Remove embedchain js (#1408)
This commit is contained in:
Taranjeet Singh
@@ -1,2 +0,0 @@
|
||||
node_modules
|
||||
dist
|
||||
@@ -1,56 +0,0 @@
|
||||
{
|
||||
// Configuration for JavaScript files
|
||||
"extends": [
|
||||
"airbnb-base",
|
||||
"plugin:prettier/recommended"
|
||||
],
|
||||
"rules": {
|
||||
"prettier/prettier": [
|
||||
"error",
|
||||
{
|
||||
"singleQuote": true,
|
||||
"endOfLine": "auto"
|
||||
}
|
||||
]
|
||||
},
|
||||
"overrides": [
|
||||
// Configuration for TypeScript files
|
||||
{
|
||||
"files": ["**/*.ts", "**/__tests__/*.test.ts"],
|
||||
"plugins": [
|
||||
"@typescript-eslint",
|
||||
"unused-imports",
|
||||
"simple-import-sort"
|
||||
],
|
||||
"extends": [
|
||||
"airbnb-typescript",
|
||||
"plugin:prettier/recommended"
|
||||
],
|
||||
"parserOptions": {
|
||||
"project": "./tsconfig.json"
|
||||
},
|
||||
"rules": {
|
||||
"prettier/prettier": [
|
||||
"error",
|
||||
{
|
||||
"singleQuote": true,
|
||||
"endOfLine": "auto"
|
||||
}
|
||||
],
|
||||
"@typescript-eslint/comma-dangle": "off", // Avoid conflict rule between Eslint and Prettier
|
||||
"@typescript-eslint/consistent-type-imports": "error", // Ensure `import type` is used when it's necessary
|
||||
"import/prefer-default-export": "off", // Named export is easier to refactor automatically
|
||||
"simple-import-sort/imports": "error", // Import configuration for `eslint-plugin-simple-import-sort`
|
||||
"simple-import-sort/exports": "error", // Export configuration for `eslint-plugin-simple-import-sort`
|
||||
"@typescript-eslint/no-unused-vars": "off",
|
||||
"react/jsx-filename-extension": "off", // Gives error
|
||||
"unused-imports/no-unused-imports": "error",
|
||||
"unused-imports/no-unused-vars": [
|
||||
"error",
|
||||
{ "argsIgnorePattern": "^_" }
|
||||
]
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
47
embedchain-js/.github/workflows/npm-publish.yml
vendored
47
embedchain-js/.github/workflows/npm-publish.yml
vendored
@@ -1,47 +0,0 @@
|
||||
name: Node.js Package
|
||||
|
||||
on:
|
||||
release:
|
||||
types: [created]
|
||||
|
||||
jobs:
|
||||
build:
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- uses: actions/checkout@v3
|
||||
- uses: actions/setup-node@v3
|
||||
with:
|
||||
node-version: 16
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||||
- run: npm ci
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||||
- run: npm test
|
||||
- run: npm run build
|
||||
- uses: actions/upload-artifact@v3
|
||||
with:
|
||||
name: dist
|
||||
path: dist
|
||||
- uses: actions/upload-artifact@v3
|
||||
with:
|
||||
name: types
|
||||
path: types
|
||||
|
||||
publish-npm:
|
||||
needs: build
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- uses: actions/checkout@v3
|
||||
- uses: actions/setup-node@v3
|
||||
with:
|
||||
node-version: 16
|
||||
registry-url: https://registry.npmjs.org/
|
||||
- uses: actions/download-artifact@v3
|
||||
with:
|
||||
name: dist
|
||||
path: dist
|
||||
- uses: actions/download-artifact@v3
|
||||
with:
|
||||
name: types
|
||||
path: types
|
||||
- run: npm ci
|
||||
- run: npm publish
|
||||
env:
|
||||
NODE_AUTH_TOKEN: ${{secrets.npm_token}}
|
||||
138
embedchain-js/.gitignore
vendored
138
embedchain-js/.gitignore
vendored
@@ -1,138 +0,0 @@
|
||||
# Logs
|
||||
logs
|
||||
*.log
|
||||
npm-debug.log*
|
||||
yarn-debug.log*
|
||||
yarn-error.log*
|
||||
lerna-debug.log*
|
||||
.pnpm-debug.log*
|
||||
|
||||
# Diagnostic reports (https://nodejs.org/api/report.html)
|
||||
report.[0-9]*.[0-9]*.[0-9]*.[0-9]*.json
|
||||
|
||||
# Runtime data
|
||||
pids
|
||||
*.pid
|
||||
*.seed
|
||||
*.pid.lock
|
||||
|
||||
# Directory for instrumented libs generated by jscoverage/JSCover
|
||||
lib-cov
|
||||
|
||||
# Coverage directory used by tools like istanbul
|
||||
coverage
|
||||
*.lcov
|
||||
|
||||
# nyc test coverage
|
||||
.nyc_output
|
||||
|
||||
# Grunt intermediate storage (https://gruntjs.com/creating-plugins#storing-task-files)
|
||||
.grunt
|
||||
|
||||
# Bower dependency directory (https://bower.io/)
|
||||
bower_components
|
||||
|
||||
# node-waf configuration
|
||||
.lock-wscript
|
||||
|
||||
# Compiled binary addons (https://nodejs.org/api/addons.html)
|
||||
build/Release
|
||||
|
||||
# Dependency directories
|
||||
node_modules/
|
||||
jspm_packages/
|
||||
|
||||
# Snowpack dependency directory (https://snowpack.dev/)
|
||||
web_modules/
|
||||
|
||||
# TypeScript cache
|
||||
*.tsbuildinfo
|
||||
|
||||
# Optional npm cache directory
|
||||
.npm
|
||||
|
||||
# Optional eslint cache
|
||||
.eslintcache
|
||||
|
||||
# Optional stylelint cache
|
||||
.stylelintcache
|
||||
|
||||
# Microbundle cache
|
||||
.rpt2_cache/
|
||||
.rts2_cache_cjs/
|
||||
.rts2_cache_es/
|
||||
.rts2_cache_umd/
|
||||
|
||||
# Optional REPL history
|
||||
.node_repl_history
|
||||
|
||||
# Output of 'npm pack'
|
||||
*.tgz
|
||||
|
||||
# Yarn Integrity file
|
||||
.yarn-integrity
|
||||
|
||||
# dotenv environment variable files
|
||||
.env
|
||||
.env.development.local
|
||||
.env.test.local
|
||||
.env.production.local
|
||||
.env.local
|
||||
|
||||
# parcel-bundler cache (https://parceljs.org/)
|
||||
.cache
|
||||
.parcel-cache
|
||||
|
||||
# Next.js build output
|
||||
.next
|
||||
out
|
||||
|
||||
# Nuxt.js build / generate output
|
||||
.nuxt
|
||||
dist
|
||||
|
||||
# Gatsby files
|
||||
.cache/
|
||||
# Comment in the public line in if your project uses Gatsby and not Next.js
|
||||
# https://nextjs.org/blog/next-9-1#public-directory-support
|
||||
# public
|
||||
|
||||
# vuepress build output
|
||||
.vuepress/dist
|
||||
|
||||
# vuepress v2.x temp and cache directory
|
||||
.temp
|
||||
.cache
|
||||
|
||||
# Docusaurus cache and generated files
|
||||
.docusaurus
|
||||
|
||||
# Serverless directories
|
||||
.serverless/
|
||||
|
||||
# FuseBox cache
|
||||
.fusebox/
|
||||
|
||||
# DynamoDB Local files
|
||||
.dynamodb/
|
||||
|
||||
# TernJS port file
|
||||
.tern-port
|
||||
|
||||
# Stores VSCode versions used for testing VSCode extensions
|
||||
.vscode-test
|
||||
|
||||
# yarn v2
|
||||
.yarn/cache
|
||||
.yarn/unplugged
|
||||
.yarn/build-state.yml
|
||||
.yarn/install-state.gz
|
||||
.pnp.*
|
||||
|
||||
.ideas.md
|
||||
.todos.md
|
||||
|
||||
# Custom
|
||||
dist
|
||||
types
|
||||
build
|
||||
@@ -1,4 +0,0 @@
|
||||
#!/bin/sh
|
||||
. "$(dirname "$0")/_/husky.sh"
|
||||
|
||||
npx --no -- commitlint --edit $1
|
||||
@@ -1,5 +0,0 @@
|
||||
#!/bin/sh
|
||||
. "$(dirname "$0")/_/husky.sh"
|
||||
|
||||
# Disable concurent to run `check-types` after ESLint in lint-staged
|
||||
npx lint-staged --concurrent false
|
||||
@@ -1,8 +0,0 @@
|
||||
cff-version: 1.2.0
|
||||
message: "If you use this software, please cite it as below."
