Build Chatbots Without Any Coding: Introducing Flowise

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Build Chatbots Without Any Coding: Introducing Flowise

Table of Contents:

  1. Introduction
  2. What is Flow Wise?
  3. Comparison with Lang Flow
  4. Installation Process
  5. User Interface
  6. Components in Flow Wise 6.1 Chart Flows 6.2 Marketplace 6.3 Document Loaders 6.4 Embeddings 6.5 LLMS 6.6 Memories 6.7 Prompt Template 6.8 Retriever 6.9 Text Splitters 6.10 Vector Store
  7. Building a Document Question and Answer Application 7.1 Uploading PDF Files 7.2 Text Splitting 7.3 Embedding Computation 7.4 Vector Store Creation 7.5 Conversational Retrieval Question and Answer Chain 7.6 Connecting Language Model and Vector Store
  8. Saving and Deploying the Chat Flow
  9. Conclusion

Introduction

[Add engaging introduction here, highlighting the growing popularity and demand for no code platforms that enable the creation of powerful applications using large language models. Mention the focus of the article on Flow Wise, a notable project in this field.]

What is Flow Wise?

Flow Wise is an impressive no code platform that allows users to build robust applications utilizing large language models. Similar to the popular project Lang Flow in terms of functionality and interface, Flow Wise is written in node.js and provides a user-friendly graphical interface. It offers a wide range of components and modules that can be combined to Create various applications, such as Q&A retrieval, translation, and conversational agents. In this article, we will explore the installation process of Flow Wise and Delve into its key features and capabilities.

Comparison with Lang Flow

[Highlight the similarities and differences between Flow Wise and Lang Flow, emphasizing the unique aspects of Flow Wise that set it apart from its counterpart.]

Installation Process

[Provide step-by-step instructions on how to install Flow Wise, including the requirement of node.js installation. Explain the process of starting Flow Wise either through the command line or by providing a username and password for authentication purposes.]

User Interface

[Describe the user interface of Flow Wise, highlighting its intuitive drag-and-drop functionality and providing a brief overview of each major component available within the interface, including Chart flows, marketplace, document loaders, embeddings, LLMS, memories, prompt templates, retrievers, text splitters, and vector store.]

Components in Flow Wise

6.1 Chart Flows [Provide an explanation of chart flows and their role in Flow Wise's application-building process.]

6.2 Marketplace [Discuss the marketplace feature, which allows users to access pre-built flows created by others as templates for their own applications.]

6.3 Document Loaders [Explain the document loader component and its significance in uploading and managing different file types, with a specific focus on PDF file loading.]

6.4 Embeddings [Discuss the role of embeddings in Flow Wise and the supported options, including Azure, Open AI, cohere, hacking face, and LM models.]

6.5 LLMS [Provide an overview of the available LLMS options in Flow Wise, emphasizing the usage of Open AI and the limitations regarding specific models.]

6.6 Memories [Explain the concept of memories in Flow Wise and their various types, showcasing their relevance in the information retrieval process.]

6.7 Prompt Template [Discuss the prompt template option and its importance in designing prompts to extract specific information from documents.]

6.8 Retriever [Highlight the retriever component, noting its role in retrieving relevant information based on input queries.]

6.9 Text Splitters [Explain the text splitter feature, focusing on the recursive character splitter and its function in dividing documents into smaller chunks.]

6.10 Vector Store [Discuss the vector store component, its different options such as chroma DB, in-memory vector store, find cone, and its significance as a stored knowledge base for computation and retrieval purposes.]

Building a Document Question and Answer Application

7.1 Uploading PDF Files [Guide readers through the process of uploading PDF files into Flow Wise for use in the document question and answer application example.]

7.2 Text Splitting [Explain the text splitting step, showcasing how the recursive character splitter divides the uploaded PDF document into smaller documents or chunks.]

7.3 Embedding Computation [Describe the process of computing embeddings for each document using the chosen embedding model, such as Open AI embeddings.]

7.4 Vector Store Creation [Discuss the creation of a vector store, explaining its purpose in storing embeddings and enabling semantic search and retrieval.]

7.5 Conversational Retrieval Question and Answer Chain [Explain the implementation of the conversational retrieval question and answer chain, highlighting its inputs of a language model and vector store retriever.]

7.6 Connecting Language Model and Vector Store [Provide guidance on connecting the language model component to the chain, demonstrating the integration of Open AI language model into the application.]

Saving and Deploying the Chat Flow

[Outline the process of saving and deploying the created chat flow, including options for embedding it in HTML, making API calls, and exporting it as a JSON file.]

Conclusion

[Summarize the key points discussed in the article, emphasizing the versatility and potential of Flow Wise as a no code platform for building applications powered by large language models.]

Highlights

  • Flow Wise is an impressive no code platform that enables the creation of powerful applications using large language models.
  • The user-friendly graphical interface of Flow Wise allows for easy application building through drag-and-drop functionality.
  • Key components in Flow Wise include chart flows, marketplace, document loaders, embeddings, LLMS, memories, prompt templates, retrievers, text splitters, and vector stores.
  • The installation process of Flow Wise involves node.js installation and starting the platform through the command line or with authentication credentials.
  • Building a document question and answer application in Flow Wise involves uploading PDF files, text splitting, embedding computation, and creating a vector store for information retrieval.

FAQ

Q: Can Flow Wise be used for translation applications? A: Yes, Flow Wise supports building translation applications using its components and modules.

Q: Is there support for open-source embedding models in Flow Wise? A: Yes, Flow Wise offers support for open-source embedding models like Hugging Face embeddings in addition to proprietary models.

Q: Can I customize Prompts for extracting specific information from documents in Flow Wise? A: Yes, Flow Wise provides a prompt template feature that allows users to design prompts to extract desired information from documents.

Q: How can I save and deploy my chat flow created in Flow Wise? A: Flow Wise offers options to save your chat flow, embed it in HTML, make API calls, or export it as a JSON file for deployment.

Q: Is there a community or support available for Flow Wise users? A: Yes, Flow Wise has a Discord server where users can connect with like-minded individuals and seek assistance for their projects.

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