diff --git a/docs/deployment/embedchain_ai.mdx b/docs/deployment/embedchain_ai.mdx new file mode 100644 index 00000000..a3fef41b --- /dev/null +++ b/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: + + diff --git a/docs/get-started/deployment.mdx b/docs/get-started/deployment.mdx index beba188b..6bc7c289 100644 --- a/docs/get-started/deployment.mdx +++ b/docs/get-started/deployment.mdx @@ -10,38 +10,10 @@ After successfully setting up and testing your RAG app locally, the next step is - + -## 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: diff --git a/docs/get-started/quickstart.mdx b/docs/get-started/quickstart.mdx index 710acf9c..79501504 100644 --- a/docs/get-started/quickstart.mdx +++ b/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) - - ```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. - ``` - - You can now share your app with others from our platform. - Access your app on our [platform](https://app.embedchain.ai/). - - diff --git a/docs/mint.json b/docs/mint.json index fb9bb7c3..9dadd39b 100644 --- a/docs/mint.json +++ b/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" ] }, { diff --git a/embedchain/loaders/directory_loader.py b/embedchain/loaders/directory_loader.py index d4941939..72953f75 100644 --- a/embedchain/loaders/directory_loader.py +++ b/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"]) diff --git a/examples/unacademy-ai/app.py b/examples/unacademy-ai/app.py index 5f060167..b3dc1e54 100644 --- a/examples/unacademy-ai/app.py +++ b/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"# Unacademy UPSC AI", unsafe_allow_html=True) styled_caption = """ -

🚀 An Embedchain app powered with Unacademy\'s UPSC data!

+

+🚀 An Embedchain app powered with Unacademy\'s UPSC data! +

""" 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? diff --git a/pyproject.toml b/pyproject.toml index c0e6ea10..86a18de2 100644 --- a/pyproject.toml +++ b/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 ",