Community[patch]use secret str in Tavily and HuggingFaceInferenceEmbe…

…ddings (#16109)

So the api keys don't show up in repr's 

Still need to do tests

libs/community/langchain_community/embeddings/huggingface.py

@@ -2,7 +2,7 @@
 
 import requests
 from langchain_core.embeddings import Embeddings
-from langchain_core.pydantic_v1 import BaseModel, Extra, Field
+from langchain_core.pydantic_v1 import BaseModel, Extra, Field, SecretStr
 
 DEFAULT_MODEL_NAME = "sentence-transformers/all-mpnet-base-v2"
 DEFAULT_INSTRUCT_MODEL = "hkunlp/instructor-large"
@@ -275,7 +275,7 @@ class HuggingFaceInferenceAPIEmbeddings(BaseModel, Embeddings):
     Requires a HuggingFace Inference API key and a model name.
     """
 
-    api_key: str
+    api_key: SecretStr
     """Your API key for the HuggingFace Inference API."""
     model_name: str = "sentence-transformers/all-MiniLM-L6-v2"
     """The name of the model to use for text embeddings."""
@@ -297,7 +297,7 @@ def _default_api_url(self) -> str:
 
     @property
     def _headers(self) -> dict:
-        return {"Authorization": f"Bearer {self.api_key}"}
+        return {"Authorization": f"Bearer {self.api_key.get_secret_value()}"}
 
     def embed_documents(self, texts: List[str]) -> List[List[float]]:
         """Get the embeddings for a list of texts.

libs/community/langchain_community/utilities/tavily_search.py

@@ -7,7 +7,7 @@
 
 import aiohttp
 import requests
-from langchain_core.pydantic_v1 import BaseModel, Extra, root_validator
+from langchain_core.pydantic_v1 import BaseModel, Extra, SecretStr, root_validator
 from langchain_core.utils import get_from_dict_or_env
 
 TAVILY_API_URL = "https://api.tavily.com"
@@ -16,7 +16,7 @@
 class TavilySearchAPIWrapper(BaseModel):
     """Wrapper for Tavily Search API."""
 
-    tavily_api_key: str
+    tavily_api_key: SecretStr
 
     class Config:
         """Configuration for this pydantic object."""
@@ -45,7 +45,7 @@ def raw_results(
         include_images: Optional[bool] = False,
     ) -> Dict:
         params = {
-            "api_key": self.tavily_api_key,
+            "api_key": self.tavily_api_key.get_secret_value(),
             "query": query,
             "max_results": max_results,
             "search_depth": search_depth,
@@ -126,7 +126,7 @@ async def raw_results_async(
         # Function to perform the API call
         async def fetch() -> str:
             params = {
-                "api_key": self.tavily_api_key,
+                "api_key": self.tavily_api_key.get_secret_value(),
                 "query": query,
                 "max_results": max_results,
                 "search_depth": search_depth,

libs/community/tests/unit_tests/embeddings/test_huggingface.py

@@ -0,0 +1,7 @@
+from langchain_community.embeddings.huggingface import HuggingFaceInferenceAPIEmbeddings
+
+
+def test_hugginggface_inferenceapi_embedding_documents_init() -> None:
+    """Test huggingface embeddings."""
+    embedding = HuggingFaceInferenceAPIEmbeddings(api_key="abcd123")
+    assert "abcd123" not in repr(embedding)

libs/community/tests/unit_tests/utilities/test_tavily.py

@@ -0,0 +1,7 @@
+from langchain_community.utilities.tavily_search import TavilySearchAPIWrapper
+
+
+def test_api_wrapper_api_key_not_visible() -> None:
+    """Test that an exception is raised if the API key is not present."""
+    wrapper = TavilySearchAPIWrapper(tavily_api_key="abcd123")
+    assert "abcd123" not in repr(wrapper)

libs/langchain/langchain/smith/evaluation/runner_utils.py

@@ -97,6 +97,7 @@ def get_aggregate_feedback(
             for col in df.columns
             if col.startswith("inputs.")
             or col.startswith("outputs.")
+            or col in {"input", "output"}
             or col.startswith("reference")
         ]
         return df.describe(include="all").drop(to_drop, axis=1)