instruction stringclasses 100
values | code stringlengths 78 193k | response stringlengths 259 170k | file stringlengths 59 203 |
|---|---|---|---|
Help me add docstrings to my project |
import asyncio
import importlib.util
import sys
import traceback
from collections import Counter
from pathlib import Path
from typing import Any, Dict, Optional
import typer
from rich.live import Live
from rich.panel import Panel
from rich.spinner import Spinner
from rich.table import Table
from rich.text import Text... | --- +++ @@ -1,3 +1,6 @@+"""
+Ragas CLI for running experiments from command line.
+"""
import asyncio
import importlib.util
@@ -23,29 +26,35 @@ # Create a callback for the main app to make it a group
@app.callback()
def main():
+ """Ragas CLI for running LLM evaluations"""
pass
# Rich utility functio... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/cli.py |
Improve my code by adding docstrings |
import asyncio
import logging
import typing as t
logger = logging.getLogger(__name__)
def is_event_loop_running() -> bool:
try:
loop = asyncio.get_running_loop()
except RuntimeError:
return False
else:
return loop.is_running()
def apply_nest_asyncio() -> bool:
if not is_eve... | --- +++ @@ -1,3 +1,4 @@+"""Async utils."""
import asyncio
import logging
@@ -7,6 +8,9 @@
def is_event_loop_running() -> bool:
+ """
+ Check if an event loop is currently running.
+ """
try:
loop = asyncio.get_running_loop()
except RuntimeError:
@@ -16,6 +20,12 @@
def apply_nest_a... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/async_utils.py |
Write docstrings that follow conventions |
import csv
import typing as t
from pathlib import Path
from pydantic import BaseModel
from .base import BaseBackend
class LocalCSVBackend(BaseBackend):
def __init__(
self,
root_dir: str,
):
self.root_dir = Path(root_dir)
def _get_data_dir(self, data_type: str) -> Path:
... | --- +++ @@ -1,3 +1,4 @@+"""Local CSV backend implementation for projects and datasets."""
import csv
import typing as t
@@ -9,6 +10,34 @@
class LocalCSVBackend(BaseBackend):
+ """File-based backend using CSV format for local storage.
+
+ Stores datasets and experiments as CSV files in separate subdirector... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/backends/local_csv.py |
Add docstrings for production code |
import json
import logging
import os
import typing as t
from pydantic import BaseModel
try:
from google.auth.transport.requests import Request
from google.oauth2.credentials import Credentials as UserCredentials
from google.oauth2.service_account import Credentials
from google_auth_oauthlib.flow impo... | --- +++ @@ -1,3 +1,4 @@+"""Google Drive backend for storing datasets and experiments in Google Sheets."""
import json
import logging
@@ -35,6 +36,36 @@
class GDriveBackend(BaseBackend):
+ """Backend for storing datasets and experiments in Google Drive using Google Sheets.
+
+ This backend stores datasets ... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/backends/gdrive_backend.py |
Can you add docstrings to this Python file? |
import logging
import typing as t
from importlib import metadata
from .base import BaseBackend
logger = logging.getLogger(__name__)
class BackendRegistry:
_instance = None
_backends: t.Dict[str, t.Type[BaseBackend]] = {}
_aliases: t.Dict[str, str] = {}
_discovered = False
def __new__(cls):
... | --- +++ @@ -1,3 +1,4 @@+"""Backend registry for managing and discovering project backends."""
import logging
import typing as t
@@ -9,6 +10,7 @@
class BackendRegistry:
+ """Registry for managing project backends with plugin support."""
_instance = None
_backends: t.Dict[str, t.Type[BaseBackend]] ... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/backends/registry.py |
Write docstrings including parameters and return values |
import typing as t
from abc import ABC, abstractmethod
from pydantic import BaseModel
class BaseBackend(ABC):
@abstractmethod
def load_dataset(self, name: str) -> t.List[t.Dict[str, t.Any]]:
pass
@abstractmethod
def load_experiment(self, name: str) -> t.List[t.Dict[str, t.Any]]:
pa... | --- +++ @@ -1,3 +1,4 @@+"""Base classes for project and dataset backends."""
import typing as t
from abc import ABC, abstractmethod
@@ -6,13 +7,79 @@
class BaseBackend(ABC):
+ """Abstract base class for dataset and experiment storage backends.
+
+ Backends provide persistent storage for datasets and exper... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/backends/base.py |
Add clean documentation to messy code | from __future__ import annotations
class RagasException(Exception):
def __init__(self, message: str):
self.message = message
super().__init__(message)
class ExceptionInRunner(RagasException):
def __init__(self):
msg = "The runner thread which was running the jobs raised an execepti... | --- +++ @@ -2,6 +2,9 @@
class RagasException(Exception):
+ """
+ Base exception class for ragas.
+ """
def __init__(self, message: str):
self.message = message
@@ -9,6 +12,9 @@
class ExceptionInRunner(RagasException):
+ """
+ Exception raised when an exception is raised in the exe... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/exceptions.py |
Document functions with clear intent | import typing as t
from ragas._analytics import EmbeddingUsageEvent, track
from ragas.cache import CacheInterface
from .base import BaseRagasEmbedding
from .utils import validate_texts
class OpenAIEmbeddings(BaseRagasEmbedding):
PROVIDER_NAME = "openai"
REQUIRES_CLIENT = True
DEFAULT_MODEL = "text-embe... | --- +++ @@ -8,6 +8,11 @@
class OpenAIEmbeddings(BaseRagasEmbedding):
+ """OpenAI embeddings implementation with batch optimization.
+
+ Supports both sync and async OpenAI clients with automatic detection.
+ Provides optimized batch processing for better performance.
+ """
PROVIDER_NAME = "openai... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/embeddings/openai_provider.py |
Generate consistent documentation across files |
import sys
import typing as t
from ragas.cache import CacheInterface
from .base import BaseRagasEmbedding
from .utils import run_sync_in_async, validate_texts
class GoogleEmbeddings(BaseRagasEmbedding):
PROVIDER_NAME = "google"
REQUIRES_CLIENT = False # Client is optional for Gemini (can auto-import)
... | --- +++ @@ -1,3 +1,4 @@+"""Google embeddings implementation supporting both Vertex AI and Google AI (Gemini)."""
import sys
import typing as t
@@ -9,6 +10,37 @@
class GoogleEmbeddings(BaseRagasEmbedding):
+ """Google embeddings using Vertex AI or Google AI (Gemini).
+
+ Supports both Vertex AI and Google ... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/embeddings/google_provider.py |
Add concise docstrings to each method | from __future__ import annotations
import atexit
import json
import logging
import os
import time
import typing as t
import uuid
from functools import lru_cache, wraps
from threading import Lock, Thread
from typing import List
import requests
from appdirs import user_data_dir
from pydantic import BaseModel, Field
fr... | --- +++ @@ -131,6 +131,13 @@
class AnalyticsBatcher:
def __init__(self, batch_size: int = 50, flush_interval: float = 120):
+ """
+ Initialize an AnalyticsBatcher instance.
+
+ Args:
+ batch_size (int, optional): Maximum number of events to batch before flushing. Defaults to 50.
+... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/_analytics.py |
Generate missing documentation strings |
import typing as t
from copy import deepcopy
from pydantic import BaseModel
from .base import BaseBackend
class InMemoryBackend(BaseBackend):
def __init__(self):
self._datasets: t.Dict[str, t.List[t.Dict[str, t.Any]]] = {}
self._experiments: t.Dict[str, t.List[t.Dict[str, t.Any]]] = {}
de... | --- +++ @@ -1,3 +1,4 @@+"""In-memory backend for temporary dataset and experiment storage."""
import typing as t
from copy import deepcopy
@@ -8,12 +9,44 @@
class InMemoryBackend(BaseBackend):
+ """Backend that stores datasets and experiments in memory.
+
+ This backend is designed for temporary storage o... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/backends/inmemory.py |
Generate docstrings with examples |
from __future__ import annotations
import random
class MemorableNames:
def __init__(self):
# List of adjectives (similar to what Docker uses)
self.adjectives = [
"admiring",
"adoring",
"affectionate",
"agitated",
"amazing",
... | --- +++ @@ -1,3 +1,4 @@+"""Shared utilities for project module."""
from __future__ import annotations
@@ -5,6 +6,7 @@
class MemorableNames:
+ """Generator for memorable, unique names for experiments and datasets."""
def __init__(self):
# List of adjectives (similar to what Docker uses)
@@ -1... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/backends/utils.py |
Help me document legacy Python code |
import asyncio
import threading
import typing as t
from concurrent.futures import ThreadPoolExecutor
def run_async_in_current_loop(coro: t.Awaitable[t.Any]) -> t.Any:
try:
# Try to get the current event loop
loop = asyncio.get_event_loop()
if loop.is_running():
# If the loop ... | --- +++ @@ -1,3 +1,4 @@+"""Shared utilities for embedding implementations."""
import asyncio
import threading
@@ -6,6 +7,20 @@
def run_async_in_current_loop(coro: t.Awaitable[t.Any]) -> t.Any:
+ """Run an async coroutine in the current event loop if possible.
