[Bug fix] Fix issue of missing user directory (#975)

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

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

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

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/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/overview.mdx

@@ -18,9 +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/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`
+6. Run your bot now,
-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.
+<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/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

@@ -2,15 +2,13 @@ from typing import Optional
 
 import yaml
 
-from embedchain.client import Client
+from embedchain.config import AppConfig, BaseEmbedderConfig, BaseLlmConfig, ChunkerConfig
-from embedchain.config import (AppConfig, BaseEmbedderConfig, BaseLlmConfig,
-                               ChunkerConfig)
 from embedchain.config.vectordb.base import BaseVectorDbConfig
 from embedchain.embedchain import EmbedChain
 from embedchain.embedder.base import BaseEmbedder
 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
@@ -68,9 +66,6 @@ class App(EmbedChain):
         :type system_prompt: Optional[str], optional
         :raises TypeError: LLM, database or embedder or their config is not a valid class instance.
         """
-        # Setup user directory if it doesn't exist already
-        Client.setup_dir()
-
         # Type check configs
         if config and not isinstance(config, AppConfig):
             raise TypeError(

embedchain/bots/base.py

@@ -3,8 +3,8 @@ from typing import Any
 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,
+from embedchain.helpers.json_serializable import (JSONSerializable,
-                                                 register_deserializable)
+                                                  register_deserializable)
 from embedchain.llm.openai import OpenAILlm
 from embedchain.vectordb.chroma import ChromaDB
 

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

@@ -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/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/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/data_formatter/data_formatter.py

@@ -4,7 +4,7 @@ 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
 
@@ -63,11 +63,17 @@ 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,
             ]
         )
 
@@ -106,6 +112,12 @@ class DataFormatter(JSONSerializable):
             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:

embedchain/embedchain.py

@@ -13,11 +13,10 @@ 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,
+from embedchain.models.data_type import DataType, DirectDataType, IndirectDataType, SpecialDataType
-                                         IndirectDataType, SpecialDataType)
 from embedchain.telemetry.posthog import AnonymousTelemetry
 from embedchain.utils import detect_datatype, is_valid_json_string
 from embedchain.vectordb.base import BaseVectorDB
@@ -203,7 +202,7 @@ class EmbedChain(JSONSerializable):
         self.user_asks.append([source, data_type.value, metadata])
 
         data_formatter = DataFormatter(data_type, config, kwargs)
-        documents, metadatas, _ids, new_chunks = self.load_and_embed(
+        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}:
@@ -340,7 +339,7 @@ class EmbedChain(JSONSerializable):
                 "When it should be  DirectDataType, IndirectDataType or SpecialDataType."
             )
 
-    def load_and_embed(
+    def _load_and_embed(
         self,
         loader: BaseLoader,
         chunker: BaseChunker,
@@ -457,7 +456,7 @@ 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]]:
         """
@@ -537,7 +536,9 @@ 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:
@@ -588,7 +589,9 @@ 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:

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 EmbeddingFunc(embeddings.embed_documents)
-            return embeddings.embed_documents(texts)
-
-        return embed_function

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

@@ -7,7 +7,7 @@ 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
 

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
 
 

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/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

@@ -40,9 +40,7 @@ class PostgresLoader(BaseLoader):
     def _check_query(self, query):
         if not isinstance(query, str):
             raise ValueError(
-                f"Invalid postgres query: {query}",
+                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
-                "Provide the valid source to add from postgres, \
-                    make sure you are following `https://docs.embedchain.ai/data-sources/postgres`",
             )
 
     def load_data(self, query):
@@ -54,7 +52,7 @@ class PostgresLoader(BaseLoader):
             results = self.cursor.fetchall()
             for result in results:
                 doc_content = str(result)
-                data.append({"content": doc_content, "meta_data": {"url": f"postgres_query-({query})"}})
+                data.append({"content": doc_content, "meta_data": {"url": query}})
                 data_content.append(doc_content)
             doc_id = hashlib.sha256((query + ", ".join(data_content)).encode()).hexdigest()
             return {

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}

embedchain/loaders/unstructured_file.py

@@ -1,12 +1,6 @@
 import hashlib
 
-try:
+from embedchain.helpers.json_serializable import register_deserializable
-    from langchain.document_loaders import UnstructuredFileLoader
-except ImportError:
-    raise ImportError(
-        'PDF File requires extra dependencies. Install with `pip install --upgrade "embedchain[dataloaders]"`'
-    ) from None
-from embedchain.helper.json_serializable import register_deserializable
 from embedchain.loaders.base_loader import BaseLoader
 from embedchain.utils import clean_string
 
