feat: set and get prompts for metrics (#1259)

```python
from ragas.experimental.metrics._faithfulness import FaithfulnessExperimental, LongFormAnswerPrompt

faithfulness = FaithfulnessExperimental() 
faithfulness.get_prompts()

#{'long_form_answer_prompt': <ragas.experimental.metrics._faithfulness.LongFormAnswerPrompt at 0x7fd7baa8efb0>,
#'nli_statement_prompt': <ragas.experimental.metrics._faithfulness.NLIStatementPrompt at 0x7fd7baa8f010>}

long_form_prompt = LongFormAnswerPrompt()
long_form_prompt.instruction = "my new instruction"

prompts = {"long_form_answer_prompt":long_form_prompt}
faithfulness.set_prompts(**prompts)
```

---------

Co-authored-by: Jithin James <jamesjithin97@gmail.com>

.github/workflows/ci.yaml

@@ -143,9 +143,6 @@ jobs:
           pip install .
           pip install -r requirements/dev.txt
 
-      - name: Format check
-        run: |
-          make format
       - name: Lint check
         run: make lint
       - name: Type check

docs/howtos/integrations/opik.ipynb

@@ -30,7 +30,9 @@
     "import getpass\n",
     "\n",
     "os.environ[\"OPIK_API_KEY\"] = getpass.getpass(\"Opik API Key: \")\n",
-    "os.environ[\"OPIK_WORKSPACE\"] = input(\"Comet workspace (often the same as your username): \")"
+    "os.environ[\"OPIK_WORKSPACE\"] = input(\n",
+    "    \"Comet workspace (often the same as your username): \"\n",
+    ")"
    ]
   },
   {
@@ -151,6 +153,7 @@
     "import asyncio\n",
     "from ragas.integrations.opik import OpikTracer\n",
     "\n",
+    "\n",
     "# Define the scoring function\n",
     "def compute_metric(opik_tracer, metric, row):\n",
     "    async def get_score(opik_tracer, metric, row):\n",
@@ -162,16 +165,17 @@
     "    result = loop.run_until_complete(get_score(opik_tracer, metric, row))\n",
     "    return result\n",
     "\n",
+    "\n",
     "# Score a simple example\n",
     "row = {\n",
     "    \"question\": \"What is the capital of France?\",\n",
     "    \"answer\": \"Paris\",\n",
-    "    \"contexts\": [\"Paris is the capital of France.\", \"Paris is in France.\"]\n",
+    "    \"contexts\": [\"Paris is the capital of France.\", \"Paris is in France.\"],\n",
     "}\n",
     "\n",
     "opik_tracer = OpikTracer()\n",
     "score = compute_metric(opik_tracer, answer_relevancy_metric, row)\n",
-    "print(\"Answer Relevancy score:\", score)\n"
+    "print(\"Answer Relevancy score:\", score)"
    ]
   },
   {
@@ -207,23 +211,27 @@
     "from opik import track\n",
     "from opik.opik_context import get_current_trace\n",
     "\n",
+    "\n",
     "@track\n",
     "def retrieve_contexts(question):\n",
     "    # Define the retrieval function, in this case we will hard code the contexts\n",
     "    return [\"Paris is the capital of France.\", \"Paris is in France.\"]\n",
     "\n",
+    "\n",
     "@track\n",
     "def answer_question(question, contexts):\n",
     "    # Define the answer function, in this case we will hard code the answer\n",
     "    return \"Paris\"\n",
     "\n",
+    "\n",
     "@track(name=\"Compute Ragas metric score\", capture_input=False)\n",
     "def compute_rag_score(answer_relevancy_metric, question, answer, contexts):\n",
     "    # Define the score function\n",
     "    row = {\"question\": question, \"answer\": answer, \"contexts\": contexts}\n",
     "    score = compute_metric(answer_relevancy_metric, row)\n",
     "    return score\n",
     "\n",
+    "\n",
     "@track\n",
     "def rag_pipeline(question):\n",
     "    # Define the pipeline\n",
@@ -233,9 +241,10 @@
     "    trace = get_current_trace()\n",
     "    score = compute_rag_score(answer_relevancy_metric, question, answer, contexts)\n",
     "    trace.log_feedback_score(\"answer_relevancy\", round(score, 4), category_name=\"ragas\")\n",
-    "    \n",
+    "\n",
     "    return answer\n",
     "\n",
+    "\n",
     "rag_pipeline(\"What is the capital of France?\")"
    ]
   },
@@ -297,7 +306,7 @@
     "result = evaluate(\n",
     "    fiqa_eval[\"baseline\"].select(range(3)),\n",
     "    metrics=[context_precision, faithfulness, answer_relevancy],\n",
-    "    callbacks=[opik_tracer_eval]\n",
+    "    callbacks=[opik_tracer_eval],\n",
     ")\n",
     "\n",
     "print(result)"

