Pinecone Vector Store - Hybrid Search¶

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

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%pip install llama-index-vector-stores-pinecone "transformers[torch]"

%pip install llama-index-vector-stores-pinecone "transformers[torch]"

Creating a Pinecone Index¶

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from pinecone import Pinecone, ServerlessSpec

from pinecone import Pinecone, ServerlessSpec

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import os

os.environ["PINECONE_API_KEY"] = "..."
os.environ["OPENAI_API_KEY"] = "sk-..."

api_key = os.environ["PINECONE_API_KEY"]

pc = Pinecone(api_key=api_key)

import os os.environ["PINECONE_API_KEY"] = "..." os.environ["OPENAI_API_KEY"] = "sk-..." api_key = os.environ["PINECONE_API_KEY"] pc = Pinecone(api_key=api_key)

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

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

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# dimensions are for text-embedding-ada-002
# NOTE: needs dotproduct for hybrid search

pc.create_index(
    name="quickstart",
    dimension=1536,
    metric="dotproduct",
    spec=ServerlessSpec(cloud="aws", region="us-east-1"),
)

# 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
# 	 )
# )
#

# dimensions are for text-embedding-ada-002 # NOTE: needs dotproduct for hybrid search pc.create_index( name="quickstart", dimension=1536, metric="dotproduct", spec=ServerlessSpec(cloud="aws", region="us-east-1"), ) # 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 # ) # ) #

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pinecone_index = pc.Index("quickstart")

pinecone_index = pc.Index("quickstart")

Download Data

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!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'

Load documents, build the PineconeVectorStore¶

When add_sparse_vector=True, the PineconeVectorStore will compute sparse vectors for each document.

By default, it is using simple token frequency for the sparse vectors. But, you can also specify a custom sparse embedding model.

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

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

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

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

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# set add_sparse_vector=True to compute sparse vectors during upsert
from llama_index.core 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,
    add_sparse_vector=True,
)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context
)

# set add_sparse_vector=True to compute sparse vectors during upsert from llama_index.core 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, add_sparse_vector=True, ) storage_context = StorageContext.from_defaults(vector_store=vector_store) index = VectorStoreIndex.from_documents( documents, storage_context=storage_context )

huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
	- Avoid using `tokenizers` before the fork if possible
	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)

Upserted vectors:   0%|          | 0/22 [00:00<?, ?it/s]

Query Index¶

May need to wait a minute or two for the index to be ready

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# set Logging to DEBUG for more detailed outputs
query_engine = index.as_query_engine(vector_store_query_mode="hybrid")
response = query_engine.query("What happened at Viaweb?")

# set Logging to DEBUG for more detailed outputs query_engine = index.as_query_engine(vector_store_query_mode="hybrid") response = query_engine.query("What happened at Viaweb?")

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

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

Paul Graham started Viaweb because he needed money. As the company grew, he realized he didn't want to run a big company and decided to build a subset of the vision as an open source project. Eventually, Viaweb was bought by Yahoo in the summer of 1998, which was a huge relief for Paul Graham.

Changing the sparse embedding model¶

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%pip install llama-index-sparse-embeddings-fastembed

%pip install llama-index-sparse-embeddings-fastembed

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# Clear the vector store
vector_store.clear()

# Clear the vector store vector_store.clear()

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from llama_index.sparse_embeddings.fastembed import FastEmbedSparseEmbedding

sparse_embedding_model = FastEmbedSparseEmbedding(
    model_name="prithivida/Splade_PP_en_v1"
)

vector_store = PineconeVectorStore(
    pinecone_index=pinecone_index,
    add_sparse_vector=True,
    sparse_embedding_model=sparse_embedding_model,
)

from llama_index.sparse_embeddings.fastembed import FastEmbedSparseEmbedding sparse_embedding_model = FastEmbedSparseEmbedding( model_name="prithivida/Splade_PP_en_v1" ) vector_store = PineconeVectorStore( pinecone_index=pinecone_index, add_sparse_vector=True, sparse_embedding_model=sparse_embedding_model, )

Fetching 5 files:   0%|          | 0/5 [00:00<?, ?it/s]

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index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context
)

index = VectorStoreIndex.from_documents( documents, storage_context=storage_context )

Upserted vectors:   0%|          | 0/22 [00:00<?, ?it/s]

Wait a mininute for things to upload..

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response = query_engine.query("What happened at Viaweb?")
display(Markdown(f"<b>{response}</b>"))

response = query_engine.query("What happened at Viaweb?") display(Markdown(f"{response}"))

Paul Graham started Viaweb because he needed money. He recruited a team to work on building software and services, with a focus on creating an application builder and network infrastructure. However, halfway through the summer, Paul realized he didn't want to run a big company and decided to shift his focus to building a subset of the project as an open source project. This led to the development of a new dialect of Lisp called Arc. Ultimately, Viaweb was sold to Yahoo in the summer of 1998, providing relief to Paul Graham and allowing him to transition to a new phase in his life.