Version bump (#1423 )

Add support for loading api_key from config or env variable (#1421 )
Support for Audio Files (#1416 )
2024-06-13 22:28:35 -07:00 · 2024-06-13 11:19:54 -07:00 · 2024-06-12 10:25:58 -07:00 · 2024-06-12 08:46:00 -07:00 · 2024-06-11 11:20:04 -07:00 · 2024-06-10 21:34:28 -07:00
84 changed files with 472 additions and 20218 deletions
@@ -67,6 +67,10 @@ We use `pytest` to test our code. You can run the tests by running the following
 poetry run pytest
 ```

+
+Several packages have been removed from Poetry to make the package lighter. Therefore, it is recommended to run `make install_all` to install the remaining packages and ensure all tests pass.
+
+
 Make sure that all tests pass before submitting a pull request.

 ## 🚀 Release Process
@@ -11,7 +11,7 @@ install:

 install_all:
 	poetry install --all-extras
-	poetry run pip install pinecone-text pinecone-client langchain-anthropic "unstructured[local-inference, all-docs]"
+	poetry run pip install pinecone-text pinecone-client langchain-anthropic "unstructured[local-inference, all-docs]" ollama deepgram-sdk==3.2.7 

 install_es:
 	poetry install --extras elasticsearch
@@ -26,6 +26,9 @@ llm:
    top_p: 1
    stream: false
    api_key: sk-xxx
+    model_kwargs:
+      response_format: 
+        type: json_object
    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.
@@ -83,7 +86,8 @@ cache:
      "stream": false,
      "prompt": "Use the following pieces of context to answer the query at the end.\nIf you don't know the answer, just say that you don't know, don't try to make up an answer.\n$context\n\nQuery: $query\n\nHelpful Answer:",
      "system_prompt": "Act as William Shakespeare. Answer the following questions in the style of William Shakespeare.",
-      "api_key": "sk-xxx"
+      "api_key": "sk-xxx",
+      "model_kwargs": {"response_format": {"type": "json_object"}}
    }
  },
  "vectordb": {
@@ -143,7 +147,8 @@ config = {
            'system_prompt': (
                "Act as William Shakespeare. Answer the following questions in the style of William Shakespeare."
            ),
-            'api_key': 'sk-xxx'
+            'api_key': 'sk-xxx',
+            "model_kwargs": {"response_format": {"type": "json_object"}}
        }
    },
    'vectordb': {
@@ -0,0 +1,25 @@
+---
+title: "🎤 Audio"
+---
+
+
+To use an audio as data source, just add `data_type` as `audio` and pass in the path of the audio (local or hosted).
+
+We use [Deepgram](https://developers.deepgram.com/docs/introduction) to transcribe the audiot to text, and then use the generated text as the data source.
+
+You would require an Deepgram API key which is available [here](https://console.deepgram.com/signup?jump=keys) to use this feature.
+
+### Without customization
+
+```python
+import os
+from embedchain import App
+
+os.environ["DEEPGRAM_API_KEY"] = "153xxx"
+
+app = App()
+app.add("introduction.wav", data_type="audio")
+response = app.query("What is my name and how old am I?")
+print(response)
+# Answer: Your name is Dave and you are 21 years old.
+```
@@ -9,6 +9,7 @@ Embedchain comes with built-in support for various data sources. We handle the c
  <Card title="CSV file" href="/components/data-sources/csv"></Card>
  <Card title="JSON file" href="/components/data-sources/json"></Card>
  <Card title="Text" href="/components/data-sources/text"></Card>
+  <Card title="Text File" href="/components/data-sources/text-file"></Card>
  <Card title="Directory" href="/components/data-sources/directory"></Card>
  <Card title="Web page" href="/components/data-sources/web-page"></Card>
  <Card title="Youtube Channel" href="/components/data-sources/youtube-channel"></Card>
@@ -33,6 +34,7 @@ Embedchain comes with built-in support for various data sources. We handle the c
  <Card title="Beehiiv" href="/components/data-sources/beehiiv"></Card>
  <Card title="Dropbox" href="/components/data-sources/dropbox"></Card>
  <Card title="Image" href="/components/data-sources/image"></Card>
+  <Card title="Audio" href="/components/data-sources/audio"></Card>
  <Card title="Custom" href="/components/data-sources/custom"></Card>
 </CardGroup>

@@ -0,0 +1,14 @@
+---
+title: '📄 Text file'
+---
+
+To add a .txt file, specify the data_type as `text_file`. The URL provided in the first parameter of the `add` function, should be a local path. Eg:
+
+```python
+from embedchain import App
+
+app = App()
+app.add('path/to/file.txt', data_type="text_file")
+
+app.query("Summarize the information of the text file")
+```
@@ -330,6 +330,7 @@ Setup Ollama using https://github.com/jmorganca/ollama

 ```python main.py
 import os
+os.environ["OLLAMA_HOST"] = "http://127.0.0.1:11434"
 from embedchain import App

 # load llm configuration from config.yaml file
@@ -345,6 +346,12 @@ llm:
    top_p: 1
    stream: true
    base_url: 'http://localhost:11434'
+embedder:
+  provider: ollama
+  config:
+    model: znbang/bge:small-en-v1.5-q8_0
+    base_url: http://localhost:11434
+
 ```

 </CodeGroup>
@@ -1,4 +0,0 @@
---
-title: ' 🟨 Javascript'
-url: https://github.com/embedchain/embedchain/tree/main/embedchain-js
---
@@ -1,17 +0,0 @@
---
-title: 'Embedchain.ai'
-description: 'Deploy your RAG application to embedchain.ai platform'
---
-
-## Deploy on Embedchain Platform
-
-Embedchain enables developers to deploy their LLM-powered apps in production using the Embedchain platform. The platform offers free access to context on your data through its REST API. Once the pipeline is deployed, you can update your data sources anytime after deployment.
-
-Deployment to Embedchain Platform is currently available on an invitation-only basis. To request access, please submit your information via the provided [Google Form](https://forms.gle/vigN11h7b4Ywat668). We will review your request and respond promptly.
-
-
-## Seeking help?
