Simple Vector Store¶

If you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.

In [ ]:

!pip install llama-index

!pip install llama-index

In [ ]:

import os
import openai

os.environ["OPENAI_API_KEY"] = "sk-..."
openai.api_key = os.environ["OPENAI_API_KEY"]

import os import openai os.environ["OPENAI_API_KEY"] = "sk-..." openai.api_key = os.environ["OPENAI_API_KEY"]

Load documents, build the VectorStoreIndex¶

In [ ]:

import nltk

nltk.download("stopwords")

import nltk nltk.download("stopwords")

[nltk_data] Downloading package stopwords to
[nltk_data]     /Users/jerryliu/nltk_data...
[nltk_data]   Package stopwords is already up-to-date!

Out[ ]:

True

In [ ]:

import llama_index.core

import llama_index.core

[nltk_data] Downloading package stopwords to /Users/jerryliu/Programmi
[nltk_data]     ng/gpt_index/.venv/lib/python3.10/site-
[nltk_data]     packages/llama_index/core/_static/nltk_cache...
[nltk_data]   Unzipping corpora/stopwords.zip.
[nltk_data] Downloading package punkt to /Users/jerryliu/Programming/g
[nltk_data]     pt_index/.venv/lib/python3.10/site-
[nltk_data]     packages/llama_index/core/_static/nltk_cache...
[nltk_data]   Unzipping tokenizers/punkt.zip.

In [ ]:

import logging
import sys

logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))

from llama_index.core import (
    VectorStoreIndex,
    SimpleDirectoryReader,
    load_index_from_storage,
    StorageContext,
)
from IPython.display import Markdown, display

import logging import sys logging.basicConfig(stream=sys.stdout, level=logging.INFO) logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout)) from llama_index.core import ( VectorStoreIndex, SimpleDirectoryReader, load_index_from_storage, StorageContext, ) from IPython.display import Markdown, display

Download Data

In [ ]:

!mkdir -p 'data/paul_graham/'
!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'

!mkdir -p 'data/paul_graham/' !wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'

--2024-02-12 13:21:13--  https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.111.133, 185.199.108.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 75042 (73K) [text/plain]
Saving to: ‘data/paul_graham/paul_graham_essay.txt’

data/paul_graham/pa 100%[===================>]  73.28K  --.-KB/s    in 0.02s   

2024-02-12 13:21:13 (4.76 MB/s) - ‘data/paul_graham/paul_graham_essay.txt’ saved [75042/75042]

In [ ]:

# load documents
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()

# load documents documents = SimpleDirectoryReader("./data/paul_graham/").load_data()

In [ ]:

index = VectorStoreIndex.from_documents(documents)

index = VectorStoreIndex.from_documents(documents)

INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"

In [ ]:

# save index to disk
index.set_index_id("vector_index")
index.storage_context.persist("./storage")

# save index to disk index.set_index_id("vector_index") index.storage_context.persist("./storage")

In [ ]:

# rebuild storage context
storage_context = StorageContext.from_defaults(persist_dir="storage")
# load index
index = load_index_from_storage(storage_context, index_id="vector_index")

# rebuild storage context storage_context = StorageContext.from_defaults(persist_dir="storage") # load index index = load_index_from_storage(storage_context, index_id="vector_index")

INFO:llama_index.core.indices.loading:Loading indices with ids: ['vector_index']
Loading indices with ids: ['vector_index']

Query Index¶

In [ ]:

# set Logging to DEBUG for more detailed outputs
query_engine = index.as_query_engine(response_mode="tree_summarize")
response = query_engine.query("What did the author do growing up?")

# set Logging to DEBUG for more detailed outputs query_engine = index.as_query_engine(response_mode="tree_summarize") response = query_engine.query("What did the author do growing up?")

INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"

In [ ]:

display(Markdown(f"<b>{response}</b>"))

display(Markdown(f"{response}"))

The author wrote short stories and also worked on programming, specifically on an IBM 1401 computer in 9th grade. They later transitioned to working with microcomputers, starting with a kit-built microcomputer and eventually acquiring a TRS-80. They wrote simple games, a program to predict rocket heights, and even a word processor. Although the author initially planned to study philosophy in college, they eventually switched to studying AI.

Query Index with SVM/Linear Regression

Use Karpathy's SVM-based approach. Set query as positive example, all other datapoints as negative examples, and then fit a hyperplane.

