[Feature] Add support for AIAssistant (#938 )

[Bug Fix] fix chromadb where clause for query and delete (#937 )
Co-authored-by: Deven Patel <deven298@yahoo.com>
2023-11-10 16:47:34 -08:00 · 2023-11-10 16:04:25 -08:00 · 2023-11-10 10:15:04 -08:00 · 2023-11-10 10:13:21 -08:00 · 2023-11-09 22:29:41 -08:00 · 2023-11-09 15:17:51 -08:00
241 changed files with 18360 additions and 2105 deletions
@@ -19,14 +19,23 @@ jobs:
        with:
          python-version: ${{ matrix.python-version }}
      - name: Install poetry
-        run: pip install poetry==1.4.2
+        uses: snok/install-poetry@v1
+        with:
+          version: 1.4.2
+          virtualenvs-create: true
+          virtualenvs-in-project: true
+      - name: Load cached venv
+        id: cached-poetry-dependencies
+        uses: actions/cache@v2
+        with:
+          path: .venv
+          key: venv-${{ runner.os }}-${{ hashFiles('**/poetry.lock') }}
      - name: Install dependencies
        run: poetry install --all-extras
+        if: steps.cached-poetry-dependencies.outputs.cache-hit != 'true'
      - name: Lint with ruff
        run: make lint
-      - name: Test with pytest
-        run: make test
-      - name: Generate coverage report
+      - name: Run tests and generate coverage report
        run: make coverage
      - name: Upload coverage reports to Codecov
        uses: codecov/codecov-action@v3
@@ -34,4 +43,3 @@ jobs:
          file: coverage.xml
        env:
          CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
-
@@ -76,7 +76,6 @@ docs/_build/
 target/

 # Jupyter Notebook
-.ipynb_checkpoints

 # IPython
 profile_default/
@@ -165,9 +164,14 @@ cython_debug/

 # Database
 db
+test-db

 .vscode
-/poetry.lock
 .idea/

 .DS_Store
+
+notebooks/*.yaml
+.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:

@@ -38,7 +38,7 @@ lint:
 	poetry run ruff .

 test:
-	poetry run pytest
+	poetry run pytest $(file)

 coverage:
 	poetry run pytest --cov=$(PROJECT_NAME) --cov-report=xml
@@ -1,62 +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 framework to easily create LLM powered bots over any dataset. If you want a javascript version, check out [embedchain-js](https://github.com/embedchain/embedchain/tree/main/embedchain-js)
+<p align="center">
+  <a href="https://pypi.org/project/embedchain/">
+    <img src="https://img.shields.io/pypi/v/embedchain" alt="PyPI">
+  </a>
+  <a href="https://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
-* Code documentation website loader
-* Notion and many more.
-
-You can find the full list of data types on [our documentation](https://docs.embedchain.ai/data-sources/csv).
-
-### Queries
-
-For example, you can use Embedchain to create an Elon Musk bot using the following code:
+For example, you can create an Elon Musk bot using the following code:

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

 # Create a bot instance
 os.environ["OPENAI_API_KEY"] = "YOUR API KEY"
@@ -70,9 +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.
 ```

-## 🤝 Contributing
+You can also try it in your browser with Google Colab:
+
+[![Open in Colab](https://camo.githubusercontent.com/84f0493939e0c4de4e6dbe113251b4bfb5353e57134ffd9fcab6b8714514d4d1/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667)](https://colab.research.google.com/drive/17ON1LPonnXAtLaZEebnOktstB_1cJJmh?usp=sharing)
+
+## 📖 Documentation
+Comprehensive guides and API documentation are available to help you get the most out of Embedchain:
+
+- [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/)
+
+## 🔗 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).
@@ -94,7 +131,7 @@ If you utilize this repository, please consider citing it with:
 ```
@misc{embedchain,
  author = {Taranjeet Singh, Deshraj Yadav},
-  title = {Embedchain: Framework to easily create LLM powered bots over any dataset},
+  title = {Embedchain: Data platform for LLMs - load, index, retrieve, and sync any unstructured data},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
@@ -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
@@ -0,0 +1,19 @@
+app:
+  config:
+    id: azure-openai-app
+
+llm:
+  provider: azure_openai
+  config:
+    model: gpt-35-turbo
+    deployment_name: your_llm_deployment_name
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 1
+    stream: false
+
+embedder:
+  provider: azure_openai
+  config:
+    model: text-embedding-ada-002
+    deployment_name: you_embedding_model_deployment_name
@@ -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
@@ -25,7 +30,7 @@ llm:
 vectordb:
  provider: chroma
  config:
-    collection_name: 'full-stack-app'
+    collection_name: 'my-collection-name'
    dir: db
    allow_reset: true

@@ -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
@@ -1,14 +1,14 @@
 app:
  config:
    id: 'my-app'
-    log_level: 'WARN'
+    log_level: 'WARNING'
    collect_metrics: true
    collection_name: 'my-app'

