Lazy load loaders and chunkers (#872 )

[Docs] Update embedchain docs and analytics (#871 )
Update notebooks to work with the latest version (#870 )
2023-10-30 11:20:38 -07:00 · 2023-10-30 00:38:35 -07:00 · 2023-10-29 23:06:43 -07:00 · 2023-10-29 16:50:14 -07:00 · 2023-10-29 14:37:32 -07:00 · 2023-10-29 01:20:21 -07:00
151 changed files with 13989 additions and 1453 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 +76,7 @@ docs/_build/
 target/

 # Jupyter Notebook
-.ipynb_checkpoints
+*.yaml

 # IPython
 profile_default/
@@ -165,9 +165,14 @@ 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
@@ -1,5 +1,7 @@
 # embedchain

+<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>
+
 [![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)
@@ -38,17 +40,23 @@ The documentation for embedchain can be found at [docs.embedchain.ai](https://do

 Embedchain empowers you to create ChatGPT like apps, on your own dynamic dataset.

-### Data Types Supported
+### Data types supported

 * Youtube video
 * PDF file
+* CSV file
 * Web page
+* MDX file
+* XML file
 * Sitemap
 * Doc file
-* Code documentation website loader
-* Notion and many more.
+* Notion
+* JSON file
+* OpenAPI specs
+* Code docs website
+* Unstructured file loader and many more

-You can find the full list of data types on [our documentation](https://docs.embedchain.ai/data-sources/csv).
+You can find the full list of data types on [our documentation](https://docs.embedchain.ai/data-sources/).

 ### Queries

@@ -56,7 +64,7 @@ For example, you can use Embedchain to create an Elon Musk bot using the followi

 ```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"
@@ -70,8 +78,47 @@ elon_bot.add("https://www.youtube.com/watch?v=RcYjXbSJBN8")
 # Query the bot
 elon_bot.query("How many companies does Elon Musk run and name those?")
 # Answer: Elon Musk currently runs several companies. As of my knowledge, he is the CEO and lead designer of SpaceX, the CEO and product architect of Tesla, Inc., the CEO and founder of Neuralink, and the CEO and founder of The Boring Company. However, please note that this information may change over time, so it's always good to verify the latest updates.
+
+# (Optional): Deploy app to Embedchain Platform
+app.deploy()
+# 🔑 Enter your Embedchain API key. You can find the API key at https://app.embedchain.ai/settings/keys/
+# ec-xxxxxx
+
+# 🛠️ Creating pipeline on the platform...
+# 🎉🎉🎉 Pipeline created successfully! View your pipeline: https://app.embedchain.ai/pipelines/xxxxx
+
+# 🛠️ Adding data to your pipeline...
+# ✅ Data of type: web_page, value: https://www.forbes.com/profile/elon-musk added successfully.
 ```

+## Examples
+
+| LLM          | Google Colab  | Replit   |
+|--------------|---------------|----------|
+| OpenAI       | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/openai.ipynb)           | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/openai#main.py)      |
+| Anthropic    | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/anthropic.ipynb)        | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/anthropic#main.py)   |
+| Azure OpenAI | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/azure-openai.ipynb)     | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/azureopenai#main.py) |
+| VertexAI     | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/vertex_ai.ipynb)        | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/vertexai#main.py)    |
+| Cohere       | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/cohere.ipynb)           | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/cohere#main.py)      |
+| Hugging Face | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/hugging_face_hub.ipynb) | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/huggingface#main.py) |
+| JinaChat     | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/jina.ipynb)             | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/jina#main.py)        |
+| GPT4All      | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/gpt4all.ipynb)          | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/gpt4all#main.py)     |
+| Llama2       | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/llama2.ipynb)           | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/llama2#main.py)      |
+
+| Embedding model     | Google Colab                                                                                                                                                                            | Replit                                                                                                                        |
+| ------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------- |
+| OpenAI       | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/openai.ipynb)           | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/openai#main.py)      |
+| VertexAI     | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/vertex_ai.ipynb)        | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/vertexai#main.py)    |
+| GPT4All      | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/gpt4all.ipynb)          | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/gpt4all#main.py)     |
+| Hugging Face | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/hugging_face_hub.ipynb) | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/huggingface#main.py) |
+
+| Vector DB     | Google Colab                                                                                                                                                                         | Replit                                                                                                                          |
+| ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------- |
+| ChromaDB      | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/chromadb.ipynb)      | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/chromadb#main.py)      |
+| Elasticsearch | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/elasticsearch.ipynb) | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/elasticsearchdb#main.py) |
+| Opensearch    | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/opensearch.ipynb)    | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/opensearchdb#main.py)    |
+| Pinecone      | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embedchain/embedchain/blob/main/notebooks/pinecone.ipynb)      | [![Try with Replit Badge](https://replit.com/badge?caption=Try%20with%20Replit&variant=small)](https://replit.com/@taranjeetio/pineconedb#main.py)      |
+
 ## 🤝 Contributing

 Contributions are welcome! Please check out the issues on the repository, and feel free to open a pull request.
@@ -0,0 +1,19 @@
+app:
+  config:
+    id: azure-openai-app
+
+llm:
+  provider: azure_openai
+  model: gpt-35-turbo
+  config:
+    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
@@ -1,7 +1,7 @@
 llm:
  provider: gpt4all
-  model: 'orca-mini-3b.ggmlv3.q4_0.bin'
  config:
+    model: 'orca-mini-3b.ggmlv3.q4_0.bin'
    temperature: 0.5
    max_tokens: 1000
    top_p: 1
@@ -9,5 +9,3 @@ llm:

 embedder:
  provider: gpt4all
-  config:
-    model: 'all-MiniLM-L6-v2'
@@ -6,8 +6,8 @@ app:

 llm:
  provider: gpt4all
-  model: 'orca-mini-3b.ggmlv3.q4_0.bin'
  config:
+    model: 'orca-mini-3b.ggmlv3.q4_0.bin'
    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
@@ -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.ggmlv3.q4_0.bin'
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 1
+    stream: false
+
+embedding_model:
+  provider: gpt4all
+  config:
+    model: 'all-MiniLM-L6-v2'
+    deployment_name: null
@@ -0,0 +1,4 @@
+vectordb:
+  provider: weaviate
+  config:
+    collection_name: my_weaviate_index
@@ -13,8 +13,8 @@ app:

 llm:
  provider: openai
-  model: 'gpt-3.5-turbo'
  config:
+    model: 'gpt-3.5-turbo'
    temperature: 0.5
    max_tokens: 1000
    top_p: 1
@@ -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>
@@ -23,7 +24,7 @@ Once you have obtained the key, you can use it like this:

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

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

@@ -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.
@@ -50,7 +90,7 @@ GPT4All supports generating high quality embeddings of arbitrary length document
 <CodeGroup>

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

 # load embedding model configuration from config.yaml file
 app = App.from_config(yaml_path="config.yaml")
@@ -59,8 +99,8 @@ app = App.from_config(yaml_path="config.yaml")
 ```yaml config.yaml
 llm:
  provider: gpt4all
-  model: 'orca-mini-3b.ggmlv3.q4_0.bin'
  config:
+    model: 'orca-mini-3b.ggmlv3.q4_0.bin'
    temperature: 0.5
    max_tokens: 1000
    top_p: 1
@@ -68,8 +108,6 @@ llm:

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

 </CodeGroup>
@@ -81,7 +119,7 @@ Hugging Face supports generating embeddings of arbitrary length documents of tex
 <CodeGroup>

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

 # load embedding model configuration from config.yaml file
 app = App.from_config(yaml_path="config.yaml")
@@ -90,8 +128,8 @@ app = App.from_config(yaml_path="config.yaml")
 ```yaml config.yaml
 llm:
  provider: huggingface
-  model: 'google/flan-t5-xxl'
  config:
+    model: 'google/flan-t5-xxl'
    temperature: 0.5
    max_tokens: 1000
    top_p: 0.5
@@ -112,7 +150,7 @@ Embedchain supports Google's VertexAI embeddings model through a simple interfac
 <CodeGroup>

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

 # load embedding model configuration from config.yaml file
 app = App.from_config(yaml_path="config.yaml")
@@ -121,8 +159,8 @@ app = App.from_config(yaml_path="config.yaml")
 ```yaml config.yaml
 llm:
  provider: vertexai
-  model: 'chat-bison'
  config:
+    model: 'chat-bison'
    temperature: 0.5
    top_p: 0.5

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

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

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

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

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

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

@@ -52,8 +52,8 @@ app = App.from_config(yaml_path="config.yaml")
 ```yaml config.yaml
 llm:
  provider: openai
-  model: 'gpt-3.5-turbo'
  config:
+    model: 'gpt-3.5-turbo'
    temperature: 0.5
    max_tokens: 1000
    top_p: 1
@@ -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

@@ -75,7 +110,7 @@ To use anthropic's model, please set the `ANTHROPIC_API_KEY` which you find on t

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

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

@@ -86,8 +121,8 @@ app = App.from_config(yaml_path="config.yaml")
 ```yaml config.yaml
 llm:
  provider: anthropic
-  model: 'claude-instant-1'
  config:
+    model: 'claude-instant-1'
    temperature: 0.5
    max_tokens: 1000
    top_p: 1
@@ -112,7 +147,7 @@ Once you have the API key, you are all set to use it with Embedchain.

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

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

@@ -123,8 +158,8 @@ app = App.from_config(yaml_path="config.yaml")
 ```yaml config.yaml
 llm:
  provider: cohere
-  model: large
  config:
+    model: large
    temperature: 0.5
    max_tokens: 1000
    top_p: 1
@@ -145,7 +180,7 @@ GPT4all is a free-to-use, locally running, privacy-aware chatbot. No GPU or inte
 <CodeGroup>

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

 # load llm configuration from config.yaml file
 app = App.from_config(yaml_path="config.yaml")
@@ -154,8 +189,8 @@ app = App.from_config(yaml_path="config.yaml")
 ```yaml config.yaml
 llm:
  provider: gpt4all
-  model: 'orca-mini-3b.ggmlv3.q4_0.bin'
  config:
+    model: 'orca-mini-3b.ggmlv3.q4_0.bin'
    temperature: 0.5
    max_tokens: 1000
    top_p: 1
@@ -163,8 +198,6 @@ llm:

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

@@ -179,7 +212,7 @@ Once you have the key, load the app using the config yaml file:

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

 os.environ["JINACHAT_API_KEY"] = "xxx"
 # load llm configuration from config.yaml file
@@ -215,7 +248,7 @@ Once you have the token, load the app using the config yaml file:

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

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

@@ -226,8 +259,8 @@ app = App.from_config(yaml_path="config.yaml")
 ```yaml config.yaml
 llm:
  provider: huggingface
-  model: 'google/flan-t5-xxl'
  config:
+    model: 'google/flan-t5-xxl'
    temperature: 0.5
    max_tokens: 1000
    top_p: 0.5
@@ -245,7 +278,7 @@ Once you have the token, load the app using the config yaml file:

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

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

@@ -256,8 +289,8 @@ app = App.from_config(yaml_path="config.yaml")
 ```yaml config.yaml
 llm:
  provider: llama2
-  model: 'a16z-infra/llama13b-v2-chat:df7690f1994d94e96ad9d568eac121aecf50684a0b0963b25a41cc40061269e5'
  config:
+    model: 'a16z-infra/llama13b-v2-chat:df7690f1994d94e96ad9d568eac121aecf50684a0b0963b25a41cc40061269e5'
    temperature: 0.5
    max_tokens: 1000
    top_p: 0.5
@@ -272,7 +305,7 @@ Setup Google Cloud Platform application credentials by following the instruction
 <CodeGroup>

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

 # load llm configuration from config.yaml file
 app = App.from_config(yaml_path="config.yaml")
@@ -281,8 +314,8 @@ app = App.from_config(yaml_path="config.yaml")
 ```yaml config.yaml
 llm:
  provider: vertexai
-  model: 'chat-bison'
  config:
+    model: 'chat-bison'
    temperature: 0.5
    top_p: 0.5
 ```
@@ -22,7 +22,7 @@ Utilizing a vector database alongside Embedchain is a seamless process. All you
 <CodeGroup>

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

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

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

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

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

 # load opensearch configuration from yaml file
 app = App.from_config(yaml_path="config.yaml")
@@ -119,10 +119,16 @@ Install related dependencies using the following command:
 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="config.yaml")
@@ -147,10 +153,18 @@ _Coming soon_

 ## Pinecone

+Install pinecone related dependencies using the following command:
+
+```bash
+pip install --upgrade 'embedchain[pinecone]'
+```
+
 In order to use Pinecone as vector database, set the environment variables `PINECONE_API_KEY` and `PINECONE_ENV` which you can find on [Pinecone dashboard](https://app.pinecone.io/).

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

 # load pinecone configuration from yaml file
 app = App.from_config(yaml_path="config.yaml")
@@ -165,12 +179,46 @@ vectordb:
    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" />
@@ -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 +1,35 @@
+---
+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
+import os
+from embedchain.apps.app import App
+from embedchain.models.data_type import DataType
+app = App()
+
+query = "to: me label:inbox"
+app.add(query, data_type=DataType.GMAIL)
+app.query("Summarize my email conversations")
+```
@@ -0,0 +1,36 @@
+---
+title: '📃 JSON'
+---
+
+To add any json file, use the data_type as `json`. `json` 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
+import os
+
+from embedchain.apps.app import App
+
+os.environ["OPENAI_API_KEY"] = "openai_api_key"
+
+app = App()
+
+response = app.query("What is the net worth of Elon Musk as of October 2023?")
+
+print(response)
+"I'm sorry, but I don't have access to real-time information or future predictions. Therefore, I don't know the net worth of Elon Musk as of October 2023."
+
+source_id = app.add("temp.json")
+
+response = app.query("What is the net worth of Elon Musk as of October 2023?")
+
+print(response)
+"As of October 2023, Elon Musk's net worth is $255.2 billion."
+```
+
+```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."
+}
+```
+
+
@@ -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 +1,23 @@
+---
+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. Headers are included for each line, so if you have an `age` column, `18` will be added as `age: 18`. Eg:
+
+```python
+from embedchain.apps.app import App
+import os
+
+os.environ["OPENAI_API_KEY"] = "sk-xxx"
+
+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")
+
+response = 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)
@@ -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 +17,9 @@ 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>
 </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()

@@ -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')
@@ -2,7 +2,9 @@
 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.
+The API server example can be found [here](https://github.com/embedchain/embedchain/tree/main/examples/api_server).
+
+It is a Flask based server that integrates the `embedchain` package, offering endpoints to add, query, and chat to engage in conversations with a chatbot using JSON requests.

 ### 🐳 Docker Setup

@@ -2,6 +2,10 @@
 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.
@@ -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>
@@ -9,7 +9,7 @@ description: 'Collections of all the frequently asked questions'

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

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

@@ -20,8 +20,8 @@ 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 +36,7 @@ llm:

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

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

@@ -47,8 +47,8 @@ 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.ggmlv3.q4_0.bin'
    temperature: 0.5
    max_tokens: 1000
    top_p: 1
@@ -3,30 +3,106 @@ title: 📚 Introduction
 description: '📝 Embedchain is a Data Platform for LLMs - load, index, retrieve, and sync any unstructured data'
 ---

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

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

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

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

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

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

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

-naval_bot.query("What unique capacity does Naval argue humans possess when it comes to understanding explanations or concepts?")
-# Answer: Naval argues that humans possess the unique capacity to understand explanations or concepts to the maximum extent possible in this physical reality.
+# Get relevant context using semantic search
+context = app.search("What is the net worth of Elon?", num_documents=2)
+print(context)
+# Context:
+# [
+#     {
+#         'context': 'Elon Musk PROFILEElon MuskCEO, Tesla$221.9BReal Time Net Worthas of 10/29/23Reflects change since 5 pm ET of prior trading day. 1 in the world todayPhoto by Martin Schoeller for ForbesAbout Elon MuskElon Musk cofounded six companies, including electric car maker Tesla, rocket producer SpaceX and tunneling startup Boring Company.He owns about 21% of Tesla between stock and options, but has pledged more than half his shares as collateral for personal loans of up to $3.5 billion.SpaceX, founded in',
+#         'source': 'https://www.forbes.com/profile/elon-musk',
+#         'document_id': 'some_document_id'
+#     },
+#     {
+#         'context': 'company, which is now called X.Wealth HistoryHOVER TO REVEAL NET WORTH BY YEARForbes Lists 1Forbes 400 (2023)The Richest Person In Every State (2023) 2Billionaires (2023) 1Innovative Leaders (2019) 25Powerful People (2018) 12Richest In Tech (2017)Global Game Changers (2016)More ListsPersonal StatsAge52Source of WealthTesla, SpaceX, Self MadeSelf-Made Score8Philanthropy Score1ResidenceAustin, TexasCitizenshipUnited StatesMarital StatusSingleChildren11EducationBachelor of Arts/Science, University',
+#         'source': 'https://www.forbes.com/profile/elon-musk',
+#         'document_id': 'some_document_id'
+#     }
+# ]
+```
+
+## ❓Query
+
+Embedchain empowers developers to ask questions and receive relevant answers through a user-friendly query API. Refer to the following example to learn how to utilize the query API:
+
+```python
+from embedchain import Pipeline as App
+
+# Initialize app
+app = App()
+
+# Add data source
+app.add("https://www.forbes.com/profile/elon-musk")
+
+# Get relevant answer for your query
+answer = app.query("What is the net worth of Elon?")
+print(answer)
+# Answer: The net worth of Elon Musk is $221.9 billion.
+```
+
+## 💬 Chat
+
+Embedchain allows easy chatting over your data sources using a user-friendly chat API. Check out the example below to understand how to use the chat API:
+
+```python
+from embedchain import Pipeline as App
+
+# Initialize app
+app = App()
+
+# Add data source
+app.add("https://www.forbes.com/profile/elon-musk")
+
+# Chat on your data using `.chat()`
+answer = app.chat("How much did Elon pay for Twitter?")
+print(answer)
+# Answer: Elon Musk paid $44 billion for Twitter.
+```
+
+## 🚀 Deploy
+
+Embedchain enables developers to deploy their LLM-powered apps in production using the Embedchain platform. The platform offers free access to context on your data through its REST API. Once the pipeline is deployed, you can update your data sources anytime after deployment.
+
+See the example below on how to use the deploy API:
+
+```python
+from embedchain import Pipeline as App
+
+# Initialize app
+app = App()
+
+# Add data source
+app.add("https://www.forbes.com/profile/elon-musk")
+
+# Deploy your pipeline to Embedchain Platform
+app.deploy()
+
+# 🔑 Enter your Embedchain API key. You can find the API key at https://app.embedchain.ai/settings/keys/
+# ec-xxxxxx
+
+# 🛠️ Creating pipeline on the platform...
+# 🎉🎉🎉 Pipeline created successfully! View your pipeline: https://app.embedchain.ai/pipelines/xxxxx
+
+# 🛠️ Adding data to your pipeline...
+# ✅ Data of type: web_page, value: https://www.forbes.com/profile/elon-musk added successfully.
 ```

 ## 🚀 How it works?
@@ -16,23 +16,36 @@ Creating an app involves 3 steps:
 <Steps>
  <Step title="⚙️ Import app instance">
 ```python
-from embedchain import App
+from embedchain import Pipeline as App
 app = App()
 ```
  </Step>
  <Step title="🗃️ Add data sources">
 ```python
 # Add different data sources
-elon_bot.add("https://en.wikipedia.org/wiki/Elon_Musk")
-elon_bot.add("https://www.forbes.com/profile/elon-musk")
+app.add("https://en.wikipedia.org/wiki/Elon_Musk")
+app.add("https://www.forbes.com/profile/elon-musk")
 # You can also add local data sources such as pdf, csv files etc.
-# elon_bot.add("/path/to/file.pdf")
+# app.add("/path/to/file.pdf")
 ```
  </Step>
-  <Step title="💬 Query or chat on your data and get answers">
+  <Step title="💬 Query or chat or search context on your data">
 ```python
-elon_bot.query("What is the net worth of Elon Musk today?")
+app.query("What is the net worth of Elon Musk today?")
 # Answer: The net worth of Elon Musk today is $258.7 billion.
+```
+  </Step>
+  <Step title="🚀 (Optional) Deploy your pipeline to Embedchain Platform">
+```python
+app.deploy()
+# 🔑 Enter your Embedchain API key. You can find the API key at https://app.embedchain.ai/settings/keys/
+# ec-xxxxxx
+
+# 🛠️ Creating pipeline on the platform...
+# 🎉🎉🎉 Pipeline created successfully! View your pipeline: https://app.embedchain.ai/pipelines/xxxxx
+
+# 🛠️ Adding data to your pipeline...
+# ✅ Data of type: web_page, value: https://www.forbes.com/profile/elon-musk added successfully.
 ```
  </Step>
 </Steps>
@@ -41,18 +54,28 @@ Putting it together, you can run your first app using the following code. Make s

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

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

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

-response = elon_bot.query("What is the net worth of Elon Musk today?")
+response = app.query("What is the net worth of Elon Musk today?")
 print(response)
 # Answer: The net worth of Elon Musk today is $258.7 billion.
+
+app.deploy()
+# 🔑 Enter your Embedchain API key. You can find the API key at https://app.embedchain.ai/settings/keys/
+# ec-xxxxxx
+
+# 🛠️ Creating pipeline on the platform...
+# 🎉🎉🎉 Pipeline created successfully! View your pipeline: https://app.embedchain.ai/pipelines/xxxxx
+
+# 🛠️ Adding data to your pipeline...
+# ✅ Data of type: web_page, value: https://www.forbes.com/profile/elon-musk added successfully.
 ```
@@ -39,7 +39,7 @@ os.environ['LANGCHAIN_PROJECT] = <your-project>


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

 app = App()
 app.add("https://en.wikipedia.org/wiki/Elon_Musk")
@@ -7,9 +7,20 @@
  },
  "favicon": "/favicon.png",
  "colors": {
-    "primary": "#12A7D3",
-    "light": "#81D7F7",
-    "dark": "#004E7A"
+    "primary": "#2B48EE",
+    "light": "#2B48EE",
+    "dark": "#2B48EE",
+    "background": {
+      "dark": "#020415"
+    }
+  },
+  "metadata": {
+    "og:image": "/images/og.png",
+    "twitter:site": "@embedchain"
+  },
+  "topAnchor": {
+    "name": "Documentation",
+    "icon": "book-open"
  },
  "topbarLinks": [
    {
@@ -26,13 +37,16 @@
    }
  ],
  "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/faq", "get-started/examples"]
    },
    {
      "group": "Components",
@@ -46,6 +60,7 @@
          "group": "Supported data sources",
          "pages": [
            "data-sources/csv",
+            "data-sources/json",
            "data-sources/docs-site",
            "data-sources/docx",
            "data-sources/mdx",
@@ -55,6 +70,7 @@
            "data-sources/sitemap",
            "data-sources/text",
            "data-sources/web-page",
+            "data-sources/openapi",
            "data-sources/youtube-video"
          ]
        },
@@ -98,7 +114,6 @@
    }

  ],
-
  "footerSocials": {
    "website": "https://embedchain.ai",
    "github": "https://github.com/embedchain/embedchain",
@@ -107,7 +122,19 @@
    "twitter": "https://twitter.com/embedchain",
    "linkedin": "https://www.linkedin.com/company/embedchain"
  },
-  "backgroundImage": "/background.png",
  "isWhiteLabeled": true,
-  "feedback.thumbsRating": true
+  "analytics": {
+    "posthog": {
+      "apiKey": "phc_MSQ1GVfzkm7dRpktoKUGzWPlpkYhmVpcUtmLk1RwkQs",
+      "apiHost": "https://app.embedchain.ai/ingest"
+    }
+  },
+  "feedback": {
+    "suggestEdit": true,
+    "raiseIssue": true,
+    "thumbsRating": true
+  },
+  "search": {
+    "prompt": "✨ Search embedchain docs..."
+  }
 }
@@ -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)
@@ -1,63 +0,0 @@
-import logging
-from typing import Optional
-
-from embedchain.apps.app import App
-from embedchain.config import CustomAppConfig
-from embedchain.embedder.base import BaseEmbedder
-from embedchain.helper.json_serializable import register_deserializable
-from embedchain.llm.base import BaseLlm
-from embedchain.vectordb.base import BaseVectorDB
-
-
-@register_deserializable
-class CustomApp(App):
-    """
-    Embedchain's custom app allows for most flexibility.
-
-    You can craft your own mix of various LLMs, vector databases and embedding model/functions.
-
-    Methods:
-    add(source, data_type): adds the data from the given URL to the vector db.
-    query(query): finds answer to the given query using vector database and LLM.
-    chat(query): finds answer to the given query using vector database and LLM, with conversation history.
-
-    .. deprecated:: 0.0.64
-        Use `App` instead.
-    """
-
-    def __init__(
-        self,
-        config: Optional[CustomAppConfig] = None,
-        llm: BaseLlm = None,
-        db: BaseVectorDB = None,
-        embedder: BaseEmbedder = None,
-        system_prompt: Optional[str] = None,
-    ):
-        """
-        Initialize a new `CustomApp` instance. You have to choose a LLM, database and embedder.
-
-        .. deprecated:: 0.0.64
-        Use `App` instead.
-
-        :param config: Config for the app instance. This is the most basic configuration,
-        that does not fall into the LLM, database or embedder category, defaults to None
-        :type config: Optional[CustomAppConfig], optional
-        :param llm: LLM Class instance. example: `from embedchain.llm.openai import OpenAILlm`, defaults to None
-        :type llm: BaseLlm
-        :param db: The database to use for storing and retrieving embeddings,
-        example: `from embedchain.vectordb.chroma_db import ChromaDb`, defaults to None
-        :type db: BaseVectorDB
-        :param embedder: The embedder (embedding model and function) use to calculate embeddings.
-        example: `from embedchain.embedder.gpt4all_embedder import GPT4AllEmbedder`, defaults to None
-        :type embedder: BaseEmbedder
-        :param system_prompt: System prompt that will be provided to the LLM as such, defaults to None
-        :type system_prompt: Optional[str], optional
-        :raises ValueError: LLM, database or embedder has not been defined.
-        :raises TypeError: LLM, database or embedder is not a valid class instance.
-        """
-        logging.warning(
-            "DEPRECATION WARNING: Please use `App` instead of `CustomApp`. "
-            "`CustomApp` will be removed in a future release. "
-            "Please refer to https://docs.embedchain.ai/advanced/app_types#opensourceapp for instructions."
-        )
-        super().__init__(config=config, llm=llm, db=db, embedder=embedder, system_prompt=system_prompt)
@@ -1,71 +0,0 @@
-import logging
-from typing import Optional
-
-from embedchain.apps.app import App
-from embedchain.config import (BaseLlmConfig, ChromaDbConfig,
-                               OpenSourceAppConfig)
-from embedchain.embedder.gpt4all import GPT4AllEmbedder
-from embedchain.helper.json_serializable import register_deserializable
-from embedchain.llm.gpt4all import GPT4ALLLlm
-from embedchain.vectordb.chroma import ChromaDB
-
-gpt4all_model = None
-
-
-@register_deserializable
-class OpenSourceApp(App):
-    """
-    The embedchain Open Source App.
-    Comes preconfigured with the best open source LLM, embedding model, database.
-
-    Methods:
-    add(source, data_type): adds the data from the given URL to the vector db.
-    query(query): finds answer to the given query using vector database and LLM.
-    chat(query): finds answer to the given query using vector database and LLM, with conversation history.
-
-    .. deprecated:: 0.0.64
-    Use `App` instead.
-    """
-
-    def __init__(
-        self,
-        config: OpenSourceAppConfig = None,
-        llm_config: BaseLlmConfig = None,
-        chromadb_config: Optional[ChromaDbConfig] = None,
-        system_prompt: Optional[str] = None,
-    ):
-        """
-        Initialize a new `CustomApp` instance.
-        Since it's opinionated you don't have to choose a LLM, database and embedder.
-        However, you can configure those.
-
-        .. deprecated:: 0.0.64
-        Use `App` instead.
-
-        :param config: Config for the app instance. This is the most basic configuration,
-        that does not fall into the LLM, database or embedder category, defaults to None
-        :type config: OpenSourceAppConfig, optional
-        :param llm_config: Allows you to configure the LLM, e.g. how many documents to return.
-        example: `from embedchain.config import BaseLlmConfig`, defaults to None
-        :type llm_config: BaseLlmConfig, optional
-        :param chromadb_config: Allows you to configure the open source database,
-        example: `from embedchain.config import ChromaDbConfig`, defaults to None
-        :type chromadb_config: Optional[ChromaDbConfig], optional
-        :param system_prompt: System prompt that will be provided to the LLM as such.
-        Please don't use for the time being, as it's not supported., defaults to None
-        :type system_prompt: Optional[str], optional
-        :raises TypeError: `OpenSourceAppConfig` or `BaseLlmConfig` invalid.
-        """
-        logging.warning(
-            "DEPRECATION WARNING: Please use `App` instead of `OpenSourceApp`."
-            "`OpenSourceApp` will be removed in a future release."
-            "Please refer to https://docs.embedchain.ai/advanced/app_types#customapp for instructions."
-        )
-
-        super().__init__(
-            config=config,
-            llm=GPT4ALLLlm(config=llm_config),
-            db=ChromaDB(config=chromadb_config),
-            embedder=GPT4AllEmbedder(),
-            system_prompt=system_prompt,
-        )
@@ -1,93 +0,0 @@
-from string import Template
-
-from embedchain.apps.app import App
-from embedchain.apps.open_source_app import OpenSourceApp
-from embedchain.config import AppConfig, BaseLlmConfig
-from embedchain.config.llm.base import (DEFAULT_PROMPT,
-                                        DEFAULT_PROMPT_WITH_HISTORY)
-from embedchain.helper.json_serializable import register_deserializable
-
-
-@register_deserializable
-class EmbedChainPersonApp:
-    """
-    Base class to create a person bot.
-    This bot behaves and speaks like a person.
-
-    :param person: name of the person, better if its a well known person.
-    :param config: AppConfig instance to load as configuration.
-    """
-
-    def __init__(self, person: str, config: AppConfig = None):
-        """Initialize a new person app
-
-        :param person: Name of the person that's imitated.
-        :type person: str
-        :param config: Configuration class instance, defaults to None
-        :type config: AppConfig, optional
-        """
-        self.person = person
-        self.person_prompt = f"You are {person}. Whatever you say, you will always say in {person} style."  # noqa:E501
-        super().__init__(config)
-
-    def add_person_template_to_config(self, default_prompt: str, config: BaseLlmConfig = None):
-        """
-        This method checks if the config object contains a prompt template
-        if yes it adds the person prompt to it and return the updated config
-        else it creates a config object with the default prompt added to the person prompt
-
-        :param default_prompt:  it is the default prompt for query or chat methods
-        :type default_prompt: str
-        :param config: _description_, defaults to None
-        :type config: BaseLlmConfig, optional
-        :return: The `ChatConfig` instance to use as configuration options.
-        :rtype: _type_
-        """
-        template = Template(self.person_prompt + " " + default_prompt)
-
-        if config:
-            if config.template:
-                # Add person prompt to custom user template
-                config.template = Template(self.person_prompt + " " + config.template.template)
-            else:
-                # If no user template is present, use person prompt with the default template
-                config.template = template
-        else:
-            # if no config is present at all, initialize the config with person prompt and default template
-            config = BaseLlmConfig(
-                template=template,
-            )
-
-        return config
-
-
-@register_deserializable
-class PersonApp(EmbedChainPersonApp, App):
-    """
-    The Person app.
-    Extends functionality from EmbedChainPersonApp and App
-    """
-
-    def query(self, input_query, config: BaseLlmConfig = None, dry_run=False):
-        config = self.add_person_template_to_config(DEFAULT_PROMPT, config)
-        return super().query(input_query, config, dry_run, where=None)
-
-    def chat(self, input_query, config: BaseLlmConfig = None, dry_run=False, where=None):
-        config = self.add_person_template_to_config(DEFAULT_PROMPT_WITH_HISTORY, config)
-        return super().chat(input_query, config, dry_run, where)
-
-
-@register_deserializable
-class PersonOpenSourceApp(EmbedChainPersonApp, OpenSourceApp):
-    """
-    The Person app.
-    Extends functionality from EmbedChainPersonApp and OpenSourceApp
-    """
-
-    def query(self, input_query, config: BaseLlmConfig = None, dry_run=False):
-        config = self.add_person_template_to_config(DEFAULT_PROMPT, config)
-        return super().query(input_query, config, dry_run)
-
-    def chat(self, input_query, config: BaseLlmConfig = None, dry_run=False):
-        config = self.add_person_template_to_config(DEFAULT_PROMPT_WITH_HISTORY, config)
-        return super().chat(input_query, config, dry_run)
@@ -44,6 +44,7 @@ class BaseChunker(JSONSerializable):

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

 from .add_config import AddConfig, ChunkerConfig
 from .apps.app_config import AppConfig
-from .apps.custom_app_config import CustomAppConfig
-from .apps.open_source_app_config import OpenSourceAppConfig
 from .base_config import BaseConfig
 from .embedder.base import BaseEmbedderConfig
 from .embedder.base import BaseEmbedderConfig as EmbedderConfig
 from .llm.base import BaseLlmConfig
+from .pipeline_config import PipelineConfig
 from .vectordb.chroma import ChromaDbConfig
 from .vectordb.elasticsearch import ElasticsearchDBConfig
 from .vectordb.opensearch import OpenSearchDBConfig
@@ -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 +1,44 @@
+from typing import Dict, Optional
+
+from embedchain.config.vectordb.base import BaseVectorDbConfig
+from embedchain.helper.json_serializable import register_deserializable
+
+
+@register_deserializable
+class QdrantDBConfig(BaseVectorDbConfig):
+    """
+    Config to initialize an qdrant client.
+    :param url. qdrant url or list of nodes url to be used for connection
+    """
+
+    def __init__(
+        self,
+        collection_name: Optional[str] = None,
+        dir: Optional[str] = None,
+        hnsw_config: Optional[Dict[str, any]] = None,
+        quantization_config: Optional[Dict[str, any]] = None,
+        on_disk: Optional[bool] = None,
+        **extra_params: Dict[str, any],
+    ):
+        """
+        Initializes a configuration class instance for a qdrant client.
+
+        :param collection_name: Default name for the collection, defaults to None
+        :type collection_name: Optional[str], optional
+        :param dir: Path to the database directory, where the database is stored, defaults to None
+        :type dir: Optional[str], optional
+        :param hnsw_config: Params for HNSW index
+        :type hnsw_config: Optional[Dict[str, any]], defaults to None
+        :param quantization_config: Params for quantization, if None - quantization will be disabled
+        :type quantization_config: Optional[Dict[str, any]], defaults to None
+        :param on_disk: If true - point`s payload will not be stored in memory.
+                It will be read from the disk every time it is requested.
+                This setting saves RAM by (slightly) increasing the response time.
+                Note: those payload values that are involved in filtering and are indexed - remain in RAM.
+        :type on_disk: bool, optional, defaults to None
+        """
+        self.hnsw_config = hnsw_config
+        self.quantization_config = quantization_config
+        self.on_disk = on_disk
+        self.extra_params = extra_params
+        super().__init__(collection_name=collection_name, dir=dir)
@@ -0,0 +1,16 @@
+from typing import Dict, Optional
+
+from embedchain.config.vectordb.base import BaseVectorDbConfig
+from embedchain.helper.json_serializable import register_deserializable
+
+
+@register_deserializable
+class WeaviateDBConfig(BaseVectorDbConfig):
+    def __init__(
+        self,
+        collection_name: Optional[str] = None,
+        dir: Optional[str] = None,
+        **extra_params: Dict[str, any],
+    ):
+        self.extra_params = extra_params
+        super().__init__(collection_name=collection_name, dir=dir)
@@ -1,33 +1,9 @@
+from importlib import import_module
 from embedchain.chunkers.base_chunker import BaseChunker
-from embedchain.chunkers.docs_site import DocsSiteChunker
-from embedchain.chunkers.docx_file import DocxFileChunker
-from embedchain.chunkers.images import ImagesChunker
-from embedchain.chunkers.mdx import MdxChunker
-from embedchain.chunkers.notion import NotionChunker
-from embedchain.chunkers.pdf_file import PdfFileChunker
-from embedchain.chunkers.qna_pair import QnaPairChunker
-from embedchain.chunkers.sitemap import SitemapChunker
-from embedchain.chunkers.table import TableChunker
-from embedchain.chunkers.text import TextChunker
-from embedchain.chunkers.web_page import WebPageChunker
-from embedchain.chunkers.xml import XmlChunker
-from embedchain.chunkers.youtube_video import YoutubeVideoChunker
 from embedchain.config import AddConfig
 from embedchain.config.add_config import ChunkerConfig, LoaderConfig
 from embedchain.helper.json_serializable import JSONSerializable
 from embedchain.loaders.base_loader import BaseLoader
-from embedchain.loaders.csv import CsvLoader
-from embedchain.loaders.docs_site_loader import DocsSiteLoader
-from embedchain.loaders.docx_file import DocxFileLoader
-from embedchain.loaders.images import ImagesLoader
-from embedchain.loaders.local_qna_pair import LocalQnaPairLoader
-from embedchain.loaders.local_text import LocalTextLoader
-from embedchain.loaders.mdx import MdxLoader
-from embedchain.loaders.pdf_file import PdfFileLoader
-from embedchain.loaders.sitemap import SitemapLoader
-from embedchain.loaders.web_page import WebPageLoader
-from embedchain.loaders.xml import XmlLoader
-from embedchain.loaders.youtube_video import YoutubeVideoLoader
 from embedchain.models.data_type import DataType


@@ -50,6 +26,11 @@ class DataFormatter(JSONSerializable):
        self.loader = self._get_loader(data_type=data_type, config=config.loader)
        self.chunker = self._get_chunker(data_type=data_type, config=config.chunker)

+    def _lazy_load(self, module_path: str):
+        module_path, class_name = module_path.rsplit(".", 1)
+        module = import_module(module_path)
+        return getattr(module, class_name)
+
    def _get_loader(self, data_type: DataType, config: LoaderConfig) -> BaseLoader:
        """
        Returns the appropriate data loader for the given data type.
@@ -63,63 +44,55 @@ class DataFormatter(JSONSerializable):
        :rtype: BaseLoader
        """
        loaders = {
-            DataType.YOUTUBE_VIDEO: YoutubeVideoLoader,
-            DataType.PDF_FILE: PdfFileLoader,
-            DataType.WEB_PAGE: WebPageLoader,
-            DataType.QNA_PAIR: LocalQnaPairLoader,
-            DataType.TEXT: LocalTextLoader,
-            DataType.DOCX: DocxFileLoader,
-            DataType.SITEMAP: SitemapLoader,
-            DataType.XML: XmlLoader,
-            DataType.DOCS_SITE: DocsSiteLoader,
-            DataType.CSV: CsvLoader,
-            DataType.MDX: MdxLoader,
-            DataType.IMAGES: ImagesLoader,
+            DataType.YOUTUBE_VIDEO: "embedchain.loaders.youtube_video.YoutubeVideoLoader",
+            DataType.PDF_FILE: "embedchain.loaders.pdf_file.PdfFileLoader",
+            DataType.WEB_PAGE: "embedchain.loaders.web_page.WebPageLoader",
+            DataType.QNA_PAIR: "embedchain.loaders.local_qna_pair.LocalQnaPairLoader",
+            DataType.TEXT: "embedchain.loaders.local_text.LocalTextLoader",
+            DataType.DOCX: "embedchain.loaders.docx_file.DocxFileLoader",
+            DataType.SITEMAP: "embedchain.loaders.sitemap.SitemapLoader",
+            DataType.XML: "embedchain.loaders.xml.XmlLoader",
+            DataType.DOCS_SITE: "embedchain.loaders.docs_site_loader.DocsSiteLoader",
+            DataType.CSV: "embedchain.loaders.csv.CsvLoader",
+            DataType.MDX: "embedchain.loaders.mdx.MdxLoader",
+            DataType.IMAGES: "embedchain.loaders.images.ImagesLoader",
+            DataType.UNSTRUCTURED: "embedchain.loaders.unstructured_file.UnstructuredLoader",
+            DataType.JSON: "embedchain.loaders.json.JSONLoader",
+            DataType.OPENAPI: "embedchain.loaders.openapi.OpenAPILoader",
+            DataType.GMAIL: "embedchain.loaders.gmail.GmailLoader",
+            DataType.NOTION: "embedchain.loaders.notion.NotionLoader",
        }
-        lazy_loaders = {DataType.NOTION}
        if data_type in loaders:
-            loader_class: type = loaders[data_type]
-            loader: BaseLoader = loader_class()
-            return loader
-        elif data_type in lazy_loaders:
-            if data_type == DataType.NOTION:
-                from embedchain.loaders.notion import NotionLoader
-
-                return NotionLoader()
-            else:
-                raise ValueError(f"Unsupported data type: {data_type}")
+            loader_class: type = self._lazy_load(loaders[data_type])
+            return loader_class()
        else:
            raise ValueError(f"Unsupported data type: {data_type}")

    def _get_chunker(self, data_type: DataType, config: ChunkerConfig) -> BaseChunker:
-        """Returns the appropriate chunker for the given data type.
-
-        :param data_type: The type of the data to chunk.
-        :type data_type: DataType
-        :param config: Config to initialize the chunker with.
-        :type config: ChunkerConfig
-        :raises ValueError: If an unsupported data type is provided.
-        :return: The chunker for the given data type.
-        :rtype: BaseChunker
-        """
+        """Returns the appropriate chunker for the given data type (updated for lazy loading)."""
        chunker_classes = {
-            DataType.YOUTUBE_VIDEO: YoutubeVideoChunker,
-            DataType.PDF_FILE: PdfFileChunker,
-            DataType.WEB_PAGE: WebPageChunker,
-            DataType.QNA_PAIR: QnaPairChunker,
-            DataType.TEXT: TextChunker,
-            DataType.DOCX: DocxFileChunker,
-            DataType.DOCS_SITE: DocsSiteChunker,
-            DataType.SITEMAP: SitemapChunker,
-            DataType.NOTION: NotionChunker,
-            DataType.CSV: TableChunker,
-            DataType.MDX: MdxChunker,
-            DataType.IMAGES: ImagesChunker,
-            DataType.XML: XmlChunker,
+            DataType.YOUTUBE_VIDEO: "embedchain.chunkers.youtube_video.YoutubeVideoChunker",
+            DataType.PDF_FILE: "embedchain.chunkers.pdf_file.PdfFileChunker",
+            DataType.WEB_PAGE: "embedchain.chunkers.web_page.WebPageChunker",
+            DataType.QNA_PAIR: "embedchain.chunkers.qna_pair.QnaPairChunker",
+            DataType.TEXT: "embedchain.chunkers.text.TextChunker",
+            DataType.DOCX: "embedchain.chunkers.docx_file.DocxFileChunker",
+            DataType.SITEMAP: "embedchain.chunkers.sitemap.SitemapChunker",
+            DataType.XML: "embedchain.chunkers.xml.XmlChunker",
+            DataType.DOCS_SITE: "embedchain.chunkers.docs_site.DocsSiteChunker",
+            DataType.CSV: "embedchain.chunkers.table.TableChunker",
+            DataType.MDX: "embedchain.chunkers.mdx.MdxChunker",
+            DataType.IMAGES: "embedchain.chunkers.images.ImagesChunker",
+            DataType.UNSTRUCTURED: "embedchain.chunkers.unstructured_file.UnstructuredFileChunker",
+            DataType.JSON: "embedchain.chunkers.json.JSONChunker",
+            DataType.OPENAPI: "embedchain.chunkers.openapi.OpenAPIChunker",
+            DataType.GMAIL: "embedchain.chunkers.gmail.GmailChunker",
+            DataType.NOTION: "embedchain.chunkers.notion.NotionChunker",
        }
+
        if data_type in chunker_classes:
-            chunker_class: type = chunker_classes[data_type]
-            chunker: BaseChunker = chunker_class(config)
+            chunker_class = self._lazy_load(chunker_classes[data_type])
+            chunker = chunker_class(config)
            chunker.set_data_type(data_type)
            return chunker
        else:
@@ -1,17 +1,13 @@
 import hashlib
-import importlib.metadata
 import json
 import logging
 import os
-import threading
-import uuid
+import sqlite3
 from pathlib import Path
 from typing import Any, Dict, List, Optional

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

 from embedchain.chunkers.base_chunker import BaseChunker
 from embedchain.config import AddConfig, BaseLlmConfig
@@ -23,6 +19,7 @@ from embedchain.llm.base import BaseLlm
 from embedchain.loaders.base_loader import BaseLoader
 from embedchain.models.data_type import (DataType, DirectDataType,
                                         IndirectDataType, SpecialDataType)
+from embedchain.telemetry.posthog import AnonymousTelemetry
 from embedchain.utils import detect_datatype
 from embedchain.vectordb.base import BaseVectorDB

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


 class EmbedChain(JSONSerializable):
@@ -87,13 +85,29 @@ class EmbedChain(JSONSerializable):
        self.user_asks = []

        # Send anonymous telemetry
-        self.s_id = self.config.id if self.config.id else str(uuid.uuid4())
-        self.u_id = self._load_or_generate_user_id()
-        # NOTE: Uncomment the next two lines when running tests to see if any test fires a telemetry event.
-        # if (self.config.collect_metrics):
-        #     raise ConnectionRefusedError("Collection of metrics should not be allowed.")
-        thread_telemetry = threading.Thread(target=self._send_telemetry_event, args=("init",))
-        thread_telemetry.start()
+        self._telemetry_props = {"class": self.__class__.__name__}
+        self.telemetry = AnonymousTelemetry(enabled=self.config.collect_metrics)
+        # Establish a connection to the SQLite database
+        self.connection = sqlite3.connect(SQLITE_PATH)
+        self.cursor = self.connection.cursor()
+
+        # Create the 'data_sources' table if it doesn't exist
+        self.cursor.execute(
+            """
+            CREATE TABLE IF NOT EXISTS data_sources (
+                pipeline_id TEXT,
+                hash TEXT,
+                type TEXT,
+                value TEXT,
+                metadata TEXT,
+                is_uploaded INTEGER DEFAULT 0,
+                PRIMARY KEY (pipeline_id, hash)
+            )
+        """
+        )
+        self.connection.commit()
+        # Send anonymous telemetry
+        self.telemetry.capture(event_name="init", properties=self._telemetry_props)

    @property
    def collect_metrics(self):
@@ -115,29 +129,6 @@ class EmbedChain(JSONSerializable):
            raise ValueError(f"Boolean value expected but got {type(value)}.")
        self.llm.online = value

-    def _load_or_generate_user_id(self) -> str:
-        """
-        Loads the user id from the config file if it exists, otherwise generates a new
-        one and saves it to the config file.
-
-        :return: user id
-        :rtype: str
-        """
-        if not os.path.exists(CONFIG_DIR):
-            os.makedirs(CONFIG_DIR)
-
-        if os.path.exists(CONFIG_FILE):
-            with open(CONFIG_FILE, "r") as f:
-                data = json.load(f)
-                if "user_id" in data:
-                    return data["user_id"]
-
-        u_id = str(uuid.uuid4())
-        with open(CONFIG_FILE, "w") as f:
-            json.dump({"user_id": u_id}, f)
-
-        return u_id
-
    def add(
        self,
        source: Any,
@@ -163,7 +154,7 @@ class EmbedChain(JSONSerializable):
        :raises ValueError: Invalid data type
        :param dry_run: Optional. A dry run displays the chunks to ensure that the loader and chunker work as intended.
        deafaults to False
-        :return: source_id, a md5-hash of the source, in hexadecimal representation.
+        :return: source_hash, a md5-hash of the source, in hexadecimal representation.
        :rtype: str
        """
        if config is None:
@@ -192,18 +183,40 @@ class EmbedChain(JSONSerializable):
        if not data_type:
            data_type = detect_datatype(source)

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

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

+        # Insert the data into the 'data' table
+        self.cursor.execute(
+            """
+            INSERT INTO data_sources (hash, pipeline_id, type, value, metadata)
+            VALUES (?, ?, ?, ?, ?)
+        """,
+            (source_hash, self.config.id, data_type.value, str(source), json.dumps(metadata)),
+        )
+
+        # Commit the transaction
+        self.connection.commit()
+
        if dry_run:
            data_chunks_info = {"chunks": documents, "metadata": metadatas, "count": len(documents), "type": data_type}
            logging.debug(f"Dry run info : {data_chunks_info}")
@@ -214,11 +227,16 @@ class EmbedChain(JSONSerializable):
            # it's quicker to check the variable twice than to count words when they won't be submitted.
            word_count = data_formatter.chunker.get_word_count(documents)

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

-        return source_id
+        return source_hash

    def add_local(
        self,
@@ -245,7 +263,7 @@ class EmbedChain(JSONSerializable):
        :param config: The `AddConfig` instance to use as configuration options., defaults to None
        :type config: Optional[AddConfig], optional
        :raises ValueError: Invalid data type
-        :return: source_id, a md5-hash of the source, in hexadecimal representation.
+        :return: source_hash, a md5-hash of the source, in hexadecimal representation.
        :rtype: str
        """
        logging.warning(
@@ -313,7 +331,7 @@ class EmbedChain(JSONSerializable):
        chunker: BaseChunker,
        src: Any,
        metadata: Optional[Dict[str, Any]] = None,
-        source_id: Optional[str] = None,
+        source_hash: Optional[str] = None,
        dry_run=False,
    ):
        """
@@ -324,7 +342,7 @@ class EmbedChain(JSONSerializable):
        :param src: The data to be handled by the loader. Can be a URL for
        remote sources or local content for local loaders.
        :param metadata: Optional. Metadata associated with the data source.
-        :param source_id: Hexadecimal hash of the source.
+        :param source_hash: Hexadecimal hash of the source.
        :param dry_run: Optional. A dry run returns chunks and doesn't update DB.
        :type dry_run: bool, defaults to False
        :return: (List) documents (embedded text), (List) metadata, (list) ids, (int) number of chunks
@@ -350,12 +368,15 @@ class EmbedChain(JSONSerializable):

        # get existing ids, and discard doc if any common id exist.
        where = {"url": src}
+        # if data type is qna_pair, we check for question
+        if chunker.data_type == DataType.QNA_PAIR:
+            where = {"question": src[0]}
+
        if self.config.id is not None:
            where["app_id"] = self.config.id

        db_result = self.db.get(ids=ids, where=where)  # optional filter
        existing_ids = set(db_result["ids"])
-
        if len(existing_ids):
            data_dict = {id: (doc, meta) for id, doc, meta in zip(ids, documents, metadatas)}
            data_dict = {id: value for id, value in data_dict.items() if id not in existing_ids}
@@ -379,7 +400,7 @@ class EmbedChain(JSONSerializable):
                m["app_id"] = self.config.id

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

            # Note: Metadata is the function argument
            if metadata:
@@ -432,7 +453,6 @@ class EmbedChain(JSONSerializable):
        :rtype: List[str]
        """
        query_config = config or self.llm.config
-
        if where is not None:
            where = where
        elif query_config is not None and query_config.where is not None:
@@ -453,14 +473,17 @@ class EmbedChain(JSONSerializable):

            db_query = ClipProcessor.get_text_features(query=input_query)

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

-        return contents
+        if len(contexts) > 0 and isinstance(contexts[0], tuple):
+            contexts = list(map(lambda x: x[0], contexts))
+
+        return contexts

    def query(self, input_query: str, config: BaseLlmConfig = None, dry_run=False, where: Optional[Dict] = None) -> str:
        """
@@ -485,9 +508,7 @@ class EmbedChain(JSONSerializable):
        answer = self.llm.query(input_query=input_query, contexts=contexts, config=config, dry_run=dry_run)

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

    def chat(
@@ -519,10 +540,8 @@ class EmbedChain(JSONSerializable):
        """
        contexts = self.retrieve_from_database(input_query=input_query, config=config, where=where)
        answer = self.llm.chat(input_query=input_query, contexts=contexts, config=config, dry_run=dry_run)
-
        # Send anonymous telemetry
-        thread_telemetry = threading.Thread(target=self._send_telemetry_event, args=("chat",))
-        thread_telemetry.start()
+        self.telemetry.capture(event_name="chat", properties=self._telemetry_props)

        return answer

@@ -557,36 +576,9 @@ class EmbedChain(JSONSerializable):
        """
        Resets the database. Deletes all embeddings irreversibly.
        `App` does not have to be reinitialized after using this method.
-
-        DEPRECATED IN FAVOR OF `db.reset()`
        """
-        # Send anonymous telemetry
-        thread_telemetry = threading.Thread(target=self._send_telemetry_event, args=("reset",))
-        thread_telemetry.start()
-
-        logging.warning("DEPRECATION WARNING: Please use `app.db.reset()` instead of `App.reset()`.")
        self.db.reset()
-
-    @retry(stop=stop_after_attempt(3), wait=wait_fixed(1))
-    def _send_telemetry_event(self, method: str, extra_metadata: Optional[dict] = None):
-        """
-        Send telemetry event to the embedchain server. This is anonymous. It can be toggled off in `AppConfig`.
-        """
-        if not self.config.collect_metrics:
-            return
-
-        with threading.Lock():
-            url = "https://api.embedchain.ai/api/v1/telemetry/"
-            metadata = {
-                "s_id": self.s_id,
-                "version": importlib.metadata.version(__package__ or __name__),
-                "method": method,
-                "language": "py",
-                "u_id": self.u_id,
-            }
-            if extra_metadata:
-                metadata.update(extra_metadata)
-
-            response = requests.post(url, json={"metadata": metadata})
-            if response.status_code != 200:
-                logging.warning(f"Telemetry event failed with status code {response.status_code}")
+        self.cursor.execute("DELETE FROM data_sources WHERE pipeline_id = ?", (self.config.id,))
+        self.connection.commit()
+        # Send anonymous telemetry
+        self.telemetry.capture(event_name="reset", properties=self._telemetry_props)
@@ -1,7 +1,5 @@
 from typing import Optional

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

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

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

        vector_dimension = VectorDimensions.GPT4ALL.value
@@ -41,13 +41,16 @@ class LlmFactory:

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

    @classmethod
@@ -70,12 +73,18 @@ class VectorDBFactory:
        "elasticsearch": "embedchain.vectordb.elasticsearch.ElasticsearchDB",
        "opensearch": "embedchain.vectordb.opensearch.OpenSearchDB",
        "pinecone": "embedchain.vectordb.pinecone.PineconeDB",
+        "qdrant": "embedchain.vectordb.qdrant.QdrantDB",
+        "weaviate": "embedchain.vectordb.weaviate.WeaviateDB",
+        "zilliz": "embedchain.vectordb.zilliz.ZillizVectorDB",
    }
    provider_to_config_class = {
        "chroma": "embedchain.config.vectordb.chroma.ChromaDbConfig",
        "elasticsearch": "embedchain.config.vectordb.elasticsearch.ElasticsearchDBConfig",
        "opensearch": "embedchain.config.vectordb.opensearch.OpenSearchDBConfig",
        "pinecone": "embedchain.config.vectordb.pinecone.PineconeDBConfig",
+        "qdrant": "embedchain.config.vectordb.qdrant.QdrantDBConfig",
+        "weaviate": "embedchain.config.vectordb.weaviate.WeaviateDBConfig",
+        "zilliz": "embedchain.config.vectordb.zilliz.ZillizDBConfig",
    }

    @classmethod
@@ -1,4 +1,5 @@
 import logging
+import os
 from typing import Optional

 from embedchain.config import BaseLlmConfig
@@ -9,6 +10,8 @@ from embedchain.llm.base import BaseLlm
@register_deserializable
 class AnthropicLlm(BaseLlm):
    def __init__(self, config: Optional[BaseLlmConfig] = None):
+        if "ANTHROPIC_API_KEY" not in os.environ:
+            raise ValueError("Please set the ANTHROPIC_API_KEY environment variable.")
        super().__init__(config=config)

    def get_llm_model_answer(self, prompt):
@@ -18,7 +21,9 @@ class AnthropicLlm(BaseLlm):
    def _get_answer(prompt: str, config: BaseLlmConfig) -> str:
        from langchain.chat_models import ChatAnthropic

-        chat = ChatAnthropic(temperature=config.temperature, model=config.model)
+        chat = ChatAnthropic(
+            anthropic_api_key=os.environ["ANTHROPIC_API_KEY"], temperature=config.temperature, model=config.model
+        )

        if config.max_tokens and config.max_tokens != 1000:
            logging.warning("Config option `max_tokens` is not supported by this model.")
@@ -129,8 +129,12 @@ class BaseLlm(JSONSerializable):
        :return: Search results
        :rtype: Unknown
        """
-        from langchain.tools import DuckDuckGoSearchRun
-
+        try:
+            from langchain.tools import DuckDuckGoSearchRun
+        except ImportError:
+            raise ImportError(
+                'Searching requires extra dependencies. Install with `pip install --upgrade "embedchain[dataloaders]"`'
+            ) from None
        search = DuckDuckGoSearchRun()
        logging.info(f"Access search to get answers for {input_query}")
        return search.run(input_query)
@@ -202,7 +206,6 @@ class BaseLlm(JSONSerializable):
                k["web_search_result"] = self.access_search_and_get_results(input_query)
            prompt = self.generate_prompt(input_query, contexts, **k)
            logging.info(f"Prompt: {prompt}")
-
            if dry_run:
                return prompt

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

+from langchain.callbacks.stdout import StdOutCallbackHandler
+from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
+
 from embedchain.config import BaseLlmConfig
 from embedchain.helper.json_serializable import register_deserializable
 from embedchain.llm.base import BaseLlm
@@ -12,6 +15,7 @@ class GPT4ALLLlm(BaseLlm):
        if self.config.model is None:
            self.config.model = "orca-mini-3b.ggmlv3.q4_0.bin"
        self.instance = GPT4ALLLlm._get_instance(self.config.model)
+        self.instance.streaming = self.config.stream

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

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

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

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

-        response = self.instance.generate(
-            prompt=prompt,
-            streaming=config.stream,
-            top_p=config.top_p,
-            max_tokens=config.max_tokens,
-            temp=config.temperature,
-        )
-        return response
+        callbacks = [StreamingStdOutCallbackHandler()] if config.stream else [StdOutCallbackHandler()]
+
+        response = self.instance.generate(prompts=messages, callbacks=callbacks, **kwargs)
+        answer = ""
+        for generations in response.generations:
+            answer += " ".join(map(lambda generation: generation.text, generations))
+        return answer
@@ -1,3 +1,4 @@
+import importlib
 import os
 from typing import Optional

@@ -11,6 +12,13 @@ from embedchain.llm.base import BaseLlm
@register_deserializable
 class Llama2Llm(BaseLlm):
    def __init__(self, config: Optional[BaseLlmConfig] = None):
+        try:
+            importlib.import_module("replicate")
+        except ModuleNotFoundError:
+            raise ModuleNotFoundError(
+                "The required dependencies for Llama2 are not installed."
