Build a chatbot with LangChain

query_builder 25 min

In this tutorial, you will build a chatbot that can communicate with the FAQs from DataStax’s website. You will generate embeddings with LangChain and OpenAI, which the chatbot will use to find relevant content.

You’ll also create a simple React.js chatbot frontend that works with FastAPI and Vercel’s serverless functions to connect your frontend to AstraDB and the OpenAI Completions API.

For a video demo of this tutorial, see WikiChat: Build a real-time RAG app on Wikipedia with LangChain.js, Vercel, and Astra DB.

Prerequisites

For this tutorial, you need the following:

You should also be proficient in the following tasks:

Interacting with databases.
Running a basic Python script.
Entering parameters in a user interface for Vercel.

Clone the Git repository

Clone the chatbot Git repository and switch to that directory.

git clone https://github.com/datastax/astra-db-chatbot-starter.git
cd astra-db-chatbot-starter

Create your Serverless (Vector) database

In the Astra Portal navigation menu, click Databases.
Click Create Database, and then select the Serverless (Vector) deployment type.
Enter a meaningful, human-readable Database name.

After you create a database, you can’t change its name.

Database names must start and end with a letter or number, and contain no more than 50 characters, including letters, numbers, spaces, and the special characters & + - _ ( ) < > . , @.
Select a Provider and Region to host your database.

On the Free plan, you can access a limited set of supported regions. To access lock Locked regions, you must upgrade your subscription plan.

To minimize latency in production databases, select a region that is close to your application’s users.
Click Create Database.

New databases start in Pending status, and then move to Initializing. Your database is ready to use when it reaches Active status.

Set your environment variables

On your database’s Overview tab, click Generate Token, copy the token, and then set it as the ASTRA_DB_APPLICATION_TOKEN environment variable:
```
export ASTRA_DB_APPLICATION_TOKEN=TOKEN
```

Set the ASTRA_DB_KEYSPACE environment variable to default_keyspace:

export ASTRA_DB_KEYSPACE=default_keyspace # A keyspace that exists in your database

Set the ASTRA_DB_COLLECTION environment variable to collection. Choose a different name if you already have a collection named collection.
```
export ASTRA_DB_COLLECTION=COLLECTION_NAME # Your database collection
```
On your database’s Overview tab, copy the database’s API Endpoint, and then set it as the ASTRA_DB_API_ENDPOINT environment variable:
```
export ASTRA_DB_API_ENDPOINT=API_ENDPOINT
```
In the OpenAI Platform, get your API key to use for the OPENAI_API_KEY environment variable:
```
export OPENAI_API_KEY=API_KEY
```
Set DIMENSIONS to the number of vector dimensions for your model. This tutorial uses the OpenAI text-embedding-ada-002 model, which produces vectors with 1536 dimensions.
```
export VECTOR_DIMENSION=1536
```
Set SCRAPED_FILE to the location where you want to store your scraped data:
```
export SCRAPED_FILE=scrape/scraped_results.json
```

You have now set the following environment variables:

export ASTRA_DB_API_ENDPOINT=API_ENDPOINT
export ASTRA_DB_APPLICATION_TOKEN=TOKEN
export ASTRA_DB_KEYSPACE=default_keyspace # A keyspace that exists in your database
export ASTRA_DB_COLLECTION=COLLECTION_NAME # Your database collection
export OPENAI_API_KEY=API_KEY
export VECTOR_DIMENSION=1536
export SCRAPED_FILE=scrape/scraped_results.json

Set up the code

Verify that pip is version 23.0 or later:
```
pip --version
```
Upgrade pip if needed:
```
python -m pip install --upgrade pip
```

Install Python dependencies:

npm install
pip install -r requirements.txt

Load the tutorial data:
```
python populate_db/create_collection.py
```
Run the load_data.py script to load the scraped website data from scraped_results.json:
```
python populate_db/load_data.py
```
OpenAI has rate limits, which could affect how effectively you can complete this tutorial. For more, see OpenAI’s rate limits.
scraped_results.json structure
{ [ { "url": url_of_webpage_scraped_1, "title": title_of_webpage_scraped_1, "content": content_of_webpage_scraped_1 }, ... ] }
For more about the embeddings, see About the tutorial.

Deploy your chatbot locally

Start the web server that you installed with the dependencies:
```
npm run dev
```
Because the web server continues to run in the terminal, open a second terminal and export your environment variables again.

Test your API by sending it a request:

curl -X POST localhost:8000/api/chat \
--header "Content-Type: application/json" \
--data '{ "prompt": "Can encryptions keys be exchanged for a particular table?" }'

Response

The response is a string that you might need to convert to JSON.

"{\"text\": \"\\nNo, encryptions keys cannot be exchanged for a particular table. However, you can use the GRANT command to configure access to rows within the table. You can also create an entirely new keyspace with a different name if desired.\", \"url\": \"https://docs.datastax.com/en/dse68-security/docs/secFAQ.html\"}"

Open http://localhost:3000 to view and test the chatbot in your browser.

Deploy your chatbot with Vercel

To see the chatbot in action, you can deploy it to a serverless environment, such as Vercel.

