Update package version to 0.1.56 (#1137 )

[Feature] Add support for docker in fullstack app (#1134 )
[BugFixes] slack loader (#1135 )
2024-01-07 23:50:49 +05:30 · 2024-01-07 23:49:20 +05:30 · 2024-01-07 23:13:25 +05:30 · 2024-01-07 13:03:42 +05:30 · 2024-01-07 11:00:35 +05:30 · 2024-01-06 13:57:32 +05:30
505 changed files with 30797 additions and 3545 deletions
@@ -1 +1 @@
-OPENAI_API_KEY=
+OPENAI_API_KEY="your-openai-api-key"
@@ -5,7 +5,7 @@ body:
 - type: markdown
  attributes:
    value: >
-      #### Before submitting a bug, please make sure the issue hasn't been already addressed by searching through [the existing and past issues](https://github.com/gventuri/pandas-ai/issues?q=is%3Aissue+sort%3Acreated-desc+).
+      #### Before submitting a bug, please make sure the issue hasn't been already addressed by searching through [the existing and past issues](https://github.com/embedchain/embedchain/issues?q=is%3Aissue+sort%3Acreated-desc+).
 - type: textarea
  attributes:
    label: 🐛 Describe the bug
@@ -23,10 +23,10 @@ jobs:
        run: |
          curl -sSL https://install.python-poetry.org | python3 -
          echo "$HOME/.local/bin" >> $GITHUB_PATH
-  
+
      - name: Install dependencies
        run: poetry install
-  
+
      - name: Build a binary wheel and a source tarball
        run: poetry build

@@ -3,7 +3,15 @@ name: ci
 on:
  push:
    branches: [main]
+    paths:
+      - 'embedchain/**'
+      - 'tests/**'
+      - 'examples/**'
  pull_request:
+    paths:
+      - 'embedchain/**'
+      - 'tests/**'
+      - 'examples/**'

 jobs:
  build:
@@ -19,14 +27,23 @@ jobs:
        with:
          python-version: ${{ matrix.python-version }}
      - name: Install poetry
-        run: pip install poetry==1.4.2
+        uses: snok/install-poetry@v1
+        with:
+          version: 1.4.2
+          virtualenvs-create: true
+          virtualenvs-in-project: true
+      - name: Load cached venv
+        id: cached-poetry-dependencies
+        uses: actions/cache@v2
+        with:
+          path: .venv
+          key: venv-${{ runner.os }}-${{ hashFiles('**/poetry.lock') }}
      - name: Install dependencies
        run: poetry install --all-extras
+        if: steps.cached-poetry-dependencies.outputs.cache-hit != 'true'
      - name: Lint with ruff
-        run: make ci_lint
-      - name: Test with pytest
-        run: make ci_test
-      - name: Generate coverage report
+        run: make lint
+      - name: Run tests and generate coverage report
        run: make coverage
      - name: Upload coverage reports to Codecov
        uses: codecov/codecov-action@v3
@@ -34,4 +51,3 @@ jobs:
          file: coverage.xml
        env:
          CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
-
@@ -76,7 +76,6 @@ docs/_build/
 target/

 # Jupyter Notebook
-.ipynb_checkpoints

 # IPython
 profile_default/
@@ -165,9 +164,17 @@ cython_debug/

 # Database
 db
+test-db

 .vscode
-/poetry.lock
 .idea/

 .DS_Store
+
+notebooks/*.yaml
+.ipynb_checkpoints/
+
+!configs/*.yaml
+
+# cache db
+*.db
@@ -1,10 +1,10 @@
 # Contributing to embedchain

-Let us make contributing easy, collaborative and fun.
+Let us make contribution easy, collaborative and fun.

 ## Submit your Contribution through PR

-To make a contribution, follow the following steps:
+To make a contribution, follow these steps:

 1. Fork and clone this repository
 2. Do the changes on your fork with dedicated feature branch `feature/f1`
@@ -35,7 +35,7 @@ poetry shell

 ### 📌 Pre-commit

-To ensure our standards, make sure to install pre-commit before star to contribute.
+To ensure our standards, make sure to install pre-commit before starting to contribute.

 ```bash
 pre-commit install
@@ -51,7 +51,7 @@ make lint

 Make sure that the linter does not report any errors or warnings before submitting a pull request.

-### Code Format with `black`
+### Code Formatting with `black`

 We use `black` to reformat the code by running the following command:

@@ -18,6 +18,9 @@ install_es:
 install_opensearch:
 	poetry install --extras opensearch

+install_milvus:
+	poetry install --extras milvus
+
 shell:
 	poetry shell

@@ -28,20 +31,15 @@ format:
 	$(PYTHON) -m black .
 	$(PYTHON) -m isort .

-lint:
-	$(PYTHON) -m ruff .
-
 clean:
 	rm -rf dist build *.egg-info

-test:
-	$(PYTHON) -m pytest
-
-ci_lint:
+lint:
 	poetry run ruff .

-ci_test:
-	poetry run pytest
+# for example: make test file=tests/test_factory.py
+test:
+	poetry run pytest $(file)

 coverage:
 	poetry run pytest --cov=$(PROJECT_NAME) --cov-report=xml
@@ -1,62 +1,70 @@
-# embedchain
+<p align="center">
+  <img src="docs/logo/dark.svg" width="400px" alt="Embedchain Logo">
+</p>

-[![PyPI](https://img.shields.io/pypi/v/embedchain)](https://pypi.org/project/embedchain/)
-[![Slack](https://img.shields.io/badge/slack-embedchain-brightgreen.svg?logo=slack)](https://join.slack.com/t/embedchain/shared_invite/zt-22uwz3c46-Zg7cIh5rOBteT_xe1jwLDw)
-[![Discord](https://dcbadge.vercel.app/api/server/6PzXDgEjG5?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/)
-[![Open in Colab](https://camo.githubusercontent.com/84f0493939e0c4de4e6dbe113251b4bfb5353e57134ffd9fcab6b8714514d4d1/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667)](https://colab.research.google.com/drive/138lMWhENGeEu7Q1-6lNbNTHGLZXBBz_B?usp=sharing)
-[![codecov](https://codecov.io/gh/embedchain/embedchain/graph/badge.svg?token=EMRRHZXW1Q)](https://codecov.io/gh/embedchain/embedchain)
+<p align="center">
+  <a href="https://runacap.com/ross-index/q3-2023/" target="_blank" rel="noopener"><img style="width: 260px; height: 56px" src="https://runacap.com/wp-content/uploads/2023/10/ROSS_badge_black_Q3_2023.svg" alt="ROSS Index - Fastest Growing Open-Source Startups in Q3 2023 | Runa Capital" width="260" height="56"/></a>
+</p>

-Embedchain is a framework to easily create LLM powered bots over any dataset. If you want a javascript version, check out [embedchain-js](https://github.com/embedchain/embedchain/tree/main/embedchain-js)
+<p align="center">
+  <a href="https://pypi.org/project/embedchain/">
+    <img src="https://img.shields.io/pypi/v/embedchain" alt="PyPI">
+  </a>
+  <a href="https://pepy.tech/project/embedchain">
+    <img src="https://static.pepy.tech/badge/embedchain" alt="Downloads">
+  </a>
+  <a href="https://embedchain.ai/slack">
+    <img src="https://img.shields.io/badge/slack-embedchain-brightgreen.svg?logo=slack" alt="Slack">
+  </a>
+  <a href="https://embedchain.ai/discord">
+    <img src="https://dcbadge.vercel.app/api/server/6PzXDgEjG5?style=flat" alt="Discord">
+  </a>
+  <a href="https://twitter.com/embedchain">
+    <img src="https://img.shields.io/twitter/follow/embedchain" alt="Twitter">
+  </a>
+  <a href="https://colab.research.google.com/drive/138lMWhENGeEu7Q1-6lNbNTHGLZXBBz_B?usp=sharing">
+    <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open in Colab">
+  </a>
+  <a href="https://codecov.io/gh/embedchain/embedchain">
+    <img src="https://codecov.io/gh/embedchain/embedchain/graph/badge.svg?token=EMRRHZXW1Q" alt="codecov">
+  </a>
+</p>

-## Community
+<hr />

-* Join embedchain community on slack by accepting [this invite](https://join.slack.com/t/embedchain/shared_invite/zt-22uwz3c46-Zg7cIh5rOBteT_xe1jwLDw)

-## 🤝 Schedule a 1-on-1 Session
+> ### Checkout our latest [Sadhguru AI app](https://sadhguru-ai.streamlit.app/) built using Embedchain.

-Book a [1-on-1 Session](https://cal.com/taranjeetio/ec) with Taranjeet, the founder, to discuss any issues, provide feedback, or explore how we can improve Embedchain for you.
+## What is Embedchain?
+
+Embedchain is an Open Source RAG Framework that makes it easy to create and deploy AI apps. At its core, Embedchain follows the design principle of being *"Conventional but Configurable"* to serve both software engineers and machine learning engineers.
+
+Embedchain streamlines the creation of RAG applications, offering a seamless process for managing various types of unstructured data. It efficiently segments data into manageable chunks, generates relevant embeddings, and stores them in a vector database for optimized retrieval. With a suite of diverse APIs, it enables users to extract contextual information, find precise answers, or engage in interactive chat conversations, all tailored to their own data.

 ## 🔧 Quick install

+### Python API
+
 ```bash
-pip install --upgrade embedchain
+pip install embedchain
 ```

-## 🔍 Demo
+## ✨ Live demo

-Try out embedchain in your browser:
+Checkout the [Chat with PDF](https://embedchain.ai/demo/chat-pdf) live demo we created using Embedchain. You can find the source code [here](https://github.com/embedchain/embedchain/tree/main/examples/chat-pdf).

-[![Open in Colab](https://camo.githubusercontent.com/84f0493939e0c4de4e6dbe113251b4bfb5353e57134ffd9fcab6b8714514d4d1/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667)](https://colab.research.google.com/drive/17ON1LPonnXAtLaZEebnOktstB_1cJJmh?usp=sharing)
+## 🔍 Usage

-## 📖 Documentation
+<!-- Demo GIF or Image -->
+<p align="center">
+  <img src="docs/images/cover.gif" width="900px" alt="Embedchain Demo">
+</p>

-The documentation for embedchain can be found at [docs.embedchain.ai](https://docs.embedchain.ai).
-
-## 💻 Usage
-
-Embedchain empowers you to create ChatGPT like apps, on your own dynamic dataset.
-
-### Data Types Supported
-
-* Youtube video
-* PDF file
-* Web page
-* Sitemap
-* Doc file
-* Code documentation website loader
-* Notion and many more.
-
-You can find the full list of data types on [our documentation](https://docs.embedchain.ai/data-sources/csv).
-
-### Queries
-
-For example, you can use Embedchain to create an Elon Musk bot using the following code:
+For example, you can create an Elon Musk bot using the following code:

 ```python
 import os
-from embedchain import App
+from embedchain import Pipeline as App

 # Create a bot instance
 os.environ["OPENAI_API_KEY"] = "YOUR API KEY"
@@ -65,14 +73,35 @@ elon_bot = App()
 # Embed online resources
 elon_bot.add("https://en.wikipedia.org/wiki/Elon_Musk")
 elon_bot.add("https://www.forbes.com/profile/elon-musk")
-elon_bot.add("https://www.youtube.com/watch?v=RcYjXbSJBN8")

