Merge remote-tracking branch 'origin/main' into hotfix/integrations

# Conflicts:
#	pyproject.toml

Author: penguine-ip

docs/content/docs/(custom)/metrics-dag.mdx

@@ -62,7 +62,7 @@ You'll also need to supply any additional arguments such as `expected_output` an
 The `DAGMetric` can be used to evaluate single-turn LLM interactions based on LLM-as-a-judge decision-trees.
 
 ```python
-from deepeval.dag import DeepAcyclicGraph
+from deepeval.metrics.dag import DeepAcyclicGraph
 from deepeval.metrics import DAGMetric
 
 dag = DeepAcyclicGraph(root_nodes=[...])

docs/content/docs/introduction.mdx

@@ -34,19 +34,6 @@ DeepEval is local-first: your evaluations run in your own environment. When your
 team needs shared dashboards, regression tracking, observability, or production
 monitoring, DeepEval integrates natively with [Confident AI](https://www.confident-ai.com).
 
-## Why DeepEval Exists
-
-LLM applications are hard to test with traditional assertions. The same input can
-produce multiple acceptable outputs, failures are often semantic, and quality can
-depend on tool calls, multi-step reasoning, state, retrieved context, or conversation
-history.
-
-DeepEval exists to make those behaviors testable without forcing every team into
-one workflow. You can write Pytest-style checks for CI/CD, run evals from
-standalone scripts and notebooks, score saved test cases or full datasets,
-evaluate entire traces or individual spans, and bring the same feedback loop into
-agentic coding workflows.
-
 ## Who is DeepEval For?
 
 DeepEval was designed for a technical audience and here are the main personas we serve well: