fix(langchain): handle secret str api keys [backport 2.1]

ddtrace/contrib/langchain/patch.py

@@ -7,6 +7,7 @@
 
 import langchain
 from langchain.callbacks.openai_info import get_openai_token_cost_for_model
+from pydantic import SecretStr
 
 from ddtrace import config
 from ddtrace.constants import ERROR_TYPE
@@ -140,8 +141,11 @@ def _extract_model_name(instance):
 
 
 def _format_api_key(api_key):
-    # type: (str) -> str
+    # type: (str | SecretStr) -> str
     """Obfuscate a given LLM provider API key by returning the last four characters."""
+    if hasattr(api_key, "get_secret_value"):
+        api_key = api_key.get_secret_value()
+
     if not api_key or len(api_key) < 4:
         return ""
     return "...%s" % api_key[-4:]
@@ -695,7 +699,7 @@ def traced_similarity_search(langchain, pin, func, instance, args, kwargs):
                 instance._index.configuration.server_variables.get("project_name", ""),
             )
             api_key = instance._index.configuration.api_key.get("ApiKeyAuth", "")
-            span.set_tag_str(API_KEY, "...%s" % api_key[-4:])  # override api_key for Pinecone
+            span.set_tag_str(API_KEY, _format_api_key(api_key))  # override api_key for Pinecone
         documents = func(*args, **kwargs)
         span.set_metric("langchain.response.document_count", len(documents))
         for idx, document in enumerate(documents):

docs/spelling_wordlist.txt

@@ -176,6 +176,7 @@ proxying
 psutil
 psycopg
 py
+pydantic
 pyenv
 PyFrameObject
 pylibmc

releasenotes/notes/langchain-api-key-secret-str-b51ef4f3be0b7315.yaml

@@ -0,0 +1,4 @@
+---
+fixes:
+  - |
+    langchain: This fix resolves an issue with tagging pydantic `SecretStr` type api keys.

tests/contrib/langchain/test_langchain.py

@@ -53,6 +53,9 @@ def langchain(ddtrace_config_langchain, mock_logs, mock_metrics):
     with override_config("langchain", ddtrace_config_langchain):
         # ensure that mock OpenAI API key is passed in
         os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY", "<not-a-real-key>")
+        os.environ["COHERE_API_KEY"] = os.getenv("COHERE_API_KEY", "<not-a-real-key>")
+        os.environ["HUGGINGFACEHUB_API_TOKEN"] = os.getenv("HUGGINGFACEHUB_API_TOKEN", "<not-a-real-key>")
+        os.environ["AI21_API_KEY"] = os.getenv("AI21_API_KEY", "<not-a-real-key>")
         patch()
         import langchain
 
@@ -1078,9 +1081,7 @@ def test_pinecone_vectorstore_similarity_search(langchain, request_vcr):
             api_key=os.getenv("PINECONE_API_KEY", "<not-a-real-key>"),
             environment=os.getenv("PINECONE_ENV", "<not-a-real-env>"),
         )
-        embed = langchain.embeddings.OpenAIEmbeddings(
-            model="text-embedding-ada-002", openai_api_key=os.getenv("OPENAI_API_KEY", "<not-a-real-key>")
-        )
+        embed = langchain.embeddings.OpenAIEmbeddings(model="text-embedding-ada-002")
         index = pinecone.Index(index_name="langchain-retrieval")
         vectorstore = langchain.vectorstores.Pinecone(index, embed.embed_query, "text")
         vectorstore.similarity_search("Who was Alan Turing?", 1)
@@ -1100,9 +1101,7 @@ def test_pinecone_vectorstore_retrieval_chain(langchain, request_vcr):
             api_key=os.getenv("PINECONE_API_KEY", "<not-a-real-key>"),
             environment=os.getenv("PINECONE_ENV", "<not-a-real-env>"),
         )
-        embed = langchain.embeddings.OpenAIEmbeddings(
-            model="text-embedding-ada-002", openai_api_key=os.getenv("OPENAI_API_KEY", "<not-a-real-key>")
-        )
+        embed = langchain.embeddings.OpenAIEmbeddings(model="text-embedding-ada-002")
         index = pinecone.Index(index_name="langchain-retrieval")
         vectorstore = langchain.vectorstores.Pinecone(index, embed.embed_query, "text")
 
@@ -1127,9 +1126,7 @@ def test_pinecone_vectorstore_retrieval_chain_39(langchain, request_vcr):
             api_key=os.getenv("PINECONE_API_KEY", "<not-a-real-key>"),
             environment=os.getenv("PINECONE_ENV", "<not-a-real-env>"),
         )
-        embed = langchain.embeddings.OpenAIEmbeddings(
-            model="text-embedding-ada-002", openai_api_key=os.getenv("OPENAI_API_KEY", "<not-a-real-key>")
-        )
+        embed = langchain.embeddings.OpenAIEmbeddings(model="text-embedding-ada-002")
         index = pinecone.Index(index_name="langchain-retrieval")
         vectorstore = langchain.vectorstores.Pinecone(index, embed.embed_query, "text")
 
@@ -1152,9 +1149,7 @@ def test_vectorstore_similarity_search_metrics(langchain, request_vcr, mock_metr
             api_key=os.getenv("PINECONE_API_KEY", "<not-a-real-key>"),
             environment=os.getenv("PINECONE_ENV", "<not-a-real-env>"),
         )
-        embed = langchain.embeddings.OpenAIEmbeddings(
-            model="text-embedding-ada-002", openai_api_key=os.getenv("OPENAI_API_KEY", "<not-a-real-key>")
-        )
+        embed = langchain.embeddings.OpenAIEmbeddings(model="text-embedding-ada-002")
         index = pinecone.Index(index_name="langchain-retrieval")
         vectorstore = langchain.vectorstores.Pinecone(index, embed.embed_query, "text")
         vectorstore.similarity_search("Who was Alan Turing?", 1)
@@ -1205,9 +1200,7 @@ def test_vectorstore_logs(langchain, ddtrace_config_langchain, request_vcr, mock
             api_key=os.getenv("PINECONE_API_KEY", "<not-a-real-key>"),
             environment=os.getenv("PINECONE_ENV", "<not-a-real-env>"),
         )
-        embed = langchain.embeddings.OpenAIEmbeddings(
-            model="text-embedding-ada-002", openai_api_key=os.getenv("OPENAI_API_KEY", "<not-a-real-key>")
-        )
+        embed = langchain.embeddings.OpenAIEmbeddings(model="text-embedding-ada-002")
         index = pinecone.Index(index_name="langchain-retrieval")
         vectorstore = langchain.vectorstores.Pinecone(index, embed.embed_query, "text")
         vectorstore.similarity_search("Who was Alan Turing?", 1)