Updating Pinecone Index - Unable to set index ID with IngestionPipeline flow

[luisedelgado]

Hey all,

I'm using llama-index in my project to manipulate my Pinecone vector store. Specifically, I'm using the IngestionPipeline flow to insert vectors into the store, and now I'm needing to implement a way to update an index's vectors if the underlying data changes. But the problem is that the Pinecone documentation for both its fetch and delete API makes it clear that the request needs to include the IDs of the records you are planning on fetching/deleting (note this is different from the document ids), and we don't get an option to specify these when using the IngestionPipeline flow. So ideally I'd expect to be able to do the following with llama-index:
1. Insert vectors into Pinecone while specifying the entry ID(s) (preferably with the IngestionPipeline flow)
2. Fetch the full set of vectors that was inserted by passing the ID(s) I used.

Has anyone done this? It seems like a fairly straight-forward scenario for which I can't find any information online :/

Thanks in advance for any info!

[dosubot[bot]]

Hey there, @luisedelgado! I'm here to help you with any bugs, questions, or contributions you have. Let's tackle this issue together!

To insert vectors into Pinecone with specified entry IDs using the IngestionPipeline flow and later fetch the full set of vectors by passing these IDs, you can follow these steps:

1. Insert Vectors with Specified Entry IDs:

   Use the upsert method to insert vectors into Pinecone. Each vector should be associated with a unique ID.

   import pinecone
   
   # Initialize Pinecone
   pinecone.init(api_key='your-api-key', environment='your-environment')
   
   # Connect to the index
   index = pinecone.Index('your-index-name')
   
   # Prepare your vectors and IDs
   vectors = [
       {"id": "vector1", "values": [0.1, 0.2, 0.3]},
       {"id": "vector2", "values": [0.4, 0.5, 0.6]},
       # Add more vectors as needed
   ]
   
   # Upsert vectors into the index
   index.upsert(vectors=vectors)

2. Fetch Vectors by Passing IDs:

   Use the fetch method to retrieve vectors from Pinecone by their IDs.

   # List of IDs to fetch
   ids_to_fetch = ["vector1", "vector2"]
   
   # Fetch vectors from the index
   fetched_vectors = index.fetch(ids=ids_to_fetch)
   
   # Print the fetched vectors
   print(fetched_vectors)

Additionally, if you are using the LlamaIndex framework, you can leverage the PineconeVectorStore class to manage your vectors. Here is an example of how to add and query vectors using this class:

from llama_index.vector_stores.pinecone import PineconeVectorStore
from llama_index.schema import BaseNode

# Initialize PineconeVectorStore
vector_store = PineconeVectorStore.from_params(
    api_key='your-api-key',
    index_name='your-index-name',
    environment='your-environment'
)

# Create nodes with embeddings
nodes = [
    BaseNode(node_id="vector1", embedding=[0.1, 0.2, 0.3]),
    BaseNode(node_id="vector2", embedding=[0.4, 0.5, 0.6]),
    # Add more nodes as needed
]

# Add nodes to the vector store
vector_store.add(nodes)

# Query the vector store
query_result = vector_store.query(VectorStoreQuery(
    query_embedding=[0.1, 0.2, 0.3],
    similarity_top_k=2
))

# Print the query result
print(query_result)

This approach provides a more structured way to manage your vectors and perform queries using the LlamaIndex framework ^[1][2][3][4].

To continue talking to Dosu, mention @dosu.

[luisedelgado]

@dosu is there a way to do this with LlamaIndex' IngestionPipeline?

[dosubot[bot]]

To use LlamaIndex's IngestionPipeline to insert vectors into Pinecone with specified entry IDs and then fetch the full set of vectors using those IDs, you can follow these steps:

1. Insert Vectors into Pinecone:

   Use the add method of the PineconeVectorStore class to insert vectors. This method takes a list of BaseNode objects, each containing an embedding and metadata. The method returns a list of IDs for the inserted vectors.

   from llama_index.core.schema import TextNode
   from llama_index.vector_stores.pinecone import PineconeVectorStore
   import pinecone
   import os
   
   # Initialize Pinecone
   api_key = os.environ["PINECONE_API_KEY"]
   environment = os.environ["PINECONE_ENVIRONMENT"]
   pinecone.init(api_key=api_key, environment=environment)
   index_name = "your_index_name"
   pinecone.create_index(index_name, dimension=1536, metric="euclidean", pod_type="p1")
   pinecone_index = pinecone.Index(index_name)
   
   # Initialize PineconeVectorStore
   pinecone_vector_store = PineconeVectorStore(pinecone_index=pinecone_index)
   
   # Assuming you have a list of nodes with embeddings
   nodes = [
       TextNode(node_id="id1", text="Sample text 1", embedding=[0.1, 0.2, 0.3]),
       TextNode(node_id="id2", text="Sample text 2", embedding=[0.4, 0.5, 0.6])
   ]
   
   # Insert nodes into Pinecone
   ids = pinecone_vector_store.add(nodes)
   print("Inserted IDs:", ids)

2. Fetch Vectors Using IDs:

   Use the query method of the PineconeVectorStore class to fetch vectors using their IDs. This method returns a VectorStoreQueryResult object containing the nodes, their similarities, and their IDs.

   from llama_index.core.vector_stores.types import VectorStoreQuery
   
   # Create a query object with the IDs of the vectors you want to fetch
   query = VectorStoreQuery(ids=["id1", "id2"])
   
   # Fetch the vectors from Pinecone
   result = pinecone_vector_store.query(query)
   print("Fetched Nodes:", result.nodes)
   print("Fetched IDs:", result.ids)

Here is the complete code:

from llama_index.core.schema import TextNode
from llama_index.vector_stores.pinecone import PineconeVectorStore
from llama_index.core.vector_stores.types import VectorStoreQuery
import pinecone
import os

# Initialize Pinecone
api_key = os.environ["PINECONE_API_KEY"]
environment = os.environ["PINECONE_ENVIRONMENT"]
pinecone.init(api_key=api_key, environment=environment)
index_name = "your_index_name"
pinecone.create_index(index_name, dimension=1536, metric="euclidean", pod_type="p1")
pinecone_index = pinecone.Index(index_name)

# Initialize PineconeVectorStore
pinecone_vector_store = PineconeVectorStore(pinecone_index=pinecone_index)

# Assuming you have a list of nodes with embeddings
nodes = [
    TextNode(node_id="id1", text="Sample text 1", embedding=[0.1, 0.2, 0.3]),
    TextNode(node_id="id2", text="Sample text 2", embedding=[0.4, 0.5, 0.6])
]

# Insert nodes into Pinecone
ids = pinecone_vector_store.add(nodes)
print("Inserted IDs:", ids)

# Create a query object with the IDs of the vectors you want to fetch
query = VectorStoreQuery(ids=["id1", "id2"])

# Fetch the vectors from Pinecone
result = pinecone_vector_store.query(query)
print("Fetched Nodes:", result.nodes)
print("Fetched IDs:", result.ids)

This code demonstrates how to insert vectors into Pinecone with specified entry IDs and then fetch the full set of vectors using those IDs ^[1][2][3][4].

To continue talking to Dosu, mention @dosu.