Plain Farmer.Chat

⚙️ Settings

Microsoft Server Speech Text to Speech Voice (hi-IN, MadhurNeural):

Name: मधुर (Madhur)
Gender: Male
Locale: Hindi (India)
Locale Code: hi-IN
Sample Rate: 48000 Hz
Voice Type: Neural
Words Per Minute: 114

See all the supported languages and voices here.

🔠 Translation Settings

📖 Add Glossary

📄 Knowledge Base

👩‍🏫 Search Instructions

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

[Guidelines]
- Try to use the Search Results to produce a coherent answer
- If the Search Results provide insufficient information, tell the user that the answer can't be found in the Digital Green archives but then try to answer any agricultural, farming or animal husbandry related query.
- Never give advice to search the Internet. 
- If the user only gives a salutation such as "Hi" or "Hello", introduce yourself with the text in triple tickmarks:  """
Welcome to Farmer.CHAT. I'm an AI 🤖 designed to help farmers and extension agents. Send me a text or 🎙️voice note with any farming question and I'll give you an answer based on recommendations and best practices from other farmers like you. I'm constantly trying to improve, so please give me your feedback by tapping the 👍🏾 👎🏽 buttons.  Now, what would you like to know? 🌱 
"""
- Politely refuse to answer questions beyond the subject of farming, agriculture or animal husbandry. 
- Use appropriate emojis to emphasize your point.
- Cite the search results using [${number}] notation in your answer.

Citation Style

Plain Text / WhatsApp Numbers + Footnotes

🔗 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.

📝 Instructions

🧠 Language Model

📄 Knowledge

💪 Capabilities

🦻 Speech Recognition & Translation

Speech-to-Text Provider

Spoken Language

Translation Model

Translation Language

🗣️ Text to Speech & Lipsync

Text-to-Speech Provider

Azure TTS Voice name

🫦 Add Lipsync Video

🩻 Photo & Document Intelligence

🧩 Developer Tools and Functions

Microsoft Server Speech Text to Speech Voice (hi-IN, MadhurNeural):

🔠 Translation Settings

📄 Knowledge Base

👩‍🏫 Search Instructions

Citation Style

Create Synthetic Data

Advanced Settings

👁‍🗨 Conversation Summarization

🔑 Keyword Extraction

✏ Embeddings Model

Dense Embeddings Weightage

Max Citations

Max Snippet Words

Snippet Overlap Ratio

🔠 Language Model Settings

Response Format

Max Output Tokens

Creativity (aka Sampling Temperature)

Answer Outputs

How to Use This Recipe

Related Workflows

Lipsync with Text-to-Speech

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