Retrieval-Augmented Generation (RAG) with Atlas Vector Search

Set up the environment.

1. Initialize your .NET project.

   Run the following commands in your terminal to create a new directory named MyCompany.RAG and initialize your project:

   dotnet new console -o MyCompany.RAG
   cd MyCompany.RAG

2. Install and import dependencies.

   Run the following commands:

   dotnet add package MongoDB.Driver --version 3.1.0
   dotnet add package PdfPig
   dotnet add package OpenAI

3. Set your environment variables.

   Export the following environment variables, set them in PowerShell, or use your IDE's environment variable manager to make these variables available to your project.

   export OPENAI_API_KEY="<api-key>"
   export ATLAS_CONNECTION_STRING="<connection-string>"

Replace the <api-key> placeholder value with your OpenAI API key.

Replace the <connection-string> placeholder value with the SRV connection string for your Atlas cluster.

Your connection string should use the following format:

mongodb+srv://<db_username>:<db_password>@<clusterName>.<hostname>.mongodb.net

Create a function to generate vector embeddings.

Create a new class named OpenAIService in a file of the same name by pasting the following code. This code defines an async Task named GetEmbeddingsAsync to generate a array of embeddings for an array of given string inputs. This function uses OpenAI's text-embedding-3-small model to generate an embedding for a given input.

namespace MyCompany.RAG;
using OpenAI.Embeddings;
using System;
using System.Threading.Tasks;
public class OpenAIService
{
    private static readonly string? OpenAIApiKey = Environment.GetEnvironmentVariable("OPENAI_API_KEY");
    private static readonly string EmbeddingModelName = "text-embedding-3-small";
    private static readonly EmbeddingClient EmbeddingClient = new(model: EmbeddingModelName, apiKey: OpenAIApiKey);
    public async Task<Dictionary<string, float[]>> GetEmbeddingsAsync(string[] texts)
    {
        Dictionary<string, float[]> documentData = new Dictionary<string, float[]>();
        try
        {
            var result = await EmbeddingClient.GenerateEmbeddingsAsync(texts);
            var embeddingCount = result.Value.Count;
            foreach (var index in Enumerable.Range(0, embeddingCount))
            {
                // Pair each embedding with the text used to generate it.
                documentData[texts[index]] = result.Value[index].ToFloats().ToArray();
            }
        }
        catch (Exception e)
        {
            throw new ApplicationException(e.Message);
        }
        return documentData;
    }
}

Ingest data into Atlas.

In this section, you ingest sample data into Atlas that LLMs don't have access to.

1. Load and split the data.

   Create a new class named PdfIngester in a file of the same name by pasting the following code. This code contains a few functions to do the following:

   • Load a PDF that contains a MongoDB earnings report.

   • Use PdfPig to parse the PDF into text.

   • Split the text into chunks, specifying the chunk size (number of characters) and chunk overlap (number of overlapping characters between consecutive chunks).

   PdfIngester.cs

   namespace MyCompany.RAG;
   using System;
   using System.Net.Http;
   using System.IO;
   using System.Threading.Tasks;
   using System.Collections.Generic;
   using System.Text;
   using UglyToad.PdfPig;
   using UglyToad.PdfPig.Content;
   public class PdfIngester
   {
       public async Task<String> DownloadPdf(string url, string path, string fileName)
       {
           using (HttpClient client = new HttpClient())
           {
               try
               {
                   byte[] pdfBytes = await client.GetByteArrayAsync(url);
                   await File.WriteAllBytesAsync(path + fileName, pdfBytes);
                   return "PDF downloaded and saved to " + path + fileName;
               }
               catch (HttpRequestException e)
               {
                   throw new ApplicationException("Error downloading the PDF: " + e.Message);
               }
               catch (IOException e)
               {
                   throw new ApplicationException("Error writing the file to disk: " + e.Message);
               }
           }
       }
       
       public List<string> ConvertPdfToChunkedText(string filePath)
       {
           List<string> textChunks;
           using (var document = PdfDocument.Open(filePath))
           {
               StringBuilder fullText = new StringBuilder();
               foreach (Page page in document.GetPages())
               {
                   fullText.Append(page.Text + "\n");
               }
               textChunks = ChunkText(fullText.ToString(), 400, 20);
           }
           var chunkCount = textChunks.Count;
           if (chunkCount == 0)
           {
               throw new ApplicationException("Unable to chunk PDF contents into text.");
           }
           Console.WriteLine($"Successfully chunked the PDF text into {chunkCount} chunks.");
           return textChunks;
       }
       
       static List<string> ChunkText(string text, int chunkSize, int overlap)
       {
           List<string> chunks = new List<string>();
           int start = 0;
           int textLength = text.Length;
           while (start < textLength)
           {
               int end = start + chunkSize;
               if (end > textLength)
               {
                   end = textLength;
               }
               string chunk = text.Substring(start, end - start);
               chunks.Add(chunk);
               // Increment starting point, considering the overlap
               start += chunkSize - overlap;
               if (start >= textLength) break;
           }
           return chunks;
       }
   }

2. Prepare to store the data and embeddings in Atlas.

   Create a new class named MongoDBDataService in a file of the same name by pasting the following code. This code defines an async Task named AddDocumentsAsync to add documents to Atlas. This function uses the Collection.InsertManyAsync() C# Driver method to insert a list of the BsonDocument type. This code stores the embeddings alongside the chunked data in the rag_db.test collection in your Atlas cluster.

   MongoDBDataService.cs

   namespace MyCompany.RAG;
   using MongoDB.Driver;
   using MongoDB.Bson;
   public class MongoDBDataService
   {
       private static readonly string? ConnectionString = Environment.GetEnvironmentVariable("ATLAS_CONNECTION_STRING");
       private static readonly MongoClient Client = new MongoClient(ConnectionString);
       private static readonly IMongoDatabase Database = Client.GetDatabase("rag_db");
       private static readonly IMongoCollection<BsonDocument> Collection = Database.GetCollection<BsonDocument>("test");
       public async Task<string> AddDocumentsAsync(Dictionary<string, float[]> embeddings)
       {
           var documents = new List<BsonDocument>();
           foreach( KeyValuePair<string, float[]> var in embeddings )
           {
               var document = new BsonDocument
               {
                   {
                       "text", var.Key
                   },
                   {
                       "embedding", new BsonArray(var.Value)
                   }
               };
               documents.Add(document);
           }
           await Collection.InsertManyAsync(documents);
           return $"Successfully inserted {embeddings.Count} documents into Atlas.";
       }
   }

3. Convert the data to vector embeddings.

   Create a new class named EmbeddingGenerator in a file of the same name by pasting the following code. This code prepares the chunked documents for ingestion by creating a list of documents with their corresponding vector embeddings. You generate these embeddings using the GetEmbeddingsAsync function that you defined earlier.

   EmbeddingGenerator.cs

   namespace MyCompany.RAG;
   public class EmbeddingGenerator
   {
       private readonly MongoDBDataService _dataService = new();
       private readonly OpenAIService _openAiService = new();
       public async Task<string> GenerateEmbeddings(List<string> textChunks)
       {
           Console.WriteLine("Generating embeddings.");
           Dictionary<string, float[]> docs = new Dictionary<string, float[]>();
           try
           {
               // Pass the text chunks to OpenAI to generate vector embeddings
               var embeddings = await _openAiService.GetEmbeddingsAsync(textChunks.ToArray());
               
               // Pair each embedding with the text chunk used to generate it
               int index = 0;
               foreach (var embedding in embeddings)
               {
                   docs[textChunks[index]] = embedding.Value;
                   index++;
               }
           }
           catch (Exception e)
           {
               throw new ApplicationException("Error creating embeddings for text chunks: "  + e.Message);
           }
           // Add a new document to the MongoDB collection for each text and vector embedding pair
           var result = await _dataService.AddDocumentsAsync(docs);
           return result;
       }
   }

4. Update the Program.cs file.

   Paste this code in your Program.cs:

   Program.cs

   using MyCompany.RAG;
   const string pdfUrl = "https://investors.mongodb.com/node/12236/pdf";
   const string savePath = "<path-name>";
   const string fileName = "investor-report.pdf";
   var pdfIngester = new PdfIngester();
   var pdfDownloadResult = await pdfIngester.DownloadPdf(pdfUrl, savePath, fileName);
   Console.WriteLine(pdfDownloadResult);
   var textChunks = pdfIngester.ConvertPdfToChunkedText(savePath + fileName);
   if (textChunks.Any()) {
       var embeddingGenerator = new EmbeddingGenerator();
       var embeddingGenerationResult = await embeddingGenerator.GenerateEmbeddings(textChunks);
       Console.WriteLine(embeddingGenerationResult);
   }

   This code:

   • Uses the PdfIngester to load and chunk the PDF into text segments

   • Uses the EmbeddingGenerator to generate embeddings for each text chunk from the PDF, and write the text chunks and embeddings to the rag_db.test collection

   Replace the <path-name> placeholder with the path where you want to download the report. On a macOS system, the path should resemble /Users/<username>/MyCompany.RAG/. The path should end with a trailing slash.

5. Compile and run your project to generate embeddings.

   dotnet run MyCompany.RAG.csproj

   VIEW OUTPUT

   PDF downloaded and saved to <PATH>
   Successfully chunked the PDF text into 73 chunks.
   Generating embeddings.
   Successfully inserted 73 documents into Atlas.

Use Atlas Vector Search to retrieve documents.

In this section, you set up Atlas Vector Search to retrieve documents from your vector database. To create an Atlas Vector Search index for a collection using the MongoDB C# driver v3.1.0 or later, perform the following steps:

1. Define the Atlas Vector Search index.

   Add a new CreateVectorIndex() method in the file named MongoDBDataService.cs to define the search index. This code connects to your Atlas cluster and creates an index of the vectorSearch type on the rag_db.test collection.

   MongoDBDataService.cs

   namespace MyCompany.RAG;
   using MongoDB.Driver;
   using MongoDB.Bson;
   public class DataService
   {
       private static readonly string? ConnectionString = Environment.GetEnvironmentVariable("ATLAS_CONNECTION_STRING");
       private static readonly MongoClient Client = new MongoClient(ConnectionString);
       private static readonly IMongoDatabase Database = Client.GetDatabase("rag_db");
       private static readonly IMongoCollection<BsonDocument> Collection = Database.GetCollection<BsonDocument>("test");
       public async Task<string> AddDocumentsAsync(Dictionary<string, float[]> embeddings)
       {
           // Method details...
       }
       public string CreateVectorIndex()
       {
           var searchIndexView = Collection.SearchIndexes;
           var name = "vector_index";
           var type = SearchIndexType.VectorSearch;
           var definition = new BsonDocument
           {
               { "fields", new BsonArray
                   {
                       new BsonDocument
                       {
                           { "type", "vector" },
                           { "path", "embedding" },
                           { "numDimensions", 1536 },
                           { "similarity", "cosine" }
                       }
                   }
               }
           };
           var model = new CreateSearchIndexModel(name, type, definition);
           try
           {
               searchIndexView.CreateOne(model);
               Console.WriteLine($"New search index named {name} is building.");
               // Polling for index status
               Console.WriteLine("Polling to check if the index is ready. This may take up to a minute.");
               bool queryable = false;
               while (!queryable)
               {
                   var indexes = searchIndexView.List();
                   foreach (var index in indexes.ToEnumerable())
                   {
                       if (index["name"] == name)
                       {
                           queryable = index["queryable"].AsBoolean;
                       }
                   }
                   if (!queryable)
                   {
                       Thread.Sleep(5000);
                   }
               }
           }
           catch (Exception e)
           {
               throw new ApplicationException("Error creating the vector index: "  + e.Message);
           }
           return $"{name} is ready for querying.";
       }
   }

2. Update the Program.cs file.

   Replace the code in Program.cs with the following code to create the index:

   Program.cs

   using MyCompany.RAG;
   var dataService = new MongoDBDataService();
   var result = dataService.CreateVectorIndex();
   Console.WriteLine(result);

3. Compile and run your project to create the index.

   dotnet run MyCompany.RAG.csproj

4. Define a function to retrieve relevant data.

   Add a new PerformVectorQuery method in the file named MongoDBDataService.cs to retrieve relevant documents. To learn more, refer to Run Vector Search Queries.

   MongoDBDataService.cs

   namespace MyCompany.RAG;
   using MongoDB.Driver;
   using MongoDB.Bson;
   public class MongoDBDataService
   {
       private static readonly string? ConnectionString = Environment.GetEnvironmentVariable("ATLAS_CONNECTION_STRING");
       private static readonly MongoClient Client = new MongoClient(ConnectionString);
       private static readonly IMongoDatabase Database = Client.GetDatabase("rag_db");
       private static readonly IMongoCollection<BsonDocument> Collection = Database.GetCollection<BsonDocument>("test");
       
       public async Task<string> AddDocumentsAsync(Dictionary<string, float[]> embeddings)
       {
           // Method details...
       }
       public string CreateVectorIndex()
       {
           // Method details...
       }
       public List<BsonDocument>? PerformVectorQuery(float[] vector)
       {
           var vectorSearchStage = new BsonDocument
           {
               {
                   "$vectorSearch",
                   new BsonDocument
                   {
                       { "index", "vector_index" },
                       { "path", "embedding" },
                       { "queryVector", new BsonArray(vector) },
                       { "exact", true },
                       { "limit", 5 }
                   }
               }
           };
           var projectStage = new BsonDocument
           {
               {
                   "$project",
                   new BsonDocument
                   {
                       { "_id", 0 },
                       { "text", 1 },
                       { "score", 
                           new BsonDocument
                           {
                               { "$meta", "vectorSearchScore"}
                           }
                       }
                   }
               }
           };
           var pipeline = new[] { vectorSearchStage, projectStage };
           return Collection.Aggregate<BsonDocument>(pipeline).ToList();
       }
   }

5. Test retrieving the data.

   1. Create a new class named PerformTestQuery in a file of the same name by pasting the following code. This code transforms a text input string into vector embeddings, and queries the database for matching results. It uses the GetEmbeddingsAsync function to create embeddings from the search query. Then, it runs the query to return semantically-similar documents.

      PerformTestQuery.cs

      namespace MyCompany.RAG;
      public class PerformTestQuery
      {
          private readonly MongoDBDataService _dataService = new();
          private readonly OpenAIService _openAiService = new();
          public async Task<string> GetQueryResults(string question)
          {
              // Get the vector embedding for the query
              var query = question;
              var queryEmbeddings = await _openAiService.GetEmbeddingsAsync([query]);
              // Query the vector database for applicable query results
              var matchingDocuments = _dataService.PerformVectorQuery(queryEmbeddings[query]);
              // Construct a string from the query results for performing QA with the LLM
              var sb = new System.Text.StringBuilder();
              if (matchingDocuments != null)
              {
                  foreach (var doc in matchingDocuments)
                  {
                      sb.AppendLine($"Text: {doc.GetValue("text").ToString()}");
                      sb.AppendLine($"Score: {doc.GetValue("score").ToString()}");
                  }
              }
              else
              {
                  return "No matching documents found.";
              }
              return sb.ToString();
          }
      }

   2. Update the Program.cs file.

      Replace the code in Program.cs with the following code to perform a test query:

      Program.cs

      using MyCompany.RAG;
      var query = "AI Technology";
      var queryCoordinator = new PerformTestQuery();
      var result = await queryCoordinator.GetQueryResults(query);
      Console.WriteLine(result);

   3. Compile and run your project to check the query results.

      dotnet run MyCompany.RAG.csproj

      VIEW OUTPUT

      Text: time series queries—and the general availability of Atlas Stream Processing to build sophisticated,event-driven applications with real-time data.MongoDB continues to expand its AI ecosystem with the announcement of the MongoDB AI Applications Program (MAAP),
      which provides customers with reference architectures, pre-built partner integrations, and professional services to helpthem quickly build AI
      Score: 0.72528624534606934
      Text: hem quickly build AI-powered applications. Accenture will establish a center of excellence focused on MongoDB projects,and is the first global systems integrator to join MAAP.Bendigo and Adelaide Bank partnered with MongoDB to modernize their core banking technology. With the help ofMongoDB Relational Migrator and generative AI-powered modernization tools, Bendigo and Adelaide Bank decomposed anou
      Score: 0.71915638446807861
      Text: and regulatory issues relating to the use of new and evolving technologies, such asartificial intelligence, in our offerings or partnerships; the growth and expansion of the market for database products and our ability to penetrate thatmarket; our ability to integrate acquired businesses and technologies successfully or achieve the expected benefits of such acquisitions; our ability tomaintain the
      Score: 0.70376789569854736
      Text: architecture is particularly well-suited for the variety and scale of data required by AI-powered applications. We are confident MongoDB will be a substantial beneficiary of this next wave of application development."First Quarter Fiscal 2025 Financial HighlightsRevenue: Total revenue was $450.6 million for the first quarter of fiscal 2025, an increase of 22% year-over-year.Subscription revenue wa
      Score: 0.67905724048614502
      Text: tures, services orenhancements; our ability to effectively expand our sales and marketing organization; our ability to continue to build and maintain credibility with thedeveloper community; our ability to add new customers or increase sales to our existing customers; our ability to maintain, protect, enforce andenhance our intellectual property; the effects of social, ethical and regulatory issue
      Score: 0.64435118436813354

Generate responses with the LLM.

In this section, you generate responses by prompting an LLM to use the retrieved documents as context. This example uses the function you just defined to retrieve matching documents from the database, and additionally:

• Accesses the gpt-4o-mini model from OpenAI.

• Instructs the LLM to include the user's question and retrieved documents in the prompt.

• Prompts the LLM about MongoDB's latest AI announcements.

1. Add the imports, the new ChatClient information, and a new method called GenerateAnswer in the file named OpenAIService.cs.

   OpenAIService.cs

   namespace MyCompany.RAG;
   using OpenAI.Embeddings;
   using OpenAI.Chat;
   using System;
   using System.Text;
   using System.Threading.Tasks;
   public class OpenAIService
   {
       private static readonly string? OpenAIApiKey = Environment.GetEnvironmentVariable("OPENAI_API_KEY");
       private static readonly string EmbeddingModelName = "text-embedding-3-small";
       private static readonly EmbeddingClient EmbeddingClient = new(model: EmbeddingModelName, apiKey: OpenAIApiKey);
       private static readonly string ChatModelName = "gpt-4o-mini";
       private static readonly ChatClient ChatClient = new(model: ChatModelName, apiKey: OpenAIApiKey);
       public async Task<Dictionary<string, float[]>> GetEmbeddingsAsync(string[] texts)
       {
           // Method details...
       }
       public async Task<string> GenerateAnswer(string question, string context)
       {   
           string prompt = $"""
                            Answer the following question based on the given context.
                            Context: {context}
                            Question: {question}
                            """;
           byte[] binaryContent = Encoding.UTF8.GetBytes(prompt);
           IEnumerable<ChatMessage> messages = new List<ChatMessage>([prompt]);
           ChatCompletion responses = await ChatClient.CompleteChatAsync(messages, new ChatCompletionOptions { MaxOutputTokenCount = 400 });
           var summaryResponse = responses.Content[0].Text;
           if (summaryResponse is null)
           {
               throw new ApplicationException("No response from the chat client.");
           }
           return summaryResponse;
       }
   }

2. Create a RAGPipeline class.

   Create a new class named RAGPipeline in a file of the same name by pasting the following code. This code coordinates the following components:

   • GetEmbeddingsAsync function: transform the string query into vector embeddings.