|
||||
authors:
|
||||
- family-names: "Singh"
|
||||
given-names: "Taranjeet"
|
||||
title: "Embedchain"
|
||||
date-released: 2023-06-25
|
||||
url: "https://github.com/embedchain/embedchainjs"
|
||||
@@ -1,201 +0,0 @@
|
||||
Apache License
|
||||
Version 2.0, January 2004
|
||||
http://www.apache.org/licenses/
|
||||
|
||||
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@@ -1,254 +0,0 @@
|
||||
# embedchainjs
|
||||
|
||||
[](https://discord.gg/CUU9FPhRNt)
|
||||
[](https://twitter.com/embedchain)
|
||||
[](https://embedchain.substack.com/)
|
||||
|
||||
embedchain is a framework to easily create LLM powered bots over any dataset. embedchainjs is Javascript version of embedchain. If you want a python version, check out [embedchain-python](https://github.com/embedchain/embedchain)
|
||||
|
||||
# 🤝 Let's Talk Embedchain!
|
||||
|
||||
Schedule a [Feedback Session](https://cal.com/taranjeetio/ec) with Taranjeet, the founder, to discuss any issues, provide feedback, or explore improvements.
|
||||
|
||||
# How it works
|
||||
|
||||
It abstracts the entire process of loading dataset, chunking it, creating embeddings and then storing in vector database.
|
||||
|
||||
You can add a single or multiple dataset using `.add` and `.addLocal` function and then use `.query` function to find an answer from the added datasets.
|
||||
|
||||
If you want to create a Naval Ravikant bot which has 2 of his blog posts, as well as a question and answer pair you supply, all you need to do is add the links to the blog posts and the QnA pair and embedchain will create a bot for you.
|
||||
|
||||
```javascript
|
||||
const dotenv = require("dotenv");
|
||||
dotenv.config();
|
||||
const { App } = require("embedchain");
|
||||
|
||||
//Run the app commands inside an async function only
|
||||
async function testApp() {
|
||||
const navalChatBot = await App();
|
||||
|
||||
// Embed Online Resources
|
||||
await navalChatBot.add("web_page", "https://nav.al/feedback");
|
||||
await navalChatBot.add("web_page", "https://nav.al/agi");
|
||||
await navalChatBot.add(
|
||||
"pdf_file",
|
||||
"https://navalmanack.s3.amazonaws.com/Eric-Jorgenson_The-Almanack-of-Naval-Ravikant_Final.pdf"
|
||||
);
|
||||
|
||||
// Embed Local Resources
|
||||
await navalChatBot.addLocal("qna_pair", [
|
||||
"Who is Naval Ravikant?",
|
||||
"Naval Ravikant is an Indian-American entrepreneur and investor.",
|
||||
]);
|
||||
|
||||
const result = await navalChatBot.query(
|
||||
"What unique capacity does Naval argue humans possess when it comes to understanding explanations or concepts?"
|
||||
);
|
||||
console.log(result);
|
||||
// answer: Naval argues that humans possess the unique capacity to understand explanations or concepts to the maximum extent possible in this physical reality.
|
||||
}
|
||||
|
||||
testApp();
|
||||
```
|
||||
|
||||
# Getting Started
|
||||
|
||||
## Installation
|
||||
|
||||
- First make sure that you have the package installed. If not, then install it using `npm`
|
||||
|
||||
```bash
|
||||
npm install embedchain && npm install -S openai@^3.3.0
|
||||
```
|
||||
|
||||
- Currently, it is only compatible with openai 3.X, not the latest version 4.X. Please make sure to use the right version, otherwise you will see the `ChromaDB` error `TypeError: OpenAIApi.Configuration is not a constructor`
|
||||
|
||||
- Make sure that dotenv package is installed and your `OPENAI_API_KEY` in a file called `.env` in the root folder. You can install dotenv by
|
||||
|
||||
```js
|
||||
npm install dotenv
|
||||
```
|
||||
|
||||
- Download and install Docker on your device by visiting [this link](https://www.docker.com/). You will need this to run Chroma vector database on your machine.
|
||||
|
||||
- Run the following commands to setup Chroma container in Docker
|
||||
|
||||
```bash
|
||||
git clone https://github.com/chroma-core/chroma.git
|
||||
cd chroma
|
||||
docker-compose up -d --build
|
||||
```
|
||||
|
||||
- Once Chroma container has been set up, run it inside Docker
|
||||
|
||||
## Usage
|
||||
|
||||
- We use OpenAI's embedding model to create embeddings for chunks and ChatGPT API as LLM to get answer given the relevant docs. Make sure that you have an OpenAI account and an API key. If you have dont have an API key, you can create one by visiting [this link](https://platform.openai.com/account/api-keys).
|
||||
|
||||
- Once you have the API key, set it in an environment variable called `OPENAI_API_KEY`
|
||||
|
||||
```js
|
||||
// Set this inside your .env file
|
||||
OPENAI_API_KEY = "sk-xxxx";
|
||||
```
|
||||
|
||||
- Load the environment variables inside your .js file using the following commands
|
||||
|
||||
```js
|
||||
const dotenv = require("dotenv");
|
||||
dotenv.config();
|
||||
```
|
||||
|
||||
- Next import the `App` class from embedchain and use `.add` function to add any dataset.
|
||||
- Now your app is created. You can use `.query` function to get the answer for any query.
|
||||
|
||||
```js
|
||||
const dotenv = require("dotenv");
|
||||
dotenv.config();
|
||||
const { App } = require("embedchain");
|
||||
|
||||
async function testApp() {
|
||||
const navalChatBot = await App();
|
||||
|
||||
// Embed Online Resources
|
||||
await navalChatBot.add("web_page", "https://nav.al/feedback");
|
||||
await navalChatBot.add("web_page", "https://nav.al/agi");
|
||||
await navalChatBot.add(
|
||||
"pdf_file",
|
||||
"https://navalmanack.s3.amazonaws.com/Eric-Jorgenson_The-Almanack-of-Naval-Ravikant_Final.pdf"
|
||||
);
|
||||
|
||||
// Embed Local Resources
|
||||
await navalChatBot.addLocal("qna_pair", [
|
||||
"Who is Naval Ravikant?",
|
||||
"Naval Ravikant is an Indian-American entrepreneur and investor.",
|
||||
]);
|
||||
|
||||
const result = await navalChatBot.query(
|
||||
"What unique capacity does Naval argue humans possess when it comes to understanding explanations or concepts?"
|
||||
);
|
||||
console.log(result);
|
||||
// answer: Naval argues that humans possess the unique capacity to understand explanations or concepts to the maximum extent possible in this physical reality.
|
||||
}
|
||||
|
||||
testApp();
|
||||
```
|
||||
|
||||
- If there is any other app instance in your script or app, you can change the import as
|
||||
|
||||
```javascript
|
||||
const { App: EmbedChainApp } = require("embedchain");
|
||||
|
||||
// or
|
||||
|
||||
const { App: ECApp } = require("embedchain");
|
||||
```
|
||||
|
||||
## Format supported
|
||||
|
||||
We support the following formats:
|
||||
|
||||
### PDF File
|
||||
|
||||
To add any pdf file, use the data_type as `pdf_file`. Eg:
|
||||
|
||||
```javascript
|
||||
await app.add("pdf_file", "a_valid_url_where_pdf_file_can_be_accessed");
|
||||
```
|
||||
|
||||
### Web Page
|
||||
|
||||
To add any web page, use the data_type as `web_page`. Eg:
|
||||
|
||||
```javascript
|
||||
await app.add("web_page", "a_valid_web_page_url");
|
||||
```
|
||||
|
||||
### QnA Pair
|
||||
|
||||
To supply your own QnA pair, use the data_type as `qna_pair` and enter a tuple. Eg:
|
||||
|
||||
```javascript
|
||||
await app.addLocal("qna_pair", ["Question", "Answer"]);
|
||||
```
|
||||
|
||||
### More Formats coming soon
|
||||
|
||||
- If you want to add any other format, please create an [issue](https://github.com/embedchain/embedchainjs/issues) and we will add it to the list of supported formats.
|
||||
|
||||
## Testing
|
||||
|
||||
Before you consume valuable tokens, you should make sure that the embedding you have done works and that it's receiving the correct document from the database.
|
||||
|
||||
For this you can use the `dryRun` method.
|
||||
|
||||
Following the example above, add this to your script:
|
||||
|
||||
```js
|
||||
let result = await naval_chat_bot.dryRun("What unique capacity does Naval argue humans possess when it comes to understanding explanations or concepts?");console.log(result);
|
||||
|
||||
'''
|
||||
Use the following pieces of context to answer the query at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.
|
||||
terms of the unseen. And I think that’s critical. That is what humans do uniquely that no other creature, no other computer, no other intelligence—biological or artificial—that we have ever encountered does. And not only do we do it uniquely, but if we were to meet an alien species that also had the power to generate these good explanations, there is no explanation that they could generate that we could not understand. We are maximally capable of understanding. There is no concept out there that is possible in this physical reality that a human being, given sufficient time and resources and
|
||||
Query: What unique capacity does Naval argue humans possess when it comes to understanding explanations or concepts?
|
||||
Helpful Answer:
|
||||
'''
|
||||
```
|
||||
|
||||
_The embedding is confirmed to work as expected. It returns the right document, even if the question is asked slightly different. No prompt tokens have been consumed._
|
||||
|
||||
**The dry run will still consume tokens to embed your query, but it is only ~1/15 of the prompt.**
|
||||
|
||||
# How does it work?
|
||||
|
||||
Creating a chat bot over any dataset needs the following steps to happen
|
||||
|
||||
- load the data
|
||||
- create meaningful chunks
|
||||
- create embeddings for each chunk
|
||||
- store the chunks in vector database
|
||||
|
||||
Whenever a user asks any query, following process happens to find the answer for the query
|
||||
|
||||
- create the embedding for query
|
||||
- find similar documents for this query from vector database
|
||||
- pass similar documents as context to LLM to get the final answer.
|
||||
|
||||
The process of loading the dataset and then querying involves multiple steps and each steps has nuances of it is own.
|
||||
|
||||
- How should I chunk the data? What is a meaningful chunk size?
|
||||
- How should I create embeddings for each chunk? Which embedding model should I use?
|
||||
- How should I store the chunks in vector database? Which vector database should I use?
|
||||
- Should I store meta data along with the embeddings?
|
||||
- How should I find similar documents for a query? Which ranking model should I use?
|
||||
|
||||
These questions may be trivial for some but for a lot of us, it needs research, experimentation and time to find out the accurate answers.
|
||||
|
||||
embedchain is a framework which takes care of all these nuances and provides a simple interface to create bots over any dataset.
|
||||
|
||||
In the first release, we are making it easier for anyone to get a chatbot over any dataset up and running in less than a minute. All you need to do is create an app instance, add the data sets using `.add` function and then use `.query` function to get the relevant answer.
|
||||
|
||||
# Team
|
||||
|
||||
## Author
|
||||
|
||||
- Taranjeet Singh ([@taranjeetio](https://twitter.com/taranjeetio))
|
||||
|
||||
## Maintainer
|
||||
|
||||
- [cachho](https://github.com/cachho)
|
||||
- [sahilyadav902](https://github.com/sahilyadav902)
|
||||
|
||||
## Citation
|
||||
|
||||
If you utilize this repository, please consider citing it with:
|
||||
```
|
||||
@misc{embedchain,
|
||||
author = {Taranjeet Singh},
|
||||
title = {Embechain: Framework to easily create LLM powered bots over any dataset},
|
||||
year = {2023},
|
||||
publisher = {GitHub},
|
||||
journal = {GitHub repository},
|
||||
howpublished = {\url{https://github.com/embedchain/embedchainjs}},
|
||||
}
|
||||
```
|
||||
@@ -1 +0,0 @@
|
||||
module.exports = { extends: ['@commitlint/config-conventional'] };
|
||||
@@ -1,66 +0,0 @@
|
||||
import { EmbedChainApp } from '../embedchain';
|
||||
|
||||
const mockAdd = jest.fn();
|
||||
const mockAddLocal = jest.fn();
|
||||
const mockQuery = jest.fn();
|
||||
|
||||
jest.mock('../embedchain', () => {
|
||||
return {
|
||||
EmbedChainApp: jest.fn().mockImplementation(() => {
|
||||
return {
|
||||
add: mockAdd,
|
||||
addLocal: mockAddLocal,
|
||||
query: mockQuery,
|
||||
};
|
||||
}),
|
||||
};
|
||||
});
|
||||
|
||||
describe('Test App', () => {
|
||||
beforeEach(() => {
|
||||
jest.clearAllMocks();
|
||||
});
|
||||
|
||||
it('tests the App', async () => {
|
||||
mockQuery.mockResolvedValue(
|
||||
'Naval argues that humans possess the unique capacity to understand explanations or concepts to the maximum extent possible in this physical reality.'
