Farmer.Chat - with current weather data

Gooey.AI's Copilot is the best chatbot builder anywhere, combining your choice of LLMs (GPT4o, GPT4o-mini, Gemini, Claude3.5, Mixtral or LLaMA3), knowledge docs from any link or doc/PDF (with table support!), speech recognition, text-to-speech and Lipsync, editable synthetic data pipelines, built in 👍🏿 👎🏼 feedback, conversation analysis, WhatsApp, slack, Facebook and API integrations and much more.

In this example, we present Farmer.CHAT. It uses a collection of documents and transcripts of 100s of videos representing best practices to answer common questions from Indian farmers. 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 farmer's questions. Simply change the Instructions and Knowledge docs & tap Submit and then Save to make it your own.

To know more, head over to our COPILOT HELP GUIDE

2y ago

Instructions

Prompt Library

Language Model

Knowledge Base

Show as Links

💪 Capabilities

🦻 Speech Recognition & Translation

🗣️ Text to Speech & Lipsync

Text-to-Speech Provider

Voice name (Google TTS)

Please refer to the list of voice names here

🫦 Add Lipsync Video

🩻 Photo & Document Intelligence

📊 Analytics & Evaluation

🛠️ Developer Tools and Functions

Functions

Variables

platform

string

weather

object

⚙️ Settings

🗣️ Google Text-to-Speech Settings

Speaking rate

1.0 is the normal native speed of the speaker

Pitch

Increase/Decrease semitones from the original pitch

📄 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

Avoid Repetition

Response Format

Max Output Tokens

Creativity (aka Sampling Temperature)

Answer Outputs

Run cost = 4 credits

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

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🐞 Debug

How to Use This Recipe

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