[BugFix] Skip checking thread when making sqlite connection (#978)

Co-authored-by: Deven Patel <deven298@yahoo.com>

.github/workflows/ci.yml

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

configs/chroma.yaml

@@ -23,4 +23,3 @@ embedder:
   provider: openai
   config:
     model: 'text-embedding-ada-002'
-    deployment_name: 'test-deployment'

configs/gpt4.yaml

@@ -0,0 +1,8 @@
+llm:
+  provider: openai
+  config:
+    model: 'gpt-4'
+    temperature: 0.5
+    max_tokens: 1000
+    top_p: 1
+    stream: false

configs/opensearch.yaml

@@ -1,7 +1,7 @@
 app:
   config:
     id: 'my-app'
-    log_level: 'WARN'
+    log_level: 'WARNING'
     collect_metrics: true
     collection_name: 'my-app'
 
@@ -30,4 +30,4 @@ embedder:
   provider: openai
   config:
     model: 'text-embedding-ada-002'
-    deployment_name: null
+    deployment_name: 'my-app'

docs/components/embedding-models.mdx

@@ -55,8 +55,8 @@ 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["AZURE_OPENAI_ENDPOINT"] = "https://xxx.openai.azure.com/"
+os.environ["AZURE_OPENAI_API_KEY"] = "xxx"
 os.environ["OPENAI_API_VERSION"] = "xxx"
 
 app = App.from_config(yaml_path="config.yaml")

docs/components/llms.mdx

@@ -237,7 +237,7 @@ llm:
 Install related dependencies using the following command:
 
 ```bash
-pip install --upgrade 'embedchain[huggingface_hub]'
+pip install --upgrade 'embedchain[huggingface-hub]'
 ```
 
 First, set `HUGGINGFACE_ACCESS_TOKEN` in environment variable which you can obtain from [their platform](https://huggingface.co/settings/tokens).

docs/data-sources/discourse.mdx

@@ -0,0 +1,44 @@
+---
+title: '🗨️ Discourse'
+---
+
+You can now easily load data from your community built with [Discourse](https://discourse.org/).
+
+## Example
+
+1. Setup the Discourse Loader with your community url.
+```Python
+from embedchain.loaders.discourse import DiscourseLoader
+
+dicourse_loader = DiscourseLoader(config={"domain": "https://community.openai.com"})
+```
+
+2. Once you setup the loader, you can create an app and load data using the above discourse loader
+```Python
+import os
+from embedchain.pipeline import Pipeline as App
+
+os.environ["OPENAI_API_KEY"] = "sk-xxx"
+
+app = App()
+
+app.add("openai after:2023-10-1", data_type="discourse", loader=dicourse_loader)
+
+question = "Where can I find the OpenAI API status page?"
+app.query(question)
+# Answer: You can find the OpenAI API status page at https:/status.openai.com/.
+```
+
+NOTE: The `add` function of the app will accept any executable search query to load data. Refer [Discourse API Docs](https://docs.discourse.org/#tag/Search) to learn more about search queries.
+
+3. We automatically create a chunker to chunk your discourse data, however if you wish to provide your own chunker class. Here is how you can do that:
+```Python
+
+from embedchain.chunkers.discourse import DiscourseChunker
+from embedchain.config.add_config import ChunkerConfig
+
+discourse_chunker_config = ChunkerConfig(chunk_size=1000, chunk_overlap=0, length_function=len)
+discourse_chunker = DiscourseChunker(config=discourse_chunker_config)
+
+app.add("openai", data_type='discourse', loader=dicourse_loader, chunker=discourse_chunker)
+```

docs/data-sources/gmail.mdx

@@ -24,12 +24,11 @@ To use this you need to save `credentials.json` in the directory from where you
 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
+from embedchain import Pipeline as App
+
 app = App()
 
-query = "to: me label:inbox"
-app.add(query, data_type=DataType.GMAIL)
+gmail_filter = "to: me label:inbox"
+app.add(gmail_filter, data_type="gmail")
 app.query("Summarize my email conversations")
 ```

docs/data-sources/json.mdx

@@ -2,52 +2,43 @@
 title: '📃 JSON'
 ---
 
-To add any json file, use the data_type as `json`. Headers are included for each line, so if you have an `age` column, `18` will be added as `age: 18`. Eg:
+To add any json file, use the data_type as `json`. Headers are included for each line, so for example if you have a json like `{"age": 18}`, then it will be added as `age: 18`.
 
 Here are the supported sources for loading `json`:
+
 ```
 1. URL - valid url to json file that ends with ".json" extension.
 2. Local file - valid url to local json file that ends with ".json" extension.
 3. String - valid json string (e.g. - app.add('{"foo": "bar"}'))
 ```
 
-If you would like to add other data structures (e.x. list, dict etc.), do:
-```python
-    import json
-    a = {"foo": "bar"}
-    valid_json_string_data = json.dumps(a, indent=0)
+<Tip>
+If you would like to add other data structures (e.g. list, dict etc.), convert it to a valid json first using `json.dumps()` function.
+</Tip>
 
-    b = [{"foo": "bar"}]
-    valid_json_string_data = json.dumps(b, indent=0)
-```
-Example:
-```python
-import os
+## Example
 
-from embedchain.apps.app import App
+<CodeGroup>
 
-os.environ["OPENAI_API_KEY"] = "openai_api_key"
+```python python
+from embedchain import Pipeline as App
 
 app = App()
 
-response = app.query("What is the net worth of Elon Musk as of October 2023?")
+# Add json file
+app.add("temp.json")
 
-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."
+app.query("What is the net worth of Elon Musk as of October 2023?")
+# As of October 2023, Elon Musk's net worth is $255.2 billion.
 ```
-temp.json
-```json
+
+
+```json temp.json
 {
     "question": "What is your net worth, Elon Musk?",
     "answer": "As of October 2023, Elon Musk's net worth is $255.2 billion, making him one of the wealthiest individuals in the world."
 }
 ```
+</CodeGroup>
 
 

docs/data-sources/mysql.mdx

@@ -0,0 +1,48 @@
+---
+title: '🐬 MySQL'
+---
+
+1. Setup the MySQL loader by configuring the SQL db.
+```Python
+from embedchain.loaders.mysql import MySQLLoader
+
+config = {
+    "host": "host",
+    "port": "port",
+    "database": "database",
+    "user": "username",
+    "password": "password",
+}
+
+mysql_loader = MySQLLoader(config=config)
+```
+
+For more details on how to setup with valid config, check MySQL [documentation](https://dev.mysql.com/doc/connector-python/en/connector-python-connectargs.html).
+
+2. Once you setup the loader, you can create an app and load data using the above MySQL loader
+```Python
+import os
+from embedchain.pipeline import Pipeline as App
+
+app = App()
+
+app.add("SELECT * FROM table_name;", data_type='mysql', loader=mysql_loader)
+# Adds `(1, 'What is your net worth, Elon Musk?', "As of October 2023, Elon Musk's net worth is $255.2 billion.")`
+
+response = app.query(question)
+# Answer: As of October 2023, Elon Musk's net worth is $255.2 billion.
+```
+
+NOTE: The `add` function of the app will accept any executable query to load data. DO NOT pass the `CREATE`, `INSERT` queries in `add` function.
+
+3. We automatically create a chunker to chunk your SQL data, however if you wish to provide your own chunker class. Here is how you can do that:
+``Python
+
+from embedchain.chunkers.mysql import MySQLChunker
+from embedchain.config.add_config import ChunkerConfig
+
+mysql_chunker_config = ChunkerConfig(chunk_size=1000, chunk_overlap=0, length_function=len)
+mysql_chunker = MySQLChunker(config=mysql_chunker_config)
+
+app.add("SELECT * FROM table_name;", data_type='mysql', loader=mysql_loader, chunker=mysql_chunker)
+```

docs/data-sources/openapi.mdx

@@ -2,13 +2,10 @@
 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:
+To add any OpenAPI spec yaml file (currently the json file will be detected as JSON data type), use the data_type as 'openapi'. 'openapi' allows remote urls and conventional file paths.
 
 ```python
-from embedchain.apps.app import App
-import os
-
-os.environ["OPENAI_API_KEY"] = "sk-xxx"
+from embedchain import Pipeline as App
 
 app = App()
 