@@ -15,6 +9,13 @@ from embedchain.utils import clean_string
 class UnstructuredLoader(BaseLoader):
     def load_data(self, url):
         """Load data from a Unstructured file."""
+        try:
+            from langchain.document_loaders import UnstructuredFileLoader
+        except ImportError:
+            raise ImportError(
+                'Unstructured file requires extra dependencies. Install with `pip install --upgrade "embedchain[dataloaders]"`'  # noqa: E501
+            ) from None
+
         loader = UnstructuredFileLoader(url)
         data = []
         all_content = []

embedchain/loaders/web_page.py

@@ -10,22 +10,24 @@ except ImportError:
         'Webpage 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
 
 
 @register_deserializable
 class WebPageLoader(BaseLoader):
+    # Shared session for all instances
+    _session = requests.Session()
+
     def load_data(self, url):
-        """Load data from a web page."""
+        """Load data from a web page using a shared requests session."""
-        response = requests.get(url)
+        response = self._session.get(url, timeout=30)
+        response.raise_for_status()
         data = response.content
         content = self._get_clean_content(data, url)
 
-        meta_data = {
+        meta_data = {"url": url}
-            "url": url,
-        }
 
         doc_id = hashlib.sha256((content + url).encode()).hexdigest()
         return {
@@ -86,3 +88,7 @@ class WebPageLoader(BaseLoader):
             )
 
         return content
+
+    @classmethod
+    def close_session(cls):
+        cls._session.close()

embedchain/loaders/xml.py

@@ -6,7 +6,7 @@ except ImportError:
     raise ImportError(
         'XML file requires extra dependencies. Install with `pip install --upgrade "embedchain[dataloaders]"`'
     ) from None
-from embedchain.helper.json_serializable import register_deserializable
+from embedchain.helpers.json_serializable import register_deserializable
 from embedchain.loaders.base_loader import BaseLoader
 from embedchain.utils import clean_string
 

embedchain/loaders/youtube_channel.py

@@ -0,0 +1,77 @@
+import concurrent.futures
+import hashlib
+import logging
+
+from tqdm import tqdm
+
+from embedchain.loaders.base_loader import BaseLoader
+from embedchain.loaders.youtube_video import YoutubeVideoLoader
+
+
+class YoutubeChannelLoader(BaseLoader):
+    """Loader for youtube channel."""
+
+    def load_data(self, channel_name):
+        try:
+            import yt_dlp
+        except ImportError as e:
+            raise ValueError(
+                "YoutubeLoader requires extra dependencies. Install with `pip install --upgrade 'embedchain[youtube_channel]'`"  # noqa: E501
+            ) from e
+
+        data = []
+        data_urls = []
+        youtube_url = f"https://www.youtube.com/{channel_name}/videos"
+        youtube_video_loader = YoutubeVideoLoader()
+
+        def _get_yt_video_links():
+            try:
+                ydl_opts = {
+                    "quiet": True,
+                    "extract_flat": True,
+                }
+                with yt_dlp.YoutubeDL(ydl_opts) as ydl:
+                    info_dict = ydl.extract_info(youtube_url, download=False)
+                    if "entries" in info_dict:
+                        videos = [entry["url"] for entry in info_dict["entries"]]
+                        return videos
+            except Exception:
+                logging.error(f"Failed to fetch youtube videos for channel: {channel_name}")
+                return []
+
+        def _load_yt_video(video_link):
+            try:
+                each_load_data = youtube_video_loader.load_data(video_link)
+                if each_load_data:
+                    return each_load_data.get("data")
+            except Exception as e:
+                logging.error(f"Failed to load youtube video {video_link}: {e}")
+            return None
+
+        def _add_youtube_channel():
+            video_links = _get_yt_video_links()
+            logging.info("Loading videos from youtube channel...")
+            with concurrent.futures.ThreadPoolExecutor() as executor:
+                # Submitting all tasks and storing the future object with the video link
+                future_to_video = {
+                    executor.submit(_load_yt_video, video_link): video_link for video_link in video_links
+                }
+
+                for future in tqdm(
+                    concurrent.futures.as_completed(future_to_video), total=len(video_links), desc="Processing videos"
+                ):
+                    video = future_to_video[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.error(f"Failed to process youtube video {video}: {e}")
+
+        _add_youtube_channel()
+        doc_id = hashlib.sha256((youtube_url + ", ".join(data_urls)).encode()).hexdigest()
+        return {
+            "doc_id": doc_id,
+            "data": data,
+        }

embedchain/loaders/youtube_video.py

@@ -6,7 +6,7 @@ except ImportError:
     raise ImportError(
         'YouTube video requires extra dependencies. Install with `pip install --upgrade "embedchain[dataloaders]"`'
     ) from None
-from embedchain.helper.json_serializable import register_deserializable
+from embedchain.helpers.json_serializable import register_deserializable
 from embedchain.loaders.base_loader import BaseLoader
 from embedchain.utils import clean_string
 
