Duplicate from mabrow05/sales_qa

Co-authored-by: Michael Brown <mabrow05@users.noreply.huggingface.co>

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
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+*.bz2 filter=lfs diff=lfs merge=lfs -text
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+*.lfs.* filter=lfs diff=lfs merge=lfs -text
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+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+title: Sales QA Bot
+emoji: 📈
+colorFrom: red
+colorTo: blue
+sdk: gradio
+sdk_version: 3.20.0
+app_file: app.py
+pinned: false
+duplicated_from: mabrow05/sales_qa
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+# -*- coding: utf-8 -*-
+
+#!pip install gradio
+#!pip install -U sentence-transformers
+#!pip install langchain
+#!pip install openai
+#!pip install -U chromadb
+
+import gradio as gr
+from sentence_transformers import SentenceTransformer, CrossEncoder, util
+from langchain.llms import OpenAI
+from langchain.docstore.document import Document
+from langchain.prompts import PromptTemplate
+from langchain.chains.question_answering import load_qa_chain
+from langchain.chains.qa_with_sources import load_qa_with_sources_chain
+from langchain import LLMMathChain, SQLDatabase, SQLDatabaseChain, LLMChain
+from langchain.agents import initialize_agent, Tool
+from langchain.agents import ZeroShotAgent, AgentExecutor
+from langchain.memory import ConversationBufferWindowMemory
+
+from langchain.schema import AIMessage, HumanMessage
+
+import sqlite3
+import pandas as pd
+import json
+
+from functools import partial
+
+import chromadb
+import os
+
+#cxn = sqlite3.connect('./data/mbr.db')
+
+"""# import models"""
+
+bi_encoder = SentenceTransformer('multi-qa-MiniLM-L6-cos-v1')
+bi_encoder.max_seq_length = 256     #Truncate long passages to 256 tokens
+
+#The bi-encoder will retrieve top_k documents. We use a cross-encoder, to re-rank the results list to improve the quality
+cross_encoder = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
+
+"""# setup vector db
+- chromadb
+- https://docs.trychroma.com/getting-started
+"""
+
+from chromadb.config import Settings
+
+chroma_client = chromadb.Client(settings=Settings(
+    chroma_db_impl="duckdb+parquet",
+    persist_directory="./data/mychromadb/" # Optional, defaults to .chromadb/ in the current directory
+))
+
+#!ls ./data/mychromadb/
+#collection = chroma_client.create_collection(name="benefit_collection")
+collection = chroma_client.get_collection(name="plan_collection", embedding_function=bi_encoder)
+faq_collection = chroma_client.get_collection(name="faq_collection", embedding_function=bi_encoder)
+"""### vector db search examples"""
+
+def rtrv(qry, collection, top_k=20):
+  results = collection.query(
+    query_embeddings=[ bi_encoder.encode(qry) ],
+    n_results=top_k,
+    )
+  return results
+
+def vdb_src(qry, collection, src, top_k=20):
+  results = collection.query(
+    query_embeddings=[ bi_encoder.encode(qry) ],
+    n_results=top_k,
+    where={"source": src},
+    )
+  return results
+
+def vdb_where(qry, collection, where, top_k=20):
+  results = collection.query(
+    query_embeddings=[ bi_encoder.encode(qry) ],
+    n_results=top_k,
+    where=where,
+    )
+  return results
+  
+def vdb_pretty(qry, collection, top_k=10):
+  results = collection.query(
+    query_embeddings=[ bi_encoder.encode(qry) ],
+    n_results=top_k,
+    include=["metadatas", "documents", "distances","embeddings"]
+    )
+  rslt_pd = pd.DataFrame(results ).explode(['ids','documents', 'metadatas', 'distances', 'embeddings'])
+  rslt_fmt = pd.concat([rslt_pd.drop(['metadatas'], axis=1), rslt_pd['metadatas'].apply(pd.Series)], axis=1 )
+  return rslt_fmt
+
+# qry = 'Why should I chose Medicare Advantage over traditional Medicare?'
+# rslt_fmt = vdb_pretty(qry, top_k=10)
+# rslt_fmt
+
+# doc_lst = rslt_fmt[['documents']].values.tolist()
+# len(doc_lst)
+
+"""# Introduction
+- example of the kind of question answering that is possible with this tool
+- assumes we are answering for a member with a Healthy Options Card
+
+*When will I get my card?*
+
+# semantic search functions
+"""
+
+# choosing to use rerank for this use case as a baseline
+
+def rernk(query, collection=collection, where=None, top_k=20, top_n = 5):
+  rtrv_rslts = vdb_where(query, collection=collection, where=where, top_k=top_k)
+  rtrv_ids = rtrv_rslts.get('ids')[0]
+  rtrv_docs = rtrv_rslts.get('documents')[0]
+
+  ##### Re-Ranking #####
+  cross_inp = [[query, doc] for doc in rtrv_docs]
+  cross_scores = cross_encoder.predict(cross_inp)
+
+  # Sort results by the cross-encoder scores
+  combined = list(zip(rtrv_ids, list(cross_scores)))
+  sorted_tuples = sorted(combined, key=lambda x: x[1], reverse=True)
+  sorted_ids = [t[0] for t in sorted_tuples[:top_n]]
+  predictions = collection.get(ids=sorted_ids, include=["documents","metadatas"])
+  return predictions
+  #return cross_scores
+
+## version w/o re-rank
+# def get_text_fmt(qry):
+  # prediction_text = []
+  # predictions = rtrv(qry, top_k = 5)
+  # docs = predictions['documents'][0]
+  # meta = predictions['metadatas'][0]
+  # for i in range(len(docs)):
+    # result = Document(page_content=docs[i], metadata=meta[i]) 
+    # prediction_text.append(result)
+  # return prediction_text
+  
+def get_text_fmt(qry, collection=collection, where=None):
+  prediction_text = []
+  predictions = rernk(qry, collection=collection, where=where, top_k=20, top_n = 5)
+  docs = predictions['documents']
+  meta = predictions['metadatas']
+  for i in range(len(docs)):
+    result = Document(page_content=docs[i], metadata=meta[i]) 
+    prediction_text.append(result)
+  return prediction_text
+
+# get_text_fmt('why should I choose a medicare advantage plan over traditional medicare?')
+
+"""# LLM based qa functions"""
+
+llm = OpenAI(temperature=0)
+# default model
+# model_name: str = "text-davinci-003"
+# instruction fine-tuned, sometimes referred to as GPT-3.5
+
+template = """You are a friendly AI assistant for the insurance company Humana. 
+Given the following extracted parts of a long document and a question, create a succinct final answer. 
+If you don't know the answer, just say that you don't know. Don't try to make up an answer.
+If the question is not about Humana, politely inform the user that you are tuned to only answer questions about Humana.
+QUESTION: {question}
+=========
+{summaries}
+=========
+FINAL ANSWER:"""
+PROMPT = PromptTemplate(template=template, input_variables=["summaries", "question"])
+
+chain_qa = load_qa_with_sources_chain(llm=llm, chain_type="stuff", prompt=PROMPT, verbose=False)
+
+def get_llm_response(message, collection=collection, where=None):
+  mydocs = get_text_fmt(message, collection, where)
+  responses = chain_qa({"input_documents":mydocs, "question":message})
+  return responses
+
+
+get_llm_response_humana = partial(get_llm_response, where={'company':'humana'})
+get_llm_response_essence = partial(get_llm_response, where={'company':'essence'})
+get_llm_response_faq = partial(get_llm_response, collection=faq_collection)
+
+# rslt = get_llm_response('can I buy shrimp?')
+# rslt['output_text']
+
+# for d in rslt['input_documents']:
+#   print(d.page_content)
+#   print(d.metadata['url'])
+
+# rslt['output_text']
+
+"""# Database query"""
+## setup member database
+## only do this once
+# d = {'mbr_fname':['bruce'], 
+#      'mbr_lname':['broussard'], 
+#      'mbr_id':[456] , 
+#      'policy_id':['H1036-236'], 
+#      'accumulated_out_of_pocket':[3800],
+#      'accumulated_routine_footcare_visits':[6],
+#      'accumulated_trasportation_trips':[22],
+#      'accumulated_drug_cost':[7500],
+#      }
+# df = pd.DataFrame(data=d, columns=['mbr_fname', 'mbr_lname', 'mbr_id', 'policy_id', 'accumulated_out_of_pocket', 'accumulated_routine_footcare_visits', 'accumulated_trasportation_trips','accumulated_drug_cost'])
+# df.to_sql(name='mbr_details', con=cxn, if_exists='replace')
+
+# # sample db query
+# qry = '''select accumulated_routine_footcare_visits 
+# from mbr_details'''
+# foot_det = pd.read_sql(qry, cxn)
+# foot_det.values[0][0]
+
+#db = SQLDatabase.from_uri("sqlite:///./data/mbr.db")
+
+#db_chain = SQLDatabaseChain(llm=llm, database=db, verbose=True, return_intermediate_steps=True)
+
+#def db_qry(qry):
+#  responses = db_chain('my mbr_id is 456 ;'+str(qry) ) ############### hardcode mbr id 456 for demo
+#  return responses
+
+"""# Math
+- default version
+"""
+
+llm_math_chain = LLMMathChain(llm=llm, verbose=True)
+
+# llm_math_chain.run('what is the square root of 49?')
