feat: Add Quivr chatbot example (#2827)

The commit adds a new Quivr chatbot example to the repository. The
example demonstrates how to create a simple chatbot using Quivr and
Chainlit. Users can upload a text file and ask questions about its
content. The commit includes the necessary files, installation
instructions, and usage guidelines.

.gitignore

@@ -89,3 +89,7 @@ backend/.env.test
 **/*.egg-info
 
 .coverage
+backend/core/examples/chatbot/.files/*
+backend/core/examples/chatbot/.python-version
+backend/core/examples/chatbot/.chainlit/config.toml
+backend/core/examples/chatbot/.chainlit/translations/en-US.json

backend/core/examples/chatbot/README.md

@@ -0,0 +1,45 @@
+# Quivr Chatbot Example
+
+This example demonstrates how to create a simple chatbot using Quivr and Chainlit. The chatbot allows users to upload a text file and then ask questions about its content.
+
+## Prerequisites
+
+- Python 3.8 or higher
+
+## Installation
+
+1. Clone the repository or navigate to the `backend/core/examples/chatbot` directory.
+
+2. Install the required dependencies:
+
+   ```
+   pip install -r requirements.txt
+   ```
+
+## Running the Chatbot
+
+1. Start the Chainlit server:
+
+   ```
+   chainlit run main.py
+   ```
+
+2. Open your web browser and go to the URL displayed in the terminal (usually `http://localhost:8000`).
+
+## Using the Chatbot
+
+1. When the chatbot interface loads, you will be prompted to upload a text file.
+
+2. Click on the upload area and select a `.txt` file from your computer. The file size should not exceed 20MB.
+
+3. After uploading, the chatbot will process the file and inform you when it's ready.
+
+4. You can now start asking questions about the content of the uploaded file.
+
+5. Type your questions in the chat input and press Enter. The chatbot will respond based on the information in the uploaded file.
+
+## How It Works
+
+The chatbot uses the Quivr library to create a "brain" from the uploaded text file. This brain is then used to answer questions about the file's content. The Chainlit library provides the user interface and handles the chat interactions.
+
+Enjoy chatting with your documents!

backend/core/examples/chatbot/chainlit.md

@@ -0,0 +1,45 @@
+# Quivr Chatbot Example
+
+This example demonstrates how to create a simple chatbot using Quivr and Chainlit. The chatbot allows users to upload a text file and then ask questions about its content.
+
+## Prerequisites
+
+- Python 3.8 or higher
+
+## Installation
+
+1. Clone the repository or navigate to the `backend/core/examples/chatbot` directory.
+
+2. Install the required dependencies:
+
+   ```
+   pip install -r requirements.txt
+   ```
+
+## Running the Chatbot
+
+1. Start the Chainlit server:
+
+   ```
+   chainlit run main.py
+   ```
+
+2. Open your web browser and go to the URL displayed in the terminal (usually `http://localhost:8000`).
+
+## Using the Chatbot
+
+1. When the chatbot interface loads, you will be prompted to upload a text file.
+
+2. Click on the upload area and select a `.txt` file from your computer. The file size should not exceed 20MB.
+
+3. After uploading, the chatbot will process the file and inform you when it's ready.
+
+4. You can now start asking questions about the content of the uploaded file.
+
+5. Type your questions in the chat input and press Enter. The chatbot will respond based on the information in the uploaded file.
+
+## How It Works
+
+The chatbot uses the Quivr library to create a "brain" from the uploaded text file. This brain is then used to answer questions about the file's content. The Chainlit library provides the user interface and handles the chat interactions.
+
+Enjoy chatting with your documents!

backend/core/examples/chatbot/main.py

@@ -0,0 +1,63 @@
+import tempfile
+
+import chainlit as cl
+from quivr_core import Brain
+
+
+@cl.on_chat_start
+async def on_chat_start():
+    files = None
+
+    # Wait for the user to upload a file
+    while files is None:
+        files = await cl.AskFileMessage(
+            content="Please upload a text .txt file to begin!",
+            accept=["text/plain"],
+            max_size_mb=20,
+            timeout=180,
+        ).send()
+
+    file = files[0]
+
+    msg = cl.Message(content=f"Processing `{file.name}`...", disable_feedback=True)
+    await msg.send()
+
+    with open(file.path, "r", encoding="utf-8") as f:
+        text = f.read()
+
+    with tempfile.NamedTemporaryFile(
+        mode="w", suffix=".txt", delete=False
+    ) as temp_file:
+        temp_file.write(text)
+        temp_file.flush()
+        temp_file_path = temp_file.name
+
+    brain = Brain.from_files(name="user_brain", file_paths=[temp_file_path])
+
+    # Store the file path in the session
+    cl.user_session.set("file_path", temp_file_path)
+
+    # Let the user know that the system is ready
+    msg.content = f"Processing `{file.name}` done. You can now ask questions!"
+    await msg.update()
+
+    cl.user_session.set("brain", brain)
+
+
+@cl.on_message
+async def main(message: cl.Message):
+    brain = cl.user_session.get("brain")  # type: Brain
+
+    if brain is None:
+        await cl.Message(content="Please upload a file first.").send()
+        return
+
+    # Prepare the message for streaming
+    msg = cl.Message(content="")
+    await msg.send()
+
+    # Use the ask_stream method for streaming responses
+    async for chunk in brain.ask_streaming(message.content):
+        await msg.stream_token(chunk.answer)
+
+    await msg.send()

backend/core/examples/chatbot/requirements.txt

@@ -0,0 +1,5 @@
+quivr-core==0.0.7
+langchain-community==0.2.6
+faiss-cpu==1.8.0
+langchain-openai==0.1.14
+chainlit==1.0.0