-
-If you run into issues with deployment, please feel free to reach out to us via any of the following methods:
-
-<Snippet file="get-help.mdx" />
@@ -13,7 +13,6 @@ After successfully setting up and testing your RAG app locally, the next step is
  <Card title="Streamlit.io" href="/deployment/streamlit_io"></Card>
  <Card title="Gradio.app" href="/deployment/gradio_app"></Card>
  <Card title="Huggingface.co" href="/deployment/huggingface_spaces"></Card>
-  <Card title="Embedchain.ai" href="/deployment/embedchain_ai"></Card>
 </CardGroup>

 ## Seeking help?
@@ -155,8 +155,7 @@
        "deployment/railway",
        "deployment/streamlit_io",
        "deployment/gradio_app",
-        "deployment/huggingface_spaces",
-        "deployment/embedchain_ai"
+        "deployment/huggingface_spaces"
      ]
    },
    {
@@ -236,8 +235,7 @@
        "contribution/guidelines",
        "contribution/dev",
        "contribution/docs",
-        "contribution/python",
-        "contribution/javascript"
+        "contribution/python"
      ]
    },
    {
@@ -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": "^_" }
-          ]
-        }
-      }
-    ]
-  }
-  
@@ -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
-      - run: npm ci
-      - 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}}
@@ -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 @@
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-                           Version 2.0, January 2004
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-
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@@ -1,254 +0,0 @@
-# embedchainjs
-
-[![Discord](https://dcbadge.vercel.app/api/server/CUU9FPhRNt?style=flat)](https://discord.gg/CUU9FPhRNt)
-[![Twitter](https://img.shields.io/twitter/follow/embedchain)](https://twitter.com/embedchain)
-[![Substack](https://img.shields.io/badge/Substack-%23006f5c.svg?logo=substack)](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'],
-};
@@ -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"]
-}
@@ -0,0 +1,22 @@
+from typing import Optional
+
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+
+from embedchain.chunkers.base_chunker import BaseChunker
+from embedchain.config.add_config import ChunkerConfig
+from embedchain.helpers.json_serializable import register_deserializable
+
+
+@register_deserializable
+class AudioChunker(BaseChunker):
+    """Chunker for audio."""
+
+    def __init__(self, config: Optional[ChunkerConfig] = None):
+        if config is None:
+            config = ChunkerConfig(chunk_size=1000, chunk_overlap=0, length_function=len)
+        text_splitter = RecursiveCharacterTextSplitter(
+            chunk_size=config.chunk_size,
+            chunk_overlap=config.chunk_overlap,
+            length_function=config.length_function,
+        )
+        super().__init__(text_splitter)
@@ -12,4 +12,4 @@ class OllamaEmbedderConfig(BaseEmbedderConfig):
        base_url: Optional[str] = None,
    ):
        super().__init__(model)
-        self.base_url = base_url or "http://127.0.0.1:11434"
+        self.base_url = base_url or "http://localhost:11434"
@@ -81,6 +81,7 @@ class DataFormatter(JSONSerializable):
            DataType.DROPBOX: "embedchain.loaders.dropbox.DropboxLoader",
            DataType.TEXT_FILE: "embedchain.loaders.text_file.TextFileLoader",
            DataType.EXCEL_FILE: "embedchain.loaders.excel_file.ExcelFileLoader",
+            DataType.AUDIO: "embedchain.loaders.audio.AudioLoader",
        }

        if data_type == DataType.CUSTOM or loader is not None:
@@ -129,6 +130,7 @@ class DataFormatter(JSONSerializable):
            DataType.DROPBOX: "embedchain.chunkers.common_chunker.CommonChunker",
            DataType.TEXT_FILE: "embedchain.chunkers.common_chunker.CommonChunker",
            DataType.EXCEL_FILE: "embedchain.chunkers.excel_file.ExcelFileChunker",
+            DataType.AUDIO: "embedchain.chunkers.audio.AudioChunker",
        }

        if chunker is not None:
@@ -2,7 +2,7 @@ import logging
 from typing import Optional

 try:
-    import ollama
+    from ollama import Client
 except ImportError:
    raise ImportError("Ollama Embedder requires extra dependencies. Install with `pip install ollama`") from None

@@ -19,11 +19,12 @@ class OllamaEmbedder(BaseEmbedder):
    def __init__(self, config: Optional[OllamaEmbedderConfig] = None):
        super().__init__(config=config)

-        local_models = ollama.list()["models"]
+        client = Client(host=config.base_url)
+        local_models = client.list()["models"]
        if not any(model.get("name") == self.config.model for model in local_models):
            logger.info(f"Pulling {self.config.model} from Ollama!")
-            ollama.pull(self.config.model)
-        embeddings = OllamaEmbeddings(model=self.config.model, base_url=self.config.base_url)
+            client.pull(self.config.model)
+        embeddings = OllamaEmbeddings(model=self.config.model, base_url=config.base_url)
        embedding_fn = BaseEmbedder._langchain_default_concept(embeddings)
        self.set_embedding_fn(embedding_fn=embedding_fn)

@@ -17,18 +17,17 @@ logger = logging.getLogger(__name__)
@register_deserializable
 class AnthropicLlm(BaseLlm):
    def __init__(self, config: Optional[BaseLlmConfig] = None):
-        if "ANTHROPIC_API_KEY" not in os.environ:
-            raise ValueError("Please set the ANTHROPIC_API_KEY environment variable.")
        super().__init__(config=config)
+        if not self.config.api_key and "ANTHROPIC_API_KEY" not in os.environ:
+            raise ValueError("Please set the ANTHROPIC_API_KEY environment variable or pass it in the config.")

    def get_llm_model_answer(self, prompt):
        return AnthropicLlm._get_answer(prompt=prompt, config=self.config)

    @staticmethod
    def _get_answer(prompt: str, config: BaseLlmConfig) -> str:
-        chat = ChatAnthropic(
-            anthropic_api_key=os.environ["ANTHROPIC_API_KEY"], temperature=config.temperature, model_name=config.model
-        )
+        api_key = config.api_key or os.getenv("ANTHROPIC_API_KEY")
+        chat = ChatAnthropic(anthropic_api_key=api_key, temperature=config.temperature, model_name=config.model)

        if config.max_tokens and config.max_tokens != 1000:
            logger.warning("Config option `max_tokens` is not supported by this model.")
@@ -12,9 +12,6 @@ from embedchain.llm.base import BaseLlm
@register_deserializable
 class CohereLlm(BaseLlm):
    def __init__(self, config: Optional[BaseLlmConfig] = None):
-        if "COHERE_API_KEY" not in os.environ:
-            raise ValueError("Please set the COHERE_API_KEY environment variable.")
-
        try:
            importlib.import_module("cohere")
        except ModuleNotFoundError:
@@ -24,6 +21,8 @@ class CohereLlm(BaseLlm):
            ) from None

        super().__init__(config=config)
+        if not self.config.api_key and "COHERE_API_KEY" not in os.environ:
+            raise ValueError("Please set the COHERE_API_KEY environment variable or pass it in the config.")

