Trying to get Sentry integration to work in the demo

pinecone_demo/docs_retrieval_langchain.py

@@ -3,33 +3,47 @@
 from langchain.embeddings.openai import OpenAIEmbeddings
 from langchain.chat_models import ChatOpenAI
 from langchain.chains import RetrievalQA
-
+import sentry_sdk
+from sentry_sdk.integrations.opentelemetry import SentrySpanProcessor, SentryPropagator
 from traceloop.sdk import Traceloop
 
+sentry_sdk.init(
+    dsn="https://0b61b297b1e1a9aa6287f392dc96aa34@o4506150677970945.ingest.sentry.io/4506150679150592",
+    enable_tracing=True,
+    sample_rate=1.0,
+    # set the instrumenter to use OpenTelemetry instead of Sentry
+    instrumenter="otel",
+)
+
+# Traceloop.init(
+#     disable_batch=True, processor=SentrySpanProcessor(), propagator=SentryPropagator()
+# )
+
 Traceloop.init(disable_batch=True)
 
-index_name = 'gpt-4-langchain-docs-fast'
-model_name = 'text-embedding-ada-002'
+from opentelemetry import trace
+from opentelemetry.propagate import set_global_textmap
+from sentry_sdk.integrations.opentelemetry import SentrySpanProcessor, SentryPropagator
+
+provider = trace.get_tracer_provider()
+provider.add_span_processor(SentrySpanProcessor())
+set_global_textmap(SentryPropagator())
+
+index_name = "gpt-4-langchain-docs-fast"
+model_name = "text-embedding-ada-002"
 
 index = pinecone.Index(index_name)
 
 embed = OpenAIEmbeddings(
     model=model_name,
 )
 
-vectorstore = Pinecone(
-    index, embed.embed_query, "text"
-)
+vectorstore = Pinecone(index, embed.embed_query, "text")
 
-llm = ChatOpenAI(
-    model_name='gpt-3.5-turbo',
-    temperature=0.0
-)
+llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0.0)
 
 qa = RetrievalQA.from_chain_type(
-    llm=llm,
-    chain_type="stuff",
-    retriever=vectorstore.as_retriever()
+    llm=llm, chain_type="stuff", retriever=vectorstore.as_retriever()
 )
 
 print(qa.run("how do I build an agent with LangChain?"))