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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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## Usage
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1. S3 Settings
2. environments settings
3. Execute `docker-compose up -d`
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### S3
1. Create a bucket.
2. Turn off the "Block all public access" setting for the bucket. ![image ](assets/block_public_access.png )
3. Add the following text to Bucket Policy.
```json
{
"Version": "2012-10-17",
"Statement": [
{
"Sid": "AllowPublicRead",
"Effect": "Allow",
"Principal": {
"AWS": "*"
},
"Action": "s3:GetObject",
"Resource": "arn:aws:s3:::{your-bucket-name}/*"
}
]
}
```
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### Environment
These environmental variables are essential, so please set them.
```
BOT_NAME: your custom bot name
OPENAI_API_KEY: openai api key
AWS_ACCESS_KEY_ID
AWS_SECRET_ACCESS_KEY
AWS_REGION
AWS_S3_BUCKET
```
These environment variables are necessary to use the following tools:
If you want to use it, set it up, and if you don't need it, you don't have to set it up.
```
SERPAPI_API_KEY: need to append google search tool
BING_SEARCH_URL, BING_SUBSCRIPTION_KEY: need to append bing search tool
```
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## Tools
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## TODO
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- [ ] memory saving
- [ ] session manage
- [ ] convert to alpaca
- [ ] requests.get refactoring
- [ ] prompt upgrade
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- [ ] give a tool to create tools
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- [ ] etc.
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## Reference
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Thanks to the following repositories.
- https://github.com/jerryjliu/llama_index
- https://github.com/hwchase17/langchain
- https://github.com/microsoft/visual-chatgpt