Merge pull request #1678 from vinodkiran/FEATURE/marketplace-revamp

Marketplace: Revamped UI
2026-06-28 21:00:58 +03:00 · 2024-02-08 03:53:46 -05:00
parent f7588007b6 08c07802f5
commit 64018b72e8
57 changed files with 598 additions and 127 deletions
@@ -1,5 +1,7 @@
 {
    "description": "Use OpenAI Function Agent and Chain to automatically decide which API to call, generating url and body request from conversation",
+    "categories": "Buffer Memory,ChainTool,API Chain,ChatOpenAI,OpenAI Function Agent,Langchain",
+    "framework": "Langchain",
    "nodes": [
        {
            "width": 300,
@@ -1,5 +1,7 @@
 {
    "description": "Given API docs, agent automatically decide which API to call, generating url and body request from conversation",
+    "categories": "Buffer Memory,ChainTool,API Chain,ChatOpenAI,Conversational Agent,Langchain",
+    "framework": "Langchain",
    "nodes": [
        {
            "width": 300,
@@ -1,5 +1,7 @@
 {
    "description": "Output antonym of given user input using few-shot prompt template built with examples",
+    "categories": "Few Shot Prompt,ChatOpenAI,LLM Chain,Langchain",
+    "framework": "Langchain",
    "nodes": [
        {
            "width": 300,
@@ -1,5 +1,7 @@
 {
    "description": "Use AutoGPT - Autonomous agent with chain of thoughts for self-guided task completion",
+    "categories": "AutoGPT,SERP Tool,File Read/Write,ChatOpenAI,Pinecone,Langchain",
+    "framework": "Langchain",
    "nodes": [
        {
            "width": 300,
@@ -1,5 +1,7 @@
 {
    "description": "Use BabyAGI to create tasks and reprioritize for a given objective",
+    "categories": "BabyAGI,ChatOpenAI,Pinecone,Langchain",
+    "framework": "Langchain",
    "nodes": [
        {
            "width": 300,
@@ -1,5 +1,7 @@
 {
    "description": "Analyse and summarize CSV data",
+    "categories": "CSV Agent,ChatOpenAI,Langchain",
+    "framework": "Langchain",
    "nodes": [
        {
            "width": 300,
@@ -1,5 +1,7 @@
 {
    "description": "Engage with data sources such as YouTube Transcripts, Google, and more through intelligent Q&A interactions",
+    "categories": "Memory Vector Store,SearchAPI,ChatOpenAI,Conversational Retrieval QA Chain,Langchain",
+    "framework": "Langchain",
    "nodes": [
        {
            "width": 300,
@@ -1,5 +1,7 @@
 {
    "description": "Use ChatGPT Plugins within LangChain abstractions with GET and POST Tools",
+    "categories": "ChatGPT Plugin,HTTP GET/POST,ChatOpenAI,MRKL Agent,Langchain",
+    "framework": "Langchain",
    "nodes": [
        {
            "width": 300,
@@ -1,5 +1,7 @@
 {
    "description": "Use Anthropic Claude with 200k context window to ingest whole document for QnA",
+    "categories": "Buffer Memory,Prompt Template,Conversation Chain,ChatAnthropic,Langchain",
+    "framework": "Langchain",
    "nodes": [
        {
            "width": 300,
@@ -1,5 +1,7 @@
 {
    "description": "Answer question based on retrieved documents (context) with built-in memory to remember conversation using LlamaIndex",
+    "categories": "Text File,Prompt Template,ChatOpenAI,Conversation Chain,Pinecone,LlamaIndex,Redis",
+    "framework": "LlamaIndex",
    "badge": "NEW",
    "nodes": [
        {
@@ -1,5 +1,7 @@
 {
    "description": "A conversational agent for a chat model which utilize chat specific prompts",
+    "categories": "Calculator Tool,Buffer Memory,SerpAPI,ChatOpenAI,Conversational Agent,Langchain",
+    "framework": "Langchain",
    "nodes": [
        {
            "width": 300,
@@ -1,6 +1,8 @@
 {
    "description": "Agent optimized for vector retrieval during conversation and answering questions based on previous dialogue.",
+    "categories": "Retriever Tool,Buffer Memory,ChatOpenAI,Conversational Retrieval Agent, Pinecone,Langchain",
    "badge": "POPULAR",
+    "framework": "Langchain",
    "nodes": [
        {
            "width": 300,
