├── .gitattributes
├── app.py
├── requirements.txt
└── test_server.py


/.gitattributes:
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1 | # Auto detect text files and perform LF normalization
2 | * text=auto
3 | 


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/app.py:
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 1 | import streamlit as st
 2 | from langchain.document_loaders.csv_loader import CSVLoader
 3 | from langchain.vectorstores import FAISS
 4 | from langchain.embeddings.openai import OpenAIEmbeddings
 5 | from langchain.prompts import PromptTemplate
 6 | from langchain.chat_models import ChatOpenAI
 7 | from langchain.chains import LLMChain
 8 | from dotenv import load_dotenv
 9 | 
10 | load_dotenv()
11 | 
12 | # 1. Vectorise the sales response csv data
13 | loader = CSVLoader(file_path="sales_response.csv")
14 | documents = loader.load()
15 | 
16 | embeddings = OpenAIEmbeddings()
17 | db = FAISS.from_documents(documents, embeddings)
18 | 
19 | # 2. Function for similarity search
20 | 
21 | 
22 | def retrieve_info(query):
23 |     similar_response = db.similarity_search(query, k=3)
24 | 
25 |     page_contents_array = [doc.page_content for doc in similar_response]
26 | 
27 |     # print(page_contents_array)
28 | 
29 |     return page_contents_array
30 | 
31 | 
32 | # 3. Setup LLMChain & prompts
33 | llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-16k-0613")
34 | 
35 | template = """
36 | You are a world class business development representative. 
37 | I will share a prospect's message with you and you will give me the best answer that 
38 | I should send to this prospect based on past best practies, 
39 | and you will follow ALL of the rules below:
40 | 
41 | 1/ Response should be very similar or even identical to the past best practies, 
42 | in terms of length, ton of voice, logical arguments and other details
43 | 
44 | 2/ If the best practice are irrelevant, then try to mimic the style of the best practice to prospect's message
45 | 
46 | Below is a message I received from the prospect:
47 | {message}
48 | 
49 | Here is a list of best practies of how we normally respond to prospect in similar scenarios:
50 | {best_practice}
51 | 
52 | Please write the best response that I should send to this prospect:
53 | """
54 | 
55 | prompt = PromptTemplate(
56 |     input_variables=["message", "best_practice"],
57 |     template=template
58 | )
59 | 
60 | chain = LLMChain(llm=llm, prompt=prompt)
61 | 
62 | 
63 | # 4. Retrieval augmented generation
64 | def generate_response(message):
65 |     best_practice = retrieve_info(message)
66 |     response = chain.run(message=message, best_practice=best_practice)
67 |     return response
68 | 
69 | 
70 | # 5. Build an app with streamlit
71 | def main():
72 |     st.set_page_config(
73 |         page_title="Customer response generator", page_icon=":bird:")
74 | 
75 |     st.header("Customer response generator :bird:")
76 |     message = st.text_area("customer message")
77 | 
78 |     if message:
79 |         st.write("Generating best practice message...")
80 | 
81 |         result = generate_response(message)
82 | 
83 |         st.info(result)
84 | 
85 | 
86 | if __name__ == '__main__':
87 |     main()
88 | 


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/requirements.txt:
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1 | fastapi[all]
2 | openai
3 | python-dotenv
4 | pydantic==1.*
5 | langchain
6 | faiss-cpu
7 | tiktoken


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/test_server.py:
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 1 | import requests
 2 | 
 3 | print(
 4 |     requests.post(
 5 |         "http://0.0.0.0:10000",
 6 |         json={
 7 |             "message": "Would be very interesting in having a conversation. Where are you based?"
 8 |         }
 9 |     ).json()
10 | )
11 | 


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