
    }

    resp, err := s.client.CreateEmbeddings(ctx, req)
    if err != nil {
        return nil, fmt.Errorf("failed to create embedding: %w", err)
    }

    if len(resp.Data) == 0 {
        return nil, fmt.Errorf("no embedding data returned")
    }

    return resp.Data[0].Embedding, nil
}

// BatchGenerateEmbeddings generates embeddings in batch (performance optimization)
func (s *EmbeddingService) BatchGenerateEmbeddings(ctx context.Context, texts []string) ([][]float32, error) {
    // OpenAI supports up to 2048 inputs per request
    const batchSize = 100
    var allEmbeddings [][]float32

    for i := 0; i < len(texts); i += batchSize {
        end := i + batchSize
        if end > len(texts) {
            end = len(texts)
        }

        batch := texts[i:end]
        req := openai.EmbeddingRequest{
            Input: batch,
            Model: s.model,
        }

        resp, err := s.client.CreateEmbeddings(ctx, req)
        if err != nil {
            return nil, fmt.Errorf("batch embedding failed: %w", err)
        }

        for _, data := range resp.Data {
            allEmbeddings = append(allEmbeddings, data.Embedding)
        }
    }

    return allEmbeddings, nil
}

4. Text Chunker

// internal/chunker/text_splitter.go
package chunker

import (
    "strings"
    "unicode/utf8"
)

type TextSplitter struct {
    ChunkSize    int     // Characters per chunk
    ChunkOverlap int     // Overlap characters
}

func NewTextSplitter(chunkSize, overlap int) *TextSplitter {
    return &TextSplitter{
        ChunkSize:    chunkSize,
        ChunkOverlap: overlap,
    }
}

// SplitText splits long text into multiple chunks
func (ts *TextSplitter) SplitText(text string) []string {
    if utf8.RuneCountInString(text) <= ts.ChunkSize {
        return []string{text}
    }

    var chunks []string
    runes := []rune(text)
    start := 0

    for start < len(runes) {
        end := start + ts.ChunkSize
        if end > len(runes) {
            end = len(runes)
        }

        // Try to split at sentence boundaries
        if end < len(runes) {
            // Find the nearest period, question mark, or newline
            for i := end; i > start+ts.ChunkSize/2; i-- {
                if runes[i] == '。' || runes[i] == '？' || runes[i] == '\n' || runes[i] == '.' {
                    end = i + 1
                    break
                }
            }
        }

        chunk := string(runes[start:end])
        chunks = append(chunks, strings.TrimSpace(chunk))

        // Calculate next start position (considering overlap)
        start = end - ts.ChunkOverlap
        if start < 0 {
            start = 0
        }
    }

    return chunks
}

// SplitByParagraph splits by paragraph (suitable for structured documents)
func (ts *TextSplitter) SplitByParagraph(text string) []string {
    paragraphs := strings.Split(text, "\n\n")
    var chunks []string

    currentChunk := ""
    for _, para := range paragraphs {
        para = strings.TrimSpace(para)
        if para == "" {
            continue
        }

        if utf8.RuneCountInString(currentChunk+para) <= ts.ChunkSize {
            if currentChunk != "" {
                currentChunk += "\n\n"
            }
            currentChunk += para
        } else {
            if currentChunk != "" {
                chunks = append(chunks, currentChunk)
            }
            // If single paragraph exceeds ChunkSize, split further
            if utf8.RuneCountInString(para) > ts.ChunkSize {
                chunks = append(chunks, ts.SplitText(para)...)
            } else {
                currentChunk = para
            }
        }
    }

    if currentChunk != "" {
        chunks = append(chunks, currentChunk)
    }

    return chunks
}

5. Vector Database Operations

// internal/vectordb/qdrant.go
package vectordb

import (
    "context"
    "fmt"
    "github.com/google/uuid"
    pb "github.com/qdrant/go-client/qdrant"
    "google.golang.org/grpc"
    "google.golang.org/grpc/credentials/insecure"
    "your-project/pkg/models"
)

type QdrantClient struct {
    client         pb.PointsClient
    collectionName string
}

func NewQdrantClient(address, collectionName string) (*QdrantClient, error) {
    conn, err := grpc.Dial(address, grpc.WithTransportCredentials(insecure.NewCredentials()))
    if err != nil {
        return nil, fmt.Errorf("failed to connect to Qdrant: %w", err)
    }

    return &QdrantClient{
        client:         pb.NewPointsClient(conn),
        collectionName: collectionName,
    }, nil
}

// UpsertDocuments inserts or updates documents
func (q *QdrantClient) UpsertDocuments(ctx context.Context, docs []models.Document) error {
    points := make([]*pb.PointStruct, 0, len(docs))

    for _, doc := range docs {
        if doc.ID == "" {
            doc.ID = uuid.New().String()
        }

        // Convert metadata to payload
        payload := make(map[string]*pb.Value)
        payload["content"] = &pb.Value{
            Kind: &pb.Value_StringValue{StringValue: doc.Content},
        }

        for k, v := range doc.Metadata {
            if strVal, ok := v.(string); ok {
                payload[k] = &pb.Value{
                    Kind: &pb.Value_StringValue{StringValue: strVal},
                }
            }
        }

        point := &pb.PointStruct{
            Id: &pb.PointId{
                PointIdOptions: &pb.PointId_Uuid{Uuid: doc.ID},
            },
            Vectors: &pb.Vectors{
                VectorsOptions: &pb.Vectors_Vector{
                    Vector: &pb.Vector{Data: doc.Embedding},
                },
            },
            Payload: payload,
        }

        points = append(points, point)
    }

    _, err := q.client.Upsert(ctx, &pb.UpsertPoints{
        CollectionName: q.collectionName,
        Points:         points,
    })

    return err
}

// Search performs semantic search
func (q *QdrantClient) Search(ctx context.Context, queryVector []float32, topK int) ([]models.SearchResult, error) {
    resp, err := q.client.Search(ctx, &pb.SearchPoints{
        CollectionName: q.collectionName,
        Vector:         queryVector,
        Limit:          uint64(topK),
        WithPayload:    &pb.WithPayloadSelector{SelectorOptions: &pb.WithPayloadSelector_Enable{Enable: true}},
    })

    if err != nil {
        return nil, fmt.Errorf("search failed: %w", err)
    }

    results := make([]models.SearchResult, 0, len(resp.Result))
    for _, point := range resp.Result {
        content := ""
        metadata := make(map[string]interface{})

        if payload := point.Payload; payload != nil {
            if contentVal, ok := payload["content"]; ok {
                if strVal := contentVal.GetStringValue(); strVal != "" {
                    content = strVal
                }
            }

            for k, v := range payload {
                if k != "content" {
                    if strVal := v.GetStringValue(); strVal != "" {
                        metadata[k] = strVal
                    }
                }
            }
        }

        results = append(results, models.SearchResult{
            Document: models.Document{
                ID:       point.Id.GetUuid(),
                Content:  content,
                Metadata: metadata,
            },
            Score: float64(point.Score),
        })
    }

    return results, nil
}

6. RAG Core Pipeline

// internal/rag/pipeline.go
package rag

import (
    "context"
    "fmt"
    "strings"
    "github.com/sashabaranov/go-openai"
    "your-project/internal/embedding"
    "your-project/internal/vectordb"
    "your-project/pkg/models"
)

type RAGPipeline struct {
    embeddingService *embedding.EmbeddingService
    vectorDB         *vectordb.QdrantClient
    llmClient        *openai.Client
    topK             int
}

func NewRAGPipeline(
    embService *embedding.EmbeddingService,
    vdb *vectordb.QdrantClient,
    openaiKey string,
    topK int,
) *RAGPipeline {
    return &RAGPipeline{
        embeddingService: embService,
        vectorDB:         vdb,
        llmClient:        openai.NewClient(openaiKey),
        topK:             topK,
    }
}

// Query executes RAG query
func (r *RAGPipeline) Query(ctx context.Context, question string) (*models.RAGResponse, error) {
    // 1. Vectorize the question
    queryVector, err := r.embeddingService.GenerateEmbedding(ctx, question)
    if err != nil {
        return nil, fmt.Errorf("failed to generate query embedding: %w", err)
    }

    // 2. Retrieve relevant documents
    searchResults, err := r.vectorDB.Search(ctx, queryVector, r.topK)
    if err != nil {
        return nil, fmt.Errorf("failed to search documents: %w", err)
    }

    if len(searchResults) == 0 {
        return &models.RAGResponse{
            Answer:  "Sorry, I couldn't find any relevant information.",
            Sources: []models.SearchResult{},
        }, nil
    }

    // 3. Build context
    context := r.buildContext(searchResults)

    // 4. Call LLM to generate answer
    answer, tokenUsed, err := r.generateAnswer(ctx, question, context)
    if err != nil {
        return nil, fmt.Errorf("failed to generate answer: %w", err)
    }

    return &models.RAGResponse{
        Answer:    answer,
        Sources:   searchResults,
        TokenUsed: tokenUsed,
    }, nil
}

// buildContext builds context prompt
func (r *RAGPipeline) buildContext(results []models.SearchResult) string {
    var sb strings.Builder
    sb.WriteString("Here is the relevant reference information:\n\n")

    for i, result := range results {
        sb.WriteString(fmt.Sprintf("【Reference %d】\n%s\n\n", i+1, result.Document.Content))
    }

    return sb.String()
}

// generateAnswer calls LLM to generate final answer
func (r *RAGPipeline) generateAnswer(ctx context.Context, question, context string) (string, int, error) {
    prompt := fmt.Sprintf(`You are a professional technical assistant. Please answer the user's question based on the following reference information.

%s

User Question: %s

Please note:
1. Answer only based on the reference information, do not fabricate content
2. If the reference information is insufficient, please clearly state so
3. Answers should be accurate, professional, and easy to understand
4. You can cite reference information numbers

Your answer:`, context, question)

    req := openai.ChatCompletionRequest{
        Model: openai.GPT4TurboPreview,
        Messages: []openai.ChatCompletionMessage{
            {
                Role:    openai.ChatMessageRoleSystem,
                Content: "You are a professional technical assistant who excels at accurately answering questions based on provided reference materials.",
            },
            {
                Role:    openai.ChatMessageRoleUser,
                Content: prompt,
            },
        },
        Temperature: 0.7,
        MaxTokens:   1000,
    }

    resp, err := r.llmClient.CreateChatCompletion(ctx, req)
    if err != nil {
        return "", 0, err
    }

    if len(resp.Choices) == 0 {
        return "", 0, fmt.Errorf("no response from LLM")
    }

    return resp.Choices[0].Message.Content, resp.Usage.TotalTokens, nil
}

7. HTTP API Interface

// cmd/main.go
package main

import (
    "context"
    "log"
    "net/http"
    "os"

    "github.com/gin-gonic/gin"
    "github.com/joho/godotenv"
    "your-project/internal/chunker"
    "your-project/internal/embedding"
    "your-project/internal/rag"
    "your-project/internal/vectordb"
    "your-project/pkg/models"
)

type Server struct {
    ragPipeline *rag.RAGPipeline
    embService  *embedding.EmbeddingService
    vectorDB    *vectordb.QdrantClient
    chunker     *chunker.TextSplitter
}

func main() {
    // Load environment variables
    if err := godotenv.Load(); err != nil {
        log.Println("Warning: .env file not found")
    }

    // Initialize services
    embService := embedding.NewEmbeddingService(os.Getenv("OPENAI_API_KEY"))
    
    vectorDB, err := vectordb.NewQdrantClient(
        os.Getenv("QDRANT_URL"),
        os.Getenv("COLLECTION_NAME"),
    )
    if err != nil {
        log.Fatal("Failed to connect to Qdrant:", err)
    }

    ragPipeline := rag.NewRAGPipeline(embService, vectorDB, os.Getenv("OPENAI_API_KEY"), 5)
    textSplitter := chunker.NewTextSplitter(500, 50)

    server := &Server{
        ragPipeline: ragPipeline,
        embService:  embService,
        vectorDB:    vectorDB,
        chunker:     textSplitter,
    }

    // Setup routes
    r := gin.Default()
    
    r.POST("/ingest", server.handleIngest)
    r.POST("/query", server.handleQuery)
    r.GET("/health", func(c *gin.Context) {
        c.JSON(http.StatusOK, gin.H{"status": "healthy"})
    })

    log.Println("Server starting on :8080")
    if err := r.Run(":8080"); err != nil {
        log.Fatal("Failed to start server:", err)
    }
}

// handleIngest handles document ingestion
func (s *Server) handleIngest(c *gin.Context) {
    var req struct {
        Text     string                 `json:"text" binding:"required"`
        Metadata map[string]interface{} `json:"metadata"`
    }

    if err := c.ShouldBindJSON(&req); err != nil {
        c.JSON(http.StatusBadRequest, gin.H{"error": err.Error()})
        return
    }

    ctx := context.Background()

    // 1. Chunk the text
    chunks := s.chunker.SplitText(req.Text)

    // 2. Generate embeddings
    embeddings, err := s.embService.BatchGenerateEmbeddings(ctx, chunks)
    if err != nil {
        c.JSON(http.StatusInternalServerError, gin.H{"error": "Failed to generate embeddings"})
        return
    }

    // 3. Build documents
    docs := make([]models.Document, len(chunks))
    for i, chunk := range chunks {
        docs[i] = models.Document{
            Content:   chunk,
            Metadata:  req.Metadata,
            Embedding: embeddings[i],
        }
    }