@@ -1,6 +1,8 @@
 {
    "description": "Text file QnA using conversational retrieval QA chain",
+    "categories": "TextFile,ChatOpenAI,Conversational Retrieval QA Chain,Pinecone,Langchain",
    "badge": "POPULAR",
+    "framework": "Langchain",
    "nodes": [
        {
            "width": 300,
@@ -1,6 +1,8 @@
 {
    "description": "Flowise Docs Github QnA using conversational retrieval QA chain",
+    "categories": "Memory Vector Store,Github Loader,ChatOpenAI,Conversational Retrieval QA Chain,Langchain",
    "badge": "POPULAR",
+    "framework": "Langchain",
    "nodes": [
        {
            "width": 300,
@@ -1,5 +1,7 @@
 {
    "description": "Simple LLM Chain using HuggingFace Inference API on falcon-7b-instruct model",
+    "categories": "HuggingFace,LLM Chain,Langchain",
+    "framework": "Langchain",
    "nodes": [
        {
            "width": 300,
@@ -1,5 +1,7 @@
 {
    "description": "Split flows based on if else condition",
+    "categories": "IfElse Function,ChatOpenAI,OpenAI,LLM Chain,Langchain",
+    "framework": "Langchain",
    "badge": "new",
    "nodes": [
        {
@@ -1,6 +1,8 @@
 {
    "description": "Generate image using Replicate Stability text-to-image generative AI model",
    "badge": "NEW",
+    "categories": "Replicate,ChatOpenAI,LLM Chain,Langchain",
+    "framework": "Langchain",
    "nodes": [
        {
            "width": 300,
@@ -1,6 +1,8 @@
 {
    "description": "Detect text that could generate harmful output and prevent it from being sent to the language model",
    "badge": "NEW",
+    "categories": "Moderation,ChatOpenAI,LLM Chain,Langchain",
+    "framework": "Langchain",
    "nodes": [
        {
            "width": 300,
@@ -1,6 +1,8 @@
 {
    "description": "Return response as a list (array) instead of a string/text",
    "badge": "NEW",
+    "categories": "CSV Output Parser,ChatOpenAI,LLM Chain,Langchain",
+    "framework": "Langchain",
    "nodes": [
        {
            "width": 300,
@@ -1,6 +1,8 @@
 {
    "description": "QnA chain using Ollama local LLM, LocalAI embedding model, and Faiss local vector store",
    "badge": "POPULAR",
+    "categories": "Text File,ChatOllama,Conversational Retrieval QA Chain,Faiss,Langchain",
+    "framework": "Langchain",
    "nodes": [
        {
            "width": 300,
@@ -1,5 +1,7 @@
 {
    "description": "Use long term memory like Zep to differentiate conversations between users with sessionId",
+    "categories": "ChatOpenAI,Conversational Retrieval QA Chain,Zep Memory,Qdrant,Langchain",
+    "framework": "Langchain",
    "nodes": [
        {
            "width": 300,
@@ -1,6 +1,8 @@
 {
    "description": "Upsert multiple files with metadata and filter by it using conversational retrieval QA chain",
+    "categories": "Text File,PDF File,ChatOpenAI,Conversational Retrieval QA Chain,Pinecone,Langchain",
    "badge": "POPULAR",
+    "framework": "Langchain",
    "nodes": [
        {
            "width": 300,
@@ -1,5 +1,7 @@
 {
    "description": "A chain that automatically picks an appropriate prompt from multiple prompts",
+    "categories": "ChatOpenAI,Multi Prompt Chain,Langchain",
+    "framework": "Langchain",
    "nodes": [
        {
            "width": 300,
@@ -1,5 +1,7 @@
 {
    "description": "A chain that automatically picks an appropriate retriever from multiple different vector databases",
+    "categories": "ChatOpenAI,Multi Retrieval QA Chain,Pinecone,Chroma,Supabase,Langchain",
+    "framework": "Langchain",
    "nodes": [
        {
            "width": 300,
@@ -1,5 +1,7 @@
 {
    "description": "Use the agent to choose between multiple different vector databases, with the ability to use other tools",
+    "categories": "Buffer Memory,ChatOpenAI,Chain Tool,Retrieval QA Chain,Redis,Faiss,Conversational Agent,Langchain",
+    "framework": "Langchain",
    "nodes": [
        {
            "width": 300,
@@ -1,5 +1,7 @@
 {