+
+ This handles Jupyter environments correctl... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/embeddings/utils.py |
Create docstrings for each class method |
import typing as t
from ragas.cache import CacheInterface
from .base import BaseRagasEmbedding
from .utils import batch_texts, run_sync_in_async, validate_texts
class HuggingFaceEmbeddings(BaseRagasEmbedding):
PROVIDER_NAME = "huggingface"
REQUIRES_MODEL = True
def __init__(
self,
mod... | --- +++ @@ -1,3 +1,4 @@+"""HuggingFace embeddings implementation supporting both local and API-based models."""
import typing as t
@@ -8,6 +9,11 @@
class HuggingFaceEmbeddings(BaseRagasEmbedding):
+ """HuggingFace embeddings supporting both local and API-based models.
+
+ Supports sentence-transformers f... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/embeddings/huggingface_provider.py |
Document this module using docstrings | from __future__ import annotations
import typing as t
import warnings
from uuid import UUID
from datasets import Dataset
from langchain_core.callbacks import BaseCallbackHandler, BaseCallbackManager
from langchain_core.embeddings import Embeddings as LangchainEmbeddings
from langchain_core.language_models import Base... | --- +++ @@ -75,6 +75,33 @@ _pbar: t.Optional[tqdm] = None,
return_executor: bool = False,
) -> t.Union[EvaluationResult, Executor]:
+ """
+ Async version of evaluate that performs evaluation without applying nest_asyncio.
+
+ This function is the async-first implementation that doesn't patch the even... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/evaluation.py |
Help me comply with documentation standards | from __future__ import annotations
import logging
import threading
import typing as t
from dataclasses import dataclass, field
import numpy as np
from tqdm.auto import tqdm
from ragas.async_utils import apply_nest_asyncio, as_completed, process_futures, run
from ragas.run_config import RunConfig
from ragas.utils imp... | --- +++ @@ -17,6 +17,30 @@
@dataclass
class Executor:
+ """
+ Executor class for running asynchronous jobs with progress tracking and error handling.
+
+ Attributes
+ ----------
+ desc : str
+ Description for the progress bar
+ show_progress : bool
+ Whether to show the progress bar
+... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/executor.py |
Write docstrings for backend logic | from __future__ import annotations
import asyncio
import inspect
import typing as t
import warnings
from abc import ABC, abstractmethod
from dataclasses import field
import numpy as np
from langchain_core.embeddings import Embeddings
from langchain_openai.embeddings import OpenAIEmbeddings
from pydantic.dataclasses i... | --- +++ @@ -27,8 +27,20 @@
class BaseRagasEmbedding(ABC):
+ """Modern abstract base class for Ragas embedding implementations.
+
+ This class provides a consistent interface for embedding text using various
+ providers. Implementations should provide both sync and async methods for
+ embedding single te... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/embeddings/base.py |
Generate docstrings with parameter types | import functools
import hashlib
import inspect
import json
import logging
import sys
from abc import ABC, abstractmethod
from typing import Any, Optional
from pydantic import BaseModel, GetCoreSchemaHandler
from pydantic_core import CoreSchema, core_schema
logger = logging.getLogger(__name__)
class CacheInterface(A... | --- +++ @@ -14,23 +14,53 @@
class CacheInterface(ABC):
+ """Abstract base class defining the interface for cache implementations.
+
+ This class provides a standard interface that all cache implementations must follow.
+ It supports basic cache operations like get, set and key checking.
+ """
@ab... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/cache.py |
Add docstrings to improve readability | from __future__ import annotations
import typing as t
from langchain.smith import RunEvalConfig
from ragas.integrations.langchain import EvaluatorChain
if t.TYPE_CHECKING:
from langsmith.schemas import Dataset as LangsmithDataset
from ragas.testset import Testset
try:
from langsmith import Client
... | --- +++ @@ -23,6 +23,37 @@ def upload_dataset(
dataset: Testset, dataset_name: str, dataset_desc: str = ""
) -> LangsmithDataset:
+ """
+ Uploads a new dataset to LangSmith, converting it from a TestDataset object to a
+ pandas DataFrame before upload. If a dataset with the specified name already
+ ex... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/integrations/langsmith.py |
Add docstrings explaining edge cases | import json
import typing as t
from ragas.messages import AIMessage, HumanMessage
def get_last_orchestration_value(traces: t.List[t.Dict[str, t.Any]], key: str):
last_index = -1
last_value = None
for i, trace in enumerate(traces):
orchestration = trace.get("trace", {}).get("orchestrationTrace", {... | --- +++ @@ -5,6 +5,13 @@
def get_last_orchestration_value(traces: t.List[t.Dict[str, t.Any]], key: str):
+ """
+ Iterates through the traces to find the last occurrence of a specified key
+ within the orchestrationTrace.
+
+ Returns:
+ (index, value): Tuple where index is the last index at which ... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/integrations/amazon_bedrock.py |
Generate NumPy-style docstrings |
__all__ = ["Experiment", "experiment", "version_experiment"]
import asyncio
import typing as t
from pathlib import Path
from pydantic import BaseModel
from tqdm import tqdm
from ragas.backends.base import BaseBackend
from ragas.dataset import Dataset, DataTable
from ragas.utils import find_git_root, memorable_names... | --- +++ @@ -1,3 +1,4 @@+"""Experiments hold the results of an experiment against a dataset."""
__all__ = ["Experiment", "experiment", "version_experiment"]
@@ -24,6 +25,23 @@ create_branch: bool = True,
stage_all: bool = False,
) -> str:
+ """Version control the current state of the codebase for an ex... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/experiment.py |
Generate docstrings for each module |
from __future__ import annotations
import json
import logging
import typing as t
import uuid
from typing import Any, Dict, List, Optional, Union
from ragas.dataset_schema import (
MultiTurnSample,
SingleTurnSample,
)
from ragas.messages import AIMessage, HumanMessage, ToolCall, ToolMessage
logger = logging.... | --- +++ @@ -1,3 +1,92 @@+"""
+AG-UI Protocol Integration for Ragas.
+
+This module provides conversion utilities and row enrichment for AG-UI protocol
+agents. It supports converting AG-UI streaming events to Ragas message format
+and running rows against AG-UI FastAPI endpoints for use with the @experiment
+decorator ... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/integrations/ag_ui.py |
Add docstrings including usage examples | import json
from typing import Any, Dict, List, Union
from ragas.messages import AIMessage, HumanMessage, ToolCall, ToolMessage
def convert_to_ragas_messages(
messages: List[Dict[str, Any]],
) -> List[Union[HumanMessage, AIMessage, ToolMessage]]:
def convert_tool_calls(tool_calls_data: List[Dict[str, Any]])... | --- +++ @@ -7,8 +7,32 @@ def convert_to_ragas_messages(
messages: List[Dict[str, Any]],
) -> List[Union[HumanMessage, AIMessage, ToolMessage]]:
+ """
+ Convert Swarm messages to Ragas message format.
+
+ Parameters
+ ----------
+ messages : List[Union[Response, Dict]]
+ List of messages to c... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/integrations/swarm.py |
Generate docstrings with examples | from __future__ import annotations
import logging
import typing as t
import warnings
from ragas.dataset_schema import EvaluationDataset
if t.TYPE_CHECKING:
pass
logger = logging.getLogger(__name__)
def _process_search_results(search_results: t.Dict[str, t.List]) -> t.List[str]:
retrieved_contexts = []
... | --- +++ @@ -14,6 +14,19 @@
def _process_search_results(search_results: t.Dict[str, t.List]) -> t.List[str]:
+ """
+ Extracts relevant text from search results while issuing warnings for unsupported result types.
+
+ Parameters
+ ----------
+ search_results : Dict[str, List]
+ A r2r result obje... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/integrations/r2r.py |
Generate documentation strings for clarity | from __future__ import annotations
import json
import random
import typing as t
from abc import ABC, abstractmethod
from collections import defaultdict
from dataclasses import dataclass, field
from uuid import UUID
import numpy as np
from pydantic import BaseModel, field_validator
from ragas.callbacks import parse_r... | --- +++ @@ -27,19 +27,61 @@
class BaseSample(BaseModel):
+ """
+ Base class for evaluation samples.
+ """
def to_dict(self) -> t.Dict:
+ """
+ Get the dictionary representation of the sample without attributes that are None.
+ """
return self.model_dump(exclude_none=Tru... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/dataset_schema.py |
Generate docstrings for each module |
__all__ = ["observe", "logger", "LangfuseTrace", "sync_trace", "add_query_param"]
import asyncio
import logging
import typing as t
from datetime import datetime
from urllib.parse import parse_qsl, urlencode, urlparse, urlunparse
if t.TYPE_CHECKING:
from langfuse import Langfuse, observe
from langfuse.api imp... | --- +++ @@ -1,3 +1,4 @@+"""Utils to help to interact with langfuse traces"""
__all__ = ["observe", "logger", "LangfuseTrace", "sync_trace", "add_query_param"]
@@ -96,6 +97,16 @@ async def sync_trace(
trace_id: t.Optional[str] = None, max_retries: int = 10, delay: float = 2
) -> LangfuseTrace:
+ """Wait fo... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/integrations/tracing/langfuse.py |
Fully document this Python code with docstrings | from __future__ import annotations
import asyncio
import inspect
import logging
import threading
import typing as t
from abc import ABC, abstractmethod
from dataclasses import dataclass, field
import instructor
from langchain_community.chat_models.vertexai import ChatVertexAI
from langchain_community.llms import Vert... | --- +++ @@ -46,6 +46,7 @@
def is_multiple_completion_supported(llm: BaseLanguageModel) -> bool:
+ """Return whether the given LLM supports n-completion."""
for llm_type in MULTIPLE_COMPLETION_SUPPORTED:
if isinstance(llm, llm_type):
return True
@@ -68,6 +69,7 @@ self.run_config... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/llms/base.py |
Add docstrings that explain logic | import typing as t
from ragas.llms.adapters.base import StructuredOutputAdapter
if t.TYPE_CHECKING:
from ragas.llms.litellm_llm import LiteLLMStructuredLLM
class LiteLLMAdapter(StructuredOutputAdapter):
def create_llm(
self,
client: t.Any,
model: str,
provider: str,
... | --- +++ @@ -7,6 +7,11 @@
class LiteLLMAdapter(StructuredOutputAdapter):
+ """
+ Adapter using LiteLLM for structured outputs.
+
+ Supports: All 100+ LiteLLM providers (Gemini, Ollama, vLLM, Groq, etc.)
+ """
def create_llm(
self,
@@ -15,6 +20,18 @@ provider: str,
**kwarg... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/llms/adapters/litellm.py |
Create structured documentation for my script | import typing as t
from abc import ABC, abstractmethod
class StructuredOutputAdapter(ABC):
@abstractmethod
def create_llm(
self,
client: t.Any,
model: str,
provider: str,
**kwargs,
) -> t.Any:
pass | --- +++ @@ -3,6 +3,12 @@
class StructuredOutputAdapter(ABC):
+ """
+ Base class for structured output adapters.
+
+ Provides a simple interface for adapters that support structured output
+ from different backends (Instructor, LiteLLM, etc).
+ """
@abstractmethod
def create_llm(
@@ -12,4 ... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/llms/adapters/base.py |
Add minimal docstrings for each function | import typing as t
from ragas.llms.adapters.base import StructuredOutputAdapter
from ragas.llms.base import InstructorLLM, InstructorModelArgs, _get_instructor_client
class InstructorAdapter(StructuredOutputAdapter):
def create_llm(
self,
client: t.Any,
model: str,
provider: str,... | --- +++ @@ -5,6 +5,11 @@
class InstructorAdapter(StructuredOutputAdapter):
+ """
+ Adapter using Instructor library for structured outputs.
+
+ Supports: OpenAI, Anthropic, Azure, Groq, Mistral, Cohere, Google, etc.
+ """
def create_llm(
self,
@@ -13,6 +18,21 @@ provider: str,
... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/llms/adapters/instructor.py |
Create simple docstrings for beginners | import asyncio
import inspect
import logging
import threading
import typing as t
from ragas._analytics import LLMUsageEvent, track
from ragas.cache import CacheInterface, cacher
from ragas.llms.base import InstructorBaseRagasLLM, InstructorTypeVar
logger = logging.getLogger(__name__)
class LiteLLMStructuredLLM(Inst... | --- +++ @@ -12,6 +12,14 @@
class LiteLLMStructuredLLM(InstructorBaseRagasLLM):
+ """
+ LLM wrapper using LiteLLM for structured outputs.
+
+ Works with all 100+ LiteLLM-supported providers including Gemini,
+ Ollama, vLLM, Groq, and many others.
+
+ The LiteLLM client should be initialized with struc... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/llms/litellm_llm.py |
Add missing documentation to my Python functions | import typing as t
from abc import ABC, abstractmethod
from pydantic import GetCoreSchemaHandler
from pydantic_core import CoreSchema, core_schema
class Loss(ABC):
@abstractmethod
def __call__(self, predicted: t.List, actual: t.List) -> float:
raise NotImplementedError
@classmethod
def __ge... | --- +++ @@ -6,6 +6,9 @@
class Loss(ABC):
+ """
+ Abstract base class for all loss functions.
+ """
@abstractmethod
def __call__(self, predicted: t.List, actual: t.List) -> float:
@@ -15,6 +18,9 @@ def __get_pydantic_core_schema__(
cls, source_type: t.Any, handler: GetCoreSchemaHand... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/losses.py |
Add docstrings explaining edge cases | import typing as t
from ragas.llms.adapters.instructor import InstructorAdapter
from ragas.llms.adapters.litellm import LiteLLMAdapter
ADAPTERS = {
"instructor": InstructorAdapter(),
"litellm": LiteLLMAdapter(),
}
def get_adapter(name: str) -> t.Any:
if name not in ADAPTERS:
raise ValueError(f"U... | --- +++ @@ -10,12 +10,33 @@
def get_adapter(name: str) -> t.Any:
+ """
+ Get adapter by name.
+
+ Args:
+ name: Adapter name ("instructor" or "litellm")
+
+ Returns:
+ StructuredOutputAdapter instance
+
+ Raises:
+ ValueError: If adapter name is unknown
+ """
if name not ... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/llms/adapters/__init__.py |
Document functions with clear intent |
import asyncio
import logging
import typing as t
from typing import Dict, List
from langchain_core.outputs import Generation, LLMResult
from langchain_core.prompt_values import PromptValue
from ragas._analytics import LLMUsageEvent, track
from ragas.llms.base import BaseRagasLLM
from ragas.run_config import RunConfi... | --- +++ @@ -1,3 +1,4 @@+"""OCI Gen AI LLM wrapper implementation for Ragas."""
import asyncio
import logging
@@ -32,6 +33,12 @@
class OCIGenAIWrapper(BaseRagasLLM):
+ """
+ OCI Gen AI LLM wrapper for Ragas.
+
+ This wrapper provides direct integration with Oracle Cloud Infrastructure
+ Generative AI... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/llms/oci_genai_wrapper.py |
Create Google-style docstrings for my code | import typing as t
from pydantic import BaseModel
class Message(BaseModel):
content: str
metadata: t.Optional[t.Dict[str, t.Any]] = None
class ToolCall(BaseModel):
name: str
args: t.Dict[str, t.Any]
class HumanMessage(Message):
type: t.Literal["human"] = "human"
def pretty_repr(self):... | --- +++ @@ -4,40 +4,111 @@
class Message(BaseModel):
+ """
+ Represents a generic message.
+
+ Attributes
+ ----------
+ content : str
+ The content of the message.
+ metadata : Optional[Dict[str, Any]], optional
+ Additional metadata associated with the message.
+ """
cont... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/messages.py |
Create docstrings for all classes and functions |
import typing as t
import numpy as np
from ragas.metrics.collections.base import BaseMetric
from ragas.metrics.result import MetricResult
if t.TYPE_CHECKING:
from ragas.embeddings.base import BaseRagasEmbedding
class SemanticSimilarity(BaseMetric):
embeddings: "BaseRagasEmbedding"
def __init__(
... | --- +++ @@ -1,3 +1,4 @@+"""Semantic Similarity metric."""
import typing as t
@@ -11,6 +12,44 @@
class SemanticSimilarity(BaseMetric):
+ """
+ Evaluate semantic similarity between reference and response using embeddings.
+
+ Scores the semantic similarity of ground truth with generated answer using
+ ... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/collections/_semantic_similarity.py |
Add docstrings for better understanding |
from enum import Enum
from ragas.metrics.collections.base import BaseMetric
from ragas.metrics.result import MetricResult
class DistanceMeasure(Enum):
LEVENSHTEIN = "levenshtein"
HAMMING = "hamming"
JARO = "jaro"
JARO_WINKLER = "jaro_winkler"
class ExactMatch(BaseMetric):
def __init__(
... | --- +++ @@ -1,3 +1,4 @@+"""String-based metrics v2 - Class-based implementations with automatic validation."""
from enum import Enum
@@ -13,12 +14,39 @@
class ExactMatch(BaseMetric):
+ """
+ Check if reference and response are exactly identical.
+
+ This implementation provides automatic validation an... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/collections/_string.py |
Add docstrings to existing functions |
import typing as t
from typing import List, Union
from ragas.messages import AIMessage, HumanMessage, ToolMessage
from ragas.metrics.collections.base import BaseMetric
from ragas.metrics.result import MetricResult
from .util import (
CompareOutcomeInput,
CompareOutcomeOutput,
CompareOutcomePrompt,
In... | --- +++ @@ -1,3 +1,4 @@+"""AgentGoalAccuracy metrics - Modern collections implementation."""
import typing as t
from typing import List, Union
@@ -20,6 +21,42 @@
class AgentGoalAccuracyWithReference(BaseMetric):
+ """
+ Measures if an agent achieved the user's goal compared to a reference outcome.
+
+ ... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/collections/agent_goal_accuracy/metric.py |
Create docstrings for all classes and functions |
import typing as t
import numpy as np
from ragas.metrics.collections.base import BaseMetric
from ragas.metrics.result import MetricResult
from .util import (
AnswerAccuracyInput,
AnswerAccuracyJudge1Prompt,
AnswerAccuracyJudge2Prompt,
AnswerAccuracyOutput,
)
if t.TYPE_CHECKING:
from ragas.llms.... | --- +++ @@ -1,3 +1,4 @@+"""Answer Accuracy metric v2 - Modern implementation with dual-judge evaluation."""
import typing as t
@@ -18,6 +19,47 @@
class AnswerAccuracy(BaseMetric):
+ """
+ Answer Accuracy metric using dual-judge evaluation.
+
+ Measures answer accuracy compared to ground truth using a ... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/collections/answer_accuracy/metric.py |
Write docstrings for this repository |
import typing as t
from pydantic import BaseModel, Field
from ragas.prompt.metrics.base_prompt import BasePrompt
class WorkflowInput(BaseModel):
workflow: str = Field(
..., description="The agentic workflow comprised of Human, AI and Tools"
)
class WorkflowOutput(BaseModel):
user_goal: str = ... | --- +++ @@ -1,3 +1,4 @@+"""AgentGoalAccuracy prompt classes and models."""
import typing as t
@@ -22,6 +23,7 @@
class InferGoalOutcomePrompt(BasePrompt[WorkflowInput, WorkflowOutput]):
+ """Prompt for inferring user goal and end state from a workflow."""
input_model = WorkflowInput
output_model ... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/collections/agent_goal_accuracy/util.py |
Include argument descriptions in docstrings |
from pydantic import BaseModel, Field
from ragas.prompt.metrics.base_prompt import BasePrompt
class AnswerAccuracyInput(BaseModel):
query: str = Field(..., description="The original question")
user_answer: str = Field(..., description="The user's answer to evaluate")
reference_answer: str = Field(..., ... | --- +++ @@ -1,3 +1,4 @@+"""Answer Accuracy prompt classes and models."""
from pydantic import BaseModel, Field
@@ -5,6 +6,7 @@
class AnswerAccuracyInput(BaseModel):
+ """Input model for answer accuracy evaluation."""
query: str = Field(..., description="The original question")
user_answer: str =... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/collections/answer_accuracy/util.py |
Add docstrings that explain inputs and outputs |
import typing as t
from typing import List
import numpy as np
from ragas.metrics.collections.base import BaseMetric
from ragas.metrics.result import MetricResult
from .util import (
ClassificationWithReason,
CorrectnessClassifierInput,
CorrectnessClassifierPrompt,
StatementGeneratorInput,
Statem... | --- +++ @@ -1,3 +1,4 @@+"""Answer Correctness metric v2 - Modern implementation with multi-step pipeline."""
import typing as t
from typing import List
@@ -22,6 +23,53 @@
class AnswerCorrectness(BaseMetric):
+ """
+ Answer Correctness metric using multi-step pipeline evaluation.
+
+ Measures answer cor... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/collections/answer_correctness/metric.py |
Add minimal docstrings for each function |
import typing as t
from pydantic import BaseModel, Field
from ragas.prompt.metrics.base_prompt import BasePrompt
class StatementGeneratorInput(BaseModel):
question: str = Field(..., description="The question being answered")
answer: str = Field(
..., description="The answer text to break down into... | --- +++ @@ -1,3 +1,4 @@+"""Answer Correctness prompt classes and models."""
import typing as t
@@ -7,6 +8,7 @@
class StatementGeneratorInput(BaseModel):
+ """Input model for statement generation."""
question: str = Field(..., description="The question being answered")
answer: str = Field(
@@ -15... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/collections/answer_correctness/util.py |
Create Google-style docstrings for my code |
from pydantic import BaseModel, Field
from ragas.prompt.metrics.base_prompt import BasePrompt
class AnswerRelevanceInput(BaseModel):
response: str = Field(
..., description="The response/answer to generate questions from"
)
class AnswerRelevanceOutput(BaseModel):
question: str = Field(
... | --- +++ @@ -1,3 +1,4 @@+"""Answer Relevancy prompt classes and models."""
from pydantic import BaseModel, Field
@@ -5,6 +6,7 @@
class AnswerRelevanceInput(BaseModel):
+ """Input model for answer relevance evaluation."""
response: str = Field(
..., description="The response/answer to generate... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/collections/answer_relevancy/util.py |
Provide docstrings following PEP 257 |
import typing as t
import numpy as np
from ragas.metrics.collections.base import BaseMetric
from ragas.metrics.result import MetricResult
from .util import (
AnswerRelevanceInput,
AnswerRelevanceOutput,
AnswerRelevancePrompt,
)
if t.TYPE_CHECKING:
from ragas.embeddings.base import BaseRagasEmbeddin... | --- +++ @@ -1,3 +1,4 @@+"""Answer Relevancy metrics v2 - Modern implementation with structured prompts."""
import typing as t
@@ -18,6 +19,45 @@
class AnswerRelevancy(BaseMetric):
+ """
+ Modern v2 implementation of answer relevancy evaluation.
+
+ Evaluates answer relevancy by generating multiple que... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/collections/answer_relevancy/metric.py |
Add docstrings to meet PEP guidelines |
import asyncio
import typing as t
from ragas.embeddings.base import BaseRagasEmbedding
from ragas.llms.base import InstructorBaseRagasLLM
from ragas.metrics.base import SimpleBaseMetric
from ragas.metrics.result import MetricResult
from ragas.metrics.validators import NumericValidator
class BaseMetric(SimpleBaseMet... | --- +++ @@ -1,3 +1,4 @@+"""Base class for collections metrics with modern component validation."""
import asyncio
import typing as t
@@ -10,6 +11,23 @@
class BaseMetric(SimpleBaseMetric, NumericValidator):
+ """
+ Base class for metrics collections with modern component validation.
+
+ This class inher... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/collections/base.py |
Document my Python code with docstrings |
from typing import List
from pydantic import BaseModel, Field
from ragas.prompt.metrics.base_prompt import BasePrompt
class ExtractEntitiesInput(BaseModel):
text: str = Field(..., description="The text to extract entities from")
class EntitiesList(BaseModel):
entities: List[str] = Field(
..., d... | --- +++ @@ -1,3 +1,4 @@+"""Context Entity Recall prompt classes and models."""
from typing import List
@@ -7,11 +8,13 @@
class ExtractEntitiesInput(BaseModel):
+ """Input model for entity extraction."""
text: str = Field(..., description="The text to extract entities from")
class EntitiesList(Ba... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/collections/context_entity_recall/util.py |
Create structured documentation for my script |
from typing import List
from pydantic import BaseModel, Field
from ragas.prompt.metrics.base_prompt import BasePrompt
class ContextRecallInput(BaseModel):
question: str = Field(..., description="The original question asked by the user")
context: str = Field(..., description="The retrieved context passage ... | --- +++ @@ -1,3 +1,4 @@+"""Context Recall prompt classes and models."""
from typing import List
@@ -7,6 +8,7 @@
class ContextRecallInput(BaseModel):
+ """Input model for context recall evaluation."""
question: str = Field(..., description="The original question asked by the user")
context: str =... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/collections/context_recall/util.py |
Add docstrings including usage examples |
import typing as t
from typing import List
import numpy as np
from ragas.metrics.collections.base import BaseMetric
from ragas.metrics.result import MetricResult
from .util import (
ContextPrecisionInput,
ContextPrecisionOutput,
ContextPrecisionPrompt,
)
if t.TYPE_CHECKING:
from ragas.llms.base imp... | --- +++ @@ -1,3 +1,4 @@+"""Context Precision metrics v2 - Modern implementation with function-based prompts."""
import typing as t
from typing import List
@@ -18,6 +19,41 @@
class ContextPrecisionWithReference(BaseMetric):
+ """
+ Modern v2 implementation of context precision with reference.
+
+ Evalua... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/collections/context_precision/metric.py |
Help me add docstrings to my project |
import typing as t
from typing import List, Sequence
from ragas.metrics.collections.base import BaseMetric
from ragas.metrics.result import MetricResult
from .util import (
EntitiesList,
ExtractEntitiesInput,
ExtractEntitiesPrompt,
)
if t.TYPE_CHECKING:
from ragas.llms.base import InstructorBaseRaga... | --- +++ @@ -1,3 +1,4 @@+"""Context Entity Recall metrics v2 - Modern implementation with structured prompts."""
import typing as t
from typing import List, Sequence
@@ -16,6 +17,41 @@
class ContextEntityRecall(BaseMetric):
+ """
+ Modern v2 implementation of context entity recall evaluation.
+
+ Calcul... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/collections/context_entity_recall/metric.py |
Write docstrings for data processing functions | from __future__ import annotations
import logging
import typing as t
from dataclasses import dataclass, field
import numpy as np
from pydantic import BaseModel
from ragas.dataset_schema import SingleTurnSample
from ragas.metrics._string import DistanceMeasure, NonLLMStringSimilarity
from ragas.metrics.base import (
... | --- +++ @@ -86,6 +86,14 @@
@dataclass
class LLMContextRecall(MetricWithLLM, SingleTurnMetric):
+ """
+ Estimates context recall by estimating TP and FN using annotated answer and
+ retrieved context.
+
+ Attributes
+ ----------
+ name : str
+ """
name: str = "context_recall"
_require... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/_context_recall.py |
Generate documentation strings for clarity | from __future__ import annotations
import logging
import typing as t
from dataclasses import dataclass, field
import numpy as np
from pydantic import BaseModel, Field
from ragas.dataset_schema import SingleTurnSample
from ragas.metrics._string import NonLLMStringSimilarity
from ragas.metrics.base import (
Metric... | --- +++ @@ -80,6 +80,16 @@
@dataclass
class LLMContextPrecisionWithReference(MetricWithLLM, SingleTurnMetric):
+ """
+ Average Precision is a metric that evaluates whether all of the
+ relevant items selected by the model are ranked higher or not.
+
+ Attributes
+ ----------
+ name : str
+ evalu... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/_context_precision.py |
Add docstrings to improve readability |
from pydantic import BaseModel, Field
from ragas.prompt.metrics.base_prompt import BasePrompt
class ContextPrecisionInput(BaseModel):
question: str = Field(..., description="The question being asked")
context: str = Field(..., description="The context to evaluate for usefulness")
answer: str = Field(
... | --- +++ @@ -1,3 +1,4 @@+"""Context Precision prompt classes and models."""
from pydantic import BaseModel, Field
@@ -5,6 +6,7 @@
class ContextPrecisionInput(BaseModel):
+ """Input model for context precision evaluation."""
question: str = Field(..., description="The question being asked")
contex... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/collections/context_precision/util.py |
Document my Python code with docstrings |
import typing as t
from typing import List
import numpy as np
from ragas.metrics.collections.base import BaseMetric
from ragas.metrics.result import MetricResult
from .util import (
ContextRecallInput,
ContextRecallOutput,
ContextRecallPrompt,
)
if t.TYPE_CHECKING:
from ragas.llms.base import Instr... | --- +++ @@ -1,3 +1,4 @@+"""Context Recall metrics v2 - Modern implementation with structured prompts."""
import typing as t
from typing import List
@@ -18,6 +19,41 @@
class ContextRecall(BaseMetric):
+ """
+ Modern v2 implementation of context recall evaluation.
+
+ Evaluates context recall by classify... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/collections/context_recall/metric.py |
Annotate my code with docstrings |
import typing as t
from ragas.metrics.collections.base import BaseMetric
from ragas.metrics.result import MetricResult
class CHRFScore(BaseMetric):
def __init__(
self,
name: str = "chrf_score",
kwargs: t.Optional[t.Dict[str, t.Any]] = None,
**base_kwargs,
):
super().... | --- +++ @@ -1,3 +1,4 @@+"""CHRFScore metric - Modern collections implementation."""
import typing as t
@@ -6,6 +7,39 @@
class CHRFScore(BaseMetric):
+ """
+ Calculate CHRF (Character F-score) between reference and response texts.
+
+ CHRF is a character n-gram F-score metric that correlates well with ... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/collections/chrf_score/metric.py |
Provide clean and structured docstrings |
import logging
import typing as t
from io import StringIO
import numpy as np
from ragas.metrics.collections.base import BaseMetric
from ragas.metrics.result import MetricResult
logger = logging.getLogger(__name__)
class DataCompyScore(BaseMetric):
def __init__(
self,
mode: t.Literal["rows", "... | --- +++ @@ -1,3 +1,4 @@+"""DataCompyScore metric - Modern collections implementation."""
import logging
import typing as t
@@ -12,6 +13,34 @@
class DataCompyScore(BaseMetric):
+ """
+ Compare CSV data using datacompy library to compute precision, recall, or F1 scores.
+
+ This metric compares two CSV s... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/collections/datacompy_score/metric.py |
Document helper functions with docstrings |
import typing as t
from typing import List
import numpy as np
from ragas.metrics.collections.base import BaseMetric
from ragas.metrics.result import MetricResult
from .util import (
ContextRelevanceInput,
ContextRelevanceJudge1Prompt,
ContextRelevanceJudge2Prompt,
ContextRelevanceOutput,
)
if t.TYP... | --- +++ @@ -1,3 +1,4 @@+"""Context Relevance metric v2 - Modern implementation with dual-judge evaluation."""
import typing as t
from typing import List
@@ -19,6 +20,47 @@
class ContextRelevance(BaseMetric):
+ """
+ Context Relevance metric using dual-judge evaluation.
+
+ Evaluates whether the retriev... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/collections/context_relevance/metric.py |
Add verbose docstrings with examples |
from pydantic import BaseModel, Field
from ragas.prompt.metrics.base_prompt import BasePrompt
class ContextRelevanceInput(BaseModel):
user_input: str = Field(..., description="The user's question")
context: str = Field(..., description="The context to evaluate for relevance")
class ContextRelevanceOutput... | --- +++ @@ -1,3 +1,4 @@+"""Context Relevance prompt classes and models."""
from pydantic import BaseModel, Field
@@ -5,12 +6,14 @@
class ContextRelevanceInput(BaseModel):
+ """Input model for context relevance evaluation."""
user_input: str = Field(..., description="The user's question")
context... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/collections/context_relevance/util.py |
Add detailed docstrings explaining each function |
import copy
import typing as t
from typing import Dict, List, Tuple
from pydantic import BaseModel, Field
from ragas.prompt.metrics.base_prompt import BasePrompt
if t.TYPE_CHECKING:
from ragas.llms.base import InstructorBaseRagasLLM
class ClaimDecompositionInput(BaseModel):
response: str = Field(..., des... | --- +++ @@ -1,3 +1,4 @@+"""Factual Correctness prompt classes and models."""
import copy
import typing as t
@@ -12,6 +13,7 @@
class ClaimDecompositionInput(BaseModel):
+ """Input for claim decomposition."""
response: str = Field(..., description="The response text to decompose into claims")
atomi... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/collections/factual_correctness/util.py |
Add minimal docstrings for each function |
import typing as t
from pydantic import BaseModel, Field
from ragas.prompt.metrics.base_prompt import BasePrompt
DEFAULT_REFERENCE_FREE_RUBRICS = {
"score1_description": "The response is entirely incorrect and fails to address any aspect of the user input.",
"score2_description": "The response contains part... | --- +++ @@ -1,3 +1,4 @@+"""DomainSpecificRubrics prompt classes and models."""
import typing as t
@@ -23,6 +24,7 @@
class RubricScoreInput(BaseModel):
+ """Input model for rubric-based scoring."""
user_input: t.Optional[str] = Field(
default=None, description="The input/question provided to ... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/collections/domain_specific_rubrics/util.py |
Add docstrings explaining edge cases |
import typing as t
from typing import List
import numpy as np
from ragas.metrics.collections.base import BaseMetric
from ragas.metrics.result import MetricResult
from ragas.metrics.utils import fbeta_score
from .util import (
ClaimDecompositionInput,
ClaimDecompositionOutput,
ClaimDecompositionPrompt,
... | --- +++ @@ -1,3 +1,4 @@+"""Factual Correctness metrics v2 - Modern implementation with multi-modal scoring."""
import typing as t
from typing import List
@@ -22,6 +23,51 @@
class FactualCorrectness(BaseMetric):
+ """
+ Modern v2 implementation of factual correctness evaluation.
+
+ Evaluates the factua... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/collections/factual_correctness/metric.py |
Document my Python code with docstrings |
import typing as t
from typing import List
from ragas.metrics.collections.base import BaseMetric
from ragas.metrics.result import MetricResult
from .util import (
NLIStatementInput,
NLIStatementOutput,
NLIStatementPrompt,
StatementGeneratorInput,
StatementGeneratorOutput,
StatementGeneratorPr... | --- +++ @@ -1,3 +1,4 @@+"""Faithfulness metric v2 - Modern implementation with multi-step pipeline."""
import typing as t
from typing import List
@@ -19,6 +20,46 @@
class Faithfulness(BaseMetric):
+ """
+ Faithfulness metric using multi-step pipeline evaluation.