 # Query the bot
 elon_bot.query("How many companies does Elon Musk run and name those?")
 # Answer: Elon Musk currently runs several companies. As of my knowledge, he is the CEO and lead designer of SpaceX, the CEO and product architect of Tesla, Inc., the CEO and founder of Neuralink, and the CEO and founder of The Boring Company. However, please note that this information may change over time, so it's always good to verify the latest updates.
 ```

-## 🤝 Contributing
+You can also try it in your browser with Google Colab:
+
+[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/17ON1LPonnXAtLaZEebnOktstB_1cJJmh?usp=sharing)
+
+## 📖 Documentation
+Comprehensive guides and API documentation are available to help you get the most out of Embedchain:
+
+- [Introduction](https://docs.embedchain.ai/get-started/introduction#what-is-embedchain)
+- [Getting Started](https://docs.embedchain.ai/get-started/quickstart)
+- [Examples](https://docs.embedchain.ai/examples)
+- [Supported data types](https://docs.embedchain.ai/components/data-sources/overview)
+
+## 🔗 Join the Community
+
+* Connect with fellow developers by joining our [Slack Community](https://embedchain.ai/slack) or [Discord Community](https://embedchain.ai/discord).
+
+* Dive into [GitHub Discussions](https://github.com/embedchain/embedchain/discussions), ask questions, or share your experiences.
+
+## 🤝 Schedule a 1-on-1 Session
+
+Book a [1-on-1 Session](https://cal.com/taranjeetio/ec) with the founders, to discuss any issues, provide feedback, or explore how we can improve Embedchain for you.
+
+## 🌐 Contributing

 Contributions are welcome! Please check out the issues on the repository, and feel free to open a pull request.
 For more information, please see the [contributing guidelines](CONTRIBUTING.md).
@@ -83,6 +112,10 @@ For more reference, please go through [Development Guide](https://docs.embedchai
  <img src="https://contrib.rocks/image?repo=embedchain/embedchain" />
 </a>

+## Anonymous Telemetry
+
+We collect anonymous usage metrics to enhance our package's quality and user experience. This includes data like feature usage frequency and system info, but never personal details. The data helps us prioritize improvements and ensure compatibility. If you wish to opt-out, set the environment variable `EC_TELEMETRY=false`. We prioritize data security and don't share this data externally.
+
 ## Citation

 If you utilize this repository, please consider citing it with:
@@ -90,7 +123,7 @@ If you utilize this repository, please consider citing it with:
 ```
@misc{embedchain,
  author = {Taranjeet Singh, Deshraj Yadav},
-  title = {Embedchain: Framework to easily create LLM powered bots over any dataset},
+  title = {Embedchain: The Open Source RAG Framework},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
@@ -0,0 +1,8 @@
+llm:
+  provider: anthropic
+  config:
+    model: 'claude-instant-1'
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 1
+    stream: false
@@ -0,0 +1,19 @@
+app:
+  config:
+    id: azure-openai-app
+
+llm:
+  provider: azure_openai
+  config:
+    model: gpt-35-turbo
+    deployment_name: your_llm_deployment_name
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 1
+    stream: false
+
+embedder:
+  provider: azure_openai
+  config:
+    model: text-embedding-ada-002
+    deployment_name: you_embedding_model_deployment_name
@@ -0,0 +1,24 @@
+app:
+  config:
+    id: 'my-app'
+
+llm:
+  provider: openai
+  config:
+    model: 'gpt-3.5-turbo'
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 1
+    stream: false
+
+vectordb:
+  provider: chroma
+  config:
+    collection_name: 'my-app'
+    dir: db
+    allow_reset: true
+
+embedder:
+  provider: openai
+  config:
+    model: 'text-embedding-ada-002'
@@ -0,0 +1,4 @@
+chunker:
+  chunk_size: 100
+  chunk_overlap: 20
+  length_function: 'len'
@@ -0,0 +1,7 @@
+llm:
+  provider: cohere
+  config:
+    model: large
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 1
@@ -0,0 +1,40 @@
+app:
+  config:
+    id: 'full-stack-app'
+
+chunker:
+  chunk_size: 100
+  chunk_overlap: 20
+  length_function: 'len'
+
+llm:
+  provider: openai
+  config:
+    model: 'gpt-3.5-turbo'
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 1
+    stream: false
+    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.
+
+      $context
+
+      Query: $query
+
+      Helpful Answer:
+    system_prompt: |
+      Act as William Shakespeare. Answer the following questions in the style of William Shakespeare.
+
+vectordb:
+  provider: chroma
+  config:
+    collection_name: 'my-collection-name'
+    dir: db
+    allow_reset: true
+
+embedder:
+  provider: openai
+  config:
+    model: 'text-embedding-ada-002'
@@ -0,0 +1,13 @@
+llm:
+  provider: google
+  config:
+    model: gemini-pro
+    max_tokens: 1000
+    temperature: 0.9
+    top_p: 1.0
+    stream: false
+
+embedder:
+  provider: google
+  config:
+    model: models/embedding-001
@@ -0,0 +1,8 @@
+llm:
+  provider: openai
+  config:
+    model: 'gpt-4'
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 1
+    stream: false
@@ -0,0 +1,11 @@
+llm:
+  provider: gpt4all
+  config:
+    model: 'orca-mini-3b-gguf2-q4_0.gguf'
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 1
+    stream: false
+
+embedder:
+  provider: gpt4all
@@ -0,0 +1,8 @@
+llm:
+  provider: huggingface
+  config:
+    model: 'google/flan-t5-xxl'
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 0.5
+    stream: false
@@ -0,0 +1,7 @@
+llm:
+  provider: jina
+  config:
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 1
+    stream: false
@@ -0,0 +1,8 @@
+llm:
+  provider: llama2
+  config:
+    model: 'a16z-infra/llama13b-v2-chat:df7690f1994d94e96ad9d568eac121aecf50684a0b0963b25a41cc40061269e5'
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 0.5
+    stream: false
@@ -0,0 +1,12 @@
+llm:
+  provider: ollama
+  config:
+    model: 'llama2'
+    temperature: 0.5
+    top_p: 1
+    stream: true
+
+embedder:
+  provider: huggingface
+  config:
+    model: 'BAAI/bge-small-en-v1.5'
@@ -0,0 +1,33 @@
+app:
+  config:
+    id: 'my-app'
+    log_level: 'WARNING'
+    collect_metrics: true
+    collection_name: 'my-app'
+
+llm:
+  provider: openai
+  config:
+    model: 'gpt-3.5-turbo'
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 1
+    stream: false
+
+vectordb:
+  provider: opensearch
+  config:
+    opensearch_url: 'https://localhost:9200'
+    http_auth:
+      - admin
+      - admin
+    vector_dimension: 1536
+    collection_name: 'my-app'
+    use_ssl: false
+    verify_certs: false
+
+embedder:
+  provider: openai
+  config:
+    model: 'text-embedding-ada-002'
+    deployment_name: 'my-app'
@@ -0,0 +1,25 @@
+app:
+  config:
+    id: 'open-source-app'
+    collect_metrics: false
+
+llm:
+  provider: gpt4all
+  config:
+    model: 'orca-mini-3b-gguf2-q4_0.gguf'
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 1
+    stream: false
+
+vectordb:
+  provider: chroma
+  config:
+    collection_name: 'open-source-app'
+    dir: db
+    allow_reset: true
+
+embedder:
+  provider: gpt4all
+  config:
+    deployment_name: 'test-deployment'
@@ -0,0 +1,6 @@
+vectordb:
+  provider: pinecone
+  config:
+    metric: cosine
+    vector_dimension: 1536
+    collection_name: my-pinecone-index
@@ -0,0 +1,26 @@
+pipeline:
+  config:
+    name: Example pipeline
+    id: pipeline-1  # Make sure that id is different every time you create a new pipeline
+
+vectordb:
+  provider: chroma
+  config:
+    collection_name: pipeline-1
+    dir: db
+    allow_reset: true
+
+llm:
+  provider: gpt4all
+  config:
+    model: 'orca-mini-3b-gguf2-q4_0.gguf'
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 1
+    stream: false
+
+embedding_model:
+  provider: gpt4all
+  config:
+    model: 'all-MiniLM-L6-v2'
+    deployment_name: null
@@ -0,0 +1,6 @@
+llm:
+  provider: together
+  config:
+    model: mistralai/Mixtral-8x7B-Instruct-v0.1
+    temperature: 0.5
+    max_tokens: 1000
@@ -0,0 +1,6 @@
+llm:
+  provider: vertexai
+  config:
+    model: 'chat-bison'
+    temperature: 0.5
+    top_p: 0.5
@@ -0,0 +1,4 @@
+vectordb:
+  provider: weaviate
+  config:
+    collection_name: my_weaviate_index
@@ -0,0 +1,10 @@
+install:
+	npm i -g mintlify
+
+run_local:
+	mintlify dev
+
+troubleshoot:
+	mintlify install
+
+.PHONY: install run_local troubleshoot
@@ -0,0 +1,11 @@
+<CardGroup cols={3}>
+  <Card title="Talk to founders" icon="calendar" href="https://cal.com/taranjeetio/ec">
+  Schedule a call
+  </Card>
+  <Card title="Slack" icon="slack" href="https://join.slack.com/t/embedchain/shared_invite/zt-22uwz3c46-Zg7cIh5rOBteT_xe1jwLDw" color="#4A154B">
+    Join our slack community
+  </Card>
+  <Card title="Discord" icon="discord" href="https://discord.gg/6PzXDgEjG5" color="#7289DA">
+    Join our discord community
+  </Card>
+</CardGroup>
@@ -0,0 +1,19 @@
+<p>If you can't find the specific data source, please feel free to request through one of the following channels and help us prioritize.</p>
+
+<CardGroup cols={2}>
+  <Card title="Google Form" icon="file" href="https://forms.gle/NDRCKsRpUHsz2Wcm8" color="#7387d0">
+    Fill out this form
+  </Card>
+  <Card title="Slack" icon="slack" href="https://join.slack.com/t/embedchain/shared_invite/zt-22uwz3c46-Zg7cIh5rOBteT_xe1jwLDw" color="#4A154B">
+    Let us know on our slack community
+  </Card>
+  <Card title="Discord" icon="discord" href="https://discord.gg/6PzXDgEjG5" color="#7289DA">
+    Let us know on discord community
+  </Card>
+  <Card title="GitHub" icon="github" href="https://github.com/embedchain/embedchain/issues/new?assignees=&labels=&projects=&template=feature_request.yml" color="#181717">
+  Open an issue on our GitHub
+  </Card>
+  <Card title="Schedule a call" icon="calendar" href="https://cal.com/taranjeetio/ec">
+  Schedule a call with Embedchain founder
+  </Card>
+</CardGroup>
@@ -0,0 +1,16 @@
+<p>If you can't find the specific LLM you need, no need to fret. We're continuously expanding our support for additional LLMs, and you can help us prioritize by opening an issue on our GitHub or simply reaching out to us on our Slack or Discord community.</p>
+
+<CardGroup cols={2}>
+  <Card title="Slack" icon="slack" href="https://join.slack.com/t/embedchain/shared_invite/zt-22uwz3c46-Zg7cIh5rOBteT_xe1jwLDw" color="#4A154B">
+    Let us know on our slack community
+  </Card>
+  <Card title="Discord" icon="discord" href="https://discord.gg/6PzXDgEjG5" color="#7289DA">
+    Let us know on discord community
+  </Card>
+  <Card title="GitHub" icon="github" href="https://github.com/embedchain/embedchain/issues/new?assignees=&labels=&projects=&template=feature_request.yml" color="#181717">
+  Open an issue on our GitHub
+  </Card>
+  <Card title="Schedule a call" icon="calendar" href="https://cal.com/taranjeetio/ec">
+  Schedule a call with Embedchain founder
+  </Card>
+</CardGroup>
@@ -0,0 +1,18 @@
+
+
+<p>If you can't find the specific vector database, please feel free to request through one of the following channels and help us prioritize.</p>
+
+<CardGroup cols={2}>
+  <Card title="Slack" icon="slack" href="https://join.slack.com/t/embedchain/shared_invite/zt-22uwz3c46-Zg7cIh5rOBteT_xe1jwLDw" color="#4A154B">
+    Let us know on our slack community
+  </Card>
+  <Card title="Discord" icon="discord" href="https://discord.gg/6PzXDgEjG5" color="#7289DA">
+    Let us know on discord community
+  </Card>
+  <Card title="GitHub" icon="github" href="https://github.com/embedchain/embedchain/issues/new?assignees=&labels=&projects=&template=feature_request.yml" color="#181717">
+  Open an issue on our GitHub
+  </Card>
+  <Card title="Schedule a call" icon="calendar" href="https://cal.com/taranjeetio/ec">
+  Schedule a call with Embedchain founder
+  </Card>
+</CardGroup>
@@ -1,213 +0,0 @@
---
-title: '📱 App types'
---
-
-## App Types
-
-Embedchain supports a variety of LLMs, embedding functions/models and vector databases.
-
-Our app gives you full control over which components you want to use, you can mix and match them to your hearts content.
-
-<Tip>
-Out of the box, if you just use `app = App()`, Embedchain uses what we believe to be the best configuration available. This might include paid/proprietary components. Currently, this is
-
-* LLM: OpenAi (gpt-3.5-turbo)
-* Embedder: OpenAi (text-embedding-ada-002)
-* Database: ChromaDB
-</Tip>
-
-### LLM
-
-#### Choosing an LLM
-
-The following LLM providers are supported by Embedchain:
- OPENAI
- ANTHPROPIC
- VERTEX_AI
- GPT4ALL
- AZURE_OPENAI
- LLAMA2
-
-You can choose one by importing it from `embedchain.llm`. E.g.:
-
-```python
-from embedchain import App
-from embedchain.llm.llama2 import Llama2Llm
-
-app = App(llm=Llama2Llm())
-```
-
-#### Configuration
-
-The LLMs can be configured by passing an LlmConfig object.
-
-The config options can be found [here](/advanced/query_configuration#llmconfig)
-
-```python
-from embedchain import App
-from embedchain.llm.llama2 import Llama2Llm
-from embedchain.config import LlmConfig
-
-app = App(llm=Llama2Llm(), llm_config=LlmConfig(number_documents=3, temperature=0))
-```
-
-### Embedder
-
-#### Choosing an Embedder
-
-The following providers for embedding functions are supported by Embedchain:
- OPENAI
- HUGGING_FACE
- VERTEX_AI
- GPT4ALL
- AZURE_OPENAI
-
-You can choose one by importing it from `embedchain.embedder`. E.g.:
-
-```python
-from embedchain import App
-from embedchain.embedder.vertexai import VertexAiEmbedder
-
-app = App(embedder=VertexAiEmbedder())
-```
-
-#### Configuration
-
-The LLMs can be configured by passing an EmbedderConfig object.
-
-```python
-from embedchain import App
-from embedchain.embedder.openai import OpenAiEmbedder
-from embedchain.config import EmbedderConfig
-
-app = App(embedder=OpenAiEmbedder(), embedder_config=EmbedderConfig(model="text-embedding-ada-002"))
-```
-
-<Tip>
-You can also pass an `LlmConfig` instance directly to the `query` or `chat` method.
-This creates a temporary config for that request alone, so you could, for example, use a different model (from the same provider) or get more context documents for a specific query.
-</Tip>
-
-### Vector Database
-
-#### Choosing a Vector Database
-
-The following vector databases are supported by Embedchain:
- ChromaDB
- Elasticsearch
-
-You can choose one by importing it from `embedchain.vectordb`. E.g.:
-
-```python
-from embedchain import App
-from embedchain.vectordb.elasticsearch import ElasticsearchDB
-
-app = App(db=ElasticsearchDB())
-```
-
-#### Configuration
-
-The vector databases can be configured by passing a specific config object.
-
-These vary greatly between the different vector databases.
-
-```python
-from embedchain import App
-from embedchain.vectordb.elasticsearch import ElasticsearchDB
-from embedchain.config import ElasticsearchDBConfig
-
-app = App(db=ElasticsearchDB(), db_config=ElasticsearchDBConfig())
-```
-
-### PersonApp
-
-```python
-from embedchain import PersonApp
-naval_chat_bot = PersonApp("name_of_person_or_character") #Like "Yoda"
-```
-
- `PersonApp` uses OpenAI's model, so these are paid models. 💸 You will be charged for embedding model usage and LLM usage.
- `PersonApp` uses 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 don't 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`
-
-```python
-import os
-os.environ["OPENAI_API_KEY"] = "sk-xxxx"
-```
-
-### Full Configuration Examples
-
-Embedchain previously offered fully configured classes, namely `App`, `OpenSourceApp`, `CustomApp` and `Llama2App`.
-We deprecated these apps. The reason for this decision was that it was hard to switch from to a different LLM, embedder or vector db, if you one day decided that that's what you want to do.
-The new app allows drop-in replacements, such as changing `App(llm=OpenAiLlm())` to `App(llm=Llama2Llm())`.
-
-To make the switch to our new, fully configurable app easier, we provide you with full examples for what the old classes would look like implemented as a new app.
-You can swap these in, and if you decide you want to try a different model one day, you don't have to rewrite your whole bot.
-
-#### App
-App without any configuration is still using the best options available, so you can keep using:
-
-```python
-from embedchain import App
-
-app = App()
-```
-
-#### OpenSourceApp
-
-Use this snippet to run an open source app.
-
-```python
-from embedchain import App
-from embedchain.llm.gpt4all import GPT4ALLLlm
-from embedchain.embedder.gpt4all import GPT4AllEmbedder
-from embedchain.vectordb.chroma import ChromaDB
-
-app = App(llm=GPT4ALLLlm(), embedder=GPT4AllEmbedder(), db=ChromaDB())
-```
-
-#### Llama2App
-```python
-from embedchain import App
-from embedchain.llm.llama2 import Llama2Llm
-
-app = App(llm=Llama2Llm())
-```
-
-#### CustomApp
-
-Every app is a custom app now.
-If you were previously using a `CustomApp`, you can now just change it to `App`.
-
-Here's one example, what you could do if we combined everything shown on this page.
-
-```python
-from embedchain import App
-from embedchain.config import ElasticsearchDBConfig, EmbedderConfig, LlmConfig
-from embedchain.embedder.openai import OpenAiEmbedder
-from embedchain.llm.llama2 import Llama2Llm
-from embedchain.vectordb.elasticsearch import ElasticsearchDB
-
-app = App(
-    llm=Llama2Llm(),
-    llm_config=LlmConfig(number_documents=3, temperature=0),
-    embedder=OpenAiEmbedder(),
-    embedder_config=EmbedderConfig(model="text-embedding-ada-002"),
-    db=ElasticsearchDB(),
-    db_config=ElasticsearchDBConfig(),
-)
-```
-
-### Compatibility with other apps
-
- If there is any other app instance in your script or app, you can change the import as
-
-```python
-from embedchain import App as EmbedChainApp
-from embedchain import PersonApp as EmbedChainPersonApp
-
-# or
-
-from embedchain import App as ECApp
-from embedchain import PersonApp as ECPApp
-```
@@ -1,104 +0,0 @@
---
-title: '⚙️ Custom configurations'
---
-
-Embedchain is made to work out of the box. However, for advanced users we're also offering configuration options. All of these configuration options are optional and have sane defaults.
-
-## Concept
-The main `App` class is available in the following varieties: `CustomApp`, `OpenSourceApp` and `Llama2App` and `App`. The first is fully configurable, the others are opinionated in some aspects.
-
-The `App` class has three subclasses: `llm`, `db` and `embedder`. These are the core ingredients that make up an EmbedChain app.
-App plus each one of the subclasses have a `config` attribute.
-You can pass a `Config` instance as an argument during initialization to persistently configure a class.
-These configs can be imported from `embedchain.config`
-
-There are `set` methods for some things that should not (only) be set at start-up, like `app.db.set_collection_name`.
-
-## Examples
-
-### General
-
-Here's the readme example with configuration options.
-
-```python
-from embedchain import App
-from embedchain.config import AppConfig, AddConfig, LlmConfig, ChunkerConfig
-
-# Example: set the log level for debugging
-config = AppConfig(log_level="DEBUG")
-naval_chat_bot = App(config)
-
-# Example: specify a custom collection name
-naval_chat_bot.db.set_collection_name("naval_chat_bot")
-
-# Example: define your own chunker config for `youtube_video`
-chunker_config = ChunkerConfig(chunk_size=1000, chunk_overlap=100, length_function=len)
-# Example: Add your chunker config to an AddConfig to actually use it
-add_config = AddConfig(chunker=chunker_config)
-naval_chat_bot.add("https://www.youtube.com/watch?v=3qHkcs3kG44", config=add_config)
-
-# Example: Reset to default
-add_config = AddConfig()
-naval_chat_bot.add("https://navalmanack.s3.amazonaws.com/Eric-Jorgenson_The-Almanack-of-Naval-Ravikant_Final.pdf", config=add_config)
-naval_chat_bot.add("https://nav.al/feedback", config=add_config)
-naval_chat_bot.add("https://nav.al/agi", config=add_config)
-naval_chat_bot.add(("Who is Naval Ravikant?", "Naval Ravikant is an Indian-American entrepreneur and investor."), config=add_config)
-
-# Change the number of documents.
-query_config = LlmConfig(number_documents=5)
-print(naval_chat_bot.query("What unique capacity does Naval argue humans possess when it comes to understanding explanations or concepts?", config=query_config))
-```
-
-### Custom prompt template
-
-Here's the example of using custom prompt template with `.query`
-
-```python
-from string import Template
-
-import wikipedia
-
-from embedchain import App
-from embedchain.config import LlmConfig
-
-einstein_chat_bot = App()
-
-# Embed Wikipedia page
-page = wikipedia.page("Albert Einstein")
-einstein_chat_bot.add(page.content)
-
-# Example: use your own custom template with `$context` and `$query`
-einstein_chat_template = Template(
-    """
-        You are Albert Einstein, a German-born theoretical physicist,
-        widely ranked among the greatest and most influential scientists of all time.
-
-        Use the following information about Albert Einstein to respond to
-        the human's query acting as Albert Einstein.
-        Context: $context
-
-        Keep the response brief. If you don't know the answer, just say that you don't know, don't try to make up an answer.
-
-        Human: $query
-        Albert Einstein:"""
-)
-# Example: Use the template, also add a system prompt.
-llm_config = LlmConfig(template=einstein_chat_template, system_prompt="You are Albert Einstein.")
-queries = [
-    "Where did you complete your studies?",
-    "Why did you win nobel prize?",
-    "Why did you divorce your first wife?",
-]
-for query in queries:
-    response = einstein_chat_bot.query(query, config=llm_config)
-    print("Query: ", query)
-    print("Response: ", response)
-
-# Output
-# Query:  Where did you complete your studies?
-# Response:  I completed my secondary education at the Argovian cantonal school in Aarau, Switzerland.
-# Query:  Why did you win nobel prize?
-# Response:  I won the Nobel Prize in Physics in 1921 for my services to Theoretical Physics, particularly for my discovery of the law of the photoelectric effect.
-# Query:  Why did you divorce your first wife?
-# Response:  We divorced due to living apart for five years.
-```
@@ -1,75 +0,0 @@
---
-title: '🤝 Interface types'
---
-
-## Interface Types
-
-The embedchain app exposes the following methods.
-
-### Query Interface
-
- This interface is like a question answering bot. It takes a question and gets the answer. It does not maintain context about the previous chats.❓
-
- To use this, call `.query()` function to get the answer for any query.
-
-```python
-print(naval_chat_bot.query("What unique capacity does Naval argue humans possess when it comes to understanding explanations or concepts?"))
-# answer: Naval argues that humans possess the unique capacity to understand explanations or concepts to the maximum extent possible in this physical reality.
-```
-
-### Chat Interface
-
- This interface is a chat interface that remembers previous conversations. Right now it remembers 5 conversations by default. 💬
-
- To use this, call `.chat` function to get the answer for any query.
-
-```python
-print(naval_chat_bot.chat("How to be happy in life?"))
-# answer: The most important trick to being happy is to realize happiness is a skill you develop and a choice you make. You choose to be happy, and then you work at it. It's just like building muscles or succeeding at your job. It's about recognizing the abundance and gifts around you at all times.
-
-print(naval_chat_bot.chat("who is naval ravikant?"))
-# answer: Naval Ravikant is an Indian-American entrepreneur and investor.
-
-print(naval_chat_bot.chat("what did the author say about happiness?"))
-# answer: The author, Naval Ravikant, believes that happiness is a choice you make and a skill you develop. He compares the mind to the body, stating that just as the body can be molded and changed, so can the mind. He emphasizes the importance of being present in the moment and not getting caught up in regrets of the past or worries about the future. By being present and grateful for where you are, you can experience true happiness.
-```
-
-#### Dry Run
-
-Dry Run is an option in the `add`, `query` and `chat` methods that allows the user to display the data chunks and their constructed prompt which is not sent to the LLM, to save money. It's used for [testing](/advanced/testing#dry-run).
-
-
-### Stream Response
-
- You can add config to your query method to stream responses like ChatGPT does. You would require a downstream handler to render the chunk in your desirable format. Supports both OpenAI model and OpenSourceApp. 📊
-
- To use this, instantiate a `LlmConfig` or `ChatConfig` object with `stream=True`. Then pass it to the `.chat()` or `.query()` method. The following example iterates through the chunks and prints them as they appear.
-
-```python
-app = App()
-query_config = LlmConfig(stream = True)
-resp = app.query("What unique capacity does Naval argue humans possess when it comes to understanding explanations or concepts?", query_config)
-
-for chunk in resp:
-    print(chunk, end="", flush=True)
-# answer: Naval argues that humans possess the unique capacity to understand explanations or concepts to the maximum extent possible in this physical reality.
-```
-
-### Other Methods
-
-#### Reset
-
-Resets the database and deletes all embeddings. Irreversible. Requires reinitialization afterwards.
-
-```python
-app.reset()
-```
-
-#### Count
-
-Counts the number of embeddings (chunks) in the database.
-
-```python
-print(app.db.count())
-# returns: 481
-```
@@ -1,79 +0,0 @@
---
-title: '🔍 Query configurations'
---
-
-## AppConfig
-
-| option    | description           | type                            | default                |
-|-----------|-----------------------|---------------------------------|------------------------|
-| log_level | log level             | string                          | WARNING                |
-| embedding_fn| embedding function    | chromadb.utils.embedding_functions | \{text-embedding-ada-002\} |
-| db        | vector database (experimental) | BaseVectorDB               | ChromaDB               |
-| collection_name | initial collection name for the database | string             | embedchain_store |
-| collect_metrics | collect anonymous telemetry data to improve embedchain | boolean | true |
-
-
-## AddConfig
-
-|option|description|type|default|
-|---|---|---|---|
-|chunker|chunker config|ChunkerConfig|Default values for chunker depends on the `data_type`. Please refer [ChunkerConfig](#chunker-config)|
-|loader|loader config|LoaderConfig|None|
-
-Yes, you are passing `ChunkerConfig` to `AddConfig`, like so:
-
-```python
-chunker_config = ChunkerConfig(chunk_size=100)
-add_config = AddConfig(chunker=chunker_config)
-app.add("lorem ipsum", config=add_config)
-```
-
-### ChunkerConfig
-
-|option|description|type|default|
-|---|---|---|---|
-|chunk_size|Maximum size of chunks to return|int|Default value for various `data_type` mentioned below|
-|chunk_overlap|Overlap in characters between chunks|int|Default value for various `data_type` mentioned below|
-|length_function|Function that measures the length of given chunks|typing.Callable|Default value for various `data_type` mentioned below|
-
-Default values of chunker config parameters for different `data_type`:
-
-|data_type|chunk_size|chunk_overlap|length_function|
-|---|---|---|---|
-|docx|1000|0|len|
-|text|300|0|len|
-|qna_pair|300|0|len|
-|web_page|500|0|len|
-|pdf_file|1000|0|len|
-|youtube_video|2000|0|len|
-|docs_site|500|50|len|
-|notion|300|0|len|
-
-### LoaderConfig
-
-_coming soon_
-
-## LlmConfig
-
-|option|description|type|default|
-|---|---|---|---|
-|number_documents|Absolute number of documents to pull from the database as context.|int|1
-|template|custom template for prompt. If history is used with query, $history has to be included as well.|Template|Template("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. \$context Query: \$query Helpful Answer:")|
-|model|name of the model used.|string|depends on app type|
-|temperature|Controls the randomness of the model's output. Higher values (closer to 1) make output more random, lower values make it more deterministic.|float|0|
-|max_tokens|Controls how many tokens are used. Exact implementation (whether it counts prompt and/or response) depends on the model.|int|1000|
-|top_p|Controls the diversity of words. Higher values (closer to 1) make word selection more diverse, lower values make words less diverse.|float|1|
-|history|include conversation history from your client or database.|any (recommendation: list[str])|None|
-|stream|control if response is streamed back to the user.|bool|False|
-|deployment_name|t.b.a.|str|None|
-|system_prompt|System prompt string. Unused if none.|str|None|
-|where|filter for context search.|dict|None|
-
-
-## ChatConfig
-
-All options for query and...
-
-_coming soon_
-
-`history` is not supported, as that is handled is handled automatically, the config option is not supported.
@@ -1,40 +0,0 @@
---
-title: '🧪 Testing'
---
-
-## Methods for testing
-
-### Dry Run
-
-Before you consume valueable tokens, you should make sure that data chunks are properly created and the embedding you have done works and that it's receiving the correct document from the database.
-
- For `query` or `chat` method, you can add this to your script:
-
-```python
-print(naval_chat_bot.query('Can you tell me who Naval Ravikant is?', dry_run=True))
-
-'''
-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.
-        Q: Who is Naval Ravikant?
-A: Naval Ravikant is an Indian-American entrepreneur and investor.
-        Query: Can you tell me who Naval Ravikant is?
-        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.**
-
-
- For `add` method, you can add this to your script:
-
-```python
-print(naval_chat_bot.add('https://navalmanack.s3.amazonaws.com/Eric-Jorgenson_The-Almanack-of-Naval-Ravikant_Final.pdf', dry_run=True))
-
-'''
-{'chunks': ['THE ALMANACK OF NAVAL RAVIKANT', 'GETTING RICH IS NOT JUST ABOUT LUCK;', 'HAPPINESS IS NOT JUST A TRAIT WE ARE'], 'metadata': [{'source': 'C:\\Users\\Dev\\AppData\\Local\\Temp\\tmp3g5mjoiz\\tmp.pdf', 'page': 0, 'url': 'https://navalmanack.s3.amazonaws.com/Eric-Jorgenson_The-Almanack-of-Naval-Ravikant_Final.pdf', 'data_type': 'pdf_file'}, {'source': 'C:\\Users\\Dev\\AppData\\Local\\Temp\\tmp3g5mjoiz\\tmp.pdf', 'page': 2, 'url': 'https://navalmanack.s3.amazonaws.com/Eric-Jorgenson_The-Almanack-of-Naval-Ravikant_Final.pdf', 'data_type': 'pdf_file'}, {'source': 'C:\\Users\\Dev\\AppData\\Local\\Temp\\tmp3g5mjoiz\\tmp.pdf', 'page': 2, 'url': 'https://navalmanack.s3.amazonaws.com/Eric-Jorgenson_The-Almanack-of-Naval-Ravikant_Final.pdf', 'data_type': 'pdf_file'}], 'count': 7358, 'type': <DataType.PDF_FILE: 'pdf_file'>}
-
-# less items to show for readability
-'''
-```
@@ -1,118 +0,0 @@
---
-title: '💾 Vector Database'
---
-
-We support `Chroma`, `Elasticsearch` and `OpenSearch` as vector databases.
-`Chroma` is used as a default database.
-
-## Elasticsearch
-
-### Minimal Example
-
-In order to use `Elasticsearch` as vector database we need to use App type `CustomApp`.
-
-1. Set the environment variables in a `.env` file.
-```
-OPENAI_API_KEY=sk-SECRETKEY
-ELASTICSEARCH_API_KEY=SECRETKEY==
-ELASTICSEARCH_URL=https://secret-domain.europe-west3.gcp.cloud.es.io:443
-```
-Please note that the key needs certain privileges. For testing you can just toggle off `restrict privileges` under `/app/management/security/api_keys/` in your web interface.
-
-2. Load the app
-```python
-from embedchain import CustomApp
-from embedchain.embedder.openai import OpenAIEmbedder
-from embedchain.llm.openai import OpenAILlm
-from embedchain.vectordb.elasticsearch import ElasticsearchDB
-
-es_app = CustomApp(
-    llm=OpenAILlm(),
-    embedder=OpenAIEmbedder(),
-    db=ElasticsearchDB(),
-)
-```
-
-### More custom settings
-
-You can get a URL for elasticsearch in the cloud, or run it locally. 
-The following example shows you how to configure embedchain to work with a locally running elasticsearch.
-
-Instead of using an API key, we use http login credentials. The localhost url can be defined in .env or in the config.
-
-```python
-import os
-
-from embedchain import CustomApp
-from embedchain.config import CustomAppConfig, ElasticsearchDBConfig
-from embedchain.embedder.openai import OpenAIEmbedder
-from embedchain.llm.openai import OpenAILlm
-from embedchain.vectordb.elasticsearch import ElasticsearchDB
-
-es_config = ElasticsearchDBConfig(
-    # elasticsearch url or list of nodes url with different hosts and ports.
-    es_url='https://localhost:9200',
-    # pass named parameters supported by Python Elasticsearch client
-    http_auth=("elastic", "secret"),
-    ca_certs="~/binaries/elasticsearch-8.7.0/config/certs/http_ca.crt" # your cert path
-    # verify_certs=False # Alternative, if you aren't using certs
-) # pass named parameters supported by elasticsearch-py
-
-es_app = CustomApp(
-    config=CustomAppConfig(log_level="INFO"),
-    llm=OpenAILlm(),
-    embedder=OpenAIEmbedder(),
-    db=ElasticsearchDB(config=es_config),
-)
-```
-3. This should log your connection details to the console.
-4. Alternatively to a URL, you `ElasticsearchDBConfig` accepts `es_url` as a list of nodes url with different hosts and ports.
-5. Additionally we can pass named parameters supported by Python Elasticsearch client.
-
-
-## OpenSearch 🔍
-
-To use OpenSearch as a vector database with a CustomApp, follow these simple steps:
-
-1. Set the `OPENAI_API_KEY` environment variable:
-
-```
-OPENAI_API_KEY=sk-xxxx
-```
-
-2. Define the OpenSearch configuration in your Python code:
-
-```python
-from embedchain import CustomApp
-from embedchain.config import OpenSearchDBConfig
-from embedchain.embedder.openai import OpenAIEmbedder
-from embedchain.llm.openai import OpenAILlm
-from embedchain.vectordb.opensearch import OpenSearchDB
-
-opensearch_url = "https://localhost:9200"
-http_auth = ("username", "password")
-
-db_config = OpenSearchDBConfig(
-    opensearch_url=opensearch_url,
-    http_auth=http_auth,
-    collection_name="embedchain-app",
-    use_ssl=True,
-    timeout=30,
-)
-db = OpenSearchDB(config=db_config)
-```
-
-2. Instantiate the app and add data:
-
-```python
-app = CustomApp(llm=OpenAILlm(), embedder=OpenAIEmbedder(), db=db)
-app.add("https://en.wikipedia.org/wiki/Elon_Musk")
-app.add("https://www.forbes.com/profile/elon-musk")
-app.add("https://www.britannica.com/biography/Elon-Musk")
-```
-
-3. You're all set! Start querying using the following command:
-
-```python
-app.query("What is the net worth of Elon Musk?")
-```
@@ -0,0 +1,236 @@
+---
+title: 'Custom configurations'
+---
+
+Embedchain offers several configuration options for your LLM, vector database, and embedding model. All of these configuration options are optional and have sane defaults.
+
+You can configure different components of your app (`llm`, `embedding model`, or `vector database`) through a simple yaml configuration that Embedchain offers. Here is a generic full-stack example of the yaml config:
+
+
+<Tip>
+Embedchain applications are configurable using YAML file, JSON file or by directly passing the config dictionary. Checkout the [docs here](/api-reference/pipeline/overview#usage) on how to use other formats.
+</Tip>
+
+<CodeGroup>
+```yaml config.yaml
+app:
+  config:
+    name: 'full-stack-app'
+
+llm:
+  provider: openai
+  config:
+    model: 'gpt-3.5-turbo'
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 1
+    stream: false
+    api_key: sk-xxx
+    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.
+
+      $context
+
+      Query: $query
+
+      Helpful Answer:
+    system_prompt: |
+      Act as William Shakespeare. Answer the following questions in the style of William Shakespeare.
+
+vectordb:
+  provider: chroma
+  config:
+    collection_name: 'full-stack-app'
+    dir: db
+    allow_reset: true
+
+embedder:
+  provider: openai
+  config:
+    model: 'text-embedding-ada-002'
+    api_key: sk-xxx
+
+chunker:
+  chunk_size: 2000
+  chunk_overlap: 100
+  length_function: 'len'
+  min_chunk_size: 0
+
+cache:
+  similarity_evaluation:
+    strategy: distance
+    max_distance: 1.0
+  config:
+    similarity_threshold: 0.8
+    auto_flush: 50
+```
+
+```json config.json
+{
+  "app": {
+    "config": {
+      "name": "full-stack-app"
+    }
+  },
+  "llm": {
+    "provider": "openai",
+    "config": {
+      "model": "gpt-3.5-turbo",
+      "temperature": 0.5,
+      "max_tokens": 1000,
+      "top_p": 1,
+      "stream": false,
+      "prompt": "Use the following pieces of context to answer the query at the end.\nIf you don't know the answer, just say that you don't know, don't try to make up an answer.\n$context\n\nQuery: $query\n\nHelpful Answer:",
+      "system_prompt": "Act as William Shakespeare. Answer the following questions in the style of William Shakespeare.",
+      "api_key": "sk-xxx"
+    }
+  },
+  "vectordb": {
+    "provider": "chroma",
+    "config": {
+      "collection_name": "full-stack-app",
+      "dir": "db",
+      "allow_reset": true
+    }
+  },
+  "embedder": {
+    "provider": "openai",
+    "config": {
+      "model": "text-embedding-ada-002",
+      "api_key": "sk-xxx"
+    }
+  },
+  "chunker": {
+    "chunk_size": 2000,
+    "chunk_overlap": 100,
+    "length_function": "len",
+    "min_chunk_size": 0
+  },
+  "cache": {
+    "similarity_evaluation": {
+        "strategy": "distance",
+        "max_distance": 1.0,
+    },
+    "config": {
+        "similarity_threshold": 0.8,
+        "auto_flush": 50,
+    },
+  },
+}
+```
+
+```python config.py
+config = {
+    'app': {
+        'config': {
+            'name': 'full-stack-app'
+        }
+    },
+    'llm': {
+        'provider': 'openai',
+        'config': {
+            'model': 'gpt-3.5-turbo',
+            'temperature': 0.5,
+            'max_tokens': 1000,
+            'top_p': 1,
+            'stream': False,
+            'prompt': (
+                "Use the following pieces of context to answer the query at the end.\n"
+                "If you don't know the answer, just say that you don't know, don't try to make up an answer.\n"
+                "$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'
+        }
+    },
+    'vectordb': {
+        'provider': 'chroma',
+        'config': {
+            'collection_name': 'full-stack-app',
+            'dir': 'db',
+            'allow_reset': True
+        }
+    },
+    'embedder': {
+        'provider': 'openai',
+        'config': {
+            'model': 'text-embedding-ada-002',
+            'api_key': 'sk-xxx'
+        }
+    },
+    'chunker': {
+        'chunk_size': 2000,
+        'chunk_overlap': 100,
+        'length_function': 'len',
+        'min_chunk_size': 0
+    },
+    'cache': {
+      'similarity_evaluation': {
+          'strategy': 'distance',
+          'max_distance': 1.0,
+      },
+      'config': {
+          'similarity_threshold': 0.8,
+          'auto_flush': 50,
+      },
+    },
+}
+```
+</CodeGroup>
+
+Alright, let's dive into what each key means in the yaml config above:
+
+1. `app` Section:
+    - `config`:
+        - `name` (String): The name of your full-stack application.
+        - `id` (String): The id of your full-stack application.
+        <Note>Only use this to reload already created apps. We recommend users to not create their own ids.</Note>
+        - `collect_metrics` (Boolean): Indicates whether metrics should be collected for the app, defaults to `True`
+        - `log_level` (String): The log level for the app, defaults to `WARNING`
+2. `llm` Section:
+    - `provider` (String): The provider for the language model, which is set to 'openai'. You can find the full list of llm providers in [our docs](/components/llms).
+    - `config`:
+        - `model` (String): The specific model being used, 'gpt-3.5-turbo'.
+        - `temperature` (Float): Controls the randomness of the model's output. A higher value (closer to 1) makes the output more random.
+        - `max_tokens` (Integer): Controls how many tokens are used in the response.
+        - `top_p` (Float): Controls the diversity of word selection. A higher value (closer to 1) makes word selection more diverse.
+        - `stream` (Boolean): Controls if the response is streamed back to the user (set to false).
+        - `prompt` (String): A prompt for the model to follow when generating responses, requires `$context` and `$query` variables.
+        - `system_prompt` (String): A system prompt for the model to follow when generating responses, in this case, it's set to the style of William Shakespeare.
+        -  `stream` (Boolean): Controls if the response is streamed back to the user (set to false).
+        - `number_documents` (Integer): Number of documents to pull from the vectordb as context, defaults to 1
+        - `api_key` (String): The API key for the language model.
+3. `vectordb` Section:
+    - `provider` (String): The provider for the vector database, set to 'chroma'. You can find the full list of vector database providers in [our docs](/components/vector-databases).
+    - `config`:
+        - `collection_name` (String): The initial collection name for the vectordb, set to 'full-stack-app'.
+        - `dir` (String): The directory for the local database, set to 'db'.
+        - `allow_reset` (Boolean): Indicates whether resetting the vectordb is allowed, set to true.
+    <Note>We recommend you to checkout vectordb specific config [here](https://docs.embedchain.ai/components/vector-databases)</Note>
+4. `embedder` Section:
+    - `provider` (String): The provider for the embedder, set to 'openai'. You can find the full list of embedding model providers in [our docs](/components/embedding-models).
+    - `config`:
+        - `model` (String): The specific model used for text embedding, 'text-embedding-ada-002'.
+        - `api_key` (String): The API key for the embedding model.
+5. `chunker` Section:
+    - `chunk_size` (Integer): The size of each chunk of text that is sent to the language model.
+    - `chunk_overlap` (Integer): The amount of overlap between each chunk of text.
+    - `length_function` (String): The function used to calculate the length of each chunk of text. In this case, it's set to 'len'. You can also use any function import directly as a string here.
+    - `min_chunk_size` (Integer): The minimum size of each chunk of text that is sent to the language model. Must be less than `chunk_size`, and greater than `chunk_overlap`.
+6. `cache` Section: (Optional)
+    - `similarity_evaluation` (Optional): The config for similarity evaluation strategy. If not provided, the default `distance` based similarity evaluation strategy is used.
+      - `strategy` (String): The strategy to use for similarity evaluation. Currently, only `distance` and `exact` based similarity evaluation is supported. Defaults to `distance`.
+      - `max_distance` (Float): The bound of maximum distance. Defaults to `1.0`.
+      - `positive` (Boolean): If the larger distance indicates more similar of two entities, set it `True`, otherwise `False`. Defaults to `False`.
+    - `config` (Optional): The config for initializing the cache. If not provided, sensible default values are used as mentioned below.
+      - `similarity_threshold` (Float): The threshold for similarity evaluation. Defaults to `0.8`.
+      - `auto_flush` (Integer): The number of queries after which the cache is flushed. Defaults to `20`.
+    <Note>
+    If you provide a cache section, the app will automatically configure and use a cache to store the results of the language model. This is useful if you want to speed up the response time and save inference cost of your app.
+    </Note>
+If you have questions about the configuration above, please feel free to reach out to us using one of the following methods:
+
+<Snippet file="get-help.mdx" />
@@ -0,0 +1,44 @@
+---
+title: '📊 add'
+---
+
+`add()` method is used to load the data sources from different data sources to a RAG pipeline. You can find the signature below:
+
+### Parameters
+
+<ParamField path="source" type="str">
+    The data to embed, can be a URL, local file or raw content, depending on the data type.. You can find the full list of supported data sources [here](/components/data-sources/overview).
+</ParamField>
+<ParamField path="data_type" type="str" optional>
+    Type of data source. It can be automatically detected but user can force what data type to load as.
+</ParamField>
+<ParamField path="metadata" type="dict" optional>
+    Any metadata that you want to store with the data source. Metadata is generally really useful for doing metadata filtering on top of semantic search to yield faster search and better results.
+</ParamField>
+
+## Usage
+
+### Load data from webpage
+
+```python Code example
+from embedchain import App
+
+app = App()
+app.add("https://www.forbes.com/profile/elon-musk")
+# Inserting batches in chromadb: 100%|███████████████| 1/1 [00:00<00:00,  1.19it/s]
+# Successfully saved https://www.forbes.com/profile/elon-musk (DataType.WEB_PAGE). New chunks count: 4
+```
+
+### Load data from sitemap
+
+```python Code example
+from embedchain import App
+
+app = App()
+app.add("https://python.langchain.com/sitemap.xml", data_type="sitemap")
+# Loading pages: 100%|█████████████| 1108/1108 [00:47<00:00, 23.17it/s]
+# Inserting batches in chromadb: 100%|█████████| 111/111 [04:41<00:00,  2.54s/it]
+# Successfully saved https://python.langchain.com/sitemap.xml (DataType.SITEMAP). New chunks count: 11024
+```
+
+You can find complete list of supported data sources [here](/components/data-sources/overview).
@@ -0,0 +1,131 @@
+---
+title: '💬 chat'
+---
+
+`chat()` method allows you to chat over your data sources using a user-friendly chat API. You can find the signature below:
+
+### Parameters
+
+<ParamField path="input_query" type="str">
+    Question to ask
+</ParamField>
+<ParamField path="config" type="BaseLlmConfig" optional>
+    Configure different llm settings such as prompt, temprature, number_documents etc.
+</ParamField>
+<ParamField path="dry_run" type="bool" optional>
+    The purpose is to test the prompt structure without actually running LLM inference. Defaults to `False`
+</ParamField>
+<ParamField path="where" type="dict" optional>
+    A dictionary of key-value pairs to filter the chunks from the vector database. Defaults to `None`
+</ParamField>
+<ParamField path="session_id" type="str" optional>
+    Session ID of the chat. This can be used to maintain chat history of different user sessions. Default value: `default`
+</ParamField>
+<ParamField path="citations" type="bool" optional>
+    Return citations along with the LLM answer. Defaults to `False`
+</ParamField>
+
+### Returns
+
+<ResponseField name="answer" type="str | tuple">
+  If `citations=False`, return a stringified answer to the question asked. <br />
+  If `citations=True`, returns a tuple with answer and citations respectively.
+</ResponseField>
+
+## Usage
+
+### With citations
+
+If you want to get the answer to question and return both answer and citations, use the following code snippet:
+
+```python With Citations
+from embedchain import App
+
+# Initialize app
+app = App()
+
+# Add data source
+app.add("https://www.forbes.com/profile/elon-musk")
+
+# Get relevant answer for your query
+answer, sources = app.chat("What is the net worth of Elon?", citations=True)
+print(answer)
+# Answer: The net worth of Elon Musk is $221.9 billion.
+
+print(sources)
+# [
+#    (
+#        'Elon Musk PROFILEElon MuskCEO, Tesla$247.1B$2.3B (0.96%)Real Time Net Worthas of 12/7/23 ...',
+#        {
+#           'url': 'https://www.forbes.com/profile/elon-musk', 
+#           'score': 0.89,
+#           ...
+#        }
+#    ),
+#    (
+#        '74% of the company, which is now called X.Wealth HistoryHOVER TO REVEAL NET WORTH BY YEARForbes ...',
+#        {
+#           'url': 'https://www.forbes.com/profile/elon-musk', 
+#           'score': 0.81,
+#           ...
+#        }
+#    ),
+#    (
+#        'founded in 2002, is worth nearly $150 billion after a $750 million tender offer in June 2023 ...',
+#        {
+#           'url': 'https://www.forbes.com/profile/elon-musk', 
+#           'score': 0.73,
+#           ...
+#        }
+#    )
+# ]
+```
+
+<Note>
+When `citations=True`, note that the returned `sources` are a list of tuples where each tuple has two elements (in the following order):
+1. source chunk
+2. dictionary with metadata about the source chunk
+    - `url`: url of the source
+    - `doc_id`: document id (used for book keeping purposes)
+    - `score`: score of the source chunk with respect to the question
+    - other metadata you might have added at the time of adding the source
+</Note>
+
+
+### Without citations
+
+If you just want to return answers and don't want to return citations, you can use the following example:
+
+```python Without Citations
+from embedchain import App
+
+# Initialize app
+app = App()
+
+# Add data source
+app.add("https://www.forbes.com/profile/elon-musk")
+
+# Chat on your data using `.chat()`
+answer = app.chat("What is the net worth of Elon?")
+print(answer)
+# Answer: The net worth of Elon Musk is $221.9 billion.
+```
+
+### With session id
+
+If you want to maintain chat sessions for different users, you can simply pass the `session_id` keyword argument. See the example below:
+
+```python With session id
+from embedchain import App
+
+app = App()
+app.add("https://www.forbes.com/profile/elon-musk")
+
+# Chat on your data using `.chat()`
+app.chat("What is the net worth of Elon Musk?", session_id="user1")
+# 'The net worth of Elon Musk is $250.8 billion.'
+app.chat("What is the net worth of Bill Gates?", session_id="user2")
+# "I don't know the current net worth of Bill Gates."
+app.chat("What was my last question", session_id="user1")
+# 'Your last question was "What is the net worth of Elon Musk?"'
+```
@@ -0,0 +1,19 @@
+---
+title: 🗑 delete
+---
+
+`delete_chat_history()` method allows you to delete all previous messages in a chat history.
+
+## Usage
+
+```python
+from embedchain import App
+
+app = App()
+
+app.add("https://www.forbes.com/profile/elon-musk")
+
+app.chat("What is the net worth of Elon Musk?")
+
+app.delete_chat_history()
+```
@@ -0,0 +1,31 @@
+---
+title: 🚀 deploy
+---
+
+Using the `deploy()` method, Embedchain allows developers to easily launch their LLM-powered applications on the [Embedchain Platform](https://app.embedchain.ai). This platform facilitates seamless access to your data's context via a free and user-friendly REST API. Once your pipeline is deployed, you can update your data sources at any time.
+
+The `deploy()` method not only deploys your pipeline but also efficiently manages LLMs, vector databases, embedding models, and data syncing, enabling you to focus on querying, chatting, or searching without the hassle of infrastructure management.
+
+## Usage
+
+```python
+from embedchain import App
+
+# Initialize app
+app = App()
+
+# Add data source
+app.add("https://www.forbes.com/profile/elon-musk")
+
+# Deploy your pipeline to Embedchain Platform
+app.deploy()
+
+# 🔑 Enter your Embedchain API key. You can find the API key at https://app.embedchain.ai/settings/keys/
+# ec-xxxxxx
+
+# 🛠️ Creating pipeline on the platform...
+# 🎉🎉🎉 Pipeline created successfully! View your pipeline: https://app.embedchain.ai/pipelines/xxxxx
+
+# 🛠️ Adding data to your pipeline...
+# ✅ Data of type: web_page, value: https://www.forbes.com/profile/elon-musk added successfully.
+```
@@ -0,0 +1,130 @@
+---
+title: "Pipeline"
+---
+
+Create a RAG pipeline object on Embedchain. This is the main entrypoint for a developer to interact with Embedchain APIs. A pipeline configures the llm, vector database, embedding model, and retrieval strategy of your choice.
+
+### Attributes
+
+<ParamField path="local_id" type="str">
+    Pipeline ID
+</ParamField>
+<ParamField path="name" type="str" optional>
+    Name of the pipeline
+</ParamField>
+<ParamField path="config" type="BaseConfig">
+    Configuration of the pipeline
+</ParamField>
+<ParamField path="llm" type="BaseLlm">
+    Configured LLM for the RAG pipeline
+</ParamField>
+<ParamField path="db" type="BaseVectorDB">
+    Configured vector database for the RAG pipeline
+</ParamField>
+<ParamField path="embedding_model" type="BaseEmbedder">
+    Configured embedding model for the RAG pipeline
+</ParamField>
+<ParamField path="chunker" type="ChunkerConfig">
+    Chunker configuration
+</ParamField>
+<ParamField path="client" type="Client" optional>
+    Client object (used to deploy a pipeline to Embedchain platform)
+</ParamField>
+<ParamField path="logger" type="logging.Logger">
+    Logger object
+</ParamField>
+
+## Usage
+
+You can create an embedchain pipeline instance using the following methods:
+
+### Default setting
+
+```python Code Example
+from embedchain import App
+app = App()
+```
+
+
+### Python Dict
+
+```python Code Example
+from embedchain import App
+
+config_dict = {
+  'llm': {
+    'provider': 'gpt4all',
+    'config': {
+      'model': 'orca-mini-3b-gguf2-q4_0.gguf',
+      'temperature': 0.5,
+      'max_tokens': 1000,
+      'top_p': 1,
+      'stream': False
+    }
+  },
+  'embedder': {
+    'provider': 'gpt4all'
+  }
+}
+
+# load llm configuration from config dict
+app = App.from_config(config=config_dict)
+```
+
+### YAML Config
+
+<CodeGroup>
+
+```python main.py
+from embedchain import App
+
+# load llm configuration from config.yaml file
+app = App.from_config(config_path="config.yaml")
+```
+
+```yaml config.yaml
+llm:
+  provider: gpt4all
+  config:
+    model: 'orca-mini-3b-gguf2-q4_0.gguf'
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 1
+    stream: false
+
+embedder:
+  provider: gpt4all
+```
+
+</CodeGroup>
+
+### JSON Config
+
+<CodeGroup>
+
+```python main.py
+from embedchain import App
+
+# load llm configuration from config.json file
+app = App.from_config(config_path="config.json")
+```
+
+```json config.json
+{
+  "llm": {
+    "provider": "gpt4all",
+    "config": {
+      "model": "orca-mini-3b-gguf2-q4_0.gguf",
+      "temperature": 0.5,
+      "max_tokens": 1000,
+      "top_p": 1,
+      "stream": false
+    }
+  },
+  "embedder": {
+    "provider": "gpt4all"
+  }
+}
+```
+
+</CodeGroup>
@@ -0,0 +1,109 @@
+---
+title: '❓ query'
+---
+
+`.query()` method empowers developers to ask questions and receive relevant answers through a user-friendly query API. Function signature is given below:
+
+### Parameters
+
+<ParamField path="input_query" type="str">
+    Question to ask
+</ParamField>
+<ParamField path="config" type="BaseLlmConfig" optional>
+    Configure different llm settings such as prompt, temprature, number_documents etc.
+</ParamField>
+<ParamField path="dry_run" type="bool" optional>
+    The purpose is to test the prompt structure without actually running LLM inference. Defaults to `False`
+</ParamField>
+<ParamField path="where" type="dict" optional>
+    A dictionary of key-value pairs to filter the chunks from the vector database. Defaults to `None`
+</ParamField>
+<ParamField path="citations" type="bool" optional>
+    Return citations along with the LLM answer. Defaults to `False`
+</ParamField>
+
+### Returns
+
+<ResponseField name="answer" type="str | tuple">
+  If `citations=False`, return a stringified answer to the question asked. <br />
+  If `citations=True`, returns a tuple with answer and citations respectively.
+</ResponseField>
+
+## Usage
+
+### With citations
+
+If you want to get the answer to question and return both answer and citations, use the following code snippet:
+
+```python With Citations
+from embedchain import App
+
+# Initialize app
+app = App()
+
+# Add data source
+app.add("https://www.forbes.com/profile/elon-musk")
+
+# Get relevant answer for your query
+answer, sources = app.query("What is the net worth of Elon?", citations=True)
+print(answer)
+# Answer: The net worth of Elon Musk is $221.9 billion.
+
+print(sources)
+# [
+#    (
+#        'Elon Musk PROFILEElon MuskCEO, Tesla$247.1B$2.3B (0.96%)Real Time Net Worthas of 12/7/23 ...',
+#        {
+#           'url': 'https://www.forbes.com/profile/elon-musk', 
+#           'score': 0.89,
+#           ...
+#        }
+#    ),
+#    (
+#        '74% of the company, which is now called X.Wealth HistoryHOVER TO REVEAL NET WORTH BY YEARForbes ...',
+#        {
+#           'url': 'https://www.forbes.com/profile/elon-musk', 
+#           'score': 0.81,
+#           ...
+#        }
+#    ),
+#    (
+#        'founded in 2002, is worth nearly $150 billion after a $750 million tender offer in June 2023 ...',
+#        {
+#           'url': 'https://www.forbes.com/profile/elon-musk', 
+#           'score': 0.73,
+#           ...
+#        }
+#    )
+# ]
+```
+
+<Note>
+When `citations=True`, note that the returned `sources` are a list of tuples where each tuple has two elements (in the following order):
+1. source chunk
+2. dictionary with metadata about the source chunk
+    - `url`: url of the source
+    - `doc_id`: document id (used for book keeping purposes)
+    - `score`: score of the source chunk with respect to the question
+    - other metadata you might have added at the time of adding the source
+</Note>
+
+### Without citations
+
+If you just want to return answers and don't want to return citations, you can use the following example:
+
+```python Without Citations
+from embedchain import App
+
+# Initialize app
+app = App()
+
+# Add data source
+app.add("https://www.forbes.com/profile/elon-musk")
+
+# Get relevant answer for your query
+answer = app.query("What is the net worth of Elon?")
+print(answer)
+# Answer: The net worth of Elon Musk is $221.9 billion.
+```
+
@@ -0,0 +1,17 @@
+---
+title: 🔄 reset
+---
+
+`reset()` method allows you to wipe the data from your RAG application and start from scratch.
+
+## Usage
+
+```python
+from embedchain import App
+
+app = App()
+app.add("https://www.forbes.com/profile/elon-musk")
+
+# Reset the app
+app.reset()
+```
@@ -0,0 +1,57 @@
+---
+title: '🔍 search'
+---
+
+`.search()` enables you to uncover the most pertinent context by performing a semantic search across your data sources based on a given query. Refer to the function signature below:
+
+### Parameters
+
+<ParamField path="query" type="str">
+    Question
+</ParamField>
+<ParamField path="num_documents" type="int" optional>
+    Number of relevant documents to fetch. Defaults to `3`
+</ParamField>
+
+### Returns
+
+<ResponseField name="answer" type="dict">
+    Return list of dictionaries that contain the relevant chunk and their source information.
+</ResponseField>
+
+## Usage
+
+Refer to the following example on how to use the search api:
+
+```python Code example
+from embedchain import App
+
+# Initialize app
+app = App()
+
+# Add data source
+app.add("https://www.forbes.com/profile/elon-musk")
+
+# Get relevant context using semantic search
+context = app.search("What is the net worth of Elon?", num_documents=2)
+print(context)
+# Context:
+# [
+#     {
+#         'context': 'Elon Musk PROFILEElon MuskCEO, Tesla$221.9BReal Time Net Worth ...',
+#         'metadata': {
+#             'source': 'https://www.forbes.com/profile/elon-musk',
+#             'document_id': 'some_document_id',
+#             'score': 0.404,
+#         }
+#     },
+#     {
+#         'context': 'company, which is now called X.Wealth HistoryHOVER TO REVEAL NET WORTH ...',
+#         'metadata': {
+#             'source': 'https://www.forbes.com/profile/elon-musk',
+#             'document_id': 'some_document_id',
+#             'score': 0.435,
+#         }
+#     }
+# ]
+```
@@ -0,0 +1,54 @@
+---
+title: 'AI Assistant'
+---
+
+The `AIAssistant` class, an alternative to the OpenAI Assistant API, is designed for those who prefer using large language models (LLMs) other than those provided by OpenAI. It facilitates the creation of AI Assistants with several key benefits:
+
+- **Visibility into Citations**: It offers transparent access to the sources and citations used by the AI, enhancing the understanding and trustworthiness of its responses.
+
+- **Debugging Capabilities**: Users have the ability to delve into and debug the AI's processes, allowing for a deeper understanding and fine-tuning of its performance.
+
+- **Customizable Prompts**: The class provides the flexibility to modify and tailor prompts according to specific needs, enabling more precise and relevant interactions.
+
+- **Chain of Thought Integration**: It supports the incorporation of a 'chain of thought' approach, which helps in breaking down complex queries into simpler, sequential steps, thereby improving the clarity and accuracy of responses.
+
+It is ideal for those who value customization, transparency, and detailed control over their AI Assistant's functionalities.
+
+### Arguments
+
+<ParamField path="name" type="string" optional>
+  Name for your AI assistant
+</ParamField>
+
+<ParamField path="instructions" type="string" optional>
+  How the Assistant and model should behave or respond
+</ParamField>
+
+<ParamField path="assistant_id" type="string" optional>
+  Load existing AI Assistant. If you pass this, you don't have to pass other arguments.
+</ParamField>
+
+<ParamField path="thread_id" type="string" optional>
+  Existing thread id if exists
+</ParamField>
+
+<ParamField path="yaml_path" type="str" Optional>
+    Embedchain pipeline config yaml path to use. This will define the configuration of the AI Assistant (such as configuring the LLM, vector database, and embedding model)
+</ParamField>
+
+<ParamField path="data_sources" type="list" default="[]">
+  Add data sources to your assistant. You can add in the following format: `[{"source": "https://example.com", "data_type": "web_page"}]`
+</ParamField>
+
+<ParamField path="collect_metrics" type="boolean" default="True">
+  Anonymous telemetry (doesn't collect any user information or user's files). Used to improve the Embedchain package utilization. Default is `True`.
+</ParamField>
+
+
+## Usage
+
+For detailed guidance on creating your own AI Assistant, click the link below. It provides step-by-step instructions to help you through the process:
+
+<Card title="Guide to Creating Your AI Assistant" icon="link" href="/examples/opensource-assistant">
+  Learn how to build a customized AI Assistant using the `AIAssistant` class.
+</Card>
@@ -0,0 +1,45 @@
+---
+title: 'OpenAI Assistant'
+---
+
+### Arguments
+
+<ParamField path="name" type="string">
+  Name for your AI assistant
+</ParamField>
+
+<ParamField path="instructions" type="string">
+  how the Assistant and model should behave or respond
+</ParamField>
+
+<ParamField path="assistant_id" type="string">
+  Load existing OpenAI Assistant. If you pass this, you don't have to pass other arguments.
+</ParamField>
+
+<ParamField path="thread_id" type="string">
+  Existing OpenAI thread id if exists
+</ParamField>
+
+<ParamField path="model" type="str" default="gpt-4-1106-preview">
+  OpenAI model to use
+</ParamField>
+
+<ParamField path="tools" type="list">
+  OpenAI tools to use. Default set to `[{"type": "retrieval"}]`
+</ParamField>
+
+<ParamField path="data_sources" type="list" default="[]">
+  Add data sources to your assistant. You can add in the following format: `[{"source": "https://example.com", "data_type": "web_page"}]`
+</ParamField>
+
+<ParamField path="telemetry" type="boolean" default="True">
+  Anonymous telemetry (doesn't collect any user information or user's files). Used to improve the Embedchain package utilization. Default is `True`.
+</ParamField>
+
+## Usage
+
+For detailed guidance on creating your own OpenAI Assistant, click the link below. It provides step-by-step instructions to help you through the process:
+
+<Card title="Guide to Creating Your OpenAI Assistant" icon="link" href="/examples/openai-assistant">
+  Learn how to build an OpenAI Assistant using the `OpenAIAssistant` class.
+</Card>
@@ -4,15 +4,25 @@ title: 🤝 Connect with Us

 We believe in building a vibrant and supportive community around embedchain. There are various channels through which you can connect with us, stay updated, and contribute to the ongoing discussions:

-
-* Slack: Our Slack workspace provides a platform for more structured discussions and channels dedicated to different topics. Feel free to jump in and start contributing. [Join Slack](https://join.slack.com/t/embedchain/shared_invite/zt-22uwz3c46-Zg7cIh5rOBteT_xe1jwLDw).
-
-* Discord: Join our Discord server to engage in real-time conversations with the community members and the project maintainers. It’s a great place to seek help and discuss anything related to the project. [Join Discord](https://discord.gg/CUU9FPhRNt).
-
-* Twitter: Follow us on Twitter for the latest news, announcements, and highlights from our community. It’s also a quick way to reach out to us. [Follow @embedchain](https://twitter.com/embedchain).
-
-* LinkedIn: Connect with us on LinkedIn to stay updated on official announcements, job openings, and professional networking opportunities within our community. [Follow Our Page](https://www.linkedin.com/company/embedchain/).
-
-* Newsletter: Subscribe to our newsletter for a curated list of project updates, community contributions, and upcoming events. It’s a compact way to stay in the loop with what’s happening in our community. [Subscribe Now](https://embedchain.substack.com/).
+<CardGroup cols={3}>
+  <Card title="Twitter" icon="twitter" href="https://twitter.com/embedchain">
+    Follow us on Twitter
+  </Card>
+  <Card title="Slack" icon="slack" href="https://join.slack.com/t/embedchain/shared_invite/zt-22uwz3c46-Zg7cIh5rOBteT_xe1jwLDw" color="#4A154B">
+    Join our slack community
+  </Card>
+  <Card title="Discord" icon="discord" href="https://discord.gg/6PzXDgEjG5" color="#7289DA">
+    Join our discord community
+  </Card>
+  <Card title="LinkedIn" icon="linkedin" href="https://www.linkedin.com/company/embedchain/">
+  Connect with us on LinkedIn
+  </Card>
+  <Card title="Schedule a call" icon="calendar" href="https://cal.com/taranjeetio/ec">
+  Schedule a call with Embedchain founder
+  </Card>
+  <Card title="Newsletter" icon="message" href="https://embedchain.substack.com/">
+  Subscribe to our newsletter
+  </Card>
+</CardGroup>

 We look forward to connecting with you and seeing how we can create amazing things together!
@@ -0,0 +1,16 @@
+---
+title: "🐝 Beehiiv"
+---
+
+To add any Beehiiv data sources to your app, just add the base url as the source and set the data_type to `beehiiv`.
+
+```python
+from embedchain import App
+
+app = App()
+
+# source: just add the base url and set the data_type to 'beehiiv'
+app.add('https://aibreakfast.beehiiv.com', data_type='beehiiv')
+app.query("How much is OpenAI paying developers?")
+# Answer: OpenAI is aggressively recruiting Google's top AI researchers with offers ranging between $5 to $10 million annually, primarily in stock options.
+```
@@ -0,0 +1,19 @@
+---
+title: '📊 CSV'
+---
+
+To add any csv file, use the data_type as `csv`. `csv` allows remote urls and conventional file paths. Headers are included for each line, so if you have an `age` column, `18` will be added as `age: 18`. Eg:
+
+```python
+from embedchain import App
+
+app = App()
+app.add('https://people.sc.fsu.edu/~jburkardt/data/csv/airtravel.csv', data_type="csv")
+# Or add using the local file path
+# app.add('/path/to/file.csv', data_type="csv")
+
+app.query("Summarize the air travel data")
+# Answer: The air travel data shows the number of flights for the months of July in the years 1958, 1959, and 1960. In July 1958, there were 491 flights, in July 1959 there were 548 flights, and in July 1960 there were 622 flights.
+```
+
+Note: There is a size limit allowed for csv file beyond which it can throw error. This limit is set by the LLMs. Please consider chunking large csv files into smaller csv files.
@@ -0,0 +1,41 @@
+---
+title: '⚙️ Custom'
+---
+
+When we say "custom", we mean that you can customize the loader and chunker to your needs. This is done by passing a custom loader and chunker to the `add` method.
+
+```python
+from embedchain import App
+import your_loader
+import your_chunker
+
+app = App()
+loader = your_loader()
+chunker = your_chunker()
+
+app.add("source", data_type="custom", loader=loader, chunker=chunker)
+```
+
+<Note>
+    The custom loader and chunker must be a class that inherits from the [`BaseLoader`](https://github.com/embedchain/embedchain/blob/main/embedchain/loaders/base_loader.py) and [`BaseChunker`](https://github.com/embedchain/embedchain/blob/main/embedchain/chunkers/base_chunker.py) classes respectively.
+</Note>
+
+<Note>
+    If the `data_type` is not a valid data type, the `add` method will fallback to the `custom` data type and expect a custom loader and chunker to be passed by the user.
+</Note>
+
+Example:
+
+```python
+from embedchain import App
+from embedchain.loaders.github import GithubLoader
+
+app = App()
+
+loader = GithubLoader(config={"token": "ghp_xxx"})
+
+app.add("repo:embedchain/embedchain type:repo", data_type="github", loader=loader)
+
+app.query("What is Embedchain?")
+# Answer: Embedchain is a Data Platform for Large Language Models (LLMs). It allows users to seamlessly load, index, retrieve, and sync unstructured data in order to build dynamic, LLM-powered applications. There is also a JavaScript implementation called embedchain-js available on GitHub.
+```
@@ -1,18 +1,16 @@
 ---
-title: 'Data Type Handling'
+title: 'Data type handling'
 ---

 ## Automatic data type detection
+
 The add method automatically tries to detect the data_type, based on your input for the source argument. So `app.add('https://www.youtube.com/watch?v=dQw4w9WgXcQ')` is enough to embed a YouTube video.

 This detection is implemented for all formats. It is based on factors such as whether it's a URL, a local file, the source data type, etc.

 ### Debugging automatic detection

-
-Set `log_level=DEBUG` (in [AppConfig](http://localhost:3000/advanced/query_configuration#appconfig)) and make sure it's working as intended.
-
-Otherwise, you will not know when, for instance, an invalid filepath is interpreted as raw text instead.
+Set `log_level: DEBUG` in the config yaml to debug if the data type detection is done right or not. Otherwise, you will not know when, for instance, an invalid filepath is interpreted as raw text instead.

 ### Forcing a data type

@@ -21,7 +19,7 @@ The examples below show you the keyword to force the respective `data_type`.

 Forcing can also be used for edge cases, such as interpreting a sitemap as a web_page, for reading its raw text instead of following links.

-## Remote Data Types
+## Remote data types

 <Tip>
 **Use local files in remote data types**
@@ -32,7 +30,7 @@ You can pass local files by formatting the path using the `file:` [URI scheme](h

 ## Reusing a vector database

-Default behavior is to create a persistent vector DB in the directory **./db**. You can split your application into two Python scripts: one to create a local vector DB and the other to reuse this local persistent vector DB. This is useful when you want to index hundreds of documents and separately implement a chat interface.
+Default behavior is to create a persistent vector db in the directory **./db**. You can split your application into two Python scripts: one to create a local vector db and the other to reuse this local persistent vector db. This is useful when you want to index hundreds of documents and separately implement a chat interface.

 Create a local index:

@@ -52,3 +50,15 @@ from embedchain import App
 naval_chat_bot = App()
 print(naval_chat_bot.query("What unique capacity does Naval argue humans possess when it comes to understanding explanations or concepts?"))
 ```
+
+## Resetting an app and vector database
+
+You can reset the app by simply calling the `reset` method. This will delete the vector database and all other app related files.
+
+```python
+from embedchain import App
+
+app = App()
+app.add("https://www.youtube.com/watch?v=3qHkcs3kG44")
+app.reset()
+```
@@ -0,0 +1,41 @@
+---
+title: '📁 Directory/Folder'
+---
+
+To use an entire directory as data source, just add `data_type` as `directory` and pass in the path of the local directory.
+
+### Without customization
+
+```python
+import os
+from embedchain import App
+
+os.environ["OPENAI_API_KEY"] = "sk-xxx"
+
+app = App()
+app.add("./elon-musk", data_type="directory")
+response = app.query("list all files")
+print(response)
+# Answer: Files are elon-musk-1.txt, elon-musk-2.pdf.
+```
+
+### Customization
+
+```python
+import os
+from embedchain import App
+from embedchain.loaders.directory_loader import DirectoryLoader
+
+os.environ["OPENAI_API_KEY"] = "sk-xxx"
+lconfig = {
+    "recursive": True,
+    "extensions": [".txt"]
+}
+loader = DirectoryLoader(config=lconfig)
+app = App()
+app.add("./elon-musk", loader=loader)
+response = app.query("what are all the files related to?")
+print(response)
+
+# Answer: The files are related to Elon Musk.
+```
@@ -0,0 +1,28 @@
+---
+title: "💬 Discord"
+---
+
+To add any Discord channel messages to your app, just add the `channel_id` as the source and set the `data_type` to `discord`.
+
+<Note>
+    This loader requires a Discord bot token with read messages access.
+    To obtain the token, follow the instructions provided in this tutorial: 
+    <a href="https://www.writebots.com/discord-bot-token/">How to Get a Discord Bot Token?</a>.
+</Note>
+
+```python
+import os
+from embedchain import App
+
+# add your discord "BOT" token
+os.environ["DISCORD_TOKEN"] = "xxx"
+
+app = App()
+
+app.add("1177296711023075338", data_type="discord")
+
+response = app.query("What is Joe saying about Elon Musk?")
+
+print(response)
+# Answer: Joe is saying "Elon Musk is a genius".
+```
@@ -0,0 +1,44 @@
+---
+title: '🗨️ Discourse'
+---
+
+You can now easily load data from your community built with [Discourse](https://discourse.org/).
+
+## Example
+
+1. Setup the Discourse Loader with your community url.
+```Python
+from embedchain.loaders.discourse import DiscourseLoader
+
+dicourse_loader = DiscourseLoader(config={"domain": "https://community.openai.com"})
+```
+
+2. Once you setup the loader, you can create an app and load data using the above discourse loader
+```Python
+import os
+from embedchain.pipeline import Pipeline as App
+
+os.environ["OPENAI_API_KEY"] = "sk-xxx"
+
+app = App()
+
+app.add("openai after:2023-10-1", data_type="discourse", loader=dicourse_loader)
+
+question = "Where can I find the OpenAI API status page?"
+app.query(question)
+# Answer: You can find the OpenAI API status page at https:/status.openai.com/.
+```
+
+NOTE: The `add` function of the app will accept any executable search query to load data. Refer [Discourse API Docs](https://docs.discourse.org/#tag/Search) to learn more about search queries.
+
+3. We automatically create a chunker to chunk your discourse data, however if you wish to provide your own chunker class. Here is how you can do that:
+```Python
+
+from embedchain.chunkers.discourse import DiscourseChunker
+from embedchain.config.add_config import ChunkerConfig
+
+discourse_chunker_config = ChunkerConfig(chunk_size=1000, chunk_overlap=0, length_function=len)
+discourse_chunker = DiscourseChunker(config=discourse_chunker_config)
+
+app.add("openai", data_type='discourse', loader=dicourse_loader, chunker=discourse_chunker)
+```
@@ -0,0 +1,14 @@
+---
+title: '📚 Code Docs website'
+---
+
+To add any code documentation website as a loader, use the data_type as `docs_site`. Eg:
+
+```python
+from embedchain import App
+
+app = App()
+app.add("https://docs.embedchain.ai/", data_type="docs_site")
+app.query("What is Embedchain?")
+# Answer: Embedchain is a platform that utilizes various components, including paid/proprietary ones, to provide what is believed to be the best configuration available. It uses LLM (Language Model) providers such as OpenAI, Anthpropic, Vertex_AI, GPT4ALL, Azure_OpenAI, LLAMA2, JINA, Ollama, Together and COHERE. Embedchain allows users to import and utilize these LLM providers for their applications.'
+```
@@ -1,5 +1,5 @@
 ---
-title: 'Docx File'
+title: '📄 Docx file'
 ---

 ### Docx file
@@ -7,6 +7,12 @@ title: 'Docx File'
 To add any doc/docx file, use the data_type as `docx`. `docx` allows remote urls and conventional file paths. Eg:

 ```python
+from embedchain import App
+
+app = App()
 app.add('https://example.com/content/intro.docx', data_type="docx")
-app.add('content/intro.docx', data_type="docx")
-```
+# Or add file using the local file path on your system
+# app.add('content/intro.docx', data_type="docx")
+
+app.query("Summarize the docx data?")
+```
@@ -0,0 +1,27 @@
+---
+title: '💾 Dropbox'
+---
+
+To load folders or files from your Dropbox account, configure the `data_type` parameter as `dropbox` and specify the path to the desired file or folder, starting from the root directory of your Dropbox account.
+
+For Dropbox access, an **access token** is required. Obtain this token by visiting [Dropbox Developer Apps](https://www.dropbox.com/developers/apps). There, create a new app and generate an access token for it.
+
+Ensure your app has the following settings activated:
+
+- In the Permissions section, enable `files.content.read` and `files.metadata.read`.
+
+```python
+import os
+from embedchain import Pipeline as App
+
+os.environ["DROPBOX_ACCESS_TOKEN"] = "sl.xxx"
+os.environ["OPENAI_API_KEY"] = "sk-xxx"
+
+app = App()
+
+# any path from the root of your dropbox account, you can leave it "" for the root folder
+app.add("/test", data_type="dropbox")
+
+print(app.query("Which two celebrities are mentioned here?"))
+# The two celebrities mentioned in the given context are Elon Musk and Jeff Bezos.
+```
@@ -0,0 +1,50 @@
+---
+title: 📝 Github
+---
+
+1. Setup the Github loader by configuring the Github account with username and personal access token (PAT). Check out [this](https://docs.github.com/en/enterprise-server@3.6/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens#creating-a-personal-access-token) link to learn how to create a PAT.
+```Python
+from embedchain.loaders.github import GithubLoader
+
+loader = GithubLoader(
+    config={
+        "token":"ghp_xxxx"
+        }
+    )
+```
+
+2. Once you setup the loader, you can create an app and load data using the above Github loader
+```Python
+import os
+from embedchain.pipeline import Pipeline as App
+
+os.environ["OPENAI_API_KEY"] = "sk-xxxx"
+
+app = App()
+
+app.add("repo:embedchain/embedchain type:repo", data_type="github", loader=loader)
+
+response = app.query("What is Embedchain?")
+# Answer: Embedchain is a Data Platform for Large Language Models (LLMs). It allows users to seamlessly load, index, retrieve, and sync unstructured data in order to build dynamic, LLM-powered applications. There is also a JavaScript implementation called embedchain-js available on GitHub.
+```
+The `add` function of the app will accept any valid github query with qualifiers. It only supports loading github code, repository, issues and pull-requests.
+<Note>
+You must provide qualifiers `type:` and `repo:` in the query. The `type:` qualifier can be a combination of `code`, `repo`, `pr`, `issue`. The `repo:` qualifier must be a valid github repository name.
+</Note>
+
+<Card title="Valid queries" icon="lightbulb" iconType="duotone" color="#ca8b04">
+    - `repo:embedchain/embedchain type:repo` - to load the repository
+    - `repo:embedchain/embedchain type:issue,pr` - to load the issues and pull-requests of the repository
+    - `repo:embedchain/embedchain type:issue state:closed` - to load the closed issues of the repository
+</Card>
+
+3. We automatically create a chunker to chunk your GitHub data, however if you wish to provide your own chunker class. Here is how you can do that:
+```Python
+from embedchain.chunkers.common_chunker import CommonChunker
+from embedchain.config.add_config import ChunkerConfig
+
+github_chunker_config = ChunkerConfig(chunk_size=2000, chunk_overlap=0, length_function=len)
+github_chunker = CommonChunker(config=github_chunker_config)
+
+app.add(load_query, data_type="github", loader=loader, chunker=github_chunker)
+```
@@ -0,0 +1,34 @@
+---
+title: '📬 Gmail'
+---
+
+To use GmailLoader you must install the extra dependencies with `pip install --upgrade embedchain[gmail]`.
+
+The `source` must be a valid Gmail search query, you can refer `https://support.google.com/mail/answer/7190?hl=en` to build a query.
+
+To load Gmail messages, you MUST use the data_type as `gmail`. Otherwise the source will be detected as simple `text`.
+
+To use this you need to save `credentials.json` in the directory from where you will run the loader. Follow these steps to get the credentials
+
+1. Go to the [Google Cloud Console](https://console.cloud.google.com/apis/credentials).
+2. Create a project if you don't have one already.
+3. Create an `OAuth Consent Screen` in the project. You may need to select the `external` option.
+4. Make sure the consent screen is published.
+5. Enable the [Gmail API](https://console.cloud.google.com/apis/api/gmail.googleapis.com)
+6. Create credentials from the `Credentials` tab.
+7. Select the type `OAuth Client ID`.
+8. Choose the application type `Web application`. As a name you can choose `embedchain` or any other name as per your use case.
+9. Add an authorized redirect URI for `http://localhost:8080/`.
+10. You can leave everything else at default, finish the creation.
+11. When you are done, a modal opens where you can download the details in `json` format.
+12. Put the `.json` file in your current directory and rename it to `credentials.json`
+
+```python
+from embedchain import App
+
+app = App()
+
+gmail_filter = "to: me label:inbox"
+app.add(gmail_filter, data_type="gmail")
+app.query("Summarize my email conversations")
+```
@@ -0,0 +1,28 @@
+---
+title: 'Google Drive'
+---
+
+To use GoogleDriveLoader you must install the extra dependencies with `pip install --upgrade embedchain[googledrive]`.
+
+The data_type must be `google_drive`. Otherwise, it will be considered a regular web page.
+
+Google Drive requires the setup of credentials. This can be done by following the steps below:
+
+1. Go to the [Google Cloud Console](https://console.cloud.google.com/apis/credentials).
+2. Create a project if you don't have one already.
+3. Enable the [Google Drive API](https://console.cloud.google.com/flows/enableapi?apiid=drive.googleapis.com)
+4. [Authorize credentials for desktop app](https://developers.google.com/drive/api/quickstart/python#authorize_credentials_for_a_desktop_application)
+5. When done, you will be able to download the credentials in `json` format. Rename the downloaded file to `credentials.json` and save it in `~/.credentials/credentials.json`
+6. Set the environment variable `GOOGLE_APPLICATION_CREDENTIALS=~/.credentials/credentials.json`
+
+The first time you use the loader, you will be prompted to enter your Google account credentials.
+
+
+```python
+from embedchain import Pipeline as App
+
+app = App()
+
+url = "https://drive.google.com/drive/u/0/folders/xxx-xxx"
+app.add(url, data_type="google_drive")
+```
@@ -0,0 +1,45 @@
+---
+title: "🖼️ Image"
+---
+
+
+To use an image as data source, just add `data_type` as `image` and pass in the path of the image (local or hosted).
+
+We use [GPT4 Vision](https://platform.openai.com/docs/guides/vision) to generate meaning of the image using a custom prompt, and then use the generated text as the data source.
+
+You would require an OpenAI API key with access to `gpt-4-vision-preview` model to use this feature.
+
+### Without customization
+
+```python
+import os
+from embedchain import App
+
+os.environ["OPENAI_API_KEY"] = "sk-xxx"
+
+app = App()
+app.add("./Elon-Musk.webp", data_type="image")
+response = app.query("Describe the man in the image.")
+print(response)
+# Answer: The man in the image is dressed in formal attire, wearing a dark suit jacket and a white collared shirt. He has short hair and is standing. He appears to be gazing off to the side with a reflective expression. The background is dark with faint, warm-toned vertical lines, possibly from a lit environment behind the individual or reflections. The overall atmosphere is somewhat moody and introspective.
+```
+
+### Customization
+
+```python
+import os
+from embedchain import App
+from embedchain.loaders.image import ImageLoader
+
+image_loader = ImageLoader(
+    max_tokens=100,
+    api_key="sk-xxx",
+    prompt="Is the person looking wealthy? Structure your thoughts around what you see in the image.",
+)
+
+app = App()
+app.add("./Elon-Musk.webp", data_type="image", loader=image_loader)
+response = app.query("Describe the man in the image.")
+print(response)
+# Answer: The man in the image appears to be well-dressed in a suit and shirt, suggesting that he may be in a professional or formal setting. His composed demeanor and confident posture further indicate a sense of self-assurance. Based on these visual cues, one could infer that the man may have a certain level of economic or social status, possibly indicating wealth or professional success.
+```
@@ -0,0 +1,44 @@
+---
+title: '📃 JSON'
+---
+
+To add any json file, use the data_type as `json`. Headers are included for each line, so for example if you have a json like `{"age": 18}`, then it will be added as `age: 18`.
+
+Here are the supported sources for loading `json`:
+
+```
+1. URL - valid url to json file that ends with ".json" extension.
+2. Local file - valid url to local json file that ends with ".json" extension.
+3. String - valid json string (e.g. - app.add('{"foo": "bar"}'))
+```
+
+<Tip>
+If you would like to add other data structures (e.g. list, dict etc.), convert it to a valid json first using `json.dumps()` function.
+</Tip>
+
+## Example
+
+<CodeGroup>
+
+```python python
+from embedchain import App
+
+app = App()
+
+# Add json file
+app.add("temp.json")
+
+app.query("What is the net worth of Elon Musk as of October 2023?")
+# As of October 2023, Elon Musk's net worth is $255.2 billion.
+```
+
+
+```json temp.json
+{
+    "question": "What is your net worth, Elon Musk?",
+    "answer": "As of October 2023, Elon Musk's net worth is $255.2 billion, making him one of the wealthiest individuals in the world."
+}
+```
+</CodeGroup>
+
+
@@ -0,0 +1,14 @@
+---
+title: '📝 Mdx file'
+---
+
+To add any `.mdx` file to your app, use the data_type (first argument to `.add()` method) as `mdx`. Note that this supports support mdx file present on machine, so this should be a file path. Eg:
+
+```python
+from embedchain import App
+
+app = App()
+app.add('path/to/file.mdx', data_type='mdx')
+
+app.query("What are the docs about?")
+```
@@ -0,0 +1,47 @@
+---
+title: '🐬 MySQL'
+---
+
+1. Setup the MySQL loader by configuring the SQL db.
+```Python
+from embedchain.loaders.mysql import MySQLLoader
+
+config = {
+    "host": "host",
+    "port": "port",
+    "database": "database",
+    "user": "username",
+    "password": "password",
+}
+
+mysql_loader = MySQLLoader(config=config)
+```
+
+For more details on how to setup with valid config, check MySQL [documentation](https://dev.mysql.com/doc/connector-python/en/connector-python-connectargs.html).
+
+2. Once you setup the loader, you can create an app and load data using the above MySQL loader
+```Python
+from embedchain.pipeline import Pipeline as App
+
+app = App()
+
+app.add("SELECT * FROM table_name;", data_type='mysql', loader=mysql_loader)
+# Adds `(1, 'What is your net worth, Elon Musk?', "As of October 2023, Elon Musk's net worth is $255.2 billion.")`
+
+response = app.query(question)
+# Answer: As of October 2023, Elon Musk's net worth is $255.2 billion.
+```
+
+NOTE: The `add` function of the app will accept any executable query to load data. DO NOT pass the `CREATE`, `INSERT` queries in `add` function.
+
+3. We automatically create a chunker to chunk your SQL data, however if you wish to provide your own chunker class. Here is how you can do that:
+``Python
+
+from embedchain.chunkers.mysql import MySQLChunker
+from embedchain.config.add_config import ChunkerConfig
+
+mysql_chunker_config = ChunkerConfig(chunk_size=1000, chunk_overlap=0, length_function=len)
+mysql_chunker = MySQLChunker(config=mysql_chunker_config)
+
+app.add("SELECT * FROM table_name;", data_type='mysql', loader=mysql_loader, chunker=mysql_chunker)
+```
@@ -0,0 +1,20 @@
+---
+title: '📓 Notion'
+---
+
+To use notion you must install the extra dependencies with `pip install --upgrade embedchain[community]`.
+
+To load a notion page, use the data_type as `notion`. Since it is hard to automatically detect, it is advised to specify the `data_type` when adding a notion document.
+The next argument must **end** with the `notion page id`. The id is a 32-character string. Eg:
+
+```python
+from embedchain import App
+
+app = App()
+
+app.add("cfbc134ca6464fc980d0391613959196", data_type="notion")
+app.add("my-page-cfbc134ca6464fc980d0391613959196", data_type="notion")
+app.add("https://www.notion.so/my-page-cfbc134ca6464fc980d0391613959196", data_type="notion")
+
+app.query("Summarize the notion doc")
+```
@@ -0,0 +1,22 @@
+---
+title: 🙌 OpenAPI
+---
+
+To add any OpenAPI spec yaml file (currently the json file will be detected as JSON data type), use the data_type as 'openapi'. 'openapi' allows remote urls and conventional file paths.
+
+```python
+from embedchain import App
+
+app = App()
+
+app.add("https://github.com/openai/openai-openapi/blob/master/openapi.yaml", data_type="openapi")
+# Or add using the local file path
+# app.add("configs/openai_openapi.yaml", data_type="openapi")
+
+app.query("What can OpenAI API endpoint do? Can you list the things it can learn from?")
+# Answer: The OpenAI API endpoint allows users to interact with OpenAI's models and perform various tasks such as generating text, answering questions, summarizing documents, translating languages, and more. The specific capabilities and tasks that the API can learn from may vary depending on the models and features provided by OpenAI. For more detailed information, it is recommended to refer to the OpenAI API documentation at https://platform.openai.com/docs/api-reference.
+```
+
+<Note>
+The yaml file added to the App must have the required OpenAPI fields otherwise the adding OpenAPI spec will fail. Please refer to [OpenAPI Spec Doc](https://spec.openapis.org/oas/v3.1.0)
+</Note>
@@ -0,0 +1,40 @@
+---
+title: Overview
+---
+
+Embedchain comes with built-in support for various data sources. We handle the complexity of loading unstructured data from these data sources, allowing you to easily customize your app through a user-friendly interface.
+
+<CardGroup cols={4}>
+  <Card title="📰 PDF file" href="/components/data-sources/pdf-file"></Card>
+  <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="📁 Directory/ Folder" href="/components/data-sources/directory"></Card>
+  <Card title="🌐 HTML Web page" href="/components/data-sources/web-page"></Card>
+  <Card title="📽️ Youtube Channel" href="/components/data-sources/youtube-channel"></Card>
+  <Card title="📺 Youtube Video" href="/components/data-sources/youtube-video"></Card>
+  <Card title="📚 Docs website" href="/components/data-sources/docs-site"></Card>
+  <Card title="📝 MDX file" href="/components/data-sources/mdx"></Card>
+  <Card title="📄 DOCX file" href="/components/data-sources/docx"></Card>
+  <Card title="📓 Notion" href="/components/data-sources/notion"></Card>
+  <Card title="🗺️ Sitemap" href="/components/data-sources/sitemap"></Card>
+  <Card title="🧾 XML file" href="/components/data-sources/xml"></Card>
+  <Card title="❓💬 Q&A pair" href="/components/data-sources/qna"></Card>
+  <Card title="🙌 OpenAPI" href="/components/data-sources/openapi"></Card>
+  <Card title="📬 Gmail" href="/components/data-sources/gmail"></Card>
+  <Card title="📝 Github" href="/components/data-sources/github"></Card>
+  <Card title="🐘 Postgres" href="/components/data-sources/postgres"></Card>
+  <Card title="🐬 MySQL" href="/components/data-sources/mysql"></Card>
+  <Card title="🤖 Slack" href="/components/data-sources/slack"></Card>
+  <Card title="💬 Discord" href="/components/data-sources/discord"></Card>
+  <Card title="🗨️ Discourse" href="/components/data-sources/discourse"></Card>
+  <Card title="📝 Substack" href="/components/data-sources/substack"></Card>
+  <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="⚙️ Custom" href="/components/data-sources/custom"></Card>
+</CardGroup>
+
+<br/ >
+
+<Snippet file="missing-data-source-tip.mdx" />
@@ -0,0 +1,47 @@
+---
+title: '📰 PDF file'
+---
+
+To add any pdf file, use the data_type as `pdf_file`. Eg:
+
+```python
+from embedchain import App
+
+app = App()
+
+app.add('https://arxiv.org/pdf/1706.03762.pdf', data_type='pdf_file')
+app.query("What is the paper 'attention is all you need' about?", citations=True)
+# Answer: The paper "Attention Is All You Need" proposes a new network architecture called the Transformer, which is based solely on attention mechanisms. It suggests that complex recurrent or convolutional neural networks can be replaced with a simpler architecture that connects the encoder and decoder through attention. The paper discusses how this approach can improve sequence transduction models, such as neural machine translation.
+# Contexts: 
+# [
+#     (
+#         'Provided proper attribution is ...',
+#         {
+#             'page': 0,
+#             'url': 'https://arxiv.org/pdf/1706.03762.pdf',
+#             'score': 0.3676220203221626,
+#             ...
+#         }
+#     ),
+#     (
+#         'Attention Visualizations Input ...',
+#         {
+#             'page': 12,
+#             'url': 'https://arxiv.org/pdf/1706.03762.pdf',
+#             'score': 0.41679039679873736,
+#             ...
+#         }
+#     ),
+#     (
+#         'sequence learning ...',
+#         {
+#             'page': 10,
+#             'url': 'https://arxiv.org/pdf/1706.03762.pdf',
+#             'score': 0.4188303600897153,
+#             ...
+#         }
+#     )
+# ]
+```
+
+Note that we do not support password protected pdfs.
@@ -0,0 +1,64 @@
+---
+title: '🐘 Postgres'
+---
+
+1. Setup the Postgres loader by configuring the postgres db.
+```Python
+from embedchain.loaders.postgres import PostgresLoader
+
+config = {
+    "host": "host_address",
+    "port": "port_number",
+    "dbname": "database_name",
+    "user": "username",
+    "password": "password",
+}
+
+"""
+config = {
+    "url": "your_postgres_url"
+}
+"""
+
+postgres_loader = PostgresLoader(config=config)
+
+```
+
+You can either setup the loader by passing the postgresql url or by providing the config data.
+For more details on how to setup with valid url and config, check postgres [documentation](https://www.postgresql.org/docs/current/libpq-connect.html#LIBPQ-CONNSTRING:~:text=34.1.1.%C2%A0Connection%20Strings-,%23,-Several%20libpq%20functions).
+
+NOTE: if you provide the `url` field in config, all other fields will be ignored.
+
+2. Once you setup the loader, you can create an app and load data using the above postgres loader
+```Python
+import os
+from embedchain.pipeline import Pipeline as App
+
+os.environ["OPENAI_API_KEY"] = "sk-xxx"
+
+app = App()
+
+question = "What is Elon Musk's networth?"
+response = app.query(question)
+# Answer: As of September 2021, Elon Musk's net worth is estimated to be around $250 billion, making him one of the wealthiest individuals in the world. However, please note that net worth can fluctuate over time due to various factors such as stock market changes and business ventures.
+
+app.add("SELECT * FROM table_name;", data_type='postgres', loader=postgres_loader)
+# Adds `(1, 'What is your net worth, Elon Musk?', "As of October 2023, Elon Musk's net worth is $255.2 billion.")`
+
+response = app.query(question)
+# Answer: As of October 2023, Elon Musk's net worth is $255.2 billion.
+```
+
+NOTE: The `add` function of the app will accept any executable query to load data. DO NOT pass the `CREATE`, `INSERT` queries in `add` function as they will result in not adding any data, so it is pointless.
+
+3. We automatically create a chunker to chunk your postgres data, however if you wish to provide your own chunker class. Here is how you can do that:
+```Python
+
+from embedchain.chunkers.postgres import PostgresChunker
+from embedchain.config.add_config import ChunkerConfig
+
+postgres_chunker_config = ChunkerConfig(chunk_size=1000, chunk_overlap=0, length_function=len)
+postgres_chunker = PostgresChunker(config=postgres_chunker_config)
+
+app.add("SELECT * FROM table_name;", data_type='postgres', loader=postgres_loader, chunker=postgres_chunker)
+```
@@ -1,11 +1,13 @@
 ---
-title: 'QnA Pair'
+title: '❓💬 Queston and answer pair'
 ---

-### QnA pair
-
 QnA pair is a local data type. To supply your own QnA pair, use the data_type as `qna_pair` and enter a tuple. Eg:

 ```python
+from embedchain import App
+
+app = App()
+
 app.add(("Question", "Answer"), data_type="qna_pair")
 ```
@@ -1,11 +1,13 @@
 ---
-title: 'Sitemap'
+title: '🗺️ Sitemap'
 ---

-### Sitemap
-
 Add all web pages from an xml-sitemap. Filters non-text files. Use the data_type as `sitemap`. Eg:

 ```python
+from embedchain import App
+
+app = App()
+
 app.add('https://example.com/sitemap.xml', data_type='sitemap')
 ```
@@ -0,0 +1,71 @@
+---
+title: '🤖 Slack'
+---
+
+## Pre-requisite
+- Download required packages by running `pip install --upgrade "embedchain[slack]"`.
+- Configure your slack bot token as environment variable `SLACK_USER_TOKEN`.
+    - Find your user token on your [Slack Account](https://api.slack.com/authentication/token-types)
+    - Make sure your slack user token includes [search](https://api.slack.com/scopes/search:read) scope.
+
+## Example
+
+### Get Started
+
+This will automatically retrieve data from the workspace associated with the user's token.
+
+```python
+import os
+from embedchain import App
+
+os.environ["SLACK_USER_TOKEN"] = "xoxp-xxx"
+app = App()
+
+app.add("in:general", data_type="slack")
+
+result = app.query("what are the messages in general channel?")
+
+print(result)
+```
+
+
+### Customize your SlackLoader
+1. Setup the Slack loader by configuring the Slack Webclient.
+```Python
+from embedchain.loaders.slack import SlackLoader
+
+os.environ["SLACK_USER_TOKEN"] = "xoxp-*"
+
+config = {
+    'base_url': slack_app_url,
+    'headers': web_headers,
+    'team_id': slack_team_id,
+}
+
+loader = SlackLoader(config)
+```
+
+NOTE: you can also pass the `config` with `base_url`, `headers`, `team_id` to setup your SlackLoader.
+
+2. Once you setup the loader, you can create an app and load data using the above slack loader
+```Python
+import os
+from embedchain.pipeline import Pipeline as App
+
+app = App()
+
+app.add("in:random", data_type="slack", loader=loader)
+question = "Which bots are available in the slack workspace's random channel?"
+# Answer: The available bot in the slack workspace's random channel is the Embedchain bot.
+```
+
+3. We automatically create a chunker to chunk your slack data, however if you wish to provide your own chunker class. Here is how you can do that:
+```Python
+from embedchain.chunkers.slack import SlackChunker
+from embedchain.config.add_config import ChunkerConfig
+
+slack_chunker_config = ChunkerConfig(chunk_size=1000, chunk_overlap=0, length_function=len)
+slack_chunker = SlackChunker(config=slack_chunker_config)
+
+app.add(slack_chunker, data_type="slack", loader=loader, chunker=slack_chunker)
+```
@@ -0,0 +1,16 @@
+---
+title: "📝 Substack"
+---
+
+To add any Substack data sources to your app, just add the main base url as the source and set the data_type to `substack`.
+
+```python
+from embedchain import App
+
+app = App()
+
+# source: for any substack just add the root URL
+app.add('https://www.lennysnewsletter.com', data_type='substack')
+app.query("Who is Brian Chesky?")
+# Answer: Brian Chesky is the co-founder and CEO of Airbnb.
+```
@@ -1,5 +1,5 @@
 ---
-title: 'Text'
+title: '📝 Text'
 ---

 ### Text
@@ -7,7 +7,11 @@ title: 'Text'
 Text is a local data type. To supply your own text, use the data_type as `text` and enter a string. The text is not processed, this can be very versatile. Eg:

 ```python
+from embedchain import App
+
+app = App()
+
 app.add('Seek wealth, not money or status. Wealth is having assets that earn while you sleep. Money is how we transfer time and wealth. Status is your place in the social hierarchy.', data_type='text')
 ```

-Note: This is not used in the examples because in most cases you will supply a whole paragraph or file, which did not fit.
+Note: This is not used in the examples because in most cases you will supply a whole paragraph or file, which did not fit.
@@ -1,11 +1,13 @@
 ---
-title: 'Web page'
+title: '🌐 HTML Web page'
 ---

