Pinecone Vector Store

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

!pip install llama-index>=0.9.31 pinecone-client>=3.0.0

import logging
import sys
import os

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

Creating a Pinecone Index

from pinecone import Pinecone, ServerlessSpec

os.environ[
    "PINECONE_API_KEY"
] = "<Your Pinecone API key, from app.pinecone.io>"
os.environ["OPENAI_API_KEY"] = "sk-..."

api_key = os.environ["PINECONE_API_KEY"]

pc = Pinecone(api_key=api_key)

# delete if needed
# pc.delete_index("quickstart")

# dimensions are for text-embedding-ada-002

pc.create_index(
    name="quickstart",
    dimension=1536,
    metric="euclidean",
    spec=ServerlessSpec(cloud="aws", region="us-west-2"),
)

# If you need to create a PodBased Pinecone index, you could alternatively do this:
#
# from pinecone import Pinecone, PodSpec
#
# pc = Pinecone(api_key='xxx')
#
# pc.create_index(
# 	 name='my-index',
# 	 dimension=1536,
# 	 metric='cosine',
# 	 spec=PodSpec(
# 		 environment='us-east1-gcp',
# 		 pod_type='p1.x1',
# 		 pods=1
# 	 )
# )
#

pinecone_index = pc.Index("quickstart")

Load documents, build the PineconeVectorStore and VectorStoreIndex

from llama_index import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores import PineconeVectorStore
from IPython.display import Markdown, display

Download Data

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

Will not apply HSTS. The HSTS database must be a regular and non-world-writable file.
ERROR: could not open HSTS store at '/home/loganm/.wget-hsts'. HSTS will be disabled.
--2024-01-16 11:56:25--  https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.111.133, 185.199.110.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|: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 0.04s   

2024-01-16 11:56:25 (1.79 MB/s) - ‘data/paul_graham/paul_graham_essay.txt’ saved [75042/75042]

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

# initialize without metadata filter
from llama_index.storage.storage_context import StorageContext

if "OPENAI_API_KEY" not in os.environ:
    raise EnvironmentError(f"Environment variable OPENAI_API_KEY is not set")

vector_store = PineconeVectorStore(pinecone_index=pinecone_index)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context
)

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"

Query Index

May take a minute or so for the index to be ready!

# set Logging to DEBUG for more detailed outputs
query_engine = index.as_query_engine()
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"

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

The author, growing up, worked on writing and programming. They wrote short stories and tried writing programs on an IBM 1401 computer. They later got a microcomputer and started programming more extensively, writing simple games and a word processor.