   • PerformVectorQuery function: retrieve semantically-similar results from the database.

   • GenerateAnswer function: pass the documents retrieved from the database to the LLM to generate the response.

   RAGPipeline.cs

   namespace MyCompany.RAG;
   public class RAGPipeline
   {
       private readonly MongoDBDataService _dataService = new();
       private readonly OpenAIService _openAiService = new();
       public async Task<string> GenerateResults(string question)
       {
           // Get the vector embedding for the query
           var query = question;
           var queryEmbedding = await _openAiService.GetEmbeddingsAsync([query]);
           // Query the vector database for applicable query results
           var matchingDocuments = _dataService.PerformVectorQuery(queryEmbedding[query]);
           // Construct a string from the query results for performing QA with the LLM
           var sb = new System.Text.StringBuilder();
           if (matchingDocuments != null)
           {
               foreach (var doc in matchingDocuments)
               {
                   sb.AppendLine($"Text: {doc.GetValue("text").ToString()}");
               }
           }
           else
           {
               return "No matching documents found.";
           }
           return await _openAiService.GenerateAnswer(question, sb.ToString());
       }
   }

3. Update the Program.cs file.

   Replace the code in Program.cs with the following code to call your RAG pipeline:

   Program.cs

   using MyCompany.RAG;
   var question = "In a few sentences, what are MongoDB's latest AI announcements?";
   var ragPipeline = new RAGPipeline();
   var result = await ragPipeline.GenerateResults(question);
   Console.WriteLine(result);

4. Compile and run your project to perform RAG. The generated response might vary.

   dotnet run MyCompany.RAG.csproj

   VIEW OUTPUT

   MongoDB has recently announced the MongoDB AI Applications Program (MAAP),
   which aims to support customers in building AI-powered applications through
   reference architectures, pre-built partner integrations, and professional
   services. Additionally, the program includes a partnership with Accenture,
   which will establish a center of excellence focused on MongoDB projects. These
   initiatives demonstrate MongoDB's commitment to expanding its AI ecosystem and
   its strategy to adapt its document-based architecture for the demands of
   AI-driven application development.

Set up the environment.

1. Initialize your Go project.

   Run the following commands in your terminal to create a new directory named rag-mongodb and initialize your project:

   mkdir rag-mongodb
   cd rag-mongodb
   go mod init rag-mongodb

2. Install and import dependencies.

   Run the following commands:

   go get github.com/joho/godotenv
   go get go.mongodb.org/mongo-driver/mongo
   go get github.com/tmc/langchaingo/llms
   go get github.com/tmc/langchaingo/documentloaders
   go get github.com/tmc/langchaingo/embeddings/huggingface
   go get github.com/tmc/langchaingo/llms/huggingface
   go get github.com/tmc/langchaingo/prompts

3. Create a .env file.

   In your project, create a .env file to store your Atlas connection string and Hugging Face access token.

   .env

   HUGGINGFACEHUB_API_TOKEN = "<access-token>"
   ATLAS_CONNECTION_STRING = "<connection-string>"

   Replace the <access-token> placeholder value with your Hugging Face access token.

   Replace the <connection-string> placeholder value with the SRV connection string for your Atlas cluster.

   Your connection string should use the following format:

   mongodb+srv://<db_username>:<db_password>@<clusterName>.<hostname>.mongodb.net

Create a function to generate vector embeddings.

In this section, you create a function that:

• Loads the mxbai-embed-large-v1 embedding model from Hugging Face's model hub.

• Creates vector embeddings from the inputted data.

1. Run the following command to create a directory that stores common functions, including one that you'll reuse to create embeddings.

   mkdir common && cd common

2. Create a file called get-embeddings.go in the common directory, and paste the following code into it:

   get-embeddings.go

   package common
   import (
   	"context"
   	"log"
   	"github.com/tmc/langchaingo/embeddings/huggingface"
   )
   func GetEmbeddings(documents []string) [][]float32 {
   	hf, err := huggingface.NewHuggingface(
   		huggingface.WithModel("mixedbread-ai/mxbai-embed-large-v1"),
   		huggingface.WithTask("feature-extraction"))
   	if err != nil {
   		log.Fatalf("failed to connect to Hugging Face: %v", err)
   	}
   	embs, err := hf.EmbedDocuments(context.Background(), documents)
   	if err != nil {
   		log.Fatalf("failed to generate embeddings: %v", err)
   	}
   	return embs
   }

Ingest data into Atlas.

In this section, you ingest sample data into Atlas that LLMs don't have access to. The following code uses the Go library for LangChain and Go driver to do the following:

• Create a HTML file that contains a MongoDB earnings report.

• Split the data into chunks, specifying the chunk size (number of characters) and chunk overlap (number of overlapping characters between consecutive chunks).

• Create vector embeddings from the chunked data by using the GetEmbeddings function that you defined.

• Store these embeddings alongside the chunked data in the rag_db.test collection in your Atlas cluster.

1. Navigate to the root of the rag-mongodb project directory.

2. Create a file called ingest-data.go in your project, and paste the following code into it:

   ingest-data.go

   package main
   import (
   	"context"
   	"fmt"
   	"io"
   	"log"
   	"net/http"
   	"os"
   	"rag-mongodb/common" // Module that contains the embedding function
   	"github.com/joho/godotenv"
   	"github.com/tmc/langchaingo/documentloaders"
   	"github.com/tmc/langchaingo/textsplitter"
   	"go.mongodb.org/mongo-driver/mongo"
   	"go.mongodb.org/mongo-driver/mongo/options"
   )
   type DocumentToInsert struct {
   	PageContent string    `bson:"pageContent"`
   	Embedding   []float32 `bson:"embedding"`
   }
   func downloadReport(filename string) {
   	_, err := os.Stat(filename)
   	if err == nil {
   		return
   	}
   	url := "https://investors.mongodb.com/node/12236"
   	fmt.Println("Downloading ", url, " to ", filename)
   	resp, err := http.Get(url)
   	if err != nil {
   		log.Fatalf("failed to connect to download the report: %v", err)
   	}
   	defer func() { _ = resp.Body.Close() }()
   	f, err := os.Create(filename)
   	if err != nil {
   		return
   	}
   	defer func() { _ = f.Close() }()
   	_, err = io.Copy(f, resp.Body)
   	if err != nil {
   		log.Fatalf("failed to copy the report: %v", err)
   	}
   }
   func main() {
   	ctx := context.Background()
   	filename := "investor-report.html"
   	downloadReport(filename)
   	f, err := os.Open(filename)
   	if err != nil {
   		defer func() { _ = f.Close() }()
   		log.Fatalf("failed to open the report: %v", err)
   	}
   	defer func() { _ = f.Close() }()
   	html := documentloaders.NewHTML(f)
   	split := textsplitter.NewRecursiveCharacter()
   	split.ChunkSize = 400
   	split.ChunkOverlap = 20
   	docs, err := html.LoadAndSplit(context.Background(), split)
   	if err != nil {
   		log.Fatalf("failed to chunk the HTML into documents: %v", err)
   	}
   	fmt.Printf("Successfully chunked the HTML into %v documents.\n", len(docs))
   	if err := godotenv.Load(); err != nil {
   		log.Fatal("no .env file found")
   	}
   	// Connect to your Atlas cluster
   	uri := os.Getenv("ATLAS_CONNECTION_STRING")
   	if uri == "" {
   		log.Fatal("set your 'ATLAS_CONNECTION_STRING' environment variable.")
   	}
   	clientOptions := options.Client().ApplyURI(uri)
   	client, err := mongo.Connect(ctx, clientOptions)
   	if err != nil {
   		log.Fatalf("failed to connect to the server: %v", err)
   	}
   	defer func() { _ = client.Disconnect(ctx) }()
   	// Set the namespace
   	coll := client.Database("rag_db").Collection("test")
   	fmt.Println("Generating embeddings.")
   	var pageContents []string
   	for i := range docs {
   		pageContents = append(pageContents, docs[i].PageContent)
   	}
   	embeddings := common.GetEmbeddings(pageContents)
   	docsToInsert := make([]interface{}, len(embeddings))
   	for i := range embeddings {
   		docsToInsert[i] = DocumentToInsert{
   			PageContent: pageContents[i],
   			Embedding:   embeddings[i],
   		}
   	}
   	result, err := coll.InsertMany(ctx, docsToInsert)
   	if err != nil {
   		log.Fatalf("failed to insert documents: %v", err)
   	}
   	fmt.Printf("Successfully inserted %v documents into Atlas\n", len(result.InsertedIDs))
   }

3. Run the following command to execute the code:

   go run ingest-data.go

   VIEW OUTPUT

   Successfully chunked the HTML into 163 documents.
   Generating embeddings.
   Successfully inserted document with id: &{ObjectID("66faffcd60da3f6d4f990fa4")}
   Successfully inserted document with id: &{ObjectID("66faffce60da3f6d4f990fa5")}
   ...

Use Atlas Vector Search to retrieve documents.