 llm:
  provider: openai
-  model: 'gpt-3.5-turbo'
  config:
+    model: 'gpt-3.5-turbo'
    temperature: 0.5
    max_tokens: 1000
    top_p: 1
@@ -30,4 +30,4 @@ embedder:
  provider: openai
  config:
    model: 'text-embedding-ada-002'
-    deployment_name: null
+    deployment_name: 'my-app'
@@ -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 +1,6 @@
+vectordb:
+  provider: pinecone
+  config:
+    metric: cosine
+    vector_dimension: 1536
+    collection_name: my-pinecone-index
@@ -0,0 +1,26 @@
+pipeline:
+  config:
+    name: Example pipeline
+    id: pipeline-1  # Make sure that id is different every time you create a new pipeline
+
+vectordb:
+  provider: chroma
+  config:
+    collection_name: pipeline-1
+    dir: db
+    allow_reset: true
+
+llm:
+  provider: gpt4all
+  config:
+    model: 'orca-mini-3b-gguf2-q4_0.gguf'
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 1
+    stream: false
+
+embedding_model:
+  provider: gpt4all
+  config:
+    model: 'all-MiniLM-L6-v2'
+    deployment_name: null
@@ -1,6 +1,6 @@
 llm:
  provider: vertexai
-  model: 'chat-bison'
  config:
+    model: 'chat-bison'
    temperature: 0.5
    top_p: 0.5
@@ -0,0 +1,4 @@
+vectordb:
+  provider: weaviate
+  config:
+    collection_name: my_weaviate_index
@@ -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'.
@@ -1,66 +0,0 @@
---
-title: '🔍 Query configurations'
---
-
-## AppConfig
-
-| option    | description           | type                            | default                |
-|-----------|-----------------------|---------------------------------|------------------------|
-| log_level | log level             | string                          | WARNING                |
-| embedding_fn| embedding function    | chromadb.utils.embedding_functions | \{text-embedding-ada-002\} |
-| db        | vector database (experimental) | BaseVectorDB               | ChromaDB               |
-| collection_name | initial collection name for the database | string             | embedchain_store |
-| collect_metrics | collect anonymous telemetry data to improve embedchain | boolean | true |
-
-
-## AddConfig
-
-|option|description|type|default|
-|---|---|---|---|
-|chunker|chunker config|ChunkerConfig|Default values for chunker depends on the `data_type`. Please refer [ChunkerConfig](#chunker-config)|
-|loader|loader config|LoaderConfig|None|
-
-Yes, you are passing `ChunkerConfig` to `AddConfig`, like so:
-
-```python
-chunker_config = ChunkerConfig(chunk_size=100)
-add_config = AddConfig(chunker=chunker_config)
-app.add("lorem ipsum", config=add_config)
-```
-
-### ChunkerConfig
-
-|option|description|type|default|
-|---|---|---|---|
-|chunk_size|Maximum size of chunks to return|int|Default value for various `data_type` mentioned below|
-|chunk_overlap|Overlap in characters between chunks|int|Default value for various `data_type` mentioned below|
-|length_function|Function that measures the length of given chunks|typing.Callable|Default value for various `data_type` mentioned below|
-
-Default values of chunker config parameters for different `data_type`:
-
-|data_type|chunk_size|chunk_overlap|length_function|
-|---|---|---|---|
-|docx|1000|0|len|
-|text|300|0|len|
-|qna_pair|300|0|len|
-|web_page|500|0|len|
-|pdf_file|1000|0|len|
-|youtube_video|2000|0|len|
-|docs_site|500|50|len|
-|notion|300|0|len|
-
-## BaseLlmConfig
-
-|option|description|type|default|
-|---|---|---|---|
-|number_documents|Absolute number of documents to pull from the database as context.|int|1
-|template|custom template for prompt. If history is used with query, $history has to be included as well.|Template|Template("Use the following pieces of context to answer the query at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer. \$context Query: \$query Helpful Answer:")|
-|model|name of the model used.|string|depends on app type|
-|temperature|Controls the randomness of the model's output. Higher values (closer to 1) make output more random, lower values make it more deterministic.|float|0|
-|max_tokens|Controls how many tokens are used. Exact implementation (whether it counts prompt and/or response) depends on the model.|int|1000|
-|top_p|Controls the diversity of words. Higher values (closer to 1) make word selection more diverse, lower values make words less diverse.|float|1|
-|history|include conversation history from your client or database.|any (recommendation: list[str])|None|
-|stream|control if response is streamed back to the user.|bool|False|
-|deployment_name|t.b.a.|str|None|
-|system_prompt|System prompt string. Unused if none.|str|None|
-|where|filter for context search.|dict|None|
@@ -1,40 +0,0 @@
---
-title: '🧪 Testing'
---
-
-## Methods for testing
-
-### Dry Run
-
-Before you consume valueable tokens, you should make sure that data chunks are properly created and the embedding you have done works and that it's receiving the correct document from the database.
-
- For `query` or `chat` method, you can add this to your script:
-
-```python
-print(naval_chat_bot.query('Can you tell me who Naval Ravikant is?', dry_run=True))
-
-'''
-Use the following pieces of context to answer the query at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.
-        Q: Who is Naval Ravikant?
-A: Naval Ravikant is an Indian-American entrepreneur and investor.
-        Query: Can you tell me who Naval Ravikant is?
-        Helpful Answer:
-'''
-```
-
-_The embedding is confirmed to work as expected. It returns the right document, even if the question is asked slightly different. No prompt tokens have been consumed._
-
-The dry run will still consume tokens to embed your query, but it is only **~1/15 of the prompt.**
-
-
- For `add` method, you can add this to your script:
-
-```python
-print(naval_chat_bot.add('https://navalmanack.s3.amazonaws.com/Eric-Jorgenson_The-Almanack-of-Naval-Ravikant_Final.pdf', dry_run=True))
-
-'''
-{'chunks': ['THE ALMANACK OF NAVAL RAVIKANT', 'GETTING RICH IS NOT JUST ABOUT LUCK;', 'HAPPINESS IS NOT JUST A TRAIT WE ARE'], 'metadata': [{'source': 'C:\\Users\\Dev\\AppData\\Local\\Temp\\tmp3g5mjoiz\\tmp.pdf', 'page': 0, 'url': 'https://navalmanack.s3.amazonaws.com/Eric-Jorgenson_The-Almanack-of-Naval-Ravikant_Final.pdf', 'data_type': 'pdf_file'}, {'source': 'C:\\Users\\Dev\\AppData\\Local\\Temp\\tmp3g5mjoiz\\tmp.pdf', 'page': 2, 'url': 'https://navalmanack.s3.amazonaws.com/Eric-Jorgenson_The-Almanack-of-Naval-Ravikant_Final.pdf', 'data_type': 'pdf_file'}, {'source': 'C:\\Users\\Dev\\AppData\\Local\\Temp\\tmp3g5mjoiz\\tmp.pdf', 'page': 2, 'url': 'https://navalmanack.s3.amazonaws.com/Eric-Jorgenson_The-Almanack-of-Naval-Ravikant_Final.pdf', 'data_type': 'pdf_file'}], 'count': 7358, 'type': <DataType.PDF_FILE: 'pdf_file'>}
-
-# less items to show for readability
-'''
-```
@@ -1,118 +0,0 @@
---
-title: '💾 Vector Database'
---
-
-We support `Chroma`, `Elasticsearch` and `OpenSearch` as vector databases.
-`Chroma` is used as a default database.
-
-## Elasticsearch
-
-### Minimal Example
-
-In order to use `Elasticsearch` as vector database we need to use App type `CustomApp`.
-
-1. Set the environment variables in a `.env` file.
-```
-OPENAI_API_KEY=sk-SECRETKEY
-ELASTICSEARCH_API_KEY=SECRETKEY==
-ELASTICSEARCH_URL=https://secret-domain.europe-west3.gcp.cloud.es.io:443
-```
-Please note that the key needs certain privileges. For testing you can just toggle off `restrict privileges` under `/app/management/security/api_keys/` in your web interface.
-
-2. Load the app
-```python
-from embedchain import CustomApp
-from embedchain.embedder.openai import OpenAIEmbedder
-from embedchain.llm.openai import OpenAILlm
-from embedchain.vectordb.elasticsearch import ElasticsearchDB
-
-es_app = CustomApp(
-    llm=OpenAILlm(),
-    embedder=OpenAIEmbedder(),
-    db=ElasticsearchDB(),
-)
-```
-
-### More custom settings
-
-You can get a URL for elasticsearch in the cloud, or run it locally. 
-The following example shows you how to configure embedchain to work with a locally running elasticsearch.
-
-Instead of using an API key, we use http login credentials. The localhost url can be defined in .env or in the config.
-
-```python
-import os
-
-from embedchain import CustomApp
-from embedchain.config import CustomAppConfig, ElasticsearchDBConfig
-from embedchain.embedder.openai import OpenAIEmbedder
-from embedchain.llm.openai import OpenAILlm
-from embedchain.vectordb.elasticsearch import ElasticsearchDB
-
-es_config = ElasticsearchDBConfig(
-    # elasticsearch url or list of nodes url with different hosts and ports.
-    es_url='https://localhost:9200',
-    # pass named parameters supported by Python Elasticsearch client
-    http_auth=("elastic", "secret"),
-    ca_certs="~/binaries/elasticsearch-8.7.0/config/certs/http_ca.crt" # your cert path
-    # verify_certs=False # Alternative, if you aren't using certs
-) # pass named parameters supported by elasticsearch-py
-
-es_app = CustomApp(
-    config=CustomAppConfig(log_level="INFO"),
-    llm=OpenAILlm(),
-    embedder=OpenAIEmbedder(),
-    db=ElasticsearchDB(config=es_config),
-)
-```
-3. This should log your connection details to the console.
-4. Alternatively to a URL, you `ElasticsearchDBConfig` accepts `es_url` as a list of nodes url with different hosts and ports.
-5. Additionally we can pass named parameters supported by Python Elasticsearch client.
-
-
-## OpenSearch 🔍
-
-To use OpenSearch as a vector database with a CustomApp, follow these simple steps:
-
-1. Set the `OPENAI_API_KEY` environment variable:
-
-```
-OPENAI_API_KEY=sk-xxxx
-```
-
-2. Define the OpenSearch configuration in your Python code:
-
-```python
-from embedchain import CustomApp
-from embedchain.config import OpenSearchDBConfig
-from embedchain.embedder.openai import OpenAIEmbedder
-from embedchain.llm.openai import OpenAILlm
-from embedchain.vectordb.opensearch import OpenSearchDB
-
-opensearch_url = "https://localhost:9200"
-http_auth = ("username", "password")
-
-db_config = OpenSearchDBConfig(
-    opensearch_url=opensearch_url,
-    http_auth=http_auth,
-    collection_name="embedchain-app",
-    use_ssl=True,
-    timeout=30,
-)
-db = OpenSearchDB(config=db_config)
-```
-
-2. Instantiate the app and add data:
-
-```python
-app = CustomApp(llm=OpenAILlm(), embedder=OpenAIEmbedder(), db=db)
-app.add("https://en.wikipedia.org/wiki/Elon_Musk")
-app.add("https://www.forbes.com/profile/elon-musk")
-app.add("https://www.britannica.com/biography/Elon-Musk")
-```
-
-3. You're all set! Start querying using the following command:
-
-```python
-app.query("What is the net worth of Elon Musk?")
-```
@@ -4,15 +4,25 @@ title: 🤝 Connect with Us

