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https://github.com/metaskills/lambda-rag

LambdaRAG is a Retrieval Augmented Generation Chat AI Demo. Please read the full RAGs to Riches blog series.
https://github.com/metaskills/lambda-rag

ai express generative-ai lambda openai openai-api rag retrieval-augmented-generation vue

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LambdaRAG is a Retrieval Augmented Generation Chat AI Demo. Please read the full RAGs to Riches blog series.

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# Retrieval Augmented Generation Chat AI Demo



![LambdaRAG](./public/rags-to-riches.png)



Please read the full "RAGs to Riches" blog series:


https://dev.to/aws-heroes/rags-to-riches-part-1-generative-ai-retrieval-4pd7



## About



This OpenAI based RAG chat application that can help you learn about AI retrieval patterns. The technologies here are beginner friendly and easy to deploy to AWS Lambda. As your needs grow, feel free to productionize this application with more robust components. What is a RAG? From [IBM Research](https://research.ibm.com/blog/retrieval-augmented-generation-RAG ):



> RAG is an AI framework for retrieving facts from an external knowledge base to ground large language models (LLMs) on the most accurate, up-to-date information and to give users insight into LLMs' generative process.



![Screenshot of the LambdaRAG Demo application.](./public/lambda-rag-start-dark.png#gh-dark-mode-only)

![Screenshot of the LambdaRAG Demo application.](./public/lambda-rag-start-dark.png#gh-light-mode-only)



## Local Development



You MUST have an OpenAI API key to run this application. You can get one for free using this [Where do I find my Secret API Key?](https://help.openai.com/en/articles/4936850-where-do-i-find-my-secret-api-key) guide. Once you have your OpenAI key, create a `.env.development.local` file at the root of this project with the following, replacing `sk...` with your key:



```

OPENAI_API_KEY=sk...

```



This project supports [Development Containers](https://containers.dev) which means you can use VS Code to [open this folder in a container](https://code.visualstudio.com/docs/devcontainers/containers) and your development environment will be created for you. Run the following commands in your integrated terminal or on your local machine assuming you have Node installed.



```shell

./bin/setup

./bin/server

```



The server command will start both a front and back end development server. Use this URL to access your application. http://localhost:5173



## Technologies Used



This demo application uses a split-stack architecture. Meaning there is a distinct front-end and back-end. The front-end is a [💚 Vue.js](https://vuejs.org/) application with [🍍 Pinia](https://pinia.vuejs.org) for state and [⚡ïļ Vite](https://vitejs.dev/) for development. The front-end also uses [🌊 Tailwind CSS](https://tailwindcss.com/) along with [🌞 daisyUI](https://daisyui.com) for styling. The back-end is a [ðŸŸĻ Node.js](https://nodejs.org/) application that uses [❎ Express](https://expressjs.com/) for the HTTP framework, and [ðŸŠķ SQLite3 VSS](https://github.com/asg017/sqlite-vss) along with [🏆 better-sqlite3](https://github.com/WiseLibs/better-sqlite3) for vector storage and search. 



Throughout the post we will explore various technologies in more detail and how they help us build a RAG application while learning the basics of AI driven integrations and prompt engineering. This is such a fun space. I hope you enjoy it as much as I do! 



⚠ïļ DISCLAIMIER: I used ChatGPT to build most of this application. It has been several years since I did any heavy client-side JavaScript. I used this RAG application as an opportunity to learn Vue.js with AI's help.



## Working Backwards - Why Lambda?



So let's start with the end in mind. Our [LambdaRAG Demo](https://github.com/metaskills/lambda-rag) runs locally to make it easy to develop and learn. At some point though you may want to ship it to production or share your work with others. So why deploy to Lambda and what benefits does that deployment option offer? A few thoughts:



1. Lambda makes it easy to deploy [containerized applications](https://docs.aws.amazon.com/lambda/latest/dg/images-create.html).

2. Lambda's [Function URLs](https://docs.aws.amazon.com/lambda/latest/dg/lambda-urls.html) are managed API Gateway reverse proxies.

3. The [Lambda Web Adapter](https://github.com/awslabs/aws-lambda-web-adapter) makes streaming API responses simple.

4. Container tools like [Crypteia](https://github.com/rails-lambda/crypteia) make secure SSM-backed secrets easy.

5. Lambda containers allow images up to 10GB in size. Great for an embedded SQLite DB.



Of all of these, I think [Response Streaming](https://aws.amazon.com/blogs/compute/introducing-aws-lambda-response-streaming/) is the most powerful. A relatively new feature for Lambda, this enables our RAG to stream text back to the web client just like ChatGPT. It also allows Lambda to break the 6MB response payload and 30s timeout limit. These few lines in the project's `template.yaml` along with the Lambda Web Adapter make it all possible.



```yaml

FunctionUrlConfig:

  AuthType: NONE

  InvokeMode: RESPONSE_STREAM

```



Before you run `./bin/deploy` for the first time. Make sure you to log into the AWS Console and navigate to [SSM Parameter Store](https://docs.aws.amazon.com/systems-manager/latest/userguide/systems-manager-parameter-store.html) first. From there create a secret string parameter with the path `/lambda-rag/OPENAI_API_KEY` and paste in your OpenAI API key. 