In [ ]:

query_modes = [
    "svm",
    "linear_regression",
    "logistic_regression",
]
for query_mode in query_modes:
    # set Logging to DEBUG for more detailed outputs
    query_engine = index.as_query_engine(vector_store_query_mode=query_mode)
    response = query_engine.query("What did the author do growing up?")
    print(f"Query mode: {query_mode}")
    display(Markdown(f"<b>{response}</b>"))

query_modes = [ "svm", "linear_regression", "logistic_regression", ] for query_mode in query_modes: # set Logging to DEBUG for more detailed outputs query_engine = index.as_query_engine(vector_store_query_mode=query_mode) response = query_engine.query("What did the author do growing up?") print(f"Query mode: {query_mode}") display(Markdown(f"{response}"))

INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"

/Users/jerryliu/Programming/gpt_index/.venv/lib/python3.10/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.
  warnings.warn(

INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
Query mode: svm

The author wrote short stories and also worked on programming, specifically on an IBM 1401 computer in 9th grade. They later got a microcomputer and started programming on it, writing simple games and a word processor. They initially planned to study philosophy in college but ended up switching to AI.

INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"

/Users/jerryliu/Programming/gpt_index/.venv/lib/python3.10/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.
  warnings.warn(

INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
Query mode: linear_regression

The author wrote short stories and also worked on programming, specifically on an IBM 1401 computer in 9th grade. They later got a microcomputer and started programming on it, writing simple games and a word processor. They initially planned to study philosophy in college but ended up switching to AI.

INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"

/Users/jerryliu/Programming/gpt_index/.venv/lib/python3.10/site-packages/sklearn/svm/_classes.py:31: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.
  warnings.warn(

INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
Query mode: logistic_regression

The author wrote short stories and also worked on programming, specifically on an IBM 1401 computer in 9th grade. They later got a microcomputer and started programming on it, writing simple games and a word processor. They initially planned to study philosophy in college but eventually switched to AI.

In [ ]:

display(Markdown(f"<b>{response}</b>"))

display(Markdown(f"{response}"))

The author wrote short stories and also worked on programming, specifically on an IBM 1401 computer in 9th grade. They later got a microcomputer and started programming on it, writing simple games and a word processor. They initially planned to study philosophy in college but eventually switched to AI.

In [ ]:

print(response.source_nodes[0].text)

print(response.source_nodes[0].text)

What I Worked On

February 2021

Before college the two main things I worked on, outside of school, were writing and programming. I didn't write essays. I wrote what beginning writers were supposed to write then, and probably still are: short stories. My stories were awful. They had hardly any plot, just characters with strong feelings, which I imagined made them deep.

The first programs I tried writing were on the IBM 1401 that our school district used for what was then called "data processing." This was in 9th grade, so I was 13 or 14. The school district's 1401 happened to be in the basement of our junior high school, and my friend Rich Draves and I got permission to use it. It was like a mini Bond villain's lair down there, with all these alien-looking machines — CPU, disk drives, printer, card reader — sitting up on a raised floor under bright fluorescent lights.

The language we used was an early version of Fortran. You had to type programs on punch cards, then stack them in the card reader and press a button to load the program into memory and run it. The result would ordinarily be to print something on the spectacularly loud printer.

I was puzzled by the 1401. I couldn't figure out what to do with it. And in retrospect there's not much I could have done with it. The only form of input to programs was data stored on punched cards, and I didn't have any data stored on punched cards. The only other option was to do things that didn't rely on any input, like calculate approximations of pi, but I didn't know enough math to do anything interesting of that type. So I'm not surprised I can't remember any programs I wrote, because they can't have done much. My clearest memory is of the moment I learned it was possible for programs not to terminate, when one of mine didn't. On a machine without time-sharing, this was a social as well as a technical error, as the data center manager's expression made clear.

With microcomputers, everything changed. Now you could have a computer sitting right in front of you, on a desk, that could respond to your keystrokes as it was running instead of just churning through a stack of punch cards and then stopping. [1]

The first of my friends to get a microcomputer built it himself. It was sold as a kit by Heathkit. I remember vividly how impressed and envious I felt watching him sitting in front of it, typing programs right into the computer.

Computers were expensive in those days and it took me years of nagging before I convinced my father to buy one, a TRS-80, in about 1980. The gold standard then was the Apple II, but a TRS-80 was good enough. This was when I really started programming. I wrote simple games, a program to predict how high my model rockets would fly, and a word processor that my father used to write at least one book. There was only room in memory for about 2 pages of text, so he'd write 2 pages at a time and then print them out, but it was a lot better than a typewriter.