In this section, you set up Atlas Vector Search to retrieve documents from your vector database. Complete the following steps:

1. Create an Atlas Vector Search index on your vector embeddings.

   Create a new file named rag-vector-index.go and paste the following code. This code connects to your Atlas cluster and creates an index of the vectorSearch type on the rag_db.test collection.

   rag-vector-index.go

   package main
   import (
   	"context"
   	"log"
   	"os"
   	"time"
   	"go.mongodb.org/mongo-driver/bson"
   	"github.com/joho/godotenv"
   	"go.mongodb.org/mongo-driver/mongo"
   	"go.mongodb.org/mongo-driver/mongo/options"
   )
   func main() {
   	ctx := context.Background()
   	if err := godotenv.Load(); err != nil {
   		log.Fatal("no .env file found")
   	}
   	// Connect to your Atlas cluster
   	uri := os.Getenv("ATLAS_CONNECTION_STRING")
   	if uri == "" {
   		log.Fatal("set your 'ATLAS_CONNECTION_STRING' environment variable.")
   	}
   	clientOptions := options.Client().ApplyURI(uri)
   	client, err := mongo.Connect(ctx, clientOptions)
   	if err != nil {
   		log.Fatalf("failed to connect to the server: %v", err)
   	}
   	defer func() { _ = client.Disconnect(ctx) }()
   	// Specify the database and collection
   	coll := client.Database("rag_db").Collection("test")
   	indexName := "vector_index"
   	opts := options.SearchIndexes().SetName(indexName).SetType("vectorSearch")
   	type vectorDefinitionField struct {
   		Type          string `bson:"type"`
   		Path          string `bson:"path"`
   		NumDimensions int    `bson:"numDimensions"`
   		Similarity    string `bson:"similarity"`
   	}
   	type filterField struct {
   		Type string `bson:"type"`
   		Path string `bson:"path"`
   	}
   	type vectorDefinition struct {
   		Fields []vectorDefinitionField `bson:"fields"`
   	}
   	indexModel := mongo.SearchIndexModel{
   		Definition: vectorDefinition{
   			Fields: []vectorDefinitionField{{
   				Type:          "vector",
   				Path:          "embedding",
   				NumDimensions: 1024,
   				Similarity:    "cosine"}},
   		},
   		Options: opts,
   	}
   	log.Println("Creating the index.")
   	searchIndexName, err := coll.SearchIndexes().CreateOne(ctx, indexModel)
   	if err != nil {
   		log.Fatalf("failed to create the search index: %v", err)
   	}
   	// Await the creation of the index.
   	log.Println("Polling to confirm successful index creation.")
   	log.Println("NOTE: This may take up to a minute.")
   	searchIndexes := coll.SearchIndexes()
   	var doc bson.Raw
   	for doc == nil {
   		cursor, err := searchIndexes.List(ctx, options.SearchIndexes().SetName(searchIndexName))
   		if err != nil {
   			log.Printf("failed to list search indexes: %w", err)
   		}
   		if !cursor.Next(ctx) {
   			break
   		}
   		name := cursor.Current.Lookup("name").StringValue()
   		queryable := cursor.Current.Lookup("queryable").Boolean()
   		if name == searchIndexName && queryable {
   			doc = cursor.Current
   		} else {
   			time.Sleep(5 * time.Second)
   		}
   	}
   	log.Println("Name of Index Created: " + searchIndexName)
   }

2. Run the following command to create the index:

   go run rag-vector-index.go

3. Define a function to retrieve relevant data.

   In this step, you create a retrieval function called GetQueryResults that runs a query to retrieve relevant documents. It uses the GetEmbeddings function to create embeddings from the search query. Then, it runs the query to return semantically-similar documents.

   To learn more, refer to Run Vector Search Queries.

   In the common directory, create a new file called get-query-results.go, and paste the following code into it:

   get-query-results.go

   package common
   import (
   	"context"
   	"log"
   	"os"
   	"github.com/joho/godotenv"
   	"go.mongodb.org/mongo-driver/bson"
   	"go.mongodb.org/mongo-driver/mongo"
   	"go.mongodb.org/mongo-driver/mongo/options"
   )
   type TextWithScore struct {
   	PageContent string  `bson:"pageContent"`
   	Score       float64 `bson:"score"`
   }
   func GetQueryResults(query string) []TextWithScore {
   	ctx := context.Background()
   	if err := godotenv.Load(); err != nil {
   		log.Fatal("no .env file found")
   	}
   	// Connect to your Atlas cluster
   	uri := os.Getenv("ATLAS_CONNECTION_STRING")
   	if uri == "" {
   		log.Fatal("set your 'ATLAS_CONNECTION_STRING' environment variable.")
   	}
   	clientOptions := options.Client().ApplyURI(uri)
   	client, err := mongo.Connect(ctx, clientOptions)
   	if err != nil {
   		log.Fatalf("failed to connect to the server: %v", err)
   	}
   	defer func() { _ = client.Disconnect(ctx) }()
   	// Specify the database and collection
   	coll := client.Database("rag_db").Collection("test")
   	queryEmbedding := GetEmbeddings([]string{query})
   	vectorSearchStage := bson.D{
   		{"$vectorSearch", bson.D{
   			{"index", "vector_index"},
   			{"path", "embedding"},
   			{"queryVector", queryEmbedding[0]},
   			{"exact", true},
   			{"limit", 5},
   		}}}
   	projectStage := bson.D{
   		{"$project", bson.D{
   			{"_id", 0},
   			{"pageContent", 1},
   			{"score", bson.D{{"$meta", "vectorSearchScore"}}},
   		}}}
   	cursor, err := coll.Aggregate(ctx, mongo.Pipeline{vectorSearchStage, projectStage})
   	if err != nil {
   		log.Fatalf("failed to execute the aggregation pipeline: %v", err)
   	}
   	var results []TextWithScore
   	if err = cursor.All(context.TODO(), &results); err != nil {
   		log.Fatalf("failed to connect unmarshal retrieved documents: %v", err)
   	}
   	return results
   }

4. Test retrieving the data.

   1. In the rag-mongodb project directory, create a new file called retrieve-documents-test.go. In this step, you check that the function you just defined returns relevant results.

   2. Paste this code into your file:

      retrieve-documents-test.go

      package main
      import (
      	"fmt"
      	"rag-mongodb/common" // Module that contains the GetQueryResults function
      )
      func main() {
      	query := "AI Technology"
      	documents := common.GetQueryResults(query)
      	for _, doc := range documents {
      		fmt.Printf("Text: %s \nScore: %v \n\n", doc.PageContent, doc.Score)
      	}
      }

   3. Run the following command to execute the code:

      go run retrieve-documents-test.go

      VIEW OUTPUT

      Text: for the variety and scale of data required by AI-powered applications. We are confident MongoDB will be a substantial beneficiary of this next wave of application development."
      Score: 0.835033655166626
      Text: "As we look ahead, we continue to be incredibly excited by our large market opportunity, the potential to increase share, and become a standard within more of our customers. We also see a tremendous opportunity to win more legacy workloads, as AI has now become a catalyst to modernize these applications. MongoDB's document-based architecture is particularly well-suited for the variety and
      Score: 0.8280757665634155
      Text: to the use of new and evolving technologies, such as artificial intelligence, in our offerings or partnerships; the growth and expansion of the market for database products and our ability to penetrate that market; our ability to integrate acquired businesses and technologies successfully or achieve the expected benefits of such acquisitions; our ability to maintain the security of our software
      Score: 0.8165900111198425
      Text: MongoDB continues to expand its AI ecosystem with the announcement of the MongoDB AI Applications Program (MAAP), which provides customers with reference architectures, pre-built partner integrations, and professional services to help them quickly build AI-powered applications. Accenture will establish a center of excellence focused on MongoDB projects, and is the first global systems
      Score: 0.8023912906646729
      Text: Bendigo and Adelaide Bank partnered with MongoDB to modernize their core banking technology. With the help of MongoDB Relational Migrator and generative AI-powered modernization tools, Bendigo and Adelaide Bank decomposed an outdated consumer-servicing application into microservices and migrated off its underlying legacy relational database technology significantly faster and more easily than
      Score: 0.7959681749343872

Generate responses with the LLM.

In this section, you generate responses by prompting an LLM to use the retrieved documents as context. This example uses the function you just defined to retrieve matching documents from the database, and additionally:

• Accesses the Mistral 7B Instruct model from Hugging Face's model hub.

• Instructs the LLM to include the user's question and retrieved documents in the prompt.

• Prompts the LLM about MongoDB's latest AI announcements.

1. Create a new file called generate-responses.go, and paste the following code into it:

   generate-responses.go

   package main
   import (
   	"context"
   	"fmt"
   	"log"
   	"rag-mongodb/common" // Module that contains the GetQueryResults function
   	"strings"
   	"github.com/tmc/langchaingo/llms"
   	"github.com/tmc/langchaingo/llms/huggingface"
   	"github.com/tmc/langchaingo/prompts"
   )
   func main() {
   	ctx := context.Background()
   	query := "AI Technology"
   	documents := common.GetQueryResults(query)
   	var textDocuments strings.Builder
   	for _, doc := range documents {
   		textDocuments.WriteString(doc.PageContent)
   	}
   	question := "In a few sentences, what are MongoDB's latest AI announcements?"
   	template := prompts.NewPromptTemplate(
   		`Answer the following question based on the given context.
   			Question: {{.question}}
   			Context: {{.context}}`,
   		[]string{"question", "context"},
   	)
   	prompt, err := template.Format(map[string]any{
   		"question": question,
   		"context":  textDocuments.String(),
   	})
   	opts := llms.CallOptions{
   		Model:       "mistralai/Mistral-7B-Instruct-v0.3",
   		MaxTokens:   150,
   		Temperature: 0.1,
   	}
   	llm, err := huggingface.New(huggingface.WithModel("mistralai/Mistral-7B-Instruct-v0.3"))
   	if err != nil {
   		log.Fatalf("failed to initialize a Hugging Face LLM: %v", err)
   	}
   	completion, err := llms.GenerateFromSinglePrompt(ctx, llm, prompt, llms.WithOptions(opts))
   	if err != nil {
   		log.Fatalf("failed to generate a response from the prompt: %v", err)
   	}
   	response := strings.Split(completion, "\n\n")
   	if len(response) == 2 {
   		fmt.Printf("Prompt: %v\n\n", response[0])
   		fmt.Printf("Response: %v\n", response[1])
   	}
   }

