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README.md

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-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Document Retrieval Augmented Language Model with LangChain and OpenAI's GPT-3.5-turbo.
+
+## Description
+
+This project involves the creation of a vector database using OpenAI embeddings and Chroma DB, followed by the retrieval of document snippets through a similarity search with LangChain's retrieval system. Upon retrieval of relevant snippets, the system uses GPT-3.5 Turbo to generate responses to input questions using the retrieved snippets as context. The system also incorporates a ConversationBufferMemory to store the memory of the chat, enhancing the quality of the conversational context and the relevance of generated responses.
+
+## Contents
+
+1. **OpenAI Embeddings and Chroma DB**: Utilizes the rich semantic information in OpenAI embeddings and the efficient storage and retrieval capabilities of Chroma DB to create a performant and effective vector database.
+2. **Document Retrieval**: Uses LangChain's retrieval system to perform similarity search and retrieve relevant snippets from documents based on input queries.
+3. **Response Generation with GPT-3.5 Turbo**: Leverages the advanced language understanding and generation capabilities of GPT-3.5 Turbo to generate responses to input questions using Langchain's `RetrievalQA`.
+4. **ConversationBufferMemory**: Stores the history of the conversation to ensure context continuity and enhance the relevance of the responses generated.
+
+## Getting Started
+
+### Prerequisites
+Before you begin, ensure you have met the following requirements:
+- You have installed Python 3.x.
+- You have access to OpenAI GPT-3.5 Turbo and relevant API credentials.
+- You have set up Chroma DB on your server/machine, and the documents in the database.
+- You have access to LangChain's retrieval system.
+
+
+### Usage
+After installation, you can use the system via command line or GUI through **gradio** `app.py`.