This workflow parses USAFacts's 2022 State of the Union in Numbers and attempts to make it queryable in a chat interface.
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Examples
API
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ChatGPT (openai)
Add documents or links to give your copilot a knowledge base. When asked a question, we'll search them to generate an answer with citations.
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⚙️ Settings
How should the LLM interpret the results from your knowledge base?
Numbers ( [1] [2] [3] ..)
🔗 Shorten Citation URLs
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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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 }}.
Text Embedding 3 Large (OpenAI)
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. 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). Default: 300
Your knowledge base documents are split into overlapping snippets. This settings adjusts how much those snippets overlap. In general you shouldn't need to adjust this. Default: 5
Avoid Repetition
How many answers should the copilot generate? Additional answer outputs increase the cost of each run.
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.
Give your copilot superpowers by giving it access to tools. Powered by Function calling.
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Run cost = 4 credits Breakdown: 1 (ChatGPT (openai)) + 3/run
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Show Raw Output
Assistant
According to the report by USAFacts.org, the US government spent $6.7 trillion in 2019.
UserHow much did the US government spend in 2019?
AssistantAccording to the report by USAFacts.org, the US government spent $6.7 trillion in 2019.
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🗑️ Clear
💁♀️ Sources
1. 00_USAFacts_2022_06-27_22_digital-compressed 1.pdf
2. 00_USAFacts_2022_06-27_22_digital-compressed 1.pdf
3. 00_USAFacts_2022_06-27_22_digital-compressed 1.pdf
on
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ℹ️ Details
Have you ever wanted to create a bot that you could talk to about anything? Ever wanted to create your own https://dara.network/RadBots or https://Farmer.CHAT? This is how.
This workflow takes a dialog LLM prompt describing your character, a collection of docs & links and optional an video clip of your bot’s face and voice settings.
We use all these to build a bot that anyone can speak to about anything and you can host directly in your own site or app, or simply connect to your Facebook, WhatsApp or Instagram page.
How It Works:
PS. This is the workflow that we used to create RadBots - a collection of Turing-test videobots, authored by leading international writers, singers and playwrights - and really inspired us to create Gooey.AI so that every person and organization could create their own fantastic characters, in any personality of their choosing. It's also the workflow that powers https://Farmer.CHAT and was demo'd at the UN General Assembly in April 2023 as a multi-lingual WhatsApp bot for Indian, Ethiopian and Kenyan farmers.
References
Final Prompt
Final Response 1
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