VectorX LlamaIndex Integration Demo

# Install necessary packages
!pip install vecx-llamaindex

import os
from llama_index.embeddings.openai import OpenAIEmbedding
from vecx.vectorx import VectorX
 
# Set API keys
os.environ["OPENAI_API_KEY"] = "your-openai-api-key"
vecx_api_token = "vectorx-api-key"
 
# Initialize VectorX client
vx = VectorX(token=vecx_api_token)
 
# Generate encryption key if you don't have one
encryption_key = vx.generate_key()
# Make sure to save this key securely - you'll need it to access your encrypted vectors
print("Encryption key:", encryption_key)

from llama_index.core import Document
 
# Create sample documents with different categories and metadata
documents = [
    Document(
        text="Python is a high-level, interpreted programming language known for its readability and simplicity.",
        metadata={"category": "programming", "language": "python", "difficulty": "beginner"}
    ),
    Document(
        text="JavaScript is a scripting language that enables interactive web pages and is an essential part of web applications.",
        metadata={"category": "programming", "language": "javascript", "difficulty": "intermediate"}
    ),
    Document(
        text="Machine learning is a subset of artificial intelligence that provides systems the ability to automatically learn and improve from experience.",
        metadata={"category": "ai", "field": "machine_learning", "difficulty": "advanced"}
    ),
    Document(
        text="Deep learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning.",
        metadata={"category": "ai", "field": "deep_learning", "difficulty": "advanced"}
    ),
    Document(
        text="Vector databases are specialized database systems designed to store and query high-dimensional vectors for similarity search.",
        metadata={"category": "database", "type": "vector", "difficulty": "intermediate"}
    ),
    Document(
        text="VectorX is an encrypted vector database that provides secure and private vector search capabilities.",
        metadata={"category": "database", "type": "vector", "product": "vectorx", "difficulty": "intermediate"}
    )
]
 
print(f"Created {len(documents)} sample documents")

from vecx_llamaindex import VectorXVectorStore
from llama_index.core import StorageContext
import time
 
# Create a unique index name with timestamp to avoid conflicts
timestamp = int(time.time())
index_name = f"llamaindex_demo_{timestamp}"
 
# Set up the embedding model
embed_model = OpenAIEmbedding()
 
# Get the embedding dimension
dimension = 1536  # OpenAI's default embedding dimension
 
# Initialize the VectorX vector store
vector_store = VectorXVectorStore.from_params(
    api_token=vecx_api_token,
    encryption_key=encryption_key,
    index_name=index_name,
    dimension=dimension,
    space_type="cosine"  # Can be "cosine", "l2", or "ip"
)
 
# Create storage context with our vector store
storage_context = StorageContext.from_defaults(vector_store=vector_store)
 
print(f"Initialized VectorX vector store with index: {index_name}")

from llama_index.core import VectorStoreIndex
 
# Create a vector index
index = VectorStoreIndex.from_documents(
    documents,
    storage_context=storage_context,
    embed_model=embed_model
)
 
print("Vector index created successfully")

# Create a query engine
query_engine = index.as_query_engine()
 
# Ask a question
response = query_engine.query("What is Python?")
 
print("Query: What is Python?")
print("Response:")
print(response)

from llama_index.core.vector_stores.types import MetadataFilters, MetadataFilter, FilterOperator
 
# Create a filtered retriever to only search within AI-related documents
ai_filter = MetadataFilter(key="category", value="ai", operator=FilterOperator.EQ)
ai_filters = MetadataFilters(filters=[ai_filter])
 
# Create a filtered query engine
filtered_query_engine = index.as_query_engine(filters=ai_filters)
 
# Ask a general question but only using AI documents
response = filtered_query_engine.query("What is learning from data?")
 
print("Filtered Query (AI category only): What is learning from data?")
print("Response:")
print(response)