    def get_llm_model_answer(self, prompt):
        if self.config.system_prompt:
@@ -32,8 +31,9 @@ class CohereLlm(BaseLlm):

    @staticmethod
    def _get_answer(prompt: str, config: BaseLlmConfig) -> str:
+        api_key = config.api_key or os.getenv("COHERE_API_KEY")
        llm = Cohere(
-            cohere_api_key=os.environ["COHERE_API_KEY"],
+            cohere_api_key=api_key,
            model=config.model,
            max_tokens=config.max_tokens,
            temperature=config.temperature,
@@ -16,9 +16,6 @@ logger = logging.getLogger(__name__)
@register_deserializable
 class GoogleLlm(BaseLlm):
    def __init__(self, config: Optional[BaseLlmConfig] = None):
-        if "GOOGLE_API_KEY" not in os.environ:
-            raise ValueError("Please set the GOOGLE_API_KEY environment variable.")
-
        try:
            importlib.import_module("google.generativeai")
        except ModuleNotFoundError:
@@ -28,7 +25,11 @@ class GoogleLlm(BaseLlm):
            ) from None

        super().__init__(config)
-        genai.configure(api_key=os.environ["GOOGLE_API_KEY"])
+        if not self.config.api_key and "GOOGLE_API_KEY" not in os.environ:
+            raise ValueError("Please set the GOOGLE_API_KEY environment variable or pass it in the config.")
+
+        api_key = self.config.api_key or os.getenv("GOOGLE_API_KEY")
+        genai.configure(api_key=api_key)

    def get_llm_model_answer(self, prompt):
        if self.config.system_prompt:
@@ -19,6 +19,8 @@ from embedchain.llm.base import BaseLlm
 class GroqLlm(BaseLlm):
    def __init__(self, config: Optional[BaseLlmConfig] = None):
        super().__init__(config=config)
+        if not self.config.api_key and "GROQ_API_KEY" not in os.environ:
+            raise ValueError("Please set the GROQ_API_KEY environment variable or pass it in the config.")

    def get_llm_model_answer(self, prompt) -> str:
        response = self._get_answer(prompt, self.config)
@@ -17,9 +17,6 @@ logger = logging.getLogger(__name__)
@register_deserializable
 class HuggingFaceLlm(BaseLlm):
    def __init__(self, config: Optional[BaseLlmConfig] = None):
-        if "HUGGINGFACE_ACCESS_TOKEN" not in os.environ:
-            raise ValueError("Please set the HUGGINGFACE_ACCESS_TOKEN environment variable.")
-
        try:
            importlib.import_module("huggingface_hub")
        except ModuleNotFoundError:
@@ -29,6 +26,8 @@ class HuggingFaceLlm(BaseLlm):
            ) from None

        super().__init__(config=config)
+        if not self.config.api_key and "HUGGINGFACE_ACCESS_TOKEN" not in os.environ:
+            raise ValueError("Please set the HUGGINGFACE_ACCESS_TOKEN environment variable or pass it in the config.")

    def get_llm_model_answer(self, prompt):
        if self.config.system_prompt:
@@ -60,9 +59,10 @@ class HuggingFaceLlm(BaseLlm):
            raise ValueError("`top_p` must be > 0.0 and < 1.0")

        model = config.model
+        api_key = config.api_key or os.getenv("HUGGINGFACE_ACCESS_TOKEN")
        logger.info(f"Using HuggingFaceHub with model {model}")
        llm = HuggingFaceHub(
-            huggingfacehub_api_token=os.environ["HUGGINGFACE_ACCESS_TOKEN"],
+            huggingfacehub_api_token=api_key,
            repo_id=model,
            model_kwargs=model_kwargs,
        )
@@ -70,8 +70,9 @@ class HuggingFaceLlm(BaseLlm):

    @staticmethod
    def _from_endpoint(prompt: str, config: BaseLlmConfig) -> str:
+        api_key = config.api_key or os.getenv("HUGGINGFACE_ACCESS_TOKEN")
        llm = HuggingFaceEndpoint(
-            huggingfacehub_api_token=os.environ["HUGGINGFACE_ACCESS_TOKEN"],
+            huggingfacehub_api_token=api_key,
            endpoint_url=config.endpoint,
            task="text-generation",
            model_kwargs=config.model_kwargs,
@@ -12,9 +12,9 @@ from embedchain.llm.base import BaseLlm
@register_deserializable
 class JinaLlm(BaseLlm):
    def __init__(self, config: Optional[BaseLlmConfig] = None):
-        if "JINACHAT_API_KEY" not in os.environ:
-            raise ValueError("Please set the JINACHAT_API_KEY environment variable.")
        super().__init__(config=config)
+        if not self.config.api_key and "JINACHAT_API_KEY" not in os.environ:
+            raise ValueError("Please set the JINACHAT_API_KEY environment variable or pass it in the config.")

    def get_llm_model_answer(self, prompt):
        response = JinaLlm._get_answer(prompt, self.config)
@@ -29,13 +29,13 @@ class JinaLlm(BaseLlm):
        kwargs = {
            "temperature": config.temperature,
            "max_tokens": config.max_tokens,
+            "jinachat_api_key": config.api_key or os.environ["JINACHAT_API_KEY"],
            "model_kwargs": {},
        }
        if config.top_p:
            kwargs["model_kwargs"]["top_p"] = config.top_p
        if config.stream:
-            from langchain.callbacks.streaming_stdout import \
-                StreamingStdOutCallbackHandler
+            from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler

            chat = JinaChat(**kwargs, streaming=config.stream, callbacks=[StreamingStdOutCallbackHandler()])
        else:
@@ -19,8 +19,6 @@ class Llama2Llm(BaseLlm):
                "The required dependencies for Llama2 are not installed."
                'Please install with `pip install --upgrade "embedchain[llama2]"`'
            ) from None
-        if "REPLICATE_API_TOKEN" not in os.environ:
-            raise ValueError("Please set the REPLICATE_API_TOKEN environment variable.")

        # Set default config values specific to this llm
        if not config:
@@ -35,13 +33,17 @@ class Llama2Llm(BaseLlm):
            )

        super().__init__(config=config)
+        if not self.config.api_key and "REPLICATE_API_TOKEN" not in os.environ:
+            raise ValueError("Please set the REPLICATE_API_TOKEN environment variable or pass it in the config.")

    def get_llm_model_answer(self, prompt):
        # TODO: Move the model and other inputs into config
        if self.config.system_prompt:
            raise ValueError("Llama2 does not support `system_prompt`")
+        api_key = self.config.api_key or os.getenv("REPLICATE_API_TOKEN")
        llm = Replicate(
            model=self.config.model,
+            replicate_api_token=api_key,
            input={
                "temperature": self.config.temperature,
                "max_length": self.config.max_tokens,
@@ -21,10 +21,9 @@ from embedchain.llm.base import BaseLlm
@register_deserializable
 class NvidiaLlm(BaseLlm):
    def __init__(self, config: Optional[BaseLlmConfig] = None):
-        if "NVIDIA_API_KEY" not in os.environ:
-            raise ValueError("NVIDIA_API_KEY environment variable must be set")
-
        super().__init__(config=config)
+        if not self.config.api_key and "NVIDIA_API_KEY" not in os.environ:
+            raise ValueError("Please set the NVIDIA_API_KEY environment variable or pass it in the config.")

    def get_llm_model_answer(self, prompt):
        return self._get_answer(prompt=prompt, config=self.config)
@@ -34,7 +33,7 @@ class NvidiaLlm(BaseLlm):
        callback_manager = [StreamingStdOutCallbackHandler()] if config.stream else [StdOutCallbackHandler()]
        model_kwargs = config.model_kwargs or {}
        labels = model_kwargs.get("labels", None)
-        params = {"model": config.model}
+        params = {"model": config.model, "nvidia_api_key": config.api_key or os.getenv("NVIDIA_API_KEY")}
        if config.system_prompt:
            params["system_prompt"] = config.system_prompt
        if config.temperature:
@@ -1,3 +1,4 @@
+import logging
 from collections.abc import Iterable
 from typing import Optional, Union

@@ -5,11 +6,14 @@ from langchain.callbacks.manager import CallbackManager
 from langchain.callbacks.stdout import StdOutCallbackHandler
 from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
 from langchain_community.llms.ollama import Ollama
+from ollama import Client

 from embedchain.config import BaseLlmConfig
 from embedchain.helpers.json_serializable import register_deserializable
 from embedchain.llm.base import BaseLlm

+logger = logging.getLogger(__name__)
+

@register_deserializable
 class OllamaLlm(BaseLlm):
@@ -18,6 +22,12 @@ class OllamaLlm(BaseLlm):
        if self.config.model is None:
            self.config.model = "llama2"

+        client = Client(host=config.base_url)
+        local_models = client.list()["models"]
+        if not any(model.get("name") == self.config.model for model in local_models):
+            logger.info(f"Pulling {self.config.model} from Ollama!")
+            client.pull(self.config.model)
+
    def get_llm_model_answer(self, prompt):
        return self._get_answer(prompt=prompt, config=self.config)

@@ -36,7 +36,7 @@ class OpenAILlm(BaseLlm):
            "model": config.model or "gpt-3.5-turbo",
            "temperature": config.temperature,
            "max_tokens": config.max_tokens,
-            "model_kwargs": {},
+            "model_kwargs": config.model_kwargs or {},
        }
        api_key = config.api_key or os.environ["OPENAI_API_KEY"]
        base_url = config.base_url or os.environ.get("OPENAI_API_BASE", None)
@@ -12,9 +12,6 @@ from embedchain.llm.base import BaseLlm
@register_deserializable
 class TogetherLlm(BaseLlm):
    def __init__(self, config: Optional[BaseLlmConfig] = None):
-        if "TOGETHER_API_KEY" not in os.environ:
-            raise ValueError("Please set the TOGETHER_API_KEY environment variable.")
-
        try:
            importlib.import_module("together")
        except ModuleNotFoundError:
@@ -24,6 +21,8 @@ class TogetherLlm(BaseLlm):
            ) from None

        super().__init__(config=config)
+        if not self.config.api_key and "TOGETHER_API_KEY" not in os.environ:
+            raise ValueError("Please set the TOGETHER_API_KEY environment variable or pass it in the config.")

    def get_llm_model_answer(self, prompt):
        if self.config.system_prompt:
@@ -32,8 +31,9 @@ class TogetherLlm(BaseLlm):

    @staticmethod
    def _get_answer(prompt: str, config: BaseLlmConfig) -> str:
+        api_key = config.api_key or os.getenv("TOGETHER_API_KEY")
        llm = Together(
-            together_api_key=os.environ["TOGETHER_API_KEY"],
+            together_api_key=api_key,
            model=config.model,
            max_tokens=config.max_tokens,
            temperature=config.temperature,
@@ -0,0 +1,51 @@
+import os
+import hashlib
+import validators
+from embedchain.helpers.json_serializable import register_deserializable
+from embedchain.loaders.base_loader import BaseLoader
+
+try:
+    from deepgram import DeepgramClient, PrerecordedOptions
+except ImportError:
+    raise ImportError(
+        "Audio file requires extra dependencies. Install with `pip install deepgram-sdk==3.2.7`"
+    ) from None
+
+
+@register_deserializable
+class AudioLoader(BaseLoader):
+    def __init__(self):
+        if not os.environ.get("DEEPGRAM_API_KEY"):
+            raise ValueError("DEEPGRAM_API_KEY is not set")
+
+        DG_KEY = os.environ.get("DEEPGRAM_API_KEY")
+        self.client = DeepgramClient(DG_KEY)
+
+    def load_data(self, url: str):
+        """Load data from a audio file or URL."""
+
+        options = PrerecordedOptions(
+            model="nova-2",
+            smart_format=True,
+        )
+        if validators.url(url):
+            source = {"url": url}
+            response = self.client.listen.prerecorded.v("1").transcribe_url(source, options)
+        else:
+            with open(url, "rb") as audio:
+                source = {"buffer": audio}
+                response = self.client.listen.prerecorded.v("1").transcribe_file(source, options)
+        content = response["results"]["channels"][0]["alternatives"][0]["transcript"]
+
+        doc_id = hashlib.sha256((content + url).encode()).hexdigest()
+        metadata = {"url": url}
+
+        return {
+            "doc_id": doc_id,
+            "data": [
+                {
+                    "content": content,
+                    "meta_data": metadata,
+                }
+            ],
+        }
@@ -27,9 +27,9 @@ class CsvLoader(BaseLoader):
            return StringIO(response.text)
        elif url.scheme == "file":
            path = url.path
-            return open(path, newline="")  # Open the file using the path from the URI
+            return open(path, newline="", encoding="utf-8")  # Open the file using the path from the URI
        else:
-            return open(content, newline="")  # Treat content as a regular file path
+            return open(content, newline="", encoding="utf-8")  # Treat content as a regular file path

    @staticmethod
    def load_data(content):
@@ -41,6 +41,7 @@ class IndirectDataType(Enum):
    DROPBOX = "dropbox"
    TEXT_FILE = "text_file"
    EXCEL_FILE = "excel_file"
+    AUDIO = "audio"


 class SpecialDataType(Enum):
@@ -81,3 +82,4 @@ class DataType(Enum):
    DROPBOX = IndirectDataType.DROPBOX.value
    TEXT_FILE = IndirectDataType.TEXT_FILE.value
    EXCEL_FILE = IndirectDataType.EXCEL_FILE.value
+    AUDIO = IndirectDataType.AUDIO.value
@@ -8,7 +8,8 @@ from pathlib import Path
 from typing import cast

 from openai import OpenAI
-from openai.types.beta.threads import MessageContentText, ThreadMessage
+from openai.types.beta.threads import Message
+from openai.types.beta.threads.text_content_block import TextContentBlock

 from embedchain import Client, Pipeline
 from embedchain.config import AddConfig
@@ -130,8 +131,8 @@ class OpenAIAssistant:

    @staticmethod
    def _format_message(thread_message):
-        thread_message = cast(ThreadMessage, thread_message)
-        content = [c.text.value for c in thread_message.content if isinstance(c, MessageContentText)]
+        thread_message = cast(Message, thread_message)
+        content = [c.text.value for c in thread_message.content if isinstance(c, TextContentBlock)]
        return " ".join(content)

    @staticmethod
@@ -193,12 +193,15 @@ def read_env_file(env_file_path):
    dict: Dictionary of environment variables.
