Merge branch 'develop' into chore/doc-updates2

README.md

@@ -13,11 +13,24 @@ For more information on the recipes concept, please see see [here](https://towar
 - [Demo video](https://www.loom.com/share/e27ae40a73e9470597e2d58176ecc4b9?sid=8421a557-fbdd-47f2-85eb-4e96da96b55f) showing AI-assisted coding for managing recipes
 - [Demo video](https://www.loom.com/share/bd63433f977c4f21b2b7d44e7305473f?sid=9ad945ef-cbbd-469d-b4b5-dc8c6859fd75) showing end-user prototype
 
+# Table of Contents
+- [Design Concepts](#design-concepts)
+- [What's in this repo?](#whats-in-this-repo)
+- [What features are supported?](#what-features-are-supported)
+- [Quick start](#quick-start)
+- [Stoping/Starting the environment](#stopingstarting-the-environment)
+- [Using Recipes](#using-recipes)
+- [Additional Features](#additional-features)
+- [Managing recipes](#managing-recipes)
+- [Autogen Studio and autogen agent teams for creating data recipes](#autogen-studio-and-autogen-agent-teams-for-creating-data-recipes)
+
+For additional information, check out [CONTRIBUTION.md](CONTRIBUTION.md)
+
 # Design Concepts
 
 Data recipes have two types: (i) Exact memories, eg '*What is the population of Mali?*' which can be served directly to the user when they ask this question; (ii) Generic skills which can be run when requested for a scenario not in memory, eg a skill for 'What is the population of country X?' which can be called when the user asks something like '*What is the population of Nigeria?*'. In both cases the match to the user's intent is made using semantic search with LLM-reranking.
 
-Given the rapidly changing landscape of LLMs, we have tried as much as possible to implement data recipes in such a way that it can be integrated with various semantic architectures and frameworks. By implementing recipes using a recipes server (powered by FastAPI), it can be called from [Open AI assistant](https://platform.openai.com/docs/assistants/overview) actions and [Copilot Studio](https://www.microsoft.com/en-us/microsoft-copilot/microsoft-copilot-studio) as well from any custom code. Also included in this repo is an example of using recipes via OpenAI format plugins, as supported by frameworks such as [semantic kernel](https://learn.microsoft.com/en-us/semantic-kernel/overview/?tabs=Csharp). 
+Given the rapidly changing landscape of LLMs, we have tried as much as possible to implement data recipes in such a way that it can be integrated with various semantic architectures and frameworks. By implementing recipes using a recipes server (powered by FastAPI), it can be called from [Open AI assistant](https://platform.openai.com/docs/assistants/overview) actions and [Copilot Studio](https://www.microsoft.com/en-us/microsoft-copilot/microsoft-copilot-studio) as well as from any custom code. Also included in this repo is an example of using recipes via OpenAI format plugins, as supported by frameworks such as [semantic kernel](https://learn.microsoft.com/en-us/semantic-kernel/overview/?tabs=Csharp). 
 
 Data recipes supports datasources accessed via API, but in some cases it is preferable to ingest data in order to leverage LLM SQL capabilities. We include an initial set of data sources specific to humanitarian response in the ingestion module, which can be extended to include additional sources as required.
 
@@ -39,7 +52,7 @@ This repo contains a docker-compose environment that will run the following comp
 - (Azure) Open AI Assistant creation tools to create assistants that are aware of the data sources available in the data recipes ai environment 
 - Autogen studio agent team for helping creating recipes [ In progress ]
 
-# What can features are supported?
+# What features are supported?
 
 - Ability to create data recipes using LLMs, these can be served to end users via chat
 - Ability for end users to access memories and recipes using a chat interface. Memories will present saved results, recipes will run to get latest results
@@ -88,7 +101,7 @@ This repo contains a docker-compose environment that will run the following comp
 
 4. Download sample Humanitarian Data Exchange (HDX) API data
 
-    For a quick start, we have prepared a sample dataset extracted from the new [HDX API](https://hdx-hapi.readthedocs.io/en/latest/). You can also run the ingestion yourself (see below), but this demo file should get you started quickly.
+    For a quick start, we have prepared a sample dataset extracted from the new [HDX API](https://hdx-hapi.readthedocs.io/en/latest/). You can also run the ingestion yourself [see below](#analysis-on-ingested-data), but this demo file should get you started quickly.
 
     From [this Google folder](https://drive.google.com/drive/folders/1E4G9HM-QzxdXVNkgP3fQXsuNcABWzdus?usp=sharing), download the file starting with 'datadb' and save it into the 'data' folder of your repo.
 
@@ -106,7 +119,7 @@ This repo contains a docker-compose environment that will run the following comp
 
     Make note of the assitant ID printed, then edit your `.env` file and using it set variable `ASSISTANTS_ID`.
 
-    Note: (i) If you rerun `create_update_assistant.py` once `ASSISTANTS_ID` is set, the script will update the assistant rather than create a new one. You will need to do this if trying different models; (ii) You can also add your own data, pdf, docx, csv, xlsx files for the assistant to use, see section 'Adding your own files for the assistant to analyze' below.
+    Note: (i) If you rerun `create_update_assistant.py` once `ASSISTANTS_ID` is set, the script will update the assistant rather than create a new one. You will need to do this if trying different models; (ii) You can also add your own data, pdf, docx, csv, xlsx files for the assistant to use, see section [Adding your own files for the assistant to analyze](#adding-your-own-files-for-the-assistant-to-analyze) below.
 
     *Warning! If using **Azure**, at time of writing July 2024, [the documented approach](https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/file-search?tabs=python#update-the-assistant-to-use-the-new-vector-store) for uploading files in Python does not work while assistants in preview model. Though the python runs, the files do not appear in the UI. We recommedn you upload files in [./assistants/chat_ui/files](./assistants/chat_ui/files) in the UI yourself.*