|
||||
);
|
||||
|
||||
const navalChatBot = await new EmbedChainApp(undefined, false);
|
||||
|
||||
// Embed Online Resources
|
||||
await navalChatBot.add('web_page', 'https://nav.al/feedback');
|
||||
await navalChatBot.add('web_page', 'https://nav.al/agi');
|
||||
await navalChatBot.add(
|
||||
'pdf_file',
|
||||
'https://navalmanack.s3.amazonaws.com/Eric-Jorgenson_The-Almanack-of-Naval-Ravikant_Final.pdf'
|
||||
);
|
||||
|
||||
// Embed Local Resources
|
||||
await navalChatBot.addLocal('qna_pair', [
|
||||
'Who is Naval Ravikant?',
|
||||
'Naval Ravikant is an Indian-American entrepreneur and investor.',
|
||||
]);
|
||||
|
||||
const result = await navalChatBot.query(
|
||||
'What unique capacity does Naval argue humans possess when it comes to understanding explanations or concepts?'
|
||||
);
|
||||
|
||||
expect(mockAdd).toHaveBeenCalledWith('web_page', 'https://nav.al/feedback');
|
||||
expect(mockAdd).toHaveBeenCalledWith('web_page', 'https://nav.al/agi');
|
||||
expect(mockAdd).toHaveBeenCalledWith(
|
||||
'pdf_file',
|
||||
'https://navalmanack.s3.amazonaws.com/Eric-Jorgenson_The-Almanack-of-Naval-Ravikant_Final.pdf'
|
||||
);
|
||||
expect(mockAddLocal).toHaveBeenCalledWith('qna_pair', [
|
||||
'Who is Naval Ravikant?',
|
||||
'Naval Ravikant is an Indian-American entrepreneur and investor.',
|
||||
]);
|
||||
expect(mockQuery).toHaveBeenCalledWith(
|
||||
'What unique capacity does Naval argue humans possess when it comes to understanding explanations or concepts?'
|
||||
);
|
||||
expect(result).toBe(
|
||||
'Naval argues that humans possess the unique capacity to understand explanations or concepts to the maximum extent possible in this physical reality.'
|
||||
);
|
||||
});
|
||||
});
|
||||
@@ -1,44 +0,0 @@
|
||||
import { createHash } from 'crypto';
|
||||
import type { RecursiveCharacterTextSplitter } from 'langchain/text_splitter';
|
||||
|
||||
import type { BaseLoader } from '../loaders';
|
||||
import type { Input, LoaderResult } from '../models';
|
||||
import type { ChunkResult } from '../models/ChunkResult';
|
||||
|
||||
class BaseChunker {
|
||||
textSplitter: RecursiveCharacterTextSplitter;
|
||||
|
||||
constructor(textSplitter: RecursiveCharacterTextSplitter) {
|
||||
this.textSplitter = textSplitter;
|
||||
}
|
||||
|
||||
async createChunks(loader: BaseLoader, url: Input): Promise<ChunkResult> {
|
||||
const documents: ChunkResult['documents'] = [];
|
||||
const ids: ChunkResult['ids'] = [];
|
||||
const datas: LoaderResult = await loader.loadData(url);
|
||||
const metadatas: ChunkResult['metadatas'] = [];
|
||||
|
||||
const dataPromises = datas.map(async (data) => {
|
||||
const { content, metaData } = data;
|
||||
const chunks: string[] = await this.textSplitter.splitText(content);
|
||||
chunks.forEach((chunk) => {
|
||||
const chunkId = createHash('sha256')
|
||||
.update(chunk + metaData.url)
|
||||
.digest('hex');
|
||||
ids.push(chunkId);
|
||||
documents.push(chunk);
|
||||
metadatas.push(metaData);
|
||||
});
|
||||
});
|
||||
|
||||
await Promise.all(dataPromises);
|
||||
|
||||
return {
|
||||
documents,
|
||||
ids,
|
||||
metadatas,
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
export { BaseChunker };
|
||||
@@ -1,26 +0,0 @@
|
||||
import { RecursiveCharacterTextSplitter } from 'langchain/text_splitter';
|
||||
|
||||
import { BaseChunker } from './BaseChunker';
|
||||
|
||||
interface TextSplitterChunkParams {
|
||||
chunkSize: number;
|
||||
chunkOverlap: number;
|
||||
keepSeparator: boolean;
|
||||
}
|
||||
|
||||
const TEXT_SPLITTER_CHUNK_PARAMS: TextSplitterChunkParams = {
|
||||
chunkSize: 1000,
|
||||
chunkOverlap: 0,
|
||||
keepSeparator: false,
|
||||
};
|
||||
|
||||
class PdfFileChunker extends BaseChunker {
|
||||
constructor() {
|
||||
const textSplitter = new RecursiveCharacterTextSplitter(
|
||||
TEXT_SPLITTER_CHUNK_PARAMS
|
||||
);
|
||||
super(textSplitter);
|
||||
}
|
||||
}
|
||||
|
||||
export { PdfFileChunker };
|
||||
@@ -1,26 +0,0 @@
|
||||
import { RecursiveCharacterTextSplitter } from 'langchain/text_splitter';
|
||||
|
||||
import { BaseChunker } from './BaseChunker';
|
||||
|
||||
interface TextSplitterChunkParams {
|
||||
chunkSize: number;
|
||||
chunkOverlap: number;
|
||||
keepSeparator: boolean;
|
||||
}
|
||||
|
||||
const TEXT_SPLITTER_CHUNK_PARAMS: TextSplitterChunkParams = {
|
||||
chunkSize: 300,
|
||||
chunkOverlap: 0,
|
||||
keepSeparator: false,
|
||||
};
|
||||
|
||||
class QnaPairChunker extends BaseChunker {
|
||||
constructor() {
|
||||
const textSplitter = new RecursiveCharacterTextSplitter(
|
||||
TEXT_SPLITTER_CHUNK_PARAMS
|
||||
);
|
||||
super(textSplitter);
|
||||
}
|
||||
}
|
||||
|
||||
export { QnaPairChunker };
|
||||
@@ -1,26 +0,0 @@
|
||||
import { RecursiveCharacterTextSplitter } from 'langchain/text_splitter';
|
||||
|
||||
import { BaseChunker } from './BaseChunker';
|
||||
|
||||
interface TextSplitterChunkParams {
|
||||
chunkSize: number;
|
||||
chunkOverlap: number;
|
||||
keepSeparator: boolean;
|
||||
}
|
||||
|
||||
const TEXT_SPLITTER_CHUNK_PARAMS: TextSplitterChunkParams = {
|
||||
chunkSize: 500,
|
||||
chunkOverlap: 0,
|
||||
keepSeparator: false,
|
||||
};
|
||||
|
||||
class WebPageChunker extends BaseChunker {
|
||||
constructor() {
|
||||
const textSplitter = new RecursiveCharacterTextSplitter(
|
||||
TEXT_SPLITTER_CHUNK_PARAMS
|
||||
);
|
||||
super(textSplitter);
|
||||
}
|
||||
}
|
||||
|
||||
export { WebPageChunker };
|
||||
@@ -1,6 +0,0 @@
|
||||
import { BaseChunker } from './BaseChunker';
|
||||
import { PdfFileChunker } from './PdfFile';
|
||||
import { QnaPairChunker } from './QnaPair';
|
||||
import { WebPageChunker } from './WebPage';
|
||||
|
||||
export { BaseChunker, PdfFileChunker, QnaPairChunker, WebPageChunker };
|
||||
@@ -1,317 +0,0 @@
|
||||
/* eslint-disable max-classes-per-file */
|
||||
import type { Collection } from 'chromadb';
|
||||
import type { QueryResponse } from 'chromadb/dist/main/types';
|
||||
import * as fs from 'fs';
|
||||
import { Document } from 'langchain/document';
|
||||
import OpenAI from 'openai';
|
||||
import * as path from 'path';
|
||||
import { v4 as uuidv4 } from 'uuid';
|
||||
|
||||
import type { BaseChunker } from './chunkers';
|
||||
import { PdfFileChunker, QnaPairChunker, WebPageChunker } from './chunkers';