+
+"""# Greeting"""
+
+template = """You are an AI assistant for the insurance company Humana. 
+Your name is Jarvis and you were created on February 13, 2023.
+Offer polite, friendly greetings and brief small talk.
+Respond to thanks with, 'Glad to help.'
+If the question is not about Humana, politely guide the user to ask questions about Humana insurance benefits
+QUESTION: {question}
+=========
+FINAL ANSWER:"""
+greet_prompt = PromptTemplate(template=template, input_variables=["question"])
+
+greet_llm = LLMChain(prompt=greet_prompt, llm=llm, verbose=True)
+
+# greet_llm.run('will it snow in Lousiville tomorrow')
+
+# greet_llm.run('Thanks, that was great')
+
+"""# MRKL Chain"""
+
+tools = [
+    Tool(
+        name = "Humana Plans",
+        func=get_llm_response_humana,
+        description='''Useful for confirming benefits of Humana plans. 
+        Useful for answering questions about Humana insurance plans.
+        You should ask targeted questions.'''
+    ),
+    Tool(
+        name = "Essence Plans",
+        func=get_llm_response_essence,
+        description='''Useful for confirming benefits of Essence Healthcare plans. 
+        Useful for answering questions about Essence Healthcare plans.
+        You should ask targeted questions.'''
+    ),
+    Tool(
+        name = "FAQ",
+        func=get_llm_response_faq,
+        description='''Useful for answering general health insurance questions. Useful for answering questions about Medicare and 
+                        Medicare Advantage. '''
+    ),
+    Tool(
+        name="Calculator",
+        func=llm_math_chain.run,
+        description="""Only useful for when you need to answer questions about math, like subtracting two numbers or dividing numbers.
+                       This tool should not be used to look up facts."""
+    ),
+    #Tool(
+    #    name = "Search",
+    #    func=search.run,
+    #    description="Useful for when you need to answer questions than can not be answered using the other tools. This tool is a last resort."
+    #),
+    Tool(
+        name="Greeting",
+        func=greet_llm.run,
+        return_direct=True,
+        description="useful for when you need to respond to greetings, thanks, make small talk or answer questions about yourself"
+    ),
+]
+
+
+##### Create Agent
+
+#mrkl = initialize_agent(tools, llm, agent="zero-shot-react-description", verbose=False, return_intermediate_steps=True, max_iterations=5, early_stopping_method="generate")
+
+prefix = """Answer the following question as best as you can. You should not make up any answers. To answer the question, use the following
+            tools:"""
+suffix = """If the question is not about healthcare or Humana,
+            you should use the "Greeting" tool and pass it the question being asked. 
+            If you are not confident in which tool to use, 
+            you should use the "Greeting" tool and pass it the question being asked.
+            Remember, only answer using the information output from the 
+            tools! Begin!"
+
+{chat_history}
+Question: {input}
+{agent_scratchpad}"""
+
+prompt = ZeroShotAgent.create_prompt(
+    tools, 
+    prefix=prefix, 
+    suffix=suffix, 
+    input_variables=["input", "chat_history", "agent_scratchpad"]
+)
+
+llm_chain = LLMChain(llm=llm, prompt=prompt)
+agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)
+agent_chain = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True,
+                                                 max_iterations=5, early_stopping_method="generate", 
+                                                   return_intermediate_steps=True)
+
+
+def make_memory_buffer(history, mem_len=2):
+    mem = ConversationBufferWindowMemory(k=mem_len, memory_key="chat_history", output_key="output")        
+    hist = []
+    for user,ai in history:
+        hist+=[HumanMessage(content=user), AIMessage(content=ai)]
+        
+    mem.chat_memory.messages = hist    
+    return mem
+
+def agent_rspnd(qry, history, agent=agent_chain):
+  agent.memory = make_memory_buffer(history)
+  response = agent({"input":str(qry) })
+  return response
+
+def make_memory_buffer(history, mem_len=2):
+            
+    hist = []
+    for user,ai in history:
+        hist+=[HumanMessage(content=user), AIMessage(content=ai)]
+        
+    mem = ConversationBufferWindowMemory(k=mem_len, memory_key="chat_history", output_key="output")
+    mem.chat_memory.messages = hist
+    return mem
+
+def agent_rspnd(qry, history):
+  agent_chain = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True, 
+                                                   memory=make_memory_buffer(history),
+                                                   max_iterations=5, early_stopping_method="generate", 
+                                                   return_intermediate_steps=True)
+  response = agent_chain({"input":str(qry) })
+  return response
+
+def mrkl_rspnd(qry):
+  response = mrkl({"input":str(qry) })
+  return response
+
+# r = mrkl_rspnd("can I buy fish with the card?")