src/ragas/integrations/opik.py

@@ -1,14 +1,20 @@
 import typing as t
 
 try:
-    from opik.integrations.langchain import OpikTracer as LangchainOpikTracer # type: ignore
+    from opik.integrations.langchain import (  # type: ignore
+        OpikTracer as LangchainOpikTracer,
+    )
+
     from ragas.evaluation import RAGAS_EVALUATION_CHAIN_NAME
 except ImportError:
-    raise ImportError("Opik is not installed. Please install it using `pip install opik` to use the Opik tracer.")
+    raise ImportError(
+        "Opik is not installed. Please install it using `pip install opik` to use the Opik tracer."
+    )
 
 if t.TYPE_CHECKING:
     from langchain_core.tracers.schemas import Run
 
+
 class OpikTracer(LangchainOpikTracer):
     """
     Callback for Opik that can be used to log traces and evaluation scores to the Opik platform.
@@ -20,6 +26,7 @@ class OpikTracer(LangchainOpikTracer):
     metadata: dict
         Additional metadata to log for each trace.
     """
+
     _evaluation_run_id: t.Optional[str] = None
 
     def _persist_run(self, run: "Run"):
@@ -48,8 +55,11 @@ def _on_chain_end(self, run: "Run"):
                 span = self._span_map[run.id]
                 trace_id = span.trace_id
                 if run.outputs:
-                    self._opik_client.log_traces_feedback_scores([
-                        {"id": trace_id, "name": name, "value": round(value, 4)} for name, value in  run.outputs.items()
-                    ])
-
+                    self._opik_client.log_traces_feedback_scores(
+                        [
+                            {"id": trace_id, "name": name, "value": round(value, 4)}
+                            for name, value in run.outputs.items()
+                        ]
+                    )
+
             self._persist_run(run)

src/ragas/metrics/base.py

@@ -26,9 +26,13 @@
     from ragas.embeddings import BaseRagasEmbeddings
     from ragas.llms import BaseRagasLLM
 
+import inspect
+
 from pysbd import Segmenter
 from pysbd.languages import LANGUAGE_CODES
 
+from ragas.experimental.llms.prompt import PydanticPrompt as Prompt
+
 logger = logging.getLogger(__name__)
 
 
@@ -154,6 +158,26 @@ def init(self, run_config: RunConfig):
             )
         self.llm.set_run_config(run_config)
 
+    def get_prompts(self) -> t.Dict[str, Prompt]:
+        prompts = {}
+        for name, value in inspect.getmembers(self):
+            if isinstance(value, Prompt):
+                prompts.update({name: value})
+        return prompts
+
+    def set_prompts(self, **prompts):
+        available_prompts = self.get_prompts()
+        for key, value in prompts.items():
+            if key not in available_prompts:
+                raise ValueError(
+                    f"Prompt with name '{key}' does not exist in the metric {self.name}. Use get_prompts() to see available prompts."
+                )
+            if not isinstance(value, Prompt):
+                raise ValueError(
+                    f"Prompt with name '{key}' must be an instance of 'Prompt'"
+                )
+            setattr(self, key, value)
+
 
 @dataclass
 class MetricWithEmbeddings(Metric):