    // 4. Store in vector database
    if err := s.vectorDB.UpsertDocuments(ctx, docs); err != nil {
        c.JSON(http.StatusInternalServerError, gin.H{"error": "Failed to store documents"})
        return
    }

    c.JSON(http.StatusOK, gin.H{
        "message":    "Documents ingested successfully",
        "chunk_count": len(chunks),
    })
}

// handleQuery handles query requests
func (s *Server) handleQuery(c *gin.Context) {
    var req struct {
        Question string `json:"question" binding:"required"`
    }

    if err := c.ShouldBindJSON(&req); err != nil {
        c.JSON(http.StatusBadRequest, gin.H{"error": err.Error()})
        return
    }

    ctx := context.Background()
    response, err := s.ragPipeline.Query(ctx, req.Question)
    if err != nil {
        c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
        return
    }

    c.JSON(http.StatusOK, response)
}

🧪 Testing and Usage

1. Ingest Documents

curl -X POST http://localhost:8080/ingest \
  -H "Content-Type: application/json" \
  -d '{
    "text": "Golang is an open-source programming language developed by Google, known for its simple syntax, excellent concurrency performance, and fast compilation speed. Go is particularly suitable for building high-performance server-side applications, microservice architectures, and cloud-native applications.",
    "metadata": {
      "source": "golang-introduction",
      "category": "programming-language"
    }
  }'

2. Query Questions

curl -X POST http://localhost:8080/query \
  -H "Content-Type: application/json" \
  -d '{
    "question": "What are the characteristics of Golang?"
  }'

Response Example

{
  "answer": "Based on the reference information, Golang's main characteristics include:\n1. Simple syntax\n2. Excellent concurrency performance\n3. Fast compilation speed\n4. Particularly suitable for building high-performance server-side applications, microservice architectures, and cloud-native applications",
  "sources": [
    {
      "document": {
        "id": "xxx-xxx-xxx",
        "content": "Golang is an open-source programming language developed by Google...",
        "metadata": {
          "source": "golang-introduction"
        }
      },
      "score": 0.89
    }
  ],
  "token_used": 245
}

🚀 Production Environment Optimization

1. Performance Optimization

Using Connection Pooling

// Configure HTTP client connection pool for OpenAI API
httpClient := &http.Client{
    Timeout: 30 * time.Second,
    Transport: &http.Transport{
        MaxIdleConns:        100,
        MaxIdleConnsPerHost: 10,
        IdleConnTimeout:     90 * time.Second,
    },
}

config := openai.DefaultConfig(apiKey)
config.HTTPClient = httpClient
client := openai.NewClientWithConfig(config)

Caching Embeddings

// Use Redis to cache embeddings for common queries
type CachedEmbeddingService struct {
    embService *embedding.EmbeddingService
    cache      *redis.Client
    ttl        time.Duration
}

func (c *CachedEmbeddingService) GetEmbedding(ctx context.Context, text string) ([]float32, error) {
    // Generate cache key
    key := fmt.Sprintf("emb:%s", hashText(text))
    
    // Try to get from cache
    cached, err := c.cache.Get(ctx, key).Bytes()
    if err == nil {
        return deserializeEmbedding(cached), nil
    }
    
    // Cache miss, generate new embedding
    emb, err := c.embService.GenerateEmbedding(ctx, text)
    if err != nil {
        return nil, err
    }
    
    // Store in cache
    c.cache.Set(ctx, key, serializeEmbedding(emb), c.ttl)
    return emb, nil
}

2. Observability

Adding Logs and Metrics

import (
    "github.com/prometheus/client_golang/prometheus"
    "github.com/prometheus/client_golang/prometheus/promauto"
)

var (
    queryDuration = promauto.NewHistogramVec(
        prometheus.HistogramOpts{
            Name: "rag_query_duration_seconds",
            Help: "RAG query duration in seconds",
        },
        []string{"status"},
    )

    embeddingRequests = promauto.NewCounterVec(
        prometheus.CounterOpts{
            Name: "embedding_requests_total",
            Help: "Total number of embedding requests",
        },
        []string{"status"},
    )
)

func (r *RAGPipeline) Query(ctx context.Context, question string) (*models.RAGResponse, error) {
    start := time.Now()
    defer func() {
        duration := time.Since(start).Seconds()
        queryDuration.WithLabelValues("success").Observe(duration)
    }()

