Flowise
Build your own app - easy peasy lemon squeezy ..
Introduction
Flowise is an open-source platform designed to build and deploy customized AI flows with a user-friendly drag-and-drop interface. It allows users to create complex AI applications without extensive coding knowledge.
The core feature of Flowise is its visual flow builder, which enables users to connect various AI components like language models (LLMs), embedding models, and vector databases into functional workflows. Users can incorporate popular models like OpenAI's GPT series, Anthropic's Claude, and open-source alternatives. The platform supports multiple vector databases including Pinecone, Chroma, and Supabase.
Flowise offers various deployment options, including self-hosting on personal hardware, cloud deployment, or using Flowise Cloud for a managed experience. It supports API endpoints that allow integration with external applications and websites. The platform is highly extensible through custom components and has an active community contributing to its development.
Common use cases include building chatbots with memory and context awareness, creating knowledge bases with document retrieval capabilities, developing AI assistants for specific domains, and prototyping AI workflows before production implementation. Flowise is particularly valuable for developers and businesses looking to experiment with AI capabilities without committing to complex infrastructure or extensive development resources.
Ensure the GenAI env is up and running.
x
x
x
x
x
Log into FlowiseAI
Pt1: KB - Simple RAG
This one was fun .. Building a Pentaho Knowledge Base ..
Each product should have its own Knowledge Base - Pentaho Data Integration
To save on costs the Template: Pt1: Pentaho Knowledge Base uses - mistral:7b model.
You will need to load a model that supports Tools.
You should be familiar with FlowiseAI before tackling this flow, as we're going to focus on just a few key areas and concepts.
x
Let's start with loading the PDF document into our Qdrant vector database.
Obviously we're going to require:
a pdf loader: upload pdf
a splitter: to chunk the text - recursive character.
an embedding model: to create the vectors - nomic-embed-text
vector database: Qdrant
Document loaders allow you to load documents from different sources like PDF, TXT, CSV, Notion, Confluence etc. They are often used together with Vector Stores to be upserted as embeddings, which can then retrieved upon query.
Drag & drop the Pdf file.
Click Upload File and navigate to the PDF.
Select the option: One document per file
One document per page: each page of your original PDF will be extracted and saved as its own standalone PDF file
One document per file: each file will be treated as a separate, complete document rather than combining multiple files into a single document.
The Recursive Character Text Splitter is a technique used in natural language processing and document handling to break down long text documents into smaller, manageable chunks while preserving context and meaning.
Unlike simple character or token splitters that might cut text at arbitrary points, the Recursive Character Text Splitter works hierarchically. It first attempts to split text along natural boundaries like paragraphs, then sentences, and finally characters if needed. This recursive approach ensures that related content stays together when possible, maintaining semantic coherence within chunks.
This technique is particularly valuable when working with large language models that have context window limitations. By intelligently chunking documents, it allows for processing lengthy texts while preserving the contextual relationships needed for tasks like summarization, question answering, and information retrieval. LangChain implements this splitter to help developers manage document processing pipelines effectively.
Drag & drop a Recursive Character Text Splitter and set the Chunk Size and Chunk Overlap.
Set the Chunk size as: 500 and the Chunk Overlap as: 20
Embeddings serve as the foundation of modern natural language processing, transforming text into dense vector representations that capture semantic meaning. These numerical representations allow machines to understand relationships between words and concepts, enabling powerful applications like semantic search, clustering, and recommendation systems.
The Nomic-embed-text model offers a versatile embedding solution with configurable settings to balance performance and resource requirements. Users can adjust the dimensionality parameter (typically set between 128 and 768 dimensions), with higher dimensions capturing more nuanced semantic relationships at the cost of increased computational overhead. The model also provides batch size configuration to optimize throughput, with default settings balancing efficiency and memory usage.
Ensure vector size is the same value set in your Vector database - 768
Drag & drop the Ollama Embeddings.
