Figuring out better settings and strategy for your RAG application is hard.
With Playground, you can configure the upsert and retrieval settings, observe the results, and use it within your flow.
Jul 24, 2024 • 10 tweets • 5 min read
This is the biggest update we've had in a while.
Flowise v2.0 and Flowise Cloud
With v2.0, we've introduced Sequential Agentic Workflow.
The new agentic workflow allows you to:
⛓️Chain agents together
🔁Loopback mechanisms
🙋Human-in-the-Loop
🔶Conditional branches
Different from existing chatflow which relies LLM to act on its own, now you have greater control over the flow. Huge shoutout to @LangChainAI team for the exceptional LangGraph framework, which made all of this possible!
We're also excited to announce the closed beta release of Flowise Cloud! In addition to all existing features, cloud version also includes Evals and Logging. Join the waitlist here:
Here's 7 examples to help you get started with agentic workflow:flowiseai.com/join1/7 Agentic RAG
A self-improving RAG that checks the relevance of retrieved documents to the user's question.
If the documents are found to be irrelevant, the agent will rephrase the question and loop back to retrieve a new set of documents until they pass the relevance score.
Jun 22, 2023 • 5 tweets • 2 min read
Function usually takes in structured data. But it's hard to tell LLM to return structured data, until @OpenAI Function Calling.
In Flowise v1.2.13, we are introducing - Custom Tool ✨
Together with @LangChainAI agent, it will choose the right tool to call the function
🧵
🛠️ Custom Tool (1/2)
Create your own custom tool by specifying:
✅ Name
✅ Description
✅ Output Schema
✅ Function
May 23, 2023 • 4 tweets • 2 min read
🔍Metadata Filter is such an underrated topic.
When you have more vectors stored in vector database, it becomes harder to retrieve top relative vectors, resulting in irrelevant answer from LLM.
Watch how you can specify metadata to your documents and later filter by it 👇
With Flowise, you can specify a set of key-value pair as metadata to your document.
Most commonly used key value pairs are:
📚 document_id
🧑 user_id
The idea is to have a unique value for each document to better differentiate between them
May 17, 2023 • 9 tweets • 4 min read
🥷 We've gone stealth for the past 2 weeks. Now back with Flowise v1.2.6:
Simply embed a chat widget to your websites that connects to your flow.
Watch how you can insert a link, and have a chat bot dedicated to answer question of the website 👇
Apr 12, 2023 • 9 tweets • 4 min read
Want to quickly experiment and build LLM apps but lazy to write the repetitive code?
Introducing Flowise - an open source UI visual tool for @LangChainAI JS, written in Node Typescript.
Repo:
Here's 7 templates ready to be used 🧵 https://t.co/jN9XuwxYk2github.com/FlowiseAI/Flow…
Basic example of LLM Chain with a Prompt Template and LLM Model:
