Using Voiceflow with Mistral AI or Open Sourced AI Model

Voiceflow
17 Jan 202417:11

TLDRThis video tutorial demonstrates how to integrate an open-source AI model, Mistol, with Voil's knowledge base to create a cost-effective AI assistant. The presenter guides developers through the process of using Voil's vector database API to fetch relevant information and then pass it to Mistol for generating responses. The tutorial also covers how to filter and select the best chunks of data for higher accuracy and how to bypass Voil's built-in models to use custom models at no additional cost. The video is aimed at developers with some JavaScript knowledge, offering a more advanced application of Voil for custom application development.

Takeaways

  • 🚀 New open-source AI models like Mistol are emerging, offering cheaper and almost as accurate alternatives to GPT-3.
  • 🔍 Tools like Perplexity are being built on top of Mistol, indicating a growing ecosystem around these models.
  • 🛠️ Developers can leverage Vo's knowledge base as a vector database with third-party large language models for efficient development.
  • 📚 Vo Flow has a built-in vector database that allows users to import and vectorize data sources for quick access via the API.
  • 🔧 The video demonstrates how to use Vo Flow's knowledge base API to send questions and receive relevant chunks of information.
  • 🔄 The process involves filtering chunks based on similarity scores to ensure the most relevant information is used to answer queries.
  • 🔗 Developers can bypass Vo Flow's built-in models and use their own custom models at no additional cost.
  • 📝 The script provides a step-by-step guide on how to integrate Vo Flow with a third-party model like Mistol using JavaScript and API calls.
  • 🔧 The video includes a practical example of building an AI assistant using Vo Flow's knowledge base and a third-party model.
  • 📋 The script also explains how to use Vo Flow's functions feature to make API calls and process data in a more advanced and customizable way.
  • 📈 The example showcases the potential for developers to create complex applications by combining vector databases with various AI models.

Q & A

  • What is Mistol and how does it compare to GPT-3?

    -Mistol is an open-source AI model that has recently been developed. It is more cost-effective and almost as accurate as GPT-3, making it a promising alternative for developers and those looking for cheaper options.

  • How can developers utilize Voiceflow's knowledge base as a vector database?

    -Developers can use Voiceflow's knowledge base to import and vectorize data sources, such as URLs, which are then processed into chunks accessible via the Voiceflow API. This allows for efficient retrieval and use of information in custom applications.

  • What is the significance of the similarity score in Voiceflow's knowledge base?

    -The similarity score indicates how closely a chunk of information is related to the user's question. It helps in filtering and selecting the most relevant chunks to provide accurate and contextually appropriate responses.

  • How can Voiceflow be integrated with third-party models like Mistol?

    -Voiceflow's knowledge base API can be used to send questions and receive relevant chunks of information. These chunks can then be passed to a third-party model like Mistol, which generates a response. This process allows for the use of custom models without additional costs.

  • What is the role of the API step and functions in Voiceflow's development tools?

    -The API step allows for making API calls, while functions provide a more advanced way to handle complex tasks. Functions enable developers to make fetch calls to APIs, write JavaScript for data transformation, and define input and output variables for more structured development.

  • How does the capture step in Voiceflow work?

    -The capture step is used to capture the user's input, storing it in a variable for later use in the development process. This is particularly useful for capturing and processing user queries in custom applications.

  • What is the purpose of the system prompt in the API call to Mistol?

    -The system prompt is a directive sent to Mistol, instructing the model to answer the question using only the information provided in the chunks. This ensures that the response is tailored to the specific query and information available.

  • How can developers test and debug their Voiceflow projects?

    -Developers can use the debug messages and error handling within Voiceflow to identify and fix issues in their projects. The platform also provides a visual representation of the flow, which aids in understanding and troubleshooting.

  • What are the benefits of using Voiceflow for building AI assistants with third-party models?

    -Voiceflow offers flexibility in integrating with various third-party models, allowing developers to experiment with different options for cost, speed, and specialization. It also provides a platform for customizing responses and building complex applications.

