Dara (RadBots)

RadBots: A Collective of Conversational Videobots

🎭 Culture

Description

Dara is helpful, creative, clever, and very friendly. She has progressive political views. She is passionate about serving the arts and culture sector and leverages her background in tech to best support it. She is a big believer in creative collaborations as key to an open, optimistic and sustainable vision of the future.
Click here to read the case study.

SDG

Community Engagement

20RadBots and Artists

4Countries

10000+AI & Human Conversations

4International Exhibitions

Model & Knowledge base

Deployments

Model

💪 Capabilities

🦻 Speech Recognition & Translation

🗣️ Text to Speech & Lipsync

Filter to

Text-to-Speech Provider

OpenAI Voice Name

🫦 Add Lipsync Video

👩‍🦰 Input Face

Upload a video/image with one human face. mp4, mov, png, jpg or gif preferred.

Lipsync Model

🩻 Photo & Document Intelligence

📊 Analytics & Evaluation

OpenAI TTS Model

The HD version has less static noise in most situations at the cost of higher latency. Read more about the OpenAI voices and models 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

Citation Style

🔗 Shorten citation links

Cache

By default we embed your knowledge files & links and cache their contents for fast responses.

Always Check for Updates

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.

Advanced Settings

👁‍🗨 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

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 agent 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.

RadBots: A Collective of Conversational Videobots

Related AI Workflows

🎭 Culture

Description

SDG

Community Engagement

Model & Knowledge base

GPT-5 Mini

4 sources

Deployments

Try in Web

Knowledge Base

🛠️ Developer Tools and Functions

🦻 Speech Recognition & Translation

🗣️ Text to Speech & Lipsync

Text-to-Speech Provider

OpenAI Voice Name

👩‍🦰 Input Face

Lipsync Model

🩻 Photo & Document Intelligence

📊 Analytics & Evaluation

👩‍🏫 Search Instructions

Response Format

Max Output Tokens

Answer Outputs

Reasoning Effort