LangChain (RAG)
Neil Haddley • July 21, 2023
Using LangChain to create a medical report application
LangChain is a framework built around Large Language Models.
I used LangChain to create a medical report application.
streamlit
Streamlit is an open-source Python library used to create web applications for data science and machine learning projects.
I used streamlit to create the application's user interface.
hello world streamlit app
OpenAI's API
I updated my hello world application to call OpenAI's API.
I had to request a secret API key.
secret key
Using LangChain to call OpenAI's API.
Document pages
I used LangChain to return the text found on each page of a sample medical report.
https://www.med.unc.edu/medclerk/wp-content/uploads/sites/877/2018/10/hp4.pdf
hp4.pdf medical report
Jupyter Notebook
To improve performance and reduce costs I pre-processed the pdf file and stored the result.
I created a Jupyter Notebook to keep track of the steps I followed.
Running jupyter-notebook locally
I loaded pages from the medical report.
I broke the pages into paragraphs (texts).
Chroma
I used Chroma to create an embeddings vector store and saved the store locally.
I used OpenAI's API to generate the embeddings
I converted the query 'Does the patient smoke?' to an embedding and compared the result with the embeddings in the vector store.
I ensured that the embeddings vector store could be loaded from the local folder.
I located the most similar paragraphs and sent those (see Context Injection) and the query to OpenAI's servers.
The streamlit code
The finished application
app0.py
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1# pip install streamlit 2# python -m streamlit run app0.py 3 4import streamlit 5 6prompt = streamlit.text_input('Input your name') 7 8if prompt: 9 response = "Hello " + prompt 10 streamlit.write(response)
app1.py
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1# pip install langchain openai streamlit 2# python -m streamlit run app.py 3 4# import os 5from langchain.llms import OpenAI 6import streamlit 7 8# os.environ['OPENAI_API_KEY']='<insert key here>' 9 10llm = OpenAI(temperature=0.9) 11 12prompt = streamlit.text_input('Input your prompt') 13 14if prompt: 15 response = llm(prompt) 16 streamlit.write(response)
1
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1from langchain.document_loaders import PyPDFLoader 2 3loader = PyPDFLoader('hp4.pdf') 4pages = loader.load_and_split() 5 6 7print (pages[0].page_content)
2 3
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1from langchain.text_splitter import RecursiveCharacterTextSplitter 2 3# Define chunk size, overlap and separators 4text_splitter = RecursiveCharacterTextSplitter( 5 chunk_size = 1024, 6 chunk_overlap = 40, 7 length_function = len, 8 separators=["\n \n", " ", ""] 9) 10 11# Split the pages into paragraphs (texts) as defined above 12texts = text_splitter.split_documents(pages) 13 14len(texts) 15 16texts[1].page_content
4
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1from langchain.llms import OpenAI 2 3from langchain.embeddings import OpenAIEmbeddings 4 5from langchain.vectorstores import Chroma 6 7from langchain.agents.agent_toolkits import ( 8 create_vectorstore_agent, 9 VectorStoreToolkit, 10 VectorStoreInfo 11) 12 13 14llm = OpenAI(temperature=0.9,verbose=True) 15embeddings=OpenAIEmbeddings() 16 17save_directory = "Chroma" 18 19store = Chroma.from_documents(texts, embeddings, collection_name='hp4', persist_directory=save_directory) 20store.persist() 21 22store.get()
5
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1search = store.similarity_search_with_score('Does the patient smoke?') 2 3search
6
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1db = Chroma(persist_directory=save_directory,collection_name='hp4',embedding_function=embeddings) 2 3print(db._collection.count()) 4 5db.get()
7
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1vectorstore_info = VectorStoreInfo( 2 name="hp4", 3 description="vectore store generate from hp4.pdf", 4 vectorstore=store 5) 6 7toolkit = VectorStoreToolkit(vectorstore_info=vectorstore_info) 8 9agent_executor = create_vectorstore_agent( 10 llm=llm, 11 toolkit=toolkit, 12 verbose=True 13) 14 15response = agent_executor.run("Does the patient smoke?") 16 17response
app2.py
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1import streamlit as st 2 3from langchain.llms import OpenAI 4 5from langchain.embeddings import OpenAIEmbeddings 6 7from langchain.vectorstores import Chroma 8 9from langchain.agents.agent_toolkits import ( 10 create_vectorstore_agent, 11 VectorStoreToolkit, 12 VectorStoreInfo 13) 14 15 16llm = OpenAI(temperature=0.9,verbose=True) 17 18embeddings=OpenAIEmbeddings() 19 20save_directory = "Chroma" 21 22# load embeddings from "Chroma" directory 23db = Chroma(persist_directory=save_directory,collection_name='hp4',embedding_function=embeddings) 24 25vectorstore_info = VectorStoreInfo( 26 name="hp4", 27 description="embeddings generated from the pdf document", 28 vectorstore=db 29) 30 31toolkit = VectorStoreToolkit(vectorstore_info=vectorstore_info) 32 33agent_executor = create_vectorstore_agent( 34 llm=llm, 35 toolkit=toolkit, 36 verbose=True 37) 38 39prompt = st.text_input('Input your prompt') 40 41if prompt: 42 #response = llm(prompt) 43 response = agent_executor.run(prompt) 44 st.write(response) 45 46 with st.expander('Document Similarity Search'): 47 search = db.similarity_search_with_score(prompt) 48 st.write(search)