## OpenAI API Basics



Our backend has a very basic [`src/utils/openai.js`](https://github.com/metaskills/lambda-rag/blob/main/src/utils/openai.js) module. This exports an OpenAI client as well as a helper function to create embeddings. We cover [Embeddings](https://platform.openai.com/docs/guides/embeddings) briefly in the [Basic Architect](https://dev.to/aws-heroes/rags-to-riches-part-1-generative-ai-retrieval-4pd7) section of the first part of this series. This function simply turns a user's query into a vector embedding which is later queried against our SQLite database. There are numerous ways to create and query embeddings. For now we are going to keep it simple and use OpenAI's `text-embedding-ada-002` model which outputs 1536 dimensional embeddings.



```javascript

import { OpenAI } from "openai";



export const openai = new OpenAI({

  apiKey: process.env.OPENAI_API_KEY,

});



export const createEmbedding = async (query) => {

  const response = await openai.embeddings.create({

    model: "text-embedding-ada-002",

    input: query,

  });

  return JSON.stringify(response.data[0].embedding);

};

```



So how does OpenAI's API work to create a chat interface and how does the [Context Window](https://dev.to/aws-heroes/rags-to-riches-part-1-generative-ai-retrieval-4pd7) discussed in part one come into play? Consider the following screenshot where I tell LambdaRAG my name and then ask if it remembers.



Screenshot of the LambdaRAG Demo application.



ChatGPT is stateless, like most web applications. It has no session for the LLM model. Every time you send a message you have to send all the previous messages (context) to the [Completions](https://platform.openai.com/docs/api-reference/completions) endpoint. This is why we use [🍍 Pinia](https://pinia.vuejs.org) for client-side state management. So from an API perspective, it would look something like this below.



```javascript

await openai.chat.completions.create({

 model: "gpt-3.5-turbo-16k",

 messages: [

    { role: "user", content: "Hello my name is Ken Collins." },

    { role: "assistant", content: "Hello Ken Collins! How can I..." },

    { role: "user", content: "Do you remember my name?" },

 ]

});

```



Did you notice how the assistant responded not only with my name but also knew it was here to help us with Luxury Apparel? This is a technique called [Role Prompting](https://learnprompting.org/docs/basics/roles). We do this in the LambdaRAG Demo by prepending this role to the user's first message in the [`src-frontend/utils/roleprompt.js`](https://github.com/metaskills/lambda-rag/blob/main/src-frontend/utils/roleprompt.js) file.



You may have noticed that the LambdaRAG Demo is written entirely in 💛 JavaScript vs. Python. As you learn more about building AI applications you may eventually have to learn Python as well as more advanced frameworks like [ðŸĶœïļðŸ”— LangChain](https://js.langchain.com/docs/get_started/introduction/) or Hugging Face's [ðŸĪ— Transformers.js](https://huggingface.co/docs/transformers.js/index). All of which have JavaScript versions. I hope this trend of providing JavaScript clients will continue. It feels like a more accessible language.



In the next section, we will cover how to create embeddings with your data and query for documents using SQLite's new VSS extension.



## Proprietary Data & Embeddings



💁‍♂ïļ The LambdaRAG Demo application contains a ready-to-use SQLite database with ~5,000 products from the [Luxury Apparel Dataset](https://www.kaggle.com/datasets/chitwanmanchanda/luxury-apparel-data) on Kaggle. It also has vector embeddings pre-seeded and ready to use!



Before we dig into [sqlite-vss](https://github.com/asg017/sqlite-vss), I'd like to explain why I think this extension is so amazing. To date, I have found sqlite-vss the easiest and quickest way to explore vector embeddings. Many GenAI projects use [Supabase](https://supabase.com) which seems great but is difficult to run locally. The goal here is to learn!



As your application grows, I highly recommend looking at [Amazon OpenSearch Serverless](https://aws.amazon.com/blogs/big-data/introducing-the-vector-engine-for-amazon-opensearch-serverless-now-in-preview/). It is a fully managed, highly scalable, and cost-effective service that supports vector similarity search. It even supports [pre-filtering with FAISS](https://opensearch.org/docs/latest/search-plugins/knn/filter-search-knn/).