Set the Base URL: http://localhost:11434
Set the Model Name: nomic-embed-text
Click on Additional Parameters to assign the GPUs.
MMap (Memory Mapping) refers to a technique used to efficiently load and access large embedding files from disk. This is particularly important when working with large-scale embedding models that might not fit into memory.
x
Open-source vector database designed to handle high-dimensional vectors for performance and massive-scale AI applications.
Ensure that the Pdf File -> Qdrant & Ollama Embeddings are connected to the Qdrant vector database.
Connect with the Qdrant API credential thats been set in the Credentials section - the API key can be anything as its a local instance.
When pointing to the Qdrant server use the container name in the URL: http://ollama:11434
Set the Qdrant Collection Name: Pentaho Data Integration.
Ensure the Qdrant vector size = Embedding model vector size (in this case 768)
x
Check all connections and the Flow is saved ..
You're ready to Upload .. Click on the green database icon in top right.
You can expand each Node to check the settings.
Click on Upsert.
Once the process has completed it will display the first 20 chunks and indicate the number of added records for that document.
In Qdrant you can also view the Pentaho Data Integration collection.
To check the collection .. just click on the name which displays a bunch of options:
view each record
display collection stats
check the search quality
take snapshots
vizualize & graph results
Pt2: KB - Document Store
In Pt2: Pentaho Knowledge Base the capabilities of the Knowledge Base are extended & managed by a Document Store.
In Pt1: Knowledge Base, RAG techniques are used to retrieve the required information. Great for a few documents, but as more data sources are identified it makes sense to centralize under a managed Document Store.
FlowiseAI's document store is a critical component that enables knowledge management and retrieval capabilities within the platform. It serves as a structured repository for storing, indexing, and retrieving documents that power various AI applications, particularly those relying on RAG (Retrieval-Augmented Generation) techniques.
The system automatically chunks these documents into smaller segments and generates vector embeddings for each chunk using the configured embedding model. These embeddings capture the semantic meaning of the text, allowing for similarity-based retrieval later.
x
x
x
x
xx
x
x
x
xx
x
x
x
x
x
x
x
x
In a multi-agent architecture with supervisor and worker nodes, specialized agents work collaboratively under hierarchical coordination. This structure mirrors human organizational patterns, balancing autonomy with oversight.
The supervisor agent serves as the orchestrator, breaking down complex tasks, assigning them to appropriate worker agents, monitoring progress, resolving conflicts, and ensuring overall system coherence. Worker agents focus on specialized domains, executing assigned tasks autonomously while reporting status and results back to the supervisor.
In a software development context, this architecture might feature a Supervisor agent overseeing three worker agents. The Supervisor would interpret client requirements, develop a project roadmap, and coordinate the team's activities.
The Software Developer worker focuses exclusively on writing efficient, bug-free code according to specifications provided by the Supervisor. The Code Reviewer worker analyzes the developer's output, checking for errors, potential optimizations, and adherence to best practices. Meanwhile, the Documentation Writer worker creates user manuals, API documentation, and technical specifications that make the software accessible to end-users.
Software Development Team
If you've got this far .. then assuming you've explored FlowiseAI you're now pretty comfortable creating flows.
As this is pretty resource intensive were going to use different models that are trained specifically for the tasks.
Software Developer
StarCoder2-15B model is a 15B parameter model trained on 600+ programming languages.
x
x
x
x
x
x
You are a supervisor tasked with managing a conversation between the following workers: {team_members}.
Given the following user request, respond with the worker to act next.
Each worker will perform a task and respond with their results and status.
When finished, respond with FINISH.
Select strategically to minimize the number of steps taken.x
x
x
# Role
As a Senior Software Engineer at {company}, you are a pivotal part of our innovative development team. Your expertise and leadership drive the creation of robust, scalable software solutions that meet the needs of our diverse clientele. By applying best practices in software development, you ensure that our products are reliable, efficient, and maintainable. Your technical depth and architectural vision enable you to tackle complex challenges while mentoring others and elevating the overall quality of our engineering output.
# Tasks
- Lead the development of high-quality software solutions that align with business objectives and user needs.
- Utilize deep technical knowledge to architect, design, and implement software systems that effectively address complex problems.
- Design and implement new features for assigned tasks, ensuring seamless integration with existing systems and meeting performance requirements.
- Apply your expertise in {technology} to build robust features that enhance product capabilities.
- Adhere to established coding standards and best practices, creating maintainable and well-structured code.
- Produce fully functional, well-documented features with detailed code comments that facilitate future maintenance and knowledge transfer.
- Submit completed code to a Quality Assurance Engineer for review when necessary, incorporating feedback to improve implementations.
- Finalize and release code once it has successfully passed review and meets all quality standards.x
x
x
# Role
As a Quality Assurance Engineer at {company}, you are an integral part of our development team, ensuring that our software products meet the highest quality standards. Your meticulous attention to detail and expertise in testing methodologies are crucial for identifying defects and verifying that our code meets rigorous quality benchmarks. You serve as a critical checkpoint in the development lifecycle, collaborating with developers to improve code quality while maintaining a focus on user experience and system reliability.
# Tasks
- Ensure the delivery of high-quality software through comprehensive code review and systematic testing processes.
- Review new features designed and implemented by Senior Software Engineers, evaluating functionality, adherence to coding standards, maintainability, and integration with existing systems.
- Provide constructive feedback to development teams, guiding contributors toward best practices and fostering a culture of continuous improvement.
- Identify potential issues proactively and recommend solutions that enhance the robustness and scalability of the software.
- Design and execute test cases that thoroughly validate functionality against requirements and specifications.
- Document testing procedures, results, and identified defects using standard tracking systems and methodologies.
- Collaborate with cross-functional teams to understand requirements and ensure alignment between development and quality objectives.
- Consistently communicate review findings and feedback to Senior Software Engineers to support iterative improvement.
- Always pass back the review and feedback to Senior Software Engineer.x
x
x
# Role
You are an expert Technical Document Writer with extensive experience in creating clear, comprehensive, and user-friendly documentation for complex technical systems. Your technical background spans software development, engineering principles, and systems architecture, allowing you to understand and explain highly technical concepts. You excel at organizing information logically, writing in a precise style that eliminates ambiguity, and creating documentation that serves both technical and non-technical audiences as needed.
# Tasks
- Create comprehensive technical documentation including user manuals, API documentation, technical specifications, and system architecture documents.
- Translate complex technical information into clear, concise language appropriate for the target audience, whether they are developers, system administrators, or end-users.
- Structure documents logically with a consistent format, including appropriate headings, tables of contents, glossaries, and appendices.
- Include visual elements such as diagrams, flowcharts, and screenshots to enhance understanding of complex processes and systems.
- Implement standard technical writing best practices, including clear labeling of warnings and cautions, consistent terminology, and comprehensive indexing.
- Maintain version control for all documentation, clearly indicating changes between versions and ensuring documentation stays synchronized with product development.
- Collaborate with subject matter experts to gather accurate technical information and validate technical content for correctness.
- Adhere to industry-specific documentation standards and compliance requirements when necessary.x
x
x
x
If you thought creating a Research Agent was easy then discover Assistants. In this workshop we're going to create a Personal Assistant, but this could be anything..!
We're going to be using a Mistral model - mistral:7b
Let's create a new assistant. Click on: 'Assistants' option in the left hand menu..
Select Custom Assistant.
Click on the + Add blue button in the top right. Let's call 'Personal Assistant'. Click Add.
Just work your way down the list ..
Select Model
ChatOllama
Knowledge (Document Store)
Pentaho Data Integration
Base URL
http://ollama:11434
Model Name
mistral:7b
Temperature
0.5
Number of GPU
24 (depending on your system)
Serp API
Serp API - or any search Tool
Connect Credential
Serp API
Click the Save icon - top right
x
Was this helpful?