@@ -16,8 +13,10 @@ app.add("https://github.com/openai/openai-openapi/blob/master/openapi.yaml", dat
 # 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?")
+app.query("What can OpenAI API endpoint do? Can you list the things it can learn from?")
 # Answer: The OpenAI API endpoint allows users to interact with OpenAI's models and perform various tasks such as generating text, answering questions, summarizing documents, translating languages, and more. The specific capabilities and tasks that the API can learn from may vary depending on the models and features provided by OpenAI. For more detailed information, it is recommended to refer to the OpenAI API documentation at https://platform.openai.com/docs/api-reference.
 ```
 
-NOTE: The yaml file added to the App must have the required OpenAPI fields otherwise the adding OpenAPI spec will fail. Please refer to [OpenAPI Spec Doc](https://spec.openapis.org/oas/v3.1.0)
+<Note>
+The yaml file added to the App must have the required OpenAPI fields otherwise the adding OpenAPI spec will fail. Please refer to [OpenAPI Spec Doc](https://spec.openapis.org/oas/v3.1.0)
+</Note>

docs/data-sources/overview.mdx

@@ -18,8 +18,12 @@ Embedchain comes with built-in support for various data sources. We handle the c
   <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="📺 youtube video" href="/data-sources/youtube-video"></Card>
   <Card title="📬 Gmail" href="/data-sources/gmail"></Card>
+  <Card title="🐘 Postgres" href="/data-sources/postgres"></Card>
+  <Card title="🐬 MySQL" href="/data-sources/mysql"></Card>
+  <Card title="🤖 Slack" href="/data-sources/slack"></Card>
+  <Card title="🗨️ Discourse" href="/data-sources/discourse"></Card>
 </CardGroup>
 
 <br/ >

docs/data-sources/postgres.mdx

@@ -0,0 +1,64 @@
+---
+title: '🐘 Postgres'
+---
+
+1. Setup the Postgres loader by configuring the postgres db.
+```Python
+from embedchain.loaders.postgres import PostgresLoader
+
+config = {
+    "host": "host_address",
+    "port": "port_number",
+    "dbname": "database_name",
+    "user": "username",
+    "password": "password",
+}
+
+"""
+config = {
+    "url": "your_postgres_url"
+}
+"""
+
+postgres_loader = PostgresLoader(config=config)
+
+```
+
+You can either setup the loader by passing the postgresql url or by providing the config data.
+For more details on how to setup with valid url and config, check postgres [documentation](https://www.postgresql.org/docs/current/libpq-connect.html#LIBPQ-CONNSTRING:~:text=34.1.1.%C2%A0Connection%20Strings-,%23,-Several%20libpq%20functions).
+
+NOTE: if you provide the `url` field in config, all other fields will be ignored.
+
+2. Once you setup the loader, you can create an app and load data using the above postgres loader
+```Python
+import os
+from embedchain.pipeline import Pipeline as App
+
+os.environ["OPENAI_API_KEY"] = "sk-xxx"
+
+app = App()
+
+question = "What is Elon Musk's networth?"
+response = app.query(question)
+# Answer: As of September 2021, Elon Musk's net worth is estimated to be around $250 billion, making him one of the wealthiest individuals in the world. However, please note that net worth can fluctuate over time due to various factors such as stock market changes and business ventures.
+
+app.add("SELECT * FROM table_name;", data_type='postgres', loader=postgres_loader)
+# Adds `(1, 'What is your net worth, Elon Musk?', "As of October 2023, Elon Musk's net worth is $255.2 billion.")`
+
+response = app.query(question)
+# Answer: As of October 2023, Elon Musk's net worth is $255.2 billion.
+```
+
+NOTE: The `add` function of the app will accept any executable query to load data. DO NOT pass the `CREATE`, `INSERT` queries in `add` function as they will result in not adding any data, so it is pointless.
+
+3. We automatically create a chunker to chunk your postgres data, however if you wish to provide your own chunker class. Here is how you can do that:
+```Python
+
+from embedchain.chunkers.postgres import PostgresChunker
+from embedchain.config.add_config import ChunkerConfig
+
+postgres_chunker_config = ChunkerConfig(chunk_size=1000, chunk_overlap=0, length_function=len)
+postgres_chunker = PostgresChunker(config=postgres_chunker_config)
+
+app.add("SELECT * FROM table_name;", data_type='postgres', loader=postgres_loader, chunker=postgres_chunker)
+```

docs/data-sources/slack.mdx

@@ -0,0 +1,54 @@
+---
+title: '🤖 Slack'
+---
+
+## Pre-requisite
+- Download required packages by running `pip install --upgrade "embedchain[slack]"`.
+- Configure your slack bot token as environment variable `SLACK_USER_TOKEN`.
+    - Find your user token on your [Slack Account](https://api.slack.com/authentication/token-types)
+    - Make sure your slack user token includes [search](https://api.slack.com/scopes/search:read) scope.
+
+## Example
+1. Setup the Slack loader by configuring the Slack Webclient.
+```Python
+from embedchain.loaders.slack import SlackLoader
+
+os.environ["SLACK_USER_TOKEN"] = "xoxp-*"
+
+loader = SlackLoader()
+
+"""
+config = {
+    'base_url': slack_app_url,
+    'headers': web_headers,
+    'team_id': slack_team_id,
+}
+
+loader = SlackLoader(config)
+"""
+```
+
+NOTE: you can also pass the `config` with `base_url`, `headers`, `team_id` to setup your SlackLoader.
+
+2. Once you setup the loader, you can create an app and load data using the above slack loader
+```Python
+import os
+from embedchain.pipeline import Pipeline as App
+
+app = App()
+
+app.add("in:random", data_type="slack", loader=loader)
+question = "Which bots are available in the slack workspace's random channel?"
+# Answer: The available bot in the slack workspace's random channel is the Embedchain bot.
+```
+
+3. We automatically create a chunker to chunk your slack data, however if you wish to provide your own chunker class. Here is how you can do that:
+```Python
+from embedchain.chunkers.slack import SlackChunker
+from embedchain.config.add_config import ChunkerConfig
+
+slack_chunker_config = ChunkerConfig(chunk_size=1000, chunk_overlap=0, length_function=len)
+slack_chunker = SlackChunker(config=slack_chunker_config)
+
+app.add(slack_chunker, data_type="slack", loader=loader, chunker=slack_chunker)
+```

docs/data-sources/substack.mdx

@@ -0,0 +1,16 @@
+---
+title: "📝 Substack"
+---
+
+To add any Substack data sources to your app, just add the sitemap.xml of that url as the source and set the data_type to `substack`.
+
+```python
+from embedchain import Pipeline as App
+
+app = App()
+
+# source: for any substack just add the sitemap.xml url
+app.add('https://www.lennysnewsletter.com/sitemap.xml', data_type='substack')
+app.query("Who is Brian Chesky?")
+# Answer: Brian Chesky is the co-founder and CEO of Airbnb.
+```

docs/data-sources/youtube-video.mdx

@@ -1,5 +1,5 @@
 ---
-title: '🎥📺 Youtube video'
+title: '📺 Youtube video'
 ---
 
 

docs/examples/slack_bot.mdx

@@ -15,8 +15,21 @@ channels:read
 chat:write
 ```
 5. Now select the option `Install to Workspace` and after it's done, copy the `Bot User OAuth Token` and set it in your secrets as `SLACK_BOT_TOKEN`.
-6. Run your bot now with `python3 -m embedchain.bots.slack`
-7. Expose your bot to the internet. Default port is `5000`, which can be changed by adding `port --8080` to the startup command. You can use your machine's public IP or DNS. Otherwise, employ a proxy server like [ngrok](https://ngrok.com/) to make your local bot accessible.
+6. Run your bot now,
+<Tabs>
+    <Tab title="docker">
+        ```bash
+        docker run --name slack-bot -e OPENAI_API_KEY=sk-xxx -e SLACK_BOT_TOKEN=xxx -p 8000:8000 embedchain/slack-bot
+        ```
+    </Tab>
+    <Tab title="python">
+        ```bash
+        pip install --upgrade "embedchain[slack]"
+        python3 -m embedchain.bots.slack --port 8000
+        ```
+</Tab>
+</Tabs>
+7. Expose your bot to the internet. You can use your machine's public IP or DNS. Otherwise, employ a proxy server like [ngrok](https://ngrok.com/) to make your local bot accessible.
 8. On the Slack API website go to `Event Subscriptions` on the left Sidebar and turn on `Enable Events`.
 9. In `Request URL`, enter your server or ngrok address.
 10. After it gets verified, click on `Subscribe to bot events`, add `message.channels` Bot User Event and click on `Save Changes`.

docs/examples/telegram_bot.mdx

@@ -1,21 +1,46 @@
 ---
-title: '📱 Telegram Bot'
+title: "📱 Telegram Bot"
 ---
 
 ### 🖼️ Template Setup
 
-- Fork [this](https://replit.com/@taranjeetio/EC-Telegram-Bot-Template?v=1#README.md) replit template.
-- Set your `OPENAI_API_KEY` in Secrets.
 - Open the Telegram app and search for the `BotFather` user.
 - Start a chat with BotFather and use the `/newbot` command to create a new bot.
 - Follow the instructions to choose a name and username for your bot.
 - Once the bot is created, BotFather will provide you with a unique token for your bot.
-- Set this token as `TELEGRAM_BOT_TOKEN` in Secrets.
+
+<Tabs>
+    <Tab title="docker">
+        ```bash
+        docker run --name telegram-bot -e OPENAI_API_KEY=sk-xxx -e TELEGRAM_BOT_TOKEN=xxx -p 8000:8000 embedchain/telegram-bot
+        ```
+
+    <Note>
+    If you wish to use **Docker**, you would need to host your bot on a server.
+    You can use [ngrok](https://ngrok.com/) to expose your localhost to the
+    internet and then set the webhook using the ngrok URL.
+    </Note>
+
+    </Tab>
+    <Tab title="replit">
+    <Card>
+        Fork <ins>**[this](https://replit.com/@taranjeetio/EC-Telegram-Bot-Template?v=1#README.md)**</ins> replit template.
+    </Card>
+
+    - Set your `OPENAI_API_KEY` in Secrets.
+    - Set the unique token as `TELEGRAM_BOT_TOKEN` in Secrets.
+
+    </Tab>
+
+</Tabs>
+
 - Click on `Run` in the replit container and a URL will get generated for your bot.
 - Now set your webhook by running the following link in your browser:
+
 ```url
 https://api.telegram.org/bot<Your_Telegram_Bot_Token>/setWebhook?url=<Replit_Generated_URL>
 ```
+
 - When you get a successful response in your browser, your bot is ready to be used.
 
 ### 🚀 Usage Instructions

docs/examples/whatsapp_bot.mdx

@@ -12,10 +12,19 @@ pip install --upgrade embedchain
 
 2. Launch your WhatsApp bot:
 
+<Tabs>
+    <Tab title="docker">
+        ```bash
+        docker run --name whatsapp-bot -e OPENAI_API_KEY=sk-xxx -p 8000:8000 embedchain/whatsapp-bot
+        ```
+    </Tab>
+    <Tab title="python">
+        ```bash
+        python -m embedchain.bots.whatsapp --port 5000
+        ```
+    </Tab>
+</Tabs>
 
-```bash
-python -m embedchain.bots.whatsapp --port 5000
-```
 
 If your bot needs to be accessible online, use your machine's public IP or DNS. Otherwise, employ a proxy server like [ngrok](https://ngrok.com/) to make your local bot accessible.
 

docs/get-started/openai-assistant.mdx

@@ -8,7 +8,7 @@ Embedchain now supports [OpenAI Assistants API](https://platform.openai.com/docs
 
 At a high level, an integration of the Assistants API has the following flow:
 
-1. Create an Assistant in the API by defining it custom instructions and picking a model
+1. Create an Assistant in the API by defining custom instructions and picking a model
 2. Create a Thread when a user starts a conversation
 3. Add Messages to the Thread as the user ask questions
 4. Run the Assistant on the Thread to trigger responses. This automatically calls the relevant tools.
@@ -19,7 +19,7 @@ Creating an OpenAI Assistant using Embedchain is very simple 3 step process.
 
 Make sure that you have `OPENAI_API_KEY` set in the environment variable.
 
-```python
+```python Initialize
 from embedchain.store.assistants import OpenAIAssistant
 
 assistant = OpenAIAssistant(
@@ -28,10 +28,28 @@ assistant = OpenAIAssistant(
 )
 ```
 
+If you want to use the existing assistant, you can do something like this:
+
+```python Initialize
+# Load an assistant and create a new thread
+assistant = OpenAIAssistant(assistant_id="asst_xxx")
+
+# Load a specific thread for an assistant
+assistant = OpenAIAssistant(assistant_id="asst_xxx", thread_id="thread_xxx")
+```
+
 ### Arguments
 
-<ResponseField name="assistant_id" type="string" required>
-  Load existing OpenAI Assistant. If you pass this, you don't have to pass other arguments
+<ResponseField name="name" type="string">
+  Name for your AI assistant
+</ResponseField>
+
+<ResponseField name="instructions" type="string">
+  how the Assistant and model should behave or respond
+</ResponseField>
+
+<ResponseField name="assistant_id" type="string">
+  Load existing OpenAI Assistant. If you pass this, you don't have to pass other arguments.
 </ResponseField>
 
 <ResponseField name="thread_id" type="string">
@@ -50,17 +68,21 @@ assistant = OpenAIAssistant(
   Add data sources to your assistant. You can add in the following format: `[{"source": "https://example.com", "data_type": "web_page"}]`
 </ResponseField>
 
+<ResponseField name="telemetry" type="boolean" default="True">
+  Anonymous telemetry (doesn't collect any user information or user's files). Used to improve the Embedchain package utilization. Default is `True`.
+</ResponseField>
+
 ## Step-2: Add data to thread
 
 You can add any custom data source that is supported by Embedchain. Else, you can directly pass the file path on your local system and Embedchain propagates it to OpenAI Assistant.
-```python
+```python Add data
 assistant.add("/path/to/file.pdf")
-assistant.add("https://www.youtube.com/watch?v=U9mJuUkhUzk", data_type="youtube_video")
+assistant.add("https://www.youtube.com/watch?v=U9mJuUkhUzk")
 assistant.add("https://openai.com/blog/new-models-and-developer-products-announced-at-devday")
 ```
 
 ## Step-3: Chat with your Assistant
-```python
+```python Chat
 assistant.chat("How much OpenAI credits were offered to attendees during OpenAI DevDay?")
 # Response: 'Every attendee of OpenAI DevDay 2023 was offered $500 in OpenAI credits.'
 ```

docs/get-started/quickstart.mdx

@@ -83,3 +83,9 @@ app.deploy()
 # 🛠️ Adding data to your pipeline...
 # ✅ Data of type: web_page, value: https://www.forbes.com/profile/elon-musk added successfully.
 ```
+
+You can try it out yourself using the following Google Colab notebook:
+
+<a href="https://colab.research.google.com/drive/17ON1LPonnXAtLaZEebnOktstB_1cJJmh?usp=sharing">
+    <img src="https://camo.githubusercontent.com/84f0493939e0c4de4e6dbe113251b4bfb5353e57134ffd9fcab6b8714514d4d1/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open in Colab" />
+</a>

docs/mint.json

@@ -87,7 +87,9 @@
             "data-sources/text",
             "data-sources/web-page",
             "data-sources/openapi",
-            "data-sources/youtube-video"
+            "data-sources/youtube-video",
+            "data-sources/discourse",
+            "data-sources/substack"
           ]
         },
         "data-sources/data-type-handling"

embedchain/__init__.py

@@ -6,3 +6,6 @@ from embedchain.apps.app import App  # noqa: F401
 from embedchain.client import Client  # noqa: F401
 from embedchain.pipeline import Pipeline  # noqa: F401
 from embedchain.vectordb.chroma import ChromaDB  # noqa: F401
+
+# Setup the user directory if doesn't exist already
+Client.setup_dir()

embedchain/apps/app.py

@@ -9,7 +9,7 @@ from embedchain.embedchain import 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.helpers.json_serializable import register_deserializable
 from embedchain.llm.base import BaseLlm
 from embedchain.llm.openai import OpenAILlm
 from embedchain.utils import validate_yaml_config

embedchain/bots/base.py

@@ -1,10 +1,10 @@
 from typing import Any
 
-from embedchain import App
-from embedchain.config import AddConfig, AppConfig, BaseLlmConfig
+from embedchain import Pipeline as App
+from embedchain.config import AddConfig, BaseLlmConfig, PipelineConfig
 from embedchain.embedder.openai import OpenAIEmbedder
-from embedchain.helper.json_serializable import (JSONSerializable,
-                                                 register_deserializable)
+from embedchain.helpers.json_serializable import (JSONSerializable,
+                                                  register_deserializable)
 from embedchain.llm.openai import OpenAILlm
 from embedchain.vectordb.chroma import ChromaDB
 
@@ -12,7 +12,7 @@ from embedchain.vectordb.chroma import ChromaDB
 @register_deserializable
 class BaseBot(JSONSerializable):
     def __init__(self):
-        self.app = App(config=AppConfig(), llm=OpenAILlm(), db=ChromaDB(), embedder=OpenAIEmbedder())
+        self.app = App(config=PipelineConfig(), llm=OpenAILlm(), db=ChromaDB(), embedding_model=OpenAIEmbedder())
 
     def add(self, data: Any, config: AddConfig = None):
         """

embedchain/bots/discord.py

@@ -2,7 +2,7 @@ import argparse
 import logging
 import os
 
-from embedchain.helper.json_serializable import register_deserializable
+from embedchain.helpers.json_serializable import register_deserializable
 
 from .base import BaseBot
 

embedchain/bots/poe.py

@@ -3,7 +3,7 @@ import logging
 import os
 from typing import List, Optional
 
-from embedchain.helper.json_serializable import register_deserializable
+from embedchain.helpers.json_serializable import register_deserializable
 
 from .base import BaseBot
 

embedchain/bots/slack.py

@@ -5,7 +5,7 @@ import signal
 import sys
 
 from embedchain import App
-from embedchain.helper.json_serializable import register_deserializable
+from embedchain.helpers.json_serializable import register_deserializable
 
 from .base import BaseBot
 

embedchain/bots/whatsapp.py

@@ -4,7 +4,7 @@ import logging
 import signal
 import sys
 
-from embedchain.helper.json_serializable import register_deserializable
+from embedchain.helpers.json_serializable import register_deserializable
 
 from .base import BaseBot
 

embedchain/chunkers/base_chunker.py

@@ -1,6 +1,6 @@
 import hashlib
 
-from embedchain.helper.json_serializable import JSONSerializable
+from embedchain.helpers.json_serializable import JSONSerializable
 from embedchain.models.data_type import DataType
 
 

embedchain/chunkers/common_chunker.py

@@ -0,0 +1,22 @@
+from typing import Optional
+
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+
+from embedchain.chunkers.base_chunker import BaseChunker
+from embedchain.config.add_config import ChunkerConfig
+from embedchain.helpers.json_serializable import register_deserializable
+
+
+@register_deserializable
+class CommonChunker(BaseChunker):
+    """Common chunker for all loaders."""
+
+    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)

embedchain/chunkers/discourse.py

@@ -0,0 +1,22 @@
+from typing import Optional
+
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+
+from embedchain.chunkers.base_chunker import BaseChunker
+from embedchain.config.add_config import ChunkerConfig
+from embedchain.helpers.json_serializable import register_deserializable
+
+
+@register_deserializable
+class DiscourseChunker(BaseChunker):
+    """Chunker for discourse."""
+
+    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)

embedchain/chunkers/docs_site.py

@@ -4,7 +4,7 @@ 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
+from embedchain.helpers.json_serializable import register_deserializable
 
 
 @register_deserializable

embedchain/chunkers/docx_file.py

@@ -4,7 +4,7 @@ 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
+from embedchain.helpers.json_serializable import register_deserializable
 
 
 @register_deserializable

embedchain/chunkers/gmail.py

@@ -4,7 +4,7 @@ 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
+from embedchain.helpers.json_serializable import register_deserializable
 
 
 @register_deserializable

embedchain/chunkers/json.py

@@ -4,7 +4,7 @@ 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
+from embedchain.helpers.json_serializable import register_deserializable
 
 
 @register_deserializable

embedchain/chunkers/mdx.py

@@ -4,7 +4,7 @@ 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
+from embedchain.helpers.json_serializable import register_deserializable
 
 
 @register_deserializable

embedchain/chunkers/mysql.py

@@ -0,0 +1,22 @@
+from typing import Optional
+
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+
+from embedchain.chunkers.base_chunker import BaseChunker
+from embedchain.config.add_config import ChunkerConfig
+from embedchain.helpers.json_serializable import register_deserializable
+
+
+@register_deserializable
+class MySQLChunker(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)

embedchain/chunkers/notion.py

@@ -4,7 +4,7 @@ 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
+from embedchain.helpers.json_serializable import register_deserializable
 
 
 @register_deserializable

embedchain/chunkers/pdf_file.py

@@ -4,7 +4,7 @@ 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
+from embedchain.helpers.json_serializable import register_deserializable
 
 
 @register_deserializable

embedchain/chunkers/postgres.py

@@ -0,0 +1,22 @@
+from typing import Optional
+
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+
+from embedchain.chunkers.base_chunker import BaseChunker
+from embedchain.config.add_config import ChunkerConfig
+from embedchain.helpers.json_serializable import register_deserializable
+
+
+@register_deserializable
+class PostgresChunker(BaseChunker):
+    """Chunker for postgres."""
+
+    def __init__(self, config: Optional[ChunkerConfig] = None):
+        if config is None:
+            config = ChunkerConfig(chunk_size=1000, chunk_overlap=0, length_function=len)
+        text_splitter = RecursiveCharacterTextSplitter(
+            chunk_size=config.chunk_size,
+            chunk_overlap=config.chunk_overlap,
+            length_function=config.length_function,
+        )
+        super().__init__(text_splitter)

embedchain/chunkers/qna_pair.py

@@ -4,7 +4,7 @@ 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
+from embedchain.helpers.json_serializable import register_deserializable
 
 
 @register_deserializable

embedchain/chunkers/sitemap.py

@@ -4,7 +4,7 @@ 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
+from embedchain.helpers.json_serializable import register_deserializable
 
 
 @register_deserializable

embedchain/chunkers/slack.py

@@ -0,0 +1,22 @@
+from typing import Optional
+
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+
+from embedchain.chunkers.base_chunker import BaseChunker
+from embedchain.config.add_config import ChunkerConfig
+from embedchain.helpers.json_serializable import register_deserializable
+
+
+@register_deserializable
+class SlackChunker(BaseChunker):
+    """Chunker for postgres."""
+
+    def __init__(self, config: Optional[ChunkerConfig] = None):
+        if config is None:
+            config = ChunkerConfig(chunk_size=1000, chunk_overlap=0, length_function=len)
+        text_splitter = RecursiveCharacterTextSplitter(
+            chunk_size=config.chunk_size,
+            chunk_overlap=config.chunk_overlap,
+            length_function=config.length_function,
+        )
+        super().__init__(text_splitter)

embedchain/chunkers/substack.py

@@ -0,0 +1,22 @@
+from typing import Optional
+
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+
+from embedchain.chunkers.base_chunker import BaseChunker
+from embedchain.config.add_config import ChunkerConfig
+from embedchain.helpers.json_serializable import register_deserializable
+
+
+@register_deserializable
+class SubstackChunker(BaseChunker):
+    """Chunker for Substack."""
+
+    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)

embedchain/chunkers/text.py

@@ -4,7 +4,7 @@ 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
+from embedchain.helpers.json_serializable import register_deserializable
 
 
 @register_deserializable

embedchain/chunkers/unstructured_file.py

@@ -4,7 +4,7 @@ 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
+from embedchain.helpers.json_serializable import register_deserializable
 
 
 @register_deserializable

embedchain/chunkers/web_page.py

@@ -4,7 +4,7 @@ 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
+from embedchain.helpers.json_serializable import register_deserializable
 
 
 @register_deserializable

embedchain/chunkers/xml.py

@@ -4,7 +4,7 @@ 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
+from embedchain.helpers.json_serializable import register_deserializable
 
 
 @register_deserializable

embedchain/chunkers/youtube_video.py

@@ -4,7 +4,7 @@ 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
+from embedchain.helpers.json_serializable import register_deserializable
 
 
 @register_deserializable

embedchain/client.py

@@ -5,7 +5,7 @@ import uuid
 
 import requests
 
-from embedchain.embedchain import CONFIG_DIR, CONFIG_FILE
+from embedchain.constants import CONFIG_DIR, CONFIG_FILE
 
 
 class Client:

embedchain/config/add_config.py

@@ -3,7 +3,7 @@ from importlib import import_module
 from typing import Callable, Optional
 
 from embedchain.config.base_config import BaseConfig
-from embedchain.helper.json_serializable import register_deserializable
+from embedchain.helpers.json_serializable import register_deserializable
 
 
 @register_deserializable

embedchain/config/apps/app_config.py

@@ -1,6 +1,6 @@
 from typing import Optional
 
-from embedchain.helper.json_serializable import register_deserializable
+from embedchain.helpers.json_serializable import register_deserializable
 
 from .base_app_config import BaseAppConfig
 

embedchain/config/apps/base_app_config.py

@@ -2,7 +2,7 @@ import logging
 from typing import Optional
 
 from embedchain.config.base_config import BaseConfig
-from embedchain.helper.json_serializable import JSONSerializable
+from embedchain.helpers.json_serializable import JSONSerializable
 from embedchain.vectordb.base import BaseVectorDB
 
 

embedchain/config/base_config.py

@@ -1,6 +1,6 @@
 from typing import Any, Dict
 
-from embedchain.helper.json_serializable import JSONSerializable
+from embedchain.helpers.json_serializable import JSONSerializable
 
 
 class BaseConfig(JSONSerializable):

embedchain/config/embedder/base.py

@@ -1,6 +1,6 @@
 from typing import Optional
 
-from embedchain.helper.json_serializable import register_deserializable
+from embedchain.helpers.json_serializable import register_deserializable
 
