Implementing a chat history feature is crucial for developing conversational AI applications, as it maintains context and enhances user experience. By leveraging Azure Cosmos DB for NoSQL with the Go SDK and integrating it with LangChainGo, developers can efficiently manage and retrieve chat histories. This article provides a comprehensive guide to building such an implementation, complete with coding examples.

Prerequisites

Before diving into the implementation, ensure you have the following:

• Azure Subscription: An active Azure account. If you don’t have one, you can create a free account.
• Go Environment: Go programming language installed (version 1.21 or newer).
• Azure Cosmos DB Account: An Azure Cosmos DB account configured for NoSQL API.
• LangChainGo: The Go implementation of LangChain, a framework for building applications with language models.

Setting Up Azure Cosmos DB for NoSQL with Go SDK

1. Install the Azure Cosmos DB Go SDK

Begin by installing the Azure Cosmos DB SDK for Go:

go get -u github.com/Azure/azure-sdk-for-go/sdk/data/azcosmos

2. Authenticate the Client

To interact with Azure Cosmos DB, authenticate using either Microsoft Entra identities or an account key. Here’s how to authenticate using an account key:

package main

import (
    "github.com/Azure/azure-sdk-for-go/sdk/data/azcosmos"
    "log"
)

func main() {
    const (
        cosmosDbEndpoint = "your_cosmos_db_endpoint"
        cosmosDbKey      = "your_cosmos_db_key"
    )

    cred, err := azcosmos.NewKeyCredential(cosmosDbKey)
    if err != nil {
        log.Fatalf("Failed to create credential: %v", err)
    }

    client, err := azcosmos.NewClientWithKey(cosmosDbEndpoint, cred, nil)
    if err != nil {
        log.Fatalf("Failed to create Cosmos DB client: %v", err)
    }
}

3. Create a Database and Container

After establishing the client, create a database and a container to store chat messages:

package main

import (
    "context"
    "github.com/Azure/azure-sdk-for-go/sdk/data/azcosmos"
    "log"
)

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

    databaseName := "ChatAppDB"
    databaseProperties := azcosmos.DatabaseProperties{ID: databaseName}
    _, err := client.CreateDatabase(ctx, databaseProperties, nil)
    if err != nil {
        log.Fatalf("Failed to create database: %v", err)
    }
    database := client.NewDatabase(databaseName)

    containerName := "ChatHistory"
    containerProperties := azcosmos.ContainerProperties{
        ID: containerName,
        PartitionKeyDefinition: azcosmos.PartitionKeyDefinition{
            Paths: []string{"/userId"},
        },
    }
    throughput := azcosmos.NewManualThroughputProperties(400)
    _, err = database.CreateContainer(ctx, containerProperties, &azcosmos.CreateContainerOptions{ThroughputProperties: &throughput})
    if err != nil {
        log.Fatalf("Failed to create container: %v", err)
    }
}

Implementing Chat History Management

1. Define the Chat Message Structure

Create a Go struct to represent individual chat messages:

package main

type ChatMessage struct {
    ID        string `json:"id"`
    UserID    string `json:"userId"`
    Timestamp int64  `json:"timestamp"`
    Role      string `json:"role"` // "user" or "assistant"
    Content   string `json:"content"`
}

2. Insert Chat Messages

To store a new chat message:

package main

import (
    "context"
    "encoding/json"
    "github.com/Azure/azure-sdk-for-go/sdk/data/azcosmos"
    "log"
    "time"
)

func main() {
    ctx := context.TODO()
    container := client.NewContainer("ChatAppDB", "ChatHistory")

    message := ChatMessage{
        ID:        "unique_message_id",
        UserID:    "user123",
        Timestamp: time.Now().Unix(),
        Role:      "user",
        Content:   "Hello, how can I assist you today?",
    }

    messageBytes, err := json.Marshal(message)
    if err != nil {
        log.Fatalf("Failed to marshal message: %v", err)
    }

    partitionKey := azcosmos.NewPartitionKeyString(message.UserID)
    _, err = container.CreateItem(ctx, partitionKey, messageBytes, nil)
    if err != nil {
        log.Fatalf("Failed to insert chat message: %v", err)
    }
}

3. Retrieve Chat History

To fetch the chat history for a specific user:

package main

import (
    "context"
    "encoding/json"
    "github.com/Azure/azure-sdk-for-go/sdk/data/azcosmos"
    "log"
)

func main() {
    ctx := context.TODO()
    container := client.NewContainer("ChatAppDB", "ChatHistory")

    userID := "user123"
    partitionKey := azcosmos.NewPartitionKeyString(userID)
    query := "SELECT * FROM ChatHistory c WHERE c.userId = @userId ORDER BY c.timestamp ASC"
    queryOptions := azcosmos.QueryOptions{
        QueryParameters: []azcosmos.QueryParameter{
            {Name: "@userId", Value: userID},
        },
    }

    pager := container.NewQueryItemsPager(query, partitionKey, &queryOptions)

    var chatHistory []ChatMessage
    for pager.More() {
        response, err := pager.NextPage(ctx)
        if err != nil {
            log.Fatalf("Failed to retrieve chat history: %v", err)
        }
        for _, item := range response.Items {
            var message ChatMessage
            if err := json.Unmarshal(item, &message); err != nil {
                log.Fatalf("Failed to unmarshal chat message: %v", err)
            }
            chatHistory = append(chatHistory, message)
        }
    }
}

Conclusion

By integrating Azure Cosmos DB for NoSQL with the Go SDK and LangChainGo, developers can efficiently manage chat histories in conversational AI applications. This approach ensures scalability, reliability, and real-time accessibility of chat logs. The ability to store, retrieve, and manage chat history enables AI models to maintain conversational context, improving user experience and engagement.

Expanding this implementation further, developers can introduce indexing for faster retrieval, implement natural language processing (NLP) for analyzing chat sentiment, and leverage AI-driven insights to personalize interactions. Additional security measures such as encryption and role-based access control can further strengthen data integrity and privacy.

As conversational AI continues to evolve, an efficient chat history management system will play a critical role in creating human-like and context-aware interactions. By refining and scaling the solution, businesses can build powerful, intelligent, and seamless conversational applications, setting new standards for AI-driven customer engagement.