update to haystack 1.17.1: simplify pipelines, remove shaper

app.py

@@ -47,8 +47,9 @@ if st.session_state.get('query') and run_pressed:
                         '\n This may take a few mins and might also fail if OpenAI API server is down.'):
             answers_2 = p3.run(ip)
     placeholder_retrieval_augmented.markdown(answers_2['results'][0])
+    print(answers_2)
     with st.expander("See source:"):
-        src = answers_2['invocation_context']['documents'][0].replace("$", "\$")
+        src = answers_2['invocation_context']['documents'][0].content.replace("$", "\$")
         split_marker = "\n\n" if "\n\n" in src else "\n"
         src = " ".join(src.split(split_marker))[0:2000] + "..."
         st.write(src)

requirements.txt

@@ -1,4 +1,4 @@
-git+https://github.com/deepset-ai/haystack.git@ffd02c29f7cc83a119b6440bfbabaacdaaac3a19#egg=farm-haystack
+farm-haystack==1.17.1
 faiss-cpu==1.7.2
 sqlalchemy>=1.4.2,<2
 sqlalchemy_utils

utils/backend.py

@@ -9,7 +9,7 @@ from haystack.nodes.retriever.web import WebRetriever
 def get_plain_pipeline():
     prompt_open_ai = PromptModel(model_name_or_path="text-davinci-003", api_key=st.secrets["OPENAI_API_KEY"])
     # Now let make one PromptNode use the default model and the other one the OpenAI model:
-    plain_llm_template = PromptTemplate(name="plain_llm", prompt_text="Answer the following question: $query")
+    plain_llm_template = PromptTemplate(name="plain_llm", prompt_text="Answer the following question: {query}")
     node_openai = PromptNode(prompt_open_ai, default_prompt_template=plain_llm_template, max_length=300)
     pipeline = Pipeline()
     pipeline.add_node(component=node_openai, name="prompt_node", inputs=["Query"])
@@ -27,22 +27,21 @@ def get_retrieval_augmented_pipeline():
         model_format="sentence_transformers",
         top_k=2
     )
-    shaper = Shaper(func="join_documents", inputs={"documents": "documents"}, outputs=["documents"])
 
     default_template = PromptTemplate(
         name="question-answering",
-        prompt_text="Given the context please answer the question. Context: $documents; Question: "
-                    "$query; Answer:",
+        prompt_text="Given the context please answer the question. Context: {join(documents)}; Question: "
+                    "{query}; Answer:",
     )
+    print(default_template.prompt_text)
     # Let's initiate the PromptNode
     node = PromptNode("text-davinci-003", default_prompt_template=default_template,
                       api_key=st.secrets["OPENAI_API_KEY"], max_length=500)
 
-    # Let's create a pipeline with Shaper and PromptNode
+    # Let's create a simple retrieval augmented pipeline with the retriever + PromptNode
     pipeline = Pipeline()
     pipeline.add_node(component=retriever, name='retriever', inputs=['Query'])
-    pipeline.add_node(component=shaper, name="shaper", inputs=["retriever"])
-    pipeline.add_node(component=node, name="prompt_node", inputs=["shaper"])
+    pipeline.add_node(component=node, name="prompt_node", inputs=["retriever"])
     return pipeline
 
 
@@ -50,18 +49,16 @@ def get_retrieval_augmented_pipeline():
 def get_web_retrieval_augmented_pipeline():
     search_key = st.secrets["WEBRET_API_KEY"]
     web_retriever = WebRetriever(api_key=search_key, search_engine_provider="SerperDev")
-    shaper = Shaper(func="join_documents", inputs={"documents": "documents"}, outputs=["documents"])
     default_template = PromptTemplate(
         name="question-answering",
-        prompt_text="Given the context please answer the question. Context: $documents; Question: "
-                    "$query; Answer:",
+        prompt_text="Given the context please answer the question. Context: {join(documents)}; Question: "
+                    "{query}; Answer:",
     )
     # Let's initiate the PromptNode
     node = PromptNode("text-davinci-003", default_prompt_template=default_template,
                       api_key=st.secrets["OPENAI_API_KEY"], max_length=500)
-    # Let's create a pipeline with Shaper and PromptNode
+    # Let's create a pipeline with the webretriever + PromptNode
     pipeline = Pipeline()
     pipeline.add_node(component=web_retriever, name='retriever', inputs=['Query'])
-    pipeline.add_node(component=shaper, name="shaper", inputs=["retriever"])
-    pipeline.add_node(component=node, name="prompt_node", inputs=["shaper"])
+    pipeline.add_node(component=node, name="prompt_node", inputs=["retriever"])
     return pipeline