refactor(docs): Update Quivr documentation structure and content

README.md

@@ -73,13 +73,116 @@ Ensure you have the following installed:
           )
           print("answer:", answer)
   ```
+## Configuration
 
-## Examples 
+### Workflows
 
-| Name                                          | Description                                                                                                               |
+#### Basic RAG
-| --------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------- |
+
-| [Simple Question](./examples/simple_question) | Ask a simple question to the RAG by ingesting a single file                                                               |
+![](docs/docs/workflows/examples/basic_rag.excalidraw.png)
-| [ChatBot](./examples/chatbot)                 | Build a chatbot by ingesting a folder of files with a nice UI powered by [Chainlit](https://github.com/Chainlit/chainlit) |
+
+
+Creating a basic RAG workflow like the one above is simple, here are the steps:
+
+
+1. Add your API Keys to your environment variables
+```python
+import os
+os.environ["OPENAI_API_KEY"] = "myopenai_apikey"
+
+```
+Quivr supports APIs from Anthropic, OpenAI, and Mistral. It also supports local models using Ollama.
+
+1. Create the YAML file ``basic_rag_workflow.yaml`` and copy the following content in it
+```yaml
+workflow_config:
+  name: "standard RAG"
+  nodes:
+    - name: "START"
+      edges: ["filter_history"]
+
+    - name: "filter_history"
+      edges: ["rewrite"]
+
+    - name: "rewrite"
+      edges: ["retrieve"]
+
+    - name: "retrieve"
+      edges: ["generate_rag"]
+
+    - name: "generate_rag" # the name of the last node, from which we want to stream the answer to the user
+      edges: ["END"]
+
+# Maximum number of previous conversation iterations
+# to include in the context of the answer
+max_history: 10
+
+# Reranker configuration
+reranker_config:
+  # The reranker supplier to use
+  supplier: "cohere"
+
+  # The model to use for the reranker for the given supplier
+  model: "rerank-multilingual-v3.0"
+
+  # Number of chunks returned by the reranker
+  top_n: 5
+
+# Configuration for the LLM
+llm_config:
+
+  # maximum number of tokens passed to the LLM to generate the answer
+  max_input_tokens: 4000
+
+  # temperature for the LLM
+  temperature: 0.7
+```
+
+3. Create a Brain with the default configuration
+```python
+from quivr_core import Brain
+
+brain = Brain.from_files(name = "my smart brain",
+                        file_paths = ["./my_first_doc.pdf", "./my_second_doc.txt"],
+                        )
+
+```
+
+4. Launch a Chat
+```python
+brain.print_info()
+
+from rich.console import Console
+from rich.panel import Panel
+from rich.prompt import Prompt
+from quivr_core.config import RetrievalConfig
+
+config_file_name = "./basic_rag_workflow.yaml"
+
+retrieval_config = RetrievalConfig.from_yaml(config_file_name)
+
+console = Console()
+console.print(Panel.fit("Ask your brain !", style="bold magenta"))
+
+while True:
+    # Get user input
+    question = Prompt.ask("[bold cyan]Question[/bold cyan]")
+
+    # Check if user wants to exit
+    if question.lower() == "exit":
+        console.print(Panel("Goodbye!", style="bold yellow"))
+        break
+
+    answer = brain.ask(question, retrieval_config=retrieval_config)
+    # Print the answer with typing effect
+    console.print(f"[bold green]Quivr Assistant[/bold green]: {answer.answer}")
+
+    console.print("-" * console.width)
+
+brain.print_info()
+```
+
+5. You are now all set up to talk with your brain and test different retrieval strategies by simply changing the configuration file!
 
 ## Go further