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

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

 We look forward to connecting with you and seeing how we can create amazing things together!
@@ -8,6 +8,7 @@ Embedchain supports several embedding models from the following providers:

 <CardGroup cols={4}>
  <Card title="OpenAI" href="#openai"></Card>
+  <Card title="Azure OpenAI" href="#azure-openai"></Card>
  <Card title="GPT4All" href="#gpt4all"></Card>
  <Card title="Hugging Face" href="#hugging-face"></Card>
  <Card title="Vertex AI" href="#vertex-ai"></Card>
@@ -23,18 +24,18 @@ 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'

-# load embedding model configuration from openai.yaml file
-app = App.from_config(yaml_path="openai.yaml")
+# load embedding model configuration from config.yaml file
+app = App.from_config(yaml_path="config.yaml")

 app.add("https://en.wikipedia.org/wiki/OpenAI")
 app.query("What is OpenAI?")
 ```

-```yaml openai.yaml
+```yaml config.yaml
 embedder:
  provider: openai
  config:
@@ -43,6 +44,45 @@ embedder:

 </CodeGroup>

+## Azure OpenAI
+
+To use Azure OpenAI embedding model, you have to set some of the azure openai related environment variables as given in the code block below:
+
+<CodeGroup>
+
+```python main.py
+import os
+from embedchain import Pipeline as App
+
+os.environ["OPENAI_API_TYPE"] = "azure"
+os.environ["OPENAI_API_BASE"] = "https://xxx.openai.azure.com/"
+os.environ["OPENAI_API_KEY"] = "xxx"
+os.environ["OPENAI_API_VERSION"] = "xxx"
+
+app = App.from_config(yaml_path="config.yaml")
+```
+
+```yaml config.yaml
+llm:
+  provider: azure_openai
+  config:
+    model: gpt-35-turbo
+    deployment_name: your_llm_deployment_name
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 1
+    stream: false
+
+embedder:
+  provider: azure_openai
+  config:
+    model: text-embedding-ada-002
+    deployment_name: you_embedding_model_deployment_name
+```
+</CodeGroup>
+
+You can find the list of models and deployment name on the [Azure OpenAI Platform](https://oai.azure.com/portal).
+
 ## GPT4ALL

 GPT4All supports generating high quality embeddings of arbitrary length documents of text using a CPU optimized contrastively trained Sentence Transformer.
@@ -50,17 +90,17 @@ 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 gpt4all.yaml file
-app = App.from_config(yaml_path="gpt4all.yaml")
+# load embedding model configuration from config.yaml file
+app = App.from_config(yaml_path="config.yaml")
 ```

-```yaml gpt4all.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
@@ -68,8 +108,6 @@ llm:

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

 </CodeGroup>
@@ -81,17 +119,17 @@ 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 huggingface.yaml file
-app = App.from_config(yaml_path="huggingface.yaml")
+# load embedding model configuration from config.yaml file
+app = App.from_config(yaml_path="config.yaml")
 ```

-```yaml huggingface.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
@@ -112,17 +150,17 @@ 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 vertexai.yaml file
-app = App.from_config(yaml_path="vertexai.yaml")
+# load embedding model configuration from config.yaml file
+app = App.from_config(yaml_path="config.yaml")
 ```

-```yaml vertexai.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'

@@ -35,25 +35,25 @@ app.add("https://en.wikipedia.org/wiki/OpenAI")
 app.query("What is OpenAI?")
 ```

-If you are looking to configure the different parameters of the LLM, you can do so by loading the app using a [yaml config](https://github.com/embedchain/embedchain/blob/main/embedchain/yaml/chroma.yaml) file. 
+If you are looking to configure the different parameters of the LLM, you can do so by loading the app using a [yaml config](https://github.com/embedchain/embedchain/blob/main/configs/chroma.yaml) file.

 <CodeGroup>

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

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

-# load llm configuration from openai.yaml file
-app = App.from_config(yaml_path="openai.yaml")
+# load llm configuration from config.yaml file
+app = App.from_config(yaml_path="config.yaml")
 ```

-```yaml openai.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
@@ -65,7 +65,42 @@ llm:

 ## Azure OpenAI

-_Coming soon_
+To use Azure OpenAI model, you have to set some of the azure openai related environment variables as given in the code block below:
+
+<CodeGroup>
+
+```python main.py
+import os
+from embedchain import Pipeline as App
+
+os.environ["OPENAI_API_TYPE"] = "azure"
+os.environ["OPENAI_API_BASE"] = "https://xxx.openai.azure.com/"
+os.environ["OPENAI_API_KEY"] = "xxx"
+os.environ["OPENAI_API_VERSION"] = "xxx"
+
+app = App.from_config(yaml_path="config.yaml")
+```
+
+```yaml config.yaml
+llm:
+  provider: azure_openai
+  config:
+    model: gpt-35-turbo
+    deployment_name: your_llm_deployment_name
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 1
+    stream: false
+
+embedder:
+  provider: azure_openai
+  config:
+    model: text-embedding-ada-002
+    deployment_name: you_embedding_model_deployment_name
+```
+</CodeGroup>
+
+You can find the list of models and deployment name on the [Azure OpenAI Platform](https://oai.azure.com/portal).

 ## Anthropic

@@ -75,19 +110,19 @@ 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"

-# load llm configuration from anthropic.yaml file
-app = App.from_config(yaml_path="anthropic.yaml")
+# load llm configuration from config.yaml file
+app = App.from_config(yaml_path="config.yaml")
 ```

-```yaml anthropic.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
@@ -96,15 +131,14 @@ llm:

 </CodeGroup>

-<br />
-
-<Tip>
-You may also have to set the `OPENAI_API_KEY` if you use the OpenAI's embedding model.
-</Tip>
-
-
 ## Cohere

+Install related dependencies using the following command:
+
+```bash
+pip install --upgrade 'embedchain[cohere]'
+```
+
 Set the `COHERE_API_KEY` as environment variable which you can find on their [Account settings page](https://dashboard.cohere.com/api-keys).

 Once you have the API key, you are all set to use it with Embedchain.
@@ -113,19 +147,19 @@ 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"

-# load llm configuration from cohere.yaml file
-app = App.from_config(yaml_path="cohere.yaml")
+# load llm configuration from config.yaml file
+app = App.from_config(yaml_path="config.yaml")
 ```

-```yaml cohere.yaml
+```yaml config.yaml
 llm:
  provider: cohere
-  model: large
  config:
+    model: large
    temperature: 0.5
    max_tokens: 1000
    top_p: 1
@@ -135,22 +169,28 @@ llm:

 ## GPT4ALL

+Install related dependencies using the following command:
+
+```bash
+pip install --upgrade 'embedchain[opensource]'
+```
+
 GPT4all is a free-to-use, locally running, privacy-aware chatbot. No GPU or internet required. You can use this with Embedchain using the following code:

 <CodeGroup>

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

-# load llm configuration from gpt4all.yaml file
-app = App.from_config(yaml_path="gpt4all.yaml")
+# load llm configuration from config.yaml file
+app = App.from_config(yaml_path="config.yaml")
 ```

-```yaml gpt4all.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
@@ -158,8 +198,6 @@ llm:

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

@@ -174,14 +212,14 @@ 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 jina.yaml file
-app = App.from_config(yaml_path="jina.yaml")
+# load llm configuration from config.yaml file
+app = App.from_config(yaml_path="config.yaml")
 ```

-```yaml jina.yaml
+```yaml config.yaml
 llm:
  provider: jina
  config:
@@ -195,6 +233,13 @@ llm:

 ## Hugging Face

+
+Install related dependencies using the following command:
+
+```bash
+pip install --upgrade 'embedchain[huggingface_hub]'
+```
+
 First, set `HUGGINGFACE_ACCESS_TOKEN` in environment variable which you can obtain from [their platform](https://huggingface.co/settings/tokens).

 Once you have the token, load the app using the config yaml file:
@@ -203,19 +248,19 @@ 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"

-# load llm configuration from huggingface.yaml file
-app = App.from_config(yaml_path="huggingface.yaml")
+# load llm configuration from config.yaml file
+app = App.from_config(yaml_path="config.yaml")
 ```

-```yaml huggingface.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
@@ -233,19 +278,19 @@ 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"

-# load llm configuration from llama2.yaml file
-app = App.from_config(yaml_path="llama2.yaml")
+# load llm configuration from config.yaml file
+app = App.from_config(yaml_path="config.yaml")
 ```

-```yaml llama2.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
@@ -260,17 +305,17 @@ 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 vertexai.yaml file
-app = App.from_config(yaml_path="vertexai.yaml")
+# load llm configuration from config.yaml file
+app = App.from_config(yaml_path="config.yaml")
 ```

-```yaml vertexai.yaml
+```yaml config.yaml
 llm:
  provider: vertexai
-  model: 'chat-bison'
  config:
+    model: 'chat-bison'
    temperature: 0.5
    top_p: 0.5
 ```
@@ -22,13 +22,13 @@ 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="chroma-config-1.yaml")
+app = App.from_config(yaml_path="config1.yaml")
 ```

-```yaml chroma-config-1.yaml
+```yaml config1.yaml
 vectordb:
  provider: chroma
  config:
@@ -37,7 +37,7 @@ vectordb:
    allow_reset: true
 ```