+                'Please install with `pip install --upgrade "embedchain[llama2]"`'
+            ) from None
        if "REPLICATE_API_TOKEN" not in os.environ:
            raise ValueError("Please set the REPLICATE_API_TOKEN environment variable.")

@@ -31,7 +39,7 @@ class Llama2Llm(BaseLlm):
    def get_llm_model_answer(self, prompt):
        # TODO: Move the model and other inputs into config
        if self.config.system_prompt:
-            raise ValueError("Llama2App does not support `system_prompt`")
+            raise ValueError("Llama2 does not support `system_prompt`")
        llm = Replicate(
            model=self.config.model,
            input={
@@ -13,13 +13,9 @@ class OpenAILlm(BaseLlm):
    def __init__(self, config: Optional[BaseLlmConfig] = None):
        super().__init__(config=config)

-    def get_llm_model_answer(self, prompt):
+    def get_llm_model_answer(self, prompt) -> str:
        response = OpenAILlm._get_answer(prompt, self.config)
-
-        if self.config.stream:
-            return response
-        else:
-            return response.content
+        return response

    def _get_answer(prompt: str, config: BaseLlmConfig) -> str:
        messages = []
@@ -41,4 +37,4 @@ class OpenAILlm(BaseLlm):
            chat = ChatOpenAI(**kwargs, streaming=config.stream, callbacks=[StreamingStdOutCallbackHandler()])
        else:
            chat = ChatOpenAI(**kwargs)
-        return chat(messages)
+        return chat(messages).content
@@ -1,3 +1,4 @@
+import importlib
 import logging
 from typing import Optional

@@ -9,6 +10,13 @@ from embedchain.llm.base import BaseLlm
@register_deserializable
 class VertexAILlm(BaseLlm):
    def __init__(self, config: Optional[BaseLlmConfig] = None):
+        try:
+            importlib.import_module("vertexai")
+        except ModuleNotFoundError:
+            raise ModuleNotFoundError(
+                "The required dependencies for VertexAI are not installed."
+                'Please install with `pip install --upgrade "embedchain[vertexai]"`'
+            ) from None
        super().__init__(config=config)

    def get_llm_model_answer(self, prompt):
@@ -3,7 +3,14 @@ import logging
 from urllib.parse import urljoin, urlparse

 import requests
-from bs4 import BeautifulSoup
+
+try:
+    from bs4 import BeautifulSoup
+except ImportError:
+    raise ImportError(
+        'DocsSite requires extra dependencies. Install with `pip install --upgrade "embedchain[dataloaders]"`'
+    ) from None
+

 from embedchain.helper.json_serializable import register_deserializable
 from embedchain.loaders.base_loader import BaseLoader
@@ -1,7 +1,11 @@
 import hashlib

-from langchain.document_loaders import Docx2txtLoader
-
+try:
+    from langchain.document_loaders import Docx2txtLoader
+except ImportError:
+    raise ImportError(
+        'Docx file requires extra dependencies. Install with `pip install --upgrade "embedchain[dataloaders]"`'
+    ) from None
 from embedchain.helper.json_serializable import register_deserializable
 from embedchain.loaders.base_loader import BaseLoader

@@ -0,0 +1,123 @@
+import hashlib
+import logging
+import os
+import quopri
+from textwrap import dedent
+
+from bs4 import BeautifulSoup
+
+try:
+    from llama_hub.gmail.base import GmailReader
+except ImportError:
+    raise ImportError("Gmail requires extra dependencies. Install with `pip install embedchain[gmail]`") from None
+
+from embedchain.loaders.base_loader import BaseLoader
+from embedchain.utils import clean_string
+
+
+def get_header(text: str, header: str) -> str:
+    start_string_position = text.find(header)
+    pos_start = text.find(":", start_string_position) + 1
+    pos_end = text.find("\n", pos_start)
+    header = text[pos_start:pos_end]
+    return header.strip()
+
+
+class GmailLoader(BaseLoader):
+    def load_data(self, query):
+        """Load data from gmail."""
+        if not os.path.isfile("credentials.json"):
+            raise FileNotFoundError(
+                "You must download the valid credentials file from your google \
+                dev account. Refer this `https://cloud.google.com/docs/authentication/api-keys`"
+            )
+
+        loader = GmailReader(query=query, service=None, results_per_page=20)
+        documents = loader.load_data()
+        logging.info(f"Gmail Loader: {len(documents)} mails found for query- {query}")
+
+        data = []
+        data_contents = []
+        logging.info(f"Gmail Loader: {len(documents)} mails found")
+        for document in documents:
+            original_size = len(document.text)
+
+            snippet = document.metadata.get("snippet")
+            meta_data = {
+                "url": document.metadata.get("id"),
+                "date": get_header(document.text, "Date"),
+                "subject": get_header(document.text, "Subject"),
+                "from": get_header(document.text, "From"),
+                "to": get_header(document.text, "To"),
+                "search_query": query,
+            }
+
+            # Decode
+            decoded_bytes = quopri.decodestring(document.text)
+            decoded_str = decoded_bytes.decode("utf-8", errors="replace")
+
+            # Slice
+            mail_start = decoded_str.find("<!DOCTYPE")
+            email_data = decoded_str[mail_start:]
+
+            # Web Page HTML Processing
+            soup = BeautifulSoup(email_data, "html.parser")
+
+            tags_to_exclude = [
+                "nav",
+                "aside",
+                "form",
+                "header",
+                "noscript",
+                "svg",
+                "canvas",
+                "footer",
+                "script",
+                "style",
+            ]
+
+            for tag in soup(tags_to_exclude):
+                tag.decompose()
+
+            ids_to_exclude = ["sidebar", "main-navigation", "menu-main-menu"]
+            for id in ids_to_exclude:
+                tags = soup.find_all(id=id)
+                for tag in tags:
+                    tag.decompose()
+
+            classes_to_exclude = [
+                "elementor-location-header",
+                "navbar-header",
+                "nav",
+                "header-sidebar-wrapper",
+                "blog-sidebar-wrapper",
+                "related-posts",
+            ]
+
+            for class_name in classes_to_exclude:
+                tags = soup.find_all(class_=class_name)
+                for tag in tags:
+                    tag.decompose()
+
+            content = soup.get_text()
+            content = clean_string(content)
+
+            cleaned_size = len(content)
+            if original_size != 0:
+                logging.info(
+                    f"[{id}] Cleaned page size: {cleaned_size} characters, down from {original_size} (shrunk: {original_size-cleaned_size} chars, {round((1-(cleaned_size/original_size)) * 100, 2)}%)"  # noqa:E501
+                )
+
+            result = f"""
+            email from '{meta_data.get('from')}' to '{meta_data.get('to')}'
+            subject: {meta_data.get('subject')}
+            date: {meta_data.get('date')}
+            preview: {snippet}
+            content: f{content}
+            """
+            data_content = dedent(result)
+            data.append({"content": data_content, "meta_data": meta_data})
+            data_contents.append(data_content)
+        doc_id = hashlib.sha256((query + ", ".join(data_contents)).encode()).hexdigest()
+        response_data = {"doc_id": doc_id, "data": data}
+        return response_data
@@ -0,0 +1,24 @@
+import hashlib
+
+from langchain.document_loaders.json_loader import \
+    JSONLoader as LangchainJSONLoader
+
+from embedchain.loaders.base_loader import BaseLoader
+
+langchain_json_jq_schema = 'to_entries | map("\(.key): \(.value|tostring)") | .[]'
+
+
+class JSONLoader(BaseLoader):
+    @staticmethod
+    def load_data(content):
+        """Load a json file. Each data point is a key value pair."""
+        data = []
+        data_content = []
+        loader = LangchainJSONLoader(content, text_content=False, jq_schema=langchain_json_jq_schema)
+        docs = loader.load()
+        for doc in docs:
+            meta_data = doc.metadata
+            data.append({"content": doc.page_content, "meta_data": {"url": content, "row": meta_data["seq_num"]}})
+            data_content.append(doc.page_content)
+        doc_id = hashlib.sha256((content + ", ".join(data_content)).encode()).hexdigest()
+        return {"doc_id": doc_id, "data": data}
@@ -0,0 +1,42 @@
+import hashlib
+from io import StringIO
+from urllib.parse import urlparse
+
+import requests
+import yaml
+
+from embedchain.loaders.base_loader import BaseLoader
+
+
+class OpenAPILoader(BaseLoader):
+    @staticmethod
+    def _get_file_content(content):
+        url = urlparse(content)
+        if all([url.scheme, url.netloc]) and url.scheme not in ["file", "http", "https"]:
+            raise ValueError("Not a valid URL.")
+
+        if url.scheme in ["http", "https"]:
+            response = requests.get(content)
+            response.raise_for_status()
+            return StringIO(response.text)
+        elif url.scheme == "file":
+            path = url.path
+            return open(path)
+        else:
+            return open(content)
+
+    @staticmethod
+    def load_data(content):
+        """Load yaml file of openapi. Each pair is a document."""
+        data = []
+        file_path = content
+        data_content = []
+        with OpenAPILoader._get_file_content(content=content) as file:
+            yaml_data = yaml.load(file, Loader=yaml.Loader)
+            for i, (key, value) in enumerate(yaml_data.items()):
+                string_data = f"{key}: {value}"
+                meta_data = {"url": file_path, "row": i + 1}
+                data.append({"content": string_data, "meta_data": meta_data})
+                data_content.append(string_data)
+        doc_id = hashlib.sha256((content + ", ".join(data_content)).encode()).hexdigest()
+        return {"doc_id": doc_id, "data": data}
@@ -1,7 +1,11 @@
 import hashlib

-from langchain.document_loaders import PyPDFLoader
-
+try:
+    from langchain.document_loaders import PyPDFLoader
+except ImportError:
+    raise ImportError(
+        'PDF File requires extra dependencies. Install with `pip install --upgrade "embedchain[dataloaders]"`'
+    ) from None
 from embedchain.helper.json_serializable import register_deserializable
 from embedchain.loaders.base_loader import BaseLoader
 from embedchain.utils import clean_string
@@ -2,8 +2,14 @@ import hashlib
 import logging

 import requests
-from bs4 import BeautifulSoup
-from bs4.builder import ParserRejectedMarkup
+
+try:
+    from bs4 import BeautifulSoup
+    from bs4.builder import ParserRejectedMarkup
+except ImportError:
+    raise ImportError(
+        'Sitemap requires extra dependencies. Install with `pip install --upgrade "embedchain[dataloaders]"`'
+    ) from None

 from embedchain.helper.json_serializable import register_deserializable
 from embedchain.loaders.base_loader import BaseLoader
@@ -0,0 +1,40 @@
+import hashlib
+
+try:
+    from langchain.document_loaders import UnstructuredFileLoader
+except ImportError:
+    raise ImportError(
+        'PDF File requires extra dependencies. Install with `pip install --upgrade "embedchain[dataloaders]"`'
+    ) from None
+from embedchain.helper.json_serializable import register_deserializable
+from embedchain.loaders.base_loader import BaseLoader
+from embedchain.utils import clean_string
+
+
+@register_deserializable
+class UnstructuredLoader(BaseLoader):
+    def load_data(self, url):
+        """Load data from a Unstructured file."""
+        loader = UnstructuredFileLoader(url)
+        data = []
+        all_content = []
+        pages = loader.load_and_split()
+        if not len(pages):
+            raise ValueError("No data found")
+        for page in pages:
+            content = page.page_content
+            content = clean_string(content)
+            meta_data = page.metadata
+            meta_data["url"] = url
+            data.append(
+                {
+                    "content": content,
+                    "meta_data": meta_data,
+                }
+            )
+            all_content.append(content)
+        doc_id = hashlib.sha256((" ".join(all_content) + url).encode()).hexdigest()
+        return {
+            "doc_id": doc_id,
+            "data": data,
+        }
@@ -2,7 +2,13 @@ import hashlib
 import logging

 import requests
-from bs4 import BeautifulSoup
+
+try:
+    from bs4 import BeautifulSoup
+except ImportError:
+    raise ImportError(
+        'Webpage requires extra dependencies. Install with `pip install --upgrade "embedchain[dataloaders]"`'
+    ) from None

 from embedchain.helper.json_serializable import register_deserializable
 from embedchain.loaders.base_loader import BaseLoader
@@ -1,7 +1,11 @@
 import hashlib

-from langchain.document_loaders import UnstructuredXMLLoader
-
+try:
+    from langchain.document_loaders import UnstructuredXMLLoader
+except ImportError:
+    raise ImportError(
+        'XML file requires extra dependencies. Install with `pip install --upgrade "embedchain[dataloaders]"`'
+    ) from None
 from embedchain.helper.json_serializable import register_deserializable
 from embedchain.loaders.base_loader import BaseLoader
 from embedchain.utils import clean_string
@@ -1,7 +1,11 @@
 import hashlib

-from langchain.document_loaders import YoutubeLoader
-
+try:
+    from langchain.document_loaders import YoutubeLoader
+except ImportError:
+    raise ImportError(
+        'YouTube video requires extra dependencies. Install with `pip install --upgrade "embedchain[dataloaders]"`'
+    ) from None
 from embedchain.helper.json_serializable import register_deserializable
 from embedchain.loaders.base_loader import BaseLoader
 from embedchain.utils import clean_string
@@ -1,4 +1,3 @@
 from .embedding_functions import EmbeddingFunctions  # noqa: F401
 from .providers import Providers  # noqa: F401
-from .vector_databases import VectorDatabases  # noqa: F401
 from .vector_dimensions import VectorDimensions  # noqa: F401
@@ -25,6 +25,10 @@ class IndirectDataType(Enum):
    CSV = "csv"
    MDX = "mdx"
    IMAGES = "images"
+    UNSTRUCTURED = "unstructured"
+    JSON = "json"
+    OPENAPI = "openapi"
+    GMAIL = "gmail"


 class SpecialDataType(Enum):
@@ -49,3 +53,7 @@ class DataType(Enum):
    MDX = IndirectDataType.MDX.value
    QNA_PAIR = SpecialDataType.QNA_PAIR.value
    IMAGES = IndirectDataType.IMAGES.value
+    UNSTRUCTURED = IndirectDataType.UNSTRUCTURED.value
+    JSON = IndirectDataType.JSON.value
+    OPENAPI = IndirectDataType.OPENAPI.value
+    GMAIL = IndirectDataType.GMAIL.value
@@ -1,8 +0,0 @@
-from enum import Enum
-
-
-class VectorDatabases(Enum):
-    CHROMADB = "CHROMADB"
-    ELASTICSEARCH = "ELASTICSEARCH"
-    OPENSEARCH = "OPENSEARCH"
-    ZILLIZ = "ZILLIZ"
@@ -0,0 +1,412 @@
+import ast
+import json
+import logging
+import os
+import sqlite3
+import uuid
+
+import requests
+import yaml
+from fastapi import FastAPI, HTTPException
+
+from embedchain import Client
+from embedchain.config import PipelineConfig
+from embedchain.embedchain import CONFIG_DIR, EmbedChain
+from embedchain.embedder.base import BaseEmbedder
+from embedchain.embedder.openai import OpenAIEmbedder
+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.telemetry.posthog import AnonymousTelemetry
+from embedchain.vectordb.base import BaseVectorDB
+from embedchain.vectordb.chroma import ChromaDB
+
+SQLITE_PATH = os.path.join(CONFIG_DIR, "embedchain.db")
+
+
+@register_deserializable
+class Pipeline(EmbedChain):
+    """
+    EmbedChain pipeline lets you create a LLM powered app for your unstructured
+    data by defining a pipeline with your chosen data source, embedding model,
+    and vector database.
+    """
+
+    def __init__(
+        self,
+        id: str = None,
+        name: str = None,
+        config: PipelineConfig = None,
+        db: BaseVectorDB = None,
+        embedding_model: BaseEmbedder = None,
+        llm: BaseLlm = None,
+        yaml_path: str = None,
+        log_level=logging.INFO,
+        auto_deploy: bool = False,
+    ):
+        """
+        Initialize a new `App` instance.
+
+        :param config: Configuration for the pipeline, defaults to None
+        :type config: PipelineConfig, optional
+        :param db: The database to use for storing and retrieving embeddings, defaults to None
+        :type db: BaseVectorDB, optional
+        :param embedding_model: The embedding model used to calculate embeddings, defaults to None
+        :type embedding_model: BaseEmbedder, optional
+        :param llm: The LLM model used to calculate embeddings, defaults to None
+        :type llm: BaseLlm, optional
+        :param yaml_path: Path to the YAML configuration file, defaults to None
+        :type yaml_path: str, optional
+        :param log_level: Log level to use, defaults to logging.INFO
+        :type log_level: int, optional
+        :param auto_deploy: Whether to deploy the pipeline automatically, defaults to False
+        :type auto_deploy: bool, optional
+        :raises Exception: If an error occurs while creating the pipeline
+        """
+        if id and yaml_path:
+            raise Exception("Cannot provide both id and config. Please provide only one of them.")
+
+        if id and name:
+            raise Exception("Cannot provide both id and name. Please provide only one of them.")
+
+        if name and config:
+            raise Exception("Cannot provide both name and config. Please provide only one of them.")
+
+        logging.basicConfig(level=log_level, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s")
+        self.logger = logging.getLogger(__name__)
+
+        self.auto_deploy = auto_deploy
+
+        # Store the yaml config as an attribute to be able to send it
+        self.yaml_config = None
+        self.client = None
+        # pipeline_id from the backend
+        self.id = None
+
+        self.config = config or PipelineConfig()
+        self.name = self.config.name
+
+        self.config.id = self.local_id = str(uuid.uuid4()) if self.config.id is None else self.config.id
+
+        if yaml_path:
+            with open(yaml_path, "r") as file:
+                config_data = yaml.safe_load(file)
+                self.yaml_config = config_data
+
+        if id is not None:
+            # Init client first since user is trying to fetch the pipeline
+            # details from the platform
+            self._init_client()
+            pipeline_details = self._get_pipeline(id)
+            self.config.id = self.local_id = pipeline_details["metadata"]["local_id"]
+            self.id = id
+
+        if name is not None:
+            self.name = name
+
+        self.embedding_model = embedding_model or OpenAIEmbedder()
+        self.db = db or ChromaDB()
+        self.llm = llm or OpenAILlm()
+        self._init_db()
+
+        # Send anonymous telemetry
+        self._telemetry_props = {"class": self.__class__.__name__}
+        self.telemetry = AnonymousTelemetry(enabled=self.config.collect_metrics)
+
+        # Establish a connection to the SQLite database
+        self.connection = sqlite3.connect(SQLITE_PATH)
+        self.cursor = self.connection.cursor()
+
+        # Create the 'data_sources' table if it doesn't exist
+        self.cursor.execute(
+            """
+            CREATE TABLE IF NOT EXISTS data_sources (
+                pipeline_id TEXT,
+                hash TEXT,
+                type TEXT,
+                value TEXT,
+                metadata TEXT,
+                is_uploaded INTEGER DEFAULT 0,
+                PRIMARY KEY (pipeline_id, hash)
+            )
+        """
+        )
+        self.connection.commit()
+        # Send anonymous telemetry
+        self.telemetry.capture(event_name="init", properties=self._telemetry_props)
+
+        self.user_asks = []
+        if self.auto_deploy:
+            self.deploy()
+
+    def _init_db(self):
+        """
+        Initialize the database.
+        """
+        self.db._set_embedder(self.embedding_model)
+        self.db._initialize()
+        self.db.set_collection_name(self.db.config.collection_name)
+
+    def _init_client(self):
+        """
+        Initialize the client.
+        """
+        config = Client.load_config()
+        if config.get("api_key"):
+            self.client = Client()
+        else:
+            api_key = input(
+                "🔑 Enter your Embedchain API key. You can find the API key at https://app.embedchain.ai/settings/keys/ \n"  # noqa: E501
+            )
+            self.client = Client(api_key=api_key)
+
+    def _get_pipeline(self, id):
+        """
+        Get existing pipeline
+        """
+        print("🛠️ Fetching pipeline details from the platform...")
+        url = f"{self.client.host}/api/v1/pipelines/{id}/cli/"
+        r = requests.get(
+            url,
+            headers={"Authorization": f"Token {self.client.api_key}"},
+        )
+        if r.status_code == 404:
+            raise Exception(f"❌ Pipeline with id {id} not found!")
+
+        print(
+            f"🎉 Pipeline loaded successfully! Pipeline url: https://app.embedchain.ai/pipelines/{r.json()['id']}\n"  # noqa: E501
+        )
+        return r.json()
+
+    def _create_pipeline(self):
+        """
+        Create a pipeline on the platform.
+        """
+        print("🛠️ Creating pipeline on the platform...")
+        # self.yaml_config is a dict. Pass it inside the key 'yaml_config' to the backend
+        payload = {
+            "yaml_config": json.dumps(self.yaml_config),
+            "name": self.name,
+            "local_id": self.local_id,
+        }
+        url = f"{self.client.host}/api/v1/pipelines/cli/create/"
+        r = requests.post(
+            url,
+            json=payload,
+            headers={"Authorization": f"Token {self.client.api_key}"},
+        )
+        if r.status_code not in [200, 201]:
+            raise Exception(f"❌ Error occurred while creating pipeline. API response: {r.text}")
+
+        if r.status_code == 200:
+            print(
+                f"🎉🎉🎉 Existing pipeline found! View your pipeline: https://app.embedchain.ai/pipelines/{r.json()['id']}\n"  # noqa: E501
+            )  # noqa: E501
+        elif r.status_code == 201:
+            print(
+                f"🎉🎉🎉 Pipeline created successfully! View your pipeline: https://app.embedchain.ai/pipelines/{r.json()['id']}\n"  # noqa: E501
+            )
+        return r.json()
+
+    def _get_presigned_url(self, data_type, data_value):
+        payload = {"data_type": data_type, "data_value": data_value}
+        r = requests.post(
+            f"{self.client.host}/api/v1/pipelines/{self.id}/cli/presigned_url/",
+            json=payload,
+            headers={"Authorization": f"Token {self.client.api_key}"},
+        )
+        r.raise_for_status()
+        return r.json()
+
+    def search(self, query, num_documents=3):
+        """
+        Search for similar documents related to the query in the vector database.
+        """
+        # Send anonymous telemetry
+        self.telemetry.capture(event_name="search", properties=self._telemetry_props)
+
+        # TODO: Search will call the endpoint rather than fetching the data from the db itself when deploy=True.
+        if self.id is None:
+            where = {"app_id": self.local_id}
+            context = self.db.query(
+                query,
+                n_results=num_documents,
+                where=where,
+                skip_embedding=False,
+            )
+            result = []
+            for c in context:
+                result.append(
+                    {
+                        "context": c[0],
+                        "source": c[1],
+                        "document_id": c[2],
+                    }
+                )
+            return result
+        else:
+            # Make API call to the backend to get the results
+            NotImplementedError("Search is not implemented yet for the prod mode.")
+
+    def _upload_file_to_presigned_url(self, presigned_url, file_path):
+        try:
+            with open(file_path, "rb") as file:
+                response = requests.put(presigned_url, data=file)
+                response.raise_for_status()
+                return response.status_code == 200
+        except Exception as e:
+            self.logger.exception(f"Error occurred during file upload: {str(e)}")
+            print("❌ Error occurred during file upload!")
+            return False
+
+    def _upload_data_to_pipeline(self, data_type, data_value, metadata=None):
+        payload = {
+            "data_type": data_type,
+            "data_value": data_value,
+            "metadata": metadata,
+        }
+        try:
+            self._send_api_request(f"/api/v1/pipelines/{self.id}/cli/add/", payload)
+            # print the local file path if user tries to upload a local file
+            printed_value = metadata.get("file_path") if metadata.get("file_path") else data_value
+            print(f"✅ Data of type: {data_type}, value: {printed_value} added successfully.")
+        except Exception as e:
+            print(f"❌ Error occurred during data upload for type {data_type}!. Error: {str(e)}")
+
+    def _send_api_request(self, endpoint, payload):
+        url = f"{self.client.host}{endpoint}"
+        headers = {"Authorization": f"Token {self.client.api_key}"}
+        response = requests.post(url, json=payload, headers=headers)
+        response.raise_for_status()
+        return response
+
+    def _process_and_upload_data(self, data_hash, data_type, data_value):
+        if os.path.isabs(data_value):
+            presigned_url_data = self._get_presigned_url(data_type, data_value)
+            presigned_url = presigned_url_data["presigned_url"]
+            s3_key = presigned_url_data["s3_key"]
+            if self._upload_file_to_presigned_url(presigned_url, file_path=data_value):
+                metadata = {"file_path": data_value, "s3_key": s3_key}
+                data_value = presigned_url
+            else:
+                self.logger.error(f"File upload failed for hash: {data_hash}")
+                return False
+        else:
+            if data_type == "qna_pair":
+                data_value = list(ast.literal_eval(data_value))
+            metadata = {}
+
+        try:
+            self._upload_data_to_pipeline(data_type, data_value, metadata)
+            self._mark_data_as_uploaded(data_hash)
+            return True
+        except Exception:
+            print(f"❌ Error occurred during data upload for hash {data_hash}!")
+            return False
+
+    def _mark_data_as_uploaded(self, data_hash):
+        self.cursor.execute(
+            "UPDATE data_sources SET is_uploaded = 1 WHERE hash = ? AND pipeline_id = ?",
+            (data_hash, self.local_id),
+        )
+        self.connection.commit()
+
+    def deploy(self):
+        if self.client is None:
+            self._init_client()
+
+        pipeline_data = self._create_pipeline()
+        self.id = pipeline_data["id"]
+
+        results = self.cursor.execute(
+            "SELECT * FROM data_sources WHERE pipeline_id = ? AND is_uploaded = 0", (self.local_id,)  # noqa:E501
+        ).fetchall()
+
+        if len(results) > 0:
+            print("🛠️ Adding data to your pipeline...")
+        for result in results:
+            data_hash, data_type, data_value = result[1], result[2], result[3]
+            self._process_and_upload_data(data_hash, data_type, data_value)
+
+        # Send anonymous telemetry
+        self.telemetry.capture(event_name="deploy", properties=self._telemetry_props)
+
+    @classmethod
+    def from_config(cls, yaml_path: str, auto_deploy: bool = False):
+        """
+        Instantiate a Pipeline object from a YAML configuration file.
+
+        :param yaml_path: Path to the YAML configuration file.
+        :type yaml_path: str
+        :param auto_deploy: Whether to deploy the pipeline automatically, defaults to False
+        :type auto_deploy: bool, optional
+        :return: An instance of the Pipeline class.
+        :rtype: Pipeline
+        """
+        with open(yaml_path, "r") as file:
+            config_data = yaml.safe_load(file)
+
+        pipeline_config_data = config_data.get("pipeline", {}).get("config", {})
+        db_config_data = config_data.get("vectordb", {})
+        embedding_model_config_data = config_data.get("embedding_model", {})
+        llm_config_data = config_data.get("llm", {})
+
+        pipeline_config = PipelineConfig(**pipeline_config_data)
+
+        db_provider = db_config_data.get("provider", "chroma")
+        db = VectorDBFactory.create(db_provider, db_config_data.get("config", {}))
+
+        if llm_config_data:
+            llm_provider = llm_config_data.get("provider", "openai")
+            llm = LlmFactory.create(llm_provider, llm_config_data.get("config", {}))
+        else:
+            llm = None
+
+        embedding_model_provider = embedding_model_config_data.get("provider", "openai")
+        embedding_model = EmbedderFactory.create(
+            embedding_model_provider, embedding_model_config_data.get("config", {})
+        )
+
+        # Send anonymous telemetry
+        event_properties = {"init_type": "yaml_config"}
+        AnonymousTelemetry().capture(event_name="init", properties=event_properties)
+
+        return cls(
+            config=pipeline_config,
+            llm=llm,
+            db=db,
+            embedding_model=embedding_model,
+            yaml_path=yaml_path,
+            auto_deploy=auto_deploy,
+        )
+
+    def start(self, host="0.0.0.0", port=8000):
+        app = FastAPI()
+
+        @app.post("/add")
+        async def add_document(data_value: str, data_type: str = None):
+            """
+            Add a document to the pipeline.
+            """
+            try:
+                document = {"data_value": data_value, "data_type": data_type}
+                self.add(document)
+                return {"message": "Document added successfully"}
+            except Exception as e:
+                raise HTTPException(status_code=500, detail=str(e))
+
+        @app.post("/query")
+        async def query_documents(query: str, num_documents: int = 3):
+            """
+            Query for similar documents in the pipeline.
+            """
+            try:
+                results = self.search(query, num_documents)
+                return results
+            except Exception as e:
+                raise HTTPException(status_code=500, detail=str(e))
+
+        import uvicorn
+
+        uvicorn.run(app, host=host, port=port)
@@ -0,0 +1,67 @@
+import json
+import logging
+import os
+import uuid
+from pathlib import Path
+
+from posthog import Posthog
+
+import embedchain
+
+HOME_DIR = str(Path.home())
+CONFIG_DIR = os.path.join(HOME_DIR, ".embedchain")
+CONFIG_FILE = os.path.join(CONFIG_DIR, "config.json")
+
+logger = logging.getLogger(__name__)
+
+
+class AnonymousTelemetry:
+    def __init__(self, host="https://app.posthog.com", enabled=True):
+        self.project_api_key = "phc_XnMmNHzwxE7PVHX4mD2r8K6nfxVM48a2sq2U3N1p2lO"
+        self.host = host
+        self.posthog = Posthog(project_api_key=self.project_api_key, host=self.host)
+        self.user_id = self.get_user_id()
+        self.enabled = enabled
+
+        # Check if telemetry tracking is disabled via environment variable
+        if "EC_TELEMETRY" in os.environ and os.environ["EC_TELEMETRY"].lower() not in [
+            "1",
+            "true",
+            "yes",
+        ]:
+            self.enabled = False
+
+        if not self.enabled:
+            self.posthog.disabled = True
+
+        # Silence posthog logging
+        posthog_logger = logging.getLogger("posthog")
+        posthog_logger.disabled = True
+
+    def get_user_id(self):
+        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"]
+
+        user_id = str(uuid.uuid4())
+        with open(CONFIG_FILE, "w") as f:
+            json.dump({"user_id": user_id}, f)
+        return user_id
+
+    def capture(self, event_name, properties=None):
+        default_properties = {
+            "version": embedchain.__version__,
+            "language": "python",
+            "pid": os.getpid(),
+        }
+        properties.update(default_properties)
+
+        try:
+            self.posthog.capture(self.user_id, event_name, properties)
+        except Exception:
+            logger.exception(f"Failed to send telemetry {event_name=}")
@@ -115,6 +115,13 @@ def detect_datatype(source: Any) -> DataType:
    """
    from urllib.parse import urlparse

+    import requests
+    import yaml
+
+    def is_openapi_yaml(yaml_content):
+        # currently the following two fields are required in openapi spec yaml config
+        return "openapi" in yaml_content and "info" in yaml_content
+
    try:
        if not isinstance(source, str):
            raise ValueError("Source is not a string and thus cannot be a URL.")
@@ -155,6 +162,35 @@ def detect_datatype(source: Any) -> DataType:
            logging.debug(f"Source of `{formatted_source}` detected as `docx`.")
            return DataType.DOCX

+        if url.path.endswith(".yaml"):
+            try:
+                response = requests.get(source)
+                response.raise_for_status()
+                try:
+                    yaml_content = yaml.safe_load(response.text)
+                except yaml.YAMLError as exc:
+                    logging.error(f"Error parsing YAML: {exc}")
+                    raise TypeError(f"Not a valid data type. Error loading YAML: {exc}")
+
+                if is_openapi_yaml(yaml_content):
+                    logging.debug(f"Source of `{formatted_source}` detected as `openapi`.")
+                    return DataType.OPENAPI
+                else:
+                    logging.error(
+                        f"Source of `{formatted_source}` does not contain all the required \
+                        fields of OpenAPI yaml. Check 'https://spec.openapis.org/oas/v3.1.0'"
+                    )
+                    raise TypeError(
+                        "Not a valid data type. Check 'https://spec.openapis.org/oas/v3.1.0', \
+                        make sure you have all the required fields in YAML config data"
+                    )
+            except requests.exceptions.RequestException as e:
+                logging.error(f"Error fetching URL {formatted_source}: {e}")
+
+        if url.path.endswith(".json"):
+            logging.debug(f"Source of `{formatted_source}` detected as `json_file`.")
+            return DataType.JSON
+
        if "docs" in url.netloc or ("docs" in url.path and url.scheme != "file"):
            # `docs_site` detection via path is not accepted for local filesystem URIs,
            # because that would mean all paths that contain `docs` are now doc sites, which is too aggressive.
@@ -194,6 +230,26 @@ def detect_datatype(source: Any) -> DataType:
            logging.debug(f"Source of `{formatted_source}` detected as `xml`.")
            return DataType.XML

+        if source.endswith(".yaml"):
+            with open(source, "r") as file:
+                yaml_content = yaml.safe_load(file)
+                if is_openapi_yaml(yaml_content):
+                    logging.debug(f"Source of `{formatted_source}` detected as `openapi`.")
+                    return DataType.OPENAPI
+                else:
+                    logging.error(
+                        f"Source of `{formatted_source}` does not contain all the required \
+                                  fields of OpenAPI yaml. Check 'https://spec.openapis.org/oas/v3.1.0'"
+                    )
+                    raise ValueError(
+                        "Invalid YAML data. Check 'https://spec.openapis.org/oas/v3.1.0', \
+                        make sure to add all the required params"
+                    )
+
+        if source.endswith(".json"):
+            logging.debug(f"Source of `{formatted_source}` detected as `json`.")
+            return DataType.JSON
+
        # If the source is a valid file, that's not detectable as a type, an error is raised.
        # It does not fallback to text.
        raise ValueError(
@@ -203,6 +259,8 @@ def detect_datatype(source: Any) -> DataType:
    else:
        # Source is not a URL.

+        # TODO: check if source is gmail query
+
        # Use text as final fallback.
        logging.debug(f"Source of `{formatted_source}` detected as `text`.")
        return DataType.TEXT
@@ -1,5 +1,5 @@
 import logging
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List, Optional, Tuple

 from chromadb import Collection, QueryResult
 from langchain.docstore.document import Document
@@ -25,6 +25,8 @@ except RuntimeError:
 class ChromaDB(BaseVectorDB):
    """Vector database using ChromaDB."""

+    BATCH_SIZE = 100
+
    def __init__(self, config: Optional[ChromaDbConfig] = None):
        """Initialize a new ChromaDB instance

@@ -36,7 +38,7 @@ class ChromaDB(BaseVectorDB):
        else:
            self.config = ChromaDbConfig()

-        self.settings = Settings()
+        self.settings = Settings(anonymized_telemetry=False)
        self.settings.allow_reset = self.config.allow_reset if hasattr(self.config, "allow_reset") else False
        if self.config.chroma_settings:
            for key, value in self.config.chroma_settings.items():
@@ -123,10 +125,6 @@ class ChromaDB(BaseVectorDB):
            args["limit"] = limit
        return self.collection.get(**args)

-    def get_advanced(self, where):
-        where_clause = self._generate_where_clause(where)
-        return self.collection.get(where=where_clause, limit=1)
-
    def add(
        self,
        embeddings: List[List[float]],
@@ -149,10 +147,31 @@ class ChromaDB(BaseVectorDB):
        :param skip_embedding: Optional. If True, then the embeddings are assumed to be already generated.
        :type skip_embedding: bool
        """
-        if skip_embedding:
-            self.collection.add(embeddings=embeddings, documents=documents, metadatas=metadatas, ids=ids)
-        else:
-            self.collection.add(documents=documents, metadatas=metadatas, ids=ids)
+        size = len(documents)
+        if skip_embedding and (embeddings is None or len(embeddings) != len(documents)):
+            raise ValueError("Cannot add documents to chromadb with inconsistent embeddings")
+
+        if len(documents) != size or len(metadatas) != size or len(ids) != size:
+            raise ValueError(
+                "Cannot add documents to chromadb with inconsistent sizes. Documents size: {}, Metadata size: {},"
+                " Ids size: {}".format(len(documents), len(metadatas), len(ids))
+            )
+
+        for i in range(0, len(documents), self.BATCH_SIZE):
+            print("Inserting batches from {} to {} in chromadb".format(i, min(len(documents), i + self.BATCH_SIZE)))
+            if skip_embedding:
+                self.collection.add(
+                    embeddings=embeddings[i : i + self.BATCH_SIZE],
+                    documents=documents[i : i + self.BATCH_SIZE],
+                    metadatas=metadatas[i : i + self.BATCH_SIZE],
+                    ids=ids[i : i + self.BATCH_SIZE],
+                )
+            else:
+                self.collection.add(
+                    documents=documents[i : i + self.BATCH_SIZE],
+                    metadatas=metadatas[i : i + self.BATCH_SIZE],
+                    ids=ids[i : i + self.BATCH_SIZE],
+                )

    def _format_result(self, results: QueryResult) -> list[tuple[Document, float]]:
        """
@@ -172,7 +191,9 @@ class ChromaDB(BaseVectorDB):
            )
        ]

-    def query(self, input_query: List[str], n_results: int, where: Dict[str, any], skip_embedding: bool) -> List[str]:
+    def query(
+        self, input_query: List[str], n_results: int, where: Dict[str, any], skip_embedding: bool