    """
    env_vars = {}
+    pattern = re.compile(r"(\w+)=(.*)")  # compile regular expression for better performance
    with open(env_file_path, "r") as file:
-        for line in file:
+        lines = file.readlines()  # readlines is faster as it reads all at once
+        for line in lines:
+            line = line.strip()
            # Ignore comments and empty lines
-            if line.strip() and not line.strip().startswith("#"):
+            if line and not line.startswith("#"):
                # Assume each line is in the format KEY=VALUE
-                key_value_match = re.match(r"(\w+)=(.*)", line.strip())
+                key_value_match = pattern.match(line)
                if key_value_match:
                    key, value = key_value_match.groups()
                    env_vars[key] = value
@@ -237,6 +237,12 @@ def detect_datatype(source: Any) -> DataType:
            logger.debug(f"Source of `{formatted_source}` detected as `docx`.")
            return DataType.DOCX

+        if url.path.endswith(
+            (".mp3", ".mp4", ".mp2", ".aac", ".wav", ".flac", ".pcm", ".m4a", ".ogg", ".opus", ".webm")
+        ):
+            logger.debug(f"Source of `{formatted_source}` detected as `audio`.")
+            return DataType.AUDIO
+
        if url.path.endswith(".yaml"):
            try:
                response = requests.get(source)
@@ -385,17 +385,17 @@ files = [

 [[package]]
 name = "boto3"
-version = "1.34.121"
+version = "1.34.122"
 description = "The AWS SDK for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "boto3-1.34.121-py3-none-any.whl", hash = "sha256:4e79e400d6d44b4eee5deda6ac0ecd08a3f5a30c45a0d30712795cdc4459fd79"},
-    {file = "boto3-1.34.121.tar.gz", hash = "sha256:ec89f3e0b0dc959c418df29e14d3748c0b05ab7acf7c0b90c839e9f340a659fa"},
+    {file = "boto3-1.34.122-py3-none-any.whl", hash = "sha256:b2d7400ff84fa547e53b3d9acfa3c95d65d45b5886ba1ede1f7df4768d1cc0b1"},
+    {file = "boto3-1.34.122.tar.gz", hash = "sha256:56840d8ce91654d182f1c113f0791fa2113c3aa43230c50b4481f235348a6037"},
 ]

 [package.dependencies]
-botocore = ">=1.34.121,<1.35.0"
+botocore = ">=1.34.122,<1.35.0"
 jmespath = ">=0.7.1,<2.0.0"
 s3transfer = ">=0.10.0,<0.11.0"

@@ -404,13 +404,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]

 [[package]]
 name = "botocore"
-version = "1.34.121"
+version = "1.34.122"
 description = "Low-level, data-driven core of boto 3."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "botocore-1.34.121-py3-none-any.whl", hash = "sha256:25b05c7646a9f240cde1c8f839552a43f27e71e15c42600275dea93e219f7dd9"},
-    {file = "botocore-1.34.121.tar.gz", hash = "sha256:1a8f94b917c47dfd84a0b531ab607dc53570efb0d073d8686600f2d2be985323"},
+    {file = "botocore-1.34.122-py3-none-any.whl", hash = "sha256:6d75df3af831b62f0c7baa109728d987e0a8d34bfadf0476eb32e2f29a079a36"},
+    {file = "botocore-1.34.122.tar.gz", hash = "sha256:9374e16a36f1062c3e27816e8599b53eba99315dfac71cc84fc3aee3f5d3cbe3"},
 ]

 [package.dependencies]
@@ -815,13 +815,13 @@ numpy = "*"

 [[package]]
 name = "chromadb"
-version = "0.5.0"
+version = "0.4.24"
 description = "Chroma."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "chromadb-0.5.0-py3-none-any.whl", hash = "sha256:8193dc65c143b61d8faf87f02c44ecfa778d471febd70de517f51c5d88a06009"},
-    {file = "chromadb-0.5.0.tar.gz", hash = "sha256:7954af614a9ff7b2902ddbd0a162f33f7ec0669e2429903905c4f7876d1f766f"},
+    {file = "chromadb-0.4.24-py3-none-any.whl", hash = "sha256:3a08e237a4ad28b5d176685bd22429a03717fe09d35022fb230d516108da01da"},
+    {file = "chromadb-0.4.24.tar.gz", hash = "sha256:a5c80b4e4ad9b236ed2d4899a5b9e8002b489293f2881cb2cadab5b199ee1c72"},
 ]

 [package.dependencies]
@@ -842,6 +842,7 @@ opentelemetry-sdk = ">=1.2.0"
 orjson = ">=3.9.12"
 overrides = ">=7.3.1"
 posthog = ">=2.4.0"
+pulsar-client = ">=3.1.0"
 pydantic = ">=1.9"
 pypika = ">=0.48.9"
 PyYAML = ">=6.0.0"
@@ -891,13 +892,13 @@ files = [

 [[package]]
 name = "cohere"
-version = "5.5.5"
+version = "5.5.6"
 description = ""
 optional = false
 python-versions = "<4.0,>=3.8"
 files = [
-    {file = "cohere-5.5.5-py3-none-any.whl", hash = "sha256:b6adb5168781c8ad3fae06e56cb29d806f595a4cfee8cac3bf4b63d7c83d93c2"},
-    {file = "cohere-5.5.5.tar.gz", hash = "sha256:d8d882401d472c00133d8885d4ae83a174733e3ea6065421e61215eb3a4e1841"},
+    {file = "cohere-5.5.6-py3-none-any.whl", hash = "sha256:10b2e38489ea30493b1eae8851216bcdc2eaf9c96a7b413ed518489c845b39d9"},
+    {file = "cohere-5.5.6.tar.gz", hash = "sha256:d146b6c87a5f709912861c15bfcbc4fca11cefe5e9843dc90bad9662df65968f"},
 ]

 [package.dependencies]
@@ -905,6 +906,7 @@ boto3 = ">=1.34.0,<2.0.0"
 fastavro = ">=1.9.4,<2.0.0"
 httpx = ">=0.21.2"
 httpx-sse = ">=0.4.0,<0.5.0"
+parameterized = ">=0.9.0,<0.10.0"
 pydantic = ">=1.9.2"
 requests = ">=2.0.0,<3.0.0"
 tokenizers = ">=0.15,<0.16"
@@ -1073,13 +1075,13 @@ files = [

 [[package]]
 name = "dataclasses-json"
-version = "0.6.6"
+version = "0.6.7"
 description = "Easily serialize dataclasses to and from JSON."
 optional = false
 python-versions = "<4.0,>=3.7"
 files = [
-    {file = "dataclasses_json-0.6.6-py3-none-any.whl", hash = "sha256:e54c5c87497741ad454070ba0ed411523d46beb5da102e221efb873801b0ba85"},
-    {file = "dataclasses_json-0.6.6.tar.gz", hash = "sha256:0c09827d26fffda27f1be2fed7a7a01a29c5ddcd2eb6393ad5ebf9d77e9deae8"},
+    {file = "dataclasses_json-0.6.7-py3-none-any.whl", hash = "sha256:0dbf33f26c8d5305befd61b39d2b3414e8a407bedc2834dea9b8d642666fb40a"},
+    {file = "dataclasses_json-0.6.7.tar.gz", hash = "sha256:b6b3e528266ea45b9535223bc53ca645f5208833c29229e847b3f26a1cc55fc0"},
 ]

 [package.dependencies]
@@ -1733,13 +1735,13 @@ tool = ["click (>=6.0.0)"]

 [[package]]
 name = "google-cloud-aiplatform"
-version = "1.54.0"
+version = "1.54.1"
 description = "Vertex AI API client library"
 optional = true
 python-versions = ">=3.8"
 files = [
-    {file = "google-cloud-aiplatform-1.54.0.tar.gz", hash = "sha256:6f5187d35a32951028465804fbb42b478362bf41e2b634ddd22b150299f6e1d8"},
-    {file = "google_cloud_aiplatform-1.54.0-py2.py3-none-any.whl", hash = "sha256:7b3ed849b9fb59a01bd6f44444ccbb7d18495b867a26f913542f6b2d4c3de252"},
+    {file = "google-cloud-aiplatform-1.54.1.tar.gz", hash = "sha256:01c231961cc1a1a3b049ea3ef71fb11e77b2d56d632d020ce09e419b27ff77f2"},
+    {file = "google_cloud_aiplatform-1.54.1-py2.py3-none-any.whl", hash = "sha256:43f70fcd572f15317d769e5a0e04cfb7c0e259ead3fe581d2fba4f203ace5617"},
 ]

 [package.dependencies]
@@ -3213,14 +3215,14 @@ files = [

 [[package]]
 name = "milvus-lite"
-version = "2.4.6"
+version = "2.4.7"
 description = "A lightweight version of Milvus wrapped with Python."
 optional = true
 python-versions = ">=3.7"
 files = [
-    {file = "milvus_lite-2.4.6-py3-none-macosx_10_9_x86_64.whl", hash = "sha256:43ac9f36903b31455e50a8f1d9cb033e18971643029c89eb5c9610f01c1f2e26"},
-    {file = "milvus_lite-2.4.6-py3-none-macosx_11_0_arm64.whl", hash = "sha256:95afe2ee019c569713926747bbe18ab5944927797374fed796f00fbe564cccd6"},
-    {file = "milvus_lite-2.4.6-py3-none-manylinux2014_x86_64.whl", hash = "sha256:2f9116bfc6a0d95636d3aa144582486b622c492689f3c93c519101bd7158b7db"},
+    {file = "milvus_lite-2.4.7-py3-none-macosx_10_9_x86_64.whl", hash = "sha256:c828190118b104b05b8c8e0b5a4147811c86b54b8fb67bc2e726ad10fc0b544e"},
+    {file = "milvus_lite-2.4.7-py3-none-macosx_11_0_arm64.whl", hash = "sha256:e1537633c39879714fb15082be56a4b97f74c905a6e98e302ec01320561081af"},
+    {file = "milvus_lite-2.4.7-py3-none-manylinux2014_x86_64.whl", hash = "sha256:f016474d663045787dddf1c3aad13b7d8b61fd329220318f858184918143dcbf"},
 ]

 [[package]]
@@ -3916,13 +3918,13 @@ sympy = "*"

 [[package]]
 name = "openai"
-version = "1.32.0"
+version = "1.33.0"
 description = "The official Python library for the openai API"
 optional = false
 python-versions = ">=3.7.1"
 files = [
-    {file = "openai-1.32.0-py3-none-any.whl", hash = "sha256:953d57669f309002044fd2f678aba9f07a43256d74b3b00cd04afb5b185568ea"},
-    {file = "openai-1.32.0.tar.gz", hash = "sha256:a6df15a7ab9344b1bc2bc8d83639f68b7a7e2453c0f5e50c1666547eee86f0bd"},
+    {file = "openai-1.33.0-py3-none-any.whl", hash = "sha256:621163b56570897ab8389d187f686a53d4771fd6ce95d481c0a9611fe8bc4229"},
+    {file = "openai-1.33.0.tar.gz", hash = "sha256:1169211a7b326ecbc821cafb427c29bfd0871f9a3e0947dd9e51acb3b0f1df78"},
 ]

 [package.dependencies]
@@ -4125,57 +4127,57 @@ files = [

 [[package]]
 name = "orjson"
-version = "3.10.3"
+version = "3.10.4"
 description = "Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "orjson-3.10.3-cp310-cp310-macosx_10_15_x86_64.macosx_11_0_arm64.macosx_10_15_universal2.whl", hash = "sha256:9fb6c3f9f5490a3eb4ddd46fc1b6eadb0d6fc16fb3f07320149c3286a1409dd8"},
-    {file = "orjson-3.10.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:252124b198662eee80428f1af8c63f7ff077c88723fe206a25df8dc57a57b1fa"},
-    {file = "orjson-3.10.3-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:9f3e87733823089a338ef9bbf363ef4de45e5c599a9bf50a7a9b82e86d0228da"},
-    {file = "orjson-3.10.3-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c8334c0d87103bb9fbbe59b78129f1f40d1d1e8355bbed2ca71853af15fa4ed3"},
-    {file = "orjson-3.10.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1952c03439e4dce23482ac846e7961f9d4ec62086eb98ae76d97bd41d72644d7"},
-    {file = "orjson-3.10.3-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:c0403ed9c706dcd2809f1600ed18f4aae50be263bd7112e54b50e2c2bc3ebd6d"},
-    {file = "orjson-3.10.3-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:382e52aa4270a037d41f325e7d1dfa395b7de0c367800b6f337d8157367bf3a7"},
-    {file = "orjson-3.10.3-cp310-none-win32.whl", hash = "sha256:be2aab54313752c04f2cbaab4515291ef5af8c2256ce22abc007f89f42f49109"},
-    {file = "orjson-3.10.3-cp310-none-win_amd64.whl", hash = "sha256:416b195f78ae461601893f482287cee1e3059ec49b4f99479aedf22a20b1098b"},
-    {file = "orjson-3.10.3-cp311-cp311-macosx_10_15_x86_64.macosx_11_0_arm64.macosx_10_15_universal2.whl", hash = "sha256:73100d9abbbe730331f2242c1fc0bcb46a3ea3b4ae3348847e5a141265479700"},
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 xml = ["lxml (>=4.9.2)"]

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+
 [[package]]
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 version = "0.12.1"
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 typing-extensions = ">=4.4"

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+    {file = "pulsar_client-3.5.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:29cb5fedb969895b78301dc00a979133e69940812b8332e4de948bb0ad3db7cb"},
+    {file = "pulsar_client-3.5.0-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:e53c74bfa59b20c66adea95023169060f5048dd8d843e6ef9cd3b8ee2d23e93b"},
