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README.md
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README.md
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# EVAL
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Everything you can do with a computer through EVAL is executable like the eval method. It can understand and generate data formats for text, image, dataframe, audio (TODO), video (TODO). It must be run inside a container that is only available to it, and it can build your container environment directly through Terminal Tools.
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> **EVAL(Elastic Versatile Agent with Langchain) will execute all your requests. Like the eval method!**
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You don't have to think about how. If you tell them the results you want, they'll search, code, run, and test the Internet themselves, and they'll return the final results.
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### EVAL's FEATURE
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1. It can **understand** and **generate** data formats for text, image, dataframe, audio (TODO), video (TODO).
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2. It can **create tools** that it can use by writing and modifying code.
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3. It can **evolve** itself by executing and testing its code.
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### BUILT-IN TOOLS
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1. Search
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- Google, Bing, Wikipedia
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- Custom DB Search (Currently using Corca's Wine Data in https://www.workershop.kr/en)
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- Use GPT index to quickly find the information you need in a document and use that information to answer
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- Requests.get (Get information from anywhere you want)
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2. Terminal
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3. Python REPL
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4. Image Understanding, Generation, Editing
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- Image Understanding
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- Image Understanding: blip-image-captioning
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- Visual Question&Answering: blip-vqa
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- Image Generation: Stable Diffusion 1.5
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- Image Editing
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- Replace or remove an object: Stable Diffusion Inpainting
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- Change Image's style: InstructPix2Pix
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Thanks to [LangChain](https://github.com/hwchase17/langchain), [Visual ChatGPT](https://github.com/microsoft/visual-chatgpt), [llama index](https://github.com/jerryjliu/llama_index).
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### CUSTOM TOOLS
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We also don't know what tools EVAL will create. Every day, It will create the right tools to execute your request.
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---
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## Usage
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1. S3 Settings
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2. environments settings
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3. Execute `docker-compose up -d`
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3. `docker-compose up -d`
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### S3
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BING_SEARCH_URL, BING_SUBSCRIPTION_KEY: need to append bing search tool
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```
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## Tools
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## TODO
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- [ ] memory saving
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- [ ] session manage
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- [ ] convert to alpaca
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- [ ] requests.get refactoring
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- [ ] prompt upgrade
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- [ ] give a tool to create tools
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- [ ] etc.
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Thanks to the following repositories.
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- https://github.com/jerryjliu/llama_index
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- https://github.com/hwchase17/langchain
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- https://github.com/microsoft/visual-chatgpt
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- https://github.com/jerryjliu/llama_index
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from env import settings
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EVAL_PREFIX = """{bot_name} is designed to be able to assist with a wide range of text, visual related tasks, data analysis related tasks, auditory related tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics.
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{bot_name} is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.
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{bot_name} is able to process and understand large amounts of various types of files(image, audio, video, dataframe, etc.). As a language model, {bot_name} can not directly read various types of files(text, image, audio, video, dataframe, etc.), but it has a list of tools to finish different visual tasks.
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```
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**Option #2:**
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Use this if you want to respond directly to the human. Markdown code snippet formatted in the following schema:
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Use this if you want to respond directly to the human.
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You should replace sensitive data or encrypted data with "d1dy0uth1nk7hat1t1s7haAAat3aSy?" in action_input.
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Markdown code snippet formatted in the following schema:
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```json
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{{{{
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"action": "Final Answer",
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"action_input": string \\ You should put what you want to return to use here
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"action_input": string \\ You should put what you want to return to use here.
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}}}}
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```"""
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@tool(
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name="Terminal",
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description="Executes commands in a terminal."
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"You can install packages with pip, apt, etc."
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"Input should be valid commands, "
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"and the output will be any output from running that command.",
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)
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