Let's look at [sqlite-vss](https://github.com/asg017/sqlite-vss) a bit closer. This article [A SQLite Extension for Vector Search](https://observablehq.com/@asg017/introducing-sqlite-vss) does an amazing job covering the creation of standard tables as well as virtual tables for embeddings and how to query them both. The LambdaRAG Demo follows all these patterns closely in our [`db/create.js`](https://github.com/metaskills/lambda-rag/blob/main/db/create.js) file. Our resulting schema is:



```sql

CREATE TABLE products (

    id INTEGER PRIMARY KEY,

    name TEXT,

    category TEXT,

    subCategory TEXT,

    description TEXT,

    embedding BLOB

  );

CREATE TABLE IF NOT EXISTS "vss_products_index"(rowid integer primary key autoincrement, idx);

CREATE TABLE sqlite_sequence(name,seq);

CREATE TABLE IF NOT EXISTS "vss_products_data"(rowid integer primary key autoincrement, _);

CREATE VIRTUAL TABLE vss_products using vss0 (

    embedding(1536)

  );

```



If you want to re-create the SQLite database or build a custom dataset, you can do so by changing the `db/create.js` and running `npm run db:create`. This will drop the existing database and re-create it with data from any CSV file(s), supporting schema, or process you are willing to code up.



```shell

> npm run db:create

> [email protected] db:create

> rm -rf db/lambdarag.db && node db/create.js

Using sqlite-vss version: v0.1.1

Inserting product data...

 ██████████████████████████████████░░░░░░ 84% | ETA: 2s | 4242/5001

```



Afterward you would need to run the `npm run db:embeddings` script which uses the OpenAI API to create embeddings for each product. This takes a few minutes to complete all the API calls. The task includes a local cache to make it faster to re-run. Lastly, there is a `npm run db:clean` script that calls a `VACUUM` on the DB to remove wasted space for the virtual tables. Again, all of this is only required if you want to re-create the database or build a custom dataset. There is a `./bin/setup-db` wrapper script that does all these steps for you.



## Retrieval with Function Calling



OK, so we have a database of products and their matching vector embeddings to use for semantic search. How do we code up going from chat to retrieving items from the database? OpenAI has this amazing feature named [Function Calling](https://platform.openai.com/docs/guides/gpt/function-calling). In our demo, it allows the LLM to search for products and describe the results to you.



Screenshot of the LambdaRAG Demo application.



But how does it know? You simply describe an [array of functions](https://github.com/metaskills/lambda-rag/blob/main/src/utils/functions.json) that your application implments and during a chat completion API call. OpenAI will 1) automatically make a determination a function should be called 2) return the name of the function to call along with the needed parameters. Your request looks something like this.



```javascript

await openai.chat.completions.create({

  model: "gpt-3.5-turbo-16k",

  functions: '[{"search_products":{"parameters": {"query": "string"}}}]',

  messages: [

    { role: "user", content: "I need a cool trucker hat." }

  ]

});

```



If a function has been selected, the response will include the name of the function and parameters. Your responsibility is to check for this, then call your application's code matching the function and parameters. For LambdaGPT, this will be querying the database and returning any matching rows. We do this in our [`src/models/products.js`](https://github.com/metaskills/lambda-rag/blob/main/src/models/products.js) file.



For OpenAI to respond with the results, we send it another request that now has two additional messages included. The first is of type "function" and includes the name and parameters of the function you were asked to call. The second is of type "user" which includes the JSON data of the products returned from our retrieval process. OpenAI will now respond as if it has this knowledge all along!



```javascript

await openai.chat.completions.create({

  model: "gpt-3.5-turbo-16k",

  functions: '[{"search_products":{"parameters": {"query": "string"}}}]',

  messages: [

    { role: "user", content: "I need a cool trucker hat." },

    { role: "function", name: "search_products", content: '{"query":"trucker hats"}' },

    { role: "user", content: '[{"id":3582,"name":"Mens Patagonia Logo Trucker Hat..."}]' },

  ]

});

```



Since all messages are maintained in client-side state, you can see them using a neat debug technique. Open up the [`src-frontend/components/Message.vue`](https://github.com/metaskills/lambda-rag/blob/main/src-frontend/components/Message.vue) file and make the following change.



```diff

  'border-b-base-300': true,

  'bg-base-200': data.role === 'user',

- 'hidden': data.hidden,

+ 'hidden': false,

```



You can now see all the messages' state in the UI. This is a great way to debug your application and see what is happening. 



Screenshot of the LambdaRAG Demo application.



## More To Explore



I hope you found this quick overview of how OpenAI's chat completions can be augmented for knowledge retrieval. There is so much more to explore and do. Here are some ideas to get you started:



- All responses are streamed from the server. The `fetchResponse` in the [`src-frontend/stores/messages.js`](https://github.com/metaskills/lambda-rag/blob/main/src-frontend/stores/messages.js) Pinia store does all the work here and manages client side state.

- That same file also converts the streaming responses Markdown code into HTML. This is how the demo can build tables just like ChatGPT does.

- Sometimes the keywords passed to the search products function can be sparse. Consider making an API call to extend the keywords of the query using the original message. You can use functions here too!

- Consider adding more retrieval methods to the [`src/utils/functions.json`](https://github.com/metaskills/lambda-rag/blob/main/src/utils/functions.json) file. For example, a `find_style` by ID method that would directly query the database.



âĪïļ I hope you enjoyed these posts and find the LambdaRAG Demo application useful in learning how to use AI for knowledge retrieval. Feel free to ask questions and share your thoughts on this post. Thank you!