  • How can users get started with Voiceflow and its developer tools?

    -Users can follow the provided tutorials and examples, import pre-built functions, and explore the developer resources available on Voiceflow's Discord channel. The platform also encourages users to share their ideas for future tutorials.

Outlines

00:00

🚀 Introduction to Open Source AI Models

The paragraph discusses the emergence of new open-source AI models like Mistol, which are more cost-effective and almost as accurate as GPT. It highlights the growing trend of tools like Perplexity building on top of Mistol. The video aims to guide developers on how to use Vo's knowledge base as a vector database with third-party large language models, such as Mistol, to save time and resources. The target audience includes developers and those with a basic understanding of JavaScript, as the content delves into advanced applications of Vo Flow.

05:01

📚 Utilizing Vo Flow's Built-in Vector Database

This paragraph explains how Vo Flow has a built-in vector database and demonstrates how to use it. The speaker shows how to import data sources, vectorize them, and access them via the Vo Flow API. It also covers how to run tests, search through the knowledge base, and synthesize responses with a large language model. The paragraph emphasizes the flexibility of Vo Flow's API, which allows for custom large language model integration at no additional cost.

10:03

🔍 Advanced API Usage for Developers

The speaker introduces the concept of using Vo Flow's API step and functions for more complex tasks. Functions allow for API calls and JavaScript transformations, which are particularly useful for developers. The paragraph provides a step-by-step guide on how to create and use functions within Vo Flow, including defining input and output variables, making API calls, and processing responses. It also shows how to filter and select the most relevant information from the API response.

15:05

🛠️ Integrating with Mistol and Other Models

This section details the process of passing chunks from Vo Flow's knowledge base to Mistol or other third-party models. The speaker explains how to make API calls to Mistol, a cheaper alternative to GPT, and how to use tools like Together to access and test various open-source models. The paragraph also demonstrates how to build a custom bot using Mistol and how to publish and embed the bot on a website, showcasing the flexibility and potential of Vo Flow for building complex applications with third-party models.

📈 Conclusion and Next Steps

The final paragraph wraps up the video by summarizing the key points discussed, including the use of vector databases, the integration of third-party models, and the customization options available in Vo Flow. The speaker encourages viewers to experiment with open-source models and provides a link to download pre-built functions for convenience. The video ends with a call to action for viewers to like, subscribe, and join the Discord channel for more developer resources and future tutorial topics.

Mindmap

Keywords

💡Open-Source AI Models

Open-Source AI Models refer to artificial intelligence systems whose source code is made available for anyone to use, modify, and distribute freely. In the context of the video, these models like Mistol are highlighted as alternatives to GPT-3, offering similar accuracy but at a lower cost, making them accessible for developers and researchers with limited budgets. The video discusses how these models can be integrated into various applications, showcasing their potential for innovation and cost-effectiveness in the AI field.

💡Mistol

Mistol is an open-source AI model mentioned in the video as a cost-effective alternative to GPT-3. It is designed to be more affordable while maintaining a high level of accuracy, making it an attractive option for developers looking to build AI applications without incurring significant expenses. The video demonstrates how Mistol can be used in conjunction with other tools and platforms, such as Voiceflow, to create efficient and customized AI solutions.

💡Vector Database

A Vector Database is a type of database that stores and retrieves data as vectors, which are mathematical objects representing directions or magnitudes in a multi-dimensional space. In the video, the concept is used to describe how Voiceflow's knowledge base can function as a vector database, allowing for the efficient storage and retrieval of information in the form of vectorized text chunks. This enables developers to leverage the database for AI applications, such as building AI assistants, by querying and retrieving relevant information based on vector similarity.