-```yaml chroma-config-2.yaml
+```yaml config2.yaml
 vectordb:
  provider: chroma
  config:
@@ -52,16 +52,22 @@ vectordb:

 ## Elasticsearch

+Install related dependencies using the following command:
+
+```bash
+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="elasticsearch.yaml")
+app = App.from_config(yaml_path="config.yaml")
 ```

-```yaml elasticsearch.yaml
+```yaml config.yaml
 vectordb:
  provider: elasticsearch
  config:
@@ -74,16 +80,22 @@ vectordb:

 ## OpenSearch

+Install related dependencies using the following command:
+
+```bash
+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="opensearch.yaml")
+app = App.from_config(yaml_path="config.yaml")
 ```

-```yaml opensearch.yaml
+```yaml config.yaml
 vectordb:
  provider: opensearch
  config:
@@ -101,20 +113,32 @@ vectordb:

 ## Zilliz

+Install related dependencies using the following command:
+
+```bash
+pip install --upgrade 'embedchain[milvus]'
+```
+
+Set the Zilliz environment variables `ZILLIZ_CLOUD_URI` and `ZILLIZ_CLOUD_TOKEN` which you can find it on their [cloud platform](https://cloud.zilliz.com/).
+
 <CodeGroup>

 ```python main.py
-from embedchain import App
+import os
+from embedchain import Pipeline as App
+
+os.environ['ZILLIZ_CLOUD_URI'] = 'https://xxx.zillizcloud.com'
+os.environ['ZILLIZ_CLOUD_TOKEN'] = 'xxx'

 # load zilliz configuration from yaml file
-app = App.from_config(yaml_path="zilliz.yaml")
+app = App.from_config(yaml_path="config.yaml")
 ```

-```yaml zilliz.yaml
+```yaml 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
@@ -129,14 +153,72 @@ _Coming soon_

 ## Pinecone

-_Coming soon_
+Install pinecone related dependencies using the following command:
+
+```bash
+pip install --upgrade 'embedchain[pinecone]'
+```
+
+In order to use Pinecone as vector database, set the environment variables `PINECONE_API_KEY` and `PINECONE_ENV` which you can find on [Pinecone dashboard](https://app.pinecone.io/).
+
+<CodeGroup>
+
+```python main.py
+from embedchain import Pipeline as App
+
+# load pinecone configuration from yaml file
+app = App.from_config(yaml_path="config.yaml")
+```
+
+```yaml config.yaml
+vectordb:
+  provider: pinecone
+  config:
+    metric: cosine
+    vector_dimension: 1536
+    collection_name: my-pinecone-index
+```
+
+</CodeGroup>

 ## Qdrant

-_Coming soon_
+In order to use Qdrant as a vector database, set the environment variables `QDRANT_URL` and `QDRANT_API_KEY` which you can find on [Qdrant Dashboard](https://cloud.qdrant.io/).
+
+<CodeGroup>
+```python main.py
+from embedchain import Pipeline as App
+
+# load qdrant configuration from yaml file
+app = App.from_config(yaml_path="config.yaml")
+```
+
+```yaml config.yaml
+vectordb:
+  provider: qdrant
+  config:
+    collection_name: my_qdrant_index
+```
+</CodeGroup>

 ## Weaviate

-_Coming soon_
+In order to use Weaviate as a vector database, set the environment variables `WEAVIATE_ENDPOINT` and `WEAVIATE_API_KEY` which you can find on [Weaviate dashboard](https://console.weaviate.cloud/dashboard).
+
+<CodeGroup>
+```python main.py
+from embedchain import Pipeline as App
+
+# load weaviate configuration from yaml file
+app = App.from_config(yaml_path="config.yaml")
+```
+
+```yaml config.yaml
+vectordb:
+  provider: weaviate
+  config:
+    collection_name: my_weaviate_index
+```
+</CodeGroup>

 <Snippet file="missing-vector-db-tip.mdx" />
@@ -35,12 +35,9 @@ embedchain is built on the following stack:

 ## Team

-### Author
+### Authors

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

 ### Citation
@@ -49,8 +46,8 @@ If you utilize this repository, please consider citing it with:

 ```
@misc{embedchain,
-  author = {Taranjeet Singh},
-  title = {Embechain: Framework to easily create LLM powered bots over any dataset},
+  author = {Taranjeet Singh, Deshraj Yadav},
+  title = {Embechain: Data platform for LLMs - Load, index, retrieve and sync any unstructured data},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
@@ -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.
@@ -1,21 +1,46 @@
 ---
-title: '📱 Telegram Bot'
+title: "📱 Telegram Bot"
 ---

 ### 🖼️ Template Setup

- Fork [this](https://replit.com/@taranjeetio/EC-Telegram-Bot-Template?v=1#README.md) replit template.
- Set your `OPENAI_API_KEY` in Secrets.
 - Open the Telegram app and search for the `BotFather` user.
 - Start a chat with BotFather and use the `/newbot` command to create a new bot.
 - Follow the instructions to choose a name and username for your bot.
 - Once the bot is created, BotFather will provide you with a unique token for your bot.
- Set this token as `TELEGRAM_BOT_TOKEN` in Secrets.
+
+<Tabs>
+    <Tab title="docker">
+        ```bash
+        docker run --name telegram-bot -e OPENAI_API_KEY=sk-xxx -e TELEGRAM_BOT_TOKEN=xxx -p 8000:8000 embedchain/telegram-bot
+        ```
+
+    <Note>
+    If you wish to use **Docker**, you would need to host your bot on a server.
+    You can use [ngrok](https://ngrok.com/) to expose your localhost to the
+    internet and then set the webhook using the ngrok URL.
+    </Note>
+
+    </Tab>
+    <Tab title="replit">
+    <Card>
+        Fork <ins>**[this](https://replit.com/@taranjeetio/EC-Telegram-Bot-Template?v=1#README.md)**</ins> replit template.
+    </Card>
+
+    - Set your `OPENAI_API_KEY` in Secrets.
+    - Set the unique token as `TELEGRAM_BOT_TOKEN` in Secrets.
+
+    </Tab>
+
+</Tabs>
+
 - Click on `Run` in the replit container and a URL will get generated for your bot.
 - Now set your webhook by running the following link in your browser:
+
 ```url
 https://api.telegram.org/bot<Your_Telegram_Bot_Token>/setWebhook?url=<Replit_Generated_URL>
 ```
+
 - When you get a successful response in your browser, your bot is ready to be used.

 ### 🚀 Usage Instructions
@@ -12,10 +12,19 @@ pip install --upgrade embedchain

 2. Launch your WhatsApp bot:

+<Tabs>
+    <Tab title="docker">
+        ```bash
+        docker run --name whatsapp-bot -e OPENAI_API_KEY=sk-xxx -p 8000:8000 embedchain/whatsapp-bot
+        ```
+    </Tab>
+    <Tab title="python">
+        ```bash
+        python -m embedchain.bots.whatsapp --port 5000
+        ```
+    </Tab>
+</Tabs>

-```bash
-python -m embedchain.bots.whatsapp --port 5000
-```

 If your bot needs to be accessible online, use your machine's public IP or DNS. Otherwise, employ a proxy server like [ngrok](https://ngrok.com/) to make your local bot accessible.

@@ -0,0 +1,123 @@
+---
+title: 🔎 Examples
+description: 'Collection of Google colab notebook and Replit links for users'
+---
+
+<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
@@ -1,35 +1,108 @@
 ---
 title: 📚 Introduction
-description: '📝 Embedchain is a framework to easily create LLM powered apps on your data.'
+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'
+#     }
+# ]
+```

-# Ask questions with specific context
-naval_bot.query("What unique capacity does Naval argue humans possess when it comes to understanding explanations or concepts?", where={'chapter': 'philosophy'})
+## ❓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 +1,95 @@
+---
+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>
+
+<ResponseField name="telemetry" type="boolean" default="True">
+  Anonymous telemetry (doesn't collect any user information or user's files). Used to improve the Embedchain package utilization. Default is `True`.
+</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>
+
@@ -3,32 +3,53 @@ title: '🚀 Quickstart'
 description: '💡 Start building LLM powered apps under 30 seconds'
 ---

+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. 
+
 Install embedchain python package:

