docs: quickstart guide with interactive LLM and project structure

Makefile

@@ -161,7 +161,7 @@ build-docs: ## Build all documentation
 	@echo "Converting ipynb notebooks to md files..."
 	$(Q)MKDOCS_CI=true uv run python $(GIT_ROOT)/docs/ipynb_to_md.py
 	@echo "Building ragas documentation..."
-	$(Q)uv run --group docs mkdocs build
+	$(Q)MKDOCS_CI=false uv run --group docs mkdocs build
 
 serve-docs: ## Build and serve documentation locally
-	$(Q)uv run --group docs mkdocs serve --dirtyreload
+	$(Q)MKDOCS_CI=false uv run --group docs mkdocs serve --dirtyreload

README.md

@@ -97,21 +97,39 @@ Available templates:
 
 ### Evaluate your LLM App
 
-This is 5 main lines:
+This is a simple example evaluating a summary for accuracy:
 
 ```python
-from ragas import SingleTurnSample
-from ragas.metrics import AspectCritic
+import asyncio
+from ragas.metrics.collections import AspectCritic
+from ragas.llms import llm_factory
 
+# Setup your LLM
+llm = llm_factory("gpt-4o")
+
+# Create a metric
+metric = AspectCritic(
+    name="summary_accuracy",
+    definition="Verify if the summary is accurate and captures key information.",
+    llm=llm
+)
+
+# Evaluate
 test_data = {
     "user_input": "summarise given text\nThe company reported an 8% rise in Q3 2024, driven by strong performance in the Asian market. Sales in this region have significantly contributed to the overall growth. Analysts attribute this success to strategic marketing and product localization. The positive trend in the Asian market is expected to continue into the next quarter.",
     "response": "The company experienced an 8% increase in Q3 2024, largely due to effective marketing strategies and product adaptation, with expectations of continued growth in the coming quarter.",
 }
-evaluator_llm = LangchainLLMWrapper(ChatOpenAI(model="gpt-4o"))
-metric = AspectCritic(name="summary_accuracy",llm=evaluator_llm, definition="Verify if the summary is accurate.")
-await metric.single_turn_ascore(SingleTurnSample(**test_data))
+
+score = await metric.ascore(
+    user_input=test_data["user_input"],
+    response=test_data["response"]
+)
+print(f"Score: {score.value}")
+print(f"Reason: {score.reason}")
 ```
 
+> **Note**: Make sure your `OPENAI_API_KEY` environment variable is set.
+
 Find the complete [Quickstart Guide](https://docs.ragas.io/en/latest/getstarted/evals)
 
 ## Want help in improving your AI application using evals?