Deshraj Yadav 1 år sedan
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2 ändrade filer med 16 tillägg och 24 borttagningar
  1. 15 23
      README.md
  2. 1 1
      docs/get-started/introduction.mdx

+ 15 - 23
README.md

@@ -10,6 +10,9 @@
   <a href="https://pypi.org/project/embedchain/">
     <img src="https://img.shields.io/pypi/v/embedchain" alt="PyPI">
   </a>
+  <a href="https://pepy.tech/project/embedchain">
+    <img src="https://static.pepy.tech/badge/embedchain" alt="Downloads">
+  </a>
   <a href="https://join.slack.com/t/embedchain/shared_invite/zt-22uwz3c46-Zg7cIh5rOBteT_xe1jwLDw">
     <img src="https://img.shields.io/badge/slack-embedchain-brightgreen.svg?logo=slack" alt="Slack">
   </a>
@@ -19,30 +22,30 @@
   <a href="https://twitter.com/embedchain">
     <img src="https://img.shields.io/twitter/follow/embedchain" alt="Twitter">
   </a>
-  <a href="https://embedchain.substack.com/">
-    <img src="https://img.shields.io/badge/Substack-%23006f5c.svg?logo=substack" alt="Substack">
-  </a>
   <a href="https://colab.research.google.com/drive/138lMWhENGeEu7Q1-6lNbNTHGLZXBBz_B?usp=sharing">
     <img src="https://camo.githubusercontent.com/84f0493939e0c4de4e6dbe113251b4bfb5353e57134ffd9fcab6b8714514d4d1/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open in Colab">
   </a>
   <a href="https://codecov.io/gh/embedchain/embedchain">
     <img src="https://codecov.io/gh/embedchain/embedchain/graph/badge.svg?token=EMRRHZXW1Q" alt="codecov">
   </a>
-  <a href="https://pepy.tech/project/embedchain">
-    <img src="https://static.pepy.tech/badge/embedchain" alt="Downloads">
-  </a>
 </p>
 
 <hr />
 
+
+> ### Checkout our latest [Sadhguru AI app](https://sadhguru-ai.streamlit.app/) built using Embedchain.
+
 ## What is Embedchain?
-Embedchain is a Data Platform for Large Language Models (LLMs). Seamlessly load, index, retrieve, and sync unstructured data to build dynamic, LLM-powered applications. Check out [embedchain-js](https://github.com/embedchain/embedchain/tree/main/embedchain-js) for a JavaScript implementation.
+
+Embedchain is a production ready Open Source RAG framework - load, index, retrieve, and sync any unstructured data.
+
+Embedchain streamlines the creation of RAG applications, offering a seamless process for managing various types of unstructured data. It efficiently segments data into manageable chunks, generates relevant embeddings, and stores them in a vector database for optimized retrieval. With a suite of diverse APIs, it enables users to extract contextual information, find precise answers, or engage in interactive chat conversations, all tailored to their own data.
 
 ## 🔧 Quick install
 
 ### Python API
 ```bash
-pip install --upgrade embedchain
+pip install embedchain
 ```
 
 ### REST API
@@ -52,7 +55,7 @@ You can also run Embedchain as a REST API server using the following command:
 docker run --name embedchain -p 8080:8080 embedchain/rest-api:latest
 ```
 
-Then, navigate to http://0.0.0.0:8080/docs to interact with the API.
+Then, navigate to http://127.0.0.1:8080/docs to interact with the API.
 
 ## 🔍 Usage and Demo
 
@@ -78,17 +81,6 @@ elon_bot.add("https://www.forbes.com/profile/elon-musk")
 # Query the bot
 elon_bot.query("How many companies does Elon Musk run and name those?")
 # Answer: Elon Musk currently runs several companies. As of my knowledge, he is the CEO and lead designer of SpaceX, the CEO and product architect of Tesla, Inc., the CEO and founder of Neuralink, and the CEO and founder of The Boring Company. However, please note that this information may change over time, so it's always good to verify the latest updates.
-
-# (Optional): Deploy app to Embedchain Platform
-app.deploy()
-# 🔑 Enter your Embedchain API key. You can find the API key at https://app.embedchain.ai/settings/keys/
-# ec-xxxxxx
-
-# 🛠️ Creating pipeline on the platform...
-# 🎉🎉🎉 Pipeline created successfully! View your pipeline: https://app.embedchain.ai/pipelines/xxxxx
-
-# 🛠️ Adding data to your pipeline...
-# ✅ Data of type: web_page, value: https://www.forbes.com/profile/elon-musk added successfully.
 ```
 
 You can also try it in your browser with Google Colab:
@@ -109,7 +101,7 @@ Connect with fellow developers and users by joining our [Slack Workspace](https:
 
 ## 🤝 Schedule a 1-on-1 Session
 
-Book a [1-on-1 Session](https://cal.com/taranjeetio/ec) with Taranjeet, the founder, to discuss any issues, provide feedback, or explore how we can improve Embedchain for you.
+Book a [1-on-1 Session](https://cal.com/taranjeetio/ec) with the founders, to discuss any issues, provide feedback, or explore how we can improve Embedchain for you.
 
 ## 🌐 Contributing
 
@@ -122,9 +114,9 @@ For more reference, please go through [Development Guide](https://docs.embedchai
   <img src="https://contrib.rocks/image?repo=embedchain/embedchain" />
 </a>
 
-## Telemetry
+## Anonymous Telemetry
 
-We collect anonymous usage metrics to enhance our package's quality and user experience. This includes data like feature usage frequency and system info, but never personal details. The data helps us prioritize improvements and ensure compatibility. If you wish to opt-out, set the `app.config.collect_metrics = False` in the code. We prioritize data security and don't share this data externally.
+We collect anonymous usage metrics to enhance our package's quality and user experience. This includes data like feature usage frequency and system info, but never personal details. The data helps us prioritize improvements and ensure compatibility. If you wish to opt-out, set the environment variable `EC_TELEMETRY=false`. We prioritize data security and don't share this data externally.
 
 ## Citation
 

+ 1 - 1
docs/get-started/introduction.mdx

@@ -4,7 +4,7 @@ title: 📚 Introduction
 
 ## What is Embedchain?
 
-Embedchain is a production ready Open-Source RAG framework - load, index, retrieve, and sync any unstructured data. 
+Embedchain is a production ready Open Source RAG framework - load, index, retrieve, and sync any unstructured data.
 
 Embedchain streamlines the creation of RAG applications, offering a seamless process for managing various types of unstructured data. It efficiently segments data into manageable chunks, generates relevant embeddings, and stores them in a vector database for optimized retrieval. With a suite of diverse APIs, it enables users to extract contextual information, find precise answers, or engage in interactive chat conversations, all tailored to their own data.