 
 @register_deserializable

embedchain/config/llm/base.py

@@ -1,9 +1,9 @@
 import re
 from string import Template
-from typing import Any, Dict, Optional
+from typing import Any, Dict, List, Optional
 
 from embedchain.config.base_config import BaseConfig
-from embedchain.helper.json_serializable import register_deserializable
+from embedchain.helpers.json_serializable import register_deserializable
 
 DEFAULT_PROMPT = """
   Use the following pieces of context to answer the query at the end.
@@ -68,6 +68,7 @@ class BaseLlmConfig(BaseConfig):
         system_prompt: Optional[str] = None,
         where: Dict[str, Any] = None,
         query_type: Optional[str] = None,
+        callbacks: Optional[List] = None,
     ):
         """
         Initializes a configuration class instance for the LLM.
@@ -98,6 +99,8 @@ class BaseLlmConfig(BaseConfig):
         :type system_prompt: Optional[str], optional
         :param where: A dictionary of key-value pairs to filter the database results., defaults to None
         :type where: Dict[str, Any], optional
+        :param callbacks: Langchain callback functions to use, defaults to None
+        :type callbacks: Optional[List], optional
         :raises ValueError: If the template is not valid as template should
         contain $context and $query (and optionally $history)
         :raises ValueError: Stream is not boolean
@@ -113,6 +116,7 @@ class BaseLlmConfig(BaseConfig):
         self.deployment_name = deployment_name
         self.system_prompt = system_prompt
         self.query_type = query_type
+        self.callbacks = callbacks
 
         if type(template) is str:
             template = Template(template)

embedchain/config/pipeline_config.py

@@ -1,6 +1,6 @@
 from typing import Optional
 
-from embedchain.helper.json_serializable import register_deserializable
+from embedchain.helpers.json_serializable import register_deserializable
 
 from .apps.base_app_config import BaseAppConfig
 

embedchain/config/vectordb/chroma.py

@@ -1,7 +1,7 @@
 from typing import Optional
 
 from embedchain.config.vectordb.base import BaseVectorDbConfig
-from embedchain.helper.json_serializable import register_deserializable
+from embedchain.helpers.json_serializable import register_deserializable
 
 
 @register_deserializable

embedchain/config/vectordb/elasticsearch.py

@@ -2,7 +2,7 @@ import os
 from typing import Dict, List, Optional, Union
 
 from embedchain.config.vectordb.base import BaseVectorDbConfig
-from embedchain.helper.json_serializable import register_deserializable
+from embedchain.helpers.json_serializable import register_deserializable
 
 
 @register_deserializable

embedchain/config/vectordb/opensearch.py

@@ -1,7 +1,7 @@
 from typing import Dict, Optional, Tuple
 
 from embedchain.config.vectordb.base import BaseVectorDbConfig
-from embedchain.helper.json_serializable import register_deserializable
+from embedchain.helpers.json_serializable import register_deserializable
 
 
 @register_deserializable

embedchain/config/vectordb/pinecone.py

@@ -1,7 +1,7 @@
 from typing import Dict, Optional
 
 from embedchain.config.vectordb.base import BaseVectorDbConfig
-from embedchain.helper.json_serializable import register_deserializable
+from embedchain.helpers.json_serializable import register_deserializable
 
 
 @register_deserializable

embedchain/config/vectordb/qdrant.py

@@ -1,7 +1,7 @@
 from typing import Dict, Optional
 
 from embedchain.config.vectordb.base import BaseVectorDbConfig
-from embedchain.helper.json_serializable import register_deserializable
+from embedchain.helpers.json_serializable import register_deserializable
 
 
 @register_deserializable

embedchain/config/vectordb/weaviate.py

@@ -1,7 +1,7 @@
 from typing import Dict, Optional
 
 from embedchain.config.vectordb.base import BaseVectorDbConfig
-from embedchain.helper.json_serializable import register_deserializable
+from embedchain.helpers.json_serializable import register_deserializable
 
 
 @register_deserializable

embedchain/config/vectordb/zilliz.py

@@ -2,7 +2,7 @@ import os
 from typing import Optional
 
 from embedchain.config.vectordb.base import BaseVectorDbConfig
-from embedchain.helper.json_serializable import register_deserializable
+from embedchain.helpers.json_serializable import register_deserializable
 
 
 @register_deserializable

embedchain/constants.py

@@ -0,0 +1,8 @@
+import os
+from pathlib import Path
+
+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")

embedchain/data_formatter/data_formatter.py

@@ -1,9 +1,10 @@
 from importlib import import_module
+from typing import Any, Dict
 
 from embedchain.chunkers.base_chunker import BaseChunker
 from embedchain.config import AddConfig
 from embedchain.config.add_config import ChunkerConfig, LoaderConfig
-from embedchain.helper.json_serializable import JSONSerializable
+from embedchain.helpers.json_serializable import JSONSerializable
 from embedchain.loaders.base_loader import BaseLoader
 from embedchain.models.data_type import DataType
 
@@ -15,7 +16,7 @@ class DataFormatter(JSONSerializable):
     .add or .add_local method call
     """
 
-    def __init__(self, data_type: DataType, config: AddConfig):
+    def __init__(self, data_type: DataType, config: AddConfig, kwargs: Dict[str, Any]):
         """
         Initialize a dataformatter, set data type and chunker based on datatype.
 
@@ -24,15 +25,15 @@ class DataFormatter(JSONSerializable):
         :param config: AddConfig instance with nested loader and chunker config attributes.
         :type config: AddConfig
         """
-        self.loader = self._get_loader(data_type=data_type, config=config.loader)
-        self.chunker = self._get_chunker(data_type=data_type, config=config.chunker)
+        self.loader = self._get_loader(data_type=data_type, config=config.loader, kwargs=kwargs)
+        self.chunker = self._get_chunker(data_type=data_type, config=config.chunker, kwargs=kwargs)
 
     def _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:
+    def _get_loader(self, data_type: DataType, config: LoaderConfig, kwargs: Dict[str, Any]) -> BaseLoader:
         """
         Returns the appropriate data loader for the given data type.
 
@@ -62,14 +63,35 @@ class DataFormatter(JSONSerializable):
             DataType.OPENAPI: "embedchain.loaders.openapi.OpenAPILoader",
             DataType.GMAIL: "embedchain.loaders.gmail.GmailLoader",
             DataType.NOTION: "embedchain.loaders.notion.NotionLoader",
+            DataType.SUBSTACK: "embedchain.loaders.substack.SubstackLoader",
+            DataType.GITHUB: "embedchain.loaders.github.GithubLoader",
+            DataType.YOUTUBE_CHANNEL: "embedchain.loaders.youtube_channel.YoutubeChannelLoader",
         }
+
+        custom_loaders = set(
+            [
+                DataType.POSTGRES,
+                DataType.MYSQL,
+                DataType.SLACK,
+                DataType.DISCOURSE,
+            ]
+        )
+
         if data_type in loaders:
             loader_class: type = self._lazy_load(loaders[data_type])
             return loader_class()
-        else:
-            raise ValueError(f"Unsupported data type: {data_type}")
+        elif data_type in custom_loaders:
+            loader_class: type = kwargs.get("loader", None)
+            if loader_class is not None:
+                return loader_class
 
-    def _get_chunker(self, data_type: DataType, config: ChunkerConfig) -> BaseChunker:
+        raise ValueError(
+            f"Cant find the loader for {data_type}.\
+                    We recommend to pass the loader to use data_type: {data_type},\
+                        check `https://docs.embedchain.ai/data-sources/overview`."
+        )
+
+    def _get_chunker(self, data_type: DataType, config: ChunkerConfig, kwargs: Dict[str, Any]) -> BaseChunker:
         """Returns the appropriate chunker for the given data type (updated for lazy loading)."""
         chunker_classes = {
             DataType.YOUTUBE_VIDEO: "embedchain.chunkers.youtube_video.YoutubeVideoChunker",
@@ -89,12 +111,27 @@ class DataFormatter(JSONSerializable):
             DataType.OPENAPI: "embedchain.chunkers.openapi.OpenAPIChunker",
             DataType.GMAIL: "embedchain.chunkers.gmail.GmailChunker",
             DataType.NOTION: "embedchain.chunkers.notion.NotionChunker",
+            DataType.POSTGRES: "embedchain.chunkers.postgres.PostgresChunker",
+            DataType.MYSQL: "embedchain.chunkers.mysql.MySQLChunker",
+            DataType.SLACK: "embedchain.chunkers.slack.SlackChunker",
+            DataType.DISCOURSE: "embedchain.chunkers.discourse.DiscourseChunker",
+            DataType.SUBSTACK: "embedchain.chunkers.substack.SubstackChunker",
+            DataType.GITHUB: "embedchain.chunkers.common_chunker.CommonChunker",
+            DataType.YOUTUBE_CHANNEL: "embedchain.chunkers.common_chunker.CommonChunker",
         }
 
         if data_type in chunker_classes:
-            chunker_class = self._lazy_load(chunker_classes[data_type])
+            if "chunker" in kwargs:
+                chunker_class = kwargs.get("chunker")
+            else:
+                chunker_class = self._lazy_load(chunker_classes[data_type])
+
             chunker = chunker_class(config)
             chunker.set_data_type(data_type)
             return chunker
         else:
-            raise ValueError(f"Unsupported data type: {data_type}")
+            raise ValueError(
+                f"Cant find the chunker for {data_type}.\
+                    We recommend to pass the chunker to use data_type: {data_type},\
+                        check `https://docs.embedchain.ai/data-sources/overview`."
+            )

embedchain/embedchain.py

@@ -1,9 +1,7 @@
 import hashlib
 import json
 import logging
-import os
 import sqlite3
-from pathlib import Path
 from typing import Any, Dict, List, Optional, Tuple, Union
 
 from dotenv import load_dotenv
@@ -12,9 +10,10 @@ from langchain.docstore.document import Document
 from embedchain.chunkers.base_chunker import BaseChunker
 from embedchain.config import AddConfig, BaseLlmConfig, ChunkerConfig
 from embedchain.config.apps.base_app_config import BaseAppConfig
+from embedchain.constants import SQLITE_PATH
 from embedchain.data_formatter import DataFormatter
 from embedchain.embedder.base import BaseEmbedder
-from embedchain.helper.json_serializable import JSONSerializable
+from embedchain.helpers.json_serializable import JSONSerializable
 from embedchain.llm.base import BaseLlm
 from embedchain.loaders.base_loader import BaseLoader
 from embedchain.models.data_type import (DataType, DirectDataType,
@@ -25,12 +24,6 @@ from embedchain.vectordb.base import BaseVectorDB
 
 load_dotenv()
 
-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):
     def __init__(
@@ -81,6 +74,9 @@ class EmbedChain(JSONSerializable):
         if system_prompt:
             self.llm.config.system_prompt = system_prompt
 
+        # Fetch the history from the database if exists
+        self.llm.update_history(app_id=self.config.id)
+
         # Attributes that aren't subclass related.
         self.user_asks = []
 
