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Aimed at Kenyan farmers and Extension Agents, this bot uses a collection of documents to address common questions from coffee farmers. We load these documents into a vector database. With each question, we search the vector DB and then add the results to a GPT3 script below to create an answer to the farmer's questions. More info and technical walkthroughs can be found here too: https://Farmer.CHAT
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1mo ago
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Choose a language to translate incoming text & audio messages to English and responses back to your selected language. Useful for low-resource languages.
Filter by Language
🔠 Translate to & from English
Choose an AI model & language to translate incoming text & audio messages to English and responses back your selected language. Useful for low-resource languages.
Use Whisper Large v2 (openai) for speech translation from Detected Language to English
⚙️ Settings
See all the supported languages and voices here.
📖 Add Glossary
How should the LLM interpret the results from your knowledge base?
🔗 Shorten citation links
By default we embed your knowledge files & links and cache their contents for fast responses.
Always Check for Updates
♻️ Refresh Cache
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.
In general, you should not need to adjust these.
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.
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 }}.
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.
0
1
0.5
The maximum number of document search citations.
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).
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.
Avoid Repetition
The maximum number of tokens to generate in the completion. Increase to generate longer responses.
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.
How many answers should the copilot generate? Additional answer outputs increase the cost of each run.
Run cost = 4 credits
Breakdown: 1 (GPT-4o (openai)) + 3/run
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Karibu Mkulima.CHAT. Mimi ni AI 🤖 iliyoundwa kusaidia wakulima na mawakala wa ugani. Nitumie SMS au 🎙️notizo la sauti lenye swali lolote la ukulima na nitakupa jibu kulingana na mapendekezo na mbinu bora kutoka kwa wakulima wengine kama wewe. Ninajaribu kuboresha kila wakati, kwa hivyo tafadhali nipe maoni yako kwa kugonga vitufe 👍🏾 👎🏽. Sasa, ungependa kujua nini? 🌱
Wakati mzuri wa kupanda kahawa ni mwanzoni mwa msimu mkuu wa mvua. Hii ni wakati udongo umekuwa na unyevu hadi kina cha sentimita 60 (futi 2), ambayo husaidia miche kusitawi vizuri [*].
*. KENYA COFFEE SUSTAINABILITY MANUAL.pdf, page 29 https://gooey.ai/2/m6wd
UserWhen is the best time to plant coffee?
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