@@ -19,7 +19,7 @@ class YoutubeVideoLoader(BaseLoader):
         doc = loader.load()
         output = []
         if not len(doc):
-            raise ValueError("No data found")
+            raise ValueError(f"No data found for url: {url}")
         content = doc[0].page_content
         content = clean_string(content)
         meta_data = doc[0].metadata

embedchain/memory/message.py

@@ -1,7 +1,7 @@
 import logging
 from typing import Any, Dict, Optional
 
-from embedchain.helper.json_serializable import JSONSerializable
+from embedchain.helpers.json_serializable import JSONSerializable
 
 
 class BaseMessage(JSONSerializable):

embedchain/models/data_type.py

@@ -30,6 +30,12 @@ class IndirectDataType(Enum):
     OPENAPI = "openapi"
     GMAIL = "gmail"
     POSTGRES = "postgres"
+    MYSQL = "mysql"
+    SLACK = "slack"
+    DISCOURSE = "discourse"
+    SUBSTACK = "substack"
+    GITHUB = "github"
+    YOUTUBE_CHANNEL = "youtube_channel"
 
 
 class SpecialDataType(Enum):
@@ -59,3 +65,9 @@ class DataType(Enum):
     OPENAPI = IndirectDataType.OPENAPI.value
     GMAIL = IndirectDataType.GMAIL.value
     POSTGRES = IndirectDataType.POSTGRES.value
+    MYSQL = IndirectDataType.MYSQL.value
+    SLACK = IndirectDataType.SLACK.value
+    DISCOURSE = IndirectDataType.DISCOURSE.value
+    SUBSTACK = IndirectDataType.SUBSTACK.value
+    GITHUB = IndirectDataType.GITHUB.value
+    YOUTUBE_CHANNEL = IndirectDataType.YOUTUBE_CHANNEL.value

embedchain/pipeline.py

@@ -15,7 +15,7 @@ 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.telemetry.posthog import AnonymousTelemetry
@@ -24,6 +24,10 @@ from embedchain.vectordb.base import BaseVectorDB
 from embedchain.vectordb.chroma import ChromaDB
 
 
+# Setup the user directory if doesn't exist already
+Client.setup_dir()
+
+
 @register_deserializable
 class Pipeline(EmbedChain):
     """
@@ -64,9 +68,6 @@ class Pipeline(EmbedChain):
         :type auto_deploy: bool, optional
         :raises Exception: If an error occurs while creating the pipeline
         """
-        # Setup user directory if it doesn't exist already
-        Client.setup_dir()
-
         if id and yaml_path:
             raise Exception("Cannot provide both id and config. Please provide only one of them.")
 

embedchain/store/assistants.py

@@ -10,7 +10,7 @@ from typing import cast
 from openai import OpenAI
 from openai.types.beta.threads import MessageContentText, ThreadMessage
 
-from embedchain import Pipeline
+from embedchain import Client, Pipeline
 from embedchain.config import AddConfig
 from embedchain.data_formatter import DataFormatter
 from embedchain.models.data_type import DataType
@@ -19,6 +19,9 @@ from embedchain.utils import detect_datatype
 
 logging.basicConfig(level=logging.WARN)
 
+# Setup the user directory if doesn't exist already
+Client.setup_dir()
+
 
 class OpenAIAssistant:
     def __init__(

embedchain/telemetry/posthog.py

@@ -20,7 +20,7 @@ class AnonymousTelemetry:
         self.project_api_key = "phc_PHQDA5KwztijnSojsxJ2c1DuJd52QCzJzT2xnSGvjN2"
         self.host = host
         self.posthog = Posthog(project_api_key=self.project_api_key, host=self.host)
-        self.user_id = self.get_user_id()
+        self.user_id = self._get_user_id()
         self.enabled = enabled
 
         # Check if telemetry tracking is disabled via environment variable
@@ -38,7 +38,7 @@ class AnonymousTelemetry:
         posthog_logger = logging.getLogger("posthog")
         posthog_logger.disabled = True
 
-    def get_user_id(self):
+    def _get_user_id(self):
         if not os.path.exists(CONFIG_DIR):
             os.makedirs(CONFIG_DIR)
 

embedchain/utils.py

@@ -10,6 +10,62 @@ from schema import Optional, Or, Schema
 from embedchain.models.data_type import DataType
 