    // ... original logic
}

3. Error Handling and Retry

import "github.com/cenkalti/backoff/v4"

func (s *EmbeddingService) GenerateEmbeddingWithRetry(ctx context.Context, text string) ([]float32, error) {
    var result []float32
    
    operation := func() error {
        var err error
        result, err = s.GenerateEmbedding(ctx, text)
        return err
    }

    // Exponential backoff retry strategy
    expBackoff := backoff.NewExponentialBackOff()
    expBackoff.MaxElapsedTime = 30 * time.Second

    err := backoff.Retry(operation, expBackoff)
    return result, err
}

📊 Advanced Techniques

1. Hybrid Search

Combining keyword search and semantic search:

type HybridRetriever struct {
    vectorDB    *vectordb.QdrantClient
    keywordDB   *ElasticsearchClient
    vectorWeight float64  // 0-1, vector search weight
}

func (h *HybridRetriever) Search(ctx context.Context, query string, topK int) ([]models.SearchResult, error) {
    // 1. Execute both searches in parallel
    var vectorResults, keywordResults []models.SearchResult
    var wg sync.WaitGroup
    wg.Add(2)

    go func() {
        defer wg.Done()
        vectorResults, _ = h.vectorDB.Search(ctx, queryVector, topK)
    }()

    go func() {
        defer wg.Done()
        keywordResults, _ = h.keywordDB.Search(ctx, query, topK)
    }()

    wg.Wait()

    // 2. Fuse results (RRF - Reciprocal Rank Fusion)
    return h.fuseResults(vectorResults, keywordResults), nil
}

2. ReRank

Using Cohere ReRank API or local model to improve retrieval accuracy:

func (r *RAGPipeline) ReRank(ctx context.Context, query string, docs []models.Document) ([]models.Document, error) {
    // Call Cohere ReRank API
    client := cohere.NewClient(os.Getenv("COHERE_API_KEY"))
    
    response, err := client.Rerank(ctx, &cohere.RerankRequest{
        Query:     query,
        Documents: extractContents(docs),
        TopN:      5,
        Model:     "rerank-multilingual-v2.0",
    })
    
    if err != nil {
        return nil, err
    }

    // Reorder based on ReRank scores
    reranked := make([]models.Document, len(response.Results))
    for i, result := range response.Results {
        reranked[i] = docs[result.Index]
    }

    return reranked, nil
}

3. Multimodal RAG

Supporting multimodal content like images and tables:

// Extract image content using GPT-4 Vision
func (s *MultiModalService) ExtractImageContent(ctx context.Context, imageURL string) (string, error) {
    req := openai.ChatCompletionRequest{
        Model: openai.GPT4VisionPreview,
        Messages: []openai.ChatCompletionMessage{
            {
                Role: openai.ChatMessageRoleUser,
                MultiContent: []openai.ChatMessagePart{
                    {
                        Type: openai.ChatMessagePartTypeText,
                        Text: "Please describe in detail the content of this image, including text, charts, key information, etc.",
                    },
                    {
                        Type: openai.ChatMessagePartTypeImageURL,
                        ImageURL: &openai.ChatMessageImageURL{
                            URL: imageURL,
                        },
                    },
                },
            },
        },
    }

    resp, err := s.client.CreateChatCompletion(ctx, req)
    // ... handle response
}

🎓 Best Practices Summary

✅ DO

1. Set Chunk Size Appropriately:

   • Technical documentation: 300-500 characters
   • Conversational data: 200-300 characters
   • Long articles: 500-800 characters

2. Add Metadata:

   • Document source, timestamp, category
   • Facilitates filtering and tracing

3. Monitor Costs:

   • Track token usage
   • Use caching to reduce API calls

4. Test Retrieval Quality:

   • Prepare test sets
   • Calculate metrics like MRR, NDCG

❌ DON'T

1. Don't blindly increase topK (high cost, more noise)
2. Don't ignore error handling (API calls can fail)
3. Don't hardcode prompts (use configuration files)
4. Don't ignore security (API Key management, input validation)

🔗 Related Resources

• OpenAI API Documentation
• Qdrant Official Documentation
• go-openai GitHub
• LangChain Go

📝 Summary

This article details how to build a complete RAG system using Golang, from basic architecture to production optimization. RAG technology is rapidly evolving, and it's recommended to stay updated on the latest developments:

• GraphRAG: Knowledge graph-based retrieval
• Self-RAG: Self-reflective RAG system
• Adaptive RAG: Adaptively selecting strategies based on queries

I hope this article helps you quickly get started with Golang + RAG development and build excellent AI applications!

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Keywords: #Golang #RAG #AI #LLM #VectorDatabase #OpenAI #Qdrant #SemanticSearch #Embedding #IntelligentQA

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