add draw and serialization/deserialization to e2e pipeline examples

e2e/preview/pipelines/test_extractive_qa_pipeline.py

@@ -4,7 +4,7 @@
 from haystack.preview.components.readers import ExtractiveReader
 
 
-def test_extractive_qa_pipeline():
+def test_extractive_qa_pipeline(tmp_path):
     document_store = MemoryDocumentStore()
 
     documents = [
@@ -20,6 +20,11 @@ def test_extractive_qa_pipeline():
     qa_pipeline.add_component(instance=ExtractiveReader(model_name_or_path="deepset/tinyroberta-squad2"), name="reader")
     qa_pipeline.connect("retriever", "reader")
 
+    qa_pipeline.draw(tmp_path / "test_extractive_qa_pipeline.png")
+
+    serialized_pipeline = qa_pipeline.to_dict()
+    qa_pipeline = Pipeline.from_dict(serialized_pipeline)
+
     questions = ["Who lives in Paris?", "Who lives in Berlin?", "Who lives in Rome?"]
     answers_spywords = ["Jean", "Mark", "Giorgio"]
 

e2e/preview/pipelines/test_rag_pipelines.py

@@ -15,7 +15,7 @@
     not os.environ.get("OPENAI_API_KEY", None),
     reason="Export an env var called OPENAI_API_KEY containing the OpenAI API key to run this test.",
 )
-def test_bm25_rag_pipeline():
+def test_bm25_rag_pipeline(tmp_path):
     document_store = MemoryDocumentStore()
 
     documents = [
@@ -47,6 +47,11 @@ def test_bm25_rag_pipeline():
     rag_pipeline.connect("llm.metadata", "answer_builder.metadata")
     rag_pipeline.connect("retriever", "answer_builder.documents")
 
+    rag_pipeline.draw(tmp_path / "test_bm25_rag_pipeline.png")
+
+    serialized_pipeline = rag_pipeline.to_dict()
+    rag_pipeline = Pipeline.from_dict(serialized_pipeline)
+
     questions = ["Who lives in Paris?", "Who lives in Berlin?", "Who lives in Rome?"]
     answers_spywords = ["Jean", "Mark", "Giorgio"]
 
@@ -71,7 +76,7 @@ def test_bm25_rag_pipeline():
     not os.environ.get("OPENAI_API_KEY", None),
     reason="Export an env var called OPENAI_API_KEY containing the OpenAI API key to run this test.",
 )
-def test_embedding_retrieval_rag_pipeline():
+def test_embedding_retrieval_rag_pipeline(tmp_path):
     document_store = MemoryDocumentStore()
 
     documents = [
@@ -115,6 +120,11 @@ def test_embedding_retrieval_rag_pipeline():
     rag_pipeline.connect("llm.metadata", "answer_builder.metadata")
     rag_pipeline.connect("retriever", "answer_builder.documents")
 
+    rag_pipeline.draw(tmp_path / "test_embedding_rag_pipeline.png")
+
+    serialized_pipeline = rag_pipeline.to_dict()
+    rag_pipeline = Pipeline.from_dict(serialized_pipeline)
+
     questions = ["Who lives in Paris?", "Who lives in Berlin?", "Who lives in Rome?"]
     answers_spywords = ["Jean", "Mark", "Giorgio"]