# DegreeGuru ## Build a RAG Chatbot using Vercel AI SDK, Langchain, Upstash Vector and OpenAI [![Deploy with Vercel](https://vercel.com/button)](https://vercel.com/new/clone?repository-url=https%3A%2F%2Fgithub.com%2Fupstash%2Fdegreeguru&env=UPSTASH_REDIS_REST_URL,UPSTASH_REDIS_REST_TOKEN,UPSTASH_VECTOR_REST_URL,UPSTASH_VECTOR_REST_TOKEN,OPENAI_API_KEY&demo-title=DegreeGuru%20Demo&demo-description=A%20Demo%20Showcasing%20the%20DegreeGuru%20App&demo-url=https%3A%2F%2Fdegreeguru.vercel.app%2F&demo-image=https%3A%2F%2Fupstash.com%2Ficons%2Ffavicon-32x32.png) ![overview](figs/overview.gif) > [!NOTE] > **This project is a Community Project.** > > The project is maintained and supported by the community. Upstash may contribute but does not officially support or assume responsibility for it. **DegreeGuru** is a project designed to teach you making your own AI RAG chatbot on any custom data. Some of our favorite features: - 🕷️ Built-in crawler that scrapes the website you point it to, automatically making this data available for the AI - ⚡ Fast answers using Upstash Vector and real-time data streaming - 🛡️ Includes rate limiting to prevent API abuse This chatbot is trained on data from Stanford University as an example, but is totally domain agnostic. We've created this project so you can turn it into a chatbot with your very own data by simply modifying the `crawler.yaml` file. ## Overview 1. [Stack](#stack) 2. [Quickstart](#quickstart) 1. [Crawler](#crawler) 2. [ChatBot](#chatbot) 3. [Conclusion](#conclusion) 4. [Shortcomings](#shortcomings) ## Stack - Crawler: [scrapy](https://scrapy.org/) - Chatbot App: [Next.js](https://nextjs.org/) - Vector DB: [Upstash](https://upstash.com/) - LLM Orchestration: [Langchain.js](https://js.langchain.com) - Generative Model: [OpenAI](https://openai.com/), [gpt-3.5-turbo-1106](https://platform.openai.com/docs/models) - Embedding Model: [OpenAI](https://openai.com/), [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) - Text Streaming: [Vercel AI](https://vercel.com/ai) - Rate Limiting: [Upstash](https://upstash.com/) ## Quickstart For local development, we recommend forking this project and cloning the forked repository to your local machine by running the following command: ``` git clone git@github.com:[YOUR_GITHUB_ACCOUNT]/DegreeGuru.git ``` This project contains two primary components: the crawler and the chatbot. First, we'll take a look at how the crawler extracts information from any website you point it to. This data is automatically stored in an Upstash Vector database. If you already have a vector database available, the crawling stage can be skipped. ### Step 1: Crawler ![crawler-diagram](figs/how-this-project-works.png) The crawler is developed using Python, by [initializing a Scrapy project](https://docs.scrapy.org/en/latest/intro/tutorial.html#creating-a-project) and implementing a [custom spider](https://github.com/upstash/degreeguru/blob/master/degreegurucrawler/degreegurucrawler/spiders/configurable.py). The spider is equipped with [the `parse_page` function](https://github.com/upstash/degreeguru/blob/master/degreegurucrawler/degreegurucrawler/spiders/configurable.py#L42), invoked each time the spider visits a webpage. This callback function splits the text on the webpage into chunks, generates vector embeddings for each chunk, and upserts those vectors into your Upstash Vector Database. Each vector stored in our database includes the original text and website URL as metadata.
To run the crawler, follow these steps: > [!TIP] > If you have docker installed, you can skip the "Configure Environment Variables" and "Install Required Python Libraries" sections. Instead you can simply update the environment variables in [docker-compose.yml](https://github.com/upstash/DegreeGuru/blob/master/degreegurucrawler/docker-compose.yml) and run `docker-compose up`. This will create a container running our crawler. Don't forget to configure the crawler as explained in the following sections!

Configure Environment Variables

Before we can run our crawler, we need to configure environment variables. They let us securely store sensitive information, such as the API keys we need to communicate with OpenAI or Upstash Vector. If you don't already have an Upstash Vector Database, create one [here](https://console.upstash.com/vector) and set 1536 as the vector dimensions. We set 1536 here because that is the amount needed by the embedding model we will use. ![vector-db-create](figs/vector-db-create.png) The following environment variables should be set: ``` # Upstash Vector credentials retrieved here: https://console.upstash.com/vector UPSTASH_VECTOR_REST_URL=**** UPSTASH_VECTOR_REST_TOKEN=**** # OpenAI key retrieved here: https://platform.openai.com/api-keys OPENAI_API_KEY=**** ```

Install Required Python Libraries

To install the libraries, we suggest setting up a virtual Python environment. Before starting the installation, navigate to the `degreegurucrawler` directory. To setup a virtual environment, first install `virtualenv` package: ```bash pip install virtualenv ``` Then, create a new virtual environment and activate it: ```bash # create environment python3 -m venv venv # activate environment source venv/bin/activate ``` Finally, use [the `requirements.txt`](https://github.com/upstash/degreeguru/blob/master/degreegurucrawler/requirements.txt) to install the required libraries: ```bash pip install -r requirements.txt ```

After setting these environment variables, we are almost ready to run the crawler. The subsequent step involves configuring the crawler itself, primarily accomplished through the `crawler.yaml` file located in the `degreegurucrawler/utils` directory. Additionally, it is imperative to address a crucial setting within the `settings.py` file.

Configuring the crawler in `crawler.yaml`

The crawler.yaml has two main sections: `crawler` and `index`: ```yaml crawler: start_urls: - https://www.some.domain.com link_extractor: allow: '.*some\.domain.*' deny: - "#" - '\?' - about index: openAI_embedding_model: text-embedding-ada-002 text_splitter: chunk_size: 1000 chunk_overlap: 100 ``` In the `crawler` section, there are two subsections: - `start_urls`: the entrypoints our crawler will start searching from - `link_extractor`: a dictionary passed as arguments to [`scrapy.linkextractors.LinkExtractor`](https://docs.scrapy.org/en/latest/topics/link-extractors.html). Some important parameters are: - `allow`: Only extracts links matching the given regex(s) - `allow_domains`: Only extract links matching the given domain(s) - `deny`: Deny links matching the given regex(s) In the `index` section, there are two subsections: - `openAI_embedding_model`: The embedding model to use - `test_splitter`: a dictionary passed as arguments to [`langchain.text_splitter.RecursiveCharacterTextSplitter`](https://api.python.langchain.com/en/latest/text_splitter/langchain.text_splitter.RecursiveCharacterTextSplitter.html)

Configuring crawl depth via `settings.py`

`settings.py` file has an important setting called `DEPTH_LIMIT` which determines how many consecutive links our spider can crawl. A high value lets our crawler visit the deepest corners of a website, taking longer to finish with possibly diminishing returns. A low value could end the crawl before extracting relevant information. If pages are skipped due to the `DEPTH_LIMIT`, Scrapy logs those skipped URLs for us. Because this usually causes a lot of logs, we've disabled this option in our project. If you'd like to keep it enabled, remove [the `"scrapy.spidermiddlewares.depth"` from the `disable_loggers` in `degreegurucrawler/spider/configurable.py` file](https://github.com/upstash/degreeguru/blob/master/degreegurucrawler/degreegurucrawler/spiders/configurable.py#L22).