2. Run this command to execute the code. The generated response might vary.

   go run generate-responses.go

   VIEW OUTPUT

   Prompt: Answer the following question based on the given context.
   			Question: In a few sentences, what are MongoDB's latest AI announcements?
   			Context: for the variety and scale of data required by AI-powered applications. We are confident MongoDB will be a substantial beneficiary of this next wave of application development.""As we look ahead, we continue to be incredibly excited by our large market opportunity, the potential to increase share, and become a standard within more of our customers. We also see a tremendous opportunity to win more legacy workloads, as AI has now become a catalyst to modernize these applications. MongoDB's document-based architecture is particularly well-suited for the variety andto the use of new and evolving technologies, such as artificial intelligence, in our offerings or partnerships; the growth and expansion of the market for database products and our ability to penetrate that market; our ability to integrate acquired businesses and technologies successfully or achieve the expected benefits of such acquisitions; our ability to maintain the security of our softwareMongoDB continues to expand its AI ecosystem with the announcement of the MongoDB AI Applications Program (MAAP), which provides customers with reference architectures, pre-built partner integrations, and professional services to help them quickly build AI-powered applications. Accenture will establish a center of excellence focused on MongoDB projects, and is the first global systemsBendigo and Adelaide Bank partnered with MongoDB to modernize their core banking technology. With the help of MongoDB Relational Migrator and generative AI-powered modernization tools, Bendigo and Adelaide Bank decomposed an outdated consumer-servicing application into microservices and migrated off its underlying legacy relational database technology significantly faster and more easily than expected.
   Response: MongoDB's latest AI announcements include the launch of the MongoDB AI Applications Program (MAAP) and a partnership with Accenture to establish a center of excellence focused on MongoDB projects. Additionally, Bendigo and Adelaide Bank have partnered with MongoDB to modernize their core banking technology using MongoDB's AI-powered modernization tools.

Create your Java project and install dependencies.

1. From your IDE, create a Java project using Maven or Gradle.

2. Add the following dependencies, depending on your package manager:

   Maven

   Gradle

   If you are using Maven, add the following dependencies to the dependencies array and Bill of Materials (BOM) to the dependencyManagement array in your project's pom.xml file:

   pom.xml

   <dependencies>
      <!-- MongoDB Java Sync Driver v5.2.0 or later -->
      <dependency>
            <groupId>org.mongodb</groupId>
            <artifactId>mongodb-driver-sync</artifactId>
            <version>[5.2.0,)</version>
      </dependency>
      <!-- Java library for Hugging Face models -->
      <dependency>
            <groupId>dev.langchain4j</groupId>
            <artifactId>langchain4j-hugging-face</artifactId>
      </dependency>
      <!-- Java library for URL Document Loader -->
      <dependency>
            <groupId>dev.langchain4j</groupId>
            <artifactId>langchain4j</artifactId>
      </dependency>
      <!-- Java library for ApachePDFBox Document Parser -->
      <dependency>
            <groupId>dev.langchain4j</groupId>
            <artifactId>langchain4j-document-parser-apache-pdfbox</artifactId>
      </dependency>
   </dependencies>
   <dependencyManagement>
      <dependencies>
            <!-- Bill of Materials (BOM) to manage Java library versions -->
            <dependency>
               <groupId>dev.langchain4j</groupId>
               <artifactId>langchain4j-bom</artifactId>
               <version>0.36.2</version>
               <type>pom</type>
               <scope>import</scope>
            </dependency>
      </dependencies>
   </dependencyManagement>

   If you are using Gradle, add the following Bill of Materials (BOM) and dependencies to the dependencies array in in your project's build.gradle file:

   build.gradle

   dependencies {
      // Bill of Materials (BOM) to manage Java library versions
      implementation platform('dev.langchain4j:langchain4j-bom:0.36.2')
      // MongoDB Java Sync Driver v5.2.0 or later
      implementation 'org.mongodb:mongodb-driver-sync:5.2.0'
      // Java library for Hugging Face models
      implementation 'dev.langchain4j:langchain4j-hugging-face'
      // Java library for URL Document Loader
      implementation 'dev.langchain4j:langchain4j'
      // Java library for Apache PDFBox Document Parser
      implementation 'dev.langchain4j:langchain4j-document-parser-apache-pdfbox'
   }

3. Run your package manager to install the dependencies to your project.

Set your environment variables.

In your IDE, create a new configuration template and add the following variables to your project:

• If you are using IntelliJ IDEA, create a new Application run configuration template, then add your variables as semicolon-separated values in the Environment variables field (for example, FOO=123;BAR=456). Apply the changes and click OK.

  To learn more, see the Create a run/debug configuration from a template section of the IntelliJ IDEA documentation.

• If you are using Eclipse, create a new Java Application launch configuration, then add each variable as a new key-value pair in the Environment tab. Apply the changes and click OK.

  To learn more, see the Creating a Java application launch configuration section of the Eclipse IDE documentation.

   HUGGING_FACE_ACCESS_TOKEN=<access-token>
   ATLAS_CONNECTION_STRING=<connection-string>

Update the placeholders with the following values:

• Replace the <access-token> placeholder value with your Hugging Face access token.

• Replace the <connection-string> placeholder value with the SRV connection string for your Atlas cluster.

  Your connection string should use the following format:

  mongodb+srv://<db_username>:<db_password>@<clusterName>.<hostname>.mongodb.net

Define methods to parse and split the data.

Create a file named PDFProcessor.java and paste the following code.

This code defines the following methods:

• The parsePDFDocument method uses the Apache PDFBox library and LangChain4j URL Document Loader to load and parse a PDF file at a given URL. The method returns the parsed PDF as a langchain4j Document.

• The splitDocument method splits a given langchain4j Document into chunks according to the specified chunk size (number of characters) and chunk overlap (number of overlapping characters between consecutive chunks). The method returns a list of text segments.

import dev.langchain4j.data.document.Document;
import dev.langchain4j.data.document.DocumentParser;
import dev.langchain4j.data.document.DocumentSplitter;
import dev.langchain4j.data.document.loader.UrlDocumentLoader;
import dev.langchain4j.data.document.parser.apache.pdfbox.ApachePdfBoxDocumentParser;
import dev.langchain4j.data.document.splitter.DocumentByCharacterSplitter;
import dev.langchain4j.data.segment.TextSegment;
import java.util.List;
public class PDFProcessor {
    /** Parses a PDF document from the specified URL, and returns a
     * langchain4j Document object.
     * */
    public static Document parsePDFDocument(String url) {
        DocumentParser parser = new ApachePdfBoxDocumentParser();
        return UrlDocumentLoader.load(url, parser);
    }
    /** Splits a parsed langchain4j Document based on the specified chunking
     * parameters, and returns an array of text segments.
     */
    public static List<TextSegment> splitDocument(Document document) {
        int maxChunkSize = 400; // number of characters
        int maxChunkOverlap = 20; // number of overlapping characters between consecutive chunks
        DocumentSplitter splitter = new DocumentByCharacterSplitter(maxChunkSize, maxChunkOverlap);
        return splitter.split(document);
    }
}

Define a method to generate vector embeddings.

Create a file named EmbeddingProvider.java and paste the following code.

This code defines two methods to generate embeddings for a given input using the mxbai-embed-large-v1 open-source embedding model:

• Multiple Inputs: The getEmbeddings method accepts an array of text segment inputs (List<TextSegment>), allowing you to create multiple embeddings in a single API call. The method converts the API-provided arrays of floats to BSON arrays of doubles for storing in your Atlas cluster.

• Single Input: The getEmbedding method accepts a single String, which represents a query you want to make against your vector data. The method converts the API-provided array of floats to a BSON array of doubles to use when querying your collection.

import dev.langchain4j.data.embedding.Embedding;
import dev.langchain4j.data.segment.TextSegment;
import dev.langchain4j.model.huggingface.HuggingFaceChatModel;
import dev.langchain4j.model.huggingface.HuggingFaceEmbeddingModel;
import dev.langchain4j.model.output.Response;
import org.bson.BsonArray;
import org.bson.BsonDouble;
import java.util.List;
import static java.time.Duration.ofSeconds;
public class EmbeddingProvider {
    private static HuggingFaceEmbeddingModel embeddingModel;
    private static HuggingFaceEmbeddingModel getEmbeddingModel() {
        if (embeddingModel == null) {
            String accessToken = System.getenv("HUGGING_FACE_ACCESS_TOKEN");
            if (accessToken == null || accessToken.isEmpty()) {
                throw new RuntimeException("HUGGING_FACE_ACCESS_TOKEN env variable is not set or is empty.");
            }
            embeddingModel = HuggingFaceEmbeddingModel.builder()
                    .accessToken(accessToken)
                    .modelId("mixedbread-ai/mxbai-embed-large-v1")
                    .waitForModel(true)
                    .timeout(ofSeconds(60))
                    .build();
        }
        return embeddingModel;
    }
    /**
     * Returns the Hugging Face chat model interface used by the createPrompt() method
     * to process queries and generate responses.
     */
    private static HuggingFaceChatModel chatModel;
    public static HuggingFaceChatModel getChatModel() {
        String accessToken = System.getenv("HUGGING_FACE_ACCESS_TOKEN");
        if (accessToken == null || accessToken.isEmpty()) {
            throw new IllegalStateException("HUGGING_FACE_ACCESS_TOKEN env variable is not set or is empty.");
        }
        if (chatModel == null) {
            chatModel = HuggingFaceChatModel.builder()
                    .timeout(ofSeconds(25))
                    .modelId("mistralai/Mistral-7B-Instruct-v0.3")
                    .temperature(0.1)
                    .maxNewTokens(150)
                    .accessToken(accessToken)
                    .waitForModel(true)
                    .build();
        }
        return chatModel;
    }
    /**
     * Takes an array of text segments and returns a BSON array of embeddings to
     * store in the database.
     */
    public List<BsonArray> getEmbeddings(List<TextSegment> texts) {
        List<TextSegment> textSegments = texts.stream()
                .toList();
        Response<List<Embedding>> response = getEmbeddingModel().embedAll(textSegments);
        return response.content().stream()
                .map(e -> new BsonArray(
                        e.vectorAsList().stream()
                                .map(BsonDouble::new)
                                .toList()))
                .toList();
    }
    /**
     * Takes a single string and returns a BSON array embedding to
     * use in a vector query.
     */
    public static BsonArray getEmbedding(String text) {
        Response<Embedding> response = getEmbeddingModel().embed(text);
        return new BsonArray(
                response.content().vectorAsList().stream()
                        .map(BsonDouble::new)
                        .toList());
    }
}

Define a method to ingest data into Atlas.

