removing agents

app.py

@@ -1,206 +1,59 @@
 import os
 os.environ["OPENAI_API_KEY"]
 
-from llama_index import (
-    VectorStoreIndex,
-    SummaryIndex,
-    SimpleKeywordTableIndex,
-    SimpleDirectoryReader,
-    ServiceContext,
-    StorageContext,
-    load_index_from_storage
-)
-from llama_index.schema import IndexNode
-from llama_index.tools import QueryEngineTool, ToolMetadata
-from llama_index.llms import OpenAI
-
-llm = OpenAI(temperature=0, model="gpt-3.5-turbo")
-service_context = ServiceContext.from_defaults(llm=llm)
-
-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 = 2)
-# 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 = 2)
-# 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=2)
+from llama_index.llms.openai import OpenAI
+from llama_index.core.schema import MetadataMode
+import openai
+from openai import OpenAI as OpenAIOG
+import logging
+import sys
+llm = OpenAI(temperature=0.0, model="gpt-3.5-turbo")
+client = OpenAIOG()
+
+# Load index
+from llama_index.core import VectorStoreIndex
+from llama_index.core import StorageContext
+from llama_index.core import load_index_from_storage
+storage_context = StorageContext.from_defaults(persist_dir="arv_metadata")
+index = load_index_from_storage(storage_context)
+query_engine = index.as_query_engine(similarity_top_k=3, llm=llm)
 
-# 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,
-)
+import gradio as gr
 
+def nishauri(question: str, conversation_history: list[str]):
+    
+    context = " ".join([item["user"] + " " + item["chatbot"] for item in conversation_history])
+    response = query_engine.query(question)
 
-preamble = (" The person asking the following prompt is a person living with HIV in Kenya."
-            " For every response, recognize that they already have HIV and do not suggest that they have to get tested"
+    background = ("The person who asked the question is a person living with HIV."
+            " Recognize that they already have HIV and do not suggest that they have to get tested"
             " for HIV or take post-exposure prophylaxis, as that is not relevant, though their partners perhaps should."  
             " Do not suggest anything that is not relevant to someone who already has HIV."
-           " They are asking questions through a mobile application called Nishauri"
-           " through which they can see their lab results, appointment histories, and upcoming appointments."
-           " Here is some information that is authoritative and should guide responses, when relevant."
-           " For questions about viral load, be sure to provide specific information"
-           " about cutoffs for viral load categories. Under 50 copies/ml is low detectable level,"
+             " Do not mention in the response that the person is living with HIV."
+            " The following information about viral loads is authoritative and should override the initial response if appropriate:"
+           " Under 50 copies/ml is low detectable level,"
            " 50 - 199 copies/ml is low level viremia, 200 - 999 is high level viremia, and "
            " 1000 and above is suspected treatment failure." 
            " A high viral load or non-suppressed viral load is any viral load above 200 copies/ml."
-           " A suppressed viral load is one below 200 copies / ml."
-           " An established client is one who is on their current ART regimen for a period greater"
-           " than 6 months, had no active OI or in the previous 6 months, has adhered to scheduled"
-           " clinic visits for the previous 6 months and Viral load results has been less than 200 copies/ml"
-           " within the last 6 months."
-           " For questions about when patients should get their viral loads taken," 
-           " if they are newly initiated on ART, the first viral load sample should be taken after 3 months of"
-           " taking ART. Otherwise, if they are not new on ART, then if their previous result was below 50 to 199 cp/ml,"
-           " their viral load should be taken after every 12 months. If  their previous result was above 200cp/ml,"
-           " then viral load sample should be taken after three months."
-           " Please answer the prompt using the information retrieved" 
-           " and do not rely at all on your prior knowledge."
-           " Please keep your reply to no longer than three sentences, and please use simple language. ")
-
-prompt_intro = (" Here is the prompt: ")
-
-import gradio as gr
-
-# num_queries = 0
-# conversation_history = []
-# context = ""
-
-def nishauri(question: str, conversation_history: list[str]):
+           " A suppressed viral load is one below 200 copies / ml.")
+
+    question_final = (
+        f"The user previously asked and answered the following: {context}"
+        f" The user just asked the following question: {question}"
+        f" The following response was generated in response: {response}"
+        f" Please update the response provided only if needed, based on the following background information {background}"
+    )
+
+    completion = client.chat.completions.create(
+      model="gpt-3.5-turbo",
+        messages=[
+        {"role": "user", "content": question_final}
+      ]
+    )
     
-    # global num_queries, context
-    
-    # if num_queries == 0:
-        
-        # response = query_engine.query(preamble + prompt_intro + question)
-    
-    # if num_queries > 0:  
-    
-    context = " ".join([item["user"] + " " + item["chatbot"] for item in conversation_history])
-    response = query_engine.query(preamble +
-                                  "the user previously asked and received the following: " +
-                                  context +
-                                  prompt_intro +
-                                  question)
-
     conversation_history.append({"user": question, "chatbot": response.response})   
 
-    # num_queries += 1
-    return response, conversation_history
+    return completion.choices[0].message.content, conversation_history
 
 demo = gr.Interface(
     title = "Nishauri Chatbot Demo",