+
+ Measures how factually consistent a ... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/collections/faithfulness/metric.py |
Write docstrings for utility functions |
import typing as t
from pydantic import BaseModel, Field
from ragas.prompt.metrics.base_prompt import BasePrompt
class StatementGeneratorInput(BaseModel):
question: str = Field(..., description="The question being answered")
answer: str = Field(
..., description="The answer text to break down into... | --- +++ @@ -1,3 +1,4 @@+"""Faithfulness prompt classes and models."""
import typing as t
@@ -7,6 +8,7 @@
class StatementGeneratorInput(BaseModel):
+ """Input model for statement generation."""
question: str = Field(..., description="The question being answered")
answer: str = Field(
@@ -15,6 +17... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/collections/faithfulness/util.py |
Document this script properly |
import typing as t
from pydantic import BaseModel, Field
from ragas.prompt.metrics.base_prompt import BasePrompt
class InstanceRubricScoreInput(BaseModel):
user_input: t.Optional[str] = Field(
default=None, description="The input/question provided to the system"
)
response: t.Optional[str] = F... | --- +++ @@ -1,3 +1,4 @@+"""InstanceSpecificRubrics prompt classes and models."""
import typing as t
@@ -7,6 +8,7 @@
class InstanceRubricScoreInput(BaseModel):
+ """Input model for instance-specific rubric scoring."""
user_input: t.Optional[str] = Field(
default=None, description="The input/q... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/collections/instance_specific_rubrics/util.py |
Write docstrings that follow conventions |
from pydantic import BaseModel, Field
from ragas.prompt.metrics.base_prompt import BasePrompt
class ResponseGroundednessInput(BaseModel):
response: str = Field(..., description="The response/assertion to evaluate")
context: str = Field(..., description="The context to evaluate against")
class ResponseGro... | --- +++ @@ -1,3 +1,4 @@+"""Response Groundedness prompt classes and models."""
from pydantic import BaseModel, Field
@@ -5,12 +6,14 @@
class ResponseGroundednessInput(BaseModel):
+ """Input model for response groundedness evaluation."""
response: str = Field(..., description="The response/assertion t... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/collections/response_groundedness/util.py |
Write docstrings for algorithm functions | from __future__ import annotations
import asyncio
import logging
import typing as t
from abc import ABC, abstractmethod
from collections import Counter
from dataclasses import dataclass, field
from enum import Enum
from pydantic import ValidationError
from tqdm import tqdm
from ragas._analytics import EvaluationEven... | --- +++ @@ -49,6 +49,16 @@
class MetricType(Enum):
+ """
+ Enumeration of metric types in Ragas.
+
+ Attributes
+ ----------
+ SINGLE_TURN : str
+ Represents a single-turn metric type.
+ MULTI_TURN : str
+ Represents a multi-turn metric type.
+ """
SINGLE_TURN = "single_tur... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/base.py |
Document functions with detailed explanations |
import typing as t
from pydantic import BaseModel, Field
from ragas.metrics.collections.multi_modal_faithfulness.util import (
is_image_path_or_url,
process_image_to_base64,
)
class MultiModalRelevanceInput(BaseModel):
user_input: str = Field(..., description="The user's question or input")
respon... | --- +++ @@ -1,3 +1,4 @@+"""Utility functions and prompt classes for MultiModalRelevance metric."""
import typing as t
@@ -10,6 +11,7 @@
class MultiModalRelevanceInput(BaseModel):
+ """Input model for multimodal relevance evaluation."""
user_input: str = Field(..., description="The user's question or ... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/collections/multi_modal_relevance/util.py |
Add documentation for all methods |
import typing as t
from typing import List
import numpy as np
from ragas.metrics.collections.base import BaseMetric
from ragas.metrics.result import MetricResult
from .util import (
ResponseGroundednessInput,
ResponseGroundednessJudge1Prompt,
ResponseGroundednessJudge2Prompt,
ResponseGroundednessOut... | --- +++ @@ -1,3 +1,4 @@+"""Response Groundedness metric v2 - Modern implementation with dual-judge evaluation."""
import typing as t
from typing import List
@@ -19,6 +20,47 @@
class ResponseGroundedness(BaseMetric):
+ """
+ Response Groundedness metric using dual-judge evaluation.
+
+ Evaluates how wel... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/collections/response_groundedness/metric.py |
Help me document legacy Python code |
from typing import List
from pydantic import BaseModel, Field
from ragas.prompt.metrics.base_prompt import BasePrompt
from ragas.prompt.metrics.common import nli_statement_prompt, statement_generator_prompt
class StatementGeneratorInput(BaseModel):
question: str = Field(..., description="The question asked")
... | --- +++ @@ -1,3 +1,4 @@+"""Noise Sensitivity prompt classes and models."""
from typing import List
@@ -8,12 +9,14 @@
class StatementGeneratorInput(BaseModel):
+ """Input for statement generation."""
question: str = Field(..., description="The question asked")
text: str = Field(..., description="... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/collections/noise_sensitivity/util.py |
Create docstrings for all classes and functions |
import typing as t
from typing import List
from ragas.metrics.collections.base import BaseMetric
from ragas.metrics.result import MetricResult
from .util import (
MULTIMODAL_FAITHFULNESS_INSTRUCTION,
MultiModalFaithfulnessOutput,
build_multimodal_message_content,
)
if t.TYPE_CHECKING:
from ragas.llm... | --- +++ @@ -1,3 +1,4 @@+"""MultiModalFaithfulness metric - Collections implementation for multimodal faithfulness evaluation."""
import typing as t
from typing import List
@@ -16,6 +17,50 @@
class MultiModalFaithfulness(BaseMetric):
+ """
+ MultiModalFaithfulness metric for evaluating response faithfulnes... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/collections/multi_modal_faithfulness/metric.py |
Replace inline comments with docstrings |
import typing as t
from typing import List
from ragas.metrics.collections.base import BaseMetric
from ragas.metrics.result import MetricResult
from .util import (
MULTIMODAL_RELEVANCE_INSTRUCTION,
MultiModalRelevanceOutput,
build_multimodal_relevance_message_content,
)
if t.TYPE_CHECKING:
from ragas... | --- +++ @@ -1,3 +1,4 @@+"""MultiModalRelevance metric - Collections implementation for multimodal relevance evaluation."""
import typing as t
from typing import List
@@ -16,6 +17,51 @@
class MultiModalRelevance(BaseMetric):
+ """
+ MultiModalRelevance metric for evaluating response relevance against
+ ... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/collections/multi_modal_relevance/metric.py |
Write docstrings for algorithm functions |
import typing as t
from pydantic import BaseModel, Field
from ragas.prompt.metrics.base_prompt import BasePrompt
class SQLEquivalenceInput(BaseModel):
reference: str = Field(..., description="Reference SQL query")
response: str = Field(..., description="Generated SQL query to evaluate")
database_schema... | --- +++ @@ -1,3 +1,4 @@+"""SQLSemanticEquivalence prompt classes and models."""
import typing as t
@@ -25,6 +26,7 @@
class SQLEquivalencePrompt(BasePrompt[SQLEquivalenceInput, SQLEquivalenceOutput]):
+ """Prompt for evaluating semantic equivalence between SQL queries."""
input_model = SQLEquivalenceI... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/collections/sql_semantic_equivalence/util.py |
Generate missing documentation strings |
import typing as t
from typing import List, Optional
from ragas.metrics.collections.base import BaseMetric
from ragas.metrics.result import MetricResult
from .util import SQLEquivalenceInput, SQLEquivalenceOutput, SQLEquivalencePrompt
if t.TYPE_CHECKING:
from ragas.llms.base import InstructorBaseRagasLLM
clas... | --- +++ @@ -1,3 +1,4 @@+"""SQLSemanticEquivalence metric - Modern collections implementation."""
import typing as t
from typing import List, Optional
@@ -12,6 +13,40 @@
class SQLSemanticEquivalence(BaseMetric):
+ """
+ Evaluates semantic equivalence between a generated SQL query and a reference query.
+
+... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/collections/sql_semantic_equivalence/metric.py |
Add verbose docstrings with examples |
import typing as t
from ragas.metrics.collections.base import BaseMetric
from ragas.metrics.result import MetricResult
class RougeScore(BaseMetric):
def __init__(
self,
name: str = "rouge_score",
rouge_type: t.Literal["rouge1", "rougeL"] = "rougeL",
mode: t.Literal["fmeasure", "... | --- +++ @@ -1,3 +1,4 @@+"""Rouge Score metric v2 - Class-based implementation with automatic validation."""
import typing as t
@@ -6,6 +7,39 @@
class RougeScore(BaseMetric):
+ """
+ Calculate ROUGE score between reference and response texts.
+
+ This implementation provides automatic validation and pu... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/collections/_rouge_score.py |
Add docstrings that explain inputs and outputs |
import typing as t
from typing import Dict, List, Literal
import numpy as np
from ragas.metrics.collections.base import BaseMetric
from ragas.metrics.result import MetricResult
from .util import (
StatementFaithfulnessInput,
StatementFaithfulnessOutput,
StatementFaithfulnessPrompt,
StatementGenerato... | --- +++ @@ -1,3 +1,4 @@+"""Noise Sensitivity metrics v2 - Modern implementation with function-based prompts."""
import typing as t
from typing import Dict, List, Literal
@@ -21,6 +22,51 @@
class NoiseSensitivity(BaseMetric):
+ """
+ Modern v2 implementation of noise sensitivity evaluation.