Create a file named DataIngest.java and paste the following code.

This code uses the LangChain4j library and the MongoDB Java Sync Driver to ingest sample data into Atlas that LLMs don't have access to.

Specifically, this code does the following:

1. Connects to your Atlas cluster.

2. Loads and parses the MongoDB earnings report PDF file from the URL using the parsePDFDocument method that you previously defined.

3. Splits the data into chunks using the splitDocument method that you previously defined.

4. Creates vector embeddings from the chunked data using the GetEmbeddings method that you previously defined.

5. Stores the embeddings alongside the chunked data in the rag_db.test collection in your Atlas cluster.

   DataIngest.java

   import com.mongodb.MongoException;
   import com.mongodb.client.MongoClient;
   import com.mongodb.client.MongoClients;
   import com.mongodb.client.MongoCollection;
   import com.mongodb.client.MongoDatabase;
   import com.mongodb.client.result.InsertManyResult;
   import dev.langchain4j.data.segment.TextSegment;
   import org.bson.BsonArray;
   import org.bson.Document;
   import java.util.ArrayList;
   import java.util.List;
   public class DataIngest {
       public static void main(String[] args) {
           String uri = System.getenv("ATLAS_CONNECTION_STRING");
           if (uri == null || uri.isEmpty()) {
               throw new RuntimeException("ATLAS_CONNECTION_STRING env variable is not set or is empty.");
           }
           // establish connection and set namespace
           try (MongoClient mongoClient = MongoClients.create(uri)) {
               MongoDatabase database = mongoClient.getDatabase("rag_db");
               MongoCollection<Document> collection = database.getCollection("test");
               // parse the PDF file at the specified URL
               String url = "https://investors.mongodb.com/node/12236/pdf";
               String fileName = "mongodb_annual_report.pdf";
               System.out.println("Parsing the [" + fileName + "] file from url: " + url);
               dev.langchain4j.data.document.Document parsedDoc = PDFProcessor.parsePDFDocument(url);
               // split (or "chunk") the parsed document into text segments
               List<TextSegment> segments = PDFProcessor.splitDocument(parsedDoc);
               System.out.println(segments.size() + " text segments created successfully.");
               
               // create vector embeddings from the chunked data (i.e. text segments)
               System.out.println("Creating vector embeddings from the parsed data segments. This may take a few moments.");
               List<Document> documents = embedText(segments);
               // insert the embeddings into the Atlas collection
               try {
                   System.out.println("Ingesting data into the " + collection.getNamespace() + " collection.");
                   insertDocuments(documents, collection);
               }
               catch (MongoException me) {
                   throw new RuntimeException("Failed to insert documents", me);
               }
           } catch (MongoException me) {
               throw new RuntimeException("Failed to connect to MongoDB", me);
           } catch (Exception e) {
               throw new RuntimeException("Operation failed: ", e);
           }
       }
       
       /** 
        * Embeds text segments into vector embeddings using the EmbeddingProvider
        * class and returns a list of BSON documents containing the text and 
        * generated embeddings.
       */
       private static List<Document> embedText(List<TextSegment> segments) {
           EmbeddingProvider embeddingProvider = new EmbeddingProvider();
           List<BsonArray> embeddings = embeddingProvider.getEmbeddings(segments);
           List<Document> documents = new ArrayList<>();
           int i = 0;
           for (TextSegment segment : segments) {
               Document doc = new Document("text", segment.text()).append("embedding", embeddings.get(i));
               documents.add(doc);
               i++;
           }
           return documents;
       }
       /**
        * Inserts a list of BSON documents into the specified MongoDB collection.
        */
       private static void insertDocuments(List<Document> documents, MongoCollection<Document> collection) {
           List<String> insertedIds = new ArrayList<>();
           InsertManyResult result = collection.insertMany(documents);
           result.getInsertedIds().values()
                   .forEach(doc -> insertedIds.add(doc.toString()));
           System.out.println(insertedIds.size() + " documents inserted into the " + collection.getNamespace() + " collection successfully.");
       }
   }

Generate the embeddings.

Save and run the DataIngest.java file. The output resembles:

Parsing the [mongodb_annual_report.pdf] file from url: https://investors.mongodb.com/node/12236/pdf
72 text segments created successfully.
Creating vector embeddings from the parsed data segments. This may take a few moments...
Ingesting data into the rag_db.test collection.
72 documents inserted into the rag_db.test collection successfully.

Use Atlas Vector Search to retrieve documents.

In this section, you set up Atlas Vector Search to retrieve documents from your vector database.

1. Create a file named VectorIndex.java and paste the following code.

   This code creates an Atlas Vector Search index on your collection using the following index definition:

   • Index the embedding field in a vector index type for the rag_db.test collection. This field contains the embedding created using the embedding model.

   • Enforce 1024 vector dimensions and measure similarity between vectors using cosine.

   VectorIndex.java

   import com.mongodb.MongoException;
   import com.mongodb.client.ListSearchIndexesIterable;
   import com.mongodb.client.MongoClient;
   import com.mongodb.client.MongoClients;
   import com.mongodb.client.MongoCollection;
   import com.mongodb.client.MongoCursor;
   import com.mongodb.client.MongoDatabase;
   import com.mongodb.client.model.SearchIndexModel;
   import com.mongodb.client.model.SearchIndexType;
   import org.bson.Document;
   import org.bson.conversions.Bson;
   import java.util.Collections;
   import java.util.List;
   public class VectorIndex {
       public static void main(String[] args) {
           String uri = System.getenv("ATLAS_CONNECTION_STRING");
           if (uri == null || uri.isEmpty()) {
               throw new IllegalStateException("ATLAS_CONNECTION_STRING env variable is not set or is empty.");
           }
           // establish connection and set namespace
           try (MongoClient mongoClient = MongoClients.create(uri)) {
               MongoDatabase database = mongoClient.getDatabase("rag_db");
               MongoCollection<Document> collection = database.getCollection("test");
               // define the index details for the index model
               String indexName = "vector_index";
               Bson definition = new Document(
                       "fields",
                       Collections.singletonList(
                               new Document("type", "vector")
                                       .append("path", "embedding")
                                       .append("numDimensions", 1024)
                                       .append("similarity", "cosine")));
               SearchIndexModel indexModel = new SearchIndexModel(
                       indexName,
                       definition,
                       SearchIndexType.vectorSearch());
               // create the index using the defined model
               try {
                   List<String> result = collection.createSearchIndexes(Collections.singletonList(indexModel));
                   System.out.println("Successfully created vector index named: " + result);
                   System.out.println("It may take up to a minute for the index to build before you can query using it.");
               } catch (Exception e) {
                   throw new RuntimeException(e);
               }
               // wait for Atlas to build the index and make it queryable
               System.out.println("Polling to confirm the index has completed building.");
               waitForIndexReady(collection, indexName);
           } catch (MongoException me) {
               throw new RuntimeException("Failed to connect to MongoDB", me);
           } catch (Exception e) {
               throw new RuntimeException("Operation failed: ", e);
           }
       }
       /**
        * Polls the collection to check whether the specified index is ready to query.
        */
       public static void waitForIndexReady(MongoCollection<Document> collection, String indexName) throws InterruptedException {
           ListSearchIndexesIterable<Document> searchIndexes = collection.listSearchIndexes();
           while (true) {
               try (MongoCursor<Document> cursor = searchIndexes.iterator()) {
                   if (!cursor.hasNext()) {
                       break;
                   }
                   Document current = cursor.next();
                   String name = current.getString("name");
                   boolean queryable = current.getBoolean("queryable");
                   if (name.equals(indexName) && queryable) {
                       System.out.println(indexName + " index is ready to query");
                       return;
                   } else {
                       Thread.sleep(500);
                   }
               }
           }
       }
   }

2. Create the Atlas Vector Search index.

   Save and run the file. The output resembles:

   Successfully created a vector index named: [vector_index]
   Polling to confirm the index has completed building.
   It may take up to a minute for the index to build before you can query using it.
   vector_index index is ready to query

Create the code to generate responses with the LLM.

In this section, you generate responses by prompting an LLM to use the retrieved documents as context.

Create a new file called LLMPrompt.java, and paste the following code into it.

This code does the following:

1. Queries the rag_db.test collection for any matching documents using a retrieveDocuments method.

   This method uses the getEmbedding method that you created earlier to generate an embedding from the search query, then runs the query to return semantically-similar documents.

   To learn more, refer to Run Vector Search Queries.

2. Accesses the Mistral 7B Instruct model from Hugging Face's model hub, and creates a templated prompt using a createPrompt method.