+    ) -> List[Tuple[str, str, str]]:
        """
        Query contents from vector database based on vector similarity

@@ -185,8 +206,8 @@ class ChromaDB(BaseVectorDB):
        :param skip_embedding: Optional. If True, then the input_query is assumed to be already embedded.
        :type skip_embedding: bool
        :raises InvalidDimensionException: Dimensions do not match.
-        :return: The content of the document that matched your query.
-        :rtype: List[str]
+        :return: The content of the document that matched your query, url of the source, doc_id
+        :rtype: List[Tuple[str,str,str]]
        """
        try:
            if skip_embedding:
@@ -208,11 +229,18 @@ class ChromaDB(BaseVectorDB):
        except InvalidDimensionException as e:
            raise InvalidDimensionException(
                e.message()
-                + ". This is commonly a side-effect when an embedding function, different from the one used to add the embeddings, is used to retrieve an embedding from the database."  # noqa E501
+                + ". This is commonly a side-effect when an embedding function, different from the one used to add the"
+                " embeddings, is used to retrieve an embedding from the database."
            ) from None
        results_formatted = self._format_result(result)
-        contents = [result[0].page_content for result in results_formatted]
-        return contents
+        contexts = []
+        for result in results_formatted:
+            context = result[0].page_content
+            metadata = result[0].metadata
+            source = metadata["url"]
+            doc_id = metadata["doc_id"]
+            contexts.append((context, source, doc_id))
+        return contexts

    def set_collection_name(self, name: str):
        """
@@ -242,9 +270,9 @@ class ChromaDB(BaseVectorDB):
        """
        Resets the database. Deletes all embeddings irreversibly.
        """
-        # Delete all data from the database
+        # Delete all data from the collection
        try:
-            self.client.reset()
+            self.client.delete_collection(self.config.collection_name)
        except ValueError:
            raise ValueError(
                "For safety reasons, resetting is disabled. "
@@ -1,5 +1,5 @@
 import logging
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List, Optional, Tuple

 try:
    from elasticsearch import Elasticsearch
@@ -135,7 +135,9 @@ class ElasticsearchDB(BaseVectorDB):
        bulk(self.client, docs)
        self.client.indices.refresh(index=self._get_index())

-    def query(self, input_query: List[str], n_results: int, where: Dict[str, any], skip_embedding: bool) -> List[str]:
+    def query(
+        self, input_query: List[str], n_results: int, where: Dict[str, any], skip_embedding: bool
+    ) -> List[Tuple[str, str, str]]:
        """
        query contents from vector data base based on vector similarity

@@ -147,8 +149,9 @@ class ElasticsearchDB(BaseVectorDB):
        :type where: Dict[str, any]
        :param skip_embedding: Optional. If True, then the input_query is assumed to be already embedded.
        :type skip_embedding: bool
-        :return: Database contents that are the result of the query
-        :rtype: List[str]
+        :return: The context of the document that matched your query, url of the source, doc_id
+
+        :rtype: List[Tuple[str,str,str]]
        """
        if skip_embedding:
            query_vector = input_query
@@ -156,6 +159,7 @@ class ElasticsearchDB(BaseVectorDB):
            input_query_vector = self.embedder.embedding_fn(input_query)
            query_vector = input_query_vector[0]

+        # `https://www.elastic.co/guide/en/elasticsearch/reference/7.17/query-dsl-script-score-query.html`
        query = {
            "script_score": {
                "query": {"bool": {"must": [{"exists": {"field": "text"}}]}},
@@ -167,11 +171,17 @@ class ElasticsearchDB(BaseVectorDB):
        }
        if "app_id" in where:
            app_id = where["app_id"]
-            query["script_score"]["query"]["bool"]["must"] = [{"term": {"metadata.app_id": app_id}}]
-        _source = ["text"]
+            query["script_score"]["query"] = {"match": {"metadata.app_id": app_id}}
+        _source = ["text", "metadata.url", "metadata.doc_id"]
        response = self.client.search(index=self._get_index(), query=query, _source=_source, size=n_results)
        docs = response["hits"]["hits"]
-        contents = [doc["_source"]["text"] for doc in docs]
+        contents = []
+        for doc in docs:
+            context = doc["_source"]["text"]
+            metadata = doc["_source"]["metadata"]
+            source = metadata["url"]
+            doc_id = metadata["doc_id"]
+            contents.append(tuple((context, source, doc_id)))
        return contents

    def set_collection_name(self, name: str):
@@ -1,5 +1,5 @@
 import logging
-from typing import Dict, List, Optional, Set
+from typing import Dict, List, Optional, Set, Tuple

 try:
    from opensearchpy import OpenSearch
@@ -145,7 +145,9 @@ class OpenSearchDB(BaseVectorDB):
        bulk(self.client, docs)
        self.client.indices.refresh(index=self._get_index())

-    def query(self, input_query: List[str], n_results: int, where: Dict[str, any], skip_embedding: bool) -> List[str]:
+    def query(
+        self, input_query: List[str], n_results: int, where: Dict[str, any], skip_embedding: bool
+    ) -> List[Tuple[str, str, str]]:
        """
        query contents from vector data base based on vector similarity

@@ -157,8 +159,8 @@ class OpenSearchDB(BaseVectorDB):
        :type where: Dict[str, any]
        :param skip_embedding: Optional. If True, then the input_query is assumed to be already embedded.
        :type skip_embedding: bool
-        :return: Database contents that are the result of the query
-        :rtype: List[str]
+        :return: The content of the document that matched your query, url of the source, doc_id
+        :rtype: List[Tuple[str,str,str]]
        """
        # TODO(rupeshbansal, deshraj): Add support for skip embeddings here if already exists
        embeddings = OpenAIEmbeddings()
@@ -185,7 +187,13 @@ class OpenSearchDB(BaseVectorDB):
            pre_filter=pre_filter,
            k=n_results,
        )
-        contents = [doc.page_content for doc in docs]
+
+        contents = []
+        for doc in docs:
+            context = doc.page_content
+            source = doc.metadata["url"]
+            doc_id = doc.metadata["doc_id"]
+            contents.append(tuple((context, source, doc_id)))
        return contents

    def set_collection_name(self, name: str):
@@ -1,5 +1,5 @@
 import os
-from typing import Dict, List, Optional
+from typing import Dict, List, Optional, Tuple

 try:
    import pinecone
@@ -118,7 +118,9 @@ class PineconeDB(BaseVectorDB):
        for i in range(0, len(docs), self.BATCH_SIZE):
            self.client.upsert(docs[i : i + self.BATCH_SIZE])

-    def query(self, input_query: List[str], n_results: int, where: Dict[str, any], skip_embedding: bool) -> List[str]:
+    def query(
+        self, input_query: List[str], n_results: int, where: Dict[str, any], skip_embedding: bool
+    ) -> List[Tuple[str, str, str]]:
        """
        query contents from vector database based on vector similarity
        :param input_query: list of query string
@@ -129,16 +131,22 @@ class PineconeDB(BaseVectorDB):
        :type where: Dict[str, any]
        :param skip_embedding: Optional. if True, input_query is already embedded
        :type skip_embedding: bool
-        :return: Database contents that are the result of the query
-        :rtype: List[str]
+        :return: The content of the document that matched your query, url of the source, doc_id
+        :rtype: List[Tuple[str,str,str]]
        """
        if not skip_embedding:
            query_vector = self.embedder.embedding_fn([input_query])[0]
        else:
            query_vector = input_query
-        contents = self.client.query(vector=query_vector, filter=where, top_k=n_results, include_metadata=True)
-        embeddings = list(map(lambda content: content["metadata"]["text"], contents["matches"]))
-        return embeddings
+        data = self.client.query(vector=query_vector, filter=where, top_k=n_results, include_metadata=True)
+        contents = []
+        for doc in data["matches"]:
+            metadata = doc["metadata"]
+            context = metadata["text"]
+            source = metadata["url"]
+            doc_id = metadata["doc_id"]
+            contents.append(tuple((context, source, doc_id)))
+        return contents

    def set_collection_name(self, name: str):
        """
@@ -0,0 +1,232 @@
+import copy
+import os
+import uuid
+from typing import Dict, List, Optional, Tuple
+
+try:
+    from qdrant_client import QdrantClient
+    from qdrant_client.http import models
+    from qdrant_client.http.models import Batch
+    from qdrant_client.models import Distance, VectorParams
+except ImportError:
+    raise ImportError("Qdrant requires extra dependencies. Install with `pip install embedchain[qdrant]`") from None
+
+from embedchain.config.vectordb.qdrant import QdrantDBConfig
+from embedchain.vectordb.base import BaseVectorDB
+
+
+class QdrantDB(BaseVectorDB):
+    """
+    Qdrant as vector database
+    """
+
+    BATCH_SIZE = 10
+
+    def __init__(self, config: QdrantDBConfig = None):
+        """
+        Qdrant as vector database
+        :param config. Qdrant database config to be used for connection
+        """
+        if config is None:
+            config = QdrantDBConfig()
+        else:
+            if not isinstance(config, QdrantDBConfig):
+                raise TypeError(
+                    "config is not a `QdrantDBConfig` instance. "
+                    "Please make sure the type is right and that you are passing an instance."
+                )
+        self.config = config
+        self.client = QdrantClient(url=os.getenv("QDRANT_URL"), api_key=os.getenv("QDRANT_API_KEY"))
+        # Call parent init here because embedder is needed
+        super().__init__(config=self.config)
+
+    def _initialize(self):
+        """
+        This method is needed because `embedder` attribute needs to be set externally before it can be initialized.
+        """
+        if not self.embedder:
+            raise ValueError("Embedder not set. Please set an embedder with `set_embedder` before initialization.")
+
+        self.collection_name = self._get_or_create_collection()
+        self.metadata_keys = {"data_type", "doc_id", "url", "hash", "app_id", "text"}
+        all_collections = self.client.get_collections()
+        collection_names = [collection.name for collection in all_collections.collections]
+        if self.collection_name not in collection_names:
+            self.client.recreate_collection(
+                collection_name=self.collection_name,
+                vectors_config=VectorParams(
+                    size=self.embedder.vector_dimension,
+                    distance=Distance.COSINE,
+                    hnsw_config=self.config.hnsw_config,
+                    quantization_config=self.config.quantization_config,
+                    on_disk=self.config.on_disk,
+                ),
+            )
+
+    def _get_or_create_db(self):
+        return self.client
+
+    def _get_or_create_collection(self):
+        return f"{self.config.collection_name}-{self.embedder.vector_dimension}".lower().replace("_", "-")
+
+    def get(self, ids: Optional[List[str]] = None, where: Optional[Dict[str, any]] = None, limit: Optional[int] = None):
+        """
+        Get existing doc ids present in vector database
+
+        :param ids: _list of doc ids to check for existence
+        :type ids: List[str]
+        :param where: to filter data
+        :type where: Dict[str, any]
+        :param limit: The number of entries to be fetched
+        :type limit: Optional int, defaults to None
+        :return: All the existing IDs
+        :rtype: Set[str]
+        """
+        if ids is None or len(ids) == 0:
+            return {"ids": []}
+
+        keys = set(where.keys() if where is not None else set())
+
+        qdrant_must_filters = [
+            models.FieldCondition(
+                key="identifier",
+                match=models.MatchAny(
+                    any=ids,
+                ),
+            )
+        ]
+        if len(keys.intersection(self.metadata_keys)) != 0:
+            for key in keys.intersection(self.metadata_keys):
+                qdrant_must_filters.append(
+                    models.FieldCondition(
+                        key="metadata.{}".format(key),
+                        match=models.MatchValue(
+                            value=where.get(key),
+                        ),
+                    )
+                )
+
+        offset = 0
+        existing_ids = []
+        while offset is not None:
+            response = self.client.scroll(
+                collection_name=self.collection_name,
+                scroll_filter=models.Filter(must=qdrant_must_filters),
+                offset=offset,
+                limit=self.BATCH_SIZE,
+            )
+            offset = response[1]
+            for doc in response[0]:
+                existing_ids.append(doc.payload["identifier"])
+        return {"ids": existing_ids}
+
+    def add(
+        self,
+        embeddings: List[List[float]],
+        documents: List[str],
+        metadatas: List[object],
+        ids: List[str],
+        skip_embedding: bool,
+    ):
+        """add data in vector database
+        :param embeddings: list of embeddings for the corresponding documents to be added
+        :type documents: List[List[float]]
+        :param documents: list of texts to add
+        :type documents: List[str]
+        :param metadatas: list of metadata associated with docs
+        :type metadatas: List[object]
+        :param ids: ids of docs
+        :type ids: List[str]
+        :param skip_embedding: A boolean flag indicating if the embedding for the documents to be added is to be
+        generated or not
+        :type skip_embedding: bool
+        """
+        if not skip_embedding:
+            embeddings = self.embedder.embedding_fn(documents)
+
+        payloads = []
+        qdrant_ids = []
+        for id, document, metadata in zip(ids, documents, metadatas):
+            metadata["text"] = document
+            qdrant_ids.append(str(uuid.uuid4()))
+            payloads.append({"identifier": id, "text": document, "metadata": copy.deepcopy(metadata)})
+        for i in range(0, len(qdrant_ids), self.BATCH_SIZE):
+            self.client.upsert(
+                collection_name=self.collection_name,
+                points=Batch(
+                    ids=qdrant_ids[i : i + self.BATCH_SIZE],
+                    payloads=payloads[i : i + self.BATCH_SIZE],
+                    vectors=embeddings[i : i + self.BATCH_SIZE],
+                ),
+            )
+
+    def query(
+        self, input_query: List[str], n_results: int, where: Dict[str, any], skip_embedding: bool
+    ) -> List[Tuple[str, str, str]]:
+        """
+        query contents from vector database based on vector similarity
+        :param input_query: list of query string
+        :type input_query: List[str]
+        :param n_results: no of similar documents to fetch from database
+        :type n_results: int
+        :param where: Optional. to filter data
+        :type where: Dict[str, any]
+        :param skip_embedding: A boolean flag indicating if the embedding for the documents to be added is to be
+        generated or not
+        :type skip_embedding: bool
+        :return: The context of the document that matched your query, url of the source, doc_id
+        :rtype: List[Tuple[str,str,str]]
+        """
+        if not skip_embedding:
+            query_vector = self.embedder.embedding_fn([input_query])[0]
+        else:
+            query_vector = input_query
+
+        keys = set(where.keys() if where is not None else set())
+
+        qdrant_must_filters = []
+        if len(keys.intersection(self.metadata_keys)) != 0:
+            for key in keys.intersection(self.metadata_keys):
+                qdrant_must_filters.append(
+                    models.FieldCondition(
+                        key="payload.metadata.{}".format(key),
+                        match=models.MatchValue(
+                            value=where.get(key),
+                        ),
+                    )
+                )
+        results = self.client.search(
+            collection_name=self.collection_name,
+            query_filter=models.Filter(must=qdrant_must_filters),
+            query_vector=query_vector,
+            limit=n_results,
+        )
+
+        response = []
+        for result in results:
+            context = result.payload["text"]
+            metadata = result.payload["metadata"]
+            source = metadata["url"]
+            doc_id = metadata["doc_id"]
+            response.append(tuple((context, source, doc_id)))
+        return response
+
+    def count(self) -> int:
+        response = self.client.get_collection(collection_name=self.collection_name)
+        return response.points_count
+
+    def reset(self):
+        self.client.delete_collection(collection_name=self.collection_name)
+        self._initialize()
+
+    def set_collection_name(self, name: str):
+        """
+        Set the name of the collection. A collection is an isolated space for vectors.
+
+        :param name: Name of the collection.
+        :type name: str
+        """
+        if not isinstance(name, str):
+            raise TypeError("Collection name must be a string")
+        self.config.collection_name = name
+        self.collection_name = self._get_or_create_collection()
@@ -0,0 +1,297 @@
+import copy
+import os
+from typing import Dict, List, Optional, Tuple
+
+try:
+    import weaviate
+except ImportError:
+    raise ImportError(
+        "Weaviate requires extra dependencies. Install with `pip install --upgrade 'embedchain[weaviate]'`"
+    ) from None
+
+from embedchain.config.vectordb.weaviate import WeaviateDBConfig
+from embedchain.helper.json_serializable import register_deserializable
+from embedchain.vectordb.base import BaseVectorDB
+
+
+@register_deserializable
+class WeaviateDB(BaseVectorDB):
+    """
+    Weaviate as vector database
+    """
+
+    BATCH_SIZE = 100
+
+    def __init__(
+        self,
+        config: Optional[WeaviateDBConfig] = None,
+    ):
+        """Weaviate as vector database.
+        :param config: Weaviate database config, defaults to None
+        :type config: WeaviateDBConfig, optional
+        :raises ValueError: No config provided
+        """
+        if config is None:
+            self.config = WeaviateDBConfig()
+        else:
+            if not isinstance(config, WeaviateDBConfig):
+                raise TypeError(
+                    "config is not a `WeaviateDBConfig` instance. "
+                    "Please make sure the type is right and that you are passing an instance."
+                )
+            self.config = config
+        self.client = weaviate.Client(
+            url=os.environ.get("WEAVIATE_ENDPOINT"),
+            auth_client_secret=weaviate.AuthApiKey(api_key=os.environ.get("WEAVIATE_API_KEY")),
+            **self.config.extra_params,
+        )
+
+        # Call parent init here because embedder is needed
+        super().__init__(config=self.config)
+
+    def _initialize(self):
+        """
+        This method is needed because `embedder` attribute needs to be set externally before it can be initialized.
+        """
+
+        if not self.embedder:
+            raise ValueError("Embedder not set. Please set an embedder with `set_embedder` before initialization.")
+
+        self.index_name = self._get_index_name()
+        self.metadata_keys = {"data_type", "doc_id", "url", "hash", "app_id", "text"}
+        if not self.client.schema.exists(self.index_name):
