Merge branch 'master' into renovate/github.com-go-skynet-go-gpt4all-j.cpp-digest

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Ettore Di Giacinto 2023-05-01 23:18:34 +02:00 committed by GitHub
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@ -6,6 +6,7 @@ Here is a list of projects that can easily be integrated with the LocalAI backen
- [chatbot-ui](https://github.com/go-skynet/LocalAI/tree/master/examples/chatbot-ui/) (by [@mkellerman](https://github.com/mkellerman)) - [chatbot-ui](https://github.com/go-skynet/LocalAI/tree/master/examples/chatbot-ui/) (by [@mkellerman](https://github.com/mkellerman))
- [discord-bot](https://github.com/go-skynet/LocalAI/tree/master/examples/discord-bot/) (by [@mudler](https://github.com/mudler)) - [discord-bot](https://github.com/go-skynet/LocalAI/tree/master/examples/discord-bot/) (by [@mudler](https://github.com/mudler))
- [slack-bot](https://github.com/go-skynet/LocalAI/tree/master/examples/slack-bot/) (by [@mudler](https://github.com/mudler))
## Want to contribute? ## Want to contribute?

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examples/langchain/.gitignore vendored Normal file
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models/ggml-koala-13B-4bit-128g
models/ggml-gpt4all-j

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FROM node:latest
COPY ./langchainjs-localai-example /app
WORKDIR /app
RUN npm install
RUN npm run build
ENTRYPOINT [ "npm", "run", "start" ]

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# langchain
Example of using langchain in TypeScript, with the standard OpenAI llm module, and LocalAI.
Example for python langchain to follow at a later date
Set up to make it easy to modify the `index.mts` file to look like any langchain example file.
**Please Note** - This is a tech demo example at this time. ggml-gpt4all-j has pretty terrible results for most langchain applications with the settings used in this example.
## Setup
```bash
# Clone LocalAI
git clone https://github.com/go-skynet/LocalAI
cd LocalAI/examples/langchain
# (optional) - Edit the example code in typescript.
# vi ./langchainjs-localai-example/index.ts
# Download gpt4all-j to models/
wget https://gpt4all.io/models/ggml-gpt4all-j.bin -O models/ggml-gpt4all-j
# start with docker-compose
docker-compose up --build
```
## Copyright
Some of the example code in index.mts is adapted from the langchainjs project and is Copyright (c) Harrison Chase. Used under the terms of the MIT license, as is the remainder of this code.

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version: '3.6'
services:
api:
image: quay.io/go-skynet/local-ai:latest
build:
context: ../../
dockerfile: Dockerfile.dev
ports:
- 8080:8080
environment:
- DEBUG=true
- MODELS_PATH=/models
volumes:
- ./models:/models:cached
command: ["/usr/bin/local-ai" ]
langchainjs:
build:
context: .
dockerfile: JS.Dockerfile
environment:
- 'OPENAI_API_KEY=sk-XXXXXXXXXXXXXXXXXXXX'
- 'OPENAI_API_HOST=http://api:8080/v1'
- 'MODEL_NAME=gpt-3.5-turbo' #gpt-3.5-turbo' # ggml-gpt4all-j' # ggml-koala-13B-4bit-128g'

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node_modules/
dist/

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{
// Use IntelliSense to learn about possible attributes.
// Hover to view descriptions of existing attributes.
// For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
"version": "0.2.0",
"configurations": [
{
"type": "node",
"request": "launch",
"name": "Launch Program",
// "skipFiles": [
// "<node_internals>/**"
// ],
"program": "${workspaceFolder}\\dist\\index.mjs",
"outFiles": [
"${workspaceFolder}/**/*.js"
]
}
]
}

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{
"name": "langchainjs-localai-example",
"version": "0.1.0",
"description": "Trivial Example of using langchain + the OpenAI API + LocalAI together",
"main": "index.mjs",
"scripts": {
"build": "tsc --build",
"clean": "tsc --build --clean",
"start": "node --trace-warnings dist/index.mjs"
},
"author": "dave@gray101.com",
"license": "MIT",
"devDependencies": {
"@types/node": "^18.16.3",
"typescript": "^5.0.4"
},
"dependencies": {
"langchain": "^0.0.67",
"typeorm": "^0.3.15"
}
}

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import { OpenAIChat } from "langchain/llms/openai";
import { loadQAStuffChain } from "langchain/chains";
import { Document } from "langchain/document";
import { initializeAgentExecutorWithOptions } from "langchain/agents";
import {Calculator} from "langchain/tools/calculator";
const pathToLocalAi = process.env['OPENAI_API_HOST'] || 'http://api:8080/v1';
const fakeApiKey = process.env['OPENAI_API_KEY'] || '-';
const modelName = process.env['MODEL_NAME'] || 'gpt-3.5-turbo';
function getModel(): OpenAIChat {
return new OpenAIChat({
prefixMessages: [
{
role: "system",
content: "You are a helpful assistant that answers in pirate language",
},
],
modelName: modelName,
maxTokens: 50,
openAIApiKey: fakeApiKey,
maxRetries: 2
}, {
basePath: pathToLocalAi,
apiKey: fakeApiKey,
});
}
// Minimal example.
export const run = async () => {
const model = getModel();
console.log(`about to model.call at ${new Date().toUTCString()}`);
const res = await model.call(
"What would be a good company name a company that makes colorful socks?"
);
console.log(`${new Date().toUTCString()}`);
console.log({ res });
};
await run();
// This example uses the `StuffDocumentsChain`
export const run2 = async () => {
const model = getModel();
const chainA = loadQAStuffChain(model);
const docs = [
new Document({ pageContent: "Harrison went to Harvard." }),
new Document({ pageContent: "Ankush went to Princeton." }),
];
const resA = await chainA.call({
input_documents: docs,
question: "Where did Harrison go to college?",
});
console.log({ resA });
};
await run2();
// Quickly thrown together example of using tools + agents.
// This seems like it should work, but it doesn't yet.
export const temporarilyBrokenToolTest = async () => {
const model = getModel();
const executor = await initializeAgentExecutorWithOptions([new Calculator(true)], model, {
agentType: "zero-shot-react-description",
});
console.log("Loaded agent.");
const input = `What is the value of (500 *2) + 350 - 13?`;
console.log(`Executing with input "${input}"...`);
const result = await executor.call({ input });
console.log(`Got output ${result.output}`);
}
await temporarilyBrokenToolTest();

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{
"compilerOptions": {
"target": "es2022",
"lib": ["ES2022", "DOM"],
"module": "ES2022",
"moduleResolution": "node",
"strict": true,
"esModuleInterop": true,
"allowSyntheticDefaultImports": true,
"isolatedModules": true,
"outDir": "./dist"
},
"include": ["src", "test"],
"exclude": ["node_modules", "dist"]
}

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{{.Input}}

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name: gpt-3.5-turbo
parameters:
model: ggml-gpt4all-j # ggml-koala-13B-4bit-128g
top_k: 80
temperature: 0.2
top_p: 0.7
context_size: 1024
threads: 4
stopwords:
- "HUMAN:"
- "GPT:"
roles:
user: " "
system: " "
template:
completion: completion
chat: completion # gpt4all

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The prompt below is a question to answer, a task to complete, or a conversation to respond to; decide which and write an appropriate response.
### Prompt:
{{.Input}}
### Response:

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SLACK_APP_TOKEN=xapp-1-...
SLACK_BOT_TOKEN=xoxb-...
OPENAI_API_KEY=sk-...
OPENAI_API_BASE=http://api:8080
OPENAI_MODEL=gpt-3.5-turbo
OPENAI_TIMEOUT_SECONDS=60
#OPENAI_SYSTEM_TEXT="You proofread text. When you receive a message, you will check
#for mistakes and make suggestion to improve the language of the given text"
USE_SLACK_LANGUAGE=true
SLACK_APP_LOG_LEVEL=INFO
TRANSLATE_MARKDOWN=true

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# Slack bot
Slackbot using: https://github.com/seratch/ChatGPT-in-Slack
## Setup
```bash
# Clone LocalAI
git clone https://github.com/go-skynet/LocalAI
cd LocalAI/examples/slack-bot
git clone https://github.com/seratch/ChatGPT-in-Slack
# (optional) Checkout a specific LocalAI tag
# git checkout -b build <TAG>
# Download gpt4all-j to models/
wget https://gpt4all.io/models/ggml-gpt4all-j.bin -O models/ggml-gpt4all-j
# Set the discord bot options (see: https://github.com/seratch/ChatGPT-in-Slack)
cp -rfv .env.example .env
vim .env
# start with docker-compose
docker-compose up -d --build
```

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version: '3.6'
services:
api:
image: quay.io/go-skynet/local-ai:latest
build:
context: ../../
dockerfile: Dockerfile.dev
ports:
- 8080:8080
environment:
- DEBUG=true
- MODELS_PATH=/models
volumes:
- ./models:/models:cached
command: ["/usr/bin/local-ai" ]
bot:
build:
context: ./ChatGPT-in-Slack
dockerfile: Dockerfile
env_file:
- .env

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examples/slack-bot/models Symbolic link
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../chatbot-ui/models