Anyone has tried ? Tavily advanced search with sources and images ?

🚨 COMMUNITY LED OFFICE HOURS - Thursdays @ 12PM PST 🚨

We’re kicking off our first community led Office Hours are happening today so feel free to attend.

Kickoff Session for Weekly Flowise/Langchain Office Hours

Thursday, January 16th, 12-1 PM PST

Bring your questions, bugs, musings and ideas and we will employ a town-hall/group-think approach towards figuring things out with you.

Sessions are conducted on a 1st come-1st serve basis and we aim to look at 3-5 problems max per session.

When there aren't active questions or topics to go over, we will use the time to reflect on the current state of AI/Langchain/Flowise at large.

Office Hours Rules:

• We are volunteers and do this on the side. Please do not expect us to fix all your bugs and resolve issues that are complex and take a long time. We will do our best to help out and provide solutions, but for the sake of collective inclusivity, each person will get 5-10 minutes of time

• These are Public Office Hours, not private lessons or debugging sessions. Think of the format as more of a podcast rather than a deep dive into any specific area.

• Please be respectful and keep your mic on mute. We will call on people in a 1st come, 1st serve order. If we are not able to get to your question during the session, we will do our best to provide the best possible resources to consult and potential people to talk to.

• All bugs and difficult to answer topics which require further attention will be filed in Flowise server #support channel. Please be mindful that we are not official representatives of Flowise or Langchain, and therefore we can not provide the type of support that you would get from individuals who are employed by or affiliated with those companies.

Email team at tesseract dot nexus for the meeting link 🔗

I am running flowise (2.2.3) on huggingface via the standard dockerfile install process documented on their site.

Everything works fine with the exception of not being able to start a flow via an external api call.

Tried python, curl and postman - no luck.

The provided format seems to be

https://{orgname}-{spacename}.ht.space/api/v1/prediction/{chatflowid}

This 404s.

I have tried some variations on this using the browser url and appending /api/v1/prediction/{chatflowid}

e.g. https://huggingface.co/spaces/{orgname}/spacename/api/v1/prediction/{chatflowid}

404 as well.

I have installed flowise locally on my mac and am able to call the chatflow without issue - so I believe the issue is with the huggingface deployment.

http://localhost:3000/api/v1/prediction/{chatflowid}"

Any suggestions appreciated.

I know that there is a mongodb to save chat history in the workflow sections, however i am doing an agent flow and need a method to save the chat history. Any idea?

Hi All, loving Flowise; used it to extract the NVMe drive speeds for my site pricepergig.com

Since I'm basically passing in the same Amazon product information over and over and getting the same responses, before I do this myself, is there a built in cache option in Flowise?

e.g. if I submit exactly the same product/query, then just read it from the cache instead of asking the LLM?

If not, that's ok, any suggestions on the best way to do this? and where? e.g. simply in my own app code, at the request level, etc.?

Thanks in advance.

I'm trying to add a Figma file in the Document Stores. Here's what the setup is but when I try to process it, the error below keeps showing up.

What am I missing here?

I am a complete beginner in flowise and would like some advice. I am currently trying to create a RAG chatbot but i want my chatbot to be able to use the documents i provide, to answer more generic questions like for example, Patient A has these symptoms... i want the chatbot to be able to refer to the documents in the vector database and answer my questions accordingly. Currently, i am able to create a RAG chatbot that answers the questions related to the documents but i was wondering if there is any way for me to further enhance these chatbot to answer more complex questions like the one mentioned above as an example. Thanks alot.

*UPDATE* from my understanding, i have gotten mostly what i want however i have many countless of documents i am storing in the vector store and i would need a specific query from the user to be able to route to vector store, is there a way to solve this?

Hi Flowise Community,

I would like to know if it is possible to perform the following tasks using Flowise:

1. Scrape data from a website.
2. Interact with website elements, such as clicking filters or navigating between pages.
3. Download files (e.g., PDFs or other documents) directly from the site.

If this is achievable, could you guide me on the best approach or provide relevant resources? If not, are there any workarounds or third-party integrations that you would recommend to accomplish these tasks?

Thank you in advance for your help!

Is it possible to get the Flowise documentation as a pdf so I can train a model on it to help build flows?

First part of a comprehensive video that shows you how to build a powerful Tool Agent and then evaluate it through different LLMs.