@@ -137,6 +133,7 @@ class EmbedChain(JSONSerializable):
         metadata: Optional[Dict[str, Any]] = None,
         config: Optional[AddConfig] = None,
         dry_run=False,
+        **kwargs: Dict[str, Any],
     ):
         """
         Adds the data from the given URL to the vector db.
@@ -180,21 +177,6 @@ class EmbedChain(JSONSerializable):
         if data_type:
             try:
                 data_type = DataType(data_type)
-                if data_type == DataType.JSON:
-                    if isinstance(source, str):
-                        if not is_valid_json_string(source):
-                            raise ValueError(
-                                f"Invalid json input: {source}",
-                                "Provide the correct JSON formatted source, \
-                                    refer `https://docs.embedchain.ai/data-sources/json`",
-                            )
-                    elif not isinstance(source, str):
-                        raise ValueError(
-                            "Invaid content input. \
-                            If you want to upload (list, dict, etc.), do \
-                                `json.dump(data, indent=0)` and add the stringified JSON. \
-                                    Check - `https://docs.embedchain.ai/data-sources/json`"
-                        )
             except ValueError:
                 raise ValueError(
                     f"Invalid data_type: '{data_type}'.",
@@ -218,9 +200,10 @@ class EmbedChain(JSONSerializable):
             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 = DataFormatter(data_type, config, kwargs)
+        documents, metadatas, _ids, new_chunks = self._load_and_embed(
             data_formatter.loader, data_formatter.chunker, source, metadata, source_hash, dry_run
         )
         if data_type in {DataType.DOCS_SITE}:
@@ -265,6 +248,7 @@ class EmbedChain(JSONSerializable):
         data_type: Optional[DataType] = None,
         metadata: Optional[Dict[str, Any]] = None,
         config: Optional[AddConfig] = None,
+        **kwargs: Dict[str, Any],
     ):
         """
         Adds the data from the given URL to the vector db.
@@ -290,7 +274,13 @@ class EmbedChain(JSONSerializable):
         logging.warning(
             "The `add_local` method is deprecated and will be removed in future versions. Please use the `add` method for both local and remote files."  # noqa: E501
         )
-        return self.add(source=source, data_type=data_type, metadata=metadata, config=config)
+        return self.add(
+            source=source,
+            data_type=data_type,
+            metadata=metadata,
+            config=config,
+            kwargs=kwargs,
+        )
 
     def _get_existing_doc_id(self, chunker: BaseChunker, src: Any):
         """
@@ -350,7 +340,7 @@ class EmbedChain(JSONSerializable):
                 "When it should be  DirectDataType, IndirectDataType or SpecialDataType."
             )
 
-    def load_and_embed(
+    def _load_and_embed(
         self,
         loader: BaseLoader,
         chunker: BaseChunker,
@@ -467,7 +457,7 @@ class EmbedChain(JSONSerializable):
             )
         ]
 
-    def retrieve_from_database(
+    def _retrieve_from_database(
         self, input_query: str, config: Optional[BaseLlmConfig] = None, where=None, citations: bool = False
     ) -> Union[List[Tuple[str, str, str]], List[str]]:
         """
@@ -488,13 +478,13 @@ class EmbedChain(JSONSerializable):
         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:
-            where = query_config.where
         else:
             where = {}
+            if query_config is not None and query_config.where is not None:
+                where = query_config.where
 
-        if self.config.id is not None:
-            where.update({"app_id": self.config.id})
+            if self.config.id is not None:
+                where.update({"app_id": self.config.id})
 
         # We cannot query the database with the input query in case of an image search. This is because we need
         # to bring down both the image and text to the same dimension to be able to compare them.
@@ -547,7 +537,9 @@ class EmbedChain(JSONSerializable):
         :rtype: str, if citations is False, otherwise Tuple[str,List[Tuple[str,str,str]]]
         """
         citations = kwargs.get("citations", False)
-        contexts = self.retrieve_from_database(input_query=input_query, config=config, where=where, citations=citations)
+        contexts = self._retrieve_from_database(
+            input_query=input_query, config=config, where=where, citations=citations
+        )
         if citations and len(contexts) > 0 and isinstance(contexts[0], tuple):
             contexts_data_for_llm_query = list(map(lambda x: x[0], contexts))
         else:
@@ -598,7 +590,9 @@ class EmbedChain(JSONSerializable):
         :rtype: str, if citations is False, otherwise Tuple[str,List[Tuple[str,str,str]]]
         """
         citations = kwargs.get("citations", False)
-        contexts = self.retrieve_from_database(input_query=input_query, config=config, where=where, citations=citations)
+        contexts = self._retrieve_from_database(
+            input_query=input_query, config=config, where=where, citations=citations
+        )
         if citations and len(contexts) > 0 and isinstance(contexts[0], tuple):
             contexts_data_for_llm_query = list(map(lambda x: x[0], contexts))
         else:
@@ -608,6 +602,9 @@ class EmbedChain(JSONSerializable):
             input_query=input_query, contexts=contexts_data_for_llm_query, config=config, dry_run=dry_run
         )
 
+        # add conversation in memory
+        self.llm.add_history(self.config.id, input_query, answer)
+
         # Send anonymous telemetry
         self.telemetry.capture(event_name="chat", properties=self._telemetry_props)
 
@@ -651,5 +648,16 @@ class EmbedChain(JSONSerializable):
         self.db.reset()
         self.cursor.execute("DELETE FROM data_sources WHERE pipeline_id = ?", (self.config.id,))
         self.connection.commit()
+        self.delete_history()
         # Send anonymous telemetry
         self.telemetry.capture(event_name="reset", properties=self._telemetry_props)
+
+    def get_history(self, num_rounds: int = 10, display_format: bool = True):
+        return self.llm.memory.get_recent_memories(
+            app_id=self.config.id,
+            num_rounds=num_rounds,
+            display_format=display_format,
+        )
+
+    def delete_history(self):
+        self.llm.memory.delete_chat_history(app_id=self.config.id)

embedchain/embedder/base.py

@@ -3,12 +3,20 @@ from typing import Any, Callable, Optional
 from embedchain.config.embedder.base import BaseEmbedderConfig
 
 try:
-    from chromadb.api.types import Documents, Embeddings
+    from chromadb.api.types import Embeddable, EmbeddingFunction, Embeddings
 except RuntimeError:
     from embedchain.utils import use_pysqlite3
 
     use_pysqlite3()
-    from chromadb.api.types import Documents, Embeddings
+    from chromadb.api.types import Embeddable, EmbeddingFunction, Embeddings
+
+
+class EmbeddingFunc(EmbeddingFunction):
+    def __init__(self, embedding_fn: Callable[[list[str]], list[str]]):
+        self.embedding_fn = embedding_fn
+
+    def __call__(self, input: Embeddable) -> Embeddings:
+        return self.embedding_fn(input)
 
 
 class BaseEmbedder:
@@ -66,7 +74,4 @@ class BaseEmbedder:
         :rtype: Callable
         """
 
-        def embed_function(texts: Documents) -> Embeddings:
-            return embeddings.embed_documents(texts)
-
-        return embed_function
+        return EmbeddingFunc(embeddings.embed_documents)

embedchain/embedder/chroma_embeddings.py

@@ -1,104 +0,0 @@
-"""
-Note that this file is copied from Chroma repository. We will remove this file once the fix in
-ChromaDB's repository.
-"""
-
-from typing import Optional
-
-from chromadb.api.types import Documents, Embeddings
-
-
-class OpenAIEmbeddingFunction:
-    def __init__(
-        self,
-        api_key: Optional[str] = None,
-        model_name: str = "text-embedding-ada-002",
-        organization_id: Optional[str] = None,
-        api_base: Optional[str] = None,
-        api_type: Optional[str] = None,
-        api_version: Optional[str] = None,
-        deployment_id: Optional[str] = None,
-    ):
-        """
-        Initialize the OpenAIEmbeddingFunction.
-        Args:
-            api_key (str, optional): Your API key for the OpenAI API. If not
-                provided, it will raise an error to provide an OpenAI API key.
-            organization_id(str, optional): The OpenAI organization ID if applicable
-            model_name (str, optional): The name of the model to use for text
-                embeddings. Defaults to "text-embedding-ada-002".
-            api_base (str, optional): The base path for the API. If not provided,
-                it will use the base path for the OpenAI API. This can be used to
-                point to a different deployment, such as an Azure deployment.
-            api_type (str, optional): The type of the API deployment. This can be
-                used to specify a different deployment, such as 'azure'. If not
-                provided, it will use the default OpenAI deployment.
-            api_version (str, optional): The api version for the API. If not provided,
-                it will use the api version for the OpenAI API. This can be used to
-                point to a different deployment, such as an Azure deployment.
-            deployment_id (str, optional): Deployment ID for Azure OpenAI.
-
-        """
-        try:
-            import openai
-        except ImportError:
-            raise ValueError("The openai python package is not installed. Please install it with `pip install openai`")
-
-        if api_key is not None:
-            openai.api_key = api_key
-        # If the api key is still not set, raise an error
-        elif openai.api_key is None:
-            raise ValueError(
-                "Please provide an OpenAI API key. You can get one at https://platform.openai.com/account/api-keys"
-            )
-
-        if api_base is not None:
-            openai.api_base = api_base
-
-        if api_version is not None:
-            openai.api_version = api_version
-
-        self._api_type = api_type
-        if api_type is not None:
-            openai.api_type = api_type
-
-        if organization_id is not None:
-            openai.organization = organization_id
-
-        self._v1 = openai.__version__.startswith("1.")
-        if self._v1:
-            if api_type == "azure":
-                self._client = openai.AzureOpenAI(
-                    api_key=api_key, api_version=api_version, azure_endpoint=api_base
-                ).embeddings
-            else:
-                self._client = openai.OpenAI(api_key=api_key, base_url=api_base).embeddings
-        else:
-            self._client = openai.Embedding
-        self._model_name = model_name
-        self._deployment_id = deployment_id
-
-    def __call__(self, input: Documents) -> Embeddings:
-        # replace newlines, which can negatively affect performance.
-        input = [t.replace("\n", " ") for t in input]
-
-        # Call the OpenAI Embedding API
-        if self._v1:
-            embeddings = self._client.create(input=input, model=self._deployment_id or self._model_name).data
-
-            # Sort resulting embeddings by index
-            sorted_embeddings = sorted(embeddings, key=lambda e: e.index)  # type: ignore
-
-            # Return just the embeddings
-            return [result.embedding for result in sorted_embeddings]
-        else:
-            if self._api_type == "azure":
-                embeddings = self._client.create(input=input, engine=self._deployment_id or self._model_name)["data"]
-            else:
-                embeddings = self._client.create(input=input, model=self._model_name)["data"]
-
-            # Sort resulting embeddings by index
-            sorted_embeddings = sorted(embeddings, key=lambda e: e["index"])  # type: ignore
-
-            # Return just the embeddings
-            return [result["embedding"] for result in sorted_embeddings]

embedchain/embedder/openai.py

@@ -1,23 +1,23 @@
 import os
 from typing import Optional
 
-from langchain.embeddings import OpenAIEmbeddings
+from chromadb.utils.embedding_functions import OpenAIEmbeddingFunction
+from langchain.embeddings import AzureOpenAIEmbeddings
 
 from embedchain.config import BaseEmbedderConfig
 from embedchain.embedder.base import BaseEmbedder
 from embedchain.models import VectorDimensions
 
