latest 2 pipelines

app.py

@@ -5,6 +5,8 @@ import sys
 
 import streamlit as st
 from haystack import Pipeline
+from haystack.nodes import Shaper, PromptNode, PromptTemplate
+from haystack.schema import Document
 
 logging.basicConfig(
     level=logging.DEBUG,
@@ -17,26 +19,56 @@ p_1 = None
 p_2 = None
 
 
-def app_init():
-    # indexing_pipeline = Pipeline.load_from_yaml("pipeline.yaml", pipeline_name="indexing")
-    # file_paths = glob.glob("data/*")
-    # ds = indexing_pipeline.get_node("DocumentStore")
-    # ds.delete_all_documents()
-    # indexing_pipeline.run(file_paths=file_paths)
-    # ds.update_embeddings(indexing_pipeline.get_node("Retriever"))
-    # ds.save(config_path="my_faiss_config.json", index_path="my_faiss_index.faiss")
+def get_plain_pipeline():
+    prompt_open_ai = PromptModel(model_name_or_path="text-davinci-003", api_key=api_key)
 
-    os.environ["OPENAI_API_KEY"] = st.secrets["OPENAI_API_KEY"]
 
-    global p_1
-    p_1 = Pipeline.load_from_yaml("pipeline.yaml", pipeline_name="query_1")
+    # 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
+
+def get_ret_aug_pipeline():
+    ds = FAISSDocumentStore(faiss_index_path="my_faiss_index.faiss",
+                            faiss_config_path="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=api_key, max_length=500)
 
-    global p_2
-    p_2 = Pipeline.load_from_yaml("pipeline.yaml", pipeline_name="query_2")
+    # Let's create a pipeline with Shaper and PromptNode
+    pipe = Pipeline()
+    pipe.add_node(component=retriever, name='retriever', inputs=['Query'])
+    pipe.add_node(component=shaper, name="shaper", inputs=["retriever"])
+    pipe.add_node(component=node, name="prompt_node", inputs=["shaper"])
+    return pipe
+
+
+def app_init():
+
+    os.environ["OPENAI_API_KEY"] = st.secrets["OPENAI_API_KEY"]
+    p1 = get_plain_pipeline()
+    p2 = get_ret_aug_pipeline()
+    return p1, p2
 
 
 def main():
-    app_init()
+    p1, p2 = app_init()
     st.title("Haystack Demo")
     input = st.text_input("Query ...")
 
@@ -49,13 +81,13 @@ def main():
 
     with col_1:
         st.text("PLAIN")
-        answers = p_1.run(input)["answers"]
+        answers = p1.run(input)["answers"]
         for ans in answers:
             st.text(ans.answer)
 
     with col_2:
         st.write(query_type.upper())
-        answers = p_2.run(input)["answers"]
+        answers = p2.run(input)["answers"]
         for ans in answers:
             st.text(ans.answer)
 

my_faiss_index.json

@@ -0,0 +1 @@
+{"faiss_index_factory_str": "Flat"}