# Create a more complex filter: database category AND intermediate difficulty
category_filter = MetadataFilter(key="category", value="database", operator=FilterOperator.EQ)
difficulty_filter = MetadataFilter(key="difficulty", value="intermediate", operator=FilterOperator.EQ)
 
complex_filters = MetadataFilters(filters=[category_filter, difficulty_filter])
 
# Create a query engine with the complex filters
complex_filtered_engine = index.as_query_engine(filters=complex_filters)
 
# Query with the complex filters
response = complex_filtered_engine.query("Tell me about databases")
 
print("Complex Filtered Query (database category AND intermediate difficulty): Tell me about databases")
print("Response:")
print(response)

from llama_index.core.retrievers import VectorIndexRetriever
 
# Create a retriever with custom parameters
retriever = VectorIndexRetriever(
    index=index,
    similarity_top_k=3,  # Return top 3 most similar results
    filters=ai_filters  # Use our AI category filter from before
)
 
# Retrieve nodes for a query
nodes = retriever.retrieve("What is deep learning?")
 
print(f"Retrieved {len(nodes)} nodes for query: 'What is deep learning?' (with AI category filter)")
print("\nRetrieved content:")
for i, node in enumerate(nodes):
    print(f"\nNode {i+1}:")
    print(f"Text: {node.node.text}")
    print(f"Metadata: {node.node.metadata}")
    print(f"Score: {node.score:.4f}")

from llama_index.core.query_engine import RetrieverQueryEngine
 
# Create a query engine with our custom retriever
custom_query_engine = RetrieverQueryEngine.from_args(
    retriever=retriever,
    verbose=True  # Enable verbose mode to see the retrieved nodes
)
 
# Query using the custom retriever query engine
response = custom_query_engine.query("Explain the difference between machine learning and deep learning")
 
print("\nFinal Response:")
print(response)

from llama_index.core.vector_stores.types import VectorStoreQuery
 
# Generate an embedding for our query
query_text = "What are vector databases?"
query_embedding = embed_model.get_text_embedding(query_text)
 
# Create a VectorStoreQuery
vector_store_query = VectorStoreQuery(
    query_embedding=query_embedding,
    similarity_top_k=2,
    filters=MetadataFilters(filters=[MetadataFilter(key="category", value="database", operator=FilterOperator.EQ)])
)
 
# Execute the query directly on the vector store
query_result = vector_store.query(vector_store_query)
 
print(f"Direct VectorStore query: '{query_text}'")
print(f"Retrieved {len(query_result.nodes)} results with database category filter:")
for i, (node, score) in enumerate(zip(query_result.nodes, query_result.similarities)):
    print(f"\nResult {i+1}:")
    print(f"Text: {node.text}")
    print(f"Metadata: {node.metadata}")
    print(f"Similarity score: {score:.4f}")

# To reconnect to an existing index in a future session, you would use:
def reconnect_to_index(api_token, encryption_key, index_name):
    # Initialize the vector store with existing index
    vector_store = VectorXVectorStore.from_params(
        api_token=api_token,
        encryption_key=encryption_key,
        index_name=index_name
    )
    
    # Create storage context
    storage_context = StorageContext.from_defaults(vector_store=vector_store)
    
    # Load the index
    index = VectorStoreIndex.from_vector_store(
        vector_store,
        embed_model=OpenAIEmbedding()
    )
    
    return index
 
# Example usage (commented out as we already have our index)
# reconnected_index = reconnect_to_index(vecx_api_token, encryption_key, index_name)
# query_engine = reconnected_index.as_query_engine()
# response = query_engine.query("What is VectorX?")
# print(response)
 
print(f"To reconnect to this index in the future, use:\n")
print(f"API Token: {vecx_api_token}")
print(f"Encryption Key: {encryption_key}")
print(f"Index Name: {index_name}")

# Comment this out if you want to keep your index
# vx.delete_index(index_name)
# print(f"Index {index_name} deleted")