Merge branch 'main' into feat/lighter-embeddings

pyproject.toml

@@ -7,6 +7,7 @@ dependencies = [
     "langchain",
     "openai",
     "pysbd>=0.3.4",
+    "nest-asyncio",
 ]
 dynamic = ["version", "readme"]
 

src/ragas/embeddings/base.py

@@ -64,7 +64,7 @@ def __init__(
             openai_api_key = api_key
 
         super(BaseAzureOpenAIEmbeddings, self).__init__(
-            azure_endpoint=azure_endpoint,
+            azure_endpoint=azure_endpoint,  # type: ignore (pydantic bug I think)
             deployment=deployment,
             api_version=api_version,
             api_key=openai_api_key,
@@ -152,6 +152,5 @@ def predict(self, texts: List[List[str]]) -> List[List[float]]:
 
 
 def embedding_factory() -> RagasEmbeddings:
-    oai_key = os.getenv("OPENAI_API_KEY", "no-key")
-    openai_embeddings = OpenAIEmbeddings(api_key=oai_key)
+    openai_embeddings = OpenAIEmbeddings()
     return openai_embeddings

src/ragas/evaluation.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import typing as t
 from dataclasses import dataclass, field
 
 import numpy as np
@@ -133,6 +134,7 @@ def __post_init__(self):
             value = np.mean(self.scores[cn])
             self[cn] = value
             if cn not in self.binary_columns:
+                value = t.cast(float, value)
                 values.append(value + 1e-10)
 
     def to_pandas(self, batch_size: int | None = None, batched: bool = False):

src/ragas/llms/base.py

@@ -49,7 +49,7 @@ async def agenerate(
         self,
         prompts: ChatPromptTemplate,
         n: int = 1,
-        temperature: float = 0,
+        temperature: float = 1e-8,
         callbacks: t.Optional[Callbacks] = None,
     ) -> LLMResult:
         ...

src/ragas/llms/langchain.py

@@ -188,11 +188,11 @@ def generate(
         self,
         prompts: list[ChatPromptTemplate],
         n: int = 1,
-        temperature: float = 0,
+        temperature: float = 1e-8,
         callbacks: t.Optional[Callbacks] = None,
     ) -> LLMResult:
         # set temperature to 0.2 for multiple completions
-        temperature = 0.2 if n > 1 else 0
+        temperature = 0.2 if n > 1 else 1e-8
         if isBedrock(self.llm) and ("model_kwargs" in self.llm.__dict__):
             self.llm.model_kwargs = {"temperature": temperature}
         else: