1. Installation

!pip install vecx-llamaindex

2. Setting up VectorX and OpenAI credentials

import os
from llama_index.embeddings.openai import OpenAIEmbedding
from vecx.vectorx import VectorX

# Set API keys
os.environ["OPENAI_API_KEY"] = "sk-proj..."
vecx_api_token = "..."

# Initialize VectorX client
vx = VectorX(token=vecx_api_token)

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)

3. Creating Sample Documents

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")

vx.delete_index("llamaIndex_testing")

vx.list_indexes()

index = vx.get_index("llamaIndex_testing", encryption_key)
index.describe()

4. Setting up VectorX with LlamaIndex

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_testing"

# 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}")

5. Creating a Vector Index from Documents

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")

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

# Create a query engine
index = reconnect_to_index(vecx_api_token, encryption_key, index_name)
query_engine = index.as_query_engine()

# Ask a question
response = query_engine.query("Which is the tallest mountain in the world?")

# print("Query: What are javascript?")
print("Response:")
print(response)

6. Basic Retrieval with Query Engine

query_embedding = embed_model.get_text_embedding(
    "What is programming language?"
)

vec_index = vx.get_index(index_name, encryption_key)

results = vec_index.query(
    vector=query_embedding, top_k=1, include_vectors=True
)

print(results)

text = "Mount Kilimanjaro is the tallest mountain in africa"

vector = embed_model.get_text_embedding(text)

vec_index.upsert(
    [
        {
            "id": "vector_1",
            "vector": vector,
            "meta": {
                text: text,
            },
        }
    ]
)

7. Using Metadata Filters

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 vector database?")

# print("Filtered Query (AI category only): What is learning from data?")
print("Response:")
print(response)

8. Advanced Filtering with Multiple Conditions

# Create a more complex filter: database category AND intermediate difficulty
category_filter = MetadataFilter(
    key="category", value="ai", 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("what is ML")

print(
    "Complex Filtered Query (database category AND intermediate difficulty): Tell me about databases"
)
print("Response:")
print(response)

9. Custom Retriever Setup

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}")

10. Using a Custom Retriever with a Query Engine

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)

11. Direct VectorStore Querying

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}")