Though I liked programming, I didn't plan to study it in college. In college I was going to study philosophy, which sounded much more powerful. It seemed, to my naive high school self, to be the study of the ultimate truths, compared to which the things studied in other fields would be mere domain knowledge. What I discovered when I got to college was that the other fields took up so much of the space of ideas that there wasn't much left for these supposed ultimate truths. All that seemed left for philosophy were edge cases that people in other fields felt could safely be ignored.

I couldn't have put this into words when I was 18. All I knew at the time was that I kept taking philosophy courses and they kept being boring. So I decided to switch to AI.

AI was in the air in the mid 1980s, but there were two things especially that made me want to work on it: a novel by Heinlein called The Moon is a Harsh Mistress, which featured an intelligent computer called Mike, and a PBS documentary that showed Terry Winograd using SHRDLU. I haven't tried rereading The Moon is a Harsh Mistress, so I don't know how well it has aged, but when I read it I was drawn entirely into its world. It seemed only a matter of time before we'd have Mike, and when I saw Winograd using SHRDLU, it seemed like that time would be a few years at most.

Query Index with custom embedding string

In [ ]:

from llama_index.core import QueryBundle

from llama_index.core import QueryBundle

In [ ]:

query_bundle = QueryBundle(
    query_str="What did the author do growing up?",
    custom_embedding_strs=["The author grew up painting."],
)
query_engine = index.as_query_engine()
response = query_engine.query(query_bundle)

query_bundle = QueryBundle( query_str="What did the author do growing up?", custom_embedding_strs=["The author grew up painting."], ) query_engine = index.as_query_engine() response = query_engine.query(query_bundle)

INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"

In [ ]:

display(Markdown(f"<b>{response}</b>"))

display(Markdown(f"{response}"))

The context does not provide information about what the author did growing up.

Use maximum marginal relevance

Instead of ranking vectors purely by similarity, adds diversity to the documents by penalizing documents similar to ones that have already been found based on MMR . A lower mmr_treshold increases diversity.

In [ ]:

query_engine = index.as_query_engine(
    vector_store_query_mode="mmr", vector_store_kwargs={"mmr_threshold": 0.2}
)
response = query_engine.query("What did the author do growing up?")

query_engine = index.as_query_engine( vector_store_query_mode="mmr", vector_store_kwargs={"mmr_threshold": 0.2} ) response = query_engine.query("What did the author do growing up?")

INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"

Get Sources¶

In [ ]:

print(response.get_formatted_sources())

print(response.get_formatted_sources())

> Source (Doc id: c4118521-8f55-4a4d-819a-2db546b6491e): What I Worked On

February 2021

Before college the two main things I worked on, outside of schoo...

> Source (Doc id: 74f77233-e4fe-4389-9820-76dd9f765af6): Which meant being easy to use and inexpensive. It was lucky for us that we were poor, because tha...

Query Index with Filters¶

We can also filter our queries using metadata

In [ ]:

from llama_index.core import Document

doc = Document(text="target", metadata={"tag": "target"})

index.insert(doc)

from llama_index.core import Document doc = Document(text="target", metadata={"tag": "target"}) index.insert(doc)

INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"

In [ ]:

from llama_index.core.vector_stores import ExactMatchFilter, MetadataFilters

filters = MetadataFilters(
    filters=[ExactMatchFilter(key="tag", value="target")]
)

retriever = index.as_retriever(
    similarity_top_k=20,
    filters=filters,
)

source_nodes = retriever.retrieve("What did the author do growing up?")

from llama_index.core.vector_stores import ExactMatchFilter, MetadataFilters filters = MetadataFilters( filters=[ExactMatchFilter(key="tag", value="target")] ) retriever = index.as_retriever( similarity_top_k=20, filters=filters, ) source_nodes = retriever.retrieve("What did the author do growing up?")

INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"

In [ ]:

# retrieves only our target node, even though we set the top k to 20
print(len(source_nodes))

# retrieves only our target node, even though we set the top k to 20 print(len(source_nodes))

1

In [ ]:

print(source_nodes[0].text)
print(source_nodes[0].metadata)

print(source_nodes[0].text) print(source_nodes[0].metadata)

target
{'tag': 'target'}