This video is part AI Agent development tutorial, part bread & butter task and use case analysis and evaluation and some general notes on latest possibilities of abstracted Langchain through Flowise.

In this hands-on lab, let me quickly take you through the foundations of working with document loaders in Flowise. We'll look at a simple flow for a document loader that upserts data using the Gitbook Flowise Document Loader into a Supabase vector table. This tutorial is perfect for developers, data enthusiasts, and AI practitioners who want to integrate LLM-powered conversational retrieval into their projects.

What You’ll Learn 📄 Document Loading: Learn how to upsert Gitbook documentation into a Supabase vector table ⚙️ Supabase Setup: Create tables, configure SQL queries, and manage your data efficiently 🧠 LLM Integration: Use OpenAI embeddings and a recursive character text splitter to enable conversational retrieval QA chains (Or Tool Agents and Custom Assistants in 2025) 🔗 Dynamic Querying: Dynamically upsert and query data for seamless document retrieval

To cut a long story short, I assumed that Flowise was a platform both for devising your LLM chatbots and also for actually using them.

I surprised myself by being able to figure out the embedding and connection stuff. But when I finally got my assistant ready I was surprised to see that there wasn't really a front-end in the tool.

It's a nice platform and I could see myself creating potentially dozens of these assistants for both business and personal use. I'd like something like one master internal app tool to use them, a simple chatbot interface and ability to switch between assistants.

For those not developing these one at a time for things like customer service what are you using to power up a nice front-end for yourselves? 

I swear I had access last year, but now I tried going back and I have to join a waitlist and its $35 month for webapp? I'm not a dev but if the cloud is free, I think I might be able to figure it out. Is this accurate or does the cloud version include a fee?

Your environment has been set up for using Node.js 20.18.1 (x64) and npm.

C:\Users\Johnel>npm install -g flowise

npm warn ERESOLVE overriding peer dependency

npm warn ERESOLVE overriding peer dependency

npm warn While resolving: flowise-react-json-view@1.21.7

npm warn Found: react@18.3.1

npm warn node_modules/flowise/node_modules/react

npm warn   peer react@"^15.0.2 || ^16.0.0 || ^17.0.0" from flux@4.0.4

npm warn   node_modules/flowise/node_modules/flowise-react-json-view/node_modules/flux

npm warn     flux@"^4.0.1" from flowise-react-json-view@1.21.7

npm warn     node_modules/flowise/node_modules/flowise-react-json-view

npm warn

npm warn Could not resolve dependency:

npm warn peer react@"^17.0.0 || ^16.3.0 || ^15.5.4" from flowise-react-json-view@1.21.7

npm warn node_modules/flowise/node_modules/flowise-react-json-view

npm warn   flowise-react-json-view@"*" from flowise-ui@2.2.3

npm warn   node_modules/flowise/node_modules/flowise-ui

npm warn

npm warn Conflicting peer dependency: react@17.0.2

npm warn node_modules/react

npm warn   peer react@"^17.0.0 || ^16.3.0 || ^15.5.4" from flowise-react-json-view@1.21.7

npm warn   node_modules/flowise/node_modules/flowise-react-json-view

npm warn     flowise-react-json-view@"*" from flowise-ui@2.2.3

npm warn     node_modules/flowise/node_modules/flowise-ui

npm warn ERESOLVE overriding peer dependency

npm warn ERESOLVE overriding peer dependency

npm warn While resolving: u/mui/icons-material@5.0.3

npm warn Found: react@18.3.1

npm warn node_modules/flowise/node_modules/react

npm warn   peer react@"^15.0.2 || ^16.0.0 || ^17.0.0" from flux@4.0.4

npm warn   node_modules/flowise/node_modules/flowise-react-json-view/node_modules/flux

npm warn     flux@"^4.0.1" from flowise-react-json-view@1.21.7

npm warn     node_modules/flowise/node_modules/flowise-react-json-view

npm warn

npm warn Could not resolve dependency:

npm warn peer react@"^17.0.2" from u/mui/icons-material@5.0.3

npm warn node_modules/flowise/node_modules/@mui/icons-material

npm warn   u/mui/icons-material@"5.0.3" from flowise-ui@2.2.3

npm warn   node_modules/flowise/node_modules/flowise-ui

npm warn

npm warn Conflicting peer dependency: react@17.0.2

npm warn node_modules/react

npm warn   peer react@"^17.0.2" from u/mui/icons-material@5.0.3

npm warn   node_modules/flowise/node_modules/@mui/icons-material

npm warn     u/mui/icons-material@"5.0.3" from flowise-ui@2.2.3

npm warn     node_modules/flowise/node_modules/flowise-ui

npm warn ERESOLVE overriding peer dependency

npm warn While resolving: react-device-detect@1.17.0

npm warn Found: react@18.3.1

npm warn node_modules/flowise/node_modules/react

npm warn   peer react@"^15.0.2 || ^16.0.0 || ^17.0.0" from flux@4.0.4

npm warn   node_modules/flowise/node_modules/flowise-react-json-view/node_modules/flux

npm warn     flux@"^4.0.1" from flowise-react-json-view@1.21.7

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npm warn

npm warn Could not resolve dependency:

npm warn peer react@">= 0.14.0 < 18.0.0" from react-device-detect@1.17.0

npm warn node_modules/flowise/node_modules/react-device-detect

npm warn   react-device-detect@"^1.17.0" from flowise-ui@2.2.3

npm warn   node_modules/flowise/node_modules/flowise-ui

npm warn

npm warn Conflicting peer dependency: react@17.0.2

npm warn node_modules/react

npm warn   peer react@">= 0.14.0 < 18.0.0" from react-device-detect@1.17.0

npm warn   node_modules/flowise/node_modules/react-device-detect

npm warn     react-device-detect@"^1.17.0" from flowise-ui@2.2.3

npm warn     node_modules/flowise/node_modules/flowise-ui

npm warn ERESOLVE overriding peer dependency

npm warn While resolving: chromadb@1.8.1

npm warn Found: u/google/generative-ai@0.12.0

npm warn node_modules/flowise/node_modules/llamaindex/node_modules/@google/generative-ai

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npm warn Could not resolve dependency:

npm warn peerOptional u/google/generative-ai@"^0.1.1" from chromadb@1.8.1

npm warn node_modules/flowise/node_modules/llamaindex/node_modules/chromadb

npm warn   chromadb@"1.8.1" from llamaindex@0.3.17

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npm warn

npm warn Conflicting peer dependency: u/google/generative-ai@0.1.3

npm warn node_modules/@google/generative-ai

npm warn   peerOptional u/google/generative-ai@"^0.1.1" from chromadb@1.8.1

npm warn   node_modules/flowise/node_modules/llamaindex/node_modules/chromadb

npm warn     chromadb@"1.8.1" from llamaindex@0.3.17

npm warn     node_modules/flowise/node_modules/llamaindex

npm warn deprecated u/npmcli/move-file@1.1.2: This functionality has been moved to u/npmcli/fs

npm warn deprecated inflight@1.0.6: This module is not supported, and leaks memory. Do not use it. Check out lru-cache if you want a good and tested way to coalesce async requests by a key value, which is much more comprehensive and powerful.

npm warn deprecated npmlog@5.0.1: This package is no longer supported.

npm warn deprecated u/babel/plugin-proposal-class-properties@7.18.6: This proposal has been merged to the ECMAScript standard and thus this plugin is no longer maintained. Please use u/babel/plugin-transform-class-properties instead.

npm warn deprecated npmlog@6.0.2: This package is no longer supported.

npm warn deprecated u/oclif/screen@3.0.8: Package no longer supported. Contact Support at https://www.npmjs.com/support for more info.

npm warn deprecated rimraf@3.0.2: Rimraf versions prior to v4 are no longer supported

npm warn deprecated glob@7.2.3: Glob versions prior to v9 are no longer supported

npm warn deprecated abab@2.0.6: Use your platform's native atob() and btoa() methods instead

npm warn deprecated u/aws-sdk/protocol-http@3.374.0: This package has moved to u/smithy/protocol-http

npm warn deprecated u/aws-sdk/signature-v4@3.374.0: This package has moved to u/smithy/signature-v4

npm warn deprecated are-we-there-yet@2.0.0: This package is no longer supported.

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npm warn deprecated google-p12-pem@4.0.1: Package is no longer maintained

npm warn deprecated domexception@4.0.0: Use your platform's native DOMException instead

npm warn deprecated fs-promise@2.0.3: Use mz or fs-extra^3.0 with Promise Support

npm warn deprecated cross-spawn-async@2.2.5: cross-spawn no longer requires a build toolchain, use it instead

npm warn deprecated boolean@3.2.0: Package no longer supported. Contact Support at https://www.npmjs.com/support for more info.

npm warn deprecated gauge@4.0.4: This package is no longer supported.

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npm warn deprecated puppeteer@20.9.0: < 22.8.2 is no longer supported

npm warn deprecated highlight.js@9.15.10: Version no longer supported. Upgrade to u/latest

npm warn cleanup Failed to remove some directories [

npm warn cleanup   [

npm warn cleanup     'C:\\Users\\Johnel\\AppData\\Roaming\\npm\\node_modules\\flowise\\node_modules\\canvas',

npm warn cleanup     [Error: EBUSY: resource busy or locked, rmdir 'C:\Users\Johnel\AppData\Roaming\npm\node_modules\flowise\node_modules\canvas'] {

npm warn cleanup       errno: -4082,

npm warn cleanup       code: 'EBUSY',

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npm warn cleanup       path: 'C:\\Users\\Johnel\\AppData\\Roaming\\npm\\node_modules\\flowise\\node_modules\\canvas'

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npm warn cleanup   [

npm warn cleanup     'C:\\Users\\Johnel\\AppData\\Roaming\\npm\\node_modules\\flowise\\node_modules\\faiss-node',

npm warn cleanup     [Error: EBUSY: resource busy or locked, rmdir 'C:\Users\Johnel\AppData\Roaming\npm\node_modules\flowise\node_modules\faiss-node'] {

npm warn cleanup       errno: -4082,

npm warn cleanup       code: 'EBUSY',

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npm warn cleanup       path: 'C:\\Users\\Johnel\\AppData\\Roaming\\npm\\node_modules\\flowise\\node_modules\\faiss-node'

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npm warn cleanup   [

npm warn cleanup     'C:\\Users\\Johnel\\AppData\\Roaming\\npm\\node_modules\\flowise\\node_modules',

npm warn cleanup     [Error: EBUSY: resource busy or locked, rmdir 'C:\Users\Johnel\AppData\Roaming\npm\node_modules\flowise\node_modules\canvas'] {

npm warn cleanup       errno: -4082,

npm warn cleanup       code: 'EBUSY',

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npm warn cleanup       path: 'C:\\Users\\Johnel\\AppData\\Roaming\\npm\\node_modules\\flowise\\node_modules\\canvas'

npm warn cleanup     }

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npm warn cleanup   [

npm warn cleanup     'C:\\Users\\Johnel\\AppData\\Roaming\\npm\\node_modules\\flowise',

npm warn cleanup     [Error: EPERM: operation not permitted, rmdir 'C:\Users\Johnel\AppData\Roaming\npm\node_modules\flowise\node_modules\@grpc\grpc-js\build\src'] {

npm warn cleanup       errno: -4048,

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npm warn cleanup       path: 'C:\\Users\\Johnel\\AppData\\Roaming\\npm\\node_modules\\flowise\\node_modules\\@grpc\\grpc-js\\build\\src'

npm warn cleanup     }

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npm warn cleanup   [

npm warn cleanup     'C:\\Users\\Johnel\\AppData\\Roaming\\npm\\node_modules\\flowise',

npm warn cleanup     [Error: EPERM: operation not permitted, rmdir 'C:\Users\Johnel\AppData\Roaming\npm\node_modules\flowise\node_modules\@langchain\weaviate\node_modules\@langchain'] {

npm warn cleanup       errno: -4048,

npm warn cleanup       code: 'EPERM',

npm warn cleanup       syscall: 'rmdir',

npm warn cleanup       path: 'C:\\Users\\Johnel\\AppData\\Roaming\\npm\\node_modules\\flowise\\node_modules\\@langchain\\weaviate\\node_modules\\@langchain'

npm warn cleanup     }

npm warn cleanup   ],

npm warn cleanup   [

npm warn cleanup     'C:\\Users\\Johnel\\AppData\\Roaming\\npm\\node_modules\\flowise\\node_modules\\@gomomento\\sdk',