-from .chroma_embeddings import OpenAIEmbeddingFunction
-
 
 class OpenAIEmbedder(BaseEmbedder):
     def __init__(self, config: Optional[BaseEmbedderConfig] = None):
         super().__init__(config=config)
+
         if self.config.model is None:
             self.config.model = "text-embedding-ada-002"
 
         if self.config.deployment_name:
-            embeddings = OpenAIEmbeddings(deployment=self.config.deployment_name)
+            embeddings = AzureOpenAIEmbeddings(deployment=self.config.deployment_name)
             embedding_fn = BaseEmbedder._langchain_default_concept(embeddings)
         else:
             if os.getenv("OPENAI_API_KEY") is None and os.getenv("OPENAI_ORGANIZATION") is None:

embedchain/helpers/callbacks.py

@@ -0,0 +1,73 @@
+import queue
+from typing import Any, Dict, List, Union
+
+from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
+from langchain.schema import LLMResult
+
+STOP_ITEM = "[END]"
+"""
+This is a special item that is used to signal the end of the stream.
+"""
+
+
+class StreamingStdOutCallbackHandlerYield(StreamingStdOutCallbackHandler):
+    """
+    This is a callback handler that yields the tokens as they are generated.
+    For a usage example, see the :func:`generate` function below.
+    """
+
+    q: queue.Queue
+    """
+    The queue to write the tokens to as they are generated.
+    """
+
+    def __init__(self, q: queue.Queue) -> None:
+        """
+        Initialize the callback handler.
+        q: The queue to write the tokens to as they are generated.
+        """
+        super().__init__()
+        self.q = q
+
+    def on_llm_start(self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) -> None:
+        """Run when LLM starts running."""
+        with self.q.mutex:
+            self.q.queue.clear()
+
+    def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
+        """Run on new LLM token. Only available when streaming is enabled."""
+        self.q.put(token)
+
+    def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
+        """Run when LLM ends running."""
+        self.q.put(STOP_ITEM)
+
+    def on_llm_error(self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any) -> None:
+        """Run when LLM errors."""
+        self.q.put("%s: %s" % (type(error).__name__, str(error)))
+        self.q.put(STOP_ITEM)
+
+
+def generate(rq: queue.Queue):
+    """
+    This is a generator that yields the items in the queue until it reaches the stop item.
+
+    Usage example:
+    ```
+    def askQuestion(callback_fn: StreamingStdOutCallbackHandlerYield):
+        llm = OpenAI(streaming=True, callbacks=[callback_fn])
+        return llm(prompt="Write a poem about a tree.")
+
+    @app.route("/", methods=["GET"])
+    def generate_output():
+        q = Queue()
+        callback_fn = StreamingStdOutCallbackHandlerYield(q)
+        threading.Thread(target=askQuestion, args=(callback_fn,)).start()
+        return Response(generate(q), mimetype="text/event-stream")
+    ```
+    """
+    while True:
+        result: str = rq.get()
+        if result == STOP_ITEM or result is None:
+            break
+        yield result

embedchain/helpers/json_serializable.py

@@ -33,7 +33,7 @@ def register_deserializable(cls: Type[T]) -> Type[T]:
     Returns:
         Type: The same class, after registration.
     """
-    JSONSerializable.register_class_as_deserializable(cls)
+    JSONSerializable._register_class_as_deserializable(cls)
     return cls
 
 
@@ -183,7 +183,7 @@ class JSONSerializable:
             return cls.deserialize(json_str)
 
     @classmethod
-    def register_class_as_deserializable(cls, target_class: Type[T]) -> None:
+    def _register_class_as_deserializable(cls, target_class: Type[T]) -> None:
         """
         Register a class as deserializable. This is a classmethod and globally shared.
 

embedchain/llm/anthropic.py

@@ -3,7 +3,7 @@ import os
 from typing import Optional
 
 from embedchain.config import BaseLlmConfig
-from embedchain.helper.json_serializable import register_deserializable
+from embedchain.helpers.json_serializable import register_deserializable
 from embedchain.llm.base import BaseLlm
 
 

embedchain/llm/azure_openai.py

@@ -2,7 +2,7 @@ import logging
 from typing import Optional
 
 from embedchain.config import BaseLlmConfig
-from embedchain.helper.json_serializable import register_deserializable
+from embedchain.helpers.json_serializable import register_deserializable
 from embedchain.llm.base import BaseLlm
 
 

embedchain/llm/base.py

@@ -1,14 +1,15 @@
 import logging
 from typing import Any, Dict, Generator, List, Optional
 
-from langchain.memory import ConversationBufferMemory
-from langchain.schema import BaseMessage
+from langchain.schema import BaseMessage as LCBaseMessage
 
 from embedchain.config import BaseLlmConfig
 from embedchain.config.llm.base import (DEFAULT_PROMPT,
                                         DEFAULT_PROMPT_WITH_HISTORY_TEMPLATE,
                                         DOCS_SITE_PROMPT_TEMPLATE)
-from embedchain.helper.json_serializable import JSONSerializable
+from embedchain.helpers.json_serializable import JSONSerializable
+from embedchain.memory.base import ECChatMemory
+from embedchain.memory.message import ChatMessage
 
 
 class BaseLlm(JSONSerializable):
@@ -23,7 +24,7 @@ class BaseLlm(JSONSerializable):
         else:
             self.config = config
 
-        self.memory = ConversationBufferMemory()
+        self.memory = ECChatMemory()
         self.is_docs_site_instance = False
         self.online = False
         self.history: Any = None
@@ -44,11 +45,18 @@ class BaseLlm(JSONSerializable):
         """
         self.history = history
 
-    def update_history(self):
+    def update_history(self, app_id: str):
         """Update class history attribute with history in memory (for chat method)"""
-        chat_history = self.memory.load_memory_variables({})["history"]
+        chat_history = self.memory.get_recent_memories(app_id=app_id, num_rounds=10)
         if chat_history:
-            self.set_history(chat_history)
+            self.set_history([str(history) for history in chat_history])
+
+    def add_history(self, app_id: str, question: str, answer: str, metadata: Optional[Dict[str, Any]] = None):
+        chat_message = ChatMessage()
+        chat_message.add_user_message(question, metadata=metadata)
+        chat_message.add_ai_message(answer, metadata=metadata)
+        self.memory.add(app_id=app_id, chat_message=chat_message)
+        self.update_history(app_id=app_id)
 
     def generate_prompt(self, input_query: str, contexts: List[str], **kwargs: Dict[str, Any]) -> str:
         """
@@ -165,7 +173,6 @@ class BaseLlm(JSONSerializable):
         for chunk in answer:
             streamed_answer = streamed_answer + chunk
             yield chunk
-        self.memory.chat_memory.add_ai_message(streamed_answer)
         logging.info(f"Answer: {streamed_answer}")
 
     def query(self, input_query: str, contexts: List[str], config: BaseLlmConfig = None, dry_run=False):
@@ -257,8 +264,6 @@ class BaseLlm(JSONSerializable):
             if self.online:
                 k["web_search_result"] = self.access_search_and_get_results(input_query)
 
-            self.update_history()
-
             prompt = self.generate_prompt(input_query, contexts, **k)
             logging.info(f"Prompt: {prompt}")
 
@@ -267,16 +272,9 @@ class BaseLlm(JSONSerializable):
 
             answer = self.get_answer_from_llm(prompt)
 
-            self.memory.chat_memory.add_user_message(input_query)
-
             if isinstance(answer, str):
-                self.memory.chat_memory.add_ai_message(answer)
                 logging.info(f"Answer: {answer}")
 
-                # NOTE: Adding to history before and after. This could be seen as redundant.
-                # If we change it, we have to change the tests (no big deal).
-                self.update_history()
-
                 return answer
             else:
                 # this is a streamed response and needs to be handled differently.
@@ -287,7 +285,7 @@ class BaseLlm(JSONSerializable):
                 self.config: BaseLlmConfig = BaseLlmConfig.deserialize(prev_config)
 
     @staticmethod
-    def _get_messages(prompt: str, system_prompt: Optional[str] = None) -> List[BaseMessage]:
+    def _get_messages(prompt: str, system_prompt: Optional[str] = None) -> List[LCBaseMessage]:
         """
         Construct a list of langchain messages
 

embedchain/llm/cohere.py

@@ -5,7 +5,7 @@ from typing import Optional
 from langchain.llms import Cohere
 
 from embedchain.config import BaseLlmConfig
-from embedchain.helper.json_serializable import register_deserializable
+from embedchain.helpers.json_serializable import register_deserializable
 from embedchain.llm.base import BaseLlm
 
 

embedchain/llm/gpt4all.py

@@ -4,7 +4,7 @@ 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.helpers.json_serializable import register_deserializable
 from embedchain.llm.base import BaseLlm
 
 

embedchain/llm/huggingface.py

@@ -5,7 +5,7 @@ from typing import Optional
 from langchain.llms import HuggingFaceHub
 
 from embedchain.config import BaseLlmConfig
-from embedchain.helper.json_serializable import register_deserializable
+from embedchain.helpers.json_serializable import register_deserializable
 from embedchain.llm.base import BaseLlm
 
 
@@ -20,7 +20,7 @@ class HuggingFaceLlm(BaseLlm):
         except ModuleNotFoundError:
             raise ModuleNotFoundError(
                 "The required dependencies for HuggingFaceHub are not installed."
-                'Please install with `pip install --upgrade "embedchain[huggingface_hub]"`'
+                'Please install with `pip install --upgrade "embedchain[huggingface-hub]"`'
             ) from None
 
         super().__init__(config=config)

embedchain/llm/jina.py

@@ -5,7 +5,7 @@ from langchain.chat_models import JinaChat
 from langchain.schema import HumanMessage, SystemMessage
 
 from embedchain.config import BaseLlmConfig
-from embedchain.helper.json_serializable import register_deserializable
+from embedchain.helpers.json_serializable import register_deserializable
 from embedchain.llm.base import BaseLlm
 
 

embedchain/llm/llama2.py

@@ -5,7 +5,7 @@ from typing import Optional
 from langchain.llms import Replicate
 
 from embedchain.config import BaseLlmConfig
-from embedchain.helper.json_serializable import register_deserializable
+from embedchain.helpers.json_serializable import register_deserializable
 from embedchain.llm.base import BaseLlm
 
 

embedchain/llm/openai.py

@@ -4,7 +4,7 @@ from langchain.chat_models import ChatOpenAI
 from langchain.schema import HumanMessage, SystemMessage
 
 from embedchain.config import BaseLlmConfig
-from embedchain.helper.json_serializable import register_deserializable
+from embedchain.helpers.json_serializable import register_deserializable
 from embedchain.llm.base import BaseLlm
 
 
@@ -34,7 +34,8 @@ class OpenAILlm(BaseLlm):
             from langchain.callbacks.streaming_stdout import \
                 StreamingStdOutCallbackHandler
 
-            chat = ChatOpenAI(**kwargs, streaming=config.stream, callbacks=[StreamingStdOutCallbackHandler()])
+            callbacks = config.callbacks if config.callbacks else [StreamingStdOutCallbackHandler()]
+            chat = ChatOpenAI(**kwargs, streaming=config.stream, callbacks=callbacks)
         else:
             chat = ChatOpenAI(**kwargs)
         return chat(messages).content

embedchain/llm/vertex_ai.py

@@ -3,7 +3,7 @@ import logging
 from typing import Optional
 
 from embedchain.config import BaseLlmConfig
-from embedchain.helper.json_serializable import register_deserializable
+from embedchain.helpers.json_serializable import register_deserializable
 from embedchain.llm.base import BaseLlm
 
