Update context_precision.md (#2262)

Correct misleading and wrong definition of context precision in docs,
with examples

## Issue Link / Problem Description
<!-- Link to related issue or describe the problem this PR solves -->
- Fixes #2013
- OR describe the issue: What problem does this solve? How can it be
replicated?

The current definition of context precision is misleading (see
discussion in linked issue). This PR is aimed at correcting the
definition.

## Changes Made
<!-- Describe what you changed and why -->
- Corrected the definition of context precision
- Added examples to illustrate the metric better
- 

## References
<!-- Link to related issues, discussions, forums, or external resources
-->
- Related issues:
#2013
- Documentation: 
- External references: 
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Author: anupamck

docs/concepts/metrics/available_metrics/context_precision.md

@@ -1,5 +1,7 @@
 # Context Precision
-Context Precision is a metric that measures the proportion of relevant chunks in the `retrieved_contexts`. It is calculated as the mean of the precision@k for each chunk in the context. Precision@k is the ratio of the number of relevant chunks at rank k to the total number of chunks at rank k.
+Context Precision is a metric that evaluates the retriever’s ability to rank relevant chunks higher than irrelevant ones for a given query in the retrieved context. Specifically, it assesses the degree to which relevant chunks in the retrieved context are placed at the top of the ranking.
+
+It is calculated as the mean of the precision@k for each chunk in the context. Precision@k is the ratio of the number of relevant chunks at rank k to the total number of chunks at rank k.
 
 $$
 \text{Context Precision@K} = \frac{\sum_{k=1}^{K} \left( \text{Precision@k} \times v_k \right)}{\text{Total number of relevant items in the top } K \text{ results}}
@@ -41,6 +43,50 @@ Output
 0.9999999999
 ```
 
+Note that even if an irrelevant chunk is present at the second position in the array, context precision remains the same.  
+
+```python
+from ragas import SingleTurnSample
+from ragas.metrics import LLMContextPrecisionWithoutReference
+
+context_precision = LLMContextPrecisionWithoutReference(llm=evaluator_llm)
+
+sample = SingleTurnSample(
+    user_input="Where is the Eiffel Tower located?",
+    response="The Eiffel Tower is located in Paris.",
+    retrieved_contexts=["The Eiffel Tower is located in Paris.", "The Brandenburg Gate is located in Berlin."], 
+)
+
+
+await context_precision.single_turn_ascore(sample)
+```
+Output
+```
+0.9999999999
+```
+
+However, if this irrelevant chunk is placed at the first position, context precision reduces. 
+
+```python
+from ragas import SingleTurnSample
+from ragas.metrics import LLMContextPrecisionWithoutReference
+
+context_precision = LLMContextPrecisionWithoutReference(llm=evaluator_llm)
+
+sample = SingleTurnSample(
+    user_input="Where is the Eiffel Tower located?",
+    response="The Eiffel Tower is located in Paris.",
+    retrieved_contexts=["The Brandenburg Gate is located in Berlin.", "The Eiffel Tower is located in Paris." ], 
+)
+
+
+await context_precision.single_turn_ascore(sample)
+```
+Output
+```
+0.49999999995
+```
+
 ### Context Precision with reference
 
 `LLMContextPrecisionWithReference` metric is can be used when you have both retrieved contexts and also reference context associated with a `user_input`. To estimate if a retrieved contexts is relevant or not this method uses the LLM to compare each of the retrieved context or chunk present in `retrieved_contexts` with `reference`.