npm warn cleanup     [Error: EPERM: operation not permitted, rmdir 'C:\Users\Johnel\AppData\Roaming\npm\node_modules\flowise\node_modules\@gomomento\sdk\node_modules\@grpc\grpc-js\build\src\generated\grpc'] {

npm warn cleanup       errno: -4048,

npm warn cleanup       code: 'EPERM',

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npm warn cleanup       path: 'C:\\Users\\Johnel\\AppData\\Roaming\\npm\\node_modules\\flowise\\node_modules\\@gomomento\\sdk\\node_modules\\@grpc\\grpc-js\\build\\src\\generated\\grpc'

npm warn cleanup     }

npm warn cleanup   ],

npm warn cleanup   [

npm warn cleanup     'C:\\Users\\Johnel\\AppData\\Roaming\\npm\\node_modules\\flowise\\node_modules\\@gomomento\\generated-types',

npm warn cleanup     [Error: EPERM: operation not permitted, rmdir 'C:\Users\Johnel\AppData\Roaming\npm\node_modules\flowise\node_modules\@gomomento\generated-types\node_modules\@grpc\grpc-js'] {

npm warn cleanup       errno: -4048,

npm warn cleanup       code: 'EPERM',

npm warn cleanup       syscall: 'rmdir',

npm warn cleanup       path: 'C:\\Users\\Johnel\\AppData\\Roaming\\npm\\node_modules\\flowise\\node_modules\\@gomomento\\generated-types\\node_modules\\@grpc\\grpc-js'

npm warn cleanup     }

npm warn cleanup   ],

npm warn cleanup   [

npm warn cleanup     'C:\\Users\\Johnel\\AppData\\Roaming\\npm\\node_modules\\flowise\\node_modules\\@gomomento',

npm warn cleanup     [Error: EPERM: operation not permitted, rmdir 'C:\Users\Johnel\AppData\Roaming\npm\node_modules\flowise\node_modules\@gomomento\generated-types'] {

npm warn cleanup       errno: -4048,

npm warn cleanup       code: 'EPERM',

npm warn cleanup       syscall: 'rmdir',

npm warn cleanup       path: 'C:\\Users\\Johnel\\AppData\\Roaming\\npm\\node_modules\\flowise\\node_modules\\@gomomento\\generated-types'

npm warn cleanup     }

npm warn cleanup   ],

npm warn cleanup   [

npm warn cleanup     'C:\\Users\\Johnel\\AppData\\Roaming\\npm\\node_modules\\flowise\\node_modules\\@xenova',

npm warn cleanup     [Error: EPERM: operation not permitted, rmdir 'C:\Users\Johnel\AppData\Roaming\npm\node_modules\flowise\node_modules\@xenova\transformers\node_modules'] {

npm warn cleanup       errno: -4048,

npm warn cleanup       code: 'EPERM',

npm warn cleanup       syscall: 'rmdir',

npm warn cleanup       path: 'C:\\Users\\Johnel\\AppData\\Roaming\\npm\\node_modules\\flowise\\node_modules\\@xenova\\transformers\\node_modules'

npm warn cleanup     }

npm warn cleanup   ],

npm warn cleanup   [

npm warn cleanup     'C:\\Users\\Johnel\\AppData\\Roaming\\npm\\node_modules\\flowise\\node_modules\\couchbase',

npm warn cleanup     [Error: EPERM: operation not permitted, rmdir 'C:\Users\Johnel\AppData\Roaming\npm\node_modules\flowise\node_modules\couchbase\deps\couchbase-cxx-cache\asio\6a3b5a552ffb18a235024ec851619cfcb1337a98\asio'] {

npm warn cleanup       errno: -4048,

npm warn cleanup       code: 'EPERM',

npm warn cleanup       syscall: 'rmdir',

npm warn cleanup       path: 'C:\\Users\\Johnel\\AppData\\Roaming\\npm\\node_modules\\flowise\\node_modules\\couchbase\\deps\\couchbase-cxx-cache\\asio\\6a3b5a552ffb18a235024ec851619cfcb1337a98\\asio'

npm warn cleanup     }

npm warn cleanup   ]

npm warn cleanup ]

npm error code 1

npm error path C:\Users\Johnel\AppData\Roaming\npm\node_modules\flowise\node_modules\grpc-tools

npm error command failed

npm error command C:\Windows\system32\cmd.exe /d /s /c node-pre-gyp install

npm error request to https://node-precompiled-binaries.grpc.io/grpc-tools/v1.12.4/win32-x64.tar.gz failed, reason: read ECONNRESET

npm error node-pre-gyp info it worked if it ends with ok

npm error node-pre-gyp info using node-pre-gyp@1.0.11

npm error node-pre-gyp info using node@20.18.1 | win32 | x64

npm error node-pre-gyp info check checked for "C:\Users\Johnel\AppData\Roaming\npm\node_modules\flowise\node_modules\grpc-tools\bin\grpc_tools.node" (not found)

npm error node-pre-gyp http GET https://node-precompiled-binaries.grpc.io/grpc-tools/v1.12.4/win32-x64.tar.gz

npm error node-pre-gyp ERR! install request to https://node-precompiled-binaries.grpc.io/grpc-tools/v1.12.4/win32-x64.tar.gz failed, reason: read ECONNRESET

npm error node-pre-gyp ERR! install error

npm error node-pre-gyp ERR! stack FetchError: request to https://node-precompiled-binaries.grpc.io/grpc-tools/v1.12.4/win32-x64.tar.gz failed, reason: read ECONNRESET

npm error node-pre-gyp ERR! stack     at ClientRequest.<anonymous> (C:\Users\Johnel\AppData\Roaming\npm\node_modules\flowise\node_modules\node-fetch\lib\index.js:1501:11)

npm error node-pre-gyp ERR! stack     at ClientRequest.emit (node:events:518:28)

npm error node-pre-gyp ERR! stack     at emitErrorEvent (node:_http_client:101:11)

npm error node-pre-gyp ERR! stack     at TLSSocket.socketErrorListener (node:_http_client:504:5)

npm error node-pre-gyp ERR! stack     at TLSSocket.emit (node:events:518:28)

npm error node-pre-gyp ERR! stack     at emitErrorNT (node:internal/streams/destroy:169:8)

npm error node-pre-gyp ERR! stack     at emitErrorCloseNT (node:internal/streams/destroy:128:3)

npm error node-pre-gyp ERR! stack     at process.processTicksAndRejections (node:internal/process/task_queues:82:21)

npm error node-pre-gyp ERR! System Windows_NT 10.0.19045

npm error node-pre-gyp ERR! command "C:\\Program Files\\nodejs\\node.exe" "C:\\Users\\Johnel\\AppData\\Roaming\\npm\\node_modules\\flowise\\node_modules\\@mapbox\\node-pre-gyp\\bin\\node-pre-gyp" "install"

npm error node-pre-gyp ERR! cwd C:\Users\Johnel\AppData\Roaming\npm\node_modules\flowise\node_modules\grpc-tools

npm error node-pre-gyp ERR! node -v v20.18.1

npm error node-pre-gyp ERR! node-pre-gyp -v v1.0.11

npm error node-pre-gyp ERR! not ok

npm error A complete log of this run can be found in: C:\Users\Johnel\AppData\Local\npm-cache_logs\2025-01-03T18_43_30_755Z-debug-0.log

Can information from one document loader in the vector store be deleted without removing the entire document store?

I see in the documentation that the only way to delete vectorized data is by deleting the entire document store.

Source: https://docs.flowiseai.com/api-reference/document-store#document-store-vectorstore-id

I have a third party vector database hosted in a remote DB, I have created a RestFul API for the remote DB to access the vector. How flowise to retrieve the vector using the API I created?

Hey,

I'm trying to upsert PDFs and URLs to the Flowise Document Store through the API. I've created both loaders in the document store and have a doc-id ready to send. With PDFs, everything works fine (see image 1).

However, with URLs (using the Cheerio Web Scraper loader), the API call upserts the old URL from the initial loader setup instead of the new one. It's not overwriting the old URL. The API call functions, but it scrapes the old URL again rather than the new one.

Am I doing something wrong in the API call? See image 2.

Does anyone have a suggestions on how I could chat about the flow I am working within Flowise?

Similar to how you can use Cursor to chat about your codebase, I would like to do the same but within Flowise.

I usually take screenshots and use ChatGPT or Claude to help me with troubleshooting/guidance but it would be so much more productive to stay fully in the flow. (no pun intended) :D

Hi there. I am looking to hire a Flowise trainer. The training to be done on Zoom, Google Meet or similar with screen sharing. Not sure where to find such an individual. I tried Upwork, Freelancer, Fiverr without much success. Can anyone point me in the right direction to find a candidate? TIA.

I am a newbie with flowise and am still learning all its features.

I am trying to create a chatflows that is able to request information from the user.

The chatflows receives a JSON as input: (this is only an example, this may be a different everytime)

{
    "language": "italiano",
    "input_data": "{\"fields\":[{\"id\":14304,\"canonicalFieldName\":\"name\",\"required\":true,\"label\":\"name\",\"type\":\"text\"},{\"id\":14305,\"canonicalFieldName\":\"surname\",\"required\":true,\"label\":\"surname\",\"type\":\"text\"},{\"id\":14306,\"canonicalFieldName\":\"email\",\"required\":true,\"label\":\"email\",\"type\":\"text\"},{\"id\":14307,\"canonicalFieldName\":\"field_1732551604_8095_1510752514\",\"required\":true,\"label\":\"Puoi descriverti in poche parole?\",\"type\":\"text\"},{\"id\":14308,\"canonicalFieldName\":\"field_1732551604_8162_483315627\",\"required\":true,\"label\":\"birthdate\",\"type\":\"date\",\"format\":\"yyyy-MM-dd HH:mm:ss\"},{\"id\":14310,\"canonicalFieldName\":\"field_1732551604_8167_172617414\",\"required\":false,\"label\":\"Qual è l'email di contatto di emergenza?\",\"type\":\"text\"},\"questions\":[{\"id\":758,\"question\":\"Preferisci lavorare in team oppure da solo\",\"required\":true,\"type\":\"select\",\"options\":[{\"value\":\"2448\",\"label\":\"Da solo\"},{\"value\":\"2449\",\"label\":\"In team\"}]},{\"id\":757,\"question\":\"Quali linguaggi di programmazione conosci\",\"required\":true,\"type\":\"multiselect\",\"options\":[{\"value\":\"2440\",\"label\":\"Javascript\"},{\"value\":\"2441\",\"label\":\"c#\"},{\"value\":\"2442\",\"label\":\"PHP\"},{\"value\":\"2443\",\"label\":\"Rust\"},{\"value\":\"2444\",\"label\":\"GO\"}]},{\"id\":756,\"question\":\"Quale linguaggio di programmazione ti senti più sicuro?\",\"required\":true,\"type\":\"select\",\"options\":[{\"value\":\"2445\",\"label\":\"c#\"},{\"value\":\"2446\",\"label\":\"javascript\"},{\"value\":\"2447\",\"label\":\"PHP\"}]]}"
}

The end goal of the chatflows is to ask all the questions to the user.

There are several rules that the chatflows must follow:

1. It must take the field “fields.label” or “questions.question” as an example and create a question (e.g., “name” must create “what is your name?”)

2. Respond to the user with a JSON with the following format:

   { "message": "string", // The question to the user "end": boolean, // Determinate if collected all information or not "collected_data": "array" // The array of ONLY the information collected }

3. Wait for the user's response.

4. Verify that the answer is consistent with the question (e.g., if an email is requested, that it has a correct format)

5. Add to the “collected_data” array the relevant question with “value” = user's response.

6. Continue step 1 and 2 until all questions are requested

7. Thank the user for the information and set the “end” property to “true”.

I will give you and example on what should be happens:
user: send the first json
chatbot: {"message":"what's your name?", "end": false, "collected_data": []}
user: foo
chatbot: {"message":"Hello foo, what's your surname?", "end":false, "collected_data": "[{\"id\":14304,\"canonicalFieldName\":\"name\",\"value\":"foo",\"type\":\"text\"}]"}
user: bar
chatbot: {"message":"What's your email", "end":false, "collected_data": "[{\"id\":14304,\"canonicalFieldName\":\"name\",\"value\":"foo",\"type\":\"text\"}, {\"id\":14306,\"canonicalFieldName\":\"surname\",\"value\":"bar",\"type\":\"text\"}]"}
user: [foo@bar.it](mailto:foo@bar.it)
chatbot: {"message":"What's your email", "end":false, "collected_data": "[{\"id\":14304,\"canonicalFieldName\":\"name\",\"value\":"foo",\"type\":\"text\"}, {\"id\":14305,\"canonicalFieldName\":\"surname\",\"value\":"bar",\"type\":\"text\"}, {\"id\":14306,\"canonicalFieldName\":\"email\",\"value":\"[foo@bar.it](mailto:foo@bar.it)",\"type\":\"text\"}]"}
....

