Update app.py

app.py

@@ -17,10 +17,133 @@ from llama_index.llms import OpenAI
 llm = OpenAI(temperature=0, model="gpt-3.5-turbo")
 service_context = ServiceContext.from_defaults(llm=llm)
 
-PERSIST_DIR = "arv_metadata"
-storage_context = StorageContext.from_defaults(persist_dir=PERSIST_DIR)
-index = load_index_from_storage(storage_context)
-query_engine = index.as_query_engine(similarity_top_k=3, llm=OpenAI(model="gpt-3.5-turbo"))
+arv_index = load_index_from_storage(StorageContext.from_defaults(persist_dir = "./arv/"))
+# arv_summary_index = load_index_from_storage(StorageContext.from_defaults(persist_dir = "./arv_summary/"))
+arv_vector_query_engine = arv_index.as_query_engine(similarity_top_k = 3)
+# arv_summary_query_engine = arv_summary_index.as_query_engine()
+
+nishauri_index = load_index_from_storage(StorageContext.from_defaults(persist_dir = "./nishauri/"))
+# nishauri_summary_index = load_index_from_storage(StorageContext.from_defaults(persist_dir = "./nishauri_summary/"))
+nishauri_vector_query_engine = nishauri_index.as_query_engine(similarity_top_k = 3)
+# nishauri_summary_query_engine = nishauri_summary_index.as_query_engine()
+
+from llama_index.agent import OpenAIAgent
+
+agents = {}
+
+# define tools
+query_engine_tools = [
+    QueryEngineTool(
+        query_engine=arv_vector_query_engine,
+        metadata=ToolMetadata(
+            name="arv_vector_tool",
+            description=(
+                "Useful for retrieving specific context about HIV care and treatment."
+            ),
+        ),
+    ),
+    # QueryEngineTool(
+    #     query_engine=arv_summary_query_engine,
+    #     metadata=ToolMetadata(
+    #         name="arv_summary_tool",
+    #         description=(
+    #             "Useful for summarization questions related to HIV care and treatment."
+    #         ),
+    #     ),
+    # ),
+]
+
+# build agent
+function_llm = OpenAI(model="gpt-3.5-turbo-0613", temperature = 0)
+agent = OpenAIAgent.from_tools(
+    query_engine_tools,
+    llm=function_llm,
+    verbose=True,
+)
+
+agents["arv"] = agent
+
+# define tools
+query_engine_tools = [
+    QueryEngineTool(
+        query_engine=nishauri_vector_query_engine,
+        metadata=ToolMetadata(
+            name="nishauri_vector_tool",
+            description=(
+                "Useful for retrieving specific context about the Nishauri mobile application through which users are asking questions"
+            ),
+        ),
+    ),
+    # QueryEngineTool(
+    #     query_engine=nishauri_summary_query_engine,
+    #     metadata=ToolMetadata(
+    #         name="nishauri_summary_tool",
+    #         description=(
+    #             "Useful for summarization questions related to the Nishauri mobile application through which users are asking questions"
+    #         ),
+    #     ),
+    # ),
+]
+
+# build agent
+function_llm = OpenAI(model="gpt-3.5-turbo-0613", temperature = 0)
+agent = OpenAIAgent.from_tools(
+    query_engine_tools,
+    llm=function_llm,
+    verbose=True,
+)
+
+agents["nishauri"] = agent
+
+# define top-level nodes
+nodes = []
+
+arv_summary = (
+    "This content contains care and treatment guidance for people living with HIV."
+    " Use this source to answer questions about ARV medications, side effects from medication,"
+    " understanding viral loads, and any question about HIV care and treatment."
+    " This is the default source to use for answering any question that isn't about how to find"
+    " information in the Nishauri app."
+)
+node = IndexNode(text=arv_summary, index_id="arv")
+nodes.append(node)
+
+nishauri_summary = (
+    "This content contains guidance on the Nishauri mobile application through which users are asking questions."
+    " Reference this document when users ask questions such as how to find their viral load"
+    " or lab histories, how to find their appointment histories,"
+    " and want to know how to change their upcoming appointments."
+    " Do not use this to answer any other questions."
+)
+node = IndexNode(text=nishauri_summary, index_id="nishauri")
+nodes.append(node)
+
+# define top-level retriever
+vector_index = VectorStoreIndex(nodes)
+vector_retriever = vector_index.as_retriever(similarity_top_k=1)
+
+# define recursive retriever
+from llama_index.retrievers import RecursiveRetriever
+from llama_index.query_engine import RetrieverQueryEngine
+from llama_index.response_synthesizers import get_response_synthesizer
+
+# note: can pass `agents` dict as `query_engine_dict` since every agent can be used as a query engine
+recursive_retriever = RecursiveRetriever(
+    "vector",
+    retriever_dict={"vector": vector_retriever},
+    query_engine_dict=agents,
+    verbose=True,
+)
+
+response_synthesizer = get_response_synthesizer(
+    # service_context=service_context,
+    response_mode="compact",
+)
+query_engine = RetrieverQueryEngine.from_args(
+    recursive_retriever,
+    response_synthesizer=response_synthesizer,
+    service_context=service_context,
+)
 
 
 preamble = (" The person asking the following prompt is a person living with HIV in Kenya."