In the Vercel Dashboard, search for and import the third-party Git repo from https://github.com/datastax/astra-db-chatbot-starter.
Select the Next.js Framework Preset option.
Set the Environment Variables.
Click Deploy.
Wait a few minutes for your chatbot to deploy.

After you deploy in Vercel the first time, auto-deployment will trigger for each subsequent commit.

For more about using Vercel, see the Vercel documentation.

About the tutorial

Embeddings

We define our embedding model to match the one used in the vectorization step. We use LangChain’s OpenAI library to create a connection to their API for completions as well as embeddings.

The load_data.py script loads the scraped website data that lives in scraped_results.json with the following structure:

scraped_results.json

{
  [
    {
      "url": url_of_webpage_scraped_1,
      "title": title_of_webpage_scraped_1,
      "content": content_of_webpage_scraped_1
    },
    ...
  ]
}

To learn more about how this data is scraped, you can find the scraper and instructions in the README.

This is a portion of the text scraped from the Datastax Premium Support FAQ page:

input_json = {
    "url": "https://www.datastax.com/services/support/premium-support/faq",
    "title": "FAQ | DataStax Premium Support",
    "content": (
        "What is DataStax Premium Support?\n"
        "Premium Support consists of three add-on offerings available to enhance your existing DataStax support experience\n"
        "There are three Premium offerings currently available:\n"
        "Premium Cloud Engineer\n"
        "Named Engineer\n"
        "Technical Account Manager\n"
        "Can I purchase a Premium Support subscription for any DataStax product? \n"
        "You can purchase Premium Support as an add-on to DataStax products that are generally available. Please contact PremiumSupport@datastax.com if you have any questions.\n"
        "What is included in each of the different DataStax technical support offerings?"
        # ...
    )
}

The text in input_json['content'] is split on the newline character. The lines that are questions are stored in question_lines.

all_lines = [
    0["What is DataStax Premium Support?"]
    1["Premium Support consists of three add-on offerings available to enhance your existing DataStax support experience"]
    2["There are three Premium offerings currently available:"]
    3["Premium Cloud Engineer"]
    4["Named Engineer"]
    5["Technical Account Manager"]
    6["Can I purchase a Premium Support subscription for any DataStax product?"]
    7["You can purchase Premium Support as an add-on to DataStax products that are generally available. Please contact PremiumSupport@datastax.com if you have any questions."]
    8["What is included in each of the different DataStax technical support offerings?"]
    # ...
]

question_lines = [0, 6, 8] # ...

There are questions on lines 0, 6, 8 … This means that the answers will be on lines 1 to 5, 7 …

Now the answer lines are joined into a list and the output data structure is defined:

q_and_a_output_data = {
    "url": "https://www.datastax.com/services/support/premium-support/faq"
    "title": "FAQ | DataStax Premium Support"
    "questions": [
        ["What is DataStax Premium Support?"],
        ["Can I purchase a Premium Support subscription for any DataStax product?"],
        # ...
    ]
    "answers": [
        [(
            "Premium Support consists of three add-on offerings available to enhance your existing DataStax support experience\n"
            "There are three Premium offerings currently available:\n"
            "Premium Cloud Engineer\n"
            "Named Engineer\n"
            "Named Engineer\n"
            "Technical Account Manager"
        )],
        [(
            "You can purchase Premium Support as an add-on to DataStax products that are generally available. "
            "Please contact PremiumSupport@datastax.com if you have any questions."
        )]
        # ...
    ]
}

Once the text is split into answers and questions, each question and answer pair is iterated. The concatenated text of each question and answer is embedded with the OpenAI text-embedding-ada-002 model.

The first iteration through the question and answer data gives the following result:

question_id = 1
document_id = "https://www.datastax.com/services/support/premium-support/faq"
question    = "What is DataStax Premium Support?"
answer      = (
    "Premium Support consists of three add-on offerings available to enhance your existing DataStax support experience\n"
    "There are three Premium offerings currently available:\n"
    "Premium Cloud Engineer\n"
    "Named Engineer\n"
    "Technical Account Manager"
)
# (The embedding is a 1 x 1536 vector that we've abbreviated)
embedding   = [-0.0077333027, -0.02274981, -0.036987208, ..., 0.052548025]

At each step, the data is pushed to the database:

to_insert = {
    "insertOne": {
        "document": {
            "document_id": document_id,
            "question_id": question_id,
            "answer":answer,
            "question":question,
            "$vector":embedding
        }
    }
}

response = requests.request(
    "POST",
    request_url,
    headers=request_headers,
    data=json.dumps(to_insert)
)

The collection is now populated with documents containing the question and answer data and the vector embeddings.

Backend API

The API is split between a few files:

index.py contains the actual FastAPI code. It defines an API with a single route: /api/chat. Upon receipt of a POST request, the function calls the send_to_openai function from chatbot_utils.py.
chatbot_utils.py loads authentication credentials from your environment variables and connects to your database.

Conclusion

Congratulations! In this tutorial, you completed the following:

Created a Serverless (Vector) database.
Added data scraped from our FAQ pages.
Used the scraped data to add context to prompts to the OpenAI API.
Held a chat session with an LLM.

You can now customize the code to scrape your own sites, change the prompts, and change the frontend to improve your users' experience.