【LangChain系列】第四节：向量数据库与嵌入

import os
from langchain_openai import OpenAI
from dotenv import load_dotenv, find_dotenv
from langchain_community.document_loaders.pdf import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter

_ = load_dotenv(find_dotenv())
client = OpenAI(
    api_key=os.getenv("OPENAI_API_KEY")
)
loaders = [
    PyPDFLoader("docs/cs229_lectures/MachineLearning-Lecture01.pdf"), # Duplicate documents on purpose - messy data
    PyPDFLoader("docs/cs229_lectures/MachineLearning-Lecture01.pdf"),
    PyPDFLoader("docs/cs229_lectures/MachineLearning-Lecture02.pdf"),
    PyPDFLoader("docs/cs229_lectures/MachineLearning-Lecture03.pdf"),
]
docs = []
for loader in loaders:
    docs.extend(loader.load())
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1500, chunk_overlap=150)
splits = text_splitter.split_documents(docs)
print("Length of splits: ", len(splits)) # Length of splits:  209

from langchain_openai import OpenAIEmbeddings
import numpy as np

embedding = OpenAIEmbeddings()
sentence1 = "i like dogs"
sentence2 = "i like canines"
sentence3 = "the weather is ugly outside"
embedding1 = embedding.embed_query(sentence1)
embedding2 = embedding.embed_query(sentence2)
embedding3 = embedding.embed_query(sentence3)
print(np.dot(embedding1, embedding2))  # 0.9631227500523609
print(np.dot(embedding1, embedding3))  # 0.7703257495981695
print(np.dot(embedding2, embedding3))  # 0.7591627401108028

from langchain.vectorstores import Chroma

persist_directory = "docs/chroma/"
# this code is only for ipynb files
# !rm -rf ./docs/chroma  # remove old database files if any 
vectordb = Chroma.from_documents(
    documents=splits, embedding=embedding, persist_directory=persist_directory
)
print(vectordb._collection.count())  # 209

question = "is there an email i can ask for help"
docs = vectordb.similarity_search(question, k=3)
print("Length of context docs: ", len(docs))  # 3
print(docs[0].page_content)

cs229-qa@cs.stanford.edu. This goes to an acc ount that's read by all the TAs and me. So 
rather than sending us email individually, if you send email to this account, it will 
actually let us get back to you maximally quickly with answers to your questions.  
If you're asking questions about homework probl ems, please say in the subject line which 
assignment and which question the email refers to, since that will also help us to route 
your question to the appropriate TA or to me  appropriately and get the response back to 
you quickly.  
Let's see. Skipping ahead — let's see — for homework, one midterm, one open and term 
project. Notice on the honor code. So one thi ng that I think will help you to succeed and 
do well in this class and even help you to enjoy this cla ss more is if you form a study 
group.  
So start looking around where you' re sitting now or at the end of class today, mingle a 
little bit and get to know your classmates. I strongly encourage you to form study groups 
and sort of have a group of people to study with and have a group of your fellow students 
to talk over these concepts with. You can also  post on the class news group if you want to 
use that to try to form a study group.  
But some of the problems sets in this cla ss are reasonably difficult.  People that have 
taken the class before may tell you they were very difficult. And just I bet it would be 
more fun for you, and you'd probably have a be tter learning experience if you form a

vectordb.persist()