    "description": "An agent that uses OpenAI's Function Calling functionality to pick the tool and args to call",
+    "categories": "Buffer Memory,Custom Tool, SerpAPI,OpenAI Function,Calculator Tool,ChatOpenAI,Langchain",
+    "framework": "Langchain",
    "nodes": [
        {
            "width": 300,
@@ -1,5 +1,7 @@
 {
    "description": "OpenAI Assistant that has instructions and can leverage models, tools, and knowledge to respond to user queries",
+    "categories": "Custom Tool, SerpAPI,OpenAI Assistant,Calculator Tool,Langchain",
+    "framework": "Langchain",
    "badge": "NEW",
    "nodes": [
        {
@@ -1,6 +1,8 @@
 {
    "description": "Use chat history to rephrase user question, and answer the rephrased question using retrieved docs from vector store",
+    "categories": "ChatOpenAI,LLM Chain,SingleStore,Langchain",
    "badge": "POPULAR",
+    "framework": "Langchain",
    "nodes": [
        {
            "width": 300,
@@ -1,5 +1,7 @@
 {
    "description": "Use output from a chain as prompt for another chain",
+    "categories": "Custom Tool,OpenAI,LLM Chain,Langchain",
+    "framework": "Langchain",
    "nodes": [
        {
            "width": 300,
@@ -1,6 +1,8 @@
 {
    "description": "Stateless query engine designed to answer question over your data using LlamaIndex",
+    "categories": "ChatAnthropic,Compact and Refine,Pinecone,LlamaIndex",
    "badge": "NEW",
+    "framework": "LlamaIndex",
    "nodes": [
        {
            "width": 300,
@@ -1,5 +1,7 @@
 {
    "description": "An agent that uses ReAct logic to decide what action to take",
+    "categories": "Calculator Tool,SerpAPI,ChatOpenAI,MRKL Agent,Langchain",
+    "framework": "Langchain",
    "nodes": [
        {
            "width": 300,
@@ -1,5 +1,7 @@
 {
    "description": "Use Replicate API that runs Llama 13b v2 model with LLMChain",
+    "categories": "Replicate,LLM Chain,Langchain",
+    "framework": "Langchain",
    "nodes": [
        {
            "width": 300,
@@ -1,5 +1,7 @@
 {
    "description": "Answer questions over a SQL database",
+    "categories": "ChatOpenAI,Sql Database Chain,Langchain",
+    "framework": "Langchain",
    "nodes": [
        {
            "width": 300,
@@ -1,5 +1,7 @@
 {
    "description": "Manually construct prompts to query a SQL database",
+    "categories": "IfElse Function,Variable Set/Get,Custom JS Function,ChatOpenAI,LLM Chain,Langchain",
+    "framework": "Langchain",
    "badge": "new",
    "nodes": [
        {
@@ -1,5 +1,7 @@
 {
    "description": "Simple chat engine to handle back and forth conversations using LlamaIndex",
+    "categories": "BufferMemory,AzureChatOpenAI,LlamaIndex",
+    "framework": "LlamaIndex",
    "badge": "NEW",
    "nodes": [
        {
@@ -1,5 +1,7 @@
 {
    "description": "Basic example of Conversation Chain with built-in memory - works exactly like ChatGPT",
+    "categories": "Buffer Memory,ChatOpenAI,Conversation Chain,Langchain",
+    "framework": "Langchain",
    "badge": "POPULAR",
    "nodes": [
        {
@@ -1,5 +1,7 @@
 {
    "description": "Basic example of stateless (no memory) LLM Chain with a Prompt Template and LLM Model",
+    "categories": "OpenAI,LLM Chain,Langchain",
+    "framework": "Langchain",
    "nodes": [
        {
            "width": 300,
@@ -1,5 +1,7 @@
 {
    "description": "Return response as a specified JSON structure instead of a string/text",
+    "categories": "Structured Output Parser,ChatOpenAI,LLM Chain,Langchain",
+    "framework": "Langchain",
    "badge": "NEW",
    "nodes": [
        {
@@ -1,5 +1,7 @@
 {
    "description": "Breaks down query into sub questions for each relevant data source, then combine into final response",
+    "categories": "Sub Question Query Engine,Sticky Note,QueryEngine Tool,Compact and Refine,ChatOpenAI,Pinecone,LlamaIndex",
+    "framework": "LlamaIndex",
    "badge": "NEW",
    "nodes": [
        {