|
||||
import type { BaseLoader } from './loaders';
|
||||
import { LocalQnaPairLoader, PdfFileLoader, WebPageLoader } from './loaders';
|
||||
import type {
|
||||
DataDict,
|
||||
DataType,
|
||||
FormattedResult,
|
||||
Input,
|
||||
LocalInput,
|
||||
Metadata,
|
||||
Method,
|
||||
RemoteInput,
|
||||
} from './models';
|
||||
import { ChromaDB } from './vectordb';
|
||||
import type { BaseVectorDB } from './vectordb/BaseVectorDb';
|
||||
|
||||
const openai = new OpenAI({
|
||||
apiKey: process.env.OPENAI_API_KEY,
|
||||
});
|
||||
|
||||
class EmbedChain {
|
||||
dbClient: any;
|
||||
|
||||
// TODO: Definitely assign
|
||||
collection!: Collection;
|
||||
|
||||
userAsks: [DataType, Input][] = [];
|
||||
|
||||
initApp: Promise<void>;
|
||||
|
||||
collectMetrics: boolean;
|
||||
|
||||
sId: string; // sessionId
|
||||
|
||||
constructor(db?: BaseVectorDB, collectMetrics: boolean = true) {
|
||||
if (!db) {
|
||||
this.initApp = this.setupChroma();
|
||||
} else {
|
||||
this.initApp = this.setupOther(db);
|
||||
}
|
||||
|
||||
this.collectMetrics = collectMetrics;
|
||||
|
||||
// Send anonymous telemetry
|
||||
this.sId = uuidv4();
|
||||
this.sendTelemetryEvent('init');
|
||||
}
|
||||
|
||||
async setupChroma(): Promise<void> {
|
||||
const db = new ChromaDB();
|
||||
await db.initDb;
|
||||
this.dbClient = db.client;
|
||||
if (db.collection) {
|
||||
this.collection = db.collection;
|
||||
} else {
|
||||
// TODO: Add proper error handling
|
||||
console.error('No collection');
|
||||
}
|
||||
}
|
||||
|
||||
async setupOther(db: BaseVectorDB): Promise<void> {
|
||||
await db.initDb;
|
||||
// TODO: Figure out how we can initialize an unknown database.
|
||||
// this.dbClient = db.client;
|
||||
// this.collection = db.collection;
|
||||
this.userAsks = [];
|
||||
}
|
||||
|
||||
static getLoader(dataType: DataType) {
|
||||
const loaders: { [t in DataType]: BaseLoader } = {
|
||||
pdf_file: new PdfFileLoader(),
|
||||
web_page: new WebPageLoader(),
|
||||
qna_pair: new LocalQnaPairLoader(),
|
||||
};
|
||||
return loaders[dataType];
|
||||
}
|
||||
|
||||
static getChunker(dataType: DataType) {
|
||||
const chunkers: { [t in DataType]: BaseChunker } = {
|
||||
pdf_file: new PdfFileChunker(),
|
||||
web_page: new WebPageChunker(),
|
||||
qna_pair: new QnaPairChunker(),
|
||||
};
|
||||
return chunkers[dataType];
|
||||
}
|
||||
|
||||
public async add(dataType: DataType, url: RemoteInput) {
|
||||
const loader = EmbedChain.getLoader(dataType);
|
||||
const chunker = EmbedChain.getChunker(dataType);
|
||||
this.userAsks.push([dataType, url]);
|
||||
const { documents, countNewChunks } = await this.loadAndEmbed(
|
||||
loader,
|
||||
chunker,
|
||||
url
|
||||
);
|
||||
|
||||
if (this.collectMetrics) {
|
||||
const wordCount = documents.reduce(
|
||||
(sum, document) => sum + document.split(' ').length,
|
||||
0
|
||||
);
|
||||
|
||||
this.sendTelemetryEvent('add', {
|
||||
data_type: dataType,
|
||||
word_count: wordCount,
|
||||
chunks_count: countNewChunks,
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
public async addLocal(dataType: DataType, content: LocalInput) {
|
||||
const loader = EmbedChain.getLoader(dataType);
|
||||
const chunker = EmbedChain.getChunker(dataType);
|
||||
this.userAsks.push([dataType, content]);
|
||||
const { documents, countNewChunks } = await this.loadAndEmbed(
|
||||
loader,
|
||||
chunker,
|
||||
content
|
||||
);
|
||||
|
||||
if (this.collectMetrics) {
|
||||
const wordCount = documents.reduce(
|
||||
(sum, document) => sum + document.split(' ').length,
|
||||
0
|
||||
);
|
||||
|
||||
this.sendTelemetryEvent('add_local', {
|
||||
data_type: dataType,
|
||||
word_count: wordCount,
|
||||
chunks_count: countNewChunks,
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
protected async loadAndEmbed(
|
||||
loader: any,
|
||||
chunker: BaseChunker,
|
||||
src: Input
|
||||
): Promise<{
|
||||
documents: string[];
|
||||
metadatas: Metadata[];
|
||||
ids: string[];
|
||||
countNewChunks: number;
|
||||
}> {
|
||||
const embeddingsData = await chunker.createChunks(loader, src);
|
||||
let { documents, ids, metadatas } = embeddingsData;
|
||||
|
||||
const existingDocs = await this.collection.get({ ids });
|
||||
const existingIds = new Set(existingDocs.ids);
|
||||
|
||||
if (existingIds.size > 0) {
|
||||
const dataDict: DataDict = {};
|
||||
for (let i = 0; i < ids.length; i += 1) {
|
||||
const id = ids[i];
|
||||
if (!existingIds.has(id)) {
|
||||
dataDict[id] = { doc: documents[i], meta: metadatas[i] };
|
||||
}
|
||||
}
|
||||
|
||||
if (Object.keys(dataDict).length === 0) {
|
||||
console.log(`All data from ${src} already exists in the database.`);
|
||||
return { documents: [], metadatas: [], ids: [], countNewChunks: 0 };
|
||||
}
|
||||
ids = Object.keys(dataDict);
|
||||
const dataValues = Object.values(dataDict);
|
||||
documents = dataValues.map(({ doc }) => doc);
|
||||
metadatas = dataValues.map(({ meta }) => meta);
|
||||
}
|
||||
|
||||
const countBeforeAddition = await this.count();
|
||||
await this.collection.add({ documents, metadatas, ids });
|
||||
const countNewChunks = (await this.count()) - countBeforeAddition;
|
||||
console.log(
|
||||
`Successfully saved ${src}. New chunks count: ${countNewChunks}`
|
||||
);
|
||||
return { documents, metadatas, ids, countNewChunks };
|
||||
}
|
||||
|
||||
static async formatResult(
|
||||
results: QueryResponse
|
||||
): Promise<FormattedResult[]> {
|
||||
return results.documents[0].map((document: any, index: number) => {
|
||||
const metadata = results.metadatas[0][index] || {};
|
||||
// TODO: Add proper error handling
|
||||
const distance = results.distances ? results.distances[0][index] : null;
|
||||
return [new Document({ pageContent: document, metadata }), distance];
|
||||
});
|
||||
}
|
||||
|
||||
static async getOpenAiAnswer(prompt: string) {
|
||||
const messages: OpenAI.Chat.CreateChatCompletionRequestMessage[] = [
|
||||
{ role: 'user', content: prompt },
|
||||
];
|
||||
const response = await openai.chat.completions.create({
|
||||
model: 'gpt-3.5-turbo',
|
||||
messages,
|
||||
temperature: 0,
|
||||
max_tokens: 1000,
|
||||
top_p: 1,
|
||||
});
|
||||
return (
|
||||
response.choices[0].message?.content ?? 'Response could not be processed.'
