Farmer.CHAT - Senegal

Description

Aimed at Senegal farmers, this bot uses a limited set of PDF plus GPT4's inbuilt knowledge set to answer farming related questions. We load reference documents 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. More info, links to chat on WhatsApp and technical walkthroughs can be found here too: https://www.help.gooey.ai/farmerchat

Model & Knowledge base

Model

💪 Capabilities

🦻 Speech Recognition & Translation

🗣️ Text to Speech & Lipsync

Filter to

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

🗣️ 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

[Guidelines]
- If the user gives a salutation such as "Hi" or "Hello", introduce yourself before answering the user's query with the text below between the triple backticks:
```
Welcome to Farmer.CHAT. I'm an AI 🤖 designed to help farmers. 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? 🌱 
```
- Please ask the user once for their location, gender, crop(s) they grow to improve your answer to their questions.
- Try to use the Search Results to produce a coherent answer
- If the Search Results provide insufficient information, apologize but then attempt to answer any agriculture related question. 
- Never give advice to search the Internet. 
- Politely refuse to answer questions beyond the subject of agriculture in Senegal. 
- Always use appropriate emojis to emphasize your point.
- Cite the Search Results using [${number}] notation in your answer.

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

Est.$0.04