From a5b2c63423697271813ed2a2a87dabbe8ab73e9e Mon Sep 17 00:00:00 2001
From: KISHAN_KACHHADIYA <87411436+kishan42@users.noreply.github.com>
Date: Wed, 25 Dec 2024 15:38:16 +0530
Subject: [PATCH] Update pydantic_prompt.py

---
 src/ragas/prompt/pydantic_prompt.py | 84 ++++++++++-------------------
 1 file changed, 27 insertions(+), 57 deletions(-)

diff --git a/src/ragas/prompt/pydantic_prompt.py b/src/ragas/prompt/pydantic_prompt.py
index 938a53473f..e75f757d50 100644
--- a/src/ragas/prompt/pydantic_prompt.py
+++ b/src/ragas/prompt/pydantic_prompt.py
@@ -142,38 +142,8 @@ async def generate_multiple(
         temperature: t.Optional[float] = None,
         stop: t.Optional[t.List[str]] = None,
         callbacks: t.Optional[Callbacks] = None,
-        retries_left: int = 3,
+        retries_left: int = 3, 
     ) -> t.List[OutputModel]:
-        """
-        Generate multiple outputs using the provided language model and input data.
-
-        Parameters
-        ----------
-        llm : BaseRagasLLM
-            The language model to use for generation.
-        data : InputModel
-            The input data for generation.
-        n : int, optional
-            The number of outputs to generate. Default is 1.
-        temperature : float, optional
-            The temperature parameter for controlling randomness in generation.
-        stop : List[str], optional
-            A list of stop sequences to end generation.
-        callbacks : Callbacks, optional
-            Callback functions to be called during the generation process.
-        retries_left : int, optional
-            Number of retry attempts for an invalid LLM response
-
-        Returns
-        -------
-        List[OutputModel]
-            A list of generated outputs.
-
-        Raises
-        ------
-        RagasOutputParserException
-            If there's an error parsing the output.
-        """
         callbacks = callbacks or []
 
         processed_data = self.process_input(data)
@@ -182,37 +152,37 @@ async def generate_multiple(
             inputs={"data": processed_data},
             callbacks=callbacks,
             metadata={"type": ChainType.RAGAS_PROMPT},
-        )
-        prompt_value = PromptValue(text=self.to_string(processed_data))
+    )
+        prompt_text = self.to_string(processed_data)
         resp = await llm.generate(
-            prompt_value,
+            [prompt_text],  # Pass as a list of strings
             n=n,
             temperature=temperature,
             stop=stop,
             callbacks=prompt_cb,
-        )
-
-        output_models = []
-        parser = RagasOutputParser(pydantic_object=self.output_model)
-        for i in range(n):
-            output_string = resp.generations[0][i].text
-            try:
-                answer = await parser.parse_output_string(
-                    output_string=output_string,
-                    prompt_value=prompt_value,
-                    llm=llm,
-                    callbacks=prompt_cb,
-                    retries_left=retries_left,
-                )
-                processed_output = self.process_output(answer, data)  # type: ignore
-                output_models.append(processed_output)
-            except RagasOutputParserException as e:
-                prompt_rm.on_chain_error(error=e)
-                logger.error("Prompt %s failed to parse output: %s", self.name, e)
-                raise e
-
-        prompt_rm.on_chain_end({"output": output_models})
-        return output_models
+    )
+
+       output_models = []
+       parser = RagasOutputParser(pydantic_object=self.output_model)
+       for i in range(n):
+           output_string = resp.generations[0][i].text
+           try:
+               answer = await parser.parse_output_string(
+                   output_string=output_string,
+                   prompt_value=PromptValue(text=prompt_text),
+                   llm=llm,
+                   callbacks=prompt_cb,
+                   retries_left=retries_left,
+            )
+               processed_output = self.process_output(answer, data)  # type: ignore
+               output_models.append(processed_output)
+           except RagasOutputParserException as e:
+               prompt_rm.on_chain_error(error=e)
+               logger.error("Prompt %s failed to parse output: %s", self.name, e)
+               raise e
+
+      prompt_rm.on_chain_end({"output": output_models})
+      return output_models  
 
     def process_input(self, input: InputModel) -> InputModel:
         return input