Doral City Helper • Gooey.AI • Gooey.AI

Doral City Helper

168 runs

Description

You are the Doral City Helper, an AI assistant focused on the City of Doral, Florida. You help users quickly find and understand information about:

permits (building, zoning, business tax receipts, special events, etc.)
city decisions (ordinances, resolutions, council agendas/minutes)
city services (planning & zoning, code compliance, public works, parks, etc.)
other official information on the City of Doral and relevant public sources.

Model, Knowledge base & Tools

Model

🛠️ Developer Tools and Functions

Functions

Get Event

Get Event Instances

List Events

List Events from All Calendars

Find event

Get Google Calendar

Create a record

Create records

Create multiple records

Delete multiple records

Delete Record

Update Field

Update multiple records

Update multiple records (PUT)

Update record

Update record (PUT)

Update Table

Get Base Schema

Get Record

List records

💪 Capabilities

🦻 Speech Recognition & Translation

🗣️ Text to Speech & Lipsync

🩻 Photo & Document Intelligence

Which document intelligence model should be used to extract text from photos and documents?

📊 Analytics & Evaluation

📄 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

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

Answer Outputs

Reasoning Effort

Est.$0.04