Reduce AI Hallucinations By Adding Custom Knowledgebases

Indexing

Overview of FlowHunt’s Schedule Feature

In today’s post, we’ll explore the schedules feature in FlowHunt and how it can be used to effectively index your domain. By setting up schedules, you can ensure that your chatbot has access to the most up-to-date information, reducing the likelihood of AI hallucinations and improving the accuracy of responses.

FlowHunt
August 13, 2024

Importance of Domain Indexing for Chatbots

For a chatbot to provide accurate and relevant responses, it must have access to well-indexed content. By regularly crawling your domain, URLs, or sitemaps, the chatbot can maintain a comprehensive understanding of the content, leading to more precise answers.

Reducing LLM Hallucinations with Accurate Data

AI hallucinations occur when a model generates information that is not based on the provided data. This can be mitigated by ensuring the chatbot has access to the most accurate and recent information from your domain through regular indexing.

Understanding website indexing

What is the Schedule Feature?

The Schedule feature in FlowHunt allows you to automate the process of crawling your domain, specific URLs, or even sitemaps. This ensures that your chatbot remains informed about the latest updates on your site.

Types of Content You Can Index (Domains, URLs, Sitemaps)

FlowHunt provides flexibility in what you can crawl—whether it’s an entire domain, specific URLs, or structured sitemaps. This feature is especially useful for websites that frequently update their content, such as blogs or e-commerce sites.

Setting Up Crawl Frequencies: Daily, Weekly, Monthly, Yearly

You can set the frequency of your crawls to match the update schedule of your website. For example, if you post new content daily, setting the crawl frequency to daily ensures that your chatbot stays updated with the latest information.

Benefits of Using Schedules for Domain Indexing

Ensuring Content Accuracy and Relevance

Regularly scheduled crawls guarantee that all the information indexed by the chatbot is current, leading to more accurate responses. This is critical for providing users with reliable and up-to-date information.

Enhancing Chatbot Responses with Updated Data

With access to the latest content, your chatbot can generate responses that are both relevant and accurate. This capability is particularly valuable for websites that offer product comparisons, detailed reviews, or have extensive FAQs.

Minimizing the Risk of Hallucinations in AI Responses

By consistently indexing your domain, you minimize the risk of AI hallucinations, where the chatbot might generate responses based on outdated or irrelevant data. This leads to a more reliable and trustworthy user experience.

Practical Use Cases for Domain Indexing

Website Curators

Website curators can benefit greatly from the schedules feature by ensuring that all content on the site is indexed and easily accessible by the chatbot. This makes the chatbot a powerful tool for navigating and providing information on the website.

Product Comparisons for E-Shops

E-commerce platforms can use this feature to allow the chatbot to make accurate product comparisons. By having access to all relevant product details, the chatbot can guide customers through their decision-making process more effectively.

General Website Curatorship and Information Retrieval

Beyond e-commerce, any website that requires detailed information retrieval —such as educational platforms, service providers, or content libraries—can benefit from this feature. The chatbot can serve as a comprehensive guide, directing users to the specific information they need.

Step-by-Step Guide to Building a Chatbot Using the Schedule Feature

Navigating to the Schedules Tab

To start using the schedules feature, navigate to the Schedules tab in FlowHunt. This is where you’ll set up your crawl schedule to index your domain or specific content on your site.

Creating a New Schedule

Click on Create New Schedule to begin the process. Here, you’ll be prompted to select the domain, URLs, or sitemap you wish to index.

Selecting Domains, URLs, or Sitemaps for Crawling

For best results, if your website has a structured sitemap, use it for the crawl. Sitemaps provide a comprehensive list of URLs on your site, making it easier for the chatbot to index all relevant content.

Choosing the Crawl Frequency

Next, choose how often the domain or sitemap should be crawled. For sites with frequent updates, a daily crawl might be necessary. For others, a weekly or monthly crawl might suffice.

Building a Flow in FlowHunt

Accessing the “My Flows” Tab

After setting up your schedule, move over to the My Flows tab. This is where you’ll create a new flow that will utilize the indexed content.

Creating and Naming Your Flow

Start by giving your flow a descriptive name that reflects the focus of your project. This makes it easier to identify the flow later on.

Understanding the Flow Canvas

The flow canvas is your workspace in FlowHunt. It’s designed to be intuitive, allowing you to drag and drop components, connect them, and create a logical sequence that guides the AI agent from input to output.

Essential Components of a Flow

Input Component: Capturing User Queries

The input component is where the user’s query will be entered. This serves as the starting point of your flow, capturing the question or topic that the user wants to explore.

Output Component: Delivering AI Responses

The output component is where the AI agent ’s response will be delivered. This is the final product of your flow, containing the information retrieved and processed by the tool.

Adding Query Expansion for Improved Search Results

To enhance the LLM ’s (Large Language Model) understanding of user queries, add a query expansion component. This component paraphrases input queries into multiple alternatives, improving the semantic search capabilities of your chatbot.

Enhancing AI Responses with Additional Components

Chat History Integration

Adding chat history integration ensures that the chatbot remembers previous interactions, allowing it to adapt its responses based on the user’s past queries. This leads to a more personalized user experience.

Incorporating LLMs: Choosing GPT-3.5 Turbo for Cost-Effectiveness

For the query expansion component, you can incorporate an LLM such as GPT-3.5 Turbo. While this model is not the fastest or most powerful, it is cost-effective and sufficient for handling queries without generating new content.

Using Document Retrievers for Accessing Indexed Content

The document retriever component is crucial for accessing the information from your crawled pages. Since you are using schedules to index your domain, this component will be the primary source of data for the chatbot’s responses.

Setting Up the Document Retriever Component

Connecting the Document Retriever to Query Expansion

Link the document retriever component to the query expansion component. This connection allows the chatbot to pull relevant information from your indexed content based on the expanded query.

Linking the Schedule to the Document Retriever

Next, add your schedule to the document retriever. This ensures that the chatbot is pulling information from the most recent crawl of your domain or sitemap.

Adjusting Settings for Optimal Output

You can adjust the settings within the document retriever component to refine the output. This might involve tweaking how much information is retrieved or which parts of the content are prioritized in the response.

Prompting and Generating Content in Your Flow

Adding the Prompt Tool to Your Flow

With your data ready, it’s time to generate text responses. Add the prompt tool to your flow, connecting it to the document retriever as the context and the input component as the input.

Using the Document Retriever as Context

The document retriever serves as the context for the prompt tool, providing the necessary background information that the chatbot will use to generate its responses.

Fine-Tuning Prompts for Desired Responses

You can customize the prompt to guide the chatbot’s responses more effectively. This might involve specifying the tone, style, or particular information the chatbot should include in its answers.

Our prompt:

you are a website curator that only answers based on the content you receive from the document retriever. and if you do not know the answer let the user know.

Your task is to answer customer queries in INPUT with consideration of previous conversation in CHAT HISTORY.

If CONTEXT is provided, use it to generate the answer.

— CONTEXT START
{context}
— CONTEXT END

— CHAT HISTORY START
{chat_history}
— CHAT HISTORY END

— INPUT START
{input}
— INPUT END

Answer in Language: {lang}

Format answer with markdown.

ANSWER:

Finalizing Your Flow

Connecting the Generator Component to an LLM

Finally, connect the generator component to a powerful LLM. This will allow the chatbot to produce the final output that is delivered to the user.

Setting Up the Output for User Interactions

Ensure that the output is configured to meet your chatbot’s goals, whether it’s providing links, generating content, or offering guidance based on the user’s query.

Improving User Experience with Linked Content

Since your chatbot uses indexed and crawled information, you can improve the user experience by providing links to relevant content. Add a document widget to your flow and connect it to the document retriever, giving users direct access to the pages they need.

Conclusion

Recap of Key Points

In this guide, we’ve covered how to use the schedules feature in FlowHunt to index your domain and improve the accuracy of your chatbot. By regularly crawling your site, you ensure that the chatbot has access to the latest information, reducing the chances of AI hallucinations.

Final Thoughts on Reducing LLM Hallucinations

Reducing AI hallucinations is critical for maintaining user trust and ensuring that your chatbot delivers high-quality, accurate information. By leveraging the schedules feature in FlowHunt, you can keep your chatbot’s knowledge base up to date, providing reliable answers to your users’ queries.

Here’s a screenshot of the completed Flow: