[fix] dot file and docs (#1044)

docs/deployment/embedchain_ai.mdx

@@ -0,0 +1,38 @@
+---
+title: 'Embedchain.ai'
+description: 'Deploy your RAG application to embedchain.ai platform'
+---
+
+## Deploy on Embedchain Platform
+
+Embedchain enables developers to deploy their LLM-powered apps in production using the [Embedchain platform](https://app.embedchain.ai). The platform offers free access to context on your data through its REST API. Once the pipeline is deployed, you can update your data sources anytime after deployment.
+
+See the example below on how to use the deploy your app (for free):
+
+```python
+from embedchain import Pipeline as App
+
+# Initialize app
+app = App()
+
+# Add data source
+app.add("https://www.forbes.com/profile/elon-musk")
+
+# Deploy your pipeline to Embedchain Platform
+app.deploy()
+
+# 🔑 Enter your Embedchain API key. You can find the API key at https://app.embedchain.ai/settings/keys/
+# ec-xxxxxx
+
+# 🛠️ Creating pipeline on the platform...
+# 🎉🎉🎉 Pipeline created successfully! View your pipeline: https://app.embedchain.ai/pipelines/xxxxx
+
+# 🛠️ Adding data to your pipeline...
+# ✅ Data of type: web_page, value: https://www.forbes.com/profile/elon-musk added successfully.
+```
+
+## Seeking help?
+
+If you run into issues with deployment, please feel free to reach out to us via any of the following methods:
+
+<Snippet file="get-help.mdx" />

docs/get-started/deployment.mdx

@@ -10,38 +10,10 @@ After successfully setting up and testing your RAG app locally, the next step is
   <Card title="Modal.com" href="/deployment/modal_com"></Card>
   <Card title="Render.com" href="/deployment/render_com"></Card>
   <Card title="Streamlit.io" href="/deployment/streamlit_io"></Card>
-  <Card title="Embedchain Platform" href="#option-1-deploy-on-embedchain-platform"></Card>
+  <Card title="Embedchain.ai" href="/deployment/embedchain_ai"></Card>
   <Card title="Self-hosting" href="#option-2-self-hosting"></Card>
 </CardGroup>
 
-## Deploy on Embedchain Platform
-
-Embedchain enables developers to deploy their LLM-powered apps in production using the [Embedchain platform](https://app.embedchain.ai). The platform offers free access to context on your data through its REST API. Once the pipeline is deployed, you can update your data sources anytime after deployment.
-
-See the example below on how to use the deploy your app (for free):
-
-```python
-from embedchain import Pipeline as App
-
-# Initialize app
-app = App()
-
-# Add data source
-app.add("https://www.forbes.com/profile/elon-musk")
-
-# Deploy your pipeline to Embedchain Platform
-app.deploy()
-
-# 🔑 Enter your Embedchain API key. You can find the API key at https://app.embedchain.ai/settings/keys/
-# ec-xxxxxx
-
-# 🛠️ Creating pipeline on the platform...
-# 🎉🎉🎉 Pipeline created successfully! View your pipeline: https://app.embedchain.ai/pipelines/xxxxx
-
-# 🛠️ Adding data to your pipeline...
-# ✅ Data of type: web_page, value: https://www.forbes.com/profile/elon-musk added successfully.
-```
-
 ## Self-hosting
 
 You can also deploy Embedchain as a self-hosted service using the dockerized REST API service that we provide. Please follow the [guide here](/examples/rest-api) on how to use the REST API service. Here are some tutorials on how to deploy a containerized application to different platforms like AWS, GCP, Azure etc:

docs/get-started/quickstart.mdx

@@ -65,21 +65,4 @@ Creating an app involves 3 steps:
       To learn about other features, click [here](https://docs.embedchain.ai/get-started/introduction)
     </Accordion>
   </Step>
-  <Step title="🚀 Seamlessly launch your App on the Embedchain Platform!">
-    ```python
-    app.deploy()
-    # 🔑 Enter your Embedchain API key. You can find the API key at https://app.embedchain.ai/settings/keys/
-    # ec-xxxxxx
-
-    # 🛠️ Creating pipeline on the platform...
-    # 🎉🎉🎉 Pipeline created successfully! View your pipeline: https://app.embedchain.ai/pipelines/xxxxx
-
-    # 🛠️ Adding data to your pipeline...
-    # ✅ Data of type: web_page, value: https://www.forbes.com/profile/elon-musk added successfully.
-    ```
-    <Accordion title="Share your app with others" icon="laptop-mobile">
-      You can now share your app with others from our platform.
-      Access your app on our [platform](https://app.embedchain.ai/).
-    </Accordion>
-  </Step>
 </Steps>

docs/mint.json

@@ -90,7 +90,8 @@
         "deployment/fly_io",
         "deployment/modal_com",
         "deployment/render_com",
-        "deployment/streamlit_io"
+        "deployment/streamlit_io",
+        "deployment/embedchain_ai"
       ]
     },
     {

embedchain/loaders/directory_loader.py

@@ -38,6 +38,9 @@ class DirectoryLoader(BaseLoader):
     def _process_directory(self, directory_path: Path):
         data_list = []
         for file_path in directory_path.rglob("*") if self.recursive else directory_path.glob("*"):
+            # don't include dotfiles
+            if file_path.name.startswith("."):
+                continue
             if file_path.is_file() and (not self.extensions or any(file_path.suffix == ext for ext in self.extensions)):
                 loader = self._predict_loader(file_path)
                 data_list.extend(loader.load_data(str(file_path))["data"])

examples/unacademy-ai/app.py

@@ -1,9 +1,11 @@
 import queue
 
 import streamlit as st
+
 from embedchain import Pipeline as App
 from embedchain.config import BaseLlmConfig
-from embedchain.helpers.callbacks import StreamingStdOutCallbackHandlerYield, generate
+from embedchain.helpers.callbacks import (StreamingStdOutCallbackHandlerYield,
+                                          generate)
 
 
 @st.cache_resource
@@ -19,7 +21,9 @@ assistant_avatar_url = "https://cdn-images-1.medium.com/v2/resize:fit:1200/1*LdF
 st.markdown(f"# <img src='{assistant_avatar_url}' width={35} /> Unacademy UPSC AI", unsafe_allow_html=True)
 
 styled_caption = """
-<p style="font-size: 17px; color: #aaa;">🚀 An <a href="https://github.com/embedchain/embedchain">Embedchain</a> app powered with Unacademy\'s UPSC data!</p>
+<p style="font-size: 17px; color: #aaa;">
+🚀 An <a href="https://github.com/embedchain/embedchain">Embedchain</a> app powered with Unacademy\'s UPSC data!
+</p>
 """
 st.markdown(styled_caption, unsafe_allow_html=True)
 
@@ -33,7 +37,10 @@ with st.expander(":grey[Want to create your own Unacademy UPSC AI?]"):
     ```python
     from embedchain import Pipeline as App
     unacademy_ai_app = App()
-    unacademy_ai_app.add("https://unacademy.com/content/upsc/study-material/plan-policy/atma-nirbhar-bharat-3-0/", data_type="web_page")
+    unacademy_ai_app.add(
+        "https://unacademy.com/content/upsc/study-material/plan-policy/atma-nirbhar-bharat-3-0/",
+        data_type="web_page"
+    )
     unacademy_ai_app.chat("What is Atma Nirbhar 3.0?")
     ```
 
@@ -45,7 +52,8 @@ if "messages" not in st.session_state:
     st.session_state.messages = [
         {
             "role": "assistant",
-            "content": """Hi, I'm Unacademy UPSC AI bot, who can answer any questions related to UPSC preparation. Let me help you prepare better for UPSC.\n
+            "content": """Hi, I'm Unacademy UPSC AI bot, who can answer any questions related to UPSC preparation.
+            Let me help you prepare better for UPSC.\n
 Sample questions:
 - What are the subjects in UPSC CSE?
 - What is the CSE scholarship price amount?

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "embedchain"
-version = "0.1.38"
+version = "0.1.39"
 description = "Data platform for LLMs - Load, index, retrieve and sync any unstructured data"
 authors = [
     "Taranjeet Singh <taranjeet@embedchain.ai>",