 ```bash
 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
-# Embed online resources
-elon_bot.add("https://en.wikipedia.org/wiki/Elon_Musk")
-elon_bot.add("https://www.forbes.com/profile/elon-musk")
+# Add different data sources
+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.
+# 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>
@@ -37,16 +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()

-# Embed online resources
-elon_bot.add("https://en.wikipedia.org/wiki/Elon_Musk")
-elon_bot.add("https://www.forbes.com/profile/elon-musk")
+# Add different data sources
+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.
+# 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"]
+      "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 +2,9 @@ from typing import Optional

 import yaml

-from embedchain.config import AppConfig, BaseEmbedderConfig, BaseLlmConfig
+from embedchain.client import Client
+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 +13,7 @@ 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 +40,7 @@ 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.
@@ -64,6 +68,9 @@ class App(EmbedChain):
        :type system_prompt: Optional[str], optional
        :raises TypeError: LLM, database or embedder or their config is not a valid class instance.
        """
+        # Setup user directory if it doesn't exist already
+        Client.setup_dir()
+
        # Type check configs
        if config and not isinstance(config, AppConfig):
            raise TypeError(
@@ -96,6 +103,9 @@ 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 +137,16 @@ 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 +156,6 @@ 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,
-        )
@@ -1,94 +0,0 @@
-from string import Template
-
-from embedchain.apps.app import App
-from embedchain.apps.open_source_app import OpenSourceApp
-from embedchain.config import BaseLlmConfig
-from embedchain.config.apps.base_app_config import BaseAppConfig
-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: BaseAppConfig instance to load as configuration.
-    """
-
-    def __init__(self, person: str, config: BaseAppConfig = 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: BaseAppConfig, 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, where=None)
-        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)
@@ -1,7 +1,7 @@
 from typing import Any

-from embedchain import App
-from embedchain.config import AddConfig, AppConfig, BaseLlmConfig
+from embedchain import Pipeline as App
+from embedchain.config import AddConfig, BaseLlmConfig, PipelineConfig
 from embedchain.embedder.openai import OpenAIEmbedder
 from embedchain.helper.json_serializable import (JSONSerializable,
                                                 register_deserializable)
@@ -12,7 +12,7 @@ from embedchain.vectordb.chroma import ChromaDB
@register_deserializable
 class BaseBot(JSONSerializable):
    def __init__(self):
-        self.app = App(config=AppConfig(), llm=OpenAILlm(), db=ChromaDB(), embedder=OpenAIEmbedder())
+        self.app = App(config=PipelineConfig(), llm=OpenAILlm(), db=ChromaDB(), embedding_model=OpenAIEmbedder())

    def add(self, data: Any, config: AddConfig = None):
        """
@@ -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 +1,22 @@
+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 JSONChunker(BaseChunker):
+    """Chunker for json."""
+
+    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 +1,22 @@
+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 UnstructuredFileChunker(BaseChunker):
+    """Chunker for Unstructured file."""
+
+    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.constants 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
@@ -0,0 +1,20 @@
+from typing import Dict, Optional
+
+from embedchain.config.vectordb.base import BaseVectorDbConfig
+from embedchain.helper.json_serializable import register_deserializable
+
+
+@register_deserializable
+class PineconeDBConfig(BaseVectorDbConfig):
+    def __init__(
+        self,
+        collection_name: Optional[str] = None,
+        dir: Optional[str] = None,
+        vector_dimension: int = 1536,
+        metric: Optional[str] = "cosine",
+        **extra_params: Dict[str, any],
+    ):
+        self.metric = metric
+        self.vector_dimension = vector_dimension
+        self.extra_params = extra_params
+        super().__init__(collection_name=collection_name, dir=dir)
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Deshraj YadavandGitHub	1364975396	[Feature] Add support for AIAssistant (#938 )	2023-11-10 16:47:34 -08:00
Deven Patel GitHub Deven Patel	deaa7f50f8	[Bug Fix] fix chromadb where clause for query and delete (#937 ) Co-authored-by: Deven Patel <deven298@yahoo.com>	2023-11-10 16:04:25 -08:00
Sidharth MohantyandGitHub	744ab5156f	[Bug fix] missing dir on first init for App (#934 )	2023-11-10 10:15:04 -08:00
Sidharth MohantyandGitHub	c45413969a	[refactor] Use pipeline for bots instead of App (#936 )	2023-11-10 10:13:21 -08:00
Deshraj YadavandGitHub	b314e5e080	[bug] Fix issue of missing user directory on first init (#931 )	2023-11-09 22:29:41 -08:00
Deshraj YadavandGitHub	17129e2eaa	[Improvement] Add support for reloading history for an existing app (#930 )	2023-11-09 15:17:51 -08:00
Deven Patel GitHub Deven Patel	654fd8d74c	[Improvement] Use SQLite for chat memory (#910 ) Co-authored-by: Deven Patel <deven298@yahoo.com>	2023-11-09 13:56:28 -08:00
Sidharth MohantyandGitHub	9d3568ef75	Update package version to 0.1.3 (#928 )	2023-11-09 11:33:18 -08:00
Sidharth MohantyandGitHub	14712cac88	Deploy remaining bots and fix schema validation (#927 )	2023-11-09 10:44:47 -08:00
Deshraj YadavandGitHub	0d568c758b	[Feat] Add anonymous telemetry to assistant (#924 )	2023-11-09 02:07:47 -08:00
Deshraj YadavandGitHub	7c6b88c7c5	[Docs] Update docs and improve assistant api (#923 )	2023-11-09 01:16:19 -08:00
Deshraj YadavandGitHub	32c93be46e	[chore] update poetry.lock file (#922 )	2023-11-09 00:59:29 -08:00
Deven Patel GitHub 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 YadavandGitHub	f7dd65a3de	[Feature] Add support for OpenAI assistants and support openai version >=1.0.0 (#921 )	2023-11-08 22:49:03 -08:00
Sidharth MohantyandGitHub	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 MohantyandGitHub	2b8b6d3ea9	Chunker config docs (#913 )	2023-11-08 10:25:45 -08:00
Sidharth MohantyandGitHub	936c7e389f	Deploy Full stack docker image (#914 )	2023-11-08 10:25:12 -08:00
Sidharth MohantyandGitHub	6864b4207b	Dockerize discord bot and update docs to run the bot correctly (#919 )	2023-11-08 10:24:57 -08:00
Deshraj YadavandGitHub	98eb5b54be	[Docs] Update developer documentation (#916 )	2023-11-07 19:07:47 -08:00
Deshraj YadavandGitHub	3332e6e236	[Docs] Update README (#915 )	2023-11-07 18:12:27 -08:00
Deven Patel GitHub 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 MohantyandGitHub	a1de238716	Introduce chunker config in yaml config (#907 )	2023-11-06 09:43:15 -08:00
Deshraj YadavandGitHub	f0d112254b	[version] bump package version and minor cleanup (#909 )	2023-11-05 16:34:36 -08:00
Sidharth MohantyandGitHub	830a7397ef	Add yaml config validation (#890 )	2023-11-04 22:23:55 -07:00
Deven Patel GitHub Deven Patel	5428765329	Beautify JSON docs (#906 ) Co-authored-by: Deven Patel <deven298@yahoo.com>	2023-11-03 13:52:33 -07:00
Sidharth MohantyandGitHub	23c912f2b7	Improve getting started page by adding steps (#904 )	2023-11-03 13:40:40 -07:00
Sidharth MohantyandGitHub	9c4b023297	Use either `embedder` or `embedding_model` as YAML key (#905 )	2023-11-03 13:40:16 -07:00
Deven Patel GitHub 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 MohantyandGitHub	e2546a653d	[chore] fix rest api docs and other minor fixes (#902 )	2023-11-03 09:40:48 -07:00
Deshraj YadavandGitHub	4b8cada873	[REST API] Change docker image name and update docs (#901 )	2023-11-03 01:08:27 -07:00
Deshraj YadavandGitHub	fa3ca1d08a	[REST API] Incorporate changes related to REST API docs (#900 )	2023-11-03 00:42:55 -07:00
Sidharth MohantyandGitHub	8dd5cb9602	Add `rest-api` example (#889 )	2023-11-03 00:32:51 -07:00
Deven Patel GitHub 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 GitHub 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 PatelandGitHub	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 YadavandGitHub	5022c1ae29	[misc] update poetry.lock file (#891 )	2023-11-01 11:35:44 -07:00
Deshraj YadavandGitHub	6ced756a6b	[misc] add `json` extra in pyproject.toml file (#886 )	2023-10-31 23:55:25 -07:00
Deshraj YadavandGitHub	b17268db50	[misc] remove jq as a dependency (#885 )	2023-10-31 21:02:19 -07:00
Deshraj YadavandGitHub	476da37009	[version] bump package version to v0.0.87 (#883 )	2023-10-31 12:40:02 -07:00
Deshraj YadavandGitHub	455f059c6f	[Telemetry] Update anonymous telemetry API key (#882 )	2023-10-31 12:34:54 -07:00
Deven Patel GitHub 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 GitHub 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 YadavandGitHub	30228f7f8e	[version] Update langchain to v0.0.303 (#875 )	2023-10-30 15:02:38 -07:00