+
+ Measures... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/collections/noise_sensitivity/metric.py |
Write docstrings for algorithm functions |
__all__ = ["MetricResult"]
import typing as t
from pydantic import GetCoreSchemaHandler, ValidationInfo
from pydantic_core import core_schema
class MetricResult:
def __init__(
self,
value: t.Any,
reason: t.Optional[str] = None,
traces: t.Optional[t.Dict[str, t.Any]] = None,
... | --- +++ @@ -1,3 +1,4 @@+"""MetricResult object to store the result of a metric"""
__all__ = ["MetricResult"]
@@ -8,6 +9,16 @@
class MetricResult:
+ """Class to hold the result of a metric evaluation.
+
+ This class behaves like its underlying result value but still provides access
+ to additional meta... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/result.py |
Add docstrings including usage examples |
import typing as t
from ragas.metrics.collections.base import BaseMetric
from ragas.metrics.result import MetricResult
from .util import (
DEFAULT_REFERENCE_FREE_RUBRICS,
DEFAULT_WITH_REFERENCE_RUBRICS,
RubricScoreInput,
RubricScoreOutput,
RubricScorePrompt,
format_rubrics,
)
if t.TYPE_CHECK... | --- +++ @@ -1,3 +1,4 @@+"""DomainSpecificRubrics metric - Modern collections implementation."""
import typing as t
@@ -18,6 +19,65 @@
class DomainSpecificRubrics(BaseMetric):
+ """
+ Evaluates responses using domain-specific rubrics with customizable scoring criteria.
+
+ This metric allows you to def... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/collections/domain_specific_rubrics/metric.py |
Fully document this Python code with docstrings |
__all__ = [
"DiscreteValidator",
"NumericValidator",
"RankingValidator",
"AllowedValuesType",
"get_validator_for_allowed_values",
"get_metric_type_name",
]
import typing as t
from abc import ABC
# Type alias for all possible allowed_values types across different metric types
AllowedValuesType... | --- +++ @@ -1,3 +1,4 @@+"""Validation mixins for different metric types."""
__all__ = [
"DiscreteValidator",
@@ -16,19 +17,31 @@
class BaseValidator(ABC):
+ """Base validator mixin with common validation interface."""
name: str
# Note: allowed_values is now inherited from SimpleBaseMetric bas... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/validators.py |
Create documentation strings for testing functions |
import typing as t
from ragas.metrics.collections.base import BaseMetric
from ragas.metrics.result import MetricResult
class BleuScore(BaseMetric):
def __init__(
self,
name: str = "bleu_score",
kwargs: t.Optional[t.Dict[str, t.Any]] = None,
**base_kwargs,
):
super().... | --- +++ @@ -1,3 +1,4 @@+"""BLEU Score metric v2 - Class-based implementation with automatic validation."""
import typing as t
@@ -6,6 +7,33 @@
class BleuScore(BaseMetric):
+ """
+ Calculate BLEU score between reference and response texts.
+
+ This implementation provides automatic validation and pure ... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/collections/_bleu_score.py |
Write docstrings describing each step |
import base64
import binascii
import logging
import os
import re
import typing as t
from io import BytesIO
from urllib.parse import urlparse
import requests
from PIL import Image
from pydantic import BaseModel, Field
logger = logging.getLogger(__name__)
# Constants for security/processing
ALLOWED_URL_SCHEMES = {"ht... | --- +++ @@ -1,3 +1,4 @@+"""Utility functions and prompt classes for MultiModalFaithfulness metric."""
import base64
import binascii
@@ -25,6 +26,7 @@
class MultiModalFaithfulnessInput(BaseModel):
+ """Input model for multimodal faithfulness evaluation."""
response: str = Field(..., description="The re... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/collections/multi_modal_faithfulness/util.py |
Create docstrings for each class method | from __future__ import annotations
import typing as t
import warnings
from dataclasses import dataclass, field
from ragas.dataset_schema import MultiTurnSample, SingleTurnSample
from ragas.messages import AIMessage, ToolCall
from ragas.metrics._string import ExactMatch
from ragas.metrics.base import MetricType, Multi... | --- +++ @@ -15,6 +15,34 @@
@dataclass
class ToolCallAccuracy(MultiTurnMetric):
+ """
+ Tool Call Accuracy metric measures how accurately an LLM agent makes tool calls
+ compared to reference tool calls.
+
+ The metric supports two evaluation modes:
+ 1. Strict order (default): Tool calls must match ex... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/_tool_call_accuracy.py |
Write reusable docstrings |
from __future__ import annotations
import re
import typing as t
QUOTE_RE = re.compile(
r'["\u201c\u201d\u201e\u201f\'\u2018\u2019`\u00b4](.*?)["\u201c\u201d\u201e\u201f\'\u2018\u2019`\u00b4]'
)
def normalize_text(text: str) -> str:
return re.sub(r"\s+", " ", text).strip().lower()
def extract_quoted_spans... | --- +++ @@ -1,3 +1,4 @@+"""Quoted Spans utility functions."""
from __future__ import annotations
@@ -10,10 +11,22 @@
def normalize_text(text: str) -> str:
+ """Normalize text by collapsing whitespace and lower-casing."""
return re.sub(r"\s+", " ", text).strip().lower()
def extract_quoted_spans(ans... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/collections/quoted_spans/util.py |
Add minimal docstrings for each function |
import typing as t
from ragas.metrics.collections.base import BaseMetric
from ragas.metrics.result import MetricResult
from .util import (
InstanceRubricScoreInput,
InstanceRubricScoreOutput,
InstanceRubricScorePrompt,
)
if t.TYPE_CHECKING:
from ragas.llms.base import InstructorBaseRagasLLM
class ... | --- +++ @@ -1,3 +1,4 @@+"""InstanceSpecificRubrics metric - Modern collections implementation."""
import typing as t
@@ -15,6 +16,50 @@
class InstanceSpecificRubrics(BaseMetric):
+ """
+ Evaluates responses using instance-specific rubrics where each sample has its own criteria.
+
+ Unlike DomainSpecif... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/collections/instance_specific_rubrics/metric.py |
Document all endpoints with docstrings | from __future__ import annotations
import logging
import typing as t
from dataclasses import dataclass, field
import numpy as np
from langchain_core.callbacks import Callbacks
from langchain_core.prompt_values import StringPromptValue
from ragas.dataset_schema import SingleTurnSample
from ragas.llms.base import Base... | --- +++ @@ -17,6 +17,31 @@
@dataclass
class AnswerAccuracy(MetricWithLLM, SingleTurnMetric):
+ """
+ Measures answer accuracy compared to ground truth given a user_input.
+ This metric averages two distinct judge prompts to evaluate.
+
+ Top10, Zero-shoot LLM-as-a-Judge Leaderboard:
+ 1)- nvidia/Llama... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/_nv_metrics.py |
Improve my code by adding docstrings |
import typing as t
from ragas.metrics.collections.base import BaseMetric
from ragas.metrics.result import MetricResult
from .util import count_matched_spans, extract_quoted_spans
class QuotedSpansAlignment(BaseMetric):
def __init__(
self,
name: str = "quoted_spans_alignment",
casefold:... | --- +++ @@ -1,3 +1,4 @@+"""QuotedSpansAlignment metric - Modern collections implementation."""
import typing as t
@@ -8,6 +9,41 @@
class QuotedSpansAlignment(BaseMetric):
+ """
+ Measure citation alignment for quoted spans in model-generated answers.
+
+ This metric computes the fraction of quoted spa... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/collections/quoted_spans/metric.py |
Create structured documentation for my script | from __future__ import annotations
import logging
import typing as t
from dataclasses import dataclass, field
import numpy as np
from pydantic import BaseModel, Field
from ragas.dataset_schema import SingleTurnSample
from ragas.metrics.base import (
MetricOutputType,
MetricType,
MetricWithLLM,
Single... | --- +++ @@ -200,6 +200,9 @@ return await self._ascore(row, callbacks)
async def _ascore(self, row: t.Dict, callbacks: Callbacks) -> float:
+ """
+ returns the NLI score for each (q, c, a) pair
+ """
assert self.llm is not None, "LLM is not set"
statements = await ... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/_faithfulness.py |
Generate docstrings for each module |
import typing as t
from pydantic import BaseModel, Field
from ragas.prompt.metrics.base_prompt import BasePrompt
class ExtractedKeyphrasesInput(BaseModel):
text: str = Field(..., description="The text to extract keyphrases from")
class ExtractedKeyphrases(BaseModel):
keyphrases: t.List[str] = Field(...... | --- +++ @@ -1,3 +1,4 @@+"""Summary Score prompt classes and models."""
import typing as t
@@ -7,11 +8,13 @@
class ExtractedKeyphrasesInput(BaseModel):
+ """Input model for keyphrase extraction."""
text: str = Field(..., description="The text to extract keyphrases from")
class ExtractedKeyphrases... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/collections/summary_score/util.py |
Help me write clear docstrings |
import typing as t
from ragas.messages import ToolCall
def sorted_key_for_tool_call(tc: ToolCall) -> t.Tuple[str, ...]:
key_list = [tc.name]
args = tc.args
args_names = sorted(args)
for name in args_names:
key_list.append(name)
key_list.append(str(args[name]))
return tuple(key_li... | --- +++ @@ -1,3 +1,4 @@+"""Tool Call Accuracy utility functions and models."""
import typing as t
@@ -5,6 +6,12 @@
def sorted_key_for_tool_call(tc: ToolCall) -> t.Tuple[str, ...]:
+ """
+ Generate a consistent sorting key for tool calls.
