Update README (#145)

Propose the following README updates for further clarity

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README.md

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 # langchain-weaviate
 
-This package contains the LangChain integrations for Weaviate through their `weaviate-client` SDK.
+## About
+
+This package contains the [Weaviate](https://github.com/weaviate/weaviate) integrations for [LangChain](https://github.com/langchain-ai/langchain).
+
+- **Weaviate** is an open source, AI-native vector database that helps developers create intuitive and reliable AI-powered applications.
+- **LangChain** is a framework for developing applications powered by language models.
+
+Using this package, LangChain users can conveniently set Weaviate as their vector store to store and retrieve embeddings.
+
+## Requirements
+
+To use this package, you need to have a running Weaviate instance.
+
+Weaviate can be [deployed in many different ways](https://weaviate.io/developers/weaviate/starter-guides/which-weaviate) such as in containerized environments, on Kubernetes, or in the cloud as a managed service, on-premises, or through a cloud provider such as AWS or Google Cloud.
+
+The deployment method to choose depends on your use case and infrastructure requirements.
+
+Two of the most common ways to deploy Weaviate are:
+- [Docker Compose](https://weaviate.io/developers/weaviate/installation/docker-compose)
+- [Weaviate Cloud Services (WCS)](https://console.weaviate.cloud)
 
 ## Installation and Setup
 
-- Install the LangChain partner package
+As an integration package, this assumes you have already installed LangChain. If not, please refer to the [LangChain installation guide](https://python.langchain.com/docs/get_started/installation).
+
+Then, install this package:
+
 ```bash
 pip install langchain-weaviate
 ```
+
+## Usage
+
+Please see the included [Jupyter notebook](docs/vectorstores.ipynb) for an example of how to use this package.
+
+## Further resources
+
+- [LangChain documentation](https://python.langchain.com/docs)
+- [Weaviate documentation](https://weaviate.io/developers/weaviate)