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LlamaIndex is a data framework for your LLM applications
https://docs.llamaindex.ai
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I wonder how to use llama_index to retrieve the Milvus collection after it is created and indexed using the MilvusVectorStore. #14790

Closed hingkan closed 1 month ago

hingkan commented 1 month ago

In the following code, the index can be retrieved using llama_index. ` vector_store = MilvusVectorStore(uri="http://localhost:19530/", dim=1792, overwrite=True, index_config={ 'metric_type': 'IP', 'index_type': "FLAT", })

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

But how should llama index load the Milvus index?My idea is as follows: vector_store = MilvusVectorStore(uri="http://localhost:19530/", dim=1792, overwrite=False, index_config={ 'metric_type': 'IP', 'index_type': "FLAT", }) storage_context = StorageContext.from_defaults(vector_store=vector_store) # rebuild storage context index = load_index_from_storage(storage_context) # load index But there was an unexpected error. ERROR:ValueError: No index in storage context, check if you specified the right persist_dir.

I would like to know how to use llama_index to retrieve Milvus' existing data.

dosubot[bot] commented 1 month ago

To retrieve data from an existing Milvus index using llama_index, you need to ensure that the MilvusVectorStore is correctly initialized and that the StorageContext is properly set up to load the index. Here is how you can do it:

  1. Initialize the MilvusVectorStore with the appropriate parameters.
  2. Create a StorageContext with the vector_store.
  3. Use the load_index_from_storage function to load the index from the storage context.

Here is an example code snippet:

from llama_index.indices.loading import load_index_from_storage
from llama_index.storage import StorageContext
from llama_index.vector_stores.milvus import MilvusVectorStore

# Initialize the MilvusVectorStore
vector_store = MilvusVectorStore(
    uri="http://localhost:19530",  # replace with your Milvus instance URI
    dim=1792,  # replace with the dimension of your embeddings
    overwrite=False,  # set to False to use an existing collection
    index_config={
        'metric_type': 'IP',
        'index_type': "FLAT",
    }
)

# Create a StorageContext with the vector store
storage_context = StorageContext.from_defaults(vector_store=vector_store)

# Load the index from the storage context
index = load_index_from_storage(storage_context=storage_context)

# Now you can use the index to retrieve data
query_vector = [0.1, 0.2, 0.3]  # Example query vector
documents = index.as_retriever().retrieve(query_vector)

# Print the retrieved documents
for doc in documents:
    print(f"Document ID: {doc.id_}, Text: {doc.text}")

This code will connect to the Milvus instance, load the existing index, and allow you to retrieve documents using a query vector. Make sure to replace the uri, dim, and query_vector with your actual values [1][2].

To continue talking to Dosu, mention @dosu.

hingkan commented 1 month ago

To retrieve data from an existing Milvus index using llama_index, you need to ensure that the MilvusVectorStore is correctly initialized and that the StorageContext is properly set up to load the index. Here is how you can do it:

  1. Initialize the MilvusVectorStore with the appropriate parameters.
  2. Create a StorageContext with the vector_store.
  3. Use the load_index_from_storage function to load the index from the storage context.

Here is an example code snippet:

from llama_index.indices.loading import load_index_from_storage
from llama_index.storage import StorageContext
from llama_index.vector_stores.milvus import MilvusVectorStore

# Initialize the MilvusVectorStore
vector_store = MilvusVectorStore(
    uri="http://localhost:19530",  # replace with your Milvus instance URI
    dim=1792,  # replace with the dimension of your embeddings
    overwrite=False,  # set to False to use an existing collection
    index_config={
        'metric_type': 'IP',
        'index_type': "FLAT",
    }
)

# Create a StorageContext with the vector store
storage_context = StorageContext.from_defaults(vector_store=vector_store)

# Load the index from the storage context
index = load_index_from_storage(storage_context=storage_context)

# Now you can use the index to retrieve data
query_vector = [0.1, 0.2, 0.3]  # Example query vector
documents = index.as_retriever().retrieve(query_vector)

# Print the retrieved documents
for doc in documents:
    print(f"Document ID: {doc.id_}, Text: {doc.text}")

This code will connect to the Milvus instance, load the existing index, and allow you to retrieve documents using a query vector. Make sure to replace the uri, dim, and query_vector with your actual values [1][2].

To continue talking to Dosu, mention @dosu.

But 'load_index_from_storage()' will report an error, error message 'ValueError: No index in storage context, check if you specified the right persist_dir.'

hingkan commented 1 month ago

Does llama_index only define Milvus' join and index creation, but not Milvus' data loading and corresponding index generation?

dosubot[bot] commented 1 month ago

To correctly load and retrieve data from an existing Milvus index using llama_index, you need to ensure that the index has been properly persisted and that the StorageContext is correctly configured to point to the right storage directory. Here is a more detailed approach:

  1. Persist the Index: Ensure that the index is saved to a persistent storage directory.
  2. Load the Index: Use the load_index_from_storage function to load the index from the storage context.

Here is an example of how to persist and then load the index:

Persisting the Index

from llama_index.vector_stores.milvus import MilvusVectorStore
from llama_index.core import StorageContext, VectorStoreIndex, SimpleDirectoryReader

# Initialize the MilvusVectorStore
vector_store = MilvusVectorStore(
    uri="http://localhost:19530",  # Replace with your Milvus instance URI
    dim=1792,  # Replace with the dimension of your embeddings
    overwrite=True,
    index_config={
        'metric_type': 'IP',
        'index_type': "FLAT",
    }
)

# Create a StorageContext with the vector store
storage_context = StorageContext.from_defaults(vector_store=vector_store)

# Load documents
documents = SimpleDirectoryReader(
    input_files=["./data/your_data_file.txt"]
).load_data()

# Create an index over the documents
index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context
)

# Persist the index
storage_context.persist(persist_dir="./storage")

Loading the Index

from llama_index.indices.loading import load_index_from_storage
from llama_index.storage import StorageContext
from llama_index.vector_stores.milvus import MilvusVectorStore

# Initialize the MilvusVectorStore
vector_store = MilvusVectorStore(
    uri="http://localhost:19530",  # Replace with your Milvus instance URI
    dim=1792,  # Replace with the dimension of your embeddings
    overwrite=False,
    index_config={
        'metric_type': 'IP',
        'index_type': "FLAT",
    }
)

# Create a StorageContext with the vector store
storage_context = StorageContext.from_defaults(vector_store=vector_store, persist_dir="./storage")

# Load the index from the storage context
index = load_index_from_storage(storage_context=storage_context)

# Now you can use the index to retrieve data
query_vector = [0.1, 0.2, 0.3]  # Example query vector
documents = index.as_retriever().retrieve(query_vector)

# Print the retrieved documents
for doc in documents:
    print(f"Document ID: {doc.id_}, Text: {doc.text}")

This approach ensures that the index is first persisted to a directory and then loaded from that directory. Make sure to replace the uri, dim, and query_vector with your actual values [1][2].

To continue talking to Dosu, mention @dosu.

hingkan commented 1 month ago

To correctly load and retrieve data from an existing Milvus index using , you need to ensure that the index has been properly persisted and that the is correctly configured to point to the right storage directory. Here is a more detailed approach:llama_index``StorageContext

  1. Persist the Index: Ensure that the index is saved to a persistent storage directory.
  2. Load the Index: Use the function to load the index from the storage context.load_index_from_storage

Here is an example of how to persist and then load the index:

Persisting the Index

from llama_index.vector_stores.milvus import MilvusVectorStore
from llama_index.core import StorageContext, VectorStoreIndex, SimpleDirectoryReader

# Initialize the MilvusVectorStore
vector_store = MilvusVectorStore(
    uri="http://localhost:19530",  # Replace with your Milvus instance URI
    dim=1792,  # Replace with the dimension of your embeddings
    overwrite=True,
    index_config={
        'metric_type': 'IP',
        'index_type': "FLAT",
    }
)

# Create a StorageContext with the vector store
storage_context = StorageContext.from_defaults(vector_store=vector_store)

# Load documents
documents = SimpleDirectoryReader(
    input_files=["./data/your_data_file.txt"]
).load_data()

# Create an index over the documents
index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context
)

# Persist the index
storage_context.persist(persist_dir="./storage")

Loading the Index

from llama_index.indices.loading import load_index_from_storage
from llama_index.storage import StorageContext
from llama_index.vector_stores.milvus import MilvusVectorStore

# Initialize the MilvusVectorStore
vector_store = MilvusVectorStore(
    uri="http://localhost:19530",  # Replace with your Milvus instance URI
    dim=1792,  # Replace with the dimension of your embeddings
    overwrite=False,
    index_config={
        'metric_type': 'IP',
        'index_type': "FLAT",
    }
)

# Create a StorageContext with the vector store
storage_context = StorageContext.from_defaults(vector_store=vector_store, persist_dir="./storage")

# Load the index from the storage context
index = load_index_from_storage(storage_context=storage_context)

# Now you can use the index to retrieve data
query_vector = [0.1, 0.2, 0.3]  # Example query vector
documents = index.as_retriever().retrieve(query_vector)

# Print the retrieved documents
for doc in documents:
    print(f"Document ID: {doc.id_}, Text: {doc.text}")

This approach ensures that the index is first persisted to a directory and then loaded from that directory. Make sure to replace the , , and with your actual values uri``dim``query_vector[1][2].

To continue talking to Dosu, mention @dosu.

If you use 'storage_context.persist(persist_dir="./storage")' to save the index, isn't loading similar to disk perpetuation?

hingkan commented 1 month ago

If you save the index to disk, there is no difference between using the Milvus vector library and not using the Milvus vector library

hingkan commented 1 month ago

To correctly load and retrieve data from an existing Milvus index using llama_index, you need to ensure that the index has been properly persisted and that the StorageContext is correctly configured to point to the right storage directory. Here is a more detailed approach:

  1. Persist the Index: Ensure that the index is saved to a persistent storage directory.
  2. Load the Index: Use the load_index_from_storage function to load the index from the storage context.

Here is an example of how to persist and then load the index:

Persisting the Index

from llama_index.vector_stores.milvus import MilvusVectorStore
from llama_index.core import StorageContext, VectorStoreIndex, SimpleDirectoryReader

# Initialize the MilvusVectorStore
vector_store = MilvusVectorStore(
    uri="http://localhost:19530",  # Replace with your Milvus instance URI
    dim=1792,  # Replace with the dimension of your embeddings
    overwrite=True,
    index_config={
        'metric_type': 'IP',
        'index_type': "FLAT",
    }
)

# Create a StorageContext with the vector store
storage_context = StorageContext.from_defaults(vector_store=vector_store)

# Load documents
documents = SimpleDirectoryReader(
    input_files=["./data/your_data_file.txt"]
).load_data()

# Create an index over the documents
index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context
)

# Persist the index
storage_context.persist(persist_dir="./storage")

Loading the Index

from llama_index.indices.loading import load_index_from_storage
from llama_index.storage import StorageContext
from llama_index.vector_stores.milvus import MilvusVectorStore

# Initialize the MilvusVectorStore
vector_store = MilvusVectorStore(
    uri="http://localhost:19530",  # Replace with your Milvus instance URI
    dim=1792,  # Replace with the dimension of your embeddings
    overwrite=False,
    index_config={
        'metric_type': 'IP',
        'index_type': "FLAT",
    }
)

# Create a StorageContext with the vector store
storage_context = StorageContext.from_defaults(vector_store=vector_store, persist_dir="./storage")

# Load the index from the storage context
index = load_index_from_storage(storage_context=storage_context)

# Now you can use the index to retrieve data
query_vector = [0.1, 0.2, 0.3]  # Example query vector
documents = index.as_retriever().retrieve(query_vector)

# Print the retrieved documents
for doc in documents:
    print(f"Document ID: {doc.id_}, Text: {doc.text}")

This approach ensures that the index is first persisted to a directory and then loaded from that directory. Make sure to replace the uri, dim, and query_vector with your actual values [1][2].

To continue talking to Dosu, mention @dosu.

If I use 'storage_context.persist(persist_dir="./storage")' to save the index, do I just need to store index store.json?

dosubot[bot] commented 1 month ago

No, you do not just need to store the index_store.json file. When you use storage_context.persist(persist_dir="./storage"), it persists data to disk under the specified directory, including files related to the document store, vector store, and graph store.

To load the data back, you need to re-create the storage context using the same configuration and directory:

storage_context = StorageContext.from_defaults(
    docstore=SimpleDocumentStore.from_persist_dir(persist_dir="./storage"),
    index_store=SimpleIndexStore.from_persist_dir(persist_dir="./storage"),
    vector_store=SimpleVectorStore.from_persist_dir(persist_dir="./storage"),
    graph_store=SimpleGraphStore.from_persist_dir(persist_dir="./storage"),
    property_graph_store=SimplePropertyGraphStore.from_persist_dir(persist_dir="./storage")
)

This indicates that multiple components are involved in the persistence process, not just the index_store.json file [1][2].

To continue talking to Dosu, mention @dosu.