This bot is designed to ask to help students fill out scholarship applications from their school records and identity documents. It asks for their ID documents, schoolmarks and then visually understands the data inside in them and then builds to build a profile of the user. It's a great example of using GPT4-Turbo in conjunction with advanced OCR to recognize text (which GPTV doesn't do well), combined with audio speech recognition in a WhatsApp bot using 11labs
Run
Examples
API
Integrations
GPT-4 Turbo (openai)
Add documents or links to give your copilot a knowledge base. When asked a question, we'll search them to generate an answer with citations.
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Submit Links in Bulk
Google Text-to-Speech
hi-IN-Neural2-D (Female)
Please refer to the list of voice names here
When your copilot users upload a photo or pdf, what kind of document are they mostly likely to upload? (via Azure)
Extract text from documents. (prebuilt-read)
⚙️ Settings
1.0 is the normal native speed of the speaker
1.0
Increase/Decrease semitones from the original pitch
How should the LLM interpret the results from your knowledge base?
Plain Text / WhatsApp Numbers + Footnotes
🔗 Shorten Citation URLs
To improve answer quality, pick a synthetic data maker workflow to scan & OCR any images in your documents or transcribe & translate any videos. It also can synthesize a helpful FAQ. Adds ~2 minutes of one-time processing per file.
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In general, you should not need to adjust these.
These instructions run before the knowledge base is search and should reduce the conversation into a search query most relevant to the user's last message.
Instructions to create a query for keyword/hybrid BM25 search. Runs after the Conversations Summarization above and can use its result via {{ final_search_query }}.
Text Embedding 3 Large (OpenAI)
Weightage for dense vs sparse embeddings. 0 for sparse, 1 for dense, 0.5 for equal weight.Generally speaking, dense embeddings excel at understanding the context of the query, whereas sparse vectors excel at keyword matches.
0
1
0.5
The maximum number of document search citations.
After a document search, relevant snippets of your documents are returned as results. This setting adjusts the maximum number of words in each snippet. A high snippet size allows the LLM to access more information from your document results, at the cost of being verbose and potentially exhausting input tokens (which can cause a failure of the copilot to respond). Default: 300
Your knowledge base documents are split into overlapping snippets. This settings adjusts how much those snippets overlap. In general you shouldn't need to adjust this. Default: 5
Avoid Repetition
How many answers should the copilot generate? Additional answer outputs increase the cost of each run.
The maximum number of tokens to generate in the completion. Increase to generate longer responses.
Higher values allow the LLM to take more risks. Try values larger than 1 for more creative applications or 0 to ensure that LLM gives the same answer when given the same user input.
Give your copilot superpowers by giving it access to tools. Powered by Function calling.
Save JSON as PDF
Run cost = 5 credits Breakdown: 2 (GPT-4 Turbo (openai)) + 3/run
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Show Raw Output
Assistant
Hello I'm your Saathi - your guide to opportunities. I can help you find scholarships, jobs, and other opportunities. Answer questions about them and help you apply. Can you please tell me your name?
UserHi
📎
✈ Send
🗑️ Clear
💁♀️ Sources
1. NSP National Means Cum Merit Scholarship Scheme
Generated in 9.9s on
...
ℹ️ Details
Have you ever wanted to create a bot that you could talk to about anything? Ever wanted to create your own https://dara.network/RadBots or https://Farmer.CHAT? This is how.
This workflow takes a dialog LLM prompt describing your character, a collection of docs & links and optional an video clip of your bot’s face and voice settings.
We use all these to build a bot that anyone can speak to about anything and you can host directly in your own site or app, or simply connect to your Facebook, WhatsApp or Instagram page.
How It Works:
PS. This is the workflow that we used to create RadBots - a collection of Turing-test videobots, authored by leading international writers, singers and playwrights - and really inspired us to create Gooey.AI so that every person and organization could create their own fantastic characters, in any personality of their choosing. It's also the workflow that powers https://Farmer.CHAT and was demo'd at the UN General Assembly in April 2023 as a multi-lingual WhatsApp bot for Indian, Ethiopian and Kenyan farmers.
Final Search Query
References
Final Prompt
Raw Text Response 1
Final Response 1
Generated Audio 1
🙋🏽♀️ Need more help? Join our Discord
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