 

embedchain/loaders/base_loader.py

@@ -1,4 +1,4 @@
-from embedchain.helper.json_serializable import JSONSerializable
+from embedchain.helpers.json_serializable import JSONSerializable
 
 
 class BaseLoader(JSONSerializable):

embedchain/loaders/discourse.py

@@ -0,0 +1,77 @@
+import hashlib
+import logging
+import time
+from typing import Any, Dict, Optional
+
+import requests
+
+from embedchain.loaders.base_loader import BaseLoader
+from embedchain.utils import clean_string
+
+
+class DiscourseLoader(BaseLoader):
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        super().__init__()
+        if not config:
+            raise ValueError(
+                "DiscourseLoader requires a config. Check the documentation for the correct format - `https://docs.embedchain.ai/data-sources/discourse`"  # noqa: E501
+            )
+
+        self.domain = config.get("domain")
+        if not self.domain:
+            raise ValueError(
+                "DiscourseLoader requires a domain. Check the documentation for the correct format - `https://docs.embedchain.ai/data-sources/discourse`"  # noqa: E501
+            )
+
+    def _check_query(self, query):
+        if not query or not isinstance(query, str):
+            raise ValueError(
+                "DiscourseLoader requires a query. Check the documentation for the correct format - `https://docs.embedchain.ai/data-sources/discourse`"  # noqa: E501
+            )
+
+    def _load_post(self, post_id):
+        post_url = f"{self.domain}posts/{post_id}.json"
+        response = requests.get(post_url)
+        try:
+            response.raise_for_status()
+        except Exception as e:
+            logging.error(f"Failed to load post {post_id}: {e}")
+            return
+        response_data = response.json()
+        post_contents = clean_string(response_data.get("raw"))
+        meta_data = {
+            "url": post_url,
+            "created_at": response_data.get("created_at", ""),
+            "username": response_data.get("username", ""),
+            "topic_slug": response_data.get("topic_slug", ""),
+            "score": response_data.get("score", ""),
+        }
+        data = {
+            "content": post_contents,
+            "meta_data": meta_data,
+        }
+        return data
+
+    def load_data(self, query):
+        self._check_query(query)
+        data = []
+        data_contents = []
+        logging.info(f"Searching data on discourse url: {self.domain}, for query: {query}")
+        search_url = f"{self.domain}search.json?q={query}"
+        response = requests.get(search_url)
+        try:
+            response.raise_for_status()
+        except Exception as e:
+            raise ValueError(f"Failed to search query {query}: {e}")
+        response_data = response.json()
+        post_ids = response_data.get("grouped_search_result").get("post_ids")
+        for id in post_ids:
+            post_data = self._load_post(id)
+            if post_data:
+                data.append(post_data)
+                data_contents.append(post_data.get("content"))
+            # Sleep for 0.4 sec, to avoid rate limiting. Check `https://meta.discourse.org/t/api-rate-limits/208405/6`
+            time.sleep(0.4)
+        doc_id = hashlib.sha256((query + ", ".join(data_contents)).encode()).hexdigest()
+        response_data = {"doc_id": doc_id, "data": data}
+        return response_data

embedchain/loaders/docs_site_loader.py

@@ -12,7 +12,7 @@ except ImportError:
     ) from None
 
 
-from embedchain.helper.json_serializable import register_deserializable
+from embedchain.helpers.json_serializable import register_deserializable
 from embedchain.loaders.base_loader import BaseLoader
 
 

embedchain/loaders/docx_file.py

@@ -6,7 +6,7 @@ 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.helpers.json_serializable import register_deserializable
 from embedchain.loaders.base_loader import BaseLoader
 
 

embedchain/loaders/github.py

@@ -0,0 +1,117 @@
+import concurrent.futures
+import hashlib
+import logging
+import os
+
+from tqdm import tqdm
+
+from embedchain.loaders.base_loader import BaseLoader
+from embedchain.loaders.json import JSONLoader
+from embedchain.loaders.mdx import MdxLoader
+from embedchain.utils import detect_datatype
+
+
+def _load_file_data(path):
+    data = []
+    data_content = []
+    try:
+        with open(path, "rb") as f:
+            content = f.read().decode("utf-8")
+    except Exception as e:
+        print(f"Error reading file {path}: {e}")
+        raise ValueError(f"Failed to read file {path}")
+
+    meta_data = {}
+    meta_data["url"] = path
+    data.append(
+        {
+            "content": content,
+            "meta_data": meta_data,
+        }
+    )
+    data_content.append(content)
+    doc_id = hashlib.sha256((" ".join(data_content) + path).encode()).hexdigest()
+    return {
+        "doc_id": doc_id,
+        "data": data,
+    }
+
+
+class GithubLoader(BaseLoader):
+    def load_data(self, repo_url):
+        """Load data from a git repo."""
+        try:
+            from git import Repo
+        except ImportError as e:
+            raise ValueError(
+                "GithubLoader requires extra dependencies. Install with `pip install --upgrade 'embedchain[git]'`"
+            ) from e
+
+        mdx_loader = MdxLoader()
+        json_loader = JSONLoader()
+        data = []
+        data_urls = []
+
+        def _fetch_or_clone_repo(repo_url: str, local_path: str):
+            if os.path.exists(local_path):
+                logging.info("Repository already exists. Fetching updates...")
+                repo = Repo(local_path)
+                origin = repo.remotes.origin
+                origin.fetch()
+                logging.info("Fetch completed.")
+            else:
+                logging.info("Cloning repository...")
+                Repo.clone_from(repo_url, local_path)
+                logging.info("Clone completed.")
+
+        def _load_file(file_path: str):
+            try:
+                data_type = detect_datatype(file_path).value
+            except Exception:
+                data_type = "unstructured"
+
+            if data_type == "mdx":
+                data = mdx_loader.load_data(file_path)
+            elif data_type == "json":
+                data = json_loader.load_data(file_path)
+            else:
+                data = _load_file_data(file_path)
+
+            return data.get("data", [])
+
+        def _is_file_empty(file_path):
+            return os.path.getsize(file_path) == 0
+
+        def _is_whitelisted(file_path):
+            whitelisted_extensions = ["md", "txt", "html", "json", "py", "js", "jsx", "ts", "tsx", "mdx", "rst"]
+            _, file_extension = os.path.splitext(file_path)
+            return file_extension[1:] in whitelisted_extensions
+
+        def _add_repo_files(repo_path: str):
+            with concurrent.futures.ThreadPoolExecutor(max_workers=4) as executor:
+                future_to_file = {
+                    executor.submit(_load_file, os.path.join(root, filename)): os.path.join(root, filename)
+                    for root, _, files in os.walk(repo_path)
+                    for filename in files
+                    if _is_whitelisted(os.path.join(root, filename))
+                    and not _is_file_empty(os.path.join(root, filename))  # noqa:E501
+                }
+                for future in tqdm(concurrent.futures.as_completed(future_to_file), total=len(future_to_file)):
+                    file = future_to_file[future]
+                    try:
+                        results = future.result()
+                        if results:
+                            data.extend(results)
+                            data_urls.extend([result.get("meta_data").get("url") for result in results])
+                    except Exception as e:
+                        logging.warn(f"Failed to process {file}: {e}")
+
+        source_hash = hashlib.sha256(repo_url.encode()).hexdigest()
+        repo_path = f"/tmp/{source_hash}"
+        _fetch_or_clone_repo(repo_url=repo_url, local_path=repo_path)
+        _add_repo_files(repo_path)
+        doc_id = hashlib.sha256((repo_url + ", ".join(data_urls)).encode()).hexdigest()
+        return {
+            "doc_id": doc_id,
+            "data": data,
+        }

embedchain/loaders/json.py

@@ -25,10 +25,21 @@ class JSONLoader(BaseLoader):
 
         return LLHUBJSONLoader()
 
+    @staticmethod
+    def _check_content(content):
+        if not isinstance(content, str):
+            raise ValueError(
+                "Invaid content input. \
+                If you want to upload (list, dict, etc.), do \
+                    `json.dump(data, indent=0)` and add the stringified JSON. \
+                        Check - `https://docs.embedchain.ai/data-sources/json`"
+            )
+
     @staticmethod
     def load_data(content):
         """Load a json file. Each data point is a key value pair."""
 
+        JSONLoader._check_content(content)
         loader = JSONLoader._get_llama_hub_loader()
 
         data = []

embedchain/loaders/local_qna_pair.py

@@ -1,6 +1,6 @@
 import hashlib
 
-from embedchain.helper.json_serializable import register_deserializable
+from embedchain.helpers.json_serializable import register_deserializable
 from embedchain.loaders.base_loader import BaseLoader
 
 

embedchain/loaders/local_text.py

@@ -1,6 +1,6 @@
 import hashlib
 
-from embedchain.helper.json_serializable import register_deserializable
+from embedchain.helpers.json_serializable import register_deserializable
 from embedchain.loaders.base_loader import BaseLoader
 
 

embedchain/loaders/mdx.py

@@ -1,6 +1,6 @@
 import hashlib
 
-from embedchain.helper.json_serializable import register_deserializable
+from embedchain.helpers.json_serializable import register_deserializable
 from embedchain.loaders.base_loader import BaseLoader
 
 

embedchain/loaders/mysql.py

@@ -0,0 +1,64 @@
+import hashlib
+import logging
+from typing import Any, Dict, Optional
+
+from embedchain.loaders.base_loader import BaseLoader
+from embedchain.utils import clean_string
+
+
+class MySQLLoader(BaseLoader):
+    def __init__(self, config: Optional[Dict[str, Any]]):
+        super().__init__()
+        if not config:
+            raise ValueError(
+                f"Invalid sql config: {config}.",
+                "Provide the correct config, refer `https://docs.embedchain.ai/data-sources/mysql`.",
+            )
+
+        self.config = config
+        self.connection = None
+        self.cursor = None
+        self._setup_loader(config=config)
+
+    def _setup_loader(self, config: Dict[str, Any]):
+        try:
+            import mysql.connector as sqlconnector
+        except ImportError as e:
+            raise ImportError(
+                "Unable to import required packages for MySQL loader. Run `pip install --upgrade 'embedchain[mysql]'`."  # noqa: E501
+            ) from e
+
+        try:
+            self.connection = sqlconnector.connection.MySQLConnection(**config)
+            self.cursor = self.connection.cursor()
+        except (sqlconnector.Error, IOError) as err:
+            logging.info(f"Connection failed: {err}")
+            raise ValueError(
+                f"Unable to connect with the given config: {config}.",
+                "Please provide the correct configuration to load data from you MySQL DB. \
+                    Refer `https://docs.embedchain.ai/data-sources/mysql`.",
+            )
+
+    def _check_query(self, query):
+        if not isinstance(query, str):
+            raise ValueError(
+                f"Invalid mysql query: {query}",
+                "Provide the valid query to add from mysql, \
+                    make sure you are following `https://docs.embedchain.ai/data-sources/mysql`",
+            )
+
+    def load_data(self, query):
+        self._check_query(query=query)
+        data = []
+        data_content = []
+        self.cursor.execute(query)
+        rows = self.cursor.fetchall()
+        for row in rows:
+            doc_content = clean_string(str(row))
+            data.append({"content": doc_content, "meta_data": {"url": query}})
+            data_content.append(doc_content)
+        doc_id = hashlib.sha256((query + ", ".join(data_content)).encode()).hexdigest()
+        return {
+            "doc_id": doc_id,
+            "data": data,
+        }

embedchain/loaders/notion.py

@@ -10,7 +10,7 @@ except ImportError:
     ) from None
 
 
-from embedchain.helper.json_serializable import register_deserializable
+from embedchain.helpers.json_serializable import register_deserializable
 from embedchain.loaders.base_loader import BaseLoader
 from embedchain.utils import clean_string
 

