fix: two small fixes (#3494)

# Description

Please include a summary of the changes and the related issue. Please
also include relevant motivation and context.

Closes CORE-299, CORE-255

## Checklist before requesting a review

Please delete options that are not relevant.

- [ ] My code follows the style guidelines of this project
- [ ] I have performed a self-review of my code
- [ ] I have commented hard-to-understand areas
- [ ] I have ideally added tests that prove my fix is effective or that
my feature works
- [ ] New and existing unit tests pass locally with my changes
- [ ] Any dependent changes have been merged

## Screenshots (if appropriate):

docs/docs/workflows/examples/rag_with_web_search.md

@@ -15,7 +15,8 @@ Follow the instructions below to create the agentic RAG workflow shown above, wh
 1. Add your API Keys to your environment variables
 ```python
 import os
-os.environ["OPENAI_API_KEY"] = "myopenai_apikey"
+os.environ["OPENAI_API_KEY"] = "my_openai_api_key"
+os.environ["TAVILY_API_KEY"] = "my_tavily_api_key"
 
 ```
 Check our `.env.example` file to see the possible environment variables you can configure. Quivr supports APIs from Anthropic, OpenAI, and Mistral. It also supports local models using Ollama.

examples/chatbot/basic_rag_workflow.yaml

@@ -15,6 +15,8 @@ workflow_config:
 
     - name: "generate_rag" # the name of the last node, from which we want to stream the answer to the user
       edges: ["END"]
+      tools:
+        - name: "cited_answer"
 
 # Maximum number of previous conversation iterations
 # to include in the context of the answer

examples/quivr-whisper/README.md

@@ -1,3 +1,65 @@
-# quivr-whisper
+# Quivr-Whisper
 
-Describe your project here.
+Quivr-Whisper is a web application that allows users to ask questions via audio input. It leverages OpenAI's Whisper model for speech transcription and synthesizes responses using OpenAI's text-to-speech capabilities. The application queries the Quivr API to get a response based on the transcribed audio input.
+
+
+
+https://github.com/StanGirard/quivr-whisper/assets/19614572/9cc270c9-07e4-4ce1-bcff-380f195c9313
+
+
+
+## Features
+
+- Audio input for asking questions
+- Speech transcription using OpenAI's Whisper model
+- Integration with Quivr API for intelligent responses
+- Speech synthesis of the response for audio playback
+
+## Getting Started
+
+These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
+
+### Prerequisites
+
+What things you need to install the software and how to install them:
+
+- Python 3.6+
+- pip for Python 3
+- Flask
+- OpenAI Python package
+- Requests package
+
+### Installing
+
+A step by step series of examples that tell you how to get a development environment running:
+
+1. Clone the repository to your local machine.
+```bash
+git clone https://github.com/stangirard/quivr-whisper.git
+cd Quivr-talk
+```
+
+2. Install the required packages.
+```bash
+pip install flask openai requests python-dotenv
+```
+
+3. Create a `.env` file in the root directory of the project and add your API keys and other configuration variables.
+```env
+OPENAI_API_KEY='your_openai_api_key'
+QUIVR_API_KEY='your_quivr_api_key'
+QUIVR_CHAT_ID='your_quivr_chat_id'
+QUIVR_BRAIN_ID='your_quivr_brain_id'
+QUIVR_URL='https://api.quivr.app' # Optional, only if different from the default
+```
+
+4. Run the Flask application.
+```bash
+flask run
+```
+
+Your app should now be running on `http://localhost:5000`.
+
+## Usage
+
+To use Quivr-talk, navigate to `http://localhost:5000` in your web browser, click on "Ask a question to Quivr", and record your question. Wait for the transcription and response to be synthesized, and you will hear the response played back to you.