+# print(r['output'])
+
+# print(json.dumps(r['intermediate_steps'], indent=2))
+
+#r['intermediate_steps']
+
+# from IPython.core.display import display, HTML
+
+def get_cot(r):
+  cot = '<p>'
+  try:
+    intermedObj = r['intermediate_steps']
+    cot +='<b>Input:</b> '+r['input']+'<br>'
+    for agnt_action, obs in intermedObj:
+      al = '<br>  '.join(agnt_action.log.split('\n') )
+      cot += '<b>AI chain of thought:</b> '+ al +'<br>'
+      if type(obs) is dict:
+        if obs.get('input_documents') is not None: #### this criteria doesn't work
+          for d in obs['input_documents']:
+            cot += '&nbsp;&nbsp;&nbsp;&nbsp;'+'<i>- '+str(d.page_content)+'</i>'+' <a href="'+ str(d.metadata['url']) +'">'+str(d.metadata['page'])+'</a> '+'<br>'
+          cot += '<b>Observation:</b> '+str(obs['output_text']) +'<br><br>'
+        elif obs.get('intermediate_steps') is not None:
+          cot += '<b>Query:</b> '+str(obs.get('intermediate_steps')) +'<br><br>'
+        else:  
+          pass 
+      else:
+        cot += '<b>Observation:</b> '+str(obs) +'<br><br>'
+  except:
+    pass
+  cot += '</p>'  
+  return cot
+
+# cot = get_cot(r)
+# display(HTML(cot))
+
+"""# chat example"""
+
+def chat(message, history):
+  history = history or []
+  #message = message.lower()
+  
+  response = agent_rspnd(message, history)
+  cot = get_cot(response)
+  history.append((message, response['output']))
+  return history, history, cot
+
+css=".gradio-container {background-color: whitesmoke}"
+
+xmpl_list = ["How does Humana's transportation benefit compare to Essence's?",
+             "Why should I choose a Medicare Advantage plan over Traditional Medicare?",
+            "What is the difference between a Medicare Advantage HMO plan and a PPO plan?",
+            "What is a low income subsidy plan and do I qualify for one of these plans?",
+            "Are my medications covered on a low income subsidy plan?"]
+
+with gr.Blocks(css=css) as demo:
+  history_state = gr.State()
+  response_state = gr.State()
+  gr.Markdown('# Sales QA Bot')
+  gr.Markdown("""You are a **Louisville, KY** resident who currently has **Medicare Advantage** through an insurer called 
+  **Essence Healthcare**. You don't know a lot about Medicare Advantage or your current benefits, so you may have questions about
+  how Humana's plans compare. This bot is here to help you learn about what **Humana has to offer** while answering any
+  other questions you might have. Welcome!""")
+  with gr.Row():
+    chatbot = gr.Chatbot()
+  with gr.Accordion(label='Show AI chain of thought: ', open=False,):
+    ai_cot = gr.HTML(show_label=False)
+  with gr.Row():
+    message = gr.Textbox(label='Input your question here:',
+                         placeholder='Why should I choose Medicare Advantage?',
+                         lines=1)
+    submit = gr.Button(value='Send',
+                       variant='secondary').style(full_width=False)
+  submit.click(chat,
+               inputs=[message, history_state],
+               outputs=[chatbot, history_state, ai_cot])
+  gr.Examples(
+            examples=xmpl_list,
+            inputs=message
+        )
+
+demo.launch()

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requirements.txt

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+sentence-transformers==2.2.2
+openai==0.27.2
+langchain==0.0.109
+chromadb==0.3.11
+gradio