+
+ Ensures tool calls with the same content are compared correc... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/collections/tool_call_accuracy/util.py |
Add well-formatted docstrings | import hashlib
import json
import logging
import typing as t
from dataclasses import dataclass, field
from langchain_core.callbacks import Callbacks
from ragas.cache import CacheInterface
from ragas.dataset_schema import SingleMetricAnnotation
from ragas.losses import Loss
from ragas.optimizers.base import Optimizer
... | --- +++ @@ -17,6 +17,46 @@
@dataclass
class DSPyOptimizer(Optimizer):
+ """
+ Advanced prompt optimizer using DSPy's MIPROv2.
+
+ MIPROv2 performs sophisticated prompt optimization by combining:
+ - Instruction optimization (prompt engineering)
+ - Demonstration optimization (few-shot examples)
+ -... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/optimizers/dspy_optimizer.py |
Add inline docstrings for readability |
import json
def extract_entities_prompt(text: str) -> str:
safe_text = json.dumps(text)
return f"""Given a text, extract unique entities without repetition. Ensure you consider different forms or mentions of the same entity as a single entity.
Please return the output in a JSON format that complies with th... | --- +++ @@ -1,8 +1,16 @@+"""Context Entity Recall prompts - V1-identical using exact PydanticPrompt.to_string() output."""
import json
def extract_entities_prompt(text: str) -> str:
+ """
+ V1-identical entity extraction prompt using exact PydanticPrompt.to_string() output.
+ Args:
+ text: The t... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/prompt/metrics/context_entity_recall.py |
Add minimal docstrings for each function |
import json
def claim_decomposition_prompt(
response: str, atomicity: str = "low", coverage: str = "low"
) -> str:
safe_response = json.dumps(response)
# Select examples based on atomicity and coverage configuration
if atomicity == "low" and coverage == "low":
examples = [
{
... | --- +++ @@ -1,3 +1,4 @@+"""Factual correctness prompts - V1-identical converted to functions."""
import json
@@ -5,6 +6,17 @@ def claim_decomposition_prompt(
response: str, atomicity: str = "low", coverage: str = "low"
) -> str:
+ """
+ V1-identical claim decomposition prompt with configurable atomicit... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/prompt/metrics/factual_correctness.py |
Create structured documentation for my script |
import json
def context_recall_prompt(question: str, context: str, answer: str) -> str:
# Use json.dumps() to safely escape the strings
safe_question = json.dumps(question)
safe_context = json.dumps(context)
safe_answer = json.dumps(answer)
return f"""Given a context, and an answer, analyze each... | --- +++ @@ -1,8 +1,20 @@+"""Context Recall prompt for classifying statement attributions."""
import json
def context_recall_prompt(question: str, context: str, answer: str) -> str:
+ """
+ Generate the prompt for context recall evaluation.
+
+ Args:
+ question: The original question
+ con... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/prompt/metrics/context_recall.py |
Write docstrings for this repository |
import json
def context_relevance_judge1_prompt(query: str, context: str) -> str:
safe_query = json.dumps(query)
safe_context = json.dumps(context)
return f"""### Instructions
You are a world class expert designed to evaluate the relevance score of a Context in order to answer the Question.
Your task i... | --- +++ @@ -1,8 +1,19 @@+"""Context Relevance prompts - Convert NVIDIA dual-judge templates to function format."""
import json
def context_relevance_judge1_prompt(query: str, context: str) -> str:
+ """
+ First judge template for context relevance evaluation.
+
+ Args:
+ query: The user's questi... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/prompt/metrics/context_relevance.py |
Add docstrings for utility scripts | from __future__ import annotations
import json
import logging
import os
import typing as t
from abc import ABC, abstractmethod
from langchain_core.prompt_values import StringPromptValue
from pydantic import BaseModel
from ragas._version import __version__
from ragas.utils import camel_to_snake
if t.TYPE_CHECKING:
... | --- +++ @@ -45,6 +45,9 @@ stop: t.Optional[t.List[str]] = None,
callbacks: Callbacks = [],
) -> t.Any:
+ """
+ Generate a single completion from the prompt.
+ """
pass
@abstractmethod
@@ -57,9 +60,15 @@ stop: t.Optional[t.List[str]] = None,
cal... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/prompt/base.py |
Fill in missing docstrings in my code |
import typing as t
import warnings
from typing import List
from ragas.messages import AIMessage, ToolCall
from ragas.metrics.collections.base import BaseMetric
from ragas.metrics.result import MetricResult
from .util import exact_match_args, sorted_key_for_tool_call
if t.TYPE_CHECKING:
from ragas.messages impor... | --- +++ @@ -1,3 +1,4 @@+"""Tool Call Accuracy metric - Modern collections implementation."""
import typing as t
import warnings
@@ -14,6 +15,49 @@
class ToolCallAccuracy(BaseMetric):
+ """
+ Modern implementation of Tool Call Accuracy metric.
+
+ Measures how accurately an LLM agent makes tool calls co... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/collections/tool_call_accuracy/metric.py |
Add docstrings to clarify complex logic |
import logging
import typing as t
from typing import List
from ragas.metrics.collections.base import BaseMetric
from ragas.metrics.result import MetricResult
from .util import (
AnswersGenerated,
ExtractedKeyphrases,
ExtractedKeyphrasesInput,
ExtractKeyphrasesPrompt,
GenerateAnswersInput,
Gen... | --- +++ @@ -1,3 +1,4 @@+"""Summary Score metric v2 - Modern implementation with multi-step pipeline."""
import logging
import typing as t
@@ -23,6 +24,52 @@
class SummaryScore(BaseMetric):
+ """
+ Summary Score metric using multi-step pipeline evaluation.
+
+ Measures how well a summary captures import... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/metrics/collections/summary_score/metric.py |
Add return value explanations in docstrings | from __future__ import annotations
__all__ = ["SimpleExampleStore", "SimpleInMemoryExampleStore", "DynamicFewShotPrompt"]
import gzip
import json
import typing as t
import warnings
from abc import ABC, abstractmethod
from pathlib import Path
import numpy as np
from ragas.embeddings.base import BaseRagasEmbedding as... | --- +++ @@ -24,20 +24,29 @@ def get_examples(
self, data: t.Dict, top_k: int = 5
) -> t.List[t.Tuple[t.Dict, t.Dict]]:
+ """Get top_k most similar examples to data."""
pass
@abstractmethod
def add_example(self, input: t.Dict, output: t.Dict) -> None:
+ """Add an exam... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/prompt/dynamic_few_shot.py |
Create documentation strings for testing functions |
import json
import typing as t
def nli_statement_prompt(context: str, statements: t.List[str]) -> str:
# Format inputs exactly like V1's model_dump_json(indent=4, exclude_none=True)
safe_context = json.dumps(context)
safe_statements = json.dumps(statements, indent=4).replace("\n", "\n ")
return f... | --- +++ @@ -1,9 +1,20 @@+"""Noise Sensitivity prompts - V1-identical using exact PydanticPrompt.to_string() output."""
import json
import typing as t
def nli_statement_prompt(context: str, statements: t.List[str]) -> str:
+ """
+ V1-identical NLI statement evaluation - matches PydanticPrompt.to_string() ... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/prompt/metrics/noise_sensitivity.py |
Add verbose docstrings with examples |
import json
import typing as t
def extract_keyphrases_prompt(text: str) -> str:
# Format input exactly like V1's model_dump_json(indent=4, exclude_none=True)
safe_text = json.dumps(text)
return f"""Extract keyphrases of type: Person, Organization, Location, Date/Time, Monetary Values, and Percentages.
P... | --- +++ @@ -1,9 +1,19 @@+"""Summary Score prompts - V1-identical using exact PydanticPrompt.to_string() output."""
import json
import typing as t
def extract_keyphrases_prompt(text: str) -> str:
+ """
+ V1-identical keyphrase extraction - matches PydanticPrompt.to_string() exactly.
+
+ Args:
+ ... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/prompt/metrics/summary_score.py |
Add docstrings to clarify complex logic | from __future__ import annotations
import inspect
import logging
import os
import typing as t
from .pydantic_prompt import PydanticPrompt
if t.TYPE_CHECKING:
from ragas.llms.base import BaseRagasLLM, InstructorBaseRagasLLM
logger = logging.getLogger(__name__)
class PromptMixin:
name: str = ""
def _... | --- +++ @@ -15,6 +15,10 @@
class PromptMixin:
+ """
+ Mixin class for classes that have prompts.
+ eg: [BaseSynthesizer][ragas.testset.synthesizers.base.BaseSynthesizer], [MetricWithLLM][ragas.metrics.base.MetricWithLLM]
+ """
name: str = ""
@@ -26,12 +30,23 @@ return prompts
def... | https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/src/ragas/prompt/mixin.py |
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