💡Voiceflow

Voiceflow is a platform mentioned in the video that provides tools for developers to build AI applications. It includes a built-in vector database and APIs that can be used to interact with external AI models. The video illustrates how Voiceflow can be used to streamline the development process by allowing developers to import data, vectorize it, and then use it in conjunction with third-party AI models like Mistol to generate responses. This platform is presented as a flexible and efficient tool for creating custom AI solutions.

💡API (Application Programming Interface)

An API is a set of rules and protocols that allows different software applications to communicate with each other. In the video, APIs are used to interact with both Voiceflow's knowledge base and external AI models like Mistol. The API calls are demonstrated as a means to send queries, receive vectorized data chunks, and obtain AI-generated responses. The video emphasizes the importance of APIs in integrating various components of an AI application, showcasing their role in enabling seamless data exchange and processing.

💡JavaScript

JavaScript is a widely-used programming language that enables the creation of interactive web applications. The video targets developers with some understanding of JavaScript, as it involves using JavaScript to write functions and make API calls within the Voiceflow platform. JavaScript is crucial in this context for transforming and processing data, as well as for building the logic that drives the AI application's functionality.

💡AI Assistant

An AI assistant is an artificial intelligence system designed to perform tasks that typically require human intelligence, such as answering questions, providing information, or automating routine tasks. In the video, the creation of an AI assistant is used as a practical example to demonstrate how Voiceflow's vector database and APIs can be utilized in conjunction with third-party AI models like Mistol. The AI assistant serves as a tangible application of the technologies discussed, illustrating the potential for creating intelligent and responsive software solutions.

💡Vectorization

Vectorization is the process of converting data into vectors, which are numerical representations that capture the essence of the data in a multi-dimensional space. In the context of the video, vectorization is used to transform text data into a form that can be efficiently processed by AI models. This process allows for the creation of a knowledge base where text chunks are represented as vectors, enabling the AI to find and synthesize relevant information based on vector similarity scores.

💡Customization

Customization refers to the process of tailoring a product or service to meet specific needs or preferences. In the video, customization is discussed in relation to AI models and applications. Developers can customize their AI assistants by selecting and integrating specific open-source models like Mistol, fine-tuning them to their requirements, and leveraging Voiceflow's tools to create unique and personalized AI solutions.

💡Third-Party Models

Third-Party Models are AI models developed by entities other than the primary platform or service provider. In the video, third-party models like Mistol are contrasted with proprietary models like GPT-3, highlighting their potential for cost savings and flexibility. The ability to use third-party models within Voiceflow is presented as a key feature that allows developers to experiment with various AI capabilities and build applications that suit their specific needs.

Highlights

New open-source AI models like Mistol are emerging, offering cheaper and almost as accurate alternatives to GPT.

Mistol is being integrated into various tools like Perplexity, showcasing its growing adoption.

The video demonstrates how to use Vo's knowledge base as a vector database with third-party large language models like Mistol.

Vo Flow has a built-in vector database, allowing developers to import and vectorize data sources efficiently.

The process involves uploading URLs, which Vo Flow then parses and vectorizes into accessible chunks.

Vo Flow's API can be used to search the knowledge base, find relevant chunks, and synthesize them with a large language model.

Developers can bypass Vo Flow's available models and use their own at no additional cost, leveraging the platform's API capabilities.

The response from the API includes different chunks, their similarity scores, and the ability to filter based on confidence levels.

Vo Flow's API allows for AI synthesis to be turned off, enabling the use of the knowledge base as a pure vector database.

The video provides a detailed walkthrough of using Vo Flow's API and Functions for advanced application development.

Functions in Vo Flow allow for making API calls and writing JavaScript to transform the data, offering a powerful tool for developers.

The tutorial shows how to create a function to fetch knowledge base chunks and filter them based on a similarity score threshold.

The process of connecting the knowledge base chunks to a third-party model like Mistol is demonstrated, including API calls and error handling.

The video concludes with a live demonstration of a custom bot running on Mistol, showcasing the practical application of the discussed techniques.

The presenter, Daniel, offers resources and invites viewers to join a developer chat channel for further exploration and support.

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