+    {file = "pulsar_client-3.5.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:99dbadb13967f1add57010971ed36b5a77d24afcdaea01960d0e55e56cf4ba6f"},
+    {file = "pulsar_client-3.5.0-cp39-cp39-win_amd64.whl", hash = "sha256:058887661d438796f42307dcc8054c84dea88a37683dae36498b95d7e1c39b37"},
+]
+
+[package.dependencies]
+certifi = "*"
+
+[package.extras]
+all = ["apache-bookkeeper-client (>=4.16.1)", "fastavro (>=1.9.2)", "grpcio (>=1.60.0)", "prometheus-client", "protobuf (>=3.6.1,<=3.20.3)", "ratelimit"]
+avro = ["fastavro (>=1.9.2)"]
+functions = ["apache-bookkeeper-client (>=4.16.1)", "grpcio (>=1.60.0)", "prometheus-client", "protobuf (>=3.6.1,<=3.20.3)", "ratelimit"]
+
 [[package]]
 name = "pyasn1"
 version = "0.6.0"
@@ -6721,13 +6784,13 @@ files = [

 [[package]]
 name = "typing-extensions"
-version = "4.12.1"
+version = "4.12.2"
 description = "Backported and Experimental Type Hints for Python 3.8+"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "typing_extensions-4.12.1-py3-none-any.whl", hash = "sha256:6024b58b69089e5a89c347397254e35f1bf02a907728ec7fee9bf0fe837d203a"},
-    {file = "typing_extensions-4.12.1.tar.gz", hash = "sha256:915f5e35ff76f56588223f15fdd5938f9a1cf9195c0de25130c627e4d597f6d1"},
+    {file = "typing_extensions-4.12.2-py3-none-any.whl", hash = "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d"},
+    {file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
 ]

 [[package]]
@@ -7457,4 +7520,4 @@ youtube = ["youtube-transcript-api", "yt_dlp"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<=3.13"
-content-hash = "625c803e4c73c2560c722d60d39258084e322ed0d9ad39c69510eed012731236"
+content-hash = "c63e9ce659b1148ca8685bbd81ac65adc07d8cc75abe28856525f6a247ce7856"
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "embedchain"
-version = "0.1.107"
+version = "0.1.110"
 description = "Simplest open source retrieval (RAG) framework"
 authors = [
    "Taranjeet Singh <taranjeet@embedchain.ai>",
@@ -96,7 +96,7 @@ python-dotenv = "^1.0.0"
 langchain = "^0.1.4"
 requests = "^2.31.0"
 openai = ">=1.1.1"
-chromadb = "^0.5.0"
+chromadb = "^0.4.24"
 posthog = "^3.0.2"
 rich = "^13.7.0"
 beautifulsoup4 = "^4.12.2"
@@ -19,6 +19,7 @@ from embedchain.chunkers.text import TextChunker
 from embedchain.chunkers.web_page import WebPageChunker
 from embedchain.chunkers.xml import XmlChunker
 from embedchain.chunkers.youtube_video import YoutubeVideoChunker
+from embedchain.chunkers.audio import AudioChunker
 from embedchain.config.add_config import ChunkerConfig

 chunker_config = ChunkerConfig(chunk_size=500, chunk_overlap=0, length_function=len)
@@ -45,6 +46,7 @@ chunker_common_config = {
    CommonChunker: {"chunk_size": 2000, "chunk_overlap": 0, "length_function": len},
    GoogleDriveChunker: {"chunk_size": 1000, "chunk_overlap": 0, "length_function": len},
    ExcelFileChunker: {"chunk_size": 1000, "chunk_overlap": 0, "length_function": len},
+    AudioChunker: {"chunk_size": 1000, "chunk_overlap": 0, "length_function": len},
 }


@@ -74,5 +74,6 @@ def test_get_llm_model_answer_without_system_prompt(config, mocker):
    mocked_jinachat.assert_called_once_with(
        temperature=config.temperature,
        max_tokens=config.max_tokens,
+        jinachat_api_key=os.environ["JINACHAT_API_KEY"],
        model_kwargs={"top_p": config.top_p},
    )
@@ -11,6 +11,7 @@ def ollama_llm_config():


 def test_get_llm_model_answer(ollama_llm_config, mocker):
+    mocker.patch("embedchain.llm.ollama.Client.list", return_value={"models": [{"name": "llama2"}]})
    mocker.patch("embedchain.llm.ollama.OllamaLlm._get_answer", return_value="Test answer")

    llm = OllamaLlm(ollama_llm_config)
@@ -20,6 +21,7 @@ def test_get_llm_model_answer(ollama_llm_config, mocker):


 def test_get_answer_mocked_ollama(ollama_llm_config, mocker):
+    mocker.patch("embedchain.llm.ollama.Client.list", return_value={"models": [{"name": "llama2"}]})
    mocked_ollama = mocker.patch("embedchain.llm.ollama.Ollama")
    mock_instance = mocked_ollama.return_value
    mock_instance.invoke.return_value = "Mocked answer"
@@ -96,6 +96,23 @@ def test_get_llm_model_answer_with_special_headers(config, mocker):
    )


+def test_get_llm_model_answer_with_model_kwargs(config, mocker):
+    config.model_kwargs = {"response_format": {"type": "json_object"}}
+    mocked_openai_chat = mocker.patch("embedchain.llm.openai.ChatOpenAI")
+
+    llm = OpenAILlm(config)
+    llm.get_llm_model_answer("Test query")
+
+    mocked_openai_chat.assert_called_once_with(
+        model=config.model,
+        temperature=config.temperature,
+        max_tokens=config.max_tokens,
+        model_kwargs={"top_p": config.top_p, "response_format": {"type": "json_object"}},
+        api_key=os.environ["OPENAI_API_KEY"],
+        base_url=os.environ["OPENAI_API_BASE"],
+    )
+
+
@pytest.mark.parametrize(
    "mock_return, expected",
    [
@@ -0,0 +1,98 @@
+import os
+import sys
+import hashlib
+import pytest
+from unittest.mock import mock_open, patch
+
+if sys.version_info > (3, 10):  # as `match` statement was introduced in python 3.10
+    from deepgram import PrerecordedOptions
+    from embedchain.loaders.audio import AudioLoader
+
+
+@pytest.fixture
+def setup_audio_loader(mocker):
+    mock_dropbox = mocker.patch("deepgram.DeepgramClient")
+    mock_dbx = mocker.MagicMock()
+    mock_dropbox.return_value = mock_dbx
+
+    os.environ["DEEPGRAM_API_KEY"] = "test_key"
+    loader = AudioLoader()
+    loader.client = mock_dbx
+
+    yield loader, mock_dbx
+
+    if "DEEPGRAM_API_KEY" in os.environ:
+        del os.environ["DEEPGRAM_API_KEY"]
+
+
+@pytest.mark.skipif(
+    sys.version_info < (3, 10), reason="Test skipped for Python 3.9 or lower"
+)  # as `match` statement was introduced in python 3.10
+def test_initialization(setup_audio_loader):
+    """Test initialization of AudioLoader."""
+    loader, _ = setup_audio_loader
+    assert loader is not None
+
+
+@pytest.mark.skipif(
+    sys.version_info < (3, 10), reason="Test skipped for Python 3.9 or lower"
+)  # as `match` statement was introduced in python 3.10
+def test_load_data_from_url(setup_audio_loader):
+    loader, mock_dbx = setup_audio_loader
+    url = "https://example.com/audio.mp3"
+    expected_content = "This is a test audio transcript."
+
+    mock_response = {"results": {"channels": [{"alternatives": [{"transcript": expected_content}]}]}}
+    mock_dbx.listen.prerecorded.v.return_value.transcribe_url.return_value = mock_response
+
+    result = loader.load_data(url)
+
+    doc_id = hashlib.sha256((expected_content + url).encode()).hexdigest()
+    expected_result = {
+        "doc_id": doc_id,
+        "data": [
+            {
+                "content": expected_content,
+                "meta_data": {"url": url},
+            }
+        ],
+    }
+
+    assert result == expected_result
+    mock_dbx.listen.prerecorded.v.assert_called_once_with("1")
+    mock_dbx.listen.prerecorded.v.return_value.transcribe_url.assert_called_once_with(
+        {"url": url}, PrerecordedOptions(model="nova-2", smart_format=True)
+    )
+
+
+@pytest.mark.skipif(
+    sys.version_info < (3, 10), reason="Test skipped for Python 3.9 or lower"
+)  # as `match` statement was introduced in python 3.10
+def test_load_data_from_file(setup_audio_loader):
+    loader, mock_dbx = setup_audio_loader
+    file_path = "local_audio.mp3"
+    expected_content = "This is a test audio transcript."
+
+    mock_response = {"results": {"channels": [{"alternatives": [{"transcript": expected_content}]}]}}
+    mock_dbx.listen.prerecorded.v.return_value.transcribe_file.return_value = mock_response
+
+    # Mock the file reading functionality
+    with patch("builtins.open", mock_open(read_data=b"some data")) as mock_file:
+        result = loader.load_data(file_path)
+
+    doc_id = hashlib.sha256((expected_content + file_path).encode()).hexdigest()
+    expected_result = {
+        "doc_id": doc_id,
+        "data": [
+            {
+                "content": expected_content,
+                "meta_data": {"url": file_path},
+            }
+        ],
+    }
+
+    assert result == expected_result
+    mock_dbx.listen.prerecorded.v.assert_called_once_with("1")
+    mock_dbx.listen.prerecorded.v.return_value.transcribe_file.assert_called_once_with(
+        {"buffer": mock_file.return_value}, PrerecordedOptions(model="nova-2", smart_format=True)
+    )
Author	SHA1	Message	Date
Dev Khant	439b425c61	Version bump (#1423 )	2024-06-13 22:28:35 -07:00
Dev Khant	2855f1635b	Add support for loading api_key from config or env variable (#1421 )	2024-06-13 11:19:54 -07:00
Dev Khant	08b67b4a78	Support for Audio Files (#1416 )	2024-06-12 10:25:58 -07:00
Dev Khant	1bddd46ed2	Verion bump, chromadb_version change and doc update (#1407 )	2024-06-12 08:46:00 -07:00
Pranav Puranik	6ecdadfd97	Add model_kwargs to OpenAI call (#1402 )	2024-06-11 11:20:04 -07:00
Dimitra Gerontaki	4119040005	Add documentation for text_file data type (#1410 )	2024-06-10 21:34:28 -07:00
Taranjeet Singh	873eef6ef8	Remove: EC deployment docs, and js links (#1409 )	2024-06-11 02:34:15 +05:30
Taranjeet Singh	445fed4d3f	Remove embedchain js (#1408 )	2024-06-11 01:54:56 +05:30
Dev Khant	52fd3e0dd4	Update contributing doc (#1404 )	2024-06-10 10:14:52 -07:00
Saurabh Misra	8fd0e1f3b0	⚡️ Speed up `read_env_file()` in `embedchain/utils/cli.py` (#1260 )	2024-06-09 09:11:15 -07:00
golemus	11fc4a8451	Update llms card to properly use local ollama (#1395 )	2024-06-09 09:09:49 -07:00
shuo	e22293294e	Delete embedchain/embedder/.ollama.py.swp (#1398 )	2024-06-09 09:02:38 -07:00
Dev Khant	73e53aaff1	Download Ollama model if not present (#1397 )	2024-06-08 23:43:03 -07:00
Deshraj Yadav	6fa946557f	Update package version to 0.1.108 (#1396 )	2024-06-08 10:34:15 -07:00
Youbin Choi	fb0852f585	[Bug Fix] Fix issue of loading other languages in csv file (#1225 )	2024-06-08 10:09:29 -07:00
Dev Khant	4070fc1bf0	Fix ollama embeddings for remote machine (#1394 )	2024-06-08 10:08:15 -07:00
Dev Khant	00c1fa1ec7	Fix OpenAI Assistant (#1393 )	2024-06-08 10:07:52 -07:00
				`@@ -1 +0,0 @@`
				`module.exports = { extends: ['@commitlint/config-conventional'] };`
				`@@ -1 +0,0 @@`
				`export type DataType = 'pdf_file' \| 'web_page' \| 'qna_pair';`
				`@@ -1 +0,0 @@`
				`export type Method = 'init' \| 'query' \| 'add' \| 'add_local';`