That's it! 🎉 We've configured our crawler and are ready to run it using the following command: ``` scrapy crawl configurable --logfile degreegurucrawl.log ``` Note that running this might take time. You can monitor the progress by looking at the log file `degreegurucrawl.log` or the metrics of your Upstash Vector Database dashboard as shown below. ![vector-db](figs/vector-db.png) > [!TIP] > If you want to do a dry run (without creating embeddings or a vector database), simply comment out [the line where we pass the `callback` parameter to the `Rule` object in `ConfigurableSpider`](https://github.com/upstash/degreeguru/blob/master/degreegurucrawler/degreegurucrawler/spiders/configurable.py#L38) ### Step 2: Chatbot In this section, we'll explore how to chat with the data we've just crawled and stored in our vector database. Here's an overview of what this will look like architecturally: ![chatbot-diagram](figs/infrastructure.png) Before we can run the chatbot locally, we need to set the environment variables as shown in the [`.env.local.example`](https://github.com/upstash/degreeguru/blob/master/.env.local.example) file. Rename this file and remove the `.example` ending, leaving us with `.env.local`. Your `.env.local` file should look like this: ``` # Redis tokens retrieved here: https://console.upstash.com/ UPSTASH_REDIS_REST_URL= UPSTASH_REDIS_REST_TOKEN= # Vector database tokens retrieved here: https://console.upstash.com/vector UPSTASH_VECTOR_REST_URL= UPSTASH_VECTOR_REST_TOKEN= # OpenAI key retrieved here: https://platform.openai.com/api-keys OPENAI_API_KEY= ``` The first four variables are provided by Upstash, you can visit the commented links for the place to retrieve these tokens. You can find the vector database tokens here: ![vector-db-read-only](figs/vector-db-read-only.png) The `UPSTASH_REDIS_REST_URL` and `UPSTASH_REDIS_REST_TOKEN` are needed for rate-limiting based on IP address. In order to get these secrets, go to Upstash dashboard and create a Redis database. ![redis-create](figs/redis-create.png) Finally, set the `OPENAI_API_KEY` environment variable you can get [here](https://platform.openai.com/api-keys) which allows us to vectorize user queries and generate responses. That's the setup done! 🎉 We've configured our crawler, set up all neccessary environment variables are after running `npm install` to install all local packages needed to run the app, we can start our chatbot using the command: ```bash npm run dev ``` Visit `http://localhost:3000` to see your chatbot live in action! ### Step 3: Optional tweaking You can use this chatbot in two different modes: - Streaming Mode: model responses are streamed to the web application in real-time as the model generates them. Interaction with the app is more fluid. - Non-Streaming Mode: Model responses are shown to the user once entirely generated. In this mode, DegreeGuru can explicitly provide the URLs of the web pages it uses as context.

Changing streaming mode

To turn streaming on/off, navigate to `src/app/route/guru` and open the `route.tsx` file. Setting [`returnIntermediateSteps`](https://github.com/upstash/degreeguru/blob/master/src/app/api/guru/route.tsx#L64) to `true` disables streaming, setting it to `false` enables streaming.

To customize the chatbot further, you can update the [AGENT_SYSTEM_TEMPLATE in your route.tsx file](https://github.com/upstash/DegreeGuru/blob/master/src/app/api/guru/route.tsx#L101) to better match your specific use case.
## Conclusion Congratulations on setting up your own AI chatbot! We hope you learned a lot by following along and seeing how the different parts of this app, namely the crawler, vector database, and LLM, play together. A major focus in developing this project was on its user-friendly design and adaptable settings to make this project perfect for your use case. ## Limitations The above implementation works great for a variety of use cases. There are a few limitations I'd like to mention: - Because the Upstash LangChain integration is a work-in-progress, the [`UpstashVectorStore`](https://github.com/upstash/degreeguru/blob/master/src/app/vectorstore/UpstashVectorStore.js) used with LangChain currently only implements the `similaritySearchVectorWithScore` method needed for our agent. Once we're done developing our native LangChain integration, we'll update this project accordingly. - When the non-streaming mode is enabled, the message history can cause an error after the user enters another query. - Our sources are available as URLs in the Upstash Vector Database, but we cannot show the sources explicitly when streaming. Instead, we provide the links to the chatbot as context and expect the bot to include the links in the response.

Upstash Vector + Vercel AI SDK Starter