mirror of
https://github.com/farcasclaudiu/Flowise.git
synced 2026-06-28 23:01:09 +03:00
Marketplace : Added categories to chatflows
This commit is contained in:
vinodkiran
@@ -1,5 +1,6 @@
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{
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"description": "Use OpenAI Function Agent and Chain to automatically decide which API to call, generating url and body request from conversation",
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"categories": "Buffer Memory,ChainTool,API Chain,ChatOpenAI,OpenAI Function Agent,Langchain",
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"nodes": [
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{
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"width": 300,
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@@ -1,5 +1,6 @@
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{
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"description": "Given API docs, agent automatically decide which API to call, generating url and body request from conversation",
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"categories": "Buffer Memory,ChainTool,API Chain,ChatOpenAI,Conversational Agent,Langchain",
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"nodes": [
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{
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"width": 300,
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@@ -1,5 +1,6 @@
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{
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"description": "Output antonym of given user input using few-shot prompt template built with examples",
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"categories": "Few Shot Prompt,ChatOpenAI,LLM Chain,Langchain",
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"nodes": [
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{
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"width": 300,
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@@ -1,5 +1,6 @@
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{
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"description": "Use AutoGPT - Autonomous agent with chain of thoughts for self-guided task completion",
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"categories": "AutoGPT,SERP Tool,File Read/Write,ChatOpenAI,Pinecone,Langchain",
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"nodes": [
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{
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"width": 300,
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@@ -1,5 +1,6 @@
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{
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"description": "Use BabyAGI to create tasks and reprioritize for a given objective",
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"categories": "BabyAGI,ChatOpenAI,Pinecone,Langchain",
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"nodes": [
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{
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"width": 300,
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@@ -1,5 +1,6 @@
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{
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"description": "Analyse and summarize CSV data",
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"categories": "CSV Agent,ChatOpenAI,Langchain",
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"nodes": [
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{
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"width": 300,
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@@ -1,5 +1,6 @@
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{
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"description": "Engage with data sources such as YouTube Transcripts, Google, and more through intelligent Q&A interactions",
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"categories": "Memory Vector Store,SearchAPI,ChatOpenAI,Conversational Retrieval QA Chain,Langchain",
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"nodes": [
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{
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"width": 300,
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@@ -1,5 +1,6 @@
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{
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"description": "Use ChatGPT Plugins within LangChain abstractions with GET and POST Tools",
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"categories": "ChatGPT Plugin,HTTP GET/POST,ChatOpenAI,MRKL Agent,Langchain",
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"nodes": [
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{
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"width": 300,
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@@ -1,5 +1,6 @@
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{
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"description": "Use Anthropic Claude with 200k context window to ingest whole document for QnA",
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"categories": "Buffer Memory,Prompt Template,Conversation Chain,ChatAnthropic,Langchain",
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"nodes": [
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{
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"width": 300,
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@@ -1,5 +1,6 @@
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{
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"description": "Answer question based on retrieved documents (context) with built-in memory to remember conversation using LlamaIndex",
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"categories": "Text File,Prompt Template,ChatOpenAI,Conversation Chain,Pinecone,LlamaIndex,Redis",
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"badge": "NEW",
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"nodes": [
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{
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@@ -1,5 +1,6 @@
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{
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"description": "A conversational agent for a chat model which utilize chat specific prompts",
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"categories": "Calculator Tool,Buffer Memory,SerpAPI,ChatOpenAI,Conversational Agent,Langchain",
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"nodes": [
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{
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"width": 300,
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@@ -1,5 +1,6 @@
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{
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"description": "Agent optimized for vector retrieval during conversation and answering questions based on previous dialogue.",
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"categories": "Retriever Tool,Buffer Memory,ChatOpenAI,Conversational Retrieval Agent, Pinecone,Langchain",
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"badge": "POPULAR",
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"nodes": [
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{
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@@ -1,5 +1,6 @@
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{
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"description": "Text file QnA using conversational retrieval QA chain",
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"categories": "TextFile,ChatOpenAI,Conversational Retrieval QA Chain,Pinecone,Langchain",
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"badge": "POPULAR",
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"nodes": [
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{
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@@ -1,5 +1,6 @@
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{
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"description": "Flowise Docs Github QnA using conversational retrieval QA chain",
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"categories": "Memory Vector Store,Github Loader,ChatOpenAI,Conversational Retrieval QA Chain,Langchain",
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"badge": "POPULAR",
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"nodes": [
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{
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@@ -1,5 +1,6 @@
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{
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"description": "Simple LLM Chain using HuggingFace Inference API on falcon-7b-instruct model",
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"categories": "HuggingFace,LLM Chain,Langchain",
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"nodes": [
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{
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"width": 300,
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@@ -1,5 +1,6 @@
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{
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"description": "Split flows based on if else condition",
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"categories": "IfElse Function,ChatOpenAI,OpenAI,LLM Chain,Langchain",
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"badge": "new",
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"nodes": [
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{
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@@ -1,6 +1,7 @@
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{
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"description": "Generate image using Replicate Stability text-to-image generative AI model",
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"badge": "NEW",
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"categories": "Replicate,ChatOpenAI,LLM Chain,Langchain",
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"nodes": [
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{
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"width": 300,
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@@ -1,6 +1,7 @@
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{
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"description": "Detect text that could generate harmful output and prevent it from being sent to the language model",
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"badge": "NEW",
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"categories": "Moderation,ChatOpenAI,LLM Chain,Langchain",
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"nodes": [
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{
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"width": 300,
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@@ -1,6 +1,7 @@
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{
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"description": "Return response as a list (array) instead of a string/text",
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"badge": "NEW",
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"categories": "CSV Output Parser,ChatOpenAI,LLM Chain,Langchain",
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"nodes": [
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{
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"width": 300,
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@@ -1,6 +1,7 @@
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{
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"description": "QnA chain using Ollama local LLM, LocalAI embedding model, and Faiss local vector store",
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"badge": "POPULAR",
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"categories": "Text File,ChatOllama,Conversational Retrieval QA Chain,Faiss,Langchain",
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"nodes": [
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{
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"width": 300,
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@@ -1,5 +1,6 @@
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{
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"description": "Use long term memory like Zep to differentiate conversations between users with sessionId",
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"categories": "ChatOpenAI,Conversational Retrieval QA Chain,Zep Memory,Qdrant,Langchain",
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"nodes": [
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{
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"width": 300,
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@@ -1,5 +1,6 @@
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{
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"description": "Upsert multiple files with metadata and filter by it using conversational retrieval QA chain",
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"categories": "Text File,PDF File,ChatOpenAI,Conversational Retrieval QA Chain,Pinecone,Langchain",
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"badge": "POPULAR",
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"nodes": [
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{
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@@ -1,5 +1,6 @@
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{
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"description": "A chain that automatically picks an appropriate prompt from multiple prompts",
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"categories": "ChatOpenAI,Multi Prompt Chain,Langchain",
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"nodes": [
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{
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"width": 300,
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@@ -1,5 +1,6 @@
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{
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"description": "A chain that automatically picks an appropriate retriever from multiple different vector databases",
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"categories": "ChatOpenAI,Multi Retrieval QA Chain,Pinecone,Chroma,Supabase,Langchain",
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"nodes": [
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{
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"width": 300,
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@@ -1,5 +1,6 @@
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{
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"description": "Use the agent to choose between multiple different vector databases, with the ability to use other tools",
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"categories": "Buffer Memory,ChatOpenAI,Chain Tool,Retrieval QA Chain,Redis,Faiss,Conversational Agent,Langchain",
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"nodes": [
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{
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"width": 300,
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@@ -1,5 +1,6 @@
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{
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"description": "An agent that uses OpenAI's Function Calling functionality to pick the tool and args to call",
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"categories": "Buffer Memory,Custom Tool, SerpAPI,OpenAI Function,Calculator Tool,ChatOpenAI,Langchain",
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"nodes": [
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{
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"width": 300,
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@@ -1,5 +1,6 @@
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{
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"description": "OpenAI Assistant that has instructions and can leverage models, tools, and knowledge to respond to user queries",
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"categories": "Custom Tool, SerpAPI,OpenAI Assistant,Calculator Tool,Langchain",
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"badge": "NEW",
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"nodes": [
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{
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@@ -1,5 +1,6 @@
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{
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"description": "Use chat history to rephrase user question, and answer the rephrased question using retrieved docs from vector store",
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"categories": "ChatOpenAI,LLM Chain,SingleStore,Langchain",
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"badge": "POPULAR",
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"nodes": [
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{
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@@ -1,5 +1,6 @@
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{
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"description": "Use output from a chain as prompt for another chain",
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"categories": "Custom Tool,OpenAI,LLM Chain,Langchain",
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"nodes": [
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{
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"width": 300,
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@@ -1,5 +1,6 @@
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{
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"description": "Stateless query engine designed to answer question over your data using LlamaIndex",
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"categories": "ChatAnthropic,Compact and Refine,Pinecone,LlamaIndex",
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"badge": "NEW",
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"nodes": [
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{
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@@ -1,5 +1,6 @@
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{
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"description": "An agent that uses ReAct logic to decide what action to take",
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"categories": "Calculator Tool,SerpAPI,ChatOpenAI,MRKL Agent,Langchain",
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"nodes": [
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{
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"width": 300,
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@@ -1,5 +1,6 @@
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{
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"description": "Use Replicate API that runs Llama 13b v2 model with LLMChain",
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"categories": "Replicate,LLM Chain,Langchain",
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"nodes": [
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{
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"width": 300,
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@@ -1,5 +1,6 @@
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{
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"description": "Answer questions over a SQL database",
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"categories": "ChatOpenAI,Sql Database Chain,Langchain",
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"nodes": [
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{
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"width": 300,
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@@ -1,5 +1,6 @@
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{
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"description": "Manually construct prompts to query a SQL database",
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"categories": "IfElse Function,Variable Set/Get,Custom JS Function,ChatOpenAI,LLM Chain,Langchain",
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"badge": "new",
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"nodes": [
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{
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@@ -1,5 +1,6 @@
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{
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"description": "Simple chat engine to handle back and forth conversations using LlamaIndex",
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"categories": "BufferMemory,AzureChatOpenAI,LlamaIndex",
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"badge": "NEW",
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"nodes": [
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{
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@@ -1,5 +1,6 @@
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{
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"description": "Basic example of Conversation Chain with built-in memory - works exactly like ChatGPT",
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"categories": "Buffer Memory,ChatOpenAI,Conversation Chain,Langchain",
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"badge": "POPULAR",
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"nodes": [
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{
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@@ -1,5 +1,6 @@
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{
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"description": "Basic example of stateless (no memory) LLM Chain with a Prompt Template and LLM Model",
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"categories": "OpenAI,LLM Chain,Langchain",
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"nodes": [
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{
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"width": 300,
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@@ -1,5 +1,6 @@
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{
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"description": "Return response as a specified JSON structure instead of a string/text",
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"categories": "Structured Output Parser,ChatOpenAI,LLM Chain,Langchain",
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"badge": "NEW",
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"nodes": [
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{
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@@ -1,5 +1,6 @@
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{
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"description": "Breaks down query into sub questions for each relevant data source, then combine into final response",
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"categories": "Sub Question Query Engine,Sticky Note,QueryEngine Tool,Compact and Refine,ChatOpenAI,Pinecone,LlamaIndex",
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"badge": "NEW",
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"nodes": [
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{
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@@ -1,5 +1,6 @@
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{
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"description": "Language translation using LLM Chain with a Chat Prompt Template and Chat Model",
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"categories": "Chat Prompt Template,ChatOpenAI,LLM Chain,Langchain",
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"nodes": [
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{
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"width": 300,
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@@ -1,4 +1,6 @@
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{
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"description": "QA chain for Vectara",
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"categories": "Vectara QA Chain,Vectara,Langchain",
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"nodes": [
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{
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"width": 300,
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@@ -1,5 +1,6 @@
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{
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"description": "Conversational Agent with ability to visit a website and extract information",
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"categories": "Buffer Memory,Web Browser,ChatOpenAI,Conversational Agent,Langchain",
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"nodes": [
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{
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"width": 300,
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@@ -1,5 +1,6 @@
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{
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"description": "Scrape web pages for QnA with long term memory Motorhead and return source documents",
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"categories": "HtmlToMarkdown,Cheerio Web Scraper,ChatOpenAI,Redis,Pinecone,Langchain",
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"badge": "POPULAR",
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"nodes": [
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{
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@@ -1237,6 +1237,7 @@ export class App {
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templateName: file.split('.json')[0],
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flowData: fileData.toString(),
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badge: fileDataObj?.badge,
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categories: fileDataObj?.categories,
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type: 'Chatflow',
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description: fileDataObj?.description || ''
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}
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@@ -1253,6 +1254,7 @@ export class App {
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...fileDataObj,
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id: index,
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type: 'Tool',
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categories: '',
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templateName: file.split('.json')[0]
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}
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templates.push(template)
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@@ -103,45 +103,27 @@ export const MarketplaceTable = ({ data, images, filterFunction, filterByBadge,
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</Typography>
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</TableCell>
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<TableCell key='2'>
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{row.type === 'Chatflow' && images[row.id] && (
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<div
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style={{
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display: 'flex',
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flexDirection: 'row',
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flexWrap: 'wrap',
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marginTop: 5
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}}
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>
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{images[row.id]
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.slice(0, images[row.id].length > 5 ? 5 : images[row.id].length)
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.map((img) => (
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<div
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key={img}
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style={{
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width: 35,
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height: 35,
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marginRight: 5,
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borderRadius: '50%',
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backgroundColor: 'white',
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marginTop: 5
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}}
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>
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<img
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style={{ width: '100%', height: '100%', padding: 5, objectFit: 'contain' }}
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alt=''
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src={img}
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/>
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</div>
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<div
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style={{
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display: 'flex',
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flexDirection: 'row',
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flexWrap: 'wrap',
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marginTop: 5
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}}
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>
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{row.categories &&
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row.categories
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.split(',')
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.map((tag, index) => (
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<Chip
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variant='outlined'
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key={index}
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size='small'
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label={tag.toUpperCase()}
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style={{ marginRight: 3, marginBottom: 3 }}
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/>
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))}
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{images[row.id].length > 5 && (
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<Typography
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sx={{ alignItems: 'center', display: 'flex', fontSize: '.8rem', fontWeight: 200 }}
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>
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+ {images[row.id].length - 5} More
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</Typography>
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)}
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</div>
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)}
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</div>
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</TableCell>
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<TableCell key='3'>
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<Typography>
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@@ -129,6 +129,7 @@ const Marketplace = () => {
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function filterFlows(data) {
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return (
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data.categories?.toLowerCase().indexOf(search.toLowerCase()) > -1 ||
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data.templateName.toLowerCase().indexOf(search.toLowerCase()) > -1 ||
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(data.description && data.description.toLowerCase().indexOf(search.toLowerCase()) > -1)
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)
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Block a user