Azure Open AI Summarize Meeting notes.

Balamurugan Balakreshnan

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2 min read

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May 20, 2023

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Overview

• Summarize Meeting converstation transcript
• Load Text data memory
• Clean the data
• Load the data using Langchain
• Using Azure Machine learning
• Upload meeting transcript into a folder

Code

• install libraries

pip install pdfreader
pip install langchain
pip install unstructured
pip install tiktoken
pip install faiss-cpu

• load libraries

from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.document_loaders import TextLoader

• Load environment variable for open ai configuration and keys
• Load the Azure open ai endpoint

import os
import openai
openai.api_type = "azure"
openai.api_base = "https://resourcename.openai.azure.com/"
openai.api_version = "2022-12-01"
openai.api_key = "xxxxxxxxxxx"

from langchain import OpenAI, PromptTemplate, LLMChain
from langchain.text_splitter import CharacterTextSplitter
from langchain.chains.mapreduce import MapReduceChain
from langchain.prompts import PromptTemplate

llm = OpenAI(engine="davinci",temperature=0)text_splitter = CharacterTextSplitter()

• Load the text data

from langchain.document_loaders import TextLoader
loader = TextLoader('./meetingtranscript.txt')
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)

embeddings = OpenAIEmbeddings(chunk_size=1)

• load the file

with open("./meetingtranscript.txt") as f:
    state_of_the_union = f.read()
texts = text_splitter.split_text(state_of_the_union)

• load langchain summarizer

from langchain.chains.summarize import load_summarize_chain

• now configure the ChatOpenAI

from langchain.chat_models import ChatOpenAI
llm=ChatOpenAI(temperature=0.7, engine="chatgpt", max_tokens=300)

• To sumamrize the entire text

chain = load_summarize_chain(llm, chain_type="map_reduce")
chain.run(docs)

• Now to prompt engineering to generate Action items

prompt_template = """Extract a Action items for follow-up:

{text}

summarize tasks:"""
PROMPT = PromptTemplate(template=prompt_template, input_variables=["text"])
chain = load_summarize_chain(llm, chain_type="stuff", prompt=PROMPT)
chain.run(docs)

Originally published at https://github.com.

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