missed commits

utils/backend.py

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+import streamlit as st
+from haystack import Pipeline
+from haystack.document_stores import FAISSDocumentStore
+from haystack.nodes import Shaper, PromptNode, PromptTemplate, PromptModel, EmbeddingRetriever
+from haystack.nodes.retriever.web import WebRetriever
+
+
+@st.cache_resource(show_spinner=False)
+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")
+    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"])
+    return pipeline
+
+
+@st.cache_resource(show_spinner=False)
+def get_retrieval_augmented_pipeline():
+    ds = FAISSDocumentStore(faiss_index_path="data/my_faiss_index.faiss",
+                            faiss_config_path="data/my_faiss_index.json")
+
+    retriever = EmbeddingRetriever(
+        document_store=ds,
+        embedding_model="sentence-transformers/multi-qa-mpnet-base-dot-v1",
+        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:",
+    )
+    # 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
+    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"])
+    return pipeline
+
+
+@st.cache_resource(show_spinner=False)
+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:",
+    )
+    # 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
+    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"])
+    return pipeline