docs: update docs

Author: sinedied

README.md

@@ -44,7 +44,7 @@ This application is made from multiple components:
 
 - A serverless API built with [Azure Functions](https://learn.microsoft.com/azure/azure-functions/functions-overview?pivots=programming-language-javascript) and using [LangChain.js](https://js.langchain.com/) to ingest the documents and generate responses to the user chat queries. The code is located in the `packages/api` folder.
 
-- A database to store the text extracted from the documents and the vectors generated by LangChain.js, using [Azure Cosmos DB for NoSQL](https://learn.microsoft.com/azure/cosmos-db/nosql/).
+- A database to store chat sessions and the text extracted from the documents and the vectors generated by LangChain.js, using [Azure Cosmos DB for NoSQL](https://learn.microsoft.com/azure/cosmos-db/nosql/).
 
 - A file storage to store the source documents, using [Azure Blob Storage](https://learn.microsoft.com/azure/storage/blobs/storage-blobs-introduction).
 
@@ -54,6 +54,7 @@ We use the [HTTP protocol for AI chat apps](https://aka.ms/chatprotocol) to comm
 
 - **Serverless Architecture**: Utilizes Azure Functions and Azure Static Web Apps for a fully serverless deployment.
 - **Retrieval-Augmented Generation (RAG)**: Combines the power of Azure Cosmos DB and LangChain.js to provide relevant and accurate responses.
+- **Chat Sessions History**: Maintains a personal chat history for each user, allowing them to revisit previous conversations.
 - **Scalable and Cost-Effective**: Leverages Azure's serverless offerings to provide a scalable and cost-effective solution.
 - **Local Development**: Supports local development using Ollama for testing without any cloud costs.
 

docs/images/architecture-local.drawio.png

@@ -35,7 +35,7 @@ This application is made from multiple components:
 
 - A serverless API built with [Azure Functions](https://learn.microsoft.com/azure/azure-functions/functions-overview?pivots=programming-language-javascript) and using [LangChain.js](https://js.langchain.com/) to ingest the documents and generate responses to the user chat queries. The code is located in the `packages/api` folder.
 
-- A database to store the text extracted from the documents and the vectors generated by LangChain.js, using [Azure Cosmos DB for NoSQL](https://learn.microsoft.com/azure/cosmos-db/nosql/).
+- A database to store chat sessions and the text extracted from the documents and the vectors generated by LangChain.js, using [Azure Cosmos DB for NoSQL](https://learn.microsoft.com/azure/cosmos-db/nosql/).
 
 - A file storage to store the source documents, using [Azure Blob Storage](https://learn.microsoft.com/azure/storage/blobs/storage-blobs-introduction).
 
@@ -77,9 +77,9 @@ You can now open the web app in your browser and start chatting with the bot.
 
 Our API is composed of two main endpoints:
 
-- `/documents`: This endpoint allows to upload a PDF documents in the database. Using LangChain.js, we extract the text from the PDF file, split it into smaller chunks, and generate vectors for each chunk. We store the text and the vectors in the database for later use.
+- `POST /documents`: This endpoint allows to upload a PDF documents in the database. Using LangChain.js, we extract the text from the PDF file, split it into smaller chunks, and generate vectors for each chunk. We store the text and the vectors in the database for later use.
 
-- `/chat`: This endpoint receives a list of messages, the last being the user query and returns a response generated by the LLM. It uses the documents stored in the database to generate the response. We use LangChain.js components to connect to the database, load the documents and perform a vector search after vectorizing the user query. After that, the most relevant documents are injected into the prompt, and we generate the response. While this process seems complex, LangChain.js does all the heavy lifting for us so we can focus on the application flow.
+- `POST /chats`: This endpoint receives a list of messages, the last being the user query and returns a response generated by the LLM. It uses the documents stored in the database to generate the response. We use LangChain.js components to connect to the database, load the documents and perform a vector search after vectorizing the user query. After that, the most relevant documents are injected into the prompt, and we generate the response. While this process seems complex, LangChain.js does all the heavy lifting for us so we can focus on the application flow.
 
 The `/documents` endpoint is used to ingest the documents after the application is deployed by uploading the PDFs, using either `curl` commands or the Node.js script we built (have a look at the `postup` hook in the `azure.yaml` file).