[patch] Reduce NaN Occurrences by Simple Prompt Modification for JSON Output for context_precision (#581)

## Overview
During the calculation of `context_precision`, an issue was observed
where increasing the context amount led to a surge in NaN occurrences.
Comparatively, `context_recall` does not exhibit this problem. An
investigation into the causes of the difference uncovered that the issue
stems from whether the prompts specify outputting in JSON format.

## Discovery
It was found that simply specifying JSON output for `context_precision`,
similar to what is done for `context_recall`, significantly reduces the
incidence of NaN. Utilizing JSON mode appears to be crucial, as noted in
the OpenAI reference for text generation in JSON mode:

> "If you don't include an explicit instruction to generate JSON, the
model may generate an unending stream of whitespace and the request may
run continually until it reaches the token limit." [OpenAI Text
Generation JSON Mode
Documentation](https://platform.openai.com/docs/guides/text-generation/json-mode)

## Solution
To align with best practices and address the NaN generation issue, I
propose updating the prompt for `context_precision` to explicitly
instruct the generation of output in JSON format. This small but
impactful change will bring `context_precision` in line with how
`context_recall` operates and ensure more stable and predictable
outcomes when handling larger context volumes.

## Impact
By making this explicit switch to JSON output, we not only follow the
guideline provided by OpenAI but also prevent the potential uncontrolled
behavior that can result in a heavy onslaught of NaN values. This
improvement should increase the reliability of calculations within our
system and significantly decrease the time spent debugging NaN-related
issues.

I look forward to your review and approval of this change, which will
help us maintain robustness in our context precision calculations.

Best,
i-w-a

---------

Co-authored-by: Shahules786 <[email protected]>

Author: i-w-a

src/ragas/llms/prompt.py

@@ -91,7 +91,12 @@ def to_string(self) -> str:
         """
         Generate the prompt string from the variables.
         """
-        prompt_str = self.instruction + "\n"
+        added_json_instruction = (
+            "\nOutput in only valid JSON format."
+            if self.output_type.lower() == "json"
+            else ""
+        )
+        prompt_str = self.instruction + added_json_instruction + "\n"
 
         if self.examples:
             # Format the examples to match the Langchain prompt template

src/ragas/metrics/_context_precision.py

@@ -18,7 +18,7 @@
 
 CONTEXT_PRECISION = Prompt(
     name="context_precision",
-    instruction="""Given question, answer and context verify if the context was useful in arriving at the given answer. Give verdict as "1" if useful and "0" if not. """,
+    instruction="""Given question, answer and context verify if the context was useful in arriving at the given answer. Give verdict as "1" if useful and "0" if not with json output. """,
     examples=[
         {
             "question": """What can you tell me about albert Albert Einstein?""",