How To Create A Rag Chat Bot With Retrieval-Augmented Generation

To create a Rag Chat Bot with Retrieval-Augmented Generation, you'll need to understand the basics of this technology. Retrieval-Augmented Generation, or RAG for short, is a type of AI model that combines the strengths of retrieval and generation.

RAG models work by first retrieving relevant information from a large dataset, and then using that information to generate a response. This process is called "augmentation" because it enhances the generation capabilities of the model.

To get started, you'll need to choose a retrieval model that can efficiently search through a large dataset. According to the article, a suitable retrieval model is the "Dense Passage Retriever" (DPR), which is a popular choice for its high accuracy and efficiency.

The DPR model uses a combination of dense vector representations and a query encoder to retrieve relevant passages from the dataset. This allows the model to quickly and accurately find the information it needs to generate a response.

LLM stands for Large Language Model, a type of AI that can generate human-like text.

It's a key term in the tech world, especially for developers working in the AI field.

Generative AI has made LLM a crucial concept for many professionals.

LLM is not a specific tool, but rather a type of model that can be used in various applications.

These models are trained on vast amounts of text data, enabling them to learn patterns and relationships.

This training allows LLMs to generate coherent and contextually relevant text.

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To set up a RAG chatbot, start by creating a Hugging Face account at https://huggingface.co/join and sign up. After signing up, go to Your Profile page and create a new access token called “RAG-Chatbot” or similar.

You should not share this token with anyone, so make sure to keep it secure. Next, set up a Pinecone account by filling in the Project Name, Cloud Provider, and Environment. In this case, I used “Chatbot Application” as a Project Name, GCP as Cloud Provider, and Iowa (gcp-starter) as an Environment.

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After the project is created, go into the API Keys section and make sure you have an API key available. This API key should not be shared with anyone. Now, create a directory called “Chatbot” and inside it, create a file called .env. The context inside .env should include your Hugging Face access tokens and Pinecone API Keys, which you should replace with your actual tokens.

Create an empty class called Chatbot inside a file called main.py. This class will be called when you implement the frontend UI. For now, just create the class without adding any more code to it.

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Data indexing is a crucial step in building a robust RAG chatbot. It involves breaking down text files into manageable segments and embedding them for efficient storage and retrieval. We can use a text splitter to set the dimensions of these segments, such as a chunk_size of 1000 and chunk_overlap of 4.

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To index our data, we can use the HuggingFaceEmbedding tool, which will be instrumental in embedding our text segments. This process involves deploying the embedding utility and then depositing the embedded text fragments into our vector database, such as Pinecone.

To get started with Pinecone, we need to initialise the Pinecone client in our application using the API key from the Pinecone dashboard. We can then assign an index name and check if it already exists in Pinecone. If it does exist, we can link it to the docsearch variable. If not, we can create a new index using pinecone.create_index, with cosine as the metric and a dimension of 768, suitable for HuggingFace embeddings.

Here are the key steps involved in data indexing:

To create a chatbot that can have a relevant conversation, we need to define a prompt that contains all the necessary information. This allows us to customize the model to fit our needs.

We'll tell the model to be a fortune teller and answer only relevant questions, which is a good way to start.

A prompt template is created by defining the necessary information, in this case, the model's role and the input variables. These variables will be replaced with the data chunk and the user's question.

To define the prompt template, we pass our template and input variables (context and questions) as a parameter to the PromptTemplate object.

The core of our rag chat bot is the ability to chain together different components to create a seamless conversation flow. This process starts with the Pinecone database index object, also known as docsearch, which pulls relevant documents to provide context.

The next step is to use a PromptTemplate to refine or modify the query before it's processed by our model. This is done using a prompt step, which refines the query.

Our model, llm, then processes the refined query and generates a response. Finally, the response from the model is turned into text with StrOutputParser.

Here's a breakdown of the components that are chained together:

By chaining these components together, we can create a sophisticated conversation flow that's both efficient and effective.

Now that we've built our rag_chain, it's time to integrate it into our Chatbot() class. Simply put all the code we've added into the Chatbot class.

To finalize the model, you'll need to run main.py and ask questions about your life in 2024, as a Sagittarius. This will give you a better understanding of how the rag_chain works.

After completing all the steps, you should be able to create something like the example provided, where you can interact with the chatbot and see how it responds to your questions.

Ran main.py and asked questions about my life in 2024, and I got the expected results.

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I implemented the RAG LLM chatbot using Streamlit frontend, but I won't go into details here. The Streamlit template I use for creating Chat UI and some basic functions for calling the model can be found in a separate file, streamlit.py.

The Streamlit frontend allows the bot to answer any question related to my life in 2024 using the Horoscope text I got from online articles. This is made possible by the prompt template we used.

To create a Streamlit frontend for your RAG chatbot, you'll need to use the code in streamlit.py. This code provides the Chat UI and basic functions for calling the model.

The bot doesn't answer irrelevant questions, thanks to the prompt template we used. This ensures that the chatbot provides relevant and accurate responses to user queries.

Here are the basic functions I use for calling the model:

To explore the source code of the RAG chat bot, you'll want to take a tour of the bot app source code. This will give you a comprehensive understanding of how the bot works.

The source code is organized into several folders, each containing specific files and templates. You can find these folders in the project directory.

The `.vscode` folder contains Visual Studio Code files for debugging, while the `appPackage` folder holds templates for the Teams app manifest. The `env` folder stores environment files, and the `infra` folder has templates for provisioning Azure resources.

The `src` folder is where you'll find the source code for the app. This is where the magic happens, and you can start to see how the bot is put together.

Here's a breakdown of the key files you'll find in the `src` folder:

The project files, such as `m365agents.yml`, `m365agents.local.yml`, and `m365agents.playground.yml`, also provide valuable information about the project configuration and setup.

In a team setting, a rag chat bot can be a game-changer. It helps to streamline communication and reduce misunderstandings.

For instance, a team of 10 members can use a rag chat bot to discuss project updates, ask questions, and share resources. This saves time and increases productivity.

In a fast-paced environment like a marketing team, a rag chat bot can help to keep track of multiple conversations and ensure that no important message gets lost. This is especially useful when working with remote teams or clients.

A rag chat bot can also be used to create a knowledge base for the team, where they can store and access relevant information, such as meeting notes, project timelines, and company policies.

For example, a team of software developers can use a rag chat bot to collaborate on coding projects, share code snippets, and get instant feedback from colleagues. This collaboration leads to better code quality and faster development time.

By implementing a rag chat bot, teams can work more efficiently, reduce stress, and achieve their goals faster.

Data products are a game-changer for RAG chatbots, as they prepare and inject trusted, fresh, and contextual enterprise data into the LLM, making it more accurate and personalized.

Data products can be invoked by the RAG framework to retrieve and integrate customer data from enterprise data sources in real-time, translate it into a contextually rich prompt, and feed it to the LLM along with the client's query.

This empowers RAG chatbots to deliver strategic value from GenAI, overcoming limitations like lack of business context, stale data, high retraining costs, and hallucinations.

Data products employ various methods to access customer data in real-time, including APIs, CDC, messaging, streaming, or any combination of these methods to integrate data from disparate data sources.

Here are some ideal use cases for combining RAG with real-time data products:

Data products are a game-changer for RAG chatbots. They prepare and inject trusted, fresh, and contextual enterprise data into your Large Language Model (LLM) in real-time.

By using data products, you can overcome the limitations of LLMs, such as lacking business context, training on stale data, being expensive to retrain, and generating hallucinations.

Data products are reusable data assets that bundle data with everything needed to make them accessible by authorized consumers. They can be invoked by the RAG framework to retrieve and integrate customer data from enterprise data sources in real-time.

Here are some ways data products can be used with RAG:

The LLM then generates an accurate and personalized response for the user. Data products employ various methods to access customer data in real-time, such as APIs, Change Data Capture (CDC), messaging, and streaming.

Data products are not just limited to chatbots. They can be used to accelerate issue resolution, create hyper-personalized marketing campaigns, and generate personalized cross- and up-sell recommendations for call center agents.

Microsoft 365 can be used as a data source for the RAG app, utilizing the Microsoft Graph Search API to query content.

To access SharePoint and OneDrive files, folders, pages, and news, you must create a Graph API client and grant it the Files.Read.All permission scope.

The Microsoft Graph Search API is available, making it possible to search SharePoint content without extra data ingestion required.

You just need to upload your document to SharePoint or OneDrive, and you're good to go.

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To create a chat interface, we need to interact with our documents and ask questions. This is where Panel's ChatInterface widget comes in!

We must create a function `respond` to define how the chatbot responds. This function takes the response from the previous step and formats it into a Panel object `answers`. We also append the relevant source documents in this Panel object.

The function is then called in the pn.chat.ChatInterface `callback`.

Generic LLMs have a problem admitting they don't know something, so they might just make something up instead.

Generic Large Language Models (LLMs) can be less knowledgeable about certain topics because they haven't been trained on that specific data.

There's a phenomenon called "hallucination" where the model generates responses that are inaccurate or misleading due to its lack of specialized knowledge.

Generic LLMs are not trained with detailed information in areas like specific legal rules or medical data, which are outside the scope of their training data.

This can lead to problems, so it's essential to address these issues with a simpler approach like RAG.