|
||||
);
|
||||
}
|
||||
|
||||
protected async retrieveFromDatabase(inputQuery: string) {
|
||||
const result = await this.collection.query({
|
||||
nResults: 1,
|
||||
queryTexts: [inputQuery],
|
||||
});
|
||||
const resultFormatted = await EmbedChain.formatResult(result);
|
||||
const content = resultFormatted[0][0].pageContent;
|
||||
return content;
|
||||
}
|
||||
|
||||
static generatePrompt(inputQuery: string, context: any) {
|
||||
const prompt = `Use the following pieces of context to answer the query at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.\n${context}\nQuery: ${inputQuery}\nHelpful Answer:`;
|
||||
return prompt;
|
||||
}
|
||||
|
||||
static async getAnswerFromLlm(prompt: string) {
|
||||
const answer = await EmbedChain.getOpenAiAnswer(prompt);
|
||||
return answer;
|
||||
}
|
||||
|
||||
public async query(inputQuery: string) {
|
||||
const context = await this.retrieveFromDatabase(inputQuery);
|
||||
const prompt = EmbedChain.generatePrompt(inputQuery, context);
|
||||
const answer = await EmbedChain.getAnswerFromLlm(prompt);
|
||||
this.sendTelemetryEvent('query');
|
||||
return answer;
|
||||
}
|
||||
|
||||
public async dryRun(input_query: string) {
|
||||
const context = await this.retrieveFromDatabase(input_query);
|
||||
const prompt = EmbedChain.generatePrompt(input_query, context);
|
||||
return prompt;
|
||||
}
|
||||
|
||||
/**
|
||||
* Count the number of embeddings.
|
||||
* @returns {Promise<number>}: The number of embeddings.
|
||||
*/
|
||||
public count(): Promise<number> {
|
||||
return this.collection.count();
|
||||
}
|
||||
|
||||
protected async sendTelemetryEvent(method: Method, extraMetadata?: object) {
|
||||
if (!this.collectMetrics) {
|
||||
return;
|
||||
}
|
||||
const url = 'https://api.embedchain.ai/api/v1/telemetry/';
|
||||
|
||||
// Read package version from filesystem (because it's not in the ts root dir)
|
||||
const packageJsonPath = path.join(__dirname, '..', 'package.json');
|
||||
const packageJson = JSON.parse(fs.readFileSync(packageJsonPath, 'utf8'));
|
||||
|
||||
const metadata = {
|
||||
s_id: this.sId,
|
||||
version: packageJson.version,
|
||||
method,
|
||||
language: 'js',
|
||||
...extraMetadata,
|
||||
};
|
||||
|
||||
const maxRetries = 3;
|
||||
|
||||
// Retry the fetch
|
||||
for (let i = 0; i < maxRetries; i += 1) {
|
||||
try {
|
||||
// eslint-disable-next-line no-await-in-loop
|
||||
const response = await fetch(url, {
|
||||
method: 'POST',
|
||||
body: JSON.stringify({ metadata }),
|
||||
});
|
||||
|
||||
if (response.ok) {
|
||||
// Break out of the loop if the request was successful
|
||||
break;
|
||||
} else {
|
||||
// Log the unsuccessful response (optional)
|
||||
console.error(
|
||||
`Telemetry: Attempt ${i + 1} failed with status:`,
|
||||
response.status
|
||||
);
|
||||
}
|
||||
} catch (error) {
|
||||
// Log the error (optional)
|
||||
console.error(`Telemetry: Attempt ${i + 1} failed with error:`, error);
|
||||
}
|
||||
|
||||
// If this was the last attempt, throw an error or handle the failure
|
||||
if (i === maxRetries - 1) {
|
||||
console.error('Telemetry: Max retries reached');
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
class EmbedChainApp extends EmbedChain {
|
||||
// The EmbedChain app.
|
||||
// Has two functions: add and query.
|
||||
// adds(dataType, url): adds the data from the given URL to the vector db.
|
||||
// query(query): finds answer to the given query using vector database and LLM.
