Gooey.AI

Health: Workplace Mental Health Bot

DEMO Aimed at employee engagement and wellbeing, this bot uses a collection of documents and video transcripts representing best practices from the White Swan Foundation & Manah Wellness repository to answer common questions about mental wellbeing at the workplace. We load these documents + transcripts into a vector database. With each question, we search the vector DB and then add the results to a GPT script below to create an answer to the user's questions.

📝 Instructions

🧠 Language Model

GPT-4o (openai)

📄 Knowledge

Submit Links in Bulk

💪 Capabilities

🦻 Speech Recognition & Translation

🗣️ Text to Speech & Lipsync

Text-to-Speech Provider

Azure Text-to-Speech

Azure TTS Voice name

Ananya - English (India)

🫦 Add Lipsync Video

👩‍🦰 Input Face

Upload a video or image (with a human face) to lipsync responses. mp4, mov, png, jpg or gif preferred.

Lipsync Model

SD, Low-res (~480p), Fast (Rudrabha/Wav2Lip)

🩻 Photo & Document Intelligence

🧩 Developer Tools and Functions

⚙️ Settings

Microsoft Server Speech Text to Speech Voice (en-IN, AnanyaNeural):

Name: Ananya
Gender: Female
Locale: English (India)
Locale Code: en-IN
Sample Rate: 48000 Hz
Voice Type: Neural
Words Per Minute: None

See all the supported languages and voices here.

⌖ Lipsync Face Padding

Adjust the detected face bounding box. Often leads to improved results. Recommended to give at least 10 padding for the chin region.

Head

Chin

Left Cheek

Right Cheek

📄 Knowledge Base

👩‍🏫 Search Instructions

How should the LLM interpret the results from your knowledge base?

Citation Style

Numbers ( [1] [2] [3] ..)

🔗 Shorten Citation URLs

Create Synthetic Data

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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Advanced Settings

In general, you should not need to adjust these.

👁‍🗨 Conversation Summarization

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.

🔑 Keyword Extraction

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 }}.

✏ Embeddings Model

Text Embedding 3 Large (OpenAI)

Dense Embeddings Weightage

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.

Max Citations

The maximum number of document search citations.

Max Snippet Words

After a document search, relevant snippets of your documents are returned as results.
This setting adjusts the maximum number of words in each snippet (tokens = words * 2).
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).

Snippet Overlap Ratio

Your knowledge base documents are split into overlapping snippets.
This settings adjusts how much those snippets overlap (overlap tokens = snippet tokens / overlap ratio).
In general you shouldn't need to adjust this.

🔠 Language Model Settings

Avoid Repetition

Response Format

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Max Output Tokens

The maximum number of tokens to generate in the completion. Increase to generate longer responses.

Creativity (aka Sampling Temperature)

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.

Answer Outputs

How many answers should the copilot generate? Additional answer outputs increase the cost of each run.

Run cost = 7 credits

Breakdown: 3 (GPT-4o (openai)) + 3/run + 1 (lipsync)

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Show Raw Output

Assistant

Investing in employees' mental wellbeing leads to increased productivity, engagement, and job satisfaction. Supported employees are more motivated, loyal, and less likely to engage in counterproductive behaviors. This support helps uncover hidden talents and fosters creativity and innovation. Additionally, younger generations seek meaningful work, and companies that prioritize mental health can better retain talent and reduce turnover. Ultimately, a happy workforce is a productive one, benefiting both the company financially and culturally [1]. 😊

User
Why should employers invest in the mental wellbeing of their employees?

💁‍♀️ Sources

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ℹ️ Details

How to Use This Recipe

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