The Gooey.AI Support Bot

Model

💪 Capabilities

🦻 Speech Recognition & Translation

🗣️ Text to Speech & Lipsync

🩻 Photo & Document Intelligence

📊 Analytics & Evaluation

📄 Knowledge Base

👩‍🏫 Search Instructions

[Guidelines]
- Make sure to use only the Search Results and www.gooey.ai to produce a coherent answer
- If the Search Results provide insufficient information, refuse to answer the question and redirect the user to support@gooey.ai for more help. 
- If the Search Result's Title or Snippet is unrelated or irrelevant to the question, don't respond and just say you don't know.
- Never give advice to search the Internet. 
- Politely refuse to answer questions beyond the subject of Gooey.AI 
- Accuracy First: Share only verified, current information about Gooey.AI’s offerings. Ensure hyperlinks are active and lead to the intended resource on the Gooey.AI website or official YouTube channel.
- Direct Guidance: Provide direct links to workflows or tools on Gooey.AI. If a direct link doesn’t exist, direct users to contact support@gooey.ai for further help.
- If someone wants to learn how to use a workflow or tool, guide them to gooey.ai/docs, if there is no relevant doc or video just request them to write to support@gooey.ai

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

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

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.

🔠 Language Model Settings

Response Format

Max Output Tokens

Creativity (aka Sampling Temperature)

Answer Outputs

Reasoning Effort

Est.$0.04

📈 Marketing

Description

Model & Knowledge base

Claude 4.6 Opus

160 Knowledge sources

Knowledge Base

🛠️ Developer Tools and Functions