|
||||
}
|
||||
|
||||
export { EmbedChainApp };
|
||||
@@ -1,7 +0,0 @@
|
||||
import { EmbedChainApp } from './embedchain';
|
||||
|
||||
export const App = async () => {
|
||||
const app = new EmbedChainApp();
|
||||
await app.initApp;
|
||||
return app;
|
||||
};
|
||||
@@ -1,5 +0,0 @@
|
||||
import type { Input, LoaderResult } from '../models';
|
||||
|
||||
export abstract class BaseLoader {
|
||||
abstract loadData(src: Input): Promise<LoaderResult>;
|
||||
}
|
||||
@@ -1,21 +0,0 @@
|
||||
import type { LoaderResult, QnaPair } from '../models';
|
||||
import { BaseLoader } from './BaseLoader';
|
||||
|
||||
class LocalQnaPairLoader extends BaseLoader {
|
||||
// eslint-disable-next-line class-methods-use-this
|
||||
async loadData(content: QnaPair): Promise<LoaderResult> {
|
||||
const [question, answer] = content;
|
||||
const contentText = `Q: ${question}\nA: ${answer}`;
|
||||
const metaData = {
|
||||
url: 'local',
|
||||
};
|
||||
return [
|
||||
{
|
||||
content: contentText,
|
||||
metaData,
|
||||
},
|
||||
];
|
||||
}
|
||||
}
|
||||
|
||||
export { LocalQnaPairLoader };
|
||||
@@ -1,58 +0,0 @@
|
||||
import type { TextContent } from 'pdfjs-dist/types/src/display/api';
|
||||
|
||||
import type { LoaderResult, Metadata } from '../models';
|
||||
import { cleanString } from '../utils';
|
||||
import { BaseLoader } from './BaseLoader';
|
||||
|
||||
const pdfjsLib = require('pdfjs-dist');
|
||||
|
||||
interface Page {
|
||||
page_content: string;
|
||||
}
|
||||
|
||||
class PdfFileLoader extends BaseLoader {
|
||||
static async getPagesFromPdf(url: string): Promise<Page[]> {
|
||||
const loadingTask = pdfjsLib.getDocument(url);
|
||||
const pdf = await loadingTask.promise;
|
||||
const { numPages } = pdf;
|
||||
|
||||
const promises = Array.from({ length: numPages }, async (_, i) => {
|
||||
const page = await pdf.getPage(i + 1);
|
||||
const pageText: TextContent = await page.getTextContent();
|
||||
const pageContent: string = pageText.items
|
||||
.map((item) => ('str' in item ? item.str : ''))
|
||||
.join(' ');
|
||||
|
||||
return {
|
||||
page_content: pageContent,
|
||||
};
|
||||
});
|
||||
|
||||
return Promise.all(promises);
|
||||
}
|
||||
|
||||
// eslint-disable-next-line class-methods-use-this
|
||||
async loadData(url: string): Promise<LoaderResult> {
|
||||
const pages: Page[] = await PdfFileLoader.getPagesFromPdf(url);
|
||||
const output: LoaderResult = [];
|
||||
|
||||
if (!pages.length) {
|
||||
throw new Error('No data found');
|
||||
}
|
||||
|
||||
pages.forEach((page) => {
|
||||
let content: string = page.page_content;
|
||||
content = cleanString(content);
|
||||
const metaData: Metadata = {
|
||||
url,
|
||||
};
|
||||
output.push({
|
||||
content,
|
||||
metaData,
|
||||
});
|
||||
});
|
||||
return output;
|
||||
}
|
||||
}
|
||||
|
||||
export { PdfFileLoader };
|
||||
@@ -1,51 +0,0 @@
|
||||
import axios from 'axios';
|
||||
import { JSDOM } from 'jsdom';
|
||||
|
||||
import { cleanString } from '../utils';
|
||||
import { BaseLoader } from './BaseLoader';
|
||||
|
||||
class WebPageLoader extends BaseLoader {
|
||||
// eslint-disable-next-line class-methods-use-this
|
||||
async loadData(url: string) {
|
||||
const response = await axios.get(url);
|
||||
const html = response.data;
|
||||
const dom = new JSDOM(html);
|
||||
const { document } = dom.window;
|
||||
const unwantedTags = [
|
||||
'nav',
|
||||
'aside',
|
||||
'form',
|
||||
'header',
|
||||
'noscript',
|
||||
'svg',
|
||||
'canvas',
|
||||
'footer',
|
||||
'script',
|
||||
'style',
|
||||
];
|
||||
unwantedTags.forEach((tagName) => {
|
||||
const elements = document.getElementsByTagName(tagName);
|
||||
Array.from(elements).forEach((element) => {
|
||||
// eslint-disable-next-line no-param-reassign
|
||||
(element as HTMLElement).textContent = ' ';
|
||||
});
|
||||
});
|
||||
|
||||
const output = [];
|
||||
let content = document.body.textContent;
|
||||
if (!content) {
|
||||
throw new Error('Web page content is empty.');
|
||||
}
|
||||
content = cleanString(content);
|
||||
const metaData = {
|
||||
url,
|
||||
};
|
||||
output.push({
|
||||
content,
|
||||
metaData,
|
||||
});
|
||||
return output;
|
||||
}
|
||||
}
|
||||
|
||||
export { WebPageLoader };
|
||||
@@ -1,6 +0,0 @@
|
||||
import { BaseLoader } from './BaseLoader';
|
||||
import { LocalQnaPairLoader } from './LocalQnaPair';
|
||||
import { PdfFileLoader } from './PdfFile';
|
||||
import { WebPageLoader } from './WebPage';
|
||||
|
||||
export { BaseLoader, LocalQnaPairLoader, PdfFileLoader, WebPageLoader };
|
||||
@@ -1,7 +0,0 @@
|
||||
import type { Metadata } from './Metadata';
|
||||
|
||||
export type ChunkResult = {
|
||||
documents: string[];
|
||||
ids: string[];
|
||||
metadatas: Metadata[];
|
||||
};
|
||||
@@ -1,10 +0,0 @@
|
||||
import type { ChunkResult } from './ChunkResult';
|
||||
|
||||
type Data = {
|
||||
doc: ChunkResult['documents'][0];
|
||||
meta: ChunkResult['metadatas'][0];
|
||||
};
|
||||
|
||||
export type DataDict = {
|
||||
[id: string]: Data;
|
||||
};
|
||||
@@ -1 +0,0 @@
|
||||
export type DataType = 'pdf_file' | 'web_page' | 'qna_pair';
|
||||
@@ -1,3 +0,0 @@
|
||||
import type { Document } from 'langchain/document';
|
||||
|
||||
export type FormattedResult = [Document, number | null];
|
||||
@@ -1,7 +0,0 @@
|
||||
import type { QnaPair } from './QnAPair';
|
||||
|
||||
export type RemoteInput = string;
|
||||
|
||||
export type LocalInput = QnaPair;
|
||||
|
||||
export type Input = RemoteInput | LocalInput;
|
||||
@@ -1,3 +0,0 @@
|
||||
import type { Metadata } from './Metadata';
|
||||
|
||||
export type LoaderResult = { content: any; metaData: Metadata }[];
|
||||
@@ -1,3 +0,0 @@
|
||||
export type Metadata = {
|
||||
url: string;
|
||||
};
|
||||
@@ -1 +0,0 @@
|
||||
export type Method = 'init' | 'query' | 'add' | 'add_local';
|
||||
@@ -1,4 +0,0 @@
|
||||
type Question = string;
|
||||
type Answer = string;
|
||||
|
||||
export type QnaPair = [Question, Answer];
|
||||
@@ -1,21 +0,0 @@
|
||||
import { DataDict } from './DataDict';
|
||||
import { DataType } from './DataType';
|
||||
import { FormattedResult } from './FormattedResult';
|
||||
import { Input, LocalInput, RemoteInput } from './Input';
|
||||
import { LoaderResult } from './LoaderResult';
|
||||
import { Metadata } from './Metadata';
|
||||
import { Method } from './Method';
|
||||
import { QnaPair } from './QnAPair';
|
||||
|
||||
export {
|
||||
DataDict,
|
||||
DataType,
|
||||
FormattedResult,
|
||||
Input,
|
||||
LoaderResult,
|
||||
LocalInput,
|
||||
Metadata,
|
||||
Method,
|
||||
QnaPair,
|
||||
RemoteInput,
|
||||
};
|
||||
@@ -1,26 +0,0 @@
|
||||
/**
|
||||
* This function takes in a string and performs a series of text cleaning operations.
|
||||
* @param {str} text: The text to be cleaned. This is expected to be a string.
|
||||
* @returns {str}: The cleaned text after all the cleaning operations have been performed.
|
||||
*/
|
||||
export function cleanString(text: string): string {
|
||||
// Replacement of newline characters:
|
||||
let cleanedText = text.replace(/\n/g, ' ');
|
||||
|
||||
// Stripping and reducing multiple spaces to single:
|
||||
cleanedText = cleanedText.trim().replace(/\s+/g, ' ');
|
||||
|
||||
// Removing backslashes:
|
||||
cleanedText = cleanedText.replace(/\\/g, '');
|
||||
|
||||
// Replacing hash characters:
|
||||
cleanedText = cleanedText.replace(/#/g, ' ');
|
||||
|
||||
// Eliminating consecutive non-alphanumeric characters:
|
||||
// This regex identifies consecutive non-alphanumeric characters (i.e., not a word character [a-zA-Z0-9_] and not a whitespace) in the string
|
||||
// and replaces each group of such characters with a single occurrence of that character.
|
||||
// For example, "!!! hello !!!" would become "! hello !".
|
||||
cleanedText = cleanedText.replace(/([^\w\s])\1*/g, '$1');
|
||||
|
||||
return cleanedText;
|
||||
}
|
||||
@@ -1,14 +0,0 @@
|
||||
class BaseVectorDB {
|
||||
initDb: Promise<void>;
|
||||
|
||||
constructor() {
|
||||
this.initDb = this.getClientAndCollection();
|
||||
}
|
||||
|
||||
// eslint-disable-next-line class-methods-use-this
|
||||
protected async getClientAndCollection(): Promise<void> {
|
||||
throw new Error('getClientAndCollection() method is not implemented');
|
||||
}
|
||||
}
|
||||
|
||||
export { BaseVectorDB };
|
||||
@@ -1,38 +0,0 @@
|
||||
import type { Collection } from 'chromadb';
|
||||
import { ChromaClient, OpenAIEmbeddingFunction } from 'chromadb';
|
||||
|
||||
import { BaseVectorDB } from './BaseVectorDb';
|
||||
|
||||
const embedder = new OpenAIEmbeddingFunction({
|
||||
openai_api_key: process.env.OPENAI_API_KEY ?? '',
|
||||
});
|
||||
|
||||
class ChromaDB extends BaseVectorDB {
|
||||
client: ChromaClient | undefined;
|
||||
|
||||
collection: Collection | null = null;
|
||||
|
||||
// eslint-disable-next-line @typescript-eslint/no-useless-constructor
|
||||
constructor() {
|
||||
super();
|
||||
}
|
||||
|
||||
protected async getClientAndCollection(): Promise<void> {
|
||||
this.client = new ChromaClient({ path: 'http://localhost:8000' });
|
||||
try {
|
||||
this.collection = await this.client.getCollection({
|
||||
name: 'embedchain_store',
|
||||
embeddingFunction: embedder,
|
||||
});
|
||||
} catch (err) {
|
||||
if (!this.collection) {
|
||||
this.collection = await this.client.createCollection({
|
||||
name: 'embedchain_store',
|
||||
embeddingFunction: embedder,
|
||||
});
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
export { ChromaDB };
|
||||
@@ -1,3 +0,0 @@
|
||||
import { ChromaDB } from './ChromaDb';
|
||||
|
||||
export { ChromaDB };
|
||||
@@ -1,9 +0,0 @@
|
||||
const { EmbedChainApp } = require("./embedchain/embedchain");
|
||||
|
||||
async function App() {
|
||||
const app = new EmbedChainApp();
|
||||
await app.init_app;
|
||||
return app;
|
||||
}
|
||||
|
||||
module.exports = { App };
|
||||
@@ -1,5 +0,0 @@
|
||||
module.exports = {
|
||||
preset: 'ts-jest',
|
||||
testEnvironment: 'node',
|
||||
testPathIgnorePatterns: ['.d.ts'],
|
||||
};
|
||||
@@ -1,5 +0,0 @@
|
||||
module.exports = {
|
||||
'*.{js,ts}': ['eslint --fix', 'eslint'],
|
||||
'**/*.ts?(x)': () => 'npm run check-types',
|
||||
'*.json': ['prettier --write'],
|
||||
};
|
||||
18457
embedchain-js/package-lock.json
generated
18457
embedchain-js/package-lock.json
generated
File diff suppressed because it is too large
Load Diff
@@ -1,53 +0,0 @@
|
||||
{
|
||||
"name": "embedchain",
|
||||
"version": "0.0.8",
|
||||
"description": "embedchain is a framework to easily create LLM powered bots over any dataset",
|
||||
"main": "dist/index.js",
|
||||
"types": "types/index.d.ts",
|
||||
"files": [
|
||||
"dist",
|
||||
"types"
|
||||
],
|
||||
"scripts": {
|
||||
"build": "tsc -p tsconfig.build.json --listFiles",
|
||||
"prepare": "husky install",
|
||||
"test": "jest",
|
||||
"check-types": "tsc --noEmit --pretty"
|
||||
},
|
||||
"author": "Taranjeet Singh",
|
||||
"license": "Apache-2.0",
|
||||
"dependencies": {
|
||||
"axios": "^1.4.0",
|
||||
"chromadb": "^1.5.6",
|
||||
"jsdom": "^22.1.0",
|
||||
"langchain": "^0.0.136",
|
||||
"openai": "^4.3.1",
|
||||
"pdfjs-dist": "^3.8.162",
|
||||
"uuid": "^9.0.0"
|
||||
},
|
||||
"devDependencies": {
|
||||
"@commitlint/cli": "^17.1.2",
|
||||
"@commitlint/config-conventional": "^17.1.0",
|
||||
"@commitlint/cz-commitlint": "^17.1.2",
|
||||
"@types/jest": "^29.5.1",
|
||||
"@types/jsdom": "^21.1.1",
|
||||
"@typescript-eslint/eslint-plugin": "^5.41.0",
|
||||
"@typescript-eslint/parser": "^5.41.0",
|
||||
"eslint": "^8.34.0",
|
||||
"eslint-config-airbnb-base": "^15.0.0",
|
||||
"eslint-config-airbnb-typescript": "^17.0.0",
|
||||
"eslint-config-prettier": "^8.5.0",
|
||||
"eslint-plugin-import": "^2.27.5",
|
||||
"eslint-plugin-prettier": "^4.2.1",
|
||||
"eslint-plugin-simple-import-sort": "^8.0.0",
|
||||
"eslint-plugin-testing-library": "^5.9.1",
|
||||
"eslint-plugin-unused-imports": "^2.0.0",
|
||||
"husky": "^8.0.1",
|
||||
"jest": "^29.5.0",
|
||||
"lint-staged": "^13.0.3",
|
||||
"prettier": "^2.7.1",
|
||||
"ts-jest": "^29.1.0",
|
||||
"ts-loader": "^9.4.2",
|
||||
"typescript": "^5.2.2"
|
||||
}
|
||||
}
|
||||
@@ -1,4 +0,0 @@
|
||||
{
|
||||
"extends": "./tsconfig.json",
|
||||
"exclude": ["embedchain/__tests__"]
|
||||
}
|
||||
@@ -1,15 +0,0 @@
|
||||
{
|
||||
"compilerOptions": {
|
||||
"target": "es6",
|
||||
"module": "CommonJS",
|
||||
"strict": true,
|
||||
"outDir": "dist",
|
||||
"rootDir": "embedchain",
|
||||
"sourceMap": true,
|
||||
"declaration": true,
|
||||
"declarationDir": "types",
|
||||
"esModuleInterop": true
|
||||
},
|
||||
"include": ["embedchain/**/*.ts"],
|
||||
"exclude": ["node_modules", "dist"]
|
||||
}
|
||||
Reference in New Issue
Block a user