[Docs] Update docs and improve assistant api (#923 )

[chore] update poetry.lock file (#922 )
[Feature] Add Postgres data loader (#918 )
2023-11-09 01:16:19 -08:00 · 2023-11-09 00:59:29 -08:00 · 2023-11-08 23:50:46 -08:00 · 2023-11-08 22:49:03 -08:00 · 2023-11-08 10:26:31 -08:00 · 2023-11-08 10:25:45 -08:00
183 changed files with 13173 additions and 1881 deletions
@@ -19,9 +19,20 @@ 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 lint
      - name: Run tests and generate coverage report
@@ -76,7 +76,6 @@ docs/_build/
 target/

 # Jupyter Notebook
-*.yaml

 # IPython
 profile_default/
@@ -165,12 +164,14 @@ cython_debug/

 # Database
 db
+test-db

 .vscode
-/poetry.lock
 .idea/

 .DS_Store

 notebooks/*.yaml
-.ipynb_checkpoints/
+.ipynb_checkpoints/
+
+!configs/*.yaml
@@ -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:

@@ -1,64 +1,68 @@
-# 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 Data Platform for LLMs - load, index, retrieve, and sync any unstructured data. Using embedchain, you can easily create LLM powered apps over any data. 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://join.slack.com/t/embedchain/shared_invite/zt-22uwz3c46-Zg7cIh5rOBteT_xe1jwLDw">
+    <img src="https://img.shields.io/badge/slack-embedchain-brightgreen.svg?logo=slack" alt="Slack">
+  </a>
+  <a href="https://discord.gg/CUU9FPhRNt">
+    <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://embedchain.substack.com/">
+    <img src="https://img.shields.io/badge/Substack-%23006f5c.svg?logo=substack" alt="Substack">
+  </a>
+  <a href="https://colab.research.google.com/drive/138lMWhENGeEu7Q1-6lNbNTHGLZXBBz_B?usp=sharing">
+    <img src="https://camo.githubusercontent.com/84f0493939e0c4de4e6dbe113251b4bfb5353e57134ffd9fcab6b8714514d4d1/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" 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
-
-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 a Data Platform for Large Language Models (LLMs). Seamlessly load, index, retrieve, and sync unstructured data to build dynamic, LLM-powered applications. Check out [embedchain-js](https://github.com/embedchain/embedchain/tree/main/embedchain-js) for a JavaScript implementation.

 ## 🔧 Quick install

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

-## 🔍 Demo
+### REST API
+You can also run Embedchain as a REST API server using the following command:

-Try out embedchain in your browser:
+```bash
+docker run --name embedchain -p 8080:8080 embedchain/rest-api:latest
+```

-[![Open in Colab](https://camo.githubusercontent.com/84f0493939e0c4de4e6dbe113251b4bfb5353e57134ffd9fcab6b8714514d4d1/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667)](https://colab.research.google.com/drive/17ON1LPonnXAtLaZEebnOktstB_1cJJmh?usp=sharing)
+Then, navigate to http://0.0.0.0:8080/docs to interact with the API.

-## 📖 Documentation
+## 🔍 Usage and Demo

-The documentation for embedchain can be found at [docs.embedchain.ai](https://docs.embedchain.ai).
+<!-- Demo GIF or Image -->
+<p align="center">
+  <img src="docs/images/cover.gif" width="900px" alt="Embedchain Demo">
+</p>

-## 💻 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
-* JSON file
-* Code documentation website loader
-* Notion
-* Unstructured file loader 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"
@@ -72,37 +76,40 @@ 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.
+
+# (Optional): Deploy app 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.
 ```

-## Examples
+You can also try it in your browser with Google Colab:

-| LLM          | Google Colab  | Replit   |
-|--------------|---------------|----------|
-| OpenAI       | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/openai.ipynb)           | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/openai)      |
-| Anthropic    | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/anthropic.ipynb)        | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/anthropic)   |
-| Azure OpenAI | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/azure-openai.ipynb)     | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/azureopenai) |
-| VertexAI     | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/vertex_ai.ipynb)        | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/vertexai)    |
-| Cohere       | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/cohere.ipynb)           | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/cohere)      |
-| Hugging Face | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/hugging_face_hub.ipynb) | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/huggingface) |
-| JinaChat     | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/jina.ipynb)             | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/jina)        |
-| GPT4All      | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/gpt4all.ipynb)          | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/gpt4all)     |
-| Llama2       | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/llama2.ipynb)           | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/llama2)      |
+[![Open in Colab](https://camo.githubusercontent.com/84f0493939e0c4de4e6dbe113251b4bfb5353e57134ffd9fcab6b8714514d4d1/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667)](https://colab.research.google.com/drive/17ON1LPonnXAtLaZEebnOktstB_1cJJmh?usp=sharing)

-| Embedding model     | Google Colab                                                                                                                                                                            | Replit                                                                                                                        |
-| ------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------- |
-| OpenAI       | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/openai.ipynb)           | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/openai)      |
-| VertexAI     | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/vertex_ai.ipynb)        | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/vertexai)    |
-| GPT4All      | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/gpt4all.ipynb)          | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/gpt4all)     |
-| Hugging Face | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/hugging_face_hub.ipynb) | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/huggingface) |
+## 📖 Documentation
+Comprehensive guides and API documentation are available to help you get the most out of Embedchain:

-| Vector DB     | Google Colab                                                                                                                                                                         | Replit                                                                                                                          |
-| ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------- |
-| ChromaDB      | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/chromadb.ipynb)      | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/chromadb)      |
-| Elasticsearch | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/elasticsearch.ipynb) | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/elasticsearch) |
-| Opensearch    | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/opensearch.ipynb)    | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/opensearch)    |
-| Pinecone      | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/pinecone.ipynb)      | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/pinecone)      |
+- [Getting Started](https://docs.embedchain.ai/get-started/quickstart)
+- [Introduction](https://docs.embedchain.ai/get-started/introduction#what-is-embedchain)
+- [Examples](https://docs.embedchain.ai/get-started/examples)
+- [Supported data types](https://docs.embedchain.ai/data-sources/)

-## 🤝 Contributing
+## 🔗 Join the Community
+
+Connect with fellow developers and users by joining our [Slack Workspace](https://join.slack.com/t/embedchain/shared_invite/zt-22uwz3c46-Zg7cIh5rOBteT_xe1jwLDw). Dive into discussions, ask questions, and share your experiences.
+
+## 🤝 Schedule a 1-on-1 Session
+
+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.
+
+## 🌐 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).
@@ -1,7 +1,7 @@
 llm:
  provider: anthropic
-  model: 'claude-instant-1'
  config:
+    model: 'claude-instant-1'
    temperature: 0.5
    max_tokens: 1000
    top_p: 1
@@ -4,8 +4,8 @@ app:

 llm:
  provider: azure_openai
-  model: gpt-35-turbo
  config:
+    model: gpt-35-turbo
    deployment_name: your_llm_deployment_name
    temperature: 0.5
    max_tokens: 1000
@@ -5,8 +5,8 @@ app:

 llm:
  provider: openai
-  model: 'gpt-3.5-turbo'
  config:
+    model: 'gpt-3.5-turbo'
    temperature: 0.5
    max_tokens: 1000
    top_p: 1
@@ -23,4 +23,4 @@ embedder:
  provider: openai
  config:
    model: 'text-embedding-ada-002'
-    deployment_name: null
+    deployment_name: 'test-deployment'
@@ -0,0 +1,4 @@
+chunker:
+  chunk_size: 100
+  chunk_overlap: 20
+  length_function: 'len'
@@ -1,7 +1,7 @@
 llm:
  provider: cohere
-  model: large
  config:
+    model: large
    temperature: 0.5
    max_tokens: 1000
    top_p: 1
@@ -2,10 +2,15 @@ app:
  config:
    id: 'full-stack-app'

+chunker:
+  chunk_size: 100
+  chunk_overlap: 20
+  length_function: 'len'
+
 llm:
  provider: openai
-  model: 'gpt-3.5-turbo'
  config:
+    model: 'gpt-3.5-turbo'
    temperature: 0.5
    max_tokens: 1000
    top_p: 1
@@ -1,7 +1,7 @@
 llm:
  provider: gpt4all
-  model: 'orca-mini-3b.ggmlv3.q4_0.bin'
  config:
+    model: 'orca-mini-3b-gguf2-q4_0.gguf'
    temperature: 0.5
    max_tokens: 1000
    top_p: 1
@@ -9,5 +9,3 @@ llm:

 embedder:
  provider: gpt4all
-  config:
-    model: 'all-MiniLM-L6-v2'
@@ -1,7 +1,7 @@
 llm:
  provider: huggingface
-  model: 'google/flan-t5-xxl'
  config:
+    model: 'google/flan-t5-xxl'
    temperature: 0.5
    max_tokens: 1000
    top_p: 0.5
@@ -1,7 +1,7 @@
 llm:
  provider: llama2
-  model: 'a16z-infra/llama13b-v2-chat:df7690f1994d94e96ad9d568eac121aecf50684a0b0963b25a41cc40061269e5'
  config:
+    model: 'a16z-infra/llama13b-v2-chat:df7690f1994d94e96ad9d568eac121aecf50684a0b0963b25a41cc40061269e5'
    temperature: 0.5
    max_tokens: 1000
    top_p: 0.5
@@ -7,8 +7,8 @@ app:

 llm:
  provider: openai
-  model: 'gpt-3.5-turbo'
  config:
+    model: 'gpt-3.5-turbo'
    temperature: 0.5
    max_tokens: 1000
    top_p: 1
@@ -6,8 +6,8 @@ app:

 llm:
  provider: gpt4all
-  model: 'orca-mini-3b.ggmlv3.q4_0.bin'
  config:
+    model: 'orca-mini-3b-gguf2-q4_0.gguf'
    temperature: 0.5
    max_tokens: 1000
    top_p: 1
@@ -23,5 +23,4 @@ vectordb:
 embedder:
  provider: gpt4all
  config:
-    model: 'all-MiniLM-L6-v2'
-    deployment_name: null
+    deployment_name: 'test-deployment'
@@ -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
@@ -1,6 +1,6 @@
 llm:
  provider: vertexai
-  model: 'chat-bison'
  config:
+    model: 'chat-bison'
    temperature: 0.5
    top_p: 0.5
@@ -1,5 +1,4 @@
-<Tip>
-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>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="Slack" icon="slack" href="https://join.slack.com/t/embedchain/shared_invite/zt-22uwz3c46-Zg7cIh5rOBteT_xe1jwLDw" color="#4A154B">
@@ -15,4 +14,3 @@ If you can't find the specific data source, please feel free to request through
  Schedule a call with Embedchain founder
  </Card>
 </CardGroup>
-</Tip>
@@ -1,5 +1,4 @@
-<Tip>
-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>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">
@@ -15,4 +14,3 @@ If you can't find the specific LLM you need, no need to fret. We're continuously
  Schedule a call with Embedchain founder
  </Card>
 </CardGroup>
-</Tip>
@@ -1,5 +1,6 @@
-<Tip>
-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>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">
@@ -15,4 +16,3 @@ If you can't find the specific vector database, please feel free to request thro
  Schedule a call with Embedchain founder
  </Card>
 </CardGroup>
-</Tip>
@@ -11,10 +11,15 @@ app:
  config:
    id: 'full-stack-app'

+chunker:
+  chunk_size: 100
+  chunk_overlap: 20
+  length_function: 'len'
+
 llm:
  provider: openai
-  model: 'gpt-3.5-turbo'
  config:
+    model: 'gpt-3.5-turbo'
    temperature: 0.5
    max_tokens: 1000
    top_p: 1
@@ -49,7 +54,11 @@ Alright, let's dive into what each key means in the yaml config above:
 1. `app` Section:
    - `config`:
        - `id` (String): The ID or name of your full-stack application.
-2. `llm` Section:
+2. `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.
+3. `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).
    - `model` (String): The specific model being used, 'gpt-3.5-turbo'.
    - `config`:
@@ -59,13 +68,13 @@ Alright, let's dive into what each key means in the yaml config above:
        - `stream` (Boolean): Controls if the response is streamed back to the user (set to false).
        - `template` (String): A custom template for the prompt that the model uses to generate responses.
        - `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.
-3. `vectordb` Section:
+4. `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 database, set to 'full-stack-app'.
        - `dir` (String): The directory for the database, set to 'db'.
        - `allow_reset` (Boolean): Indicates whether resetting the database is allowed, set to true.
-4. `embedder` Section:
+5. `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'.
@@ -24,7 +24,7 @@ Once you have obtained the key, you can use it like this:

 ```python main.py
 import os
-from embedchain import App
+from embedchain import Pipeline as App

 os.environ['OPENAI_API_KEY'] = 'xxx'

@@ -52,7 +52,7 @@ To use Azure OpenAI embedding model, you have to set some of the azure openai re

 ```python main.py
 import os
-from embedchain import App
+from embedchain import Pipeline as App

 os.environ["OPENAI_API_TYPE"] = "azure"
 os.environ["OPENAI_API_BASE"] = "https://xxx.openai.azure.com/"
@@ -65,8 +65,8 @@ app = App.from_config(yaml_path="config.yaml")
 ```yaml config.yaml
 llm:
  provider: azure_openai
-  model: gpt-35-turbo
  config:
+    model: gpt-35-turbo
    deployment_name: your_llm_deployment_name
    temperature: 0.5
    max_tokens: 1000
@@ -90,7 +90,7 @@ GPT4All supports generating high quality embeddings of arbitrary length document
 <CodeGroup>

 ```python main.py
-from embedchain import App
+from embedchain import Pipeline as App

 # load embedding model configuration from config.yaml file
 app = App.from_config(yaml_path="config.yaml")
@@ -99,8 +99,8 @@ app = App.from_config(yaml_path="config.yaml")
 ```yaml config.yaml
 llm:
  provider: gpt4all
-  model: 'orca-mini-3b.ggmlv3.q4_0.bin'
  config:
+    model: 'orca-mini-3b-gguf2-q4_0.gguf'
    temperature: 0.5
    max_tokens: 1000
    top_p: 1
@@ -108,8 +108,6 @@ llm:

 embedder:
  provider: gpt4all
-  config:
-    model: 'all-MiniLM-L6-v2'
 ```

 </CodeGroup>
@@ -121,7 +119,7 @@ Hugging Face supports generating embeddings of arbitrary length documents of tex
 <CodeGroup>

 ```python main.py
-from embedchain import App
+from embedchain import Pipeline as App

 # load embedding model configuration from config.yaml file
 app = App.from_config(yaml_path="config.yaml")
@@ -130,8 +128,8 @@ app = App.from_config(yaml_path="config.yaml")
 ```yaml config.yaml
 llm:
  provider: huggingface
-  model: 'google/flan-t5-xxl'
  config:
+    model: 'google/flan-t5-xxl'
    temperature: 0.5
    max_tokens: 1000
    top_p: 0.5
@@ -152,7 +150,7 @@ Embedchain supports Google's VertexAI embeddings model through a simple interfac
 <CodeGroup>

 ```python main.py
-from embedchain import App
+from embedchain import Pipeline as App

 # load embedding model configuration from config.yaml file
 app = App.from_config(yaml_path="config.yaml")
@@ -161,8 +159,8 @@ app = App.from_config(yaml_path="config.yaml")
 ```yaml config.yaml
 llm:
  provider: vertexai
-  model: 'chat-bison'
  config:
+    model: 'chat-bison'
    temperature: 0.5
    top_p: 0.5

@@ -26,7 +26,7 @@ Once you have obtained the key, you can use it like this:

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

 os.environ['OPENAI_API_KEY'] = 'xxx'

@@ -41,7 +41,7 @@ If you are looking to configure the different parameters of the LLM, you can do

 ```python main.py
 import os
-from embedchain import App
+from embedchain import Pipeline as App

 os.environ['OPENAI_API_KEY'] = 'xxx'

@@ -52,8 +52,8 @@ app = App.from_config(yaml_path="config.yaml")
 ```yaml config.yaml
 llm:
  provider: openai
-  model: 'gpt-3.5-turbo'
  config:
+    model: 'gpt-3.5-turbo'
    temperature: 0.5
    max_tokens: 1000
    top_p: 1
@@ -71,7 +71,7 @@ To use Azure OpenAI model, you have to set some of the azure openai related envi

 ```python main.py
 import os
-from embedchain import App
+from embedchain import Pipeline as App

 os.environ["OPENAI_API_TYPE"] = "azure"
 os.environ["OPENAI_API_BASE"] = "https://xxx.openai.azure.com/"
@@ -84,8 +84,8 @@ app = App.from_config(yaml_path="config.yaml")
 ```yaml config.yaml
 llm:
  provider: azure_openai
-  model: gpt-35-turbo
  config:
+    model: gpt-35-turbo
    deployment_name: your_llm_deployment_name
    temperature: 0.5
    max_tokens: 1000
@@ -110,7 +110,7 @@ To use anthropic's model, please set the `ANTHROPIC_API_KEY` which you find on t

 ```python main.py
 import os
-from embedchain import App
+from embedchain import Pipeline as App

 os.environ["ANTHROPIC_API_KEY"] = "xxx"

@@ -121,8 +121,8 @@ app = App.from_config(yaml_path="config.yaml")
 ```yaml config.yaml
 llm:
  provider: anthropic
-  model: 'claude-instant-1'
  config:
+    model: 'claude-instant-1'
    temperature: 0.5
    max_tokens: 1000
    top_p: 1
@@ -147,7 +147,7 @@ Once you have the API key, you are all set to use it with Embedchain.

 ```python main.py
 import os
-from embedchain import App
+from embedchain import Pipeline as App

 os.environ["COHERE_API_KEY"] = "xxx"

@@ -158,8 +158,8 @@ app = App.from_config(yaml_path="config.yaml")
 ```yaml config.yaml
 llm:
  provider: cohere
-  model: large
  config:
+    model: large
    temperature: 0.5
    max_tokens: 1000
    top_p: 1
@@ -180,7 +180,7 @@ GPT4all is a free-to-use, locally running, privacy-aware chatbot. No GPU or inte
 <CodeGroup>

 ```python main.py
-from embedchain import App
+from embedchain import Pipeline as App

 # load llm configuration from config.yaml file
 app = App.from_config(yaml_path="config.yaml")
@@ -189,8 +189,8 @@ app = App.from_config(yaml_path="config.yaml")
 ```yaml config.yaml
 llm:
  provider: gpt4all
-  model: 'orca-mini-3b.ggmlv3.q4_0.bin'
  config:
+    model: 'orca-mini-3b-gguf2-q4_0.gguf'
    temperature: 0.5
    max_tokens: 1000
    top_p: 1
@@ -198,8 +198,6 @@ llm:

 embedder:
  provider: gpt4all
-  config:
-    model: 'all-MiniLM-L6-v2'
 ```
 </CodeGroup>

@@ -214,7 +212,7 @@ Once you have the key, load the app using the config yaml file:

 ```python main.py
 import os
-from embedchain import App
+from embedchain import Pipeline as App

 os.environ["JINACHAT_API_KEY"] = "xxx"
 # load llm configuration from config.yaml file
@@ -250,7 +248,7 @@ Once you have the token, load the app using the config yaml file:

 ```python main.py
 import os
-from embedchain import App
+from embedchain import Pipeline as App

 os.environ["HUGGINGFACE_ACCESS_TOKEN"] = "xxx"

@@ -261,8 +259,8 @@ app = App.from_config(yaml_path="config.yaml")
 ```yaml config.yaml
 llm:
  provider: huggingface
-  model: 'google/flan-t5-xxl'
  config:
+    model: 'google/flan-t5-xxl'
    temperature: 0.5
    max_tokens: 1000
    top_p: 0.5
@@ -280,7 +278,7 @@ Once you have the token, load the app using the config yaml file:

 ```python main.py
 import os
-from embedchain import App
+from embedchain import Pipeline as App

 os.environ["REPLICATE_API_TOKEN"] = "xxx"

@@ -291,8 +289,8 @@ app = App.from_config(yaml_path="config.yaml")
 ```yaml config.yaml
 llm:
  provider: llama2
-  model: 'a16z-infra/llama13b-v2-chat:df7690f1994d94e96ad9d568eac121aecf50684a0b0963b25a41cc40061269e5'
  config:
+    model: 'a16z-infra/llama13b-v2-chat:df7690f1994d94e96ad9d568eac121aecf50684a0b0963b25a41cc40061269e5'
    temperature: 0.5
    max_tokens: 1000
    top_p: 0.5
@@ -307,7 +305,7 @@ Setup Google Cloud Platform application credentials by following the instruction
 <CodeGroup>

 ```python main.py
-from embedchain import App
+from embedchain import Pipeline as App

 # load llm configuration from config.yaml file
 app = App.from_config(yaml_path="config.yaml")
@@ -316,8 +314,8 @@ app = App.from_config(yaml_path="config.yaml")
 ```yaml config.yaml
 llm:
  provider: vertexai
-  model: 'chat-bison'
  config:
+    model: 'chat-bison'
    temperature: 0.5
    top_p: 0.5
 ```
@@ -22,7 +22,7 @@ Utilizing a vector database alongside Embedchain is a seamless process. All you
 <CodeGroup>

 ```python main.py
-from embedchain import App
+from embedchain import Pipeline as App

 # load chroma configuration from yaml file
 app = App.from_config(yaml_path="config1.yaml")
@@ -61,7 +61,7 @@ pip install --upgrade 'embedchain[elasticsearch]'
 <CodeGroup>

 ```python main.py
-from embedchain import App
+from embedchain import Pipeline as App

 # load elasticsearch configuration from yaml file
 app = App.from_config(yaml_path="config.yaml")
@@ -89,7 +89,7 @@ pip install --upgrade 'embedchain[opensearch]'
 <CodeGroup>

 ```python main.py
-from embedchain import App
+from embedchain import Pipeline as App

 # load opensearch configuration from yaml file
 app = App.from_config(yaml_path="config.yaml")
@@ -125,7 +125,7 @@ Set the Zilliz environment variables `ZILLIZ_CLOUD_URI` and `ZILLIZ_CLOUD_TOKEN`

 ```python main.py
 import os
-from embedchain import App
+from embedchain import Pipeline as App

 os.environ['ZILLIZ_CLOUD_URI'] = 'https://xxx.zillizcloud.com'
 os.environ['ZILLIZ_CLOUD_TOKEN'] = 'xxx'
@@ -138,7 +138,7 @@ app = App.from_config(yaml_path="config.yaml")
 vectordb:
  provider: zilliz
  config:
-    collection_name: 'zilliz-app'
+    collection_name: 'zilliz_app'
    uri: https://xxxx.api.gcp-region.zillizcloud.com
    token: xxx
    vector_dim: 1536
@@ -164,7 +164,7 @@ In order to use Pinecone as vector database, set the environment variables `PINE
 <CodeGroup>

 ```python main.py
-from embedchain import App
+from embedchain import Pipeline as App

 # load pinecone configuration from yaml file
 app = App.from_config(yaml_path="config.yaml")
@@ -187,7 +187,7 @@ In order to use Qdrant as a vector database, set the environment variables `QDRA

 <CodeGroup>
 ```python main.py
-from embedchain import App
+from embedchain import Pipeline as App

 # load qdrant configuration from yaml file
 app = App.from_config(yaml_path="config.yaml")
@@ -207,7 +207,7 @@ In order to use Weaviate as a vector database, set the environment variables `WE

 <CodeGroup>
 ```python main.py
-from embedchain import App
+from embedchain import Pipeline as App

 # load weaviate configuration from yaml file
 app = App.from_config(yaml_path="config.yaml")
@@ -5,7 +5,7 @@ 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
+from embedchain import Pipeline as App

 app = App()
 app.add('https://people.sc.fsu.edu/~jburkardt/data/csv/airtravel.csv', data_type="csv")
@@ -35,7 +35,7 @@ Default behavior is to create a persistent vector db in the directory **./db**.
 Create a local index:

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

 naval_chat_bot = App()
 naval_chat_bot.add("https://www.youtube.com/watch?v=3qHkcs3kG44")
@@ -45,7 +45,7 @@ naval_chat_bot.add("https://navalmanack.s3.amazonaws.com/Eric-Jorgenson_The-Alma
 You can reuse the local index with the same code, but without adding new documents:

 ```python
-from embedchain import App
+from embedchain import Pipeline as 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?"))
@@ -5,7 +5,7 @@ title: '📚🌐 Code documentation'
 To add any code documentation website as a loader, use the data_type as `docs_site`. Eg:

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

 app = App()
 app.add("https://docs.embedchain.ai/", data_type="docs_site")
@@ -7,7 +7,7 @@ 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
+from embedchain import Pipeline as App

 app = App()
 app.add('https://example.com/content/intro.docx', data_type="docx")
@@ -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 Pipeline as App
+
+app = App()
+
+gmail_filter = "to: me label:inbox"
+app.add(gmail_filter, data_type="gmail")
+app.query("Summarize my email conversations")
+```
@@ -0,0 +1,44 @@
+---
+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 Pipeline as 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>
+
+
@@ -5,7 +5,7 @@ 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
+from embedchain import Pipeline as App

 app = App()
 app.add('path/to/file.mdx', data_type='mdx')
@@ -2,13 +2,13 @@
 title: '📓 Notion'
 ---

-To use notion you must install the extra dependencies with `pip install --upgrade embedchain[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
+from embedchain import Pipeline as App

 app = App()

@@ -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 Pipeline as 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>
@@ -6,6 +6,7 @@ Embedchain comes with built-in support for various data sources. We handle the c

 <CardGroup cols={4}>
  <Card title="📊 csv" href="/data-sources/csv"></Card>
+  <Card title="📃 JSON" href="/data-sources/json"></Card>
  <Card title="📚🌐 docs site" href="/data-sources/docs-site"></Card>
  <Card title="📄 docx" href="/data-sources/docx"></Card>
  <Card title="📝 mdx" href="/data-sources/mdx"></Card>
@@ -16,7 +17,10 @@ Embedchain comes with built-in support for various data sources. We handle the c
  <Card title="📝 text" href="/data-sources/text"></Card>
  <Card title="🌐📄 web page" href="/data-sources/web-page"></Card>
  <Card title="🧾 xml" href="/data-sources/xml"></Card>
+  <Card title="🙌 OpenAPI" href="/data-sources/openapi"></Card>
  <Card title="🎥📺 youtube video" href="/data-sources/youtube-video"></Card>
+  <Card title="📬 Gmail" href="/data-sources/gmail"></Card>
+  <Card title="🐘 Postgres" href="/data-sources/postgres"></Card>
 </CardGroup>

 <br/ >
@@ -5,7 +5,7 @@ title: '📰 PDF file'
 To add any pdf file, use the data_type as `pdf_file`. Eg:

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

 app = App()

@@ -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)
+```
@@ -5,7 +5,7 @@ title: '❓💬 Queston and answer 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
+from embedchain import Pipeline as App

 app = App()

@@ -5,7 +5,7 @@ title: '🗺️ 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
+from embedchain import Pipeline as App

 app = App()

@@ -7,7 +7,7 @@ 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
+from embedchain import Pipeline as App

 app = App()

@@ -5,7 +5,7 @@ title: '🌐📄 Web page'
 To add any web page, use the data_type as `web_page`. Eg:

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

 app = App()

@@ -7,7 +7,7 @@ title: '🧾 XML file'
 To add any xml file, use the data_type as `xml`. Eg:

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

 app = App()

@@ -6,7 +6,7 @@ title: '🎥📺 Youtube video'
 To add any youtube video to your app, use the data_type (first argument to `.add()` method) as `youtube_video`. Eg:

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

 app = App()
 app.add('a_valid_youtube_url_here', data_type='youtube_video')
@@ -1,91 +0,0 @@
---
-title: '🌍 API Server'
---
-
-The API Server based on Flask integrates the `embedchain` package, offering endpoints to add, query, and chat to engage in conversations with a chatbot using JSON requests.
-
-### 🐳 Docker Setup
-
- Open variables.env, and edit it to add your 🔑 `OPENAI_API_KEY`.
- To setup your api server using docker, run the following command inside this folder using your terminal.
-
-```bash
-docker-compose up --build
-```
-
-📝 Note: The build command might take a while to install all the packages depending on your system resources.
-
-### 🚀 Usage Instructions
-
- Your api server is running on [http://localhost:5000/](http://localhost:5000/)
- To use the api server, make an api call to the endpoints `/add`, `/query` and `/chat` using the json formats discussed below.
- To add data sources to the bot (/add):
-```json
-// Request
-{
-  "data_type": "your_data_type_here",
-  "url_or_text": "your_url_or_text_here"
-}
-
-// Response
-{
-  "data": "Added data_type: url_or_text"
-}
-```
- To ask queries from the bot (/query):
-```json
-// Request
-{
-  "question": "your_question_here"
-}
-
-// Response
-{
-  "data": "your_answer_here"
-}
-```
- To chat with the bot (/chat):
-```json
-// Request
-{
-  "question": "your_question_here"
-}
-
-// Response
-{
-  "data": "your_answer_here"
-}
-```
-
-### 📡 Curl Call Formats
-
- To add data sources to the bot (/add):
-```bash
-curl -X POST \
-     -H "Content-Type: application/json" \
-     -d '{
-         "data_type": "your_data_type_here",
-         "url_or_text": "your_url_or_text_here"
-     }' \
-     http://localhost:5000/add
-```
- To ask queries from the bot (/query):
-```bash
-curl -X POST \
-     -H "Content-Type: application/json" \
-     -d '{
-         "question": "your_question_here"
-     }' \
-     http://localhost:5000/query
-```
- To chat with the bot (/chat):
-```bash
-curl -X POST \
-     -H "Content-Type: application/json" \
-     -d '{
-         "question": "your_question_here"
-     }' \
-     http://localhost:5000/chat
-```
-
-🎉 Happy Chatting! 🎉
@@ -1,5 +1,5 @@
 ---
-title: '🤖 Discord Bot'
+title: "🤖 Discord Bot"
 ---

 ### 🔑 Keys Setup
@@ -12,9 +12,11 @@ title: '🤖 Discord Bot'
 - On the left sidebar, click on `OAuth2` and go to `General`.
 - Set `Authorization Method` to `In-app Authorization`. Under `Scopes` select `bot`.
 - Under `Bot Permissions` allow the following and then click on `Save Changes`.
+
 ```text
 Send Messages (under Text Permissions)
 ```
+
 - Now under `OAuth2` and go to `URL Generator`. Under `Scopes` select `bot`.
 - Under `Bot Permissions` set the same permissions as above.
 - Now scroll down and copy the `Generated URL`. Paste it in a browser window and select the Server where you want to add the bot.
@@ -23,40 +25,46 @@ Send Messages (under Text Permissions)

 ### Take the bot online

-1. Install embedchain python package:
+<Tabs>
+    <Tab title="docker">
+        ```bash
+        docker run --name discord-bot -e OPENAI_API_KEY=sk-xxx -e DISCORD_BOT_TOKEN=xxx -p 8080:8080 embedchain/discord-bot:latest
+        ```
+    </Tab>
+    <Tab title="python">
+        ```bash
+        pip install --upgrade "embedchain[discord]"

-```bash
-pip install --upgrade "embedchain[discord]"
-```
+        python -m embedchain.bots.discord

-2. Launch your Discord bot:
-
-
-```bash
-python -m embedchain.bots.discord
-```
-
-If you prefer to see the question and not only the answer, run it with
-
-```bash
-python -m embedchain.bots.discord --include-question
-```
+        # or if you prefer to see the question and not only the answer, run it with
+        python -m embedchain.bots.discord --include-question
+        ```
+    </Tab>
+</Tabs>

 ### 🚀 Usage Instructions

 - Go to the server where you have added your bot.
+  ![Slash commands interaction with bot](https://github.com/embedchain/embedchain/assets/73601258/bf1414e3-d408-4863-b0d2-ef382a76467e)
 - You can add data sources to the bot using the slash command:
+
 ```text
-/add <data_type> <url_or_text>
+/ec add <data_type> <url_or_text>
 ```
+
 - You can ask your queries from the bot using the slash command:
+
 ```text
-/query <question>
+/ec query <question>
 ```
+
 - You can chat with the bot using the slash command:
+
 ```text
-/chat <question>
+/ec chat <question>
 ```
+
 📝 Note: To use the bot privately, you can message the bot directly by right clicking the bot and selecting `Message`.

 🎉 Happy Chatting! 🎉
@@ -2,16 +2,51 @@
 title: '🌐 Full Stack'
 ---

+The Full Stack app example can be found [here](https://github.com/embedchain/embedchain/tree/main/examples/full_stack).
+
+This guide will help you setup the full stack app on your local machine.
+
 ### 🐳 Docker Setup

- To setup full stack app using docker, run the following command inside this folder using your terminal.
+- Create a `docker-compose.yml` file and paste the following code in it.
+
+```yaml
+version: "3.9"
+
+services:
+  backend:
+    container_name: embedchain-backend
+    restart: unless-stopped
+    build:
+      context: backend
+      dockerfile: Dockerfile
+    image: embedchain/backend
+    ports:
+      - "8000:8000"
+
+  frontend:
+    container_name: embedchain-frontend
+    restart: unless-stopped
+    build:
+      context: frontend
+      dockerfile: Dockerfile
+    image: embedchain/frontend
+    ports:
+      - "3000:3000"
+    depends_on:
+      - "backend"
+```
+
+- Run the following command,

 ```bash
-docker-compose up --build
+docker-compose up
 ```

 📝 Note: The build command might take a while to install all the packages depending on your system resources.

+![Fullstack App](https://github.com/embedchain/embedchain/assets/73601258/c7c04bbb-9be7-4669-a6af-039e7e972a13)
+
 ### 🚀 Usage Instructions

 - Go to [http://localhost:3000/](http://localhost:3000/) in your browser to view the dashboard.
@@ -3,28 +3,121 @@ title: 🔎 Examples
 description: 'Collection of Google colab notebook and Replit links for users'
 ---

-| LLM          | Google Colab                                                                                                                                                                            | Replit                                                                                                                        |
-| ------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------- |
-| OpenAI       | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/openai.ipynb)           | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/openai)      |
-| Anthropic    | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/anthropic.ipynb)        | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/anthropic)   |
-| Azure OpenAI | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/azure-openai.ipynb)     | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/azureopenai) |
-| VertexAI     | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/vertex_ai.ipynb)        | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/vertexai)    |
-| Cohere       | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/cohere.ipynb)           | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/cohere)      |
-| Hugging Face | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/hugging_face_hub.ipynb) | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/huggingface) |
-| JinaChat     | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/jina.ipynb)             | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/jina)        |
-| GPT4All      | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/gpt4all.ipynb)          | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/gpt4all)     |
-| Llama2       | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/llama2.ipynb)           | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/llama2)      |
-
-| Embedding model     | Google Colab                                                                                                                                                                            | Replit                                                                                                                        |
-| ------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------- |
-| OpenAI       | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/openai.ipynb)           | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/openai)      |
-| VertexAI     | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/vertex_ai.ipynb)        | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/vertexai)    |
-| GPT4All      | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/gpt4all.ipynb)          | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/gpt4all)     |
-| Hugging Face | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/hugging_face_hub.ipynb) | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/huggingface) |
-
-| Vector DB     | Google Colab                                                                                                                                                                         | Replit                                                                                                                          |
-| ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------- |
-| ChromaDB      | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/chromadb.ipynb)      | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/chromadb)      |
-| Elasticsearch | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/elasticsearch.ipynb) | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/elasticsearch) |
-| Opensearch    | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/opensearch.ipynb)    | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/opensearch)    |
-| Pinecone      | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/pinecone.ipynb)      | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/pinecone)      |
+<table>
+  <thead>
+    <tr>
+      <th>LLM</th>
+      <th>Google Colab</th>
+      <th>Replit</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td className="align-middle">OpenAI</td>
+      <td className="align-middle"><a target="_blank" href="https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/openai.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" noZoom alt="Open In Colab"/></a></td>
+      <td className="align-middle"><a target="_blank" href="https://replit.com/@taranjeetio/openai#main.py"><img src="https://replit.com/badge?caption=Try%20with%20Replit&amp;variant=small" noZoom alt="Try with Replit Badge"/></a></td>
+    </tr>
+    <tr>
+      <td className="align-middle">Anthropic</td>
+      <td className="align-middle"><a target="_blank" href="https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/anthropic.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" noZoom alt="Open In Colab"/></a></td>
+      <td className="align-middle"><a target="_blank" href="https://replit.com/@taranjeetio/anthropic#main.py"><img src="https://replit.com/badge?caption=Try%20with%20Replit&amp;variant=small" noZoom alt="Try with Replit Badge"/></a></td>
+    </tr>
+    <tr>
+      <td className="align-middle">Azure OpenAI</td>
+      <td className="align-middle"><a target="_blank" href="https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/azure-openai.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" noZoom alt="Open In Colab"/></a></td>
+      <td className="align-middle"><a target="_blank" href="https://replit.com/@taranjeetio/azureopenai#main.py"><img src="https://replit.com/badge?caption=Try%20with%20Replit&amp;variant=small" noZoom alt="Try with Replit Badge"/></a></td>
+    </tr>
+    <tr>
+      <td className="align-middle">VertexAI</td>
+      <td className="align-middle"><a target="_blank" href="https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/vertex_ai.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" noZoom alt="Open In Colab"/></a></td>
+      <td className="align-middle"><a target="_blank" href="https://replit.com/@taranjeetio/vertexai#main.py"><img src="https://replit.com/badge?caption=Try%20with%20Replit&amp;variant=small" noZoom alt="Try with Replit Badge"/></a></td>
+    </tr>
+    <tr>
+      <td className="align-middle">Cohere</td>
+      <td className="align-middle"><a target="_blank" href="https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/cohere.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" noZoom alt="Open In Colab"/></a></td>
+      <td className="align-middle"><a target="_blank" href="https://replit.com/@taranjeetio/cohere#main.py"><img src="https://replit.com/badge?caption=Try%20with%20Replit&amp;variant=small" noZoom alt="Try with Replit Badge"/></a></td>
+    </tr>
+    <tr>
+      <td className="align-middle">Hugging Face</td>
+      <td className="align-middle"><a target="_blank" href="https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/hugging_face_hub.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" noZoom alt="Open In Colab"/></a></td>
+      <td className="align-middle"><a target="_blank" href="https://replit.com/@taranjeetio/huggingface#main.py"><img src="https://replit.com/badge?caption=Try%20with%20Replit&amp;variant=small" noZoom alt="Try with Replit Badge"/></a></td>
+    </tr>
+    <tr>
+      <td className="align-middle">JinaChat</td>
+      <td className="align-middle"><a target="_blank" href="https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/jina.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" noZoom alt="Open In Colab"/></a></td>
+      <td className="align-middle"><a target="_blank" href="https://replit.com/@taranjeetio/jina#main.py"><img src="https://replit.com/badge?caption=Try%20with%20Replit&amp;variant=small" noZoom alt="Try with Replit Badge"/></a></td>
+    </tr>
+    <tr>
+      <td className="align-middle">GPT4All</td>
+      <td className="align-middle"><a target="_blank" href="https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/gpt4all.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" noZoom alt="Open In Colab"/></a></td>
+      <td className="align-middle"><a target="_blank" href="https://replit.com/@taranjeetio/gpt4all#main.py"><img src="https://replit.com/badge?caption=Try%20with%20Replit&amp;variant=small" noZoom alt="Try with Replit Badge"/></a></td>
+    </tr>
+    <tr>
+      <td className="align-middle">Llama2</td>
+      <td className="align-middle"><a target="_blank" href="https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/llama2.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" noZoom alt="Open In Colab"/></a></td>
+      <td className="align-middle"><a target="_blank" href="https://replit.com/@taranjeetio/llama2#main.py"><img src="https://replit.com/badge?caption=Try%20with%20Replit&amp;variant=small" noZoom alt="Try with Replit Badge"/></a></td>
+    </tr>
+  </tbody>
+</table>
+<table>
+  <thead>
+    <tr>
+      <th>Embedding model</th>
+      <th>Google Colab</th>
+      <th>Replit</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td className="align-middle">OpenAI</td>
+      <td className="align-middle"><a target="_blank" href="https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/openai.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" noZoom alt="Open In Colab"/></a></td>
+      <td className="align-middle"><a target="_blank" href="https://replit.com/@taranjeetio/openai#main.py"><img src="https://replit.com/badge?caption=Try%20with%20Replit&amp;variant=small" noZoom alt="Try with Replit Badge"/></a></td>
+    </tr>
+    <tr>
+      <td className="align-middle">VertexAI</td>
+      <td className="align-middle"><a target="_blank" href="https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/vertex_ai.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" noZoom alt="Open In Colab"/></a></td>
+      <td className="align-middle"><a target="_blank" href="https://replit.com/@taranjeetio/vertexai#main.py"><img src="https://replit.com/badge?caption=Try%20with%20Replit&amp;variant=small" noZoom alt="Try with Replit Badge"/></a></td>
+    </tr>
+    <tr>
+      <td className="align-middle">GPT4All</td>
+      <td className="align-middle"><a target="_blank" href="https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/gpt4all.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" noZoom alt="Open In Colab"/></a></td>
+      <td className="align-middle"><a target="_blank" href="https://replit.com/@taranjeetio/gpt4all#main.py"><img src="https://replit.com/badge?caption=Try%20with%20Replit&amp;variant=small" noZoom alt="Try with Replit Badge"/></a></td>
+    </tr>
+    <tr>
+      <td className="align-middle">Hugging Face</td>
+      <td className="align-middle"><a target="_blank" href="https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/hugging_face_hub.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" noZoom alt="Open In Colab"/></a></td>
+      <td className="align-middle"><a target="_blank" href="https://replit.com/@taranjeetio/huggingface#main.py"><img src="https://replit.com/badge?caption=Try%20with%20Replit&amp;variant=small" noZoom alt="Try with Replit Badge"/></a></td>
+    </tr>
+  </tbody>
+</table>
+<table>
+  <thead>
+    <tr>
+      <th>Vector DB</th>
+      <th>Google Colab</th>
+      <th>Replit</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td className="align-middle">ChromaDB</td>
+      <td className="align-middle"><a target="_blank" href="https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/chromadb.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" noZoom alt="Open In Colab"/></a></td>
+      <td className="align-middle"><a target="_blank" href="https://replit.com/@taranjeetio/chromadb#main.py"><img src="https://replit.com/badge?caption=Try%20with%20Replit&amp;variant=small" noZoom alt="Try with Replit Badge"/></a></td>
+    </tr>
+    <tr>
+      <td className="align-middle">Elasticsearch</td>
+      <td className="align-middle"><a target="_blank" href="https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/elasticsearch.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" noZoom alt="Open In Colab"/></a></td>
+      <td className="align-middle"><a target="_blank" href="https://replit.com/@taranjeetio/elasticsearchdb#main.py"><img src="https://replit.com/badge?caption=Try%20with%20Replit&amp;variant=small" noZoom alt="Try with Replit Badge"/></a></td>
+    </tr>
+    <tr>
+      <td className="align-middle">Opensearch</td>
+      <td className="align-middle"><a target="_blank" href="https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/opensearch.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" noZoom alt="Open In Colab"/></a></td>
+      <td className="align-middle"><a target="_blank" href="https://replit.com/@taranjeetio/opensearchdb#main.py"><img src="https://replit.com/badge?caption=Try%20with%20Replit&amp;variant=small" noZoom alt="Try with Replit Badge"/></a></td>
+    </tr>
+    <tr>
+      <td className="align-middle">Pinecone</td>
+      <td className="align-middle"><a target="_blank" href="https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/pinecone.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" noZoom alt="Open In Colab"/></a></td>
+      <td className="align-middle"><a target="_blank" href="https://replit.com/@taranjeetio/pineconedb#main.py"><img src="https://replit.com/badge?caption=Try%20with%20Replit&amp;variant=small" noZoom alt="Try with Replit Badge"/></a></td>
+    </tr>
+  </tbody>
+</table>
@@ -3,13 +3,45 @@ title: ❓ FAQs
 description: 'Collections of all the frequently asked questions'
 ---

+#### Does Embedchain support OpenAI's Assistant APIs?
+
+Yes, it does. Please refer to the [OpenAI Assistant docs page](/get-started/openai-assistant).
+
+#### How to use `gpt-4-turbo` model released on OpenAI DevDay?
+
+<CodeGroup>
+
+```python main.py
+import os
+from embedchain import Pipeline as App
+
+os.environ['OPENAI_API_KEY'] = 'xxx'
+
+# load llm configuration from gpt4_turbo.yaml file
+app = App.from_config(yaml_path="gpt4_turbo.yaml")
+```
+
+```yaml gpt4_turbo.yaml
+llm:
+  provider: openai
+  config:
+    model: 'gpt-4-turbo'
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 1
+    stream: false
+```
+
+</CodeGroup>
+
+
 #### How to use GPT-4 as the LLM model?

 <CodeGroup>

 ```python main.py
 import os
-from embedchain import App
+from embedchain import Pipeline as App

 os.environ['OPENAI_API_KEY'] = 'xxx'

@@ -20,8 +52,8 @@ app = App.from_config(yaml_path="gpt4.yaml")
 ```yaml gpt4.yaml
 llm:
  provider: openai
-  model: 'gpt-4'
  config:
+    model: 'gpt-4'
    temperature: 0.5
    max_tokens: 1000
    top_p: 1
@@ -36,7 +68,7 @@ llm:

 ```python main.py
 import os
-from embedchain import App
+from embedchain import Pipeline as App

 os.environ['OPENAI_API_KEY'] = 'xxx'

@@ -47,8 +79,8 @@ app = App.from_config(yaml_path="opensource.yaml")
 ```yaml opensource.yaml
 llm:
  provider: gpt4all
-  model: 'orca-mini-3b.ggmlv3.q4_0.bin'
  config:
+    model: 'orca-mini-3b-gguf2-q4_0.gguf'
    temperature: 0.5
    max_tokens: 1000
    top_p: 1
@@ -3,30 +3,106 @@ title: 📚 Introduction
 description: '📝 Embedchain is a Data Platform for LLMs - load, index, retrieve, and sync any unstructured data'
 ---

-## 🤔 What is Embedchain?
+## 🌐 What is Embedchain?

-Embedchain abstracts the entire process of loading data, chunking it, creating embeddings, and storing it in a vector database.
+Embedchain simplifies data handling by automatically processing unstructured data, breaking it into chunks, generating embeddings, and storing it in a vector database.

-You can add data from different data sources using the `.add()` method. Then, simply use the `.query()` method to find answers from the added datasets.
+Through various APIs, you can obtain contextual information for queries, find answers to specific questions, and engage in chat conversations using your data.
+## 🔍 Search

-If you want to create a Naval Ravikant bot with a YouTube video, a book in PDF format, two blog posts, and a question and answer pair, all you need to do is add the respective links. Embedchain will take care of the rest, creating a bot for you.
+Embedchain lets you get most relevant context by doing semantic search over your data sources for a provided query. See the example below:

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

-naval_bot = App()
-# Add online data
-naval_bot.add("https://www.youtube.com/watch?v=3qHkcs3kG44")
-naval_bot.add("https://navalmanack.s3.amazonaws.com/Eric-Jorgenson_The-Almanack-of-Naval-Ravikant_Final.pdf")
-naval_bot.add("https://nav.al/feedback")
-naval_bot.add("https://nav.al/agi")
-naval_bot.add("The Meanings of Life", 'text', metadata={'chapter': 'philosphy'})
+# Initialize app
+app = App()

-# Add local resources
-naval_bot.add(("Who is Naval Ravikant?", "Naval Ravikant is an Indian-American entrepreneur and investor."))
+# Add data source
+app.add("https://www.forbes.com/profile/elon-musk")

-naval_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.
+# 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 Worthas of 10/29/23Reflects change since 5 pm ET of prior trading day. 1 in the world todayPhoto by Martin Schoeller for ForbesAbout Elon MuskElon Musk cofounded six companies, including electric car maker Tesla, rocket producer SpaceX and tunneling startup Boring Company.He owns about 21% of Tesla between stock and options, but has pledged more than half his shares as collateral for personal loans of up to $3.5 billion.SpaceX, founded in',
+#         'source': 'https://www.forbes.com/profile/elon-musk',
+#         'document_id': 'some_document_id'
+#     },
+#     {
+#         'context': 'company, which is now called X.Wealth HistoryHOVER TO REVEAL NET WORTH BY YEARForbes Lists 1Forbes 400 (2023)The Richest Person In Every State (2023) 2Billionaires (2023) 1Innovative Leaders (2019) 25Powerful People (2018) 12Richest In Tech (2017)Global Game Changers (2016)More ListsPersonal StatsAge52Source of WealthTesla, SpaceX, Self MadeSelf-Made Score8Philanthropy Score1ResidenceAustin, TexasCitizenshipUnited StatesMarital StatusSingleChildren11EducationBachelor of Arts/Science, University',
+#         'source': 'https://www.forbes.com/profile/elon-musk',
+#         'document_id': 'some_document_id'
+#     }
+# ]
+```
+
+## ❓Query
+
+Embedchain empowers developers to ask questions and receive relevant answers through a user-friendly query API. Refer to the following example to learn how to utilize the query API:
+
+```python
+from embedchain import Pipeline as 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.
+```
+
+## 💬 Chat
+
+Embedchain allows easy chatting over your data sources using a user-friendly chat API. Check out the example below to understand how to use the chat API:
+
+```python
+from embedchain import Pipeline as 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("How much did Elon pay for Twitter?")
+print(answer)
+# Answer: Elon Musk paid $44 billion for Twitter.
+```
+
+## 🚀 Deploy
+
+Embedchain enables developers to deploy their LLM-powered apps in production using the Embedchain platform. The platform offers free access to context on your data through its REST API. Once the pipeline is deployed, you can update your data sources anytime after deployment.
+
+See the example below on how to use the deploy API:
+
+```python
+from embedchain import Pipeline as 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.
 ```

 ## 🚀 How it works?
@@ -0,0 +1,91 @@
+---
+title: '🤖 OpenAI Assistant'
+---
+
+<img src="https://blogs.swarthmore.edu/its/wp-content/uploads/2022/05/openai.jpg"  align="center" width="500" alt="OpenAI Logo"/>
+
+Embedchain now supports [OpenAI Assistants API](https://platform.openai.com/docs/assistants/overview) which allows you to build AI assistants within your own applications. An Assistant has instructions and can leverage models, tools, and knowledge to respond to user queries.
+
+At a high level, an integration of the Assistants API has the following flow:
+
+1. Create an Assistant in the API by defining custom instructions and picking a model
+2. Create a Thread when a user starts a conversation
+3. Add Messages to the Thread as the user ask questions
+4. Run the Assistant on the Thread to trigger responses. This automatically calls the relevant tools.
+
+Creating an OpenAI Assistant using Embedchain is very simple 3 step process.
+
+## Step 1: Create OpenAI Assistant
+
+Make sure that you have `OPENAI_API_KEY` set in the environment variable.
+
+```python Initialize
+from embedchain.store.assistants import OpenAIAssistant
+
+assistant = OpenAIAssistant(
+    name="OpenAI DevDay Assistant",
+    instructions="You are an organizer of OpenAI DevDay",
+)
+```
+
+If you want to use the existing assistant, you can do something like this:
+
+```python Initialize
+# Load an assistant and create a new thread
+assistant = OpenAIAssistant(assistant_id="asst_xxx")
+
+# Load a specific thread for an assistant
+assistant = OpenAIAssistant(assistant_id="asst_xxx", thread_id="thread_xxx")
+```
+
+### Arguments
+
+<ResponseField name="name" type="string">
+  Name for your AI assistant
+</ResponseField>
+
+<ResponseField name="instructions" type="string">
+  how the Assistant and model should behave or respond
+</ResponseField>
+
+<ResponseField name="assistant_id" type="string">
+  Load existing OpenAI Assistant. If you pass this, you don't have to pass other arguments.
+</ResponseField>
+
+<ResponseField name="thread_id" type="string">
+  Existing OpenAI thread id if exists
+</ResponseField>
+
+<ResponseField name="model" type="str" default="gpt-4-1106-preview">
+  OpenAI model to use
+</ResponseField>
+
+<ResponseField name="tools" type="list">
+  OpenAI tools to use. Default set to `[{"type": "retrieval"}]`
+</ResponseField>
+
+<ResponseField name="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"}]`
+</ResponseField>
+
+## Step-2: Add data to thread
+
+You can add any custom data source that is supported by Embedchain. Else, you can directly pass the file path on your local system and Embedchain propagates it to OpenAI Assistant.
+```python Add data
+assistant.add("/path/to/file.pdf")
+assistant.add("https://www.youtube.com/watch?v=U9mJuUkhUzk")
+assistant.add("https://openai.com/blog/new-models-and-developer-products-announced-at-devday")
+```
+
+## Step-3: Chat with your Assistant
+```python Chat
+assistant.chat("How much OpenAI credits were offered to attendees during OpenAI DevDay?")
+# Response: 'Every attendee of OpenAI DevDay 2023 was offered $500 in OpenAI credits.'
+```
+
+You can try it out yourself using the following Google Colab notebook:
+
+<a href="https://colab.research.google.com/drive/1BKlXZYSl6AFRgiHZ5XIzXrXC_24kDYHQ?usp=sharing">
+    <img src="https://camo.githubusercontent.com/84f0493939e0c4de4e6dbe113251b4bfb5353e57134ffd9fcab6b8714514d4d1/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open in Colab" />
+</a>
+
@@ -11,28 +11,45 @@ Install embedchain python package:
 pip install embedchain
 ```

+<Tip>
+Embedchain now supports OpenAI's latest `gpt-4-turbo` model. Checkout the [docs here](/get-started/faq#how-to-use-gpt-4-turbo-model-released-on-openai-devday) on how to use it.
+</Tip>
+
 Creating an app involves 3 steps:

 <Steps>
  <Step title="⚙️ Import app instance">
 ```python
-from embedchain import App
+from embedchain import Pipeline as App
 app = App()
 ```
  </Step>
  <Step title="🗃️ Add data sources">
 ```python
 # Add different data sources
-elon_bot.add("https://en.wikipedia.org/wiki/Elon_Musk")
-elon_bot.add("https://www.forbes.com/profile/elon-musk")
+app.add("https://en.wikipedia.org/wiki/Elon_Musk")
+app.add("https://www.forbes.com/profile/elon-musk")
 # You can also add local data sources such as pdf, csv files etc.
-# elon_bot.add("/path/to/file.pdf")
+# app.add("/path/to/file.pdf")
 ```
  </Step>
-  <Step title="💬 Query or chat on your data and get answers">
+  <Step title="💬 Query or chat or search context on your data">
 ```python
-elon_bot.query("What is the net worth of Elon Musk today?")
+app.query("What is the net worth of Elon Musk today?")
 # Answer: The net worth of Elon Musk today is $258.7 billion.
+```
+  </Step>
+  <Step title="🚀 (Optional) Deploy your pipeline to Embedchain Platform">
+```python
+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.
 ```
  </Step>
 </Steps>
@@ -41,18 +58,28 @@ Putting it together, you can run your first app using the following code. Make s

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

 os.environ["OPENAI_API_KEY"] = "xxx"
-elon_bot = App()
+app = App()

 # Add different data sources
-elon_bot.add("https://en.wikipedia.org/wiki/Elon_Musk")
-elon_bot.add("https://www.forbes.com/profile/elon-musk")
+app.add("https://en.wikipedia.org/wiki/Elon_Musk")
+app.add("https://www.forbes.com/profile/elon-musk")
 # You can also add local data sources such as pdf, csv files etc.
-# elon_bot.add("/path/to/file.pdf")
+# app.add("/path/to/file.pdf")

-response = elon_bot.query("What is the net worth of Elon Musk today?")
+response = app.query("What is the net worth of Elon Musk today?")
 print(response)
 # Answer: The net worth of Elon Musk today is $258.7 billion.
+
+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.
 ```
@@ -39,7 +39,7 @@ os.environ['LANGCHAIN_PROJECT] = <your-project>


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

 app = App()
 app.add("https://en.wikipedia.org/wiki/Elon_Musk")
@@ -3,40 +3,70 @@
  "name": "Embedchain",
  "logo": {
    "dark": "/logo/dark.svg",
-    "light": "/logo/light.svg"
+    "light": "/logo/light.svg",
+    "href": "https://embedchain.ai/"
  },
  "favicon": "/favicon.png",
  "colors": {
-    "primary": "#12A7D3",
-    "light": "#81D7F7",
-    "dark": "#004E7A"
+    "primary": "#3B2FC9",
+    "light": "#6673FF",
+    "dark": "#3B2FC9",
+    "background": {
+      "dark": "#0f1117",
+      "light": "#fff"
+    }
  },
+  "modeToggle": {
+    "default": "dark"
+  },
+  "openapi": ["/rest-api.json"],
+  "metadata": {
+    "og:image": "/images/og.png",
+    "twitter:site": "@embedchain"
+  },
+  "anchors": [
+    {
+      "name": "Embedchain Platform",
+      "icon": "tv",
+      "url": "https://app.embedchain.ai/"
+    },
+    {
+      "name": "Join our slack",
+      "icon": "slack",
+      "url": "https://join.slack.com/t/embedchain/shared_invite/zt-22uwz3c46-Zg7cIh5rOBteT_xe1jwLDw"
+    }
+  ],
  "topbarLinks": [
    {
-      "name": "Twitter",
-      "url": "https://twitter.com/embedchain"
-    },
-    {
-      "name":"Slack",
-      "url":"https://join.slack.com/t/embedchain/shared_invite/zt-22uwz3c46-Zg7cIh5rOBteT_xe1jwLDw"
-    },
-    {
-      "name": "Discord",
-      "url": "https://discord.gg/6PzXDgEjG5"
+      "name": "Create account",
+      "url": "https://app.embedchain.ai/login/"
    }
  ],
  "topbarCtaButton": {
-    "name": "GitHub",
-    "url": "https://embedchain.ai"
+    "name": "Get started",
+    "url": "https://app.embedchain.ai"
+  },
+  "primaryTab": {
+    "name": "Docs"
  },
  "navigation": [
    {
      "group": "Get started",
-      "pages": ["get-started/quickstart", "get-started/introduction", "get-started/faq", "get-started/examples"]
+      "pages": [
+        "get-started/quickstart",
+        "get-started/introduction",
+        "get-started/openai-assistant",
+        "get-started/faq",
+        "get-started/examples"
+      ]
    },
    {
      "group": "Components",
-      "pages": ["components/llms", "components/embedding-models", "components/vector-databases"]
+      "pages": [
+        "components/llms",
+        "components/embedding-models",
+        "components/vector-databases"
+      ]
    },
    {
      "group": "Data sources",
@@ -46,6 +76,7 @@
          "group": "Supported data sources",
          "pages": [
            "data-sources/csv",
+            "data-sources/json",
            "data-sources/docs-site",
            "data-sources/docx",
            "data-sources/mdx",
@@ -55,6 +86,7 @@
            "data-sources/sitemap",
            "data-sources/text",
            "data-sources/web-page",
+            "data-sources/openapi",
            "data-sources/youtube-video"
          ]
        },
@@ -66,15 +98,33 @@
      "pages": ["advanced/configuration"]
    },
    {
-      "group": "Examples",
-      "pages": ["examples/full_stack", "examples/api_server", "examples/discord_bot", "examples/slack_bot", "examples/telegram_bot", "examples/whatsapp_bot", "examples/poe_bot"]
+      "group": "REST API",
+      "pages": [
+        "rest-api/getting-started",
+        "rest-api/create",
+        "rest-api/get-all-apps",
+        "rest-api/add-data",
+        "rest-api/get-data",
+        "rest-api/query",
+        "rest-api/deploy",
+        "rest-api/delete",
+        "rest-api/check-status"
+      ]
+    },
+    {
+      "group": "Use Cases",
+      "pages": [
+        "examples/full_stack",
+        "examples/discord_bot",
+        "examples/slack_bot",
+        "examples/telegram_bot",
+        "examples/whatsapp_bot",
+        "examples/poe_bot"
+      ]
    },
    {
      "group": "Community",
-      "pages": [
-        "community/connect-with-us",
-        "community/showcase"
-      ]
+      "pages": ["community/connect-with-us", "community/showcase"]
    },
    {
      "group": "Integrations",
@@ -92,22 +142,33 @@
    },
    {
      "group": "Product",
-      "pages": [
-        "product/release-notes"
-      ]
+      "pages": ["product/release-notes"]
    }
-
  ],
-
  "footerSocials": {
    "website": "https://embedchain.ai",
    "github": "https://github.com/embedchain/embedchain",
-    "slack":"https://join.slack.com/t/embedchain/shared_invite/zt-22uwz3c46-Zg7cIh5rOBteT_xe1jwLDw",
+    "slack": "https://join.slack.com/t/embedchain/shared_invite/zt-22uwz3c46-Zg7cIh5rOBteT_xe1jwLDw",
    "discord": "https://discord.gg/6PzXDgEjG5",
    "twitter": "https://twitter.com/embedchain",
    "linkedin": "https://www.linkedin.com/company/embedchain"
  },
-  "backgroundImage": "/background.png",
  "isWhiteLabeled": true,
-  "feedback.thumbsRating": true
+  "analytics": {
+    "posthog": {
+      "apiKey": "phc_PHQDA5KwztijnSojsxJ2c1DuJd52QCzJzT2xnSGvjN2",
+      "apiHost": "https://app.embedchain.ai/ingest"
+    }
+  },
+  "feedback": {
+    "suggestEdit": true,
+    "raiseIssue": true,
+    "thumbsRating": true
+  },
+  "search": {
+    "prompt": "✨ Search embedchain docs..."
+  },
+  "api": {
+    "baseUrl": "http://localhost:8080"
+  }
 }
@@ -0,0 +1,427 @@
+{
+    "openapi": "3.1.0",
+    "info": {
+      "title": "Embedchain REST API",
+      "description": "This is the REST API for Embedchain.",
+      "license": {
+        "name": "Apache 2.0",
+        "url": "https://github.com/embedchain/embedchain/blob/main/LICENSE"
+      },
+      "version": "0.0.1"
+    },
+    "paths": {
+      "/ping": {
+        "get": {
+          "tags": ["Utility"],
+          "summary": "Check status",
+          "description": "Endpoint to check the status of the API",
+          "operationId": "check_status_ping_get",
+          "responses": {
+            "200": {
+              "description": "Successful Response",
+              "content": { "application/json": { "schema": {} } }
+            }
+          }
+        }
+      },
+      "/apps": {
+        "get": {
+          "tags": ["Apps"],
+          "summary": "Get all apps",
+          "description": "Get all applications",
+          "operationId": "get_all_apps_apps_get",
+          "responses": {
+            "200": {
+              "description": "Successful Response",
+              "content": { "application/json": { "schema": {} } }
+            }
+          }
+        }
+      },
+      "/create": {
+        "post": {
+          "tags": ["Apps"],
+          "summary": "Create app",
+          "description": "Create a new app using App ID",
+          "operationId": "create_app_using_default_config_create_post",
+          "parameters": [
+            {
+              "name": "app_id",
+              "in": "query",
+              "required": true,
+              "schema": { "type": "string", "title": "App Id" }
+            }
+          ],
+          "requestBody": {
+            "content": {
+              "multipart/form-data": {
+                "schema": {
+                  "allOf": [
+                    {
+                      "$ref": "#/components/schemas/Body_create_app_using_default_config_create_post"
+                    }
+                  ],
+                  "title": "Body"
+                }
+              }
+            }
+          },
+          "responses": {
+            "200": {
+              "description": "Successful Response",
+              "content": {
+                "application/json": {
+                  "schema": { "$ref": "#/components/schemas/DefaultResponse" }
+                }
+              }
+            },
+            "422": {
+              "description": "Validation Error",
+              "content": {
+                "application/json": {
+                  "schema": { "$ref": "#/components/schemas/HTTPValidationError" }
+                }
+              }
+            }
+          }
+        }
+      },
+      "/{app_id}/data": {
+        "get": {
+          "tags": ["Apps"],
+          "summary": "Get data",
+          "description": "Get all data sources for an app",
+          "operationId": "get_datasources_associated_with_app_id__app_id__data_get",
+          "parameters": [
+            {
+              "name": "app_id",
+              "in": "path",
+              "required": true,
+              "schema": { "type": "string", "title": "App Id" }
+            }
+          ],
+          "responses": {
+            "200": {
+              "description": "Successful Response",
+              "content": { "application/json": { "schema": {} } }
+            },
+            "422": {
+              "description": "Validation Error",
+              "content": {
+                "application/json": {
+                  "schema": { "$ref": "#/components/schemas/HTTPValidationError" }
+                }
+              }
+            }
+          }
+        }
+      },
+      "/{app_id}/add": {
+        "post": {
+          "tags": ["Apps"],
+          "summary": "Add data",
+          "description": "Add a data source to an app.",
+          "operationId": "add_datasource_to_an_app__app_id__add_post",
+          "parameters": [
+            {
+              "name": "app_id",
+              "in": "path",
+              "required": true,
+              "schema": { "type": "string", "title": "App Id" }
+            }
+          ],
+          "requestBody": {
+            "required": true,
+            "content": {
+              "application/json": {
+                "schema": { "$ref": "#/components/schemas/SourceApp" }
+              }
+            }
+          },
+          "responses": {
+            "200": {
+              "description": "Successful Response",
+              "content": {
+                "application/json": {
+                  "schema": { "$ref": "#/components/schemas/DefaultResponse" }
+                }
+              }
+            },
+            "422": {
+              "description": "Validation Error",
+              "content": {
+                "application/json": {
+                  "schema": { "$ref": "#/components/schemas/HTTPValidationError" }
+                }
+              }
+            }
+          }
+        }
+      },
+      "/{app_id}/query": {
+        "post": {
+          "tags": ["Apps"],
+          "summary": "Query app",
+          "description": "Query an app",
+          "operationId": "query_an_app__app_id__query_post",
+          "parameters": [
+            {
+              "name": "app_id",
+              "in": "path",
+              "required": true,
+              "schema": { "type": "string", "title": "App Id" }
+            }
+          ],
+          "requestBody": {
+            "required": true,
+            "content": {
+              "application/json": {
+                "schema": { "$ref": "#/components/schemas/QueryApp" }
+              }
+            }
+          },
+          "responses": {
+            "200": {
+              "description": "Successful Response",
+              "content": {
+                "application/json": {
+                  "schema": { "$ref": "#/components/schemas/DefaultResponse" }
+                }
+              }
+            },
+            "422": {
+              "description": "Validation Error",
+              "content": {
+                "application/json": {
+                  "schema": { "$ref": "#/components/schemas/HTTPValidationError" }
+                }
+              }
+            }
+          }
+        }
+      },
+      "/{app_id}/chat": {
+        "post": {
+          "tags": ["Apps"],
+          "summary": "Chat",
+          "description": "Chat with an app.\n\napp_id: The ID of the app. Use \"default\" for the default app.\n\nmessage: The message that you want to send to the app.",
+          "operationId": "chat_with_an_app__app_id__chat_post",
+          "parameters": [
+            {
+              "name": "app_id",
+              "in": "path",
+              "required": true,
+              "schema": { "type": "string", "title": "App Id" }
+            }
+          ],
+          "requestBody": {
+            "required": true,
+            "content": {
+              "application/json": {
+                "schema": { "$ref": "#/components/schemas/MessageApp" }
+              }
+            }
+          },
+          "responses": {
+            "200": {
+              "description": "Successful Response",
+              "content": {
+                "application/json": {
+                  "schema": { "$ref": "#/components/schemas/DefaultResponse" }
+                }
+              }
+            },
+            "422": {
+              "description": "Validation Error",
+              "content": {
+                "application/json": {
+                  "schema": { "$ref": "#/components/schemas/HTTPValidationError" }
+                }
+              }
+            }
+          }
+        }
+      },
+      "/{app_id}/deploy": {
+        "post": {
+          "tags": ["Apps"],
+          "summary": "Deploy App",
+          "description": "Deploy an existing app.",
+          "operationId": "deploy_app__app_id__deploy_post",
+          "parameters": [
+            {
+              "name": "app_id",
+              "in": "path",
+              "required": true,
+              "schema": { "type": "string", "title": "App Id" }
+            }
+          ],
+          "requestBody": {
+            "required": true,
+            "content": {
+              "application/json": {
+                "schema": { "$ref": "#/components/schemas/DeployAppRequest" }
+              }
+            }
+          },
+          "responses": {
+            "200": {
+              "description": "Successful Response",
+              "content": {
+                "application/json": {
+                  "schema": { "$ref": "#/components/schemas/DefaultResponse" }
+                }
+              }
+            },
+            "422": {
+              "description": "Validation Error",
+              "content": {
+                "application/json": {
+                  "schema": { "$ref": "#/components/schemas/HTTPValidationError" }
+                }
+              }
+            }
+          }
+        }
+      },
+      "/{app_id}/delete": {
+        "delete": {
+          "tags": ["Apps"],
+          "summary": "Delete app",
+          "description": "Delete an existing app",
+          "operationId": "delete_app__app_id__delete_delete",
+          "parameters": [
+            {
+              "name": "app_id",
+              "in": "path",
+              "required": true,
+              "schema": { "type": "string", "title": "App Id" }
+            }
+          ],
+          "responses": {
+            "200": {
+              "description": "Successful Response",
+              "content": {
+                "application/json": {
+                  "schema": { "$ref": "#/components/schemas/DefaultResponse" }
+                }
+              }
+            },
+            "422": {
+              "description": "Validation Error",
+              "content": {
+                "application/json": {
+                  "schema": { "$ref": "#/components/schemas/HTTPValidationError" }
+                }
+              }
+            }
+          }
+        }
+      }
+    },
+    "components": {
+      "schemas": {
+        "Body_create_app_using_default_config_create_post": {
+          "properties": {
+            "config": { "type": "string", "format": "binary", "title": "Config" }
+          },
+          "type": "object",
+          "title": "Body_create_app_using_default_config_create_post"
+        },
+        "DefaultResponse": {
+          "properties": { "response": { "type": "string", "title": "Response" } },
+          "type": "object",
+          "required": ["response"],
+          "title": "DefaultResponse"
+        },
+        "DeployAppRequest": {
+          "properties": {
+            "api_key": {
+              "type": "string",
+              "title": "Api Key",
+              "description": "The Embedchain API key for app deployments. You get the api key on the Embedchain platform by visiting [https://app.embedchain.ai](https://app.embedchain.ai)",
+              "default": ""
+            }
+          },
+          "type": "object",
+          "title": "DeployAppRequest",
+          "example":{
+            "api_key":"ec-xxx"
+         }
+        },
+        "HTTPValidationError": {
+          "properties": {
+            "detail": {
+              "items": { "$ref": "#/components/schemas/ValidationError" },
+              "type": "array",
+              "title": "Detail"
+            }
+          },
+          "type": "object",
+          "title": "HTTPValidationError"
+        },
+        "MessageApp": {
+          "properties": {
+            "message": {
+              "type": "string",
+              "title": "Message",
+              "description": "The message that you want to send to the App.",
+              "default": ""
+            }
+          },
+          "type": "object",
+          "title": "MessageApp"
+        },
+        "QueryApp": {
+          "properties": {
+            "query": {
+              "type": "string",
+              "title": "Query",
+              "description": "The query that you want to ask the App.",
+              "default": ""
+            }
+          },
+          "type": "object",
+          "title": "QueryApp",
+          "example":{
+            "query":"Who is Elon Musk?"
+         }
+        },
+        "SourceApp": {
+          "properties": {
+            "source": {
+              "type": "string",
+              "title": "Source",
+              "description": "The source that you want to add to the App.",
+              "default": ""
+            },
+            "data_type": {
+              "anyOf": [{ "type": "string" }, { "type": "null" }],
+              "title": "Data Type",
+              "description": "The type of data to add, remove it if you want Embedchain to detect it automatically.",
+              "default": ""
+            }
+          },
+          "type": "object",
+          "title": "SourceApp",
+          "example":{
+            "source":"https://en.wikipedia.org/wiki/Elon_Musk"
+         }
+        },
+        "ValidationError": {
+          "properties": {
+            "loc": {
+              "items": { "anyOf": [{ "type": "string" }, { "type": "integer" }] },
+              "type": "array",
+              "title": "Location"
+            },
+            "msg": { "type": "string", "title": "Message" },
+            "type": { "type": "string", "title": "Error Type" }
+          },
+          "type": "object",
+          "required": ["loc", "msg", "type"],
+          "title": "ValidationError"
+        }
+      }
+    }
+  }
@@ -0,0 +1,22 @@
+---
+openapi: post /{app_id}/add
+---
+
+<RequestExample>
+
+```bash Request
+curl --request POST \
+  --url http://localhost:8080/{app_id}/add \
+  -d "source=https://www.forbes.com/profile/elon-musk" \
+  -d "data_type=web_page"
+```
+
+</RequestExample>
+
+<ResponseExample>
+
+```json Response
+{ "response": "fec7fe91e6b2d732938a2ec2e32bfe3f" }
+```
+
+</ResponseExample>
@@ -0,0 +1,3 @@
+---
+openapi: post /{app_id}/chat
+---
@@ -0,0 +1,20 @@
+---
+openapi: get /ping
+---
+
+<RequestExample>
+
+```bash Request
+  curl --request GET \
+    --url http://localhost:8080/ping
+```
+
+</RequestExample>
+
+<ResponseExample>
+
+```json Response
+{ "ping": "pong" }
+```
+
+</ResponseExample>
@@ -0,0 +1,95 @@
+---
+openapi: post /create
+---
+
+<RequestExample>
+
+```bash Request
+curl --request POST \
+  --url http://localhost:8080/create?app_id=app1 \
+  -F "config=@/path/to/config.yaml"
+```
+
+</RequestExample>
+
+<ResponseExample>
+
+```json Response
+{ "response": "App created successfully. App ID: app1" }
+```
+
+</ResponseExample>
+
+By default we will use the opensource **gpt4all** model to get started. You can also specify your own config by uploading a config YAML file.
+
+For example, create a `config.yaml` file (adjust according to your requirements):
+
+```yaml
+app:
+  config:
+    id: "default-app"
+
+llm:
+  provider: openai
+  config:
+    model: "gpt-3.5-turbo"
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 1
+    stream: false
+    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:
+
+vectordb:
+  provider: chroma
+  config:
+    collection_name: "rest-api-app"
+    dir: db
+    allow_reset: true
+
+embedder:
+  provider: openai
+  config:
+    model: "text-embedding-ada-002"
+```
+
+To learn more about custom configurations, check out the [custom configurations docs](https://docs.embedchain.ai/advanced/configuration). To explore more examples of config yamls for embedchain, visit [embedchain/configs](https://github.com/embedchain/embedchain/tree/main/configs).
+
+Now, you can upload this config file in the request body.
+
+For example,
+
+```bash Request
+curl --request POST \
+  --url http://localhost:8080/create?app_id=my-app \
+  -F "config=@/path/to/config.yaml"
+```
+
+**Note:** To use custom models, an **API key** might be required. Refer to the table below to determine the necessary API key for your provider.
+
+| Keys                       | Providers                      |
+| -------------------------- | ------------------------------ |
+| `OPENAI_API_KEY `          | OpenAI, Azure OpenAI, Jina etc |
+| `OPENAI_API_TYPE`          | Azure OpenAI                   |
+| `OPENAI_API_BASE`          | Azure OpenAI                   |
+| `OPENAI_API_VERSION`       | Azure OpenAI                   |
+| `COHERE_API_KEY`           | Cohere                         |
+| `ANTHROPIC_API_KEY`        | Anthropic                      |
+| `JINACHAT_API_KEY`         | Jina                           |
+| `HUGGINGFACE_ACCESS_TOKEN` | Huggingface                    |
+| `REPLICATE_API_TOKEN`      | LLAMA2                         |
+
+To add env variables, you can simply run the docker command with the `-e` flag.
+
+For example,
+
+```bash
+docker run --name embedchain -p 8080:8080 -e OPENAI_API_KEY=<YOUR_OPENAI_API_KEY> embedchain/rest-api:latest
+```
@@ -0,0 +1,21 @@
+---
+openapi: delete /{app_id}/delete
+---
+
+
+<RequestExample>
+
+```bash Request
+  curl --request DELETE \
+    --url http://localhost:8080/{app_id}/delete
+```
+
+</RequestExample>
+
+<ResponseExample>
+
+```json Response
+{ "response": "App with id {app_id} deleted successfully." }
+```
+
+</ResponseExample>
@@ -0,0 +1,22 @@
+---
+openapi: post /{app_id}/deploy
+---
+
+
+<RequestExample>
+
+```bash Request
+curl --request POST \
+  --url http://localhost:8080/{app_id}/deploy \
+  -d "api_key=ec-xxxx"
+```
+
+</RequestExample>
+
+<ResponseExample>
+
+```json Response
+{ "response": "App deployed successfully." }
+```
+
+</ResponseExample>
@@ -0,0 +1,33 @@
+---
+openapi: get /apps
+---
+
+<RequestExample>
+
+```bash Request
+curl --request GET \
+  --url http://localhost:8080/apps
+```
+
+</RequestExample>
+
+<ResponseExample>
+
+```json Response
+{
+  "results": [
+    {
+      "config": "config1.yaml",
+      "id": 1,
+      "app_id": "app1"
+    },
+    {
+      "config": "config2.yaml",
+      "id": 2,
+      "app_id": "app2"
+    }
+  ]
+}
+```
+
+</ResponseExample>
@@ -0,0 +1,28 @@
+---
+openapi: get /{app_id}/data
+---
+
+<RequestExample>
+
+```bash Request
+curl --request GET \
+  --url http://localhost:8080/{app_id}/data
+```
+
+</RequestExample>
+
+<ResponseExample>
+
+```json Response
+{
+  "results": [
+    {
+      "data_type": "web_page",
+      "data_value": "https://www.forbes.com/profile/elon-musk/",
+      "metadata": "null"
+    }
+  ]
+}
+```
+
+</ResponseExample>
@@ -0,0 +1,294 @@
+---
+title: "🌍 Getting Started"
+---
+
+## Quickstart
+
+To use Embedchain as a REST API service, run the following command:
+
+```bash
+docker run --name embedchain -p 8080:8080 embedchain/rest-api:latest
+```
+
+Navigate to [http://localhost:8080/docs](http://localhost:8080/docs) to interact with the API. There is a full-fledged Swagger docs playground with all the information about the API endpoints.
+
+![Swagger Docs Screenshot](https://github.com/embedchain/embedchain/assets/73601258/299d81e5-a0df-407c-afc2-6fa2c4286844)
+
+## ⚡ Steps to get started
+
+<Steps>
+  <Step title="⚙️ Create an app">
+    <Tabs>
+      <Tab title="cURL">
+      ```bash
+      curl --request POST "http://localhost:8080/create?app_id=my-app" \
+       -H "accept: application/json"
+      ```
+      </Tab>
+      <Tab title="python">
+      ```python
+      import requests
+
+      url = "http://localhost:8080/create?app_id=my-app"
+
+      payload={}
+
+      response = requests.request("POST", url, data=payload)
+
+      print(response)
+      ```
+      </Tab>
+      <Tab title="javascript">
+      ```javascript
+      const data = fetch("http://localhost:8080/create?app_id=my-app", {
+        method: "POST",
+      }).then((res) => res.json());
+
+      console.log(data);
+      ```
+      </Tab>
+      <Tab title="go">
+      ```go
+      package main
+
+      import (
+        "fmt"
+        "net/http"
+        "io/ioutil"
+      )
+
+      func main() {
+
+        url := "http://localhost:8080/create?app_id=my-app"
+
+        payload := strings.NewReader("")
+
+        req, _ := http.NewRequest("POST", url, payload)
+
+        req.Header.Add("Content-Type", "application/json")
+
+        res, _ := http.DefaultClient.Do(req)
+
+        defer res.Body.Close()
+        body, _ := ioutil.ReadAll(res.Body)
+
+        fmt.Println(res)
+        fmt.Println(string(body))
+
+      }
+      ```
+      </Tab>
+    </Tabs>
+
+  </Step>
+  <Step title="🗃️ Add data sources">
+    <Tabs>
+      <Tab title="cURL">
+        ```bash
+        curl --request POST \
+          --url http://localhost:8080/my-app/add \
+          -d "source=https://www.forbes.com/profile/elon-musk" \
+          -d "data_type=web_page"
+          ```
+      </Tab>
+      <Tab title="python">
+        ```python
+        import requests
+
+        url = "http://localhost:8080/my-app/add"
+
+        payload = "source=https://www.forbes.com/profile/elon-musk&data_type=web_page"
+        headers = {}
+
+        response = requests.request("POST", url, headers=headers, data=payload)
+
+        print(response)
+        ```
+      </Tab>
+      <Tab title="javascript">
+        ```javascript
+        const data = fetch("http://localhost:8080/my-app/add", {
+          method: "POST",
+          body: "source=https://www.forbes.com/profile/elon-musk&data_type=web_page",
+        }).then((res) => res.json());
+
+        console.log(data);
+        ```
+        </Tab>
+      <Tab title="go">
+        ```go
+        package main
+
+        import (
+          "fmt"
+          "strings"
+          "net/http"
+          "io/ioutil"
+        )
+
+        func main() {
+
+          url := "http://localhost:8080/my-app/add"
+
+          payload := strings.NewReader("source=https://www.forbes.com/profile/elon-musk&data_type=web_page")
+
+          req, _ := http.NewRequest("POST", url, payload)
+
+          req.Header.Add("Content-Type", "application/x-www-form-urlencoded")
+
+          res, _ := http.DefaultClient.Do(req)
+
+          defer res.Body.Close()
+          body, _ := ioutil.ReadAll(res.Body)
+
+          fmt.Println(res)
+          fmt.Println(string(body))
+
+        }
+        ```
+      </Tab>
+      </Tabs>
+
+  </Step>
+  <Step title="💬 Query on your data">
+    <Tabs>
+      <Tab title="cURL">
+        ```bash
+        curl --request POST \
+          --url http://localhost:8080/my-app/query \
+          -d "query=Who is Elon Musk?"
+        ```
+      </Tab>
+      <Tab title="python">
+        ```python
+        import requests
+
+        url = "http://localhost:8080/my-app/query"
+
+        payload = "query=Who is Elon Musk?"
+        headers = {}
+
+        response = requests.request("POST", url, headers=headers, data=payload)
+
+        print(response)
+        ```
+      </Tab>
+      <Tab title="javascript">
+        ```javascript
+        const data = fetch("http://localhost:8080/my-app/query", {
+          method: "POST",
+          body: "query=Who is Elon Musk?",
+        }).then((res) => res.json());
+
+        console.log(data);
+        ```
+        </Tab>
+        <Tab title="go">
+        ```go
+        package main
+
+        import (
+          "fmt"
+          "strings"
+          "net/http"
+          "io/ioutil"
+        )
+
+        func main() {
+
+          url := "http://localhost:8080/my-app/query"
+
+          payload := strings.NewReader("query=Who is Elon Musk?")
+
+          req, _ := http.NewRequest("POST", url, payload)
+
+          req.Header.Add("Content-Type", "application/x-www-form-urlencoded")
+
+          res, _ := http.DefaultClient.Do(req)
+
+          defer res.Body.Close()
+          body, _ := ioutil.ReadAll(res.Body)
+
+          fmt.Println(res)
+          fmt.Println(string(body))
+
+        }
+        ```
+      </Tab>
+    </Tabs>
+
+  </Step>
+  <Step title="🚀 (Optional) Deploy your app to Embedchain Platform">
+    <Tabs>
+      <Tab title="cURL">
+        ```bash
+        curl --request POST \
+          --url http://localhost:8080/my-app/deploy \
+          -d "api_key=ec-xxxx"
+          ```
+      </Tab>
+      <Tab title="python">
+        ```python
+        import requests
+
+        url = "http://localhost:8080/my-app/deploy"
+
+        payload = "api_key=ec-xxxx"
+
+        response = requests.request("POST", url, data=payload)
+
+        print(response)
+        ```
+      </Tab>
+      <Tab title="javascript">
+        ```javascript
+        const data = fetch("http://localhost:8080/my-app/deploy", {
+          method: "POST",
+          body: "api_key=ec-xxxx",
+        }).then((res) => res.json());
+
+        console.log(data);
+        ```
+      </Tab>
+      <Tab title="go">
+        ```go
+        package main
+
+        import (
+          "fmt"
+          "strings"
+          "net/http"
+          "io/ioutil"
+        )
+
+        func main() {
+
+          url := "http://localhost:8080/my-app/deploy"
+
+          payload := strings.NewReader("api_key=ec-xxxx")
+
+          req, _ := http.NewRequest("POST", url, payload)
+
+          req.Header.Add("Content-Type", "application/x-www-form-urlencoded")
+
+          res, _ := http.DefaultClient.Do(req)
+
+          defer res.Body.Close()
+          body, _ := ioutil.ReadAll(res.Body)
+
+          fmt.Println(res)
+          fmt.Println(string(body))
+
+        }
+        ```
+      </Tab>
+    </Tabs>
+
+  </Step>
+</Steps>
+
+And you're ready! 🎉
+
+If you run into issues, please feel free to contact us using below links:
+
+<Snippet file="get-help.mdx" />
@@ -0,0 +1,21 @@
+---
+openapi: post /{app_id}/query
+---
+
+<RequestExample>
+
+```bash Request
+curl --request POST \
+  --url http://localhost:8080/{app_id}/query \
+  -d "query=who is Elon Musk?"
+```
+
+</RequestExample>
+
+<ResponseExample>
+
+```json Response
+{ "response": "Net worth of Elon Musk is $218 Billion." }
+```
+
+</ResponseExample>
@@ -3,9 +3,6 @@ import importlib.metadata
 __version__ = importlib.metadata.version(__package__ or __name__)

 from embedchain.apps.app import App  # noqa: F401
-from embedchain.apps.custom_app import CustomApp  # noqa: F401
-from embedchain.apps.Llama2App import Llama2App  # noqa: F401
-from embedchain.apps.open_source_app import OpenSourceApp  # noqa: F401
-from embedchain.apps.person_app import (PersonApp,  # noqa: F401
-                                        PersonOpenSourceApp)
+from embedchain.client import Client  # noqa: F401
+from embedchain.pipeline import Pipeline  # noqa: F401
 from embedchain.vectordb.chroma import ChromaDB  # noqa: F401
@@ -1,38 +0,0 @@
-import logging
-from typing import Optional
-
-from embedchain.apps.app import App
-from embedchain.config import CustomAppConfig
-from embedchain.helper.json_serializable import register_deserializable
-from embedchain.llm.llama2 import Llama2Llm
-
-
-@register_deserializable
-class Llama2App(App):
-    """
-    The EmbedChain Llama2App class.
-
-    Methods:
-    add(source, data_type): adds the data from the given URL to the vector db.
-    query(query): finds answer to the given query using vector database and LLM.
-    chat(query): finds answer to the given query using vector database and LLM, with conversation history.
-
-    .. deprecated:: 0.0.64
-    Use `App` instead.
-    """
-
-    def __init__(self, config: CustomAppConfig = None, system_prompt: Optional[str] = None):
-        """
-        .. deprecated:: 0.0.64
-        Use `App` instead.
-
-        :param config: CustomAppConfig instance to load as configuration. Optional.
-        :param system_prompt: System prompt string. Optional.
-        """
-        logging.warning(
-            "DEPRECATION WARNING: Please use `App` instead of `Llama2App`. "
-            "`Llama2App` will be removed in a future release. "
-            "Please refer to https://docs.embedchain.ai/advanced/app_types#llama2app for instructions."
-        )
-
-        super().__init__(config=config, llm=Llama2Llm(), system_prompt=system_prompt)
@@ -2,7 +2,8 @@ from typing import Optional

 import yaml

-from embedchain.config import AppConfig, BaseEmbedderConfig, BaseLlmConfig
+from embedchain.config import (AppConfig, BaseEmbedderConfig, BaseLlmConfig,
+                               ChunkerConfig)
 from embedchain.config.vectordb.base import BaseVectorDbConfig
 from embedchain.embedchain import EmbedChain
 from embedchain.embedder.base import BaseEmbedder
@@ -11,6 +12,7 @@ from embedchain.factory import EmbedderFactory, LlmFactory, VectorDBFactory
 from embedchain.helper.json_serializable import register_deserializable
 from embedchain.llm.base import BaseLlm
 from embedchain.llm.openai import OpenAILlm
+from embedchain.utils import validate_yaml_config
 from embedchain.vectordb.base import BaseVectorDB
 from embedchain.vectordb.chroma import ChromaDB

@@ -37,6 +39,7 @@ class App(EmbedChain):
        embedder: BaseEmbedder = None,
        embedder_config: Optional[BaseEmbedderConfig] = None,
        system_prompt: Optional[str] = None,
+        chunker: Optional[ChunkerConfig] = None,
    ):
        """
        Initialize a new `App` instance.
@@ -96,6 +99,9 @@ class App(EmbedChain):
        if embedder is None:
            embedder = OpenAIEmbedder(config=embedder_config)

+        self.chunker = None
+        if chunker:
+            self.chunker = ChunkerConfig(**chunker)
        # Type check assignments
        if not isinstance(llm, BaseLlm):
            raise TypeError(
@@ -127,10 +133,16 @@ class App(EmbedChain):
        with open(yaml_path, "r") as file:
            config_data = yaml.safe_load(file)

+        try:
+            validate_yaml_config(config_data)
+        except Exception as e:
+            raise Exception(f"❌ Error occurred while validating the YAML config. Error: {str(e)}")
+
        app_config_data = config_data.get("app", {})
        llm_config_data = config_data.get("llm", {})
        db_config_data = config_data.get("vectordb", {})
-        embedder_config_data = config_data.get("embedder", {})
+        embedding_model_config_data = config_data.get("embedding_model", config_data.get("embedder", {}))
+        chunker_config_data = config_data.get("chunker", {})

        app_config = AppConfig(**app_config_data.get("config", {}))

@@ -140,6 +152,6 @@ class App(EmbedChain):
        db_provider = db_config_data.get("provider", "chroma")
        db = VectorDBFactory.create(db_provider, db_config_data.get("config", {}))

-        embedder_provider = embedder_config_data.get("provider", "openai")
-        embedder = EmbedderFactory.create(embedder_provider, embedder_config_data.get("config", {}))
-        return cls(config=app_config, llm=llm, db=db, embedder=embedder)
+        embedder_provider = embedding_model_config_data.get("provider", "openai")
+        embedder = EmbedderFactory.create(embedder_provider, embedding_model_config_data.get("config", {}))
+        return cls(config=app_config, llm=llm, db=db, embedder=embedder, chunker=chunker_config_data)
@@ -1,63 +0,0 @@
-import logging
-from typing import Optional
-
-from embedchain.apps.app import App
-from embedchain.config import CustomAppConfig
-from embedchain.embedder.base import BaseEmbedder
-from embedchain.helper.json_serializable import register_deserializable
-from embedchain.llm.base import BaseLlm
-from embedchain.vectordb.base import BaseVectorDB
-
-
-@register_deserializable
-class CustomApp(App):
-    """
-    Embedchain's custom app allows for most flexibility.
-
-    You can craft your own mix of various LLMs, vector databases and embedding model/functions.
-
-    Methods:
-    add(source, data_type): adds the data from the given URL to the vector db.
-    query(query): finds answer to the given query using vector database and LLM.
-    chat(query): finds answer to the given query using vector database and LLM, with conversation history.
-
-    .. deprecated:: 0.0.64
-        Use `App` instead.
-    """
-
-    def __init__(
-        self,
-        config: Optional[CustomAppConfig] = None,
-        llm: BaseLlm = None,
-        db: BaseVectorDB = None,
-        embedder: BaseEmbedder = None,
-        system_prompt: Optional[str] = None,
-    ):
-        """
-        Initialize a new `CustomApp` instance. You have to choose a LLM, database and embedder.
-
-        .. deprecated:: 0.0.64
-        Use `App` instead.
-
-        :param config: Config for the app instance. This is the most basic configuration,
-        that does not fall into the LLM, database or embedder category, defaults to None
-        :type config: Optional[CustomAppConfig], optional
-        :param llm: LLM Class instance. example: `from embedchain.llm.openai import OpenAILlm`, defaults to None
-        :type llm: BaseLlm
-        :param db: The database to use for storing and retrieving embeddings,
-        example: `from embedchain.vectordb.chroma_db import ChromaDb`, defaults to None
-        :type db: BaseVectorDB
-        :param embedder: The embedder (embedding model and function) use to calculate embeddings.
-        example: `from embedchain.embedder.gpt4all_embedder import GPT4AllEmbedder`, defaults to None
-        :type embedder: BaseEmbedder
-        :param system_prompt: System prompt that will be provided to the LLM as such, defaults to None
-        :type system_prompt: Optional[str], optional
-        :raises ValueError: LLM, database or embedder has not been defined.
-        :raises TypeError: LLM, database or embedder is not a valid class instance.
-        """
-        logging.warning(
-            "DEPRECATION WARNING: Please use `App` instead of `CustomApp`. "
-            "`CustomApp` will be removed in a future release. "
-            "Please refer to https://docs.embedchain.ai/advanced/app_types#opensourceapp for instructions."
-        )
-        super().__init__(config=config, llm=llm, db=db, embedder=embedder, system_prompt=system_prompt)
@@ -1,71 +0,0 @@
-import logging
-from typing import Optional
-
-from embedchain.apps.app import App
-from embedchain.config import (BaseLlmConfig, ChromaDbConfig,
-                               OpenSourceAppConfig)
-from embedchain.embedder.gpt4all import GPT4AllEmbedder
-from embedchain.helper.json_serializable import register_deserializable
-from embedchain.llm.gpt4all import GPT4ALLLlm
-from embedchain.vectordb.chroma import ChromaDB
-
-gpt4all_model = None
-
-
-@register_deserializable
-class OpenSourceApp(App):
-    """
-    The embedchain Open Source App.
-    Comes preconfigured with the best open source LLM, embedding model, database.
-
-    Methods:
-    add(source, data_type): adds the data from the given URL to the vector db.
-    query(query): finds answer to the given query using vector database and LLM.
-    chat(query): finds answer to the given query using vector database and LLM, with conversation history.
-
-    .. deprecated:: 0.0.64
-    Use `App` instead.
-    """
-
-    def __init__(
-        self,
-        config: OpenSourceAppConfig = None,
-        llm_config: BaseLlmConfig = None,
-        chromadb_config: Optional[ChromaDbConfig] = None,
-        system_prompt: Optional[str] = None,
-    ):
-        """
-        Initialize a new `CustomApp` instance.
-        Since it's opinionated you don't have to choose a LLM, database and embedder.
-        However, you can configure those.
-
-        .. deprecated:: 0.0.64
-        Use `App` instead.
-
-        :param config: Config for the app instance. This is the most basic configuration,
-        that does not fall into the LLM, database or embedder category, defaults to None
-        :type config: OpenSourceAppConfig, optional
-        :param llm_config: Allows you to configure the LLM, e.g. how many documents to return.
-        example: `from embedchain.config import BaseLlmConfig`, defaults to None
-        :type llm_config: BaseLlmConfig, optional
-        :param chromadb_config: Allows you to configure the open source database,
-        example: `from embedchain.config import ChromaDbConfig`, defaults to None
-        :type chromadb_config: Optional[ChromaDbConfig], optional
-        :param system_prompt: System prompt that will be provided to the LLM as such.
-        Please don't use for the time being, as it's not supported., defaults to None
-        :type system_prompt: Optional[str], optional
-        :raises TypeError: `OpenSourceAppConfig` or `BaseLlmConfig` invalid.
-        """
-        logging.warning(
-            "DEPRECATION WARNING: Please use `App` instead of `OpenSourceApp`."
-            "`OpenSourceApp` will be removed in a future release."
-            "Please refer to https://docs.embedchain.ai/advanced/app_types#customapp for instructions."
-        )
-
-        super().__init__(
-            config=config,
-            llm=GPT4ALLLlm(config=llm_config),
-            db=ChromaDB(config=chromadb_config),
-            embedder=GPT4AllEmbedder(),
-            system_prompt=system_prompt,
-        )
@@ -1,93 +0,0 @@
-from string import Template
-
-from embedchain.apps.app import App
-from embedchain.apps.open_source_app import OpenSourceApp
-from embedchain.config import AppConfig, BaseLlmConfig
-from embedchain.config.llm.base import (DEFAULT_PROMPT,
-                                        DEFAULT_PROMPT_WITH_HISTORY)
-from embedchain.helper.json_serializable import register_deserializable
-
-
-@register_deserializable
-class EmbedChainPersonApp:
-    """
-    Base class to create a person bot.
-    This bot behaves and speaks like a person.
-
-    :param person: name of the person, better if its a well known person.
-    :param config: AppConfig instance to load as configuration.
-    """
-
-    def __init__(self, person: str, config: AppConfig = None):
-        """Initialize a new person app
-
-        :param person: Name of the person that's imitated.
-        :type person: str
-        :param config: Configuration class instance, defaults to None
-        :type config: AppConfig, optional
-        """
-        self.person = person
-        self.person_prompt = f"You are {person}. Whatever you say, you will always say in {person} style."  # noqa:E501
-        super().__init__(config)
-
-    def add_person_template_to_config(self, default_prompt: str, config: BaseLlmConfig = None):
-        """
-        This method checks if the config object contains a prompt template
-        if yes it adds the person prompt to it and return the updated config
-        else it creates a config object with the default prompt added to the person prompt
-
-        :param default_prompt:  it is the default prompt for query or chat methods
-        :type default_prompt: str
-        :param config: _description_, defaults to None
-        :type config: BaseLlmConfig, optional
-        :return: The `ChatConfig` instance to use as configuration options.
-        :rtype: _type_
-        """
-        template = Template(self.person_prompt + " " + default_prompt)
-
-        if config:
-            if config.template:
-                # Add person prompt to custom user template
-                config.template = Template(self.person_prompt + " " + config.template.template)
-            else:
-                # If no user template is present, use person prompt with the default template
-                config.template = template
-        else:
-            # if no config is present at all, initialize the config with person prompt and default template
-            config = BaseLlmConfig(
-                template=template,
-            )
-
-        return config
-
-
-@register_deserializable
-class PersonApp(EmbedChainPersonApp, App):
-    """
-    The Person app.
-    Extends functionality from EmbedChainPersonApp and App
-    """
-
-    def query(self, input_query, config: BaseLlmConfig = None, dry_run=False):
-        config = self.add_person_template_to_config(DEFAULT_PROMPT, config)
-        return super().query(input_query, config, dry_run, where=None)
-
-    def chat(self, input_query, config: BaseLlmConfig = None, dry_run=False, where=None):
-        config = self.add_person_template_to_config(DEFAULT_PROMPT_WITH_HISTORY, config)
-        return super().chat(input_query, config, dry_run, where)
-
-
-@register_deserializable
-class PersonOpenSourceApp(EmbedChainPersonApp, OpenSourceApp):
-    """
-    The Person app.
-    Extends functionality from EmbedChainPersonApp and OpenSourceApp
-    """
-
-    def query(self, input_query, config: BaseLlmConfig = None, dry_run=False):
-        config = self.add_person_template_to_config(DEFAULT_PROMPT, config)
-        return super().query(input_query, config, dry_run)
-
-    def chat(self, input_query, config: BaseLlmConfig = None, dry_run=False):
-        config = self.add_person_template_to_config(DEFAULT_PROMPT_WITH_HISTORY, config)
-        return super().chat(input_query, config, dry_run)
@@ -44,6 +44,7 @@ class BaseChunker(JSONSerializable):

            for chunk in chunks:
                chunk_id = hashlib.sha256((chunk + url).encode()).hexdigest()
+                chunk_id = f"{app_id}--{chunk_id}" if app_id is not None else chunk_id
                if idMap.get(chunk_id) is None:
                    idMap[chunk_id] = True
                    chunk_ids.append(chunk_id)
@@ -0,0 +1,22 @@
+from typing import Optional
+
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+
+from embedchain.chunkers.base_chunker import BaseChunker
+from embedchain.config.add_config import ChunkerConfig
+from embedchain.helper.json_serializable import register_deserializable
+
+
+@register_deserializable
+class GmailChunker(BaseChunker):
+    """Chunker for gmail."""
+
+    def __init__(self, config: Optional[ChunkerConfig] = None):
+        if config is None:
+            config = ChunkerConfig(chunk_size=1000, chunk_overlap=0, length_function=len)
+        text_splitter = RecursiveCharacterTextSplitter(
+            chunk_size=config.chunk_size,
+            chunk_overlap=config.chunk_overlap,
+            length_function=config.length_function,
+        )
+        super().__init__(text_splitter)
@@ -0,0 +1,18 @@
+from typing import Optional
+
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+
+from embedchain.chunkers.base_chunker import BaseChunker
+from embedchain.config.add_config import ChunkerConfig
+
+
+class OpenAPIChunker(BaseChunker):
+    def __init__(self, config: Optional[ChunkerConfig] = None):
+        if config is None:
+            config = ChunkerConfig(chunk_size=1000, chunk_overlap=0, length_function=len)
+        text_splitter = RecursiveCharacterTextSplitter(
+            chunk_size=config.chunk_size,
+            chunk_overlap=config.chunk_overlap,
+            length_function=config.length_function,
+        )
+        super().__init__(text_splitter)
@@ -0,0 +1,22 @@
+from typing import Optional
+
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+
+from embedchain.chunkers.base_chunker import BaseChunker
+from embedchain.config.add_config import ChunkerConfig
+from embedchain.helper.json_serializable import register_deserializable
+
+
+@register_deserializable
+class PostgresChunker(BaseChunker):
+    """Chunker for postgres."""
+
+    def __init__(self, config: Optional[ChunkerConfig] = None):
+        if config is None:
+            config = ChunkerConfig(chunk_size=1000, chunk_overlap=0, length_function=len)
+        text_splitter = RecursiveCharacterTextSplitter(
+            chunk_size=config.chunk_size,
+            chunk_overlap=config.chunk_overlap,
+            length_function=config.length_function,
+        )
+        super().__init__(text_splitter)
@@ -0,0 +1,102 @@
+import json
+import logging
+import os
+import uuid
+
+import requests
+
+from embedchain.embedchain import CONFIG_DIR, CONFIG_FILE
+
+
+class Client:
+    def __init__(self, api_key=None, host="https://apiv2.embedchain.ai"):
+        self.config_data = self.load_config()
+        self.host = host
+
+        if api_key:
+            if self.check(api_key):
+                self.api_key = api_key
+                self.save()
+            else:
+                raise ValueError(
+                    "Invalid API key provided. You can find your API key on https://app.embedchain.ai/settings/keys."
+                )
+        else:
+            if "api_key" in self.config_data:
+                self.api_key = self.config_data["api_key"]
+                logging.info("API key loaded successfully!")
+            else:
+                raise ValueError(
+                    "You are not logged in. Please obtain an API key from https://app.embedchain.ai/settings/keys/"
+                )
+
+    @classmethod
+    def setup_dir(self):
+        """
+        Loads the user id from the config file if it exists, otherwise generates a new
+        one and saves it to the config file.
+
+        :return: user id
+        :rtype: str
+        """
+        if not os.path.exists(CONFIG_DIR):
+            os.makedirs(CONFIG_DIR)
+
+        if os.path.exists(CONFIG_FILE):
+            with open(CONFIG_FILE, "r") as f:
+                data = json.load(f)
+                if "user_id" in data:
+                    return data["user_id"]
+
+        u_id = str(uuid.uuid4())
+        with open(CONFIG_FILE, "w") as f:
+            json.dump({"user_id": u_id}, f)
+
+    @classmethod
+    def load_config(cls):
+        if not os.path.exists(CONFIG_FILE):
+            cls.setup_dir()
+
+        with open(CONFIG_FILE, "r") as config_file:
+            return json.load(config_file)
+
+    def save(self):
+        self.config_data["api_key"] = self.api_key
+        with open(CONFIG_FILE, "w") as config_file:
+            json.dump(self.config_data, config_file, indent=4)
+
+        logging.info("API key saved successfully!")
+
+    def clear(self):
+        if "api_key" in self.config_data:
+            del self.config_data["api_key"]
+            with open(CONFIG_FILE, "w") as config_file:
+                json.dump(self.config_data, config_file, indent=4)
+            self.api_key = None
+            logging.info("API key deleted successfully!")
+        else:
+            logging.warning("API key not found in the configuration file.")
+
+    def update(self, api_key):
+        if self.check(api_key):
+            self.api_key = api_key
+            self.save()
+            logging.info("API key updated successfully!")
+        else:
+            logging.warning("Invalid API key provided. API key not updated.")
+
+    def check(self, api_key):
+        validation_url = f"{self.host}/api/v1/accounts/api_keys/validate/"
+        response = requests.post(validation_url, headers={"Authorization": f"Token {api_key}"})
+        if response.status_code == 200:
+            return True
+        else:
+            logging.warning(f"Response from API: {response.text}")
+            logging.warning("Invalid API key. Unable to validate.")
+            return False
+
+    def get(self):
+        return self.api_key
+
+    def __str__(self):
+        return self.api_key
@@ -2,12 +2,11 @@

 from .add_config import AddConfig, ChunkerConfig
 from .apps.app_config import AppConfig
-from .apps.custom_app_config import CustomAppConfig
-from .apps.open_source_app_config import OpenSourceAppConfig
 from .base_config import BaseConfig
 from .embedder.base import BaseEmbedderConfig
 from .embedder.base import BaseEmbedderConfig as EmbedderConfig
 from .llm.base import BaseLlmConfig
+from .pipeline_config import PipelineConfig
 from .vectordb.chroma import ChromaDbConfig
 from .vectordb.elasticsearch import ElasticsearchDBConfig
 from .vectordb.opensearch import OpenSearchDBConfig
@@ -1,3 +1,5 @@
+import builtins
+from importlib import import_module
 from typing import Callable, Optional

 from embedchain.config.base_config import BaseConfig
@@ -18,7 +20,18 @@ class ChunkerConfig(BaseConfig):
    ):
        self.chunk_size = chunk_size if chunk_size else 2000
        self.chunk_overlap = chunk_overlap if chunk_overlap else 0
-        self.length_function = length_function if length_function else len
+        if isinstance(length_function, str):
+            self.length_function = self.load_func(length_function)
+        else:
+            self.length_function = length_function if length_function else len
+
+    def load_func(self, dotpath: str):
+        if "." not in dotpath:
+            return getattr(builtins, dotpath)
+        else:
+            module_, func = dotpath.rsplit(".", maxsplit=1)
+            m = import_module(module_)
+            return getattr(m, func)


@register_deserializable
@@ -15,7 +15,7 @@ class AppConfig(BaseAppConfig):
        self,
        log_level: str = "WARNING",
        id: Optional[str] = None,
-        collect_metrics: Optional[bool] = None,
+        collect_metrics: Optional[bool] = True,
        collection_name: Optional[str] = None,
    ):
        """
@@ -8,7 +8,7 @@ from embedchain.vectordb.base import BaseVectorDB

 class BaseAppConfig(BaseConfig, JSONSerializable):
    """
-    Parent config to initialize an instance of `App`, `OpenSourceApp` or `CustomApp`.
+    Parent config to initialize an instance of `App`.
    """

    def __init__(
@@ -1,40 +0,0 @@
-from typing import Optional
-
-from embedchain.helper.json_serializable import register_deserializable
-
-from .base_app_config import BaseAppConfig
-
-
-@register_deserializable
-class OpenSourceAppConfig(BaseAppConfig):
-    """
-    Config to initialize an embedchain custom `OpenSourceApp` instance, with extra config options.
-    """
-
-    def __init__(
-        self,
-        log_level: str = "WARNING",
-        id: Optional[str] = None,
-        collect_metrics: Optional[bool] = None,
-        model: str = "orca-mini-3b.ggmlv3.q4_0.bin",
-        collection_name: Optional[str] = None,
-    ):
-        """
-        Initializes a configuration class instance for an Open Source App.
-
-        :param log_level: Debug level ['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'], defaults to "WARNING"
-        :type log_level: str, optional
-        :param id: ID of the app. Document metadata will have this id., defaults to None
-        :type id: Optional[str], optional
-        :param collect_metrics: Send anonymous telemetry to improve embedchain, defaults to True
-        :type collect_metrics: Optional[bool], optional
-        :param model: GPT4ALL uses the model to instantiate the class.
-        Unlike `App`, it has to be provided before querying, defaults to "orca-mini-3b.ggmlv3.q4_0.bin"
-        :type model: str, optional
-        :param collection_name: Default collection name. It's recommended to use app.db.set_collection_name() instead,
-        defaults to None
-        :type collection_name: Optional[str], optional
-        """
-        self.model = model or "orca-mini-3b.ggmlv3.q4_0.bin"
-
-        super().__init__(log_level=log_level, id=id, collect_metrics=collect_metrics, collection_name=collection_name)
@@ -1,17 +1,12 @@
 from typing import Optional

-from dotenv import load_dotenv
-
 from embedchain.helper.json_serializable import register_deserializable
-from embedchain.vectordb.base import BaseVectorDB

-from .base_app_config import BaseAppConfig
-
-load_dotenv()
+from .apps.base_app_config import BaseAppConfig


@register_deserializable
-class CustomAppConfig(BaseAppConfig):
+class PipelineConfig(BaseAppConfig):
    """
    Config to initialize an embedchain custom `App` instance, with extra config options.
    """
@@ -19,20 +14,16 @@ class CustomAppConfig(BaseAppConfig):
    def __init__(
        self,
        log_level: str = "WARNING",
-        db: Optional[BaseVectorDB] = None,
        id: Optional[str] = None,
-        collect_metrics: Optional[bool] = None,
-        collection_name: Optional[str] = None,
+        name: Optional[str] = None,
+        collect_metrics: Optional[bool] = True,
    ):
        """
-        Initializes a configuration class instance for an Custom App.
-        Most of the configuration is done in the `CustomApp` class itself.
+        Initializes a configuration class instance for an App. This is the simplest form of an embedchain app.
+        Most of the configuration is done in the `App` class itself.

        :param log_level: Debug level ['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'], defaults to "WARNING"
        :type log_level: str, optional
-        :param db: A database class. It is recommended to set this directly in the `CustomApp` class, not this config,
-        defaults to None
-        :type db: Optional[BaseVectorDB], optional
        :param id: ID of the app. Document metadata will have this id., defaults to None
        :type id: Optional[str], optional
        :param collect_metrics: Send anonymous telemetry to improve embedchain, defaults to True
@@ -41,6 +32,7 @@ class CustomAppConfig(BaseAppConfig):
        defaults to None
        :type collection_name: Optional[str], optional
        """
-        super().__init__(
-            log_level=log_level, db=db, id=id, collect_metrics=collect_metrics, collection_name=collection_name
-        )
+        self._setup_logging(log_level)
+        self.id = id
+        self.name = name
+        self.collect_metrics = collect_metrics
@@ -1,37 +1,11 @@
+from importlib import import_module
+from typing import Any, Dict
+
 from embedchain.chunkers.base_chunker import BaseChunker
-from embedchain.chunkers.docs_site import DocsSiteChunker
-from embedchain.chunkers.docx_file import DocxFileChunker
-from embedchain.chunkers.images import ImagesChunker
-from embedchain.chunkers.json import JSONChunker
-from embedchain.chunkers.mdx import MdxChunker
-from embedchain.chunkers.notion import NotionChunker
-from embedchain.chunkers.pdf_file import PdfFileChunker
-from embedchain.chunkers.qna_pair import QnaPairChunker
-from embedchain.chunkers.sitemap import SitemapChunker
-from embedchain.chunkers.table import TableChunker
-from embedchain.chunkers.text import TextChunker
-from embedchain.chunkers.unstructured_file import UnstructuredFileChunker
-from embedchain.chunkers.web_page import WebPageChunker
-from embedchain.chunkers.xml import XmlChunker
-from embedchain.chunkers.youtube_video import YoutubeVideoChunker
 from embedchain.config import AddConfig
 from embedchain.config.add_config import ChunkerConfig, LoaderConfig
 from embedchain.helper.json_serializable import JSONSerializable
 from embedchain.loaders.base_loader import BaseLoader
-from embedchain.loaders.csv import CsvLoader
-from embedchain.loaders.docs_site_loader import DocsSiteLoader
-from embedchain.loaders.docx_file import DocxFileLoader
-from embedchain.loaders.images import ImagesLoader
-from embedchain.loaders.json import JSONLoader
-from embedchain.loaders.local_qna_pair import LocalQnaPairLoader
-from embedchain.loaders.local_text import LocalTextLoader
-from embedchain.loaders.mdx import MdxLoader
-from embedchain.loaders.pdf_file import PdfFileLoader
-from embedchain.loaders.sitemap import SitemapLoader
-from embedchain.loaders.unstructured_file import UnstructuredLoader
-from embedchain.loaders.web_page import WebPageLoader
-from embedchain.loaders.xml import XmlLoader
-from embedchain.loaders.youtube_video import YoutubeVideoLoader
 from embedchain.models.data_type import DataType


@@ -42,7 +16,7 @@ class DataFormatter(JSONSerializable):
    .add or .add_local method call
    """

-    def __init__(self, data_type: DataType, config: AddConfig):
+    def __init__(self, data_type: DataType, config: AddConfig, kwargs: Dict[str, Any]):
        """
        Initialize a dataformatter, set data type and chunker based on datatype.

@@ -51,10 +25,15 @@ class DataFormatter(JSONSerializable):
        :param config: AddConfig instance with nested loader and chunker config attributes.
        :type config: AddConfig
        """
-        self.loader = self._get_loader(data_type=data_type, config=config.loader)
-        self.chunker = self._get_chunker(data_type=data_type, config=config.chunker)
+        self.loader = self._get_loader(data_type=data_type, config=config.loader, kwargs=kwargs)
+        self.chunker = self._get_chunker(data_type=data_type, config=config.chunker, kwargs=kwargs)

-    def _get_loader(self, data_type: DataType, config: LoaderConfig) -> BaseLoader:
+    def _lazy_load(self, module_path: str):
+        module_path, class_name = module_path.rsplit(".", 1)
+        module = import_module(module_path)
+        return getattr(module, class_name)
+
+    def _get_loader(self, data_type: DataType, config: LoaderConfig, kwargs: Dict[str, Any]) -> BaseLoader:
        """
        Returns the appropriate data loader for the given data type.

@@ -67,68 +46,80 @@ class DataFormatter(JSONSerializable):
        :rtype: BaseLoader
        """
        loaders = {
-            DataType.YOUTUBE_VIDEO: YoutubeVideoLoader,
-            DataType.PDF_FILE: PdfFileLoader,
-            DataType.WEB_PAGE: WebPageLoader,
-            DataType.QNA_PAIR: LocalQnaPairLoader,
-            DataType.TEXT: LocalTextLoader,
-            DataType.DOCX: DocxFileLoader,
-            DataType.SITEMAP: SitemapLoader,
-            DataType.XML: XmlLoader,
-            DataType.DOCS_SITE: DocsSiteLoader,
-            DataType.CSV: CsvLoader,
-            DataType.MDX: MdxLoader,
-            DataType.IMAGES: ImagesLoader,
-            DataType.UNSTRUCTURED: UnstructuredLoader,
-            DataType.JSON: JSONLoader,
+            DataType.YOUTUBE_VIDEO: "embedchain.loaders.youtube_video.YoutubeVideoLoader",
+            DataType.PDF_FILE: "embedchain.loaders.pdf_file.PdfFileLoader",
+            DataType.WEB_PAGE: "embedchain.loaders.web_page.WebPageLoader",
+            DataType.QNA_PAIR: "embedchain.loaders.local_qna_pair.LocalQnaPairLoader",
+            DataType.TEXT: "embedchain.loaders.local_text.LocalTextLoader",
+            DataType.DOCX: "embedchain.loaders.docx_file.DocxFileLoader",
+            DataType.SITEMAP: "embedchain.loaders.sitemap.SitemapLoader",
+            DataType.XML: "embedchain.loaders.xml.XmlLoader",
+            DataType.DOCS_SITE: "embedchain.loaders.docs_site_loader.DocsSiteLoader",
+            DataType.CSV: "embedchain.loaders.csv.CsvLoader",
+            DataType.MDX: "embedchain.loaders.mdx.MdxLoader",
+            DataType.IMAGES: "embedchain.loaders.images.ImagesLoader",
+            DataType.UNSTRUCTURED: "embedchain.loaders.unstructured_file.UnstructuredLoader",
+            DataType.JSON: "embedchain.loaders.json.JSONLoader",
+            DataType.OPENAPI: "embedchain.loaders.openapi.OpenAPILoader",
+            DataType.GMAIL: "embedchain.loaders.gmail.GmailLoader",
+            DataType.NOTION: "embedchain.loaders.notion.NotionLoader",
        }
-        lazy_loaders = {DataType.NOTION}
+
+        custom_loaders = set(
+            [
+                DataType.POSTGRES,
+            ]
+        )
+
        if data_type in loaders:
-            loader_class: type = loaders[data_type]
-            loader: BaseLoader = loader_class()
-            return loader
-        elif data_type in lazy_loaders:
-            if data_type == DataType.NOTION:
-                from embedchain.loaders.notion import NotionLoader
+            loader_class: type = self._lazy_load(loaders[data_type])
+            return loader_class()
+        elif data_type in custom_loaders:
+            loader_class: type = kwargs.get("loader", None)
+            if loader_class is not None:
+                return loader_class

-                return NotionLoader()
-            else:
-                raise ValueError(f"Unsupported data type: {data_type}")
-        else:
-            raise ValueError(f"Unsupported data type: {data_type}")
+        raise ValueError(
+            f"Cant find the loader for {data_type}.\
+                    We recommend to pass the loader to use data_type: {data_type},\
+                        check `https://docs.embedchain.ai/data-sources/overview`."
+        )

-    def _get_chunker(self, data_type: DataType, config: ChunkerConfig) -> BaseChunker:
-        """Returns the appropriate chunker for the given data type.
-
-        :param data_type: The type of the data to chunk.
-        :type data_type: DataType
-        :param config: Config to initialize the chunker with.
-        :type config: ChunkerConfig
-        :raises ValueError: If an unsupported data type is provided.
-        :return: The chunker for the given data type.
-        :rtype: BaseChunker
-        """
+    def _get_chunker(self, data_type: DataType, config: ChunkerConfig, kwargs: Dict[str, Any]) -> BaseChunker:
+        """Returns the appropriate chunker for the given data type (updated for lazy loading)."""
        chunker_classes = {
-            DataType.YOUTUBE_VIDEO: YoutubeVideoChunker,
-            DataType.PDF_FILE: PdfFileChunker,
-            DataType.WEB_PAGE: WebPageChunker,
-            DataType.QNA_PAIR: QnaPairChunker,
-            DataType.TEXT: TextChunker,
-            DataType.DOCX: DocxFileChunker,
-            DataType.DOCS_SITE: DocsSiteChunker,
-            DataType.SITEMAP: SitemapChunker,
-            DataType.NOTION: NotionChunker,
-            DataType.CSV: TableChunker,
-            DataType.MDX: MdxChunker,
-            DataType.IMAGES: ImagesChunker,
-            DataType.XML: XmlChunker,
-            DataType.UNSTRUCTURED: UnstructuredFileChunker,
-            DataType.JSON: JSONChunker,
+            DataType.YOUTUBE_VIDEO: "embedchain.chunkers.youtube_video.YoutubeVideoChunker",
+            DataType.PDF_FILE: "embedchain.chunkers.pdf_file.PdfFileChunker",
+            DataType.WEB_PAGE: "embedchain.chunkers.web_page.WebPageChunker",
+            DataType.QNA_PAIR: "embedchain.chunkers.qna_pair.QnaPairChunker",
+            DataType.TEXT: "embedchain.chunkers.text.TextChunker",
+            DataType.DOCX: "embedchain.chunkers.docx_file.DocxFileChunker",
+            DataType.SITEMAP: "embedchain.chunkers.sitemap.SitemapChunker",
+            DataType.XML: "embedchain.chunkers.xml.XmlChunker",
+            DataType.DOCS_SITE: "embedchain.chunkers.docs_site.DocsSiteChunker",
+            DataType.CSV: "embedchain.chunkers.table.TableChunker",
+            DataType.MDX: "embedchain.chunkers.mdx.MdxChunker",
+            DataType.IMAGES: "embedchain.chunkers.images.ImagesChunker",
+            DataType.UNSTRUCTURED: "embedchain.chunkers.unstructured_file.UnstructuredFileChunker",
+            DataType.JSON: "embedchain.chunkers.json.JSONChunker",
+            DataType.OPENAPI: "embedchain.chunkers.openapi.OpenAPIChunker",
+            DataType.GMAIL: "embedchain.chunkers.gmail.GmailChunker",
+            DataType.NOTION: "embedchain.chunkers.notion.NotionChunker",
+            DataType.POSTGRES: "embedchain.chunkers.postgres.PostgresChunker",
        }
+
        if data_type in chunker_classes:
-            chunker_class: type = chunker_classes[data_type]
-            chunker: BaseChunker = chunker_class(config)
+            if "chunker" in kwargs:
+                chunker_class = kwargs.get("chunker")
+            else:
+                chunker_class = self._lazy_load(chunker_classes[data_type])
+
+            chunker = chunker_class(config)
            chunker.set_data_type(data_type)
            return chunker
        else:
-            raise ValueError(f"Unsupported data type: {data_type}")
+            raise ValueError(
+                f"Cant find the chunker for {data_type}.\
+                    We recommend to pass the chunker to use data_type: {data_type},\
+                        check `https://docs.embedchain.ai/data-sources/overview`."
+            )
@@ -1,20 +1,16 @@
 import hashlib
-import importlib.metadata
 import json
 import logging
 import os
-import threading
-import uuid
+import sqlite3
 from pathlib import Path
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List, Optional, Tuple, Union

-import requests
 from dotenv import load_dotenv
 from langchain.docstore.document import Document
-from tenacity import retry, stop_after_attempt, wait_fixed

 from embedchain.chunkers.base_chunker import BaseChunker
-from embedchain.config import AddConfig, BaseLlmConfig
+from embedchain.config import AddConfig, BaseLlmConfig, ChunkerConfig
 from embedchain.config.apps.base_app_config import BaseAppConfig
 from embedchain.data_formatter import DataFormatter
 from embedchain.embedder.base import BaseEmbedder
@@ -23,7 +19,8 @@ from embedchain.llm.base import BaseLlm
 from embedchain.loaders.base_loader import BaseLoader
 from embedchain.models.data_type import (DataType, DirectDataType,
                                         IndirectDataType, SpecialDataType)
-from embedchain.utils import detect_datatype
+from embedchain.telemetry.posthog import AnonymousTelemetry
+from embedchain.utils import detect_datatype, is_valid_json_string
 from embedchain.vectordb.base import BaseVectorDB

 load_dotenv()
@@ -32,6 +29,7 @@ ABS_PATH = os.getcwd()
 HOME_DIR = str(Path.home())
 CONFIG_DIR = os.path.join(HOME_DIR, ".embedchain")
 CONFIG_FILE = os.path.join(CONFIG_DIR, "config.json")
+SQLITE_PATH = os.path.join(CONFIG_DIR, "embedchain.db")


 class EmbedChain(JSONSerializable):
@@ -86,14 +84,31 @@ class EmbedChain(JSONSerializable):
        # Attributes that aren't subclass related.
        self.user_asks = []

+        self.chunker: ChunkerConfig = None
        # Send anonymous telemetry
-        self.s_id = self.config.id if self.config.id else str(uuid.uuid4())
-        self.u_id = self._load_or_generate_user_id()
-        # NOTE: Uncomment the next two lines when running tests to see if any test fires a telemetry event.
-        # if (self.config.collect_metrics):
-        #     raise ConnectionRefusedError("Collection of metrics should not be allowed.")
-        thread_telemetry = threading.Thread(target=self._send_telemetry_event, args=("init",))
-        thread_telemetry.start()
+        self._telemetry_props = {"class": self.__class__.__name__}
+        self.telemetry = AnonymousTelemetry(enabled=self.config.collect_metrics)
+        # Establish a connection to the SQLite database
+        self.connection = sqlite3.connect(SQLITE_PATH, check_same_thread=False)
+        self.cursor = self.connection.cursor()
+
+        # Create the 'data_sources' table if it doesn't exist
+        self.cursor.execute(
+            """
+            CREATE TABLE IF NOT EXISTS data_sources (
+                pipeline_id TEXT,
+                hash TEXT,
+                type TEXT,
+                value TEXT,
+                metadata TEXT,
+                is_uploaded INTEGER DEFAULT 0,
+                PRIMARY KEY (pipeline_id, hash)
+            )
+        """
+        )
+        self.connection.commit()
+        # Send anonymous telemetry
+        self.telemetry.capture(event_name="init", properties=self._telemetry_props)

    @property
    def collect_metrics(self):
@@ -115,29 +130,6 @@ class EmbedChain(JSONSerializable):
            raise ValueError(f"Boolean value expected but got {type(value)}.")
        self.llm.online = value

-    def _load_or_generate_user_id(self) -> str:
-        """
-        Loads the user id from the config file if it exists, otherwise generates a new
-        one and saves it to the config file.
-
-        :return: user id
-        :rtype: str
-        """
-        if not os.path.exists(CONFIG_DIR):
-            os.makedirs(CONFIG_DIR)
-
-        if os.path.exists(CONFIG_FILE):
-            with open(CONFIG_FILE, "r") as f:
-                data = json.load(f)
-                if "user_id" in data:
-                    return data["user_id"]
-
-        u_id = str(uuid.uuid4())
-        with open(CONFIG_FILE, "w") as f:
-            json.dump({"user_id": u_id}, f)
-
-        return u_id
-
    def add(
        self,
        source: Any,
@@ -145,6 +137,7 @@ class EmbedChain(JSONSerializable):
        metadata: Optional[Dict[str, Any]] = None,
        config: Optional[AddConfig] = None,
        dry_run=False,
+        **kwargs: Dict[str, Any],
    ):
        """
        Adds the data from the given URL to the vector db.
@@ -163,10 +156,14 @@ class EmbedChain(JSONSerializable):
        :raises ValueError: Invalid data type
        :param dry_run: Optional. A dry run displays the chunks to ensure that the loader and chunker work as intended.
        deafaults to False
-        :return: source_id, a md5-hash of the source, in hexadecimal representation.
+        :return: source_hash, a md5-hash of the source, in hexadecimal representation.
        :rtype: str
        """
-        if config is None:
+        if config is not None:
+            pass
+        elif self.chunker is not None:
+            config = AddConfig(chunker=self.chunker)
+        else:
            config = AddConfig()

        try:
@@ -189,21 +186,45 @@ class EmbedChain(JSONSerializable):
                    f"Invalid data_type: '{data_type}'.",
                    f"Please use one of the following: {[data_type.value for data_type in DataType]}",
                ) from None
+
        if not data_type:
            data_type = detect_datatype(source)

-        # `source_id` is the hash of the source argument
+        # `source_hash` is the md5 hash of the source argument
        hash_object = hashlib.md5(str(source).encode("utf-8"))
-        source_id = hash_object.hexdigest()
+        source_hash = hash_object.hexdigest()
+
+        # Check if the data hash already exists, if so, skip the addition
+        self.cursor.execute(
+            "SELECT 1 FROM data_sources WHERE hash = ? AND pipeline_id = ?", (source_hash, self.config.id)
+        )
+        existing_data = self.cursor.fetchone()
+
+        if existing_data:
+            print(f"Data with hash {source_hash} already exists. Skipping addition.")
+            return source_hash

-        data_formatter = DataFormatter(data_type, config)
        self.user_asks.append([source, data_type.value, metadata])
+
+        data_formatter = DataFormatter(data_type, config, kwargs)
        documents, metadatas, _ids, new_chunks = self.load_and_embed(
-            data_formatter.loader, data_formatter.chunker, source, metadata, source_id, dry_run
+            data_formatter.loader, data_formatter.chunker, source, metadata, source_hash, dry_run
        )
        if data_type in {DataType.DOCS_SITE}:
            self.is_docs_site_instance = True

+        # Insert the data into the 'data' table
+        self.cursor.execute(
+            """
+            INSERT INTO data_sources (hash, pipeline_id, type, value, metadata)
+            VALUES (?, ?, ?, ?, ?)
+        """,
+            (source_hash, self.config.id, data_type.value, str(source), json.dumps(metadata)),
+        )
+
+        # Commit the transaction
+        self.connection.commit()
+
        if dry_run:
            data_chunks_info = {"chunks": documents, "metadata": metadatas, "count": len(documents), "type": data_type}
            logging.debug(f"Dry run info : {data_chunks_info}")
@@ -214,11 +235,16 @@ class EmbedChain(JSONSerializable):
            # it's quicker to check the variable twice than to count words when they won't be submitted.
            word_count = data_formatter.chunker.get_word_count(documents)

-            extra_metadata = {"data_type": data_type.value, "word_count": word_count, "chunks_count": new_chunks}
-            thread_telemetry = threading.Thread(target=self._send_telemetry_event, args=("add", extra_metadata))
-            thread_telemetry.start()
+            # Send anonymous telemetry
+            event_properties = {
+                **self._telemetry_props,
+                "data_type": data_type.value,
+                "word_count": word_count,
+                "chunks_count": new_chunks,
+            }
+            self.telemetry.capture(event_name="add", properties=event_properties)

-        return source_id
+        return source_hash

    def add_local(
        self,
@@ -226,6 +252,7 @@ class EmbedChain(JSONSerializable):
        data_type: Optional[DataType] = None,
        metadata: Optional[Dict[str, Any]] = None,
        config: Optional[AddConfig] = None,
+        **kwargs: Dict[str, Any],
    ):
        """
        Adds the data from the given URL to the vector db.
@@ -245,13 +272,19 @@ class EmbedChain(JSONSerializable):
        :param config: The `AddConfig` instance to use as configuration options., defaults to None
        :type config: Optional[AddConfig], optional
        :raises ValueError: Invalid data type
-        :return: source_id, a md5-hash of the source, in hexadecimal representation.
+        :return: source_hash, a md5-hash of the source, in hexadecimal representation.
        :rtype: str
        """
        logging.warning(
            "The `add_local` method is deprecated and will be removed in future versions. Please use the `add` method for both local and remote files."  # noqa: E501
        )
-        return self.add(source=source, data_type=data_type, metadata=metadata, config=config)
+        return self.add(
+            source=source,
+            data_type=data_type,
+            metadata=metadata,
+            config=config,
+            kwargs=kwargs,
+        )

    def _get_existing_doc_id(self, chunker: BaseChunker, src: Any):
        """
@@ -269,6 +302,10 @@ class EmbedChain(JSONSerializable):
            # These types have a indirect source reference
            # As long as the reference is the same, they can be updated.
            where = {"url": src}
+            if chunker.data_type == DataType.JSON and is_valid_json_string(src):
+                url = hashlib.sha256((src).encode("utf-8")).hexdigest()
+                where = {"url": url}
+
            if self.config.id is not None:
                where.update({"app_id": self.config.id})

@@ -313,7 +350,7 @@ class EmbedChain(JSONSerializable):
        chunker: BaseChunker,
        src: Any,
        metadata: Optional[Dict[str, Any]] = None,
-        source_id: Optional[str] = None,
+        source_hash: Optional[str] = None,
        dry_run=False,
    ):
        """
@@ -324,7 +361,7 @@ class EmbedChain(JSONSerializable):
        :param src: The data to be handled by the loader. Can be a URL for
        remote sources or local content for local loaders.
        :param metadata: Optional. Metadata associated with the data source.
-        :param source_id: Hexadecimal hash of the source.
+        :param source_hash: Hexadecimal hash of the source.
        :param dry_run: Optional. A dry run returns chunks and doesn't update DB.
        :type dry_run: bool, defaults to False
        :return: (List) documents (embedded text), (List) metadata, (list) ids, (int) number of chunks
@@ -350,6 +387,10 @@ class EmbedChain(JSONSerializable):

        # get existing ids, and discard doc if any common id exist.
        where = {"url": src}
+        if chunker.data_type == DataType.JSON and is_valid_json_string(src):
+            url = hashlib.sha256((src).encode("utf-8")).hexdigest()
+            where = {"url": url}
+
        # if data type is qna_pair, we check for question
        if chunker.data_type == DataType.QNA_PAIR:
            where = {"question": src[0]}
@@ -382,7 +423,7 @@ class EmbedChain(JSONSerializable):
                m["app_id"] = self.config.id

            # Add hashed source
-            m["hash"] = source_id
+            m["hash"] = source_hash

            # Note: Metadata is the function argument
            if metadata:
@@ -420,7 +461,9 @@ class EmbedChain(JSONSerializable):
            )
        ]

-    def retrieve_from_database(self, input_query: str, config: Optional[BaseLlmConfig] = None, where=None) -> List[str]:
+    def retrieve_from_database(
+        self, input_query: str, config: Optional[BaseLlmConfig] = None, where=None, citations: bool = False
+    ) -> Union[List[Tuple[str, str, str]], List[str]]:
        """
        Queries the vector database based on the given input query.
        Gets relevant doc based on the query
@@ -431,6 +474,8 @@ class EmbedChain(JSONSerializable):
        :type config: Optional[BaseLlmConfig], optional
        :param where: A dictionary of key-value pairs to filter the database results, defaults to None
        :type where: _type_, optional
+        :param citations: A boolean to indicate if db should fetch citation source
+        :type citations: bool
        :return: List of contents of the document that matched your query
        :rtype: List[str]
        """
@@ -455,15 +500,24 @@ class EmbedChain(JSONSerializable):

            db_query = ClipProcessor.get_text_features(query=input_query)

-        contents = self.db.query(
+        contexts = self.db.query(
            input_query=db_query,
            n_results=query_config.number_documents,
            where=where,
            skip_embedding=(hasattr(config, "query_type") and config.query_type == "Images"),
+            citations=citations,
        )
-        return contents

-    def query(self, input_query: str, config: BaseLlmConfig = None, dry_run=False, where: Optional[Dict] = None) -> str:
+        return contexts
+
+    def query(
+        self,
+        input_query: str,
+        config: BaseLlmConfig = None,
+        dry_run=False,
+        where: Optional[Dict] = None,
+        **kwargs: Dict[str, Any],
+    ) -> Union[Tuple[str, List[Tuple[str, str, str]]], str]:
        """
        Queries the vector database based on the given input query.
        Gets relevant doc based on the query and then passes it to an
@@ -479,17 +533,31 @@ class EmbedChain(JSONSerializable):
        :type dry_run: bool, optional
        :param where: A dictionary of key-value pairs to filter the database results., defaults to None
        :type where: Optional[Dict[str, str]], optional
-        :return: The answer to the query or the dry run result
-        :rtype: str
+        :param kwargs: To read more params for the query function. Ex. we use citations boolean
+        param to return context along with the answer
+        :type kwargs: Dict[str, Any]
+        :return: The answer to the query, with citations if the citation flag is True
+        or the dry run result
+        :rtype: str, if citations is False, otherwise Tuple[str,List[Tuple[str,str,str]]]
        """
-        contexts = self.retrieve_from_database(input_query=input_query, config=config, where=where)
-        answer = self.llm.query(input_query=input_query, contexts=contexts, config=config, dry_run=dry_run)
+        citations = kwargs.get("citations", False)
+        contexts = self.retrieve_from_database(input_query=input_query, config=config, where=where, citations=citations)
+        if citations and len(contexts) > 0 and isinstance(contexts[0], tuple):
+            contexts_data_for_llm_query = list(map(lambda x: x[0], contexts))
+        else:
+            contexts_data_for_llm_query = contexts
+
+        answer = self.llm.query(
+            input_query=input_query, contexts=contexts_data_for_llm_query, config=config, dry_run=dry_run
+        )

        # Send anonymous telemetry
-        thread_telemetry = threading.Thread(target=self._send_telemetry_event, args=("query",))
-        thread_telemetry.start()
+        self.telemetry.capture(event_name="query", properties=self._telemetry_props)

-        return answer
+        if citations:
+            return answer, contexts
+        else:
+            return answer

    def chat(
        self,
@@ -497,6 +565,7 @@ class EmbedChain(JSONSerializable):
        config: Optional[BaseLlmConfig] = None,
        dry_run=False,
        where: Optional[Dict[str, str]] = None,
+        **kwargs: Dict[str, Any],
    ) -> str:
        """
        Queries the vector database on the given input query.
@@ -515,17 +584,31 @@ class EmbedChain(JSONSerializable):
        :type dry_run: bool, optional
        :param where: A dictionary of key-value pairs to filter the database results., defaults to None
        :type where: Optional[Dict[str, str]], optional
-        :return: The answer to the query or the dry run result
-        :rtype: str
+        :param kwargs: To read more params for the query function. Ex. we use citations boolean
+        param to return context along with the answer
+        :type kwargs: Dict[str, Any]
+        :return: The answer to the query, with citations if the citation flag is True
+        or the dry run result
+        :rtype: str, if citations is False, otherwise Tuple[str,List[Tuple[str,str,str]]]
        """
-        contexts = self.retrieve_from_database(input_query=input_query, config=config, where=where)
-        answer = self.llm.chat(input_query=input_query, contexts=contexts, config=config, dry_run=dry_run)
+        citations = kwargs.get("citations", False)
+        contexts = self.retrieve_from_database(input_query=input_query, config=config, where=where, citations=citations)
+        if citations and len(contexts) > 0 and isinstance(contexts[0], tuple):
+            contexts_data_for_llm_query = list(map(lambda x: x[0], contexts))
+        else:
+            contexts_data_for_llm_query = contexts
+
+        answer = self.llm.chat(
+            input_query=input_query, contexts=contexts_data_for_llm_query, config=config, dry_run=dry_run
+        )

        # Send anonymous telemetry
-        thread_telemetry = threading.Thread(target=self._send_telemetry_event, args=("chat",))
-        thread_telemetry.start()
+        self.telemetry.capture(event_name="chat", properties=self._telemetry_props)

-        return answer
+        if citations:
+            return answer, contexts
+        else:
+            return answer

    def set_collection_name(self, name: str):
        """
@@ -558,36 +641,9 @@ class EmbedChain(JSONSerializable):
        """
        Resets the database. Deletes all embeddings irreversibly.
        `App` does not have to be reinitialized after using this method.
-
-        DEPRECATED IN FAVOR OF `db.reset()`
        """
-        # Send anonymous telemetry
-        thread_telemetry = threading.Thread(target=self._send_telemetry_event, args=("reset",))
-        thread_telemetry.start()
-
-        logging.warning("DEPRECATION WARNING: Please use `app.db.reset()` instead of `App.reset()`.")
        self.db.reset()
-
-    @retry(stop=stop_after_attempt(3), wait=wait_fixed(1))
-    def _send_telemetry_event(self, method: str, extra_metadata: Optional[dict] = None):
-        """
-        Send telemetry event to the embedchain server. This is anonymous. It can be toggled off in `AppConfig`.
-        """
-        if not self.config.collect_metrics:
-            return
-
-        with threading.Lock():
-            url = "https://api.embedchain.ai/api/v1/telemetry/"
-            metadata = {
-                "s_id": self.s_id,
-                "version": importlib.metadata.version(__package__ or __name__),
-                "method": method,
-                "language": "py",
-                "u_id": self.u_id,
-            }
-            if extra_metadata:
-                metadata.update(extra_metadata)
-
-            response = requests.post(url, json={"metadata": metadata})
-            if response.status_code != 200:
-                logging.warning(f"Telemetry event failed with status code {response.status_code}")
+        self.cursor.execute("DELETE FROM data_sources WHERE pipeline_id = ?", (self.config.id,))
+        self.connection.commit()
+        # Send anonymous telemetry
+        self.telemetry.capture(event_name="reset", properties=self._telemetry_props)
@@ -0,0 +1,104 @@
+"""
+Note that this file is copied from Chroma repository. We will remove this file once the fix in
+ChromaDB's repository.
+"""
+
+from typing import Optional
+
+from chromadb.api.types import Documents, Embeddings
+
+
+class OpenAIEmbeddingFunction:
+    def __init__(
+        self,
+        api_key: Optional[str] = None,
+        model_name: str = "text-embedding-ada-002",
+        organization_id: Optional[str] = None,
+        api_base: Optional[str] = None,
+        api_type: Optional[str] = None,
+        api_version: Optional[str] = None,
+        deployment_id: Optional[str] = None,
+    ):
+        """
+        Initialize the OpenAIEmbeddingFunction.
+        Args:
+            api_key (str, optional): Your API key for the OpenAI API. If not
+                provided, it will raise an error to provide an OpenAI API key.
+            organization_id(str, optional): The OpenAI organization ID if applicable
+            model_name (str, optional): The name of the model to use for text
+                embeddings. Defaults to "text-embedding-ada-002".
+            api_base (str, optional): The base path for the API. If not provided,
+                it will use the base path for the OpenAI API. This can be used to
+                point to a different deployment, such as an Azure deployment.
+            api_type (str, optional): The type of the API deployment. This can be
+                used to specify a different deployment, such as 'azure'. If not
+                provided, it will use the default OpenAI deployment.
+            api_version (str, optional): The api version for the API. If not provided,
+                it will use the api version for the OpenAI API. This can be used to
+                point to a different deployment, such as an Azure deployment.
+            deployment_id (str, optional): Deployment ID for Azure OpenAI.
+
+        """
+        try:
+            import openai
+        except ImportError:
+            raise ValueError("The openai python package is not installed. Please install it with `pip install openai`")
+
+        if api_key is not None:
+            openai.api_key = api_key
+        # If the api key is still not set, raise an error
+        elif openai.api_key is None:
+            raise ValueError(
+                "Please provide an OpenAI API key. You can get one at https://platform.openai.com/account/api-keys"
+            )
+
+        if api_base is not None:
+            openai.api_base = api_base
+
+        if api_version is not None:
+            openai.api_version = api_version
+
+        self._api_type = api_type
+        if api_type is not None:
+            openai.api_type = api_type
+
+        if organization_id is not None:
+            openai.organization = organization_id
+
+        self._v1 = openai.__version__.startswith("1.")
+        if self._v1:
+            if api_type == "azure":
+                self._client = openai.AzureOpenAI(
+                    api_key=api_key, api_version=api_version, azure_endpoint=api_base
+                ).embeddings
+            else:
+                self._client = openai.OpenAI(api_key=api_key, base_url=api_base).embeddings
+        else:
+            self._client = openai.Embedding
+        self._model_name = model_name
+        self._deployment_id = deployment_id
+
+    def __call__(self, input: Documents) -> Embeddings:
+        # replace newlines, which can negatively affect performance.
+        input = [t.replace("\n", " ") for t in input]
+
+        # Call the OpenAI Embedding API
+        if self._v1:
+            embeddings = self._client.create(input=input, model=self._deployment_id or self._model_name).data
+
+            # Sort resulting embeddings by index
+            sorted_embeddings = sorted(embeddings, key=lambda e: e.index)  # type: ignore
+
+            # Return just the embeddings
+            return [result.embedding for result in sorted_embeddings]
+        else:
+            if self._api_type == "azure":
+                embeddings = self._client.create(input=input, engine=self._deployment_id or self._model_name)["data"]
+            else:
+                embeddings = self._client.create(input=input, model=self._model_name)["data"]
+
+            # Sort resulting embeddings by index
+            sorted_embeddings = sorted(embeddings, key=lambda e: e["index"])  # type: ignore
+
+            # Return just the embeddings
+            return [result["embedding"] for result in sorted_embeddings]
@@ -1,7 +1,5 @@
 from typing import Optional

-from chromadb.utils import embedding_functions
-
 from embedchain.config import BaseEmbedderConfig
 from embedchain.embedder.base import BaseEmbedder
 from embedchain.models import VectorDimensions
@@ -9,12 +7,13 @@ from embedchain.models import VectorDimensions

 class GPT4AllEmbedder(BaseEmbedder):
    def __init__(self, config: Optional[BaseEmbedderConfig] = None):
-        # Note: We could use langchains GPT4ALL embedding, but it's not available in all versions.
        super().__init__(config=config)
-        if self.config.model is None:
-            self.config.model = "all-MiniLM-L6-v2"

-        embedding_fn = embedding_functions.SentenceTransformerEmbeddingFunction(model_name=self.config.model)
+        from langchain.embeddings import \
+            GPT4AllEmbeddings as LangchainGPT4AllEmbeddings
+
+        embeddings = LangchainGPT4AllEmbeddings()
+        embedding_fn = BaseEmbedder._langchain_default_concept(embeddings)
        self.set_embedding_fn(embedding_fn=embedding_fn)

        vector_dimension = VectorDimensions.GPT4ALL.value
@@ -7,13 +7,7 @@ from embedchain.config import BaseEmbedderConfig
 from embedchain.embedder.base import BaseEmbedder
 from embedchain.models import VectorDimensions

-try:
-    from chromadb.utils import embedding_functions
-except RuntimeError:
-    from embedchain.utils import use_pysqlite3
-
-    use_pysqlite3()
-    from chromadb.utils import embedding_functions
+from .chroma_embeddings import OpenAIEmbeddingFunction


 class OpenAIEmbedder(BaseEmbedder):
@@ -30,11 +24,10 @@ class OpenAIEmbedder(BaseEmbedder):
                raise ValueError(
                    "OPENAI_API_KEY or OPENAI_ORGANIZATION environment variables not provided"
                )  # noqa:E501
-            embedding_fn = embedding_functions.OpenAIEmbeddingFunction(
+            embedding_fn = OpenAIEmbeddingFunction(
                api_key=os.getenv("OPENAI_API_KEY"),
                organization_id=os.getenv("OPENAI_ORGANIZATION"),
                model_name=self.config.model,
            )
-
        self.set_embedding_fn(embedding_fn=embedding_fn)
        self.set_vector_dimension(vector_dimension=VectorDimensions.OPENAI.value)
@@ -41,15 +41,16 @@ class LlmFactory:

 class EmbedderFactory:
    provider_to_class = {
+        "azure_openai": "embedchain.embedder.openai.OpenAIEmbedder",
        "gpt4all": "embedchain.embedder.gpt4all.GPT4AllEmbedder",
        "huggingface": "embedchain.embedder.huggingface.HuggingFaceEmbedder",
-        "vertexai": "embedchain.embedder.vertexai.VertexAIEmbedder",
-        "azure_openai": "embedchain.embedder.openai.OpenAIEmbedder",
        "openai": "embedchain.embedder.openai.OpenAIEmbedder",
+        "vertexai": "embedchain.embedder.vertexai.VertexAIEmbedder",
    }
    provider_to_config_class = {
-        "openai": "embedchain.config.embedder.base.BaseEmbedderConfig",
        "azure_openai": "embedchain.config.embedder.base.BaseEmbedderConfig",
+        "openai": "embedchain.config.embedder.base.BaseEmbedderConfig",
+        "gpt4all": "embedchain.config.embedder.base.BaseEmbedderConfig",
    }

    @classmethod
@@ -72,16 +73,18 @@ class VectorDBFactory:
        "elasticsearch": "embedchain.vectordb.elasticsearch.ElasticsearchDB",
        "opensearch": "embedchain.vectordb.opensearch.OpenSearchDB",
        "pinecone": "embedchain.vectordb.pinecone.PineconeDB",
-        "weaviate": "embedchain.vectordb.weaviate.WeaviateDB",
        "qdrant": "embedchain.vectordb.qdrant.QdrantDB",
+        "weaviate": "embedchain.vectordb.weaviate.WeaviateDB",
+        "zilliz": "embedchain.vectordb.zilliz.ZillizVectorDB",
    }
    provider_to_config_class = {
        "chroma": "embedchain.config.vectordb.chroma.ChromaDbConfig",
        "elasticsearch": "embedchain.config.vectordb.elasticsearch.ElasticsearchDBConfig",
        "opensearch": "embedchain.config.vectordb.opensearch.OpenSearchDBConfig",
        "pinecone": "embedchain.config.vectordb.pinecone.PineconeDBConfig",
-        "weaviate": "embedchain.config.vectordb.weaviate.WeaviateDBConfig",
        "qdrant": "embedchain.config.vectordb.qdrant.QdrantDBConfig",
+        "weaviate": "embedchain.config.vectordb.weaviate.WeaviateDBConfig",
+        "zilliz": "embedchain.config.vectordb.zilliz.ZillizDBConfig",
    }

    @classmethod
@@ -1,5 +1,8 @@
 from typing import Iterable, Optional, Union

+from langchain.callbacks.stdout import StdOutCallbackHandler
+from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
+
 from embedchain.config import BaseLlmConfig
 from embedchain.helper.json_serializable import register_deserializable
 from embedchain.llm.base import BaseLlm
@@ -10,8 +13,9 @@ class GPT4ALLLlm(BaseLlm):
    def __init__(self, config: Optional[BaseLlmConfig] = None):
        super().__init__(config=config)
        if self.config.model is None:
-            self.config.model = "orca-mini-3b.ggmlv3.q4_0.bin"
+            self.config.model = "orca-mini-3b-gguf2-q4_0.gguf"
        self.instance = GPT4ALLLlm._get_instance(self.config.model)
+        self.instance.streaming = self.config.stream

    def get_llm_model_answer(self, prompt):
        return self._get_answer(prompt=prompt, config=self.config)
@@ -19,13 +23,13 @@ class GPT4ALLLlm(BaseLlm):
    @staticmethod
    def _get_instance(model):
        try:
-            from gpt4all import GPT4All
+            from langchain.llms.gpt4all import GPT4All as LangchainGPT4All
        except ModuleNotFoundError:
            raise ModuleNotFoundError(
                "The GPT4All python package is not installed. Please install it with `pip install --upgrade embedchain[opensource]`"  # noqa E501
            ) from None

-        return GPT4All(model_name=model)
+        return LangchainGPT4All(model=model, allow_download=True)

    def _get_answer(self, prompt: str, config: BaseLlmConfig) -> Union[str, Iterable]:
        if config.model and config.model != self.config.model:
@@ -33,14 +37,21 @@ class GPT4ALLLlm(BaseLlm):
                "GPT4ALLLlm does not support switching models at runtime. Please create a new app instance."
            )

+        messages = []
        if config.system_prompt:
-            raise ValueError("GPT4ALLLlm does not support `system_prompt`")
+            messages.append(config.system_prompt)
+        messages.append(prompt)
+        kwargs = {
+            "temp": config.temperature,
+            "max_tokens": config.max_tokens,
+        }
+        if config.top_p:
+            kwargs["top_p"] = config.top_p

-        response = self.instance.generate(
-            prompt=prompt,
-            streaming=config.stream,
-            top_p=config.top_p,
-            max_tokens=config.max_tokens,
-            temp=config.temperature,
-        )
-        return response
+        callbacks = [StreamingStdOutCallbackHandler()] if config.stream else [StdOutCallbackHandler()]
+
+        response = self.instance.generate(prompts=messages, callbacks=callbacks, **kwargs)
+        answer = ""
+        for generations in response.generations:
+            answer += " ".join(map(lambda generation: generation.text, generations))
+        return answer
@@ -8,18 +8,17 @@ from embedchain.config import BaseLlmConfig
 from embedchain.helper.json_serializable import register_deserializable
 from embedchain.llm.base import BaseLlm

-try:
-    importlib.import_module("replicate")
-except ModuleNotFoundError:
-    raise ModuleNotFoundError(
-        "The required dependencies for Llama2 are not installed."
-        'Please install with `pip install --upgrade "embedchain[llama2]"`'
-    ) from None
-

@register_deserializable
 class Llama2Llm(BaseLlm):
    def __init__(self, config: Optional[BaseLlmConfig] = None):
+        try:
+            importlib.import_module("replicate")
+        except ModuleNotFoundError:
+            raise ModuleNotFoundError(
+                "The required dependencies for Llama2 are not installed."
+                'Please install with `pip install --upgrade "embedchain[llama2]"`'
+            ) from None
        if "REPLICATE_API_TOKEN" not in os.environ:
            raise ValueError("Please set the REPLICATE_API_TOKEN environment variable.")

@@ -40,7 +39,7 @@ class Llama2Llm(BaseLlm):
    def get_llm_model_answer(self, prompt):
        # TODO: Move the model and other inputs into config
        if self.config.system_prompt:
-            raise ValueError("Llama2App does not support `system_prompt`")
+            raise ValueError("Llama2 does not support `system_prompt`")
        llm = Replicate(
            model=self.config.model,
            input={
@@ -6,18 +6,17 @@ from embedchain.config import BaseLlmConfig
 from embedchain.helper.json_serializable import register_deserializable
 from embedchain.llm.base import BaseLlm

-try:
-    importlib.import_module("vertexai")
-except ModuleNotFoundError:
-    raise ModuleNotFoundError(
-        "The required dependencies for VertexAI are not installed."
-        'Please install with `pip install --upgrade "embedchain[vertexai]"`'
-    ) from None
-

@register_deserializable
 class VertexAILlm(BaseLlm):
    def __init__(self, config: Optional[BaseLlmConfig] = None):
+        try:
+            importlib.import_module("vertexai")
+        except ModuleNotFoundError:
+            raise ModuleNotFoundError(
+                "The required dependencies for VertexAI are not installed."
+                'Please install with `pip install --upgrade "embedchain[vertexai]"`'
+            ) from None
        super().__init__(config=config)

    def get_llm_model_answer(self, prompt):
@@ -0,0 +1,123 @@
+import hashlib
+import logging
+import os
+import quopri
+from textwrap import dedent
+
+from bs4 import BeautifulSoup
+
+try:
+    from llama_hub.gmail.base import GmailReader
+except ImportError:
+    raise ImportError("Gmail requires extra dependencies. Install with `pip install embedchain[gmail]`") from None
+
+from embedchain.loaders.base_loader import BaseLoader
+from embedchain.utils import clean_string
+
+
+def get_header(text: str, header: str) -> str:
+    start_string_position = text.find(header)
+    pos_start = text.find(":", start_string_position) + 1
+    pos_end = text.find("\n", pos_start)
+    header = text[pos_start:pos_end]
+    return header.strip()
+
+
+class GmailLoader(BaseLoader):
+    def load_data(self, query):
+        """Load data from gmail."""
+        if not os.path.isfile("credentials.json"):
+            raise FileNotFoundError(
+                "You must download the valid credentials file from your google \
+                dev account. Refer this `https://cloud.google.com/docs/authentication/api-keys`"
+            )
+
+        loader = GmailReader(query=query, service=None, results_per_page=20)
+        documents = loader.load_data()
+        logging.info(f"Gmail Loader: {len(documents)} mails found for query- {query}")
+
+        data = []
+        data_contents = []
+        logging.info(f"Gmail Loader: {len(documents)} mails found")
+        for document in documents:
+            original_size = len(document.text)
+
+            snippet = document.metadata.get("snippet")
+            meta_data = {
+                "url": document.metadata.get("id"),
+                "date": get_header(document.text, "Date"),
+                "subject": get_header(document.text, "Subject"),
+                "from": get_header(document.text, "From"),
+                "to": get_header(document.text, "To"),
+                "search_query": query,
+            }
+
+            # Decode
+            decoded_bytes = quopri.decodestring(document.text)
+            decoded_str = decoded_bytes.decode("utf-8", errors="replace")
+
+            # Slice
+            mail_start = decoded_str.find("<!DOCTYPE")
+            email_data = decoded_str[mail_start:]
+
+            # Web Page HTML Processing
+            soup = BeautifulSoup(email_data, "html.parser")
+
+            tags_to_exclude = [
+                "nav",
+                "aside",
+                "form",
+                "header",
+                "noscript",
+                "svg",
+                "canvas",
+                "footer",
+                "script",
+                "style",
+            ]
+
+            for tag in soup(tags_to_exclude):
+                tag.decompose()
+
+            ids_to_exclude = ["sidebar", "main-navigation", "menu-main-menu"]
+            for id in ids_to_exclude:
+                tags = soup.find_all(id=id)
+                for tag in tags:
+                    tag.decompose()
+
+            classes_to_exclude = [
+                "elementor-location-header",
+                "navbar-header",
+                "nav",
+                "header-sidebar-wrapper",
+                "blog-sidebar-wrapper",
+                "related-posts",
+            ]
+
+            for class_name in classes_to_exclude:
+                tags = soup.find_all(class_=class_name)
+                for tag in tags:
+                    tag.decompose()
+
+            content = soup.get_text()
+            content = clean_string(content)
+
+            cleaned_size = len(content)
+            if original_size != 0:
+                logging.info(
+                    f"[{id}] Cleaned page size: {cleaned_size} characters, down from {original_size} (shrunk: {original_size-cleaned_size} chars, {round((1-(cleaned_size/original_size)) * 100, 2)}%)"  # noqa:E501
+                )
+
+            result = f"""
+            email from '{meta_data.get('from')}' to '{meta_data.get('to')}'
+            subject: {meta_data.get('subject')}
+            date: {meta_data.get('date')}
+            preview: {snippet}
+            content: f{content}
+            """
+            data_content = dedent(result)
+            data.append({"content": data_content, "meta_data": meta_data})
+            data_contents.append(data_content)
+        doc_id = hashlib.sha256((query + ", ".join(data_contents)).encode()).hexdigest()
+        response_data = {"doc_id": doc_id, "data": data}
+        return response_data
@@ -1,24 +1,76 @@
 import hashlib
+import json
+import os
+import re

-from langchain.document_loaders.json_loader import \
-    JSONLoader as LangchainJSONLoader
+import requests

 from embedchain.loaders.base_loader import BaseLoader
+from embedchain.utils import clean_string, is_valid_json_string

-langchain_json_jq_schema = 'to_entries | map("\(.key): \(.value|tostring)") | .[]'
+VALID_URL_PATTERN = "^https:\/\/[0-9A-z.]+.[0-9A-z.]+.[a-z]+\/.*\.json$"


 class JSONLoader(BaseLoader):
+    @staticmethod
+    def _get_llama_hub_loader():
+        try:
+            from llama_hub.jsondata.base import \
+                JSONDataReader as LLHUBJSONLoader
+        except ImportError as e:
+            raise Exception(
+                f"Failed to install required packages: {e}, \
+                install them using `pip install --upgrade 'embedchain[json]`"
+            )
+
+        return LLHUBJSONLoader()
+
+    @staticmethod
+    def _check_content(content):
+        if not isinstance(content, str):
+            raise ValueError(
+                "Invaid content input. \
+                If you want to upload (list, dict, etc.), do \
+                    `json.dump(data, indent=0)` and add the stringified JSON. \
+                        Check - `https://docs.embedchain.ai/data-sources/json`"
+            )
+
    @staticmethod
    def load_data(content):
        """Load a json file. Each data point is a key value pair."""
+
+        JSONLoader._check_content(content)
+        loader = JSONLoader._get_llama_hub_loader()
+
        data = []
        data_content = []
-        loader = LangchainJSONLoader(content, text_content=False, jq_schema=langchain_json_jq_schema)
-        docs = loader.load()
+
+        content_url_str = content
+
+        # Load json data from various sources.
+        if os.path.isfile(content):
+            with open(content, "r", encoding="utf-8") as json_file:
+                json_data = json.load(json_file)
+        elif re.match(VALID_URL_PATTERN, content):
+            response = requests.get(content)
+            if response.status_code == 200:
+                json_data = response.json()
+            else:
+                raise ValueError(
+                    f"Loading data from the given url: {content} failed. \
+                    Make sure the url is working."
+                )
+        elif is_valid_json_string(content):
+            json_data = content
+            content_url_str = hashlib.sha256((content).encode("utf-8")).hexdigest()
+        else:
+            raise ValueError(f"Invalid content to load json data from: {content}")
+
+        docs = loader.load_data(json_data)
        for doc in docs:
-            meta_data = doc.metadata
-            data.append({"content": doc.page_content, "meta_data": {"url": content, "row": meta_data["seq_num"]}})
-            data_content.append(doc.page_content)
-        doc_id = hashlib.sha256((content + ", ".join(data_content)).encode()).hexdigest()
+            doc_content = clean_string(doc.text)
+            data.append({"content": doc_content, "meta_data": {"url": content_url_str}})
+            data_content.append(doc_content)
+
+        doc_id = hashlib.sha256((content_url_str + ", ".join(data_content)).encode()).hexdigest()
        return {"doc_id": doc_id, "data": data}
@@ -0,0 +1,42 @@
+import hashlib
+from io import StringIO
+from urllib.parse import urlparse
+
+import requests
+import yaml
+
+from embedchain.loaders.base_loader import BaseLoader
+
+
+class OpenAPILoader(BaseLoader):
+    @staticmethod
+    def _get_file_content(content):
+        url = urlparse(content)
+        if all([url.scheme, url.netloc]) and url.scheme not in ["file", "http", "https"]:
+            raise ValueError("Not a valid URL.")
+
+        if url.scheme in ["http", "https"]:
+            response = requests.get(content)
+            response.raise_for_status()
+            return StringIO(response.text)
+        elif url.scheme == "file":
+            path = url.path
+            return open(path)
+        else:
+            return open(content)
+
+    @staticmethod
+    def load_data(content):
+        """Load yaml file of openapi. Each pair is a document."""
+        data = []
+        file_path = content
+        data_content = []
+        with OpenAPILoader._get_file_content(content=content) as file:
+            yaml_data = yaml.load(file, Loader=yaml.Loader)
+            for i, (key, value) in enumerate(yaml_data.items()):
+                string_data = f"{key}: {value}"
+                meta_data = {"url": file_path, "row": i + 1}
+                data.append({"content": string_data, "meta_data": meta_data})
+                data_content.append(string_data)
+        doc_id = hashlib.sha256((content + ", ".join(data_content)).encode()).hexdigest()
+        return {"doc_id": doc_id, "data": data}
@@ -0,0 +1,73 @@
+import hashlib
+import logging
+from typing import Any, Dict, Optional
+
+from embedchain.loaders.base_loader import BaseLoader
+
+
+class PostgresLoader(BaseLoader):
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        super().__init__()
+        if not config:
+            raise ValueError(f"Must provide the valid config. Received: {config}")
+
+        self.connection = None
+        self.cursor = None
+        self._setup_loader(config=config)
+
+    def _setup_loader(self, config: Dict[str, Any]):
+        try:
+            import psycopg
+        except ImportError as e:
+            raise ImportError(
+                "Unable to import required packages. \
+                    Run `pip install --upgrade 'embedchain[postgres]'`"
+            ) from e
+
+        config_info = ""
+        if "url" in config:
+            config_info = config.get("url")
+        else:
+            conn_params = []
+            for key, value in config.items():
+                conn_params.append(f"{key}={value}")
+            config_info = " ".join(conn_params)
+
+        logging.info(f"Connecting to postrgres sql: {config_info}")
+        self.connection = psycopg.connect(conninfo=config_info)
+        self.cursor = self.connection.cursor()
+
+    def _check_query(self, query):
+        if not isinstance(query, str):
+            raise ValueError(
+                f"Invalid postgres query: {query}",
+                "Provide the valid source to add from postgres, \
+                    make sure you are following `https://docs.embedchain.ai/data-sources/postgres`",
+            )
+
+    def load_data(self, query):
+        self._check_query(query)
+        try:
+            data = []
+            data_content = []
+            self.cursor.execute(query)
+            results = self.cursor.fetchall()
+            for result in results:
+                doc_content = str(result)
+                data.append({"content": doc_content, "meta_data": {"url": f"postgres_query-({query})"}})
+                data_content.append(doc_content)
+            doc_id = hashlib.sha256((query + ", ".join(data_content)).encode()).hexdigest()
+            return {
+                "doc_id": doc_id,
+                "data": data,
+            }
+        except Exception as e:
+            raise ValueError(f"Failed to load data using query={query} with: {e}")
+
+    def close_connection(self):
+        if self.cursor:
+            self.cursor.close()
+            self.cursor = None
+        if self.connection:
+            self.connection.close()
+            self.connection = None
@@ -27,6 +27,9 @@ class IndirectDataType(Enum):
    IMAGES = "images"
    UNSTRUCTURED = "unstructured"
    JSON = "json"
+    OPENAPI = "openapi"
+    GMAIL = "gmail"
+    POSTGRES = "postgres"


 class SpecialDataType(Enum):
@@ -53,3 +56,6 @@ class DataType(Enum):
    IMAGES = IndirectDataType.IMAGES.value
    UNSTRUCTURED = IndirectDataType.UNSTRUCTURED.value
    JSON = IndirectDataType.JSON.value
+    OPENAPI = IndirectDataType.OPENAPI.value
+    GMAIL = IndirectDataType.GMAIL.value
+    POSTGRES = IndirectDataType.POSTGRES.value
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Deshraj Yadav	7c6b88c7c5	[Docs] Update docs and improve assistant api (#923 )	2023-11-09 01:16:19 -08:00
Deshraj Yadav	32c93be46e	[chore] update poetry.lock file (#922 )	2023-11-09 00:59:29 -08:00
Deven Patel	7de8d85199	[Feature] Add Postgres data loader (#918 ) Co-authored-by: Deven Patel <deven298@yahoo.com>	2023-11-08 23:50:46 -08:00
Deshraj Yadav	f7dd65a3de	[Feature] Add support for OpenAI assistants and support openai version >=1.0.0 (#921 )	2023-11-08 22:49:03 -08:00
Sidharth Mohanty	d8cdbe0041	Set `check_same_thread` false so that one App can be used in parallel (#911 )	2023-11-08 10:26:31 -08:00
Sidharth Mohanty	2b8b6d3ea9	Chunker config docs (#913 )	2023-11-08 10:25:45 -08:00
Sidharth Mohanty	936c7e389f	Deploy Full stack docker image (#914 )	2023-11-08 10:25:12 -08:00
Sidharth Mohanty	6864b4207b	Dockerize discord bot and update docs to run the bot correctly (#919 )	2023-11-08 10:24:57 -08:00
Deshraj Yadav	98eb5b54be	[Docs] Update developer documentation (#916 )	2023-11-07 19:07:47 -08:00
Deshraj Yadav	3332e6e236	[Docs] Update README (#915 )	2023-11-07 18:12:27 -08:00
Deven Patel	0533da72d7	[Improvement] add delete functionality to zilliz DB (#912 ) Co-authored-by: Deven Patel <deven298@yahoo.com>	2023-11-07 02:46:39 -08:00
Sidharth Mohanty	a1de238716	Introduce chunker config in yaml config (#907 )	2023-11-06 09:43:15 -08:00
Deshraj Yadav	f0d112254b	[version] bump package version and minor cleanup (#909 )	2023-11-05 16:34:36 -08:00
Sidharth Mohanty	830a7397ef	Add yaml config validation (#890 )	2023-11-04 22:23:55 -07:00
Deven Patel	5428765329	Beautify JSON docs (#906 ) Co-authored-by: Deven Patel <deven298@yahoo.com>	2023-11-03 13:52:33 -07:00
Sidharth Mohanty	23c912f2b7	Improve getting started page by adding steps (#904 )	2023-11-03 13:40:40 -07:00
Sidharth Mohanty	9c4b023297	Use either `embedder` or `embedding_model` as YAML key (#905 )	2023-11-03 13:40:16 -07:00
Deven Patel	53037b5ed8	[Feature Improvement] Update JSON Loader to support loading data from more sources (#898 ) Co-authored-by: Deven Patel <deven298@yahoo.com>	2023-11-03 10:00:27 -07:00
Sidharth Mohanty	e2546a653d	[chore] fix rest api docs and other minor fixes (#902 )	2023-11-03 09:40:48 -07:00
Deshraj Yadav	4b8cada873	[REST API] Change docker image name and update docs (#901 )	2023-11-03 01:08:27 -07:00
Deshraj Yadav	fa3ca1d08a	[REST API] Incorporate changes related to REST API docs (#900 )	2023-11-03 00:42:55 -07:00
Sidharth Mohanty	8dd5cb9602	Add `rest-api` example (#889 )	2023-11-03 00:32:51 -07:00
Deven Patel	a054f7be9c	[Data loader] Make json data loader work for other languages (#897 ) Co-authored-by: Deven Patel <deven298@yahoo.com>	2023-11-01 22:27:40 -07:00
Deven Patel	df314dc6d1	Clean json data before loading (#895 ) Co-authored-by: Deven Patel <deven298@yahoo.com>	2023-11-01 21:52:34 -07:00
Deven Patel	930280f4ce	[Feature] Add citations flag in query and chat functions of App to return context along with the answer (#859 )	2023-11-01 13:06:28 -07:00
Deshraj Yadav	5022c1ae29	[misc] update poetry.lock file (#891 )	2023-11-01 11:35:44 -07:00
Deshraj Yadav	6ced756a6b	[misc] add `json` extra in pyproject.toml file (#886 )	2023-10-31 23:55:25 -07:00
Deshraj Yadav	b17268db50	[misc] remove jq as a dependency (#885 )	2023-10-31 21:02:19 -07:00
Deshraj Yadav	476da37009	[version] bump package version to v0.0.87 (#883 )	2023-10-31 12:40:02 -07:00
Deshraj Yadav	455f059c6f	[Telemetry] Update anonymous telemetry API key (#882 )	2023-10-31 12:34:54 -07:00
Deven Patel	5255a37c93	Embedchain json url support (#878 ) Co-authored-by: Deven Patel <deven298@yahoo.com>	2023-10-30 16:19:11 -07:00
Deven Patel	68dc274f72	Embedchain json loader update (#876 ) Co-authored-by: Deven Patel <deven298@yahoo.com>	2023-10-30 15:30:49 -07:00
Deshraj Yadav	30228f7f8e	[version] Update langchain to v0.0.303 (#875 )	2023-10-30 15:02:38 -07:00
Sidharth Mohanty	e15ef79ca9	Lazy load loaders and chunkers (#872 )	2023-10-30 11:20:38 -07:00
Deshraj Yadav	bc012a7518	[Docs] Update embedchain docs and analytics (#871 )	2023-10-30 00:38:35 -07:00
Sidharth Mohanty	3b4409cfad	Update notebooks to work with the latest version (#870 )	2023-10-29 23:06:43 -07:00
Deshraj Yadav	d3726134b2	[Docs] Update docs and minor improvements in search API (#869 )	2023-10-29 16:50:14 -07:00
anujshandillya	5acb7f1c55	[fix]: updated twitter logo to new X (#868 )	2023-10-29 14:37:32 -07:00
Deshraj Yadav	81336668b3	[Feature]: Add posthog anonymous telemetry and update docs (#867 )	2023-10-29 01:20:21 -07:00
Deshraj Yadav	35c2b83015	[version] Update openai version to 0.28.0 (#866 )	2023-10-28 19:18:57 -07:00
Deshraj Yadav	cc1ee1deaa	Fix dependencies in base package (#863 )	2023-10-27 21:35:48 -07:00
Deshraj Yadav	29bd038579	[Bug fix] Fix missing dependency issue with gmail (#862 )	2023-10-27 20:02:03 -07:00
Deshraj Yadav	f6c4f86986	[Pipelines] Improvements in pipelines feature (#861 )	2023-10-27 18:42:46 -07:00
Deven Patel	68183e9dce	[Feature] Gmail Loader (#841 )	2023-10-27 18:05:08 -07:00
Deshraj Yadav	78ec91a3a9	[Bug fix] Fix sqlite related issue with api server example (#857 )	2023-10-26 21:54:45 -07:00
Deven Patel	c95d458e52	Add tests for gpt4all llm (#852 )	2023-10-26 20:41:28 -07:00
Deshraj Yadav	191ae3ec1e	Update README (#855 )	2023-10-26 11:11:05 -07:00
Deshraj Yadav	ab9598d00a	Update version to v0.0.77 (#851 )	2023-10-26 09:51:51 -07:00
Deven Patel	0f8a2e624a	[Improvement] Add support for gpt4all through langchain (#838 )	2023-10-25 22:25:00 -07:00
Deven Patel	d77e8da3f3	[Feature] Update `db.query` to return source of context (#831 )	2023-10-25 22:20:32 -07:00
Sidharth Mohanty	a27eeb3255	[Bug fix] import App shouldn't throw other llm deps errors (#837 )	2023-10-25 20:48:53 -07:00
Deshraj Yadav	413ccb83e6	[Bug fix] Fix issues related to creating pipelines (#850 )	2023-10-25 20:26:58 -07:00
Deven Patel	797bb567c6	[feat]: Add openapi spec data loader (#818 )	2023-10-25 14:19:13 -07:00
Deshraj Yadav	f2a5dc40ee	Update package version to v0.0.76 (#849 )	2023-10-25 13:40:00 -07:00
Deshraj Yadav	3979480532	[Feature] Add support for deploying local pipelines to Embedchain platform (#847 )	2023-10-25 13:36:24 -07:00
Abhinesh	76f1993e7a	Update CONTRIBUTING.md (#845 )	2023-10-25 13:31:39 -07:00
Deshraj Yadav	d783fa2b89	[chore]: Add poetry lock file (#848 )	2023-10-25 12:53:06 -07:00
Sidharth Mohanty	bbce18caac	[Bug fix] `reset()` erases everything from db (#844 )	2023-10-25 10:22:20 -07:00
Deshraj Yadav	3ce2d8a656	Bump version to v0.0.75 (#836 )	2023-10-19 21:06:49 -07:00
Deshraj Yadav	a5c86a2f5c	[Bugfix]: Fix issue of context overspilling into other apps (#835 )	2023-10-19 17:46:33 -07:00
Deshraj Yadav	d18e533adf	[Feature] Setup base for creating pipelines in embedchain (#834 )	2023-10-19 17:46:15 -07:00
Sidharth Mohanty	2b881aaad0	Cache dependencies in CI (#832 )	2023-10-19 13:59:02 -07:00
Sidharth Mohanty	39cc07608f	[chore] Update repl links to launch main.py directly (#830 )	2023-10-19 13:53:07 -07:00
Sidharth Mohanty	9894cfcced	[docs] add links to example repositories (#833 )	2023-10-19 13:51:08 -07:00
Sidharth Mohanty	b5d80be037	Remove person_app, open_source app, llama2_app with their configs (#829 )	2023-10-19 01:13:52 -07:00
Sidharth Mohanty	b7870fbd9b	[Bug fix] Table content alignment in example docs table (#828 )	2023-10-18 21:38:31 -07:00
Sidharth Mohanty	36e6d486fc	[chore] Remove `image.jpg` produced by tests and add cleanup in test (#827 )	2023-10-18 21:38:00 -07:00
Sidharth Mohanty	2d5dc84f1a	[chore] Update LLM YAML config docs (#826 )	2023-10-18 21:37:45 -07:00