   The method instructs the LLM to include the user's question and retrieved documents in the defined prompt.

3. Prompts the LLM about MongoDB's latest AI announcements, then returns a generated response.

   LLMPrompt.java

   import com.mongodb.MongoException;
   import com.mongodb.client.MongoClient;
   import com.mongodb.client.MongoClients;
   import com.mongodb.client.MongoCollection;
   import com.mongodb.client.MongoDatabase;
   import com.mongodb.client.model.search.FieldSearchPath;
   import dev.langchain4j.data.message.AiMessage;
   import dev.langchain4j.model.huggingface.HuggingFaceChatModel;
   import dev.langchain4j.model.input.Prompt;
   import dev.langchain4j.model.input.PromptTemplate;
   import org.bson.BsonArray;
   import org.bson.BsonValue;
   import org.bson.Document;
   import org.bson.conversions.Bson;
   import java.util.ArrayList;
   import java.util.HashMap;
   import java.util.List;
   import java.util.Map;
   import static com.mongodb.client.model.Aggregates.project;
   import static com.mongodb.client.model.Aggregates.vectorSearch;
   import static com.mongodb.client.model.Projections.exclude;
   import static com.mongodb.client.model.Projections.fields;
   import static com.mongodb.client.model.Projections.include;
   import static com.mongodb.client.model.Projections.metaVectorSearchScore;
   import static com.mongodb.client.model.search.SearchPath.fieldPath;
   import static com.mongodb.client.model.search.VectorSearchOptions.exactVectorSearchOptions;
   import static java.util.Arrays.asList;
   public class LLMPrompt {
       // User input: the question to answer
       static String question = "In a few sentences, what are MongoDB's latest AI announcements?";
       public static void main(String[] args) {
           String uri = System.getenv("ATLAS_CONNECTION_STRING");
           if (uri == null || uri.isEmpty()) {
               throw new IllegalStateException("ATLAS_CONNECTION_STRING env variable is not set or is empty.");
           }
           // establish connection and set namespace
           try (MongoClient mongoClient = MongoClients.create(uri)) {
               MongoDatabase database = mongoClient.getDatabase("rag_db");
               MongoCollection<Document> collection = database.getCollection("test");
               // generate a response to the user question
               try {
                   createPrompt(question, collection);
               } catch (Exception e) {
                   throw new RuntimeException("An error occurred while generating the response: ", e);
               }
           } catch (MongoException me) {
               throw new RuntimeException("Failed to connect to MongoDB ", me);
           } catch (Exception e) {
               throw new RuntimeException("Operation failed: ", e);
           }
       }
       /**
        * Returns a list of documents from the specified MongoDB collection that
        * match the user's question.
        * NOTE: Update or omit the projection stage to change the desired fields in the response
        */
       public static List<Document> retrieveDocuments(String question, MongoCollection<Document> collection) {
           try {
               // generate the query embedding to use in the vector search
               BsonArray queryEmbeddingBsonArray = EmbeddingProvider.getEmbedding(question);
               List<Double> queryEmbedding = new ArrayList<>();
               for (BsonValue value : queryEmbeddingBsonArray.stream().toList()) {
                   queryEmbedding.add(value.asDouble().getValue());
               }
               // define the pipeline stages for the vector search index
               String indexName = "vector_index";
               FieldSearchPath fieldSearchPath = fieldPath("embedding");
               int limit = 5;
               List<Bson> pipeline = asList(
                       vectorSearch(
                               fieldSearchPath,
                               queryEmbedding,
                               indexName,
                               limit,
                               exactVectorSearchOptions()),
                       project(
                               fields(
                                       exclude("_id"),
                                       include("text"),
                                       metaVectorSearchScore("score"))));
               // run the query and return the matching documents
               List<Document> matchingDocuments = new ArrayList<>();
               collection.aggregate(pipeline).forEach(matchingDocuments::add);
               return matchingDocuments;
           } catch (Exception e) {
               System.err.println("Error occurred while retrieving documents: " + e.getMessage());
               return new ArrayList<>();
           }
       }
       /**
        * Creates a templated prompt from a submitted question string and any retrieved documents,
        * then generates a response using the Hugging Face chat model.
        */
       public static void createPrompt(String question, MongoCollection<Document> collection) {
           // retrieve documents matching the user's question
           List<Document> retrievedDocuments = retrieveDocuments(question, collection);
           if (retrievedDocuments.isEmpty()) {
               System.out.println("No relevant documents found. Unable to generate a response.");
               return;
           } else
               System.out.println("Generating a response from the retrieved documents. This may take a few moments.");
           // define a prompt template
           HuggingFaceChatModel huggingFaceChatModel = EmbeddingProvider.getChatModel();
           PromptTemplate promptBuilder = PromptTemplate.from("""
                   Answer the following question based on the given context:
                   Question: {{question}}
                   Context: {{information}}
                   -------
                   """);
           // build the information string from the retrieved documents
           StringBuilder informationBuilder = new StringBuilder();
           for (Document doc : retrievedDocuments) {
               String text = doc.getString("text");
               informationBuilder.append(text).append("\n");
           }
           Map<String, Object> variables = new HashMap<>();
           variables.put("question", question);
           variables.put("information", informationBuilder);
           // generate and output the response from the chat model
           Prompt prompt = promptBuilder.apply(variables);
           AiMessage response = huggingFaceChatModel.generate(prompt.toUserMessage()).content();
           // extract the generated text to output a formatted response
           String responseText = response.text();
           String marker = "-------";
           int markerIndex = responseText.indexOf(marker);
           String generatedResponse;
           if (markerIndex != -1) {
               generatedResponse = responseText.substring(markerIndex + marker.length()).trim();
           } else {
               generatedResponse = responseText; // else fallback to the full response
           }
           // output the question and formatted response
           System.out.println("Question:\n " + question);
           System.out.println("Response:\n " + generatedResponse);
           // output the filled-in prompt and context information for demonstration purposes
           System.out.println("\n" + "---- Prompt Sent to LLM ----");
           System.out.println(prompt.text() + "\n");
       }
   }

Generate responses with the LLM.

Save and run the file. The output resembles the following, but note that the generated response might vary.

Generating a response from the retrieved documents. This may take a few moments.
Question:
 In a few sentences, what are MongoDB's latest AI announcements?
Response:
 MongoDB's latest AI announcements include the MongoDB AI Applications Program (MAAP), which provides customers with reference architectures, pre-built partner integrations, and professional services to help them quickly build AI-powered applications. Accenture will establish a center of excellence focused on MongoDB projects. These announcements highlight MongoDB's growing focus on AI application development and its potential to modernize legacy workloads.
---- Prompt Sent to LLM ----
Answer the following question based on the given context:
Question: In a few sentences, what are MongoDB's latest AI announcements?
Context: time data.
MongoDB continues to expand its AI ecosystem with the announcement of the MongoDB AI Applications Program (MAAP),
which provides customers with reference architectures, pre-built partner integrations, and professional services to help
them quickly build AI-powered applications. Accenture will establish a center of excellence focused on MongoDB projects,
and is the first global systems i
ighlights
MongoDB announced a number of new products and capabilities at MongoDB.local NYC. Highlights included the preview
of MongoDB 8.0—with significant performance improvements such as faster reads and updates, along with significantly
faster bulk inserts and time series queries—and the general availability of Atlas Stream Processing to build sophisticated,
event-driven applications with real-
ble future as well as the criticality of MongoDB to artificial intelligence application development. These forward-looking
statements include, but are not limited to, plans, objectives, expectations and intentions and other statements contained in this press release that are
not historical facts and statements identified by words such as "anticipate," "believe," "continue," "could," "estimate," "e
ve Officer of MongoDB.
"As we look ahead, we continue to be incredibly excited by our large market opportunity, the potential to increase share, and become a standard within
more of our customers. We also see a tremendous opportunity to win more legacy workloads, as AI has now become a catalyst to modernize these
applications. MongoDB's document-based architecture is particularly well-suited for t
ictable, impact on its future GAAP financial results.
Conference Call Information
MongoDB will host a conference call today, May 30, 2024, at 5:00 p.m. (Eastern Time) to discuss its financial results and business outlook. A live
webcast of the call will be available on the "Investor Relations" page of MongoDB's website at https://investors.mongodb.com. To access the call by
phone, please go to thi

Set up the environment.

1. Initialize your Node.js project.

   Run the following commands in your terminal to create a new directory named rag-mongodb and initialize your project:

   mkdir rag-mongodb
   cd rag-mongodb
   npm init -y

2. Install and import dependencies.

   Run the following command:

   npm install mongodb langchain @langchain/community @xenova/transformers @huggingface/inference pdf-parse

3. Update your package.json file.

   In your project's package.json file, specify the type field as shown in the following example, and then save the file.

   {
      "name": "rag-mongodb",
      "type": "module",
      ...

4. Create a .env file.

   In your project, create a .env file to store your Atlas connection string and Hugging Face access token.

   HUGGING_FACE_ACCESS_TOKEN = "<access-token>"
   ATLAS_CONNECTION_STRING = "<connection-string>"

   Replace the <access-token> placeholder value with your Hugging Face access token.

   Replace the <connection-string> placeholder value with the SRV connection string for your Atlas cluster.

   Your connection string should use the following format:

   mongodb+srv://<db_username>:<db_password>@<clusterName>.<hostname>.mongodb.net

   Note

   Minimum Node.js Version Requirements

   Node.js v20.x introduced the --env-file option. If you are using an older version of Node.js, add the dotenv package to your project, or use a different method to manage your environment variables.

Create a function to generate vector embeddings.

In this section, you create a function that:

• Loads the nomic-embed-text-v1 embedding model from Hugging Face's model hub.

• Creates vector embeddings from the inputted data.