embedchain/loaders/pdf_file.py

@@ -6,7 +6,7 @@ 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.helpers.json_serializable import register_deserializable
 from embedchain.loaders.base_loader import BaseLoader
 from embedchain.utils import clean_string
 

embedchain/loaders/postgres.py

@@ -0,0 +1,71 @@
+import hashlib
+import logging
+from typing import Any, Dict, Optional
+
+from embedchain.loaders.base_loader import BaseLoader
+
+
+class PostgresLoader(BaseLoader):
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        super().__init__()
+        if not config:
+            raise ValueError(f"Must provide the valid config. Received: {config}")
+
+        self.connection = None
+        self.cursor = None
+        self._setup_loader(config=config)
+
+    def _setup_loader(self, config: Dict[str, Any]):
+        try:
+            import psycopg
+        except ImportError as e:
+            raise ImportError(
+                "Unable to import required packages. \
+                    Run `pip install --upgrade 'embedchain[postgres]'`"
+            ) from e
+
+        config_info = ""
+        if "url" in config:
+            config_info = config.get("url")
+        else:
+            conn_params = []
+            for key, value in config.items():
+                conn_params.append(f"{key}={value}")
+            config_info = " ".join(conn_params)
+
+        logging.info(f"Connecting to postrgres sql: {config_info}")
+        self.connection = psycopg.connect(conninfo=config_info)
+        self.cursor = self.connection.cursor()
+
+    def _check_query(self, query):
+        if not isinstance(query, str):
+            raise ValueError(
+                f"Invalid postgres query: {query}. Provide the valid source to add from postgres, make sure you are following `https://docs.embedchain.ai/data-sources/postgres`",  # noqa:E501
+            )
+
+    def load_data(self, query):
+        self._check_query(query)
+        try:
+            data = []
+            data_content = []
+            self.cursor.execute(query)
+            results = self.cursor.fetchall()
+            for result in results:
+                doc_content = str(result)
+                data.append({"content": doc_content, "meta_data": {"url": query}})
+                data_content.append(doc_content)
+            doc_id = hashlib.sha256((query + ", ".join(data_content)).encode()).hexdigest()
+            return {
+                "doc_id": doc_id,
+                "data": data,
+            }
+        except Exception as e:
+            raise ValueError(f"Failed to load data using query={query} with: {e}")
+
+    def close_connection(self):
+        if self.cursor:
+            self.cursor.close()
+            self.cursor = None
+        if self.connection:
+            self.connection.close()
+            self.connection = None

embedchain/loaders/sitemap.py

@@ -1,7 +1,9 @@
+import concurrent.futures
 import hashlib
 import logging
 
 import requests
+from tqdm import tqdm
 
 try:
     from bs4 import BeautifulSoup
@@ -11,7 +13,7 @@ except ImportError:
         'Sitemap requires extra dependencies. Install with `pip install --upgrade "embedchain[dataloaders]"`'
     ) from None
 
-from embedchain.helper.json_serializable import register_deserializable
+from embedchain.helpers.json_serializable import register_deserializable
 from embedchain.loaders.base_loader import BaseLoader
 from embedchain.loaders.web_page import WebPageLoader
 from embedchain.utils import is_readable
@@ -19,33 +21,45 @@ from embedchain.utils import is_readable
 
 @register_deserializable
 class SitemapLoader(BaseLoader):
+    """
+    This method takes a sitemap URL as input and retrieves
+    all the URLs to use the WebPageLoader to load content
+    of each page.
+    """
+
     def load_data(self, sitemap_url):
-        """
-        This method takes a sitemap URL as input and retrieves
-        all the URLs to use the WebPageLoader to load content
-        of each page.
-        """
         output = []
         web_page_loader = WebPageLoader()
         response = requests.get(sitemap_url)
         response.raise_for_status()
 
         soup = BeautifulSoup(response.text, "xml")
-
         links = [link.text for link in soup.find_all("loc") if link.parent.name == "url"]
         if len(links) == 0:
-            # Get all <loc> tags as a fallback. This might include images.
             links = [link.text for link in soup.find_all("loc")]
 
         doc_id = hashlib.sha256((" ".join(links) + sitemap_url).encode()).hexdigest()
 
-        for link in links:
+        def load_link(link):
             try:
                 each_load_data = web_page_loader.load_data(link)
                 if is_readable(each_load_data.get("data")[0].get("content")):
-                    output.append(each_load_data.get("data"))
+                    return each_load_data.get("data")
                 else:
                     logging.warning(f"Page is not readable (too many invalid characters): {link}")
             except ParserRejectedMarkup as e:
                 logging.error(f"Failed to parse {link}: {e}")
-        return {"doc_id": doc_id, "data": [data[0] for data in output]}
+            return None
+
+        with concurrent.futures.ThreadPoolExecutor() as executor:
+            future_to_link = {executor.submit(load_link, link): link for link in links}
+            for future in tqdm(concurrent.futures.as_completed(future_to_link), total=len(links), desc="Loading pages"):
+                link = future_to_link[future]
+                try:
+                    data = future.result()
+                    if data:
+                        output.extend(data)
+                except Exception as e:
+                    logging.error(f"Error loading page {link}: {e}")
+
+        return {"doc_id": doc_id, "data": output}

embedchain/loaders/slack.py

@@ -0,0 +1,108 @@
+import hashlib
+import logging
+import os
+import ssl
+from typing import Any, Dict, Optional
+
+import certifi
+
+from embedchain.loaders.base_loader import BaseLoader
+from embedchain.utils import clean_string
+
+SLACK_API_BASE_URL = "https://www.slack.com/api/"
+
+
+class SlackLoader(BaseLoader):
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        super().__init__()
+
+        if config is not None:
+            self.config = config
+        else:
+            self.config = {"base_url": SLACK_API_BASE_URL}
+
+        self.client = None
+        self._setup_loader(self.config)
+
+    def _setup_loader(self, config: Dict[str, Any]):
+        try:
+            from slack_sdk import WebClient
+        except ImportError as e:
+            raise ImportError(
+                "Slack loader requires extra dependencies. \
+                Install with `pip install --upgrade embedchain[slack]`"
+            ) from e
+
+        if os.getenv("SLACK_USER_TOKEN") is None:
+            raise ValueError(
+                "SLACK_USER_TOKEN environment variables not provided. Check `https://docs.embedchain.ai/data-sources/slack` to learn more."  # noqa:E501
+            )
+
+        logging.info(f"Creating Slack Loader with config: {config}")
+        # get slack client config params
+        slack_bot_token = os.getenv("SLACK_USER_TOKEN")
+        ssl_cert = ssl.create_default_context(cafile=certifi.where())
+        base_url = config.get("base_url", SLACK_API_BASE_URL)
+        headers = config.get("headers")
+        # for Org-Wide App
+        team_id = config.get("team_id")
+
+        self.client = WebClient(
+            token=slack_bot_token,
+            base_url=base_url,
+            ssl=ssl_cert,
+            headers=headers,
+            team_id=team_id,
+        )
+        logging.info("Slack Loader setup successful!")
+
+    def _check_query(self, query):
+        if not isinstance(query, str):
+            raise ValueError(
+                f"Invalid query passed to Slack loader, found: {query}. Check `https://docs.embedchain.ai/data-sources/slack` to learn more."  # noqa:E501
+            )
+
+    def load_data(self, query):
+        self._check_query(query)
+        try:
+            data = []
+            data_content = []
+
+            logging.info(f"Searching slack conversations for query: {query}")
+            results = self.client.search_messages(
+                query=query,
+                sort="timestamp",
+                sort_dir="desc",
+                count=1000,
+            )
+
+            messages = results.get("messages")
+            num_message = results.get("total")
+            logging.info(f"Found {num_message} messages for query: {query}")
+
+            matches = messages.get("matches", [])
+            for message in matches:
+                url = message.get("permalink")
+                text = message.get("text")
+                content = clean_string(text)
+
+                message_meta_data_keys = ["channel", "iid", "team", "ts", "type", "user", "username"]
+                meta_data = message.fromkeys(message_meta_data_keys, "")
+                meta_data.update({"url": url})
+                data.append(
+                    {
+                        "content": content,
+                        "meta_data": meta_data,
+                    }
+                )
+                data_content.append(content)
+            doc_id = hashlib.md5((query + ", ".join(data_content)).encode()).hexdigest()
+            return {
+                "doc_id": doc_id,
+                "data": data,
+            }
+        except Exception as e:
+            logging.warning(f"Error in loading slack data: {e}")
+            raise ValueError(
+                f"Error in loading slack data: {e}. Check `https://docs.embedchain.ai/data-sources/slack` to learn more."  # noqa:E501
+            ) from e

embedchain/loaders/substack.py

@@ -0,0 +1,86 @@
+import hashlib
+import logging
+import time
+
+import requests
+
+from embedchain.helpers.json_serializable import register_deserializable
+from embedchain.loaders.base_loader import BaseLoader
+from embedchain.utils import is_readable
+
+
+@register_deserializable
+class SubstackLoader(BaseLoader):
+    """
+    This method takes a sitemap URL as input and retrieves
+    all the URLs to use the WebPageLoader to load content
+    of each page.
+    """
+
+    def load_data(self, url: str):
+        try:
+            from bs4 import BeautifulSoup
+            from bs4.builder import ParserRejectedMarkup
+        except ImportError:
+            raise ImportError(
+                'Substack requires extra dependencies. Install with `pip install --upgrade "embedchain[dataloaders]"`'
+            ) from None
+
+        output = []
+        response = requests.get(url)
+        response.raise_for_status()
+
+        soup = BeautifulSoup(response.text, "xml")
+        links = [link.text for link in soup.find_all("loc") if link.parent.name == "url" and "/p/" in link.text]
+        if len(links) == 0:
+            links = [link.text for link in soup.find_all("loc") if "/p/" in link.text]
+
+        doc_id = hashlib.sha256((" ".join(links) + url).encode()).hexdigest()
+
+        def serialize_response(soup: BeautifulSoup):
+            data = {}
+
+            h1_els = soup.find_all("h1")
+            if h1_els is not None and len(h1_els) > 0:
+                data["title"] = h1_els[1].text
+
+            description_el = soup.find("meta", {"name": "description"})
+            if description_el is not None:
+                data["description"] = description_el["content"]
+
+            content_el = soup.find("div", {"class": "available-content"})
+            if content_el is not None:
+                data["content"] = content_el.text
+
+            like_btn = soup.find("div", {"class": "like-button-container"})
+            if like_btn is not None:
+                no_of_likes_div = like_btn.find("div", {"class": "label"})
+                if no_of_likes_div is not None:
+                    data["no_of_likes"] = no_of_likes_div.text
+
+            return data
+
+        def load_link(link: str):
+            try:
+                each_load_data = requests.get(link)
+                each_load_data.raise_for_status()
+
+                soup = BeautifulSoup(response.text, "html.parser")
+                data = serialize_response(soup)
+                data = str(data)
+                if is_readable(data):
+                    return data
+                else:
+                    logging.warning(f"Page is not readable (too many invalid characters): {link}")
+            except ParserRejectedMarkup as e:
+                logging.error(f"Failed to parse {link}: {e}")
+            return None
+
+        for link in links:
+            data = load_link(link)
+            if data:
+                output.append({"content": data, "meta_data": {"url": link}})
+            # TODO: allow users to configure this
+            time.sleep(1.0)  # added to avoid rate limiting
+
+        return {"doc_id": doc_id, "data": output}