 
+def parse_content(content, type):
+    implemented = ["html.parser", "lxml", "lxml-xml", "xml", "html5lib"]
+    if type not in implemented:
+        raise ValueError(f"Parser type {type} not implemented. Please choose one of {implemented}")
+
+    from bs4 import BeautifulSoup
+
+    soup = BeautifulSoup(content, type)
+    original_size = len(str(soup.get_text()))
+
+    tags_to_exclude = [
+        "nav",
+        "aside",
+        "form",
+        "header",
+        "noscript",
+        "svg",
+        "canvas",
+        "footer",
+        "script",
+        "style",
+    ]
+    for tag in soup(tags_to_exclude):
+        tag.decompose()
+
+    ids_to_exclude = ["sidebar", "main-navigation", "menu-main-menu"]
+    for id in ids_to_exclude:
+        tags = soup.find_all(id=id)
+        for tag in tags:
+            tag.decompose()
+
+    classes_to_exclude = [
+        "elementor-location-header",
+        "navbar-header",
+        "nav",
+        "header-sidebar-wrapper",
+        "blog-sidebar-wrapper",
+        "related-posts",
+    ]
+    for class_name in classes_to_exclude:
+        tags = soup.find_all(class_=class_name)
+        for tag in tags:
+            tag.decompose()
+
+    content = soup.get_text()
+    content = clean_string(content)
+
+    cleaned_size = len(content)
+    if original_size != 0:
+        logging.info(
+            f"Cleaned page size: {cleaned_size} characters, down from {original_size} (shrunk: {original_size-cleaned_size} chars, {round((1-(cleaned_size/original_size)) * 100, 2)}%)"  # noqa:E501
+        )
+
+    return content
+
+
 def clean_string(text):
     """
     This function takes in a string and performs a series of text cleaning operations.
@@ -161,6 +217,10 @@ def detect_datatype(source: Any) -> DataType:
             logging.debug(f"Source of `{formatted_source}` detected as `csv`.")
             return DataType.CSV
 
+        if url.path.endswith(".mdx") or url.path.endswith(".md"):
+            logging.debug(f"Source of `{formatted_source}` detected as `mdx`.")
+            return DataType.MDX
+
         if url.path.endswith(".docx"):
             logging.debug(f"Source of `{formatted_source}` detected as `docx`.")
             return DataType.DOCX
@@ -200,6 +260,10 @@ def detect_datatype(source: Any) -> DataType:
             logging.debug(f"Source of `{formatted_source}` detected as `docs_site`.")
             return DataType.DOCS_SITE
 
+        if "github.com" in url.netloc:
+            logging.debug(f"Source of `{formatted_source}` detected as `github`.")
+            return DataType.GITHUB
+
         # If none of the above conditions are met, it's a general web page
         logging.debug(f"Source of `{formatted_source}` detected as `web_page`.")
         return DataType.WEB_PAGE
@@ -233,6 +297,10 @@ def detect_datatype(source: Any) -> DataType:
             logging.debug(f"Source of `{formatted_source}` detected as `xml`.")
             return DataType.XML
 
+        if source.endswith(".mdx") or source.endswith(".md"):
+            logging.debug(f"Source of `{formatted_source}` detected as `mdx`.")
+            return DataType.MDX
+
         if source.endswith(".yaml"):
             with open(source, "r") as file:
                 yaml_content = yaml.safe_load(file)

embedchain/vectordb/base.py

@@ -1,6 +1,6 @@
 from embedchain.config.vectordb.base import BaseVectorDbConfig
 from embedchain.embedder.base import BaseEmbedder
-from embedchain.helper.json_serializable import JSONSerializable
+from embedchain.helpers.json_serializable import JSONSerializable
 
 
 class BaseVectorDB(JSONSerializable):

embedchain/vectordb/chroma.py

@@ -3,9 +3,10 @@ from typing import Any, Dict, List, Optional, Tuple, Union
 
 from chromadb import Collection, QueryResult
 from langchain.docstore.document import Document
+from tqdm import tqdm
 
 from embedchain.config import ChromaDbConfig
-from embedchain.helper.json_serializable import register_deserializable
+from embedchain.helpers.json_serializable import register_deserializable
 from embedchain.vectordb.base import BaseVectorDB
 
 try:
@@ -157,8 +158,7 @@ class ChromaDB(BaseVectorDB):
                 " Ids size: {}".format(len(documents), len(metadatas), len(ids))
             )
 
-        for i in range(0, len(documents), self.BATCH_SIZE):
+        for i in tqdm(range(0, len(documents), self.BATCH_SIZE), desc="Inserting batches in chromadb"):
-            print("Inserting batches from {} to {} in chromadb".format(i, min(len(documents), i + self.BATCH_SIZE)))
             if skip_embedding:
                 self.collection.add(
                     embeddings=embeddings[i : i + self.BATCH_SIZE],