Create a file called get-embeddings.js in your project, and paste the following code:

import { pipeline } from '@xenova/transformers';
// Function to generate embeddings for a given data source
export async function getEmbedding(data) {
    const embedder = await pipeline(
        'feature-extraction', 
        'Xenova/nomic-embed-text-v1');
    const results = await embedder(data, { pooling: 'mean', normalize: true });
    return Array.from(results.data);
}

Ingest data into Atlas.

In this section, you ingest sample data into Atlas that LLMs don't have access to. The following code uses the LangChain integration and Node.js driver to do the following:

• Load a PDF that contains a MongoDB earnings report.

• Split the data into chunks, specifying the chunk size (number of characters) and chunk overlap (number of overlapping characters between consecutive chunks).

• Create vector embeddings from the chunked data by using the getEmbeddings function that you defined.

• Store these embeddings alongside the chunked data in the rag_db.test collection in your Atlas cluster.

Create a file called ingest-data.js in your project, and paste the following code:

import { PDFLoader } from "@langchain/community/document_loaders/fs/pdf";
import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";
import { MongoClient } from 'mongodb';
import { getEmbeddings } from './get-embeddings.js';
import * as fs from 'fs';
async function run() {
    const client = new MongoClient(process.env.ATLAS_CONNECTION_STRING);
    try {
        // Save online PDF as a file
        const rawData = await fetch("https://investors.mongodb.com/node/12236/pdf");
        const pdfBuffer = await rawData.arrayBuffer();
        const pdfData = Buffer.from(pdfBuffer);
        fs.writeFileSync("investor-report.pdf", pdfData);
        const loader = new PDFLoader(`investor-report.pdf`);
        const data = await loader.load();
        // Chunk the text from the PDF
        const textSplitter = new RecursiveCharacterTextSplitter({
            chunkSize: 400,
            chunkOverlap: 20,
          });
        const docs = await textSplitter.splitDocuments(data);
        console.log(`Successfully chunked the PDF into ${docs.length} documents.`);
        // Connect to your Atlas cluster
        await client.connect();
        const db = client.db("rag_db");
        const collection = db.collection("test");
        console.log("Generating embeddings and inserting documents.");
        let docCount = 0;
        await Promise.all(docs.map(async doc => {
            const embeddings = await getEmbeddings(doc.pageContent);
            
            // Insert the embeddings and the chunked PDF data into Atlas
            await collection.insertOne({
                document: doc,
                embedding: embeddings,
            });
            docCount += 1;
        }))
        console.log(`Successfully inserted ${docCount} documents.`);
    } catch (err) {
        console.log(err.stack);
    }
    finally {
        await client.close();
    }
}
run().catch(console.dir);

Then, run the following command to execute the code:

node --env-file=.env ingest-data.js

Use Atlas Vector Search to retrieve documents.

In this section, you set up Atlas Vector Search to retrieve documents from your vector database. Complete the following steps:

1. Create an Atlas Vector Search index on your vector embeddings.

   Create a new file named rag-vector-index.js and paste the following code. This code connects to your Atlas cluster and creates an index of the vectorSearch type on the rag_db.test collection.

   import { MongoClient } from 'mongodb';
   // Connect to your Atlas cluster
   const client = new MongoClient(process.env.ATLAS_CONNECTION_STRING);
   async function run() {
       try {
         const database = client.db("rag_db");
         const collection = database.collection("test");
        
         // Define your Atlas Vector Search index
         const index = {
             name: "vector_index",
             type: "vectorSearch",
             definition: {
               "fields": [
                 {
                   "type": "vector",
                   "numDimensions": 768,
                   "path": "embedding",
                   "similarity": "cosine"
                 }
               ]
             }
         }
    
         // Call the method to create the index
         const result = await collection.createSearchIndex(index);
         console.log(result);
       } finally {
         await client.close();
       }
   }
   run().catch(console.dir);

   Then, run the following command to execute the code:

   node --env-file=.env rag-vector-index.js

2. Define a function to retrieve relevant data.

   Create a new file called retrieve-documents.js.

   In this step, you create a retrieval function called getQueryResults that runs a query to retrieve relevant documents. It uses the getEmbeddings function to create embeddings from the search query. Then, it runs the query to return semantically-similar documents.

   To learn more, refer to Run Vector Search Queries.

   Paste this code into your file:

   import { MongoClient } from 'mongodb';
   import { getEmbeddings } from './get-embeddings.js';
   // Function to get the results of a vector query
   export async function getQueryResults(query) {
       // Connect to your Atlas cluster
       const client = new MongoClient(process.env.ATLAS_CONNECTION_STRING);
       
       try {
           // Get embeddings for a query
           const queryEmbeddings = await getEmbeddings(query);
           await client.connect();
           const db = client.db("rag_db");
           const collection = db.collection("test");
           const pipeline = [
               {
                   $vectorSearch: {
                       index: "vector_index",
                       queryVector: queryEmbeddings,
                       path: "embedding",
                       exact: true,
                       limit: 5
                   }
               },
               {
                   $project: {
                       _id: 0,
                       document: 1,
                   }
               }
           ];
           // Retrieve documents from Atlas using this Vector Search query
           const result = collection.aggregate(pipeline);
           const arrayOfQueryDocs = [];
           for await (const doc of result) {
               arrayOfQueryDocs.push(doc);
           }
           return arrayOfQueryDocs;
       } catch (err) {
           console.log(err.stack);
       }
       finally {
           await client.close();
       }
   }

3. Test retrieving the data.

   Create a new file called retrieve-documents-test.js. In this step, you check that the function you just defined returns relevant results.

   Paste this code into your file:

   import { getQueryResults } from './retrieve-documents.js';
   async function run() {
       try {
           const query = "AI Technology";
           const documents = await getQueryResults(query);
           documents.forEach( doc => {
               console.log(doc);
           }); 
       } catch (err) {
           console.log(err.stack);
       }
   }
   run().catch(console.dir);

   Then, run the following command to execute the code:

   node --env-file=.env retrieve-documents-test.js

   VIEW OUTPUT

   {
     document: {
       pageContent: 'MongoDB continues to expand its AI ecosystem with the announcement of the MongoDB AI Applications Program (MAAP),',
       metadata: { source: 'investor-report.pdf', pdf: [Object], loc: [Object] },
       id: null
     }
   }
   {
     document: {
       pageContent: 'artificial intelligence, in our offerings or partnerships; the growth and expansion of the market for database products and our ability to penetrate that\n' +
         'market; our ability to integrate acquired businesses and technologies successfully or achieve the expected benefits of such acquisitions; our ability to',
       metadata: { source: 'investor-report.pdf', pdf: [Object], loc: [Object] },
       id: null
     }
   }
   {
     document: {
       pageContent: 'more of our customers. We also see a tremendous opportunity to win more legacy workloads, as AI has now become a catalyst to modernize these\n' +
         "applications. MongoDB's document-based architecture is particularly well-suited for the variety and scale of data required by AI-powered applications. \n" +
         'We are confident MongoDB will be a substantial beneficiary of this next wave of application development."',
       metadata: { source: 'investor-report.pdf', pdf: [Object], loc: [Object] },
       id: null
     }
   }
   {
     document: {
       pageContent: 'which provides customers with reference architectures, pre-built partner integrations, and professional services to help\n' +
         'them quickly build AI-powered applications. Accenture will establish a center of excellence focused on MongoDB projects,\n' +
         'and is the first global systems integrator to join MAAP.',
       metadata: { source: 'investor-report.pdf', pdf: [Object], loc: [Object] },
       id: null
     }
   }
   {
     document: {
       pageContent: 'Bendigo and Adelaide Bank partnered with MongoDB to modernize their core banking technology. With the help of\n' +
         'MongoDB Relational Migrator and generative AI-powered modernization tools, Bendigo and Adelaide Bank decomposed an\n' +
         'outdated consumer-servicing application into microservices and migrated off its underlying legacy relational database',
       metadata: { source: 'investor-report.pdf', pdf: [Object], loc: [Object] },
       id: null
     }
   }

Generate responses with the LLM.

In this section, you generate responses by prompting an LLM to use the retrieved documents as context. This example uses the function you just defined to retrieve matching documents from the database, and additionally:

• Accesses the Mistral 7B Instruct model from Hugging Face's model hub.

• Instructs the LLM to include the user's question and retrieved documents in the prompt.

• Prompts the LLM about MongoDB's latest AI announcements.

Create a new file called generate-responses.js, and paste the following code into it:

import { getQueryResults } from './retrieve-documents.js';
import { HfInference } from '@huggingface/inference'
async function run() {
    try {
        // Specify search query and retrieve relevant documents
        const query = "AI Technology";
        const documents = await getQueryResults(query);
        // Build a string representation of the retrieved documents to use in the prompt
        let textDocuments = "";
        documents.forEach(doc => {
            textDocuments += doc.document.pageContent;
        });
        const question = "In a few sentences, what are MongoDB's latest AI announcements?";
        // Create a prompt consisting of the question and context to pass to the LLM
        const prompt = `Answer the following question based on the given context.
            Question: {${question}}
            Context: {${textDocuments}}
        `;
        // Connect to Hugging Face, using the access token from the environment file
        const hf = new HfInference(process.env.HUGGING_FACE_ACCESS_TOKEN);
        const llm = hf.endpoint(
            "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.3"
           );
        
        // Prompt the LLM to answer the question using the
        // retrieved documents as the context
        const output = await llm.chatCompletion({
            model: "mistralai/Mistral-7B-Instruct-v0.2",
            messages: [{ role: "user", content: prompt }],
            max_tokens: 150,
        });
        // Output the LLM's response as text.
        console.log(output.choices[0].message.content);
    } catch (err) {
        console.log(err.stack);
    }
}
run().catch(console.dir);