+            # id is a reserved field in Weaviate, hence we had to change the name of the id field to identifier
+            # The none vectorizer is crucial as we have our own custom embedding function
+            class_obj = {
+                "classes": [
+                    {
+                        "class": self.index_name,
+                        "vectorizer": "none",
+                        "properties": [
+                            {
+                                "name": "identifier",
+                                "dataType": ["text"],
+                            },
+                            {
+                                "name": "text",
+                                "dataType": ["text"],
+                            },
+                            {
+                                "name": "metadata",
+                                "dataType": [self.index_name + "_metadata"],
+                            },
+                        ],
+                    },
+                    {
+                        "class": self.index_name + "_metadata",
+                        "vectorizer": "none",
+                        "properties": [
+                            {
+                                "name": "data_type",
+                                "dataType": ["text"],
+                            },
+                            {
+                                "name": "doc_id",
+                                "dataType": ["text"],
+                            },
+                            {
+                                "name": "url",
+                                "dataType": ["text"],
+                            },
+                            {
+                                "name": "hash",
+                                "dataType": ["text"],
+                            },
+                            {
+                                "name": "app_id",
+                                "dataType": ["text"],
+                            },
+                            {
+                                "name": "text",
+                                "dataType": ["text"],
+                            },
+                        ],
+                    },
+                ]
+            }
+
+            self.client.schema.create(class_obj)
+
+    def get(self, ids: Optional[List[str]] = None, where: Optional[Dict[str, any]] = None, limit: Optional[int] = None):
+        """
+        Get existing doc ids present in vector database
+        :param ids: _list of doc ids to check for existance
+        :type ids: List[str]
+        :param where: to filter data
+        :type where: Dict[str, any]
+        :return: ids
+        :rtype: Set[str]
+        """
+
+        if ids is None or len(ids) == 0:
+            return {"ids": []}
+
+        existing_ids = []
+        cursor = None
+        has_iterated_once = False
+        while cursor is not None or not has_iterated_once:
+            has_iterated_once = True
+            results = self._query_with_cursor(
+                self.client.query.get(self.index_name, ["identifier"])
+                .with_additional(["id"])
+                .with_limit(self.BATCH_SIZE),
+                cursor,
+            )
+            fetched_results = results["data"]["Get"].get(self.index_name, [])
+            if len(fetched_results) == 0:
+                break
+            for result in fetched_results:
+                existing_ids.append(result["identifier"])
+                cursor = result["_additional"]["id"]
+
+        return {"ids": existing_ids}
+
+    def add(
+        self,
+        embeddings: List[List[float]],
+        documents: List[str],
+        metadatas: List[object],
+        ids: List[str],
+        skip_embedding: bool,
+    ):
+        """add data in vector database
+        :param embeddings: list of embeddings for the corresponding documents to be added
+        :type documents: List[List[float]]
+        :param documents: list of texts to add
+        :type documents: List[str]
+        :param metadatas: list of metadata associated with docs
+        :type metadatas: List[object]
+        :param ids: ids of docs
+        :type ids: List[str]
+        :param skip_embedding: A boolean flag indicating if the embedding for the documents to be added is to be
+        generated or not
+        :type skip_embedding: bool
+        """
+
+        print("Adding documents to Weaviate...")
+        if not skip_embedding:
+            embeddings = self.embedder.embedding_fn(documents)
+        self.client.batch.configure(batch_size=self.BATCH_SIZE, timeout_retries=3)  # Configure batch
+        with self.client.batch as batch:  # Initialize a batch process
+            for id, text, metadata, embedding in zip(ids, documents, metadatas, embeddings):
+                doc = {"identifier": id, "text": text}
+                updated_metadata = {"text": text}
+                if metadata is not None:
+                    updated_metadata.update(**metadata)
+
+                obj_uuid = batch.add_data_object(
+                    data_object=copy.deepcopy(doc), class_name=self.index_name, vector=embedding
+                )
+                metadata_uuid = batch.add_data_object(
+                    data_object=copy.deepcopy(updated_metadata),
+                    class_name=self.index_name + "_metadata",
+                    vector=embedding,
+                )
+                batch.add_reference(obj_uuid, self.index_name, "metadata", metadata_uuid, self.index_name + "_metadata")
+
+    def query(
+        self, input_query: List[str], n_results: int, where: Dict[str, any], skip_embedding: bool
+    ) -> List[Tuple[str, str, str]]:
+        """
+        query contents from vector database based on vector similarity
+        :param input_query: list of query string
+        :type input_query: List[str]
+        :param n_results: no of similar documents to fetch from database
+        :type n_results: int
+        :param where: Optional. to filter data
+        :type where: Dict[str, any]
+        :param skip_embedding: A boolean flag indicating if the embedding for the documents to be added is to be
+        generated or not
+        :type skip_embedding: bool
+        :return: The context of the document that matched your query, url of the source, doc_id
+        :rtype: List[Tuple[str,str,str]]
+        """
+        if not skip_embedding:
+            query_vector = self.embedder.embedding_fn([input_query])[0]
+        else:
+            query_vector = input_query
+        keys = set(where.keys() if where is not None else set())
+        data_fields = ["text"]
+        if len(keys.intersection(self.metadata_keys)) != 0:
+            weaviate_where_operands = []
+            for key in keys:
+                if key in self.metadata_keys:
+                    weaviate_where_operands.append(
+                        {
+                            "path": ["metadata", self.index_name + "_metadata", key],
+                            "operator": "Equal",
+                            "valueText": where.get(key),
+                        }
+                    )
+            if len(weaviate_where_operands) == 1:
+                weaviate_where_clause = weaviate_where_operands[0]
+            else:
+                weaviate_where_clause = {"operator": "And", "operands": weaviate_where_operands}
+
+            results = (
+                self.client.query.get(self.index_name, data_fields)
+                .with_where(weaviate_where_clause)
+                .with_near_vector({"vector": query_vector})
+                .with_limit(n_results)
+                .do()
+            )
+        else:
+            results = (
+                self.client.query.get(self.index_name, data_fields)
+                .with_near_vector({"vector": query_vector})
+                .with_limit(n_results)
+                .do()
+            )
+        contexts = results["data"]["Get"].get(self.index_name)
+        return contexts
+
+    def set_collection_name(self, name: str):
+        """
+        Set the name of the collection. A collection is an isolated space for vectors.
+        :param name: Name of the collection.
+        :type name: str
+        """
+        if not isinstance(name, str):
+            raise TypeError("Collection name must be a string")
+        self.config.collection_name = name
+
+    def count(self) -> int:
+        """
+        Count number of documents/chunks embedded in the database.
+        :return: number of documents
+        :rtype: int
+        """
+        data = self.client.query.aggregate(self.index_name).with_meta_count().do()
+        return data["data"]["Aggregate"].get(self.index_name)[0]["meta"]["count"]
+
+    def _get_or_create_db(self):
+        """Called during initialization"""
+        return self.client
+
+    def reset(self):
+        """
+        Resets the database. Deletes all embeddings irreversibly.
+        """
+        # Delete all data from the database
+        self.client.batch.delete_objects(
+            self.index_name, where={"path": ["identifier"], "operator": "Like", "valueText": ".*"}
+        )
+
+    # Weaviate internally by default capitalizes the class name
+    def _get_index_name(self) -> str:
+        """Get the Weaviate index for a collection
+        :return: Weaviate index
+        :rtype: str
+        """
+        return f"{self.config.collection_name}_{self.embedder.vector_dimension}".capitalize()
+
+    def _query_with_cursor(self, query, cursor):
+        if cursor is not None:
+            query.with_after(cursor)
+        results = query.do()
+        return results
@@ -1,4 +1,5 @@
-from typing import Dict, List, Optional
+import logging
+from typing import Dict, List, Optional, Tuple

 from embedchain.config import ZillizDBConfig
 from embedchain.helper.json_serializable import register_deserializable
@@ -61,6 +62,7 @@ class ZillizVectorDB(BaseVectorDB):
        :type name: str
        """
        if utility.has_collection(name):
+            logging.info(f"[ZillizDB]: found an existing collection {name}, make sure the auto-id is disabled.")
            self.collection = Collection(name)
        else:
            fields = [
@@ -124,7 +126,9 @@ class ZillizVectorDB(BaseVectorDB):
        self.collection.flush()
        self.client.flush(self.config.collection_name)

-    def query(self, input_query: List[str], n_results: int, where: Dict[str, any], skip_embedding: bool) -> List[str]:
+    def query(
+        self, input_query: List[str], n_results: int, where: Dict[str, any], skip_embedding: bool
+    ) -> List[Tuple[str, str, str]]:
        """
        Query contents from vector data base based on vector similarity

@@ -135,8 +139,8 @@ class ZillizVectorDB(BaseVectorDB):
        :param where: to filter data
        :type where: str
        :raises InvalidDimensionException: Dimensions do not match.
-        :return: The content of the document that matched your query.
-        :rtype: List[str]
+        :return: The context of the document that matched your query, url of the source, doc_id
+        :rtype: List[Tuple[str,str,str]]
        """

        if self.collection.is_empty:
@@ -145,13 +149,14 @@ class ZillizVectorDB(BaseVectorDB):
        if not isinstance(where, str):
            where = None

+        output_fields = ["text", "url", "doc_id"]
        if skip_embedding:
            query_vector = input_query
            query_result = self.client.search(
                collection_name=self.config.collection_name,
                data=query_vector,
                limit=n_results,
-                output_fields=["text"],
+                output_fields=output_fields,
            )

        else:
@@ -162,13 +167,16 @@ class ZillizVectorDB(BaseVectorDB):
                collection_name=self.config.collection_name,
                data=[query_vector],
                limit=n_results,
-                output_fields=["text"],
+                output_fields=output_fields,
            )

        doc_list = []
        for query in query_result:
-            doc_list.append(query[0]["entity"]["text"])
-
+            data = query[0]["entity"]
+            context = data["text"]
+            source = data["url"]
+            doc_id = data["doc_id"]
+            doc_list.append(tuple((context, source, doc_id)))
        return doc_list

    def count(self) -> int:
@@ -8,4 +8,9 @@ COPY . .

 EXPOSE 5000

-CMD ["python", "api_server.py"]
+ENV FLASK_APP=api_server.py
+
+ENV FLASK_RUN_EXTRA_FILES=/usr/src/api/*
+ENV FLASK_ENV=development
+
+CMD ["flask", "run", "--host=0.0.0.0", "--reload"]
@@ -1,3 +1,5 @@
+import logging
+
 from flask import Flask, jsonify, request

 from embedchain import App
@@ -5,11 +7,6 @@ from embedchain import App
 app = Flask(__name__)


-def initialize_chat_bot():
-    global chat_bot
-    chat_bot = App()
-
-
@app.route("/add", methods=["POST"])
 def add():
    data = request.get_json()
@@ -17,9 +14,10 @@ def add():
    url_or_text = data.get("url_or_text")
    if data_type and url_or_text:
        try:
-            chat_bot.add(data_type, url_or_text)
+            App().add(url_or_text, data_type=data_type)
            return jsonify({"data": f"Added {data_type}: {url_or_text}"}), 200
        except Exception:
+            logging.exception(f"Failed to add {data_type=}: {url_or_text=}")
            return jsonify({"error": f"Failed to add {data_type}: {url_or_text}"}), 500
    return jsonify({"error": "Invalid request. Please provide 'data_type' and 'url_or_text' in JSON format."}), 400

@@ -30,9 +28,10 @@ def query():
    question = data.get("question")
    if question:
        try:
-            response = chat_bot.query(question)
+            response = App().query(question)
            return jsonify({"data": response}), 200
        except Exception:
+            logging.exception(f"Failed to query {question=}")
            return jsonify({"error": "An error occurred. Please try again!"}), 500
    return jsonify({"error": "Invalid request. Please provide 'question' in JSON format."}), 400

@@ -43,13 +42,13 @@ def chat():
    question = data.get("question")
    if question:
        try:
-            response = chat_bot.chat(question)
+            response = App().chat(question)
            return jsonify({"data": response}), 200
        except Exception:
+            logging.exception(f"Failed to chat {question=}")
            return jsonify({"error": "An error occurred. Please try again!"}), 500
    return jsonify({"error": "Invalid request. Please provide 'question' in JSON format."}), 400


 if __name__ == "__main__":
-    initialize_chat_bot()
    app.run(host="0.0.0.0", port=5000, debug=False)
@@ -10,4 +10,6 @@ services:
    env_file:
      - variables.env
    ports:
-      - "5000:5000"
+      - "5000:5000"
+    volumes:
+      - .:/usr/src/api
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Sidharth Mohanty	e15ef79ca9	Lazy load loaders and chunkers (#872 )	2023-10-30 11:20:38 -07:00
Deshraj Yadav	bc012a7518	[Docs] Update embedchain docs and analytics (#871 )	2023-10-30 00:38:35 -07:00
Sidharth Mohanty	3b4409cfad	Update notebooks to work with the latest version (#870 )	2023-10-29 23:06:43 -07:00
Deshraj Yadav	d3726134b2	[Docs] Update docs and minor improvements in search API (#869 )	2023-10-29 16:50:14 -07:00
anujshandillya	5acb7f1c55	[fix]: updated twitter logo to new X (#868 )	2023-10-29 14:37:32 -07:00
Deshraj Yadav	81336668b3	[Feature]: Add posthog anonymous telemetry and update docs (#867 )	2023-10-29 01:20:21 -07:00
Deshraj Yadav	35c2b83015	[version] Update openai version to 0.28.0 (#866 )	2023-10-28 19:18:57 -07:00
Deshraj Yadav	cc1ee1deaa	Fix dependencies in base package (#863 )	2023-10-27 21:35:48 -07:00
Deshraj Yadav	29bd038579	[Bug fix] Fix missing dependency issue with gmail (#862 )	2023-10-27 20:02:03 -07:00
Deshraj Yadav	f6c4f86986	[Pipelines] Improvements in pipelines feature (#861 )	2023-10-27 18:42:46 -07:00
Deven Patel	68183e9dce	[Feature] Gmail Loader (#841 )	2023-10-27 18:05:08 -07:00
Deshraj Yadav	78ec91a3a9	[Bug fix] Fix sqlite related issue with api server example (#857 )	2023-10-26 21:54:45 -07:00
Deven Patel	c95d458e52	Add tests for gpt4all llm (#852 )	2023-10-26 20:41:28 -07:00
Deshraj Yadav	191ae3ec1e	Update README (#855 )	2023-10-26 11:11:05 -07:00
Deshraj Yadav	ab9598d00a	Update version to v0.0.77 (#851 )	2023-10-26 09:51:51 -07:00
Deven Patel	0f8a2e624a	[Improvement] Add support for gpt4all through langchain (#838 )	2023-10-25 22:25:00 -07:00
Deven Patel	d77e8da3f3	[Feature] Update `db.query` to return source of context (#831 )	2023-10-25 22:20:32 -07:00
Sidharth Mohanty	a27eeb3255	[Bug fix] import App shouldn't throw other llm deps errors (#837 )	2023-10-25 20:48:53 -07:00
Deshraj Yadav	413ccb83e6	[Bug fix] Fix issues related to creating pipelines (#850 )	2023-10-25 20:26:58 -07:00
Deven Patel	797bb567c6	[feat]: Add openapi spec data loader (#818 )	2023-10-25 14:19:13 -07:00
Deshraj Yadav	f2a5dc40ee	Update package version to v0.0.76 (#849 )	2023-10-25 13:40:00 -07:00
Deshraj Yadav	3979480532	[Feature] Add support for deploying local pipelines to Embedchain platform (#847 )	2023-10-25 13:36:24 -07:00
Abhinesh	76f1993e7a	Update CONTRIBUTING.md (#845 )	2023-10-25 13:31:39 -07:00
Deshraj Yadav	d783fa2b89	[chore]: Add poetry lock file (#848 )	2023-10-25 12:53:06 -07:00
Sidharth Mohanty	bbce18caac	[Bug fix] `reset()` erases everything from db (#844 )	2023-10-25 10:22:20 -07:00
Deshraj Yadav	3ce2d8a656	Bump version to v0.0.75 (#836 )	2023-10-19 21:06:49 -07:00
Deshraj Yadav	a5c86a2f5c	[Bugfix]: Fix issue of context overspilling into other apps (#835 )	2023-10-19 17:46:33 -07:00
Deshraj Yadav	d18e533adf	[Feature] Setup base for creating pipelines in embedchain (#834 )	2023-10-19 17:46:15 -07:00
Sidharth Mohanty	2b881aaad0	Cache dependencies in CI (#832 )	2023-10-19 13:59:02 -07:00
Sidharth Mohanty	39cc07608f	[chore] Update repl links to launch main.py directly (#830 )	2023-10-19 13:53:07 -07:00
Sidharth Mohanty	9894cfcced	[docs] add links to example repositories (#833 )	2023-10-19 13:51:08 -07:00
Sidharth Mohanty	b5d80be037	Remove person_app, open_source app, llama2_app with their configs (#829 )	2023-10-19 01:13:52 -07:00
Sidharth Mohanty	b7870fbd9b	[Bug fix] Table content alignment in example docs table (#828 )	2023-10-18 21:38:31 -07:00
Sidharth Mohanty	36e6d486fc	[chore] Remove `image.jpg` produced by tests and add cleanup in test (#827 )	2023-10-18 21:38:00 -07:00
Sidharth Mohanty	2d5dc84f1a	[chore] Update LLM YAML config docs (#826 )	2023-10-18 21:37:45 -07:00
Deshraj Yadav	b47405e1bd	Update version to v0.0.74 (#825 )	2023-10-18 17:12:51 -07:00
Muhammad Muzammil	8b64deab40	[Feature]: Unstructured File Loader Support - USF (#815 )	2023-10-18 16:43:41 -07:00
Rupesh Bansal	c8846e0e93	[Feature] Add Qdrant support (#822 )	2023-10-18 14:27:57 -07:00
Deven Patel	7641cba01d	[Feature] JSON data loader support (#816 )	2023-10-18 13:53:15 -07:00
Sidharth Mohanty	4dc1785ef1	[docs] Examples table with notebooks and replit links (#823 )	2023-10-18 10:29:41 -07:00
Sidharth Mohanty	b2286f3e34	Google Colab Notebooks for LLMs, Embedders and VectorDBs (#821 )	2023-10-18 09:04:06 -07:00
Sidharth Mohanty	65a20aa457	[Bug fix] Anthropic, Llama2 and VertexAI LLMs dependencies (#820 )	2023-10-18 01:10:46 -07:00
Rupesh Bansal	d8a7d71344	[Feature] Batch uploading in chromadb (#814 )	2023-10-17 22:22:29 -07:00
Deshraj Yadav	bb490df9a6	Bump version to v0.0.73 (#819 )	2023-10-17 22:19:22 -07:00
Rupesh Bansal	cdfd6519c8	[Feature] Add support for weaviate vector db (#782 )	2023-10-17 22:18:53 -07:00
Sidharth Mohanty	e8a2846449	Improve and add more tests (#807 )	2023-10-17 14:06:47 -07:00
Sidharth Mohanty	d065cbf934	[Bug fix] Qna pair not loading properly (#817 )	2023-10-17 11:34:03 -07:00
Sidharth Mohanty	413b107b9a	Update dependencies for fast installation (#811 )	2023-10-17 08:21:30 -07:00
Deshraj Yadav	c336292346	Update version to v0.0.72 (#809 )	2023-10-16 13:33:36 -07:00
Deshraj Yadav	adf50f1e81	[Docs] Add docs for Azure OpenAI provider (#804 )	2023-10-16 13:31:56 -07:00