question = "what did they say about matlab?"
docs = vectordb.similarity_search(question, k=5)
print(docs[0].page_content)
# Document(page_content='those homeworks will be done in either MATLA B or in Octave, which is sort of — I \nknow some people call it a free ve rsion of MATLAB, which it sort  of is, sort of isn\'t.  \nSo I guess for those of you that haven\'t s een MATLAB before, and I know most of you \nhave, MATLAB is I guess part of the programming language that makes it very easy to write codes using matrices, to write code for numerical routines, to move data around, to \nplot data. And it\'s sort of an extremely easy to  learn tool to use for implementing a lot of \nlearning algorithms.  \nAnd in case some of you want to work on your  own home computer or something if you \ndon\'t have a MATLAB license, for the purposes of  this class, there\'s also — [inaudible] \nwrite that down [inaudible] MATLAB — there\' s also a software package called Octave \nthat you can download for free off the Internet. And it has somewhat fewer features than MATLAB, but it\'s free, and for the purposes of  this class, it will work for just about \neverything.  \nSo actually I, well, so yeah, just a side comment for those of you that haven\'t seen \nMATLAB before I guess, once a colleague of mine at a different university, not at \nStanford, actually teaches another machine l earning course. He\'s taught it for many years. \nSo one day, he was in his office, and an old student of his from, lik e, ten years ago came \ninto his office and he said, "Oh, professo r, professor, thank you so much for your', metadata={'page': 8, 'source': 'docs/cs229_lectures/MachineLearning-Lecture01.pdf'})
print(docs[1].page_content)
# Document(page_content='those homeworks will be done in either MATLA B or in Octave, which is sort of — I \nknow some people call it a free ve rsion of MATLAB, which it sort  of is, sort of isn\'t.  \nSo I guess for those of you that haven\'t s een MATLAB before, and I know most of you \nhave, MATLAB is I guess part of the programming language that makes it very easy to write codes using matrices, to write code for numerical routines, to move data around, to \nplot data. And it\'s sort of an extremely easy to  learn tool to use for implementing a lot of \nlearning algorithms.  \nAnd in case some of you want to work on your  own home computer or something if you \ndon\'t have a MATLAB license, for the purposes of  this class, there\'s also — [inaudible] \nwrite that down [inaudible] MATLAB — there\' s also a software package called Octave \nthat you can download for free off the Internet. And it has somewhat fewer features than MATLAB, but it\'s free, and for the purposes of  this class, it will work for just about \neverything.  \nSo actually I, well, so yeah, just a side comment for those of you that haven\'t seen \nMATLAB before I guess, once a colleague of mine at a different university, not at \nStanford, actually teaches another machine l earning course. He\'s taught it for many years. \nSo one day, he was in his office, and an old student of his from, lik e, ten years ago came \ninto his office and he said, "Oh, professo r, professor, thank you so much for your', metadata={'page': 8, 'source': 'docs/cs229_lectures/MachineLearning-Lecture01.pdf'})

question = "what did they say about regression in the third lecture?"
docs = vectordb.similarity_search(question, k=5)

for doc in docs:
    print(doc.metadata)
# {'page': 0, 'source': 'docs/cs229_lectures/MachineLearning-Lecture03.pdf'}
# {'page': 14, 'source': 'docs/cs229_lectures/MachineLearning-Lecture03.pdf'}
# {'page': 0, 'source': 'docs/cs229_lectures/MachineLearning-Lecture02.pdf'}
# {'page': 6, 'source': 'docs/cs229_lectures/MachineLearning-Lecture03.pdf'}
# {'page': 8, 'source': 'docs/cs229_lectures/MachineLearning-Lecture01.pdf'}
print(docs[4].page_content)

into his office and he said, "Oh, professo r, professor, thank you so much for your 
machine learning class. I learned so much from it. There's this stuff that I learned in your 
class, and I now use every day. And it's help ed me make lots of money, and here's a 
picture of my big house."  
So my friend was very excited. He said, "W ow. That's great. I'm glad to hear this 
machine learning stuff was actually useful. So what was it that you learned? Was it 
logistic regression? Was it the PCA? Was it the data ne tworks? What was it that you 
learned that was so helpful?" And the student said, "Oh, it was the MATLAB."  
So for those of you that don't know MATLAB yet, I hope you do learn it. It's not hard, 
and we'll actually have a short MATLAB tutori al in one of the discussion sections for 
those of you that don't know it.  
Okay. The very last piece of logistical th ing is the discussion s ections. So discussion 
sections will be taught by the TAs, and atte ndance at discussion sections is optional, 
although they'll also be recorded and televi sed. And we'll use the discussion sections 
mainly for two things. For the next two or th ree weeks, we'll use the discussion sections 
to go over the prerequisites to this class or if some of you haven't seen probability or 
statistics for a while or maybe algebra, we'll go over those in the discussion sections as a 
refresher for those of you that want one.