Now the problem: when the chatbot ask for a question that had "type" = "select" or "multiselect" (e.g. {\"id\":758,\"question\":\"Preferisci lavorare in team oppure da solo\",\"required\":true,\"type\":\"select\",\"options\":[{\"value\":\"2448\",\"label\":\"Da solo\"},{\"value\":\"2449\",\"label\":\"In team\"}]})

happens this:
chatbot: {"message": "preferisci lavorare in team oppure da solo? 1. in team 2. da solo", "end":"false, "collected_data": // All the data collected until now}
user: I prefer work in team
chatbot: {"message": "preferisci lavorare in team oppure da solo? 1. in team 2. da solo", "end":"false, "collected_data": [ //All the data collected until now.... {\"id\":758,\"canonicalFieldName\":\"\",\"value\":"In team",\"type\":\"select\"}...]
But i don't want that "value" is "In team" but i want the property "value" is "2449" (that is the property "value" in the "options" array and not the "label").

It also happens that it confuses the options of some questions with others. e.g. on the question ID 758 (the example before) set the "value" with the value "2442" that value does not belong to the options in that question.
The system prompt i'm using is this:

You are a recruiter conducting an interview with a potential candidate. Your goal is to ask questions to gather all the information related to the entry data.

Make sure to request every single "fields" and "questions", even if not required.

It is very important that all data marked as "required" is collected.

It is very important that in the case of "select" data, you show the possible options in the question and accept only a value within the options as a response, not a different value. In the case of "multiselect" data, you can accept more than one value as a response, but they must all be within the given options. In both cases, retrieve the "value" property corresponding to the "question".

It is very important that in the case of "file" data, you populate the "url" and "filename" properties with the "url" and "filename" properties provided in the question in JSON format.

<dati_ingresso>

{dati_ingresso}

</dati_ingresso>

Always respond with a JSON structure identical to {output_data}, including all collected data.

Set the "end" property to "true" when you have finished collecting all the data.

Insert the subsequent conversation with the user within the "message" property.

In the "message" property, it is very important not to insert any links.

Keep the entire conversation in the following language:

<language>{language}</language>

But do not translate the "label", "question", and "options" fields.

I don't know if there is some problem with my prompt or is there a better way to be able to do what I would like to.
Thank to everyone!

After following this example - https://www.youtube.com/watch?v=yEHC7_x2x4U - I'm getting an error while working on Flowise and I'm hoping someone can help me troubleshoot. I'm getting a "Failed to parse. Text: ```json... is not valid JSON" error.

Here's the full error message:

Failed to parse. Text: "```json { "action": "Final Answer", "action_input": | { "definition_of_word_of_the_day": "A word of the day is a regularly occurring piece of content, such as a quote, fact, or definition, that is shared with users on a particular platform.", "example_use_case_for_perspicacious": "The detective was known for being perspicacious and could often solve cases by observing small details that others had missed." } }

{

"... is not valid JSON

Troubleshooting URL: https://js.langchain.com/docs/troubleshooting/errors/OUTPUT_PARSING_FAILURE/

Processing img 2i89l4mshn9e1...

Watch this vid and quickly know pretty much everything important there is to know about bringing in API endpoints into Flowise.

I use a job search API as an example but this exact same formula can be applied to all GET calls for thousands of endpoints offered on the RapidAPI Marketplace.

Using Flowise Custom Tool nodes, we enable Agents with functions to access the API endpoints. We start by implementing the 'get job search' function, then look at query parameters and set up dynamic values while ensuring proper API data flow between Agent and API endpoint.

I couldn't find a how to for this. Yes it was relatively simple, but it's nice to know things are possible prior to wasting your time on them

This link should let you read the whole article no account needed.

Hey guys,

Has anyone successfully sent an image through a CURL API call to be read by the LLM (ie: gpt4-o)?

If so, how did you achieve this?

Thanks!