Sidharth MohantyandGitHub	e15ef79ca9	Lazy load loaders and chunkers (#872 )	2023-10-30 11:20:38 -07:00
Deshraj YadavandGitHub	bc012a7518	[Docs] Update embedchain docs and analytics (#871 )	2023-10-30 00:38:35 -07:00
Sidharth MohantyandGitHub	3b4409cfad	Update notebooks to work with the latest version (#870 )	2023-10-29 23:06:43 -07:00
Deshraj YadavandGitHub	d3726134b2	[Docs] Update docs and minor improvements in search API (#869 )	2023-10-29 16:50:14 -07:00
anujshandillyaandGitHub	5acb7f1c55	[fix]: updated twitter logo to new X (#868 )	2023-10-29 14:37:32 -07:00
Deshraj YadavandGitHub	81336668b3	[Feature]: Add posthog anonymous telemetry and update docs (#867 )	2023-10-29 01:20:21 -07:00
Deshraj YadavandGitHub	35c2b83015	[version] Update openai version to 0.28.0 (#866 )	2023-10-28 19:18:57 -07:00
Deshraj YadavandGitHub	cc1ee1deaa	Fix dependencies in base package (#863 )	2023-10-27 21:35:48 -07:00
Deshraj YadavandGitHub	29bd038579	[Bug fix] Fix missing dependency issue with gmail (#862 )	2023-10-27 20:02:03 -07:00
Deshraj YadavandGitHub	f6c4f86986	[Pipelines] Improvements in pipelines feature (#861 )	2023-10-27 18:42:46 -07:00
Deven PatelandGitHub	68183e9dce	[Feature] Gmail Loader (#841 )	2023-10-27 18:05:08 -07:00
Deshraj YadavandGitHub	78ec91a3a9	[Bug fix] Fix sqlite related issue with api server example (#857 )	2023-10-26 21:54:45 -07:00
Deven PatelandGitHub	c95d458e52	Add tests for gpt4all llm (#852 )	2023-10-26 20:41:28 -07:00
Deshraj YadavandGitHub	191ae3ec1e	Update README (#855 )	2023-10-26 11:11:05 -07:00
Deshraj YadavandGitHub	ab9598d00a	Update version to v0.0.77 (#851 )	2023-10-26 09:51:51 -07:00
Deven PatelandGitHub	0f8a2e624a	[Improvement] Add support for gpt4all through langchain (#838 )	2023-10-25 22:25:00 -07:00
Deven PatelandGitHub	d77e8da3f3	[Feature] Update `db.query` to return source of context (#831 )	2023-10-25 22:20:32 -07:00
Sidharth MohantyandGitHub	a27eeb3255	[Bug fix] import App shouldn't throw other llm deps errors (#837 )	2023-10-25 20:48:53 -07:00
Deshraj YadavandGitHub	413ccb83e6	[Bug fix] Fix issues related to creating pipelines (#850 )	2023-10-25 20:26:58 -07:00
Deven PatelandGitHub	797bb567c6	[feat]: Add openapi spec data loader (#818 )	2023-10-25 14:19:13 -07:00
Deshraj YadavandGitHub	f2a5dc40ee	Update package version to v0.0.76 (#849 )	2023-10-25 13:40:00 -07:00
Deshraj YadavandGitHub	3979480532	[Feature] Add support for deploying local pipelines to Embedchain platform (#847 )	2023-10-25 13:36:24 -07:00
AbhineshandGitHub	76f1993e7a	Update CONTRIBUTING.md (#845 )	2023-10-25 13:31:39 -07:00
Deshraj YadavandGitHub	d783fa2b89	[chore]: Add poetry lock file (#848 )	2023-10-25 12:53:06 -07:00
Sidharth MohantyandGitHub	bbce18caac	[Bug fix] `reset()` erases everything from db (#844 )	2023-10-25 10:22:20 -07:00
Deshraj YadavandGitHub	3ce2d8a656	Bump version to v0.0.75 (#836 )	2023-10-19 21:06:49 -07:00
Deshraj YadavandGitHub	a5c86a2f5c	[Bugfix]: Fix issue of context overspilling into other apps (#835 )	2023-10-19 17:46:33 -07:00
Deshraj YadavandGitHub	d18e533adf	[Feature] Setup base for creating pipelines in embedchain (#834 )	2023-10-19 17:46:15 -07:00
Sidharth MohantyandGitHub	2b881aaad0	Cache dependencies in CI (#832 )	2023-10-19 13:59:02 -07:00
Sidharth MohantyandGitHub	39cc07608f	[chore] Update repl links to launch main.py directly (#830 )	2023-10-19 13:53:07 -07:00
Sidharth MohantyandGitHub	9894cfcced	[docs] add links to example repositories (#833 )	2023-10-19 13:51:08 -07:00
Sidharth MohantyandGitHub	b5d80be037	Remove person_app, open_source app, llama2_app with their configs (#829 )	2023-10-19 01:13:52 -07:00
Sidharth MohantyandGitHub	b7870fbd9b	[Bug fix] Table content alignment in example docs table (#828 )	2023-10-18 21:38:31 -07:00
Sidharth MohantyandGitHub	36e6d486fc	[chore] Remove `image.jpg` produced by tests and add cleanup in test (#827 )	2023-10-18 21:38:00 -07:00
Sidharth MohantyandGitHub	2d5dc84f1a	[chore] Update LLM YAML config docs (#826 )	2023-10-18 21:37:45 -07:00
Deshraj YadavandGitHub	b47405e1bd	Update version to v0.0.74 (#825 )	2023-10-18 17:12:51 -07:00
Muhammad MuzammilandGitHub	8b64deab40	[Feature]: Unstructured File Loader Support - USF (#815 )	2023-10-18 16:43:41 -07:00
Rupesh BansalandGitHub	c8846e0e93	[Feature] Add Qdrant support (#822 )	2023-10-18 14:27:57 -07:00
Deven PatelandGitHub	7641cba01d	[Feature] JSON data loader support (#816 )	2023-10-18 13:53:15 -07:00
Sidharth MohantyandGitHub	4dc1785ef1	[docs] Examples table with notebooks and replit links (#823 )	2023-10-18 10:29:41 -07:00
Sidharth MohantyandGitHub	b2286f3e34	Google Colab Notebooks for LLMs, Embedders and VectorDBs (#821 )	2023-10-18 09:04:06 -07:00
Sidharth MohantyandGitHub	65a20aa457	[Bug fix] Anthropic, Llama2 and VertexAI LLMs dependencies (#820 )	2023-10-18 01:10:46 -07:00
Rupesh BansalandGitHub	d8a7d71344	[Feature] Batch uploading in chromadb (#814 )	2023-10-17 22:22:29 -07:00
Deshraj YadavandGitHub	bb490df9a6	Bump version to v0.0.73 (#819 )	2023-10-17 22:19:22 -07:00
Rupesh BansalandGitHub	cdfd6519c8	[Feature] Add support for weaviate vector db (#782 )	2023-10-17 22:18:53 -07:00
Sidharth MohantyandGitHub	e8a2846449	Improve and add more tests (#807 )	2023-10-17 14:06:47 -07:00
Sidharth MohantyandGitHub	d065cbf934	[Bug fix] Qna pair not loading properly (#817 )	2023-10-17 11:34:03 -07:00
Sidharth MohantyandGitHub	413b107b9a	Update dependencies for fast installation (#811 )	2023-10-17 08:21:30 -07:00
Deshraj YadavandGitHub	c336292346	Update version to v0.0.72 (#809 )	2023-10-16 13:33:36 -07:00
Deshraj YadavandGitHub	adf50f1e81	[Docs] Add docs for Azure OpenAI provider (#804 )	2023-10-16 13:31:56 -07:00
Deshraj YadavandGitHub	636bc0a99d	[feature]: Improve pinecone db integration (#806 )	2023-10-15 02:26:35 -07:00
Rupesh BansalandGitHub	a7a61fae1d	[Feature] Pinecone Vector DB support (#723 )	2023-10-15 01:54:07 -07:00
Sidharth MohantyandGitHub	5ec12212e4	Improve tests (#800 )	2023-10-14 19:16:27 -07:00
Deshraj YadavandGitHub	77c90a308e	[Docs]: Clean up docs (#802 )	2023-10-14 19:14:24 -07:00