L1-student.py

#!/usr/bin/env python
# coding: utf-8

# # Lab 1 - Overview of embeddings-based retrieval

# Welcome! Here's a few notes about the Chroma course notebooks.
#  - A number of warnings pop up when running the notebooks. These are normal and can be ignored.
#  - Some operations such as calling an LLM or an opeation using generated data return unpredictable results and so your notebook outputs may differ from the video.
#   
# Enjoy the course!

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from helper_utils import word_wrap


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from pypdf import PdfReader

reader = PdfReader("microsoft_annual_report_2022.pdf")
pdf_texts = [p.extract_text().strip() for p in reader.pages]

# Filter the empty strings
pdf_texts = [text for text in pdf_texts if text]

print(word_wrap(pdf_texts[0]))


# You can view the pdf in your browser [here](./microsoft_annual_report_2022.pdf) if you would like. 

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from langchain.text_splitter import RecursiveCharacterTextSplitter, SentenceTransformersTokenTextSplitter


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character_splitter = RecursiveCharacterTextSplitter(
    separators=["\n\n", "\n", ". ", " ", ""],
    chunk_size=1000,
    chunk_overlap=0
)
character_split_texts = character_splitter.split_text('\n\n'.join(pdf_texts))

print(word_wrap(character_split_texts[10]))
print(f"\nTotal chunks: {len(character_split_texts)}")


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token_splitter = SentenceTransformersTokenTextSplitter(chunk_overlap=0, tokens_per_chunk=256)

token_split_texts = []
for text in character_split_texts:
    token_split_texts += token_splitter.split_text(text)

print(word_wrap(token_split_texts[10]))
print(f"\nTotal chunks: {len(token_split_texts)}")


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import chromadb
from chromadb.utils.embedding_functions import SentenceTransformerEmbeddingFunction

embedding_function = SentenceTransformerEmbeddingFunction()
print(embedding_function([token_split_texts[10]]))


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chroma_client = chromadb.Client()
chroma_collection = chroma_client.create_collection("microsoft_annual_report_2022", embedding_function=embedding_function)

ids = [str(i) for i in range(len(token_split_texts))]

chroma_collection.add(ids=ids, documents=token_split_texts)
chroma_collection.count()


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query = "What was the total revenue?"

results = chroma_collection.query(query_texts=[query], n_results=5)
retrieved_documents = results['documents'][0]

for document in retrieved_documents:
    print(word_wrap(document))
    print('\n')


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import os
import openai
from openai import OpenAI

from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv()) # read local .env file
openai.api_key = os.environ['OPENAI_API_KEY']

openai_client = OpenAI()


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def rag(query, retrieved_documents, model="gpt-3.5-turbo"):
    information = "\n\n".join(retrieved_documents)

    messages = [
        {
            "role": "system",
            "content": "You are a helpful expert financial research assistant. Your users are asking questions about information contained in an annual report."
            "You will be shown the user's question, and the relevant information from the annual report. Answer the user's question using only this information."
        },
        {"role": "user", "content": f"Question: {query}. \n Information: {information}"}
    ]
    
    response = openai_client.chat.completions.create(
        model=model,
        messages=messages,
    )
    content = response.choices[0].message.content
    return content


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output = rag(query=query, retrieved_documents=retrieved_documents)

print(word_wrap(output))


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