@@ -1,5 +1,7 @@
 {
    "description": "Language translation using LLM Chain with a Chat Prompt Template and Chat Model",
+    "categories": "Chat Prompt Template,ChatOpenAI,LLM Chain,Langchain",
+    "framework": "Langchain",
    "nodes": [
        {
            "width": 300,
@@ -1,4 +1,7 @@
 {
+    "description": "QA chain for Vectara",
+    "categories": "Vectara QA Chain,Vectara,Langchain",
+    "framework": "Langchain",
    "nodes": [
        {
            "width": 300,
@@ -1,5 +1,7 @@
 {
    "description": "Conversational Agent with ability to visit a website and extract information",
+    "categories": "Buffer Memory,Web Browser,ChatOpenAI,Conversational Agent",
+    "framework": "Langchain",
    "nodes": [
        {
            "width": 300,
@@ -1,5 +1,7 @@
 {
    "description": "Scrape web pages for QnA with long term memory Motorhead and return source documents",
+    "categories": "HtmlToMarkdown,Cheerio Web Scraper,ChatOpenAI,Redis,Pinecone,Langchain",
+    "framework": "Langchain",
    "badge": "POPULAR",
    "nodes": [
        {
@@ -1,5 +1,6 @@
 {
    "name": "add_contact_hubspot",
+    "framework": "Langchain",
    "description": "Add new contact to Hubspot",
    "color": "linear-gradient(rgb(85,198,123), rgb(0,230,99))",
    "iconSrc": "https://cdn.worldvectorlogo.com/logos/hubspot-1.svg",
@@ -1,5 +1,6 @@
 {
    "name": "add_airtable",
+    "framework": "Langchain",
    "description": "Add column1, column2 to Airtable",
    "color": "linear-gradient(rgb(125,71,222), rgb(128,102,23))",
    "iconSrc": "https://raw.githubusercontent.com/gilbarbara/logos/main/logos/airtable.svg",
@@ -1,5 +1,6 @@
 {
    "name": "todays_date_time",
+    "framework": "Langchain",
    "description": "Useful to get todays day, date and time.",
    "color": "linear-gradient(rgb(117,118,129), rgb(230,10,250))",
    "iconSrc": "https://raw.githubusercontent.com/gilbarbara/logos/main/logos/javascript.svg",
@@ -1,5 +1,6 @@
 {
    "name": "get_stock_movers",
+    "framework": "Langchain",
    "description": "Get the stocks that has biggest price/volume moves, e.g. actives, gainers, losers, etc.",
    "iconSrc": "https://rapidapi.com/cdn/images?url=https://rapidapi-prod-apis.s3.amazonaws.com/9c/e743343bdd41edad39a3fdffd5b974/016c33699f51603ae6fe4420c439124b.png",
    "color": "linear-gradient(rgb(191,202,167), rgb(143,202,246))",
@@ -1,5 +1,6 @@
 {
    "name": "make_webhook",
+    "framework": "Langchain",
    "description": "Useful when you need to send message to Discord",
    "color": "linear-gradient(rgb(19,94,2), rgb(19,124,59))",
    "iconSrc": "https://github.com/FlowiseAI/Flowise/assets/26460777/517fdab2-8a6e-4781-b3c8-fb92cc78aa0b",
@@ -1,5 +1,6 @@
 {
    "name": "send_message_to_discord_channel",
+    "framework": "Langchain",
    "description": "Send message to Discord channel",
    "color": "linear-gradient(rgb(155,190,84), rgb(176,69,245))",
    "iconSrc": "https://raw.githubusercontent.com/gilbarbara/logos/main/logos/discord-icon.svg",
@@ -1,5 +1,6 @@
 {
    "name": "send_message_to_slack_channel",
+    "framework": "Langchain",
    "description": "Send message to Slack channel",
    "color": "linear-gradient(rgb(155,190,84), rgb(176,69,245))",
    "iconSrc": "https://raw.githubusercontent.com/gilbarbara/logos/main/logos/slack-icon.svg",
@@ -1,5 +1,6 @@
 {
    "name": "send_message_to_teams_channel",
+    "framework": "Langchain",
    "description": "Send message to Teams channel",
    "color": "linear-gradient(rgb(155,190,84), rgb(176,69,245))",
    "iconSrc": "https://raw.githubusercontent.com/gilbarbara/logos/main/logos/microsoft-teams.svg",
@@ -1,5 +1,6 @@
 {
    "name": "sendgrid_email",
+    "framework": "Langchain",
    "description": "Send email using SendGrid",
    "color": "linear-gradient(rgb(230,108,70), rgb(222,4,98))",
    "iconSrc": "https://raw.githubusercontent.com/gilbarbara/logos/main/logos/sendgrid-icon.svg",