-### Web page
-
 To add any web page, use the data_type as `web_page`. Eg:

 ```python
+from embedchain import App
+
+app = App()
+
 app.add('a_valid_web_page_url', data_type='web_page')
-```
+```
@@ -0,0 +1,17 @@
+---
+title: '🧾 XML file'
+---
+
+### XML file
+
+To add any xml file, use the data_type as `xml`. Eg:
+
+```python
+from embedchain import App
+
+app = App()
+
+app.add('content/data.xml')
+```
+
+Note: Only the text content of the xml file will be added to the app. The tags will be ignored.
@@ -0,0 +1,20 @@
+---
+title: '📽️ Youtube Channel'
+---
+
+To add all the videos from a youtube channel to your app, use the data_type as `youtube_channel`.
+
+<Note>
+Make sure you have all the required packages installed before using this data type. You can install them by running the following command in your terminal.
+
+```bash
+pip install -u "embedchain[youtube]"
+```
+</Note>
+
+```python
+from embedchain import App
+
+app = App()
+app.add("@channel_name", data_type="youtube_channel")
+```
@@ -0,0 +1,12 @@
+---
+title: '📺 Youtube Video'
+---
+
+To add any youtube video to your app, use the data_type as `youtube_video`. Eg:
+
+```python
+from embedchain import App
+
+app = App()
+app.add('a_valid_youtube_url_here', data_type='youtube_video')
+```
@@ -0,0 +1,202 @@
+---
+title: 🧩 Embedding models
+---
+
+## Overview
+
+Embedchain supports several embedding models from the following providers:
+
+<CardGroup cols={4}>
+  <Card title="OpenAI" href="#openai"></Card>
+  <Card title="GoogleAI" href="#google-ai"></Card>
+  <Card title="Azure OpenAI" href="#azure-openai"></Card>
+  <Card title="GPT4All" href="#gpt4all"></Card>
+  <Card title="Hugging Face" href="#hugging-face"></Card>
+  <Card title="Vertex AI" href="#vertex-ai"></Card>
+</CardGroup>
+
+## OpenAI
+
+To use OpenAI embedding function, you have to set the `OPENAI_API_KEY` environment variable. You can obtain the OpenAI API key from the [OpenAI Platform](https://platform.openai.com/account/api-keys).
+
+Once you have obtained the key, you can use it like this:
+
+<CodeGroup>
+
+```python main.py
+import os
+from embedchain import App
+
+os.environ['OPENAI_API_KEY'] = 'xxx'
+
+# load embedding model configuration from config.yaml file
+app = App.from_config(config_path="config.yaml")
+
+app.add("https://en.wikipedia.org/wiki/OpenAI")
+app.query("What is OpenAI?")
+```
+
+```yaml config.yaml
+embedder:
+  provider: openai
+  config:
+    model: 'text-embedding-ada-002'
+```
+
+</CodeGroup>
+
+## Google AI
+
+To use Google AI embedding function, you have to set the `GOOGLE_API_KEY` environment variable. You can obtain the Google API key from the [Google Maker Suite](https://makersuite.google.com/app/apikey)
+
+<CodeGroup>
+```python main.py
+import os
+from embedchain import App
+
+os.environ["GOOGLE_API_KEY"] = "xxx"
+
+app = App.from_config(config_path="config.yaml")
+```
+
+```yaml config.yaml
+embedder:
+  provider: google
+  config:
+    model: 'models/embedding-001'
+    task_type: "retrieval_document"
+    title: "Embeddings for Embedchain"
+```
+</CodeGroup>
+<br/>
+<Note>
+For more details regarding the Google AI embedding model, please refer to the [Google AI documentation](https://ai.google.dev/tutorials/python_quickstart#use_embeddings).
+</Note>
+
+## Azure OpenAI
+
+To use Azure OpenAI embedding model, you have to set some of the azure openai related environment variables as given in the code block below:
+
+<CodeGroup>
+
+```python main.py
+import os
+from embedchain import App
+
+os.environ["OPENAI_API_TYPE"] = "azure"
+os.environ["AZURE_OPENAI_ENDPOINT"] = "https://xxx.openai.azure.com/"
+os.environ["AZURE_OPENAI_API_KEY"] = "xxx"
+os.environ["OPENAI_API_VERSION"] = "xxx"
+
+app = App.from_config(config_path="config.yaml")
+```
+
+```yaml config.yaml
+llm:
+  provider: azure_openai
+  config:
+    model: gpt-35-turbo
+    deployment_name: your_llm_deployment_name
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 1
+    stream: false
+
+embedder:
+  provider: azure_openai
+  config:
+    model: text-embedding-ada-002
+    deployment_name: you_embedding_model_deployment_name
+```
+</CodeGroup>
+
+You can find the list of models and deployment name on the [Azure OpenAI Platform](https://oai.azure.com/portal).
+
+## GPT4ALL
+
+GPT4All supports generating high quality embeddings of arbitrary length documents of text using a CPU optimized contrastively trained Sentence Transformer.
+
+<CodeGroup>
+
+```python main.py
+from embedchain import App
+
+# load embedding model configuration from config.yaml file
+app = App.from_config(config_path="config.yaml")
+```
+
+```yaml config.yaml
+llm:
+  provider: gpt4all
+  config:
+    model: 'orca-mini-3b-gguf2-q4_0.gguf'
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 1
+    stream: false
+
+embedder:
+  provider: gpt4all
+```
+
+</CodeGroup>
+
+## Hugging Face
+
+Hugging Face supports generating embeddings of arbitrary length documents of text using Sentence Transformer library. Example of how to generate embeddings using hugging face is given below:
+
+<CodeGroup>
+
+```python main.py
+from embedchain import App
+
+# load embedding model configuration from config.yaml file
+app = App.from_config(config_path="config.yaml")
+```
+
+```yaml config.yaml
+llm:
+  provider: huggingface
+  config:
+    model: 'google/flan-t5-xxl'
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 0.5
+    stream: false
+
+embedder:
+  provider: huggingface
+  config:
+    model: 'sentence-transformers/all-mpnet-base-v2'
+```
+
+</CodeGroup>
+
+## Vertex AI
+
+Embedchain supports Google's VertexAI embeddings model through a simple interface. You just have to pass the `model_name` in the config yaml and it would work out of the box.
+
+<CodeGroup>
+
+```python main.py
+from embedchain import App
+
+# load embedding model configuration from config.yaml file
+app = App.from_config(config_path="config.yaml")
+```
+
+```yaml config.yaml
+llm:
+  provider: vertexai
+  config:
+    model: 'chat-bison'
+    temperature: 0.5
+    top_p: 0.5
+
+embedder:
+  provider: vertexai
+  config:
+    model: 'textembedding-gecko'
+```
+
+</CodeGroup>
@@ -0,0 +1,12 @@
+---
+title: 🧩 Introduction
+---
+
+## Overview
+
+You can configure following components
+
+* [Data Source](/components/data-sources/overview)
+* [LLM](/components/llms)
+* [Embedding Model](/components/embedding-models)
+* [Vector Database](/components/vector-databases)
@@ -0,0 +1,551 @@
+---
+title: 🤖 Large language models (LLMs)
+---
+
+## Overview
+
+Embedchain comes with built-in support for various popular large language models. We handle the complexity of integrating these models for you, allowing you to easily customize your language model interactions through a user-friendly interface.
+
+<CardGroup cols={4}>
+  <Card title="OpenAI" href="#openai"></Card>
+  <Card title="Google AI" href="#google-ai"></Card>
+  <Card title="Azure OpenAI" href="#azure-openai"></Card>
+  <Card title="Anthropic" href="#anthropic"></Card>
+  <Card title="Cohere" href="#cohere"></Card>
+  <Card title="Together" href="#together"></Card>
+  <Card title="Ollama" href="#ollama"></Card>
+  <Card title="GPT4All" href="#gpt4all"></Card>
+  <Card title="JinaChat" href="#jinachat"></Card>
+  <Card title="Hugging Face" href="#hugging-face"></Card>
+  <Card title="Llama2" href="#llama2"></Card>
+  <Card title="Vertex AI" href="#vertex-ai"></Card>
+</CardGroup>
+
+## OpenAI
+
+To use OpenAI LLM models, you have to set the `OPENAI_API_KEY` environment variable. You can obtain the OpenAI API key from the [OpenAI Platform](https://platform.openai.com/account/api-keys).
+
+Once you have obtained the key, you can use it like this:
+
+```python
+import os
+from embedchain import App
+
+os.environ['OPENAI_API_KEY'] = 'xxx'
+
+app = App()
+app.add("https://en.wikipedia.org/wiki/OpenAI")
+app.query("What is OpenAI?")
+```
+
+If you are looking to configure the different parameters of the LLM, you can do so by loading the app using a [yaml config](https://github.com/embedchain/embedchain/blob/main/configs/chroma.yaml) file.
+
+<CodeGroup>
+
+```python main.py
+import os
+from embedchain import App
+
+os.environ['OPENAI_API_KEY'] = 'xxx'
+
+# load llm configuration from config.yaml file
+app = App.from_config(config_path="config.yaml")
+```
+
+```yaml config.yaml
+llm:
+  provider: openai
+  config:
+    model: 'gpt-3.5-turbo'
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 1
+    stream: false
+```
+</CodeGroup>
+
+### Function Calling
+To enable [function calling](https://platform.openai.com/docs/guides/function-calling) in your application using embedchain and OpenAI, you need to pass functions into `OpenAILlm` class as an array of functions. Here are several ways in which you can achieve that:
+
+Examples:
+<Accordion title="Using Pydantic Models">
+  ```python
+import os
+from embedchain import App
+from embedchain.llm.openai import OpenAILlm
+import requests
+from pydantic import BaseModel, Field, ValidationError, field_validator
+
+os.environ["OPENAI_API_KEY"] = "sk-xxx"
+
+class QA(BaseModel):
+    """
+    A question and answer pair.
+    """
+
+    question: str = Field(
+        ..., description="The question.", example="What is a mountain?"
+    )
+    answer: str = Field(
+        ..., description="The answer.", example="A mountain is a hill."
+    )
+    person_who_is_asking: str = Field(
+        ..., description="The person who is asking the question.", example="John"
+    )
+
+    @field_validator("question")
+    def question_must_end_with_a_question_mark(cls, v):
+        """
+        Validate that the question ends with a question mark.
+        """
+        if not v.endswith("?"):
+            raise ValueError("question must end with a question mark")
+        return v
+
+    @field_validator("answer")
+    def answer_must_end_with_a_period(cls, v):
+        """
+        Validate that the answer ends with a period.
+        """
+        if not v.endswith("."):
+            raise ValueError("answer must end with a period")
+        return v
+
+llm = OpenAILlm(config=None,functions=[QA])
+app = App(llm=llm)
+
+result = app.query("Hey I am Sid. What is a mountain? A mountain is a hill.")
+
+print(result)
+  ```
+  </Accordion>
+  
+  <Accordion title="Using OpenAI JSON schema">
+```python
+import os
+from embedchain import App
+from embedchain.llm.openai import OpenAILlm
+import requests
+from pydantic import BaseModel, Field, ValidationError, field_validator
+
+os.environ["OPENAI_API_KEY"] = "sk-xxx"
+
+json_schema = {
+    "name": "get_qa",
+    "description": "A question and answer pair and the user who is asking the question.",
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "question": {"type": "string", "description": "The question."},
+            "answer": {"type": "string", "description": "The answer."},
+            "person_who_is_asking": {
+                "type": "string",
+                "description": "The person who is asking the question.",
+            }
+        },
+        "required": ["question", "answer", "person_who_is_asking"],
+    },
+}
+
+llm = OpenAILlm(config=None,functions=[json_schema])
+app = App(llm=llm)
+
+result = app.query("Hey I am Sid. What is a mountain? A mountain is a hill.")
+
+print(result)
+  ```
+  </Accordion>
+  <Accordion title="Using actual python functions">
+  ```python
+import os
+from embedchain import App
+from embedchain.llm.openai import OpenAILlm
+import requests
+from pydantic import BaseModel, Field, ValidationError, field_validator
+
+os.environ["OPENAI_API_KEY"] = "sk-xxx"
+
+def find_info_of_pokemon(pokemon: str):
+    """
+    Find the information of the given pokemon.
+    Args:
+        pokemon: The pokemon.
+    """
+    req = requests.get(f"https://pokeapi.co/api/v2/pokemon/{pokemon}")
+    if req.status_code == 404:
+        raise ValueError("pokemon not found")
+    return req.json()
+
+llm = OpenAILlm(config=None,functions=[find_info_of_pokemon])
+app = App(llm=llm)
+
+result = app.query("Tell me more about the pokemon pikachu.")
+
+print(result)
+```
+</Accordion>
+
+## Google AI
+
+To use Google AI model, you have to set the `GOOGLE_API_KEY` environment variable. You can obtain the Google API key from the [Google Maker Suite](https://makersuite.google.com/app/apikey)
+
+<CodeGroup>
+```python main.py
+import os
+from embedchain import App
+
+os.environ["GOOGLE_API_KEY"] = "xxx"
+
+app = App.from_config(config_path="config.yaml")
+
+app.add("https://www.forbes.com/profile/elon-musk")
+
+response = app.query("What is the net worth of Elon Musk?")
+if app.llm.config.stream: # if stream is enabled, response is a generator
+    for chunk in response:
+        print(chunk)
+else:
+    print(response)
+```
+
+```yaml config.yaml
+llm:
+  provider: google
+  config:
+    model: gemini-pro
+    max_tokens: 1000
+    temperature: 0.5
+    top_p: 1
+    stream: false
+
+embedder:
+  provider: google
+  config:
+    model: 'models/embedding-001'
+    task_type: "retrieval_document"
+    title: "Embeddings for Embedchain"
+```
+</CodeGroup>
+
+## Azure OpenAI
+
+To use Azure OpenAI model, you have to set some of the azure openai related environment variables as given in the code block below:
+
+<CodeGroup>
+
+```python main.py
+import os
+from embedchain import App
+
+os.environ["OPENAI_API_TYPE"] = "azure"
+os.environ["OPENAI_API_BASE"] = "https://xxx.openai.azure.com/"
+os.environ["OPENAI_API_KEY"] = "xxx"
+os.environ["OPENAI_API_VERSION"] = "xxx"
+
+app = App.from_config(config_path="config.yaml")
+```
+
+```yaml config.yaml
+llm:
+  provider: azure_openai
+  config:
+    model: gpt-35-turbo
+    deployment_name: your_llm_deployment_name
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 1
+    stream: false
+
+embedder:
+  provider: azure_openai
+  config:
+    model: text-embedding-ada-002
+    deployment_name: you_embedding_model_deployment_name
+```
+</CodeGroup>
+
+You can find the list of models and deployment name on the [Azure OpenAI Platform](https://oai.azure.com/portal).
+
+## Anthropic
+
+To use anthropic's model, please set the `ANTHROPIC_API_KEY` which you find on their [Account Settings Page](https://console.anthropic.com/account/keys).
+
+<CodeGroup>
+
+```python main.py
+import os
+from embedchain import App
+
+os.environ["ANTHROPIC_API_KEY"] = "xxx"
+
+# load llm configuration from config.yaml file
+app = App.from_config(config_path="config.yaml")
+```
+
+```yaml config.yaml
+llm:
+  provider: anthropic
+  config:
+    model: 'claude-instant-1'
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 1
+    stream: false
+```
+
+</CodeGroup>
+
+## Cohere
+
+Install related dependencies using the following command:
+
+```bash
+pip install --upgrade 'embedchain[cohere]'
+```
+
+Set the `COHERE_API_KEY` as environment variable which you can find on their [Account settings page](https://dashboard.cohere.com/api-keys).
+
+Once you have the API key, you are all set to use it with Embedchain.
+
+<CodeGroup>
+
+```python main.py
+import os
+from embedchain import App
+
+os.environ["COHERE_API_KEY"] = "xxx"
+
+# load llm configuration from config.yaml file
+app = App.from_config(config_path="config.yaml")
+```
+
+```yaml config.yaml
+llm:
+  provider: cohere
+  config:
+    model: large
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 1
+```
+
+</CodeGroup>
+
+## Together
+
+Install related dependencies using the following command:
+
+```bash
+pip install --upgrade 'embedchain[together]'
+```
+
+Set the `TOGETHER_API_KEY` as environment variable which you can find on their [Account settings page](https://api.together.xyz/settings/api-keys).
+
+Once you have the API key, you are all set to use it with Embedchain.
+
+<CodeGroup>
+
+```python main.py
+import os
+from embedchain import App
+
+os.environ["TOGETHER_API_KEY"] = "xxx"
+
+# load llm configuration from config.yaml file
+app = App.from_config(config_path="config.yaml")
+```
+
+```yaml config.yaml
+llm:
+  provider: together
+  config:
+    model: togethercomputer/RedPajama-INCITE-7B-Base
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 1
+```
+
+</CodeGroup>
+
+## Ollama
+
+Setup Ollama using https://github.com/jmorganca/ollama
+
+<CodeGroup>
+
+```python main.py
+import os
+from embedchain import App
+
+# load llm configuration from config.yaml file
+app = App.from_config(config_path="config.yaml")
+```
+
+```yaml config.yaml
+llm:
+  provider: ollama
+  config:
+    model: 'llama2'
+    temperature: 0.5
+    top_p: 1
+    stream: true
+```
+
+</CodeGroup>
+
+## GPT4ALL
+
+Install related dependencies using the following command:
+
+```bash
+pip install --upgrade 'embedchain[opensource]'
+```
+
+GPT4all is a free-to-use, locally running, privacy-aware chatbot. No GPU or internet required. You can use this with Embedchain using the following code:
+
+<CodeGroup>
+
+```python main.py
+from embedchain import App
+
+# load llm configuration from config.yaml file
+app = App.from_config(config_path="config.yaml")
+```
+
+```yaml config.yaml
+llm:
+  provider: gpt4all
+  config:
+    model: 'orca-mini-3b-gguf2-q4_0.gguf'
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 1
+    stream: false
+
+embedder:
+  provider: gpt4all
+```
+</CodeGroup>
+
+
+## JinaChat
+
+First, set `JINACHAT_API_KEY` in environment variable which you can obtain from [their platform](https://chat.jina.ai/api).
+
+Once you have the key, load the app using the config yaml file:
+
+<CodeGroup>
+
+```python main.py
+import os
+from embedchain import App
+
+os.environ["JINACHAT_API_KEY"] = "xxx"
+# load llm configuration from config.yaml file
+app = App.from_config(config_path="config.yaml")
+```
+
+```yaml config.yaml
+llm:
+  provider: jina
+  config:
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 1
+    stream: false
+```
+</CodeGroup>
+
+
+## Hugging Face
+
+
+Install related dependencies using the following command:
+
+```bash
+pip install --upgrade 'embedchain[huggingface-hub]'
+```
+
+First, set `HUGGINGFACE_ACCESS_TOKEN` in environment variable which you can obtain from [their platform](https://huggingface.co/settings/tokens).
+
+Once you have the token, load the app using the config yaml file:
+
+<CodeGroup>
+
+```python main.py
+import os
+from embedchain import App
+
+os.environ["HUGGINGFACE_ACCESS_TOKEN"] = "xxx"
+
+# load llm configuration from config.yaml file
+app = App.from_config(config_path="config.yaml")
+```
+
+```yaml config.yaml
+llm:
+  provider: huggingface
+  config:
+    model: 'google/flan-t5-xxl'
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 0.5
+    stream: false
+```
+</CodeGroup>
+
+## Llama2
+
+Llama2 is integrated through [Replicate](https://replicate.com/).  Set `REPLICATE_API_TOKEN` in environment variable which you can obtain from [their platform](https://replicate.com/account/api-tokens).
+
+Once you have the token, load the app using the config yaml file:
+
+<CodeGroup>
+
+```python main.py
+import os
+from embedchain import App
+
+os.environ["REPLICATE_API_TOKEN"] = "xxx"
+
+# load llm configuration from config.yaml file
+app = App.from_config(config_path="config.yaml")
+```
+
+```yaml config.yaml
+llm:
+  provider: llama2
+  config:
+    model: 'a16z-infra/llama13b-v2-chat:df7690f1994d94e96ad9d568eac121aecf50684a0b0963b25a41cc40061269e5'
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 0.5
+    stream: false
+```
+</CodeGroup>
+
+## Vertex AI
+
+Setup Google Cloud Platform application credentials by following the instruction on [GCP](https://cloud.google.com/docs/authentication/external/set-up-adc). Once setup is done, use the following code to create an app using VertexAI as provider:
+
+<CodeGroup>
+
+```python main.py
+from embedchain import App
+
+# load llm configuration from config.yaml file
+app = App.from_config(config_path="config.yaml")
+```
+
+```yaml config.yaml
+llm:
+  provider: vertexai
+  config:
+    model: 'chat-bison'
+    temperature: 0.5
+    top_p: 0.5
+```
+</CodeGroup>
+
+<br/ >
+<Snippet file="missing-llm-tip.mdx" />
@@ -0,0 +1,232 @@
+---
+title: 🗄️ Vector databases
+---
+
+## Overview
+
+Utilizing a vector database alongside Embedchain is a seamless process. All you need to do is configure it within the YAML configuration file. We've provided examples for each supported database below:
+
+<CardGroup cols={4}>
+  <Card title="ChromaDB" href="#chromadb"></Card>
+  <Card title="Elasticsearch" href="#elasticsearch"></Card>
+  <Card title="OpenSearch" href="#opensearch"></Card>
+  <Card title="Zilliz" href="#zilliz"></Card>
+  <Card title="LanceDB" href="#lancedb"></Card>
+  <Card title="Pinecone" href="#pinecone"></Card>
+  <Card title="Qdrant" href="#qdrant"></Card>
+  <Card title="Weaviate" href="#weaviate"></Card>
+</CardGroup>
+
+## ChromaDB
+
+<CodeGroup>
+
+```python main.py
+from embedchain import App
+
+# load chroma configuration from yaml file
+app = App.from_config(config_path="config1.yaml")
+```
+
+```yaml config1.yaml
+vectordb:
+  provider: chroma
+  config:
+    collection_name: 'my-collection'
+    dir: db
+    allow_reset: true
+```
+
+```yaml config2.yaml
+vectordb:
+  provider: chroma
+  config:
+    collection_name: 'my-collection'
+    host: localhost
+    port: 5200
+    allow_reset: true
+```
+
+</CodeGroup>
+
+
+## Elasticsearch
+
+Install related dependencies using the following command:
+
+```bash
+pip install --upgrade 'embedchain[elasticsearch]'
+```
+
+<Note>
+You can configure the Elasticsearch connection by providing either `es_url` or `cloud_id`. If you are using the Elasticsearch Service on Elastic Cloud, you can find the `cloud_id` on the [Elastic Cloud dashboard](https://cloud.elastic.co/deployments).
+</Note>
+
+You can authorize the connection to Elasticsearch by providing either `basic_auth`, `api_key`, or `bearer_auth`.
+
+<CodeGroup>
+
+```python main.py
+from embedchain import App
+
+# load elasticsearch configuration from yaml file
+app = App.from_config(config_path="config.yaml")
+```
+
+```yaml config.yaml
+vectordb:
+  provider: elasticsearch
+  config:
+    collection_name: 'es-index'
+    cloud_id: 'deployment-name:xxxx'
+    basic_auth:
+      - elastic
+      - <your_password>
+    verify_certs: false
+```
+</CodeGroup>
+
+## OpenSearch
+
+Install related dependencies using the following command:
+
+```bash
+pip install --upgrade 'embedchain[opensearch]'
+```
+
+<CodeGroup>
+
+```python main.py
+from embedchain import App
+
+# load opensearch configuration from yaml file
+app = App.from_config(config_path="config.yaml")
+```
+
+```yaml config.yaml
+vectordb:
+  provider: opensearch
+  config:
+    collection_name: 'my-app'
+    opensearch_url: 'https://localhost:9200'
+    http_auth:
+      - admin
+      - admin
+    vector_dimension: 1536
+    use_ssl: false
+    verify_certs: false
+```
+
+</CodeGroup>
+
+## Zilliz
+
+Install related dependencies using the following command:
+
+```bash
+pip install --upgrade 'embedchain[milvus]'
+```
+
+Set the Zilliz environment variables `ZILLIZ_CLOUD_URI` and `ZILLIZ_CLOUD_TOKEN` which you can find it on their [cloud platform](https://cloud.zilliz.com/).
+
+<CodeGroup>
+
+```python main.py
+import os
+from embedchain import App
+
+os.environ['ZILLIZ_CLOUD_URI'] = 'https://xxx.zillizcloud.com'
+os.environ['ZILLIZ_CLOUD_TOKEN'] = 'xxx'
+
+# load zilliz configuration from yaml file
+app = App.from_config(config_path="config.yaml")
+```
+
+```yaml config.yaml
+vectordb:
+  provider: zilliz
+  config:
+    collection_name: 'zilliz_app'
+    uri: https://xxxx.api.gcp-region.zillizcloud.com
+    token: xxx
+    vector_dim: 1536
+    metric_type: L2
+```
+
+</CodeGroup>
+
+## LanceDB
+
+_Coming soon_
+
+## Pinecone
+
+Install pinecone related dependencies using the following command:
+
+```bash
+pip install --upgrade 'embedchain[pinecone]'
+```
+
+In order to use Pinecone as vector database, set the environment variables `PINECONE_API_KEY` and `PINECONE_ENV` which you can find on [Pinecone dashboard](https://app.pinecone.io/).
+
+<CodeGroup>
+
+```python main.py
+from embedchain import App
+
+# load pinecone configuration from yaml file
+app = App.from_config(config_path="config.yaml")
+```
+
+```yaml config.yaml
+vectordb:
+  provider: pinecone
+  config:
+    metric: cosine
+    vector_dimension: 1536
+    collection_name: my-pinecone-index
+```
+
+</CodeGroup>
+
+## Qdrant
+
+In order to use Qdrant as a vector database, set the environment variables `QDRANT_URL` and `QDRANT_API_KEY` which you can find on [Qdrant Dashboard](https://cloud.qdrant.io/).
+
+<CodeGroup>
+```python main.py
+from embedchain import App
+
+# load qdrant configuration from yaml file
+app = App.from_config(config_path="config.yaml")
+```
+
+```yaml config.yaml
+vectordb:
+  provider: qdrant
+  config:
+    collection_name: my_qdrant_index
+```
+</CodeGroup>
+
+## Weaviate
+
+In order to use Weaviate as a vector database, set the environment variables `WEAVIATE_ENDPOINT` and `WEAVIATE_API_KEY` which you can find on [Weaviate dashboard](https://console.weaviate.cloud/dashboard).
+
+<CodeGroup>
+```python main.py
+from embedchain import App
+
+# load weaviate configuration from yaml file
+app = App.from_config(config_path="config.yaml")
+```
+
+```yaml config.yaml
+vectordb:
+  provider: weaviate
+  config:
+    collection_name: my_weaviate_index
+```
+</CodeGroup>
+
+<Snippet file="missing-vector-db-tip.mdx" />
@@ -22,26 +22,12 @@ make lint format

 5. **Create a pull request**: When you are ready to contribute your changes, submit a pull request to the EmbedChain repository. Provide a clear and descriptive title for your pull request, along with a detailed description of the changes you have made.

-# Tech Stack
-
-embedchain is built on the following stack:
-
- [Langchain](https://github.com/hwchase17/langchain) as an LLM framework to load, chunk and index data
- [OpenAI's Ada embedding model](https://platform.openai.com/docs/guides/embeddings) to create embeddings
- [OpenAI's ChatGPT API](https://platform.openai.com/docs/guides/gpt/chat-completions-api) as LLM to get answers given the context
- [Chroma](https://github.com/chroma-core/chroma) as the vector database to store embeddings
- [gpt4all](https://github.com/nomic-ai/gpt4all) as an open source LLM
- [sentence-transformers](https://huggingface.co/sentence-transformers) as open source embedding model
-
 ## Team

-### Author
+### Authors

 - Taranjeet Singh ([@taranjeetio](https://twitter.com/taranjeetio))
-
-### Maintainer
-
- Deshraj Yadav ([@deshrajdry](https://twitter.com/taranjeetio))
+- Deshraj Yadav ([@deshrajdry](https://twitter.com/deshrajdry))

 ### Citation

@@ -49,8 +35,8 @@ 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},
+  author = {Taranjeet Singh, Deshraj Yadav},
+  title = {Embechain: The Open Source RAG Framework},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
@@ -1,14 +0,0 @@
---
-title: 'CSV'
---
-
-### CSV file
-
-To add any csv file, use the data_type as `csv`. `csv` allows remote urls and conventional file paths. Headers are included for each line, so if you have an `age` column, `18` will be added as `age: 18`. Eg:
-
-```python
-app.add('https://example.com/content/sheet.csv', data_type="csv")
-app.add('content/sheet.csv', data_type="csv")
-```
-
-Note: There is a size limit allowed for csv file beyond which it can throw error. This limit is set by the LLMs. Please consider chunking large csv files into smaller csv files.
@@ -1,11 +0,0 @@
---
-title: 'Code Documentation'
---
-
-### Code documentation
-
-To add any code documentation website as a loader, use the data_type as `docs_site`. Eg:
-
-```python
-app.add("https://docs.embedchain.ai/", data_type="docs_site")
-```
@@ -1,24 +0,0 @@
---
-title: 'How to add data'
---
-
-## Add Dataset
-
- This step assumes that you have already created an `App`. We are calling our app instance as `naval_chat_bot` 🤖
-
- Now use `.add` method to add any dataset.
-
-```python
-naval_chat_bot = App()
-
-# Embed Online Resources
-naval_chat_bot.add("https://www.youtube.com/watch?v=3qHkcs3kG44")
-naval_chat_bot.add("https://navalmanack.s3.amazonaws.com/Eric-Jorgenson_The-Almanack-of-Naval-Ravikant_Final.pdf")
-naval_chat_bot.add("https://nav.al/feedback")
-naval_chat_bot.add("https://nav.al/agi")
-
-# Embed Local Resources
-naval_chat_bot.add(("Who is Naval Ravikant?", "Naval Ravikant is an Indian-American entrepreneur and investor."))
-```
-
-The possible formats to add data can be found on the [Supported Data Formats](/advanced/data_types) page.
@@ -1,12 +0,0 @@
---
-title: 'Mdx'
---
-
-
-### Mdx file
-
-To add any mdx file to your app, use the data_type (first argument to `.add()` method) as `mdx`. Note that this supports support mdx file present on machine, so this should be a file path. Eg:
-
-```python
-app.add('path/to/file.mdx', data_type='mdx')
-```
@@ -1,15 +0,0 @@
---
-title: 'Notion'
---
-
-### Notion
-To use notion you must install the extra dependencies with `pip install --upgrade embedchain[notion]`.
-
-To load a notion page, use the data_type as `notion`. Since it is hard to automatically detect, forcing this is advised.
-The next argument must **end** with the `notion page id`. The id is a 32-character string. Eg:
-
-```python
-app.add("cfbc134ca6464fc980d0391613959196", "notion")
-app.add("my-page-cfbc134ca6464fc980d0391613959196", "notion")
-app.add("https://www.notion.so/my-page-cfbc134ca6464fc980d0391613959196", "notion")
-```
--- a/Show More
+++ b/Show More