endee-llamaindex 0.1.5
Vector Database for Fast ANN Searches
Requires Python: >=3.6

# Endee LlamaIndex Integration

Build powerful RAG applications with Endee vector database and LlamaIndex.

---

## Table of Contents

1. [Installation](#1-installation)
2. [Testing locally](#testing-locally)
3. [Setting up Credentials](#2-setting-up-endee-and-openai-credentials)
4. [Creating Sample Documents](#3-creating-sample-documents)
5. [Setting up Endee with LlamaIndex](#4-setting-up-endee-with-llamaindex)
6. [Creating a Vector Index](#5-creating-a-vector-index-from-documents)
7. [Basic Retrieval](#6-basic-retrieval-with-query-engine)
8. [Using Metadata Filters](#7-using-metadata-filters)
9. [Advanced Filtering](#8-advanced-filtering-with-multiple-conditions)
10. [Custom Retriever Setup](#9-custom-retriever-setup)
11. [Custom Retriever with Query Engine](#10-using-a-custom-retriever-with-a-query-engine)
12. [Direct VectorStore Querying](#11-direct-vectorstore-querying)
13. [Saving and Loading Indexes](#12-saving-and-loading-indexes)
14. [Cleanup](#13-cleanup)

---

## 1. Installation

Get started by installing the required package.

### Basic Installation (Dense-only search)

```bash
pip install endee-llamaindex
```

> **Note:** This will automatically install `endee` and `llama-index` as dependencies.

### Full Installation (with Hybrid Search support)

For hybrid search capabilities (dense + sparse vectors), install with the `hybrid` extra:

```bash
pip install endee-llamaindex[hybrid]
```

This includes FastEmbed for sparse vector encoding (SPLADE, BM25, etc.).

### GPU-Accelerated Hybrid Search

For GPU-accelerated sparse encoding:

```bash
pip install endee-llamaindex[hybrid-gpu]
```

### All Features

To install all optional dependencies:

```bash
pip install endee-llamaindex[all]
```

### Installation Options Summary

| Installation | Use Case | Includes |
|--------------|----------|----------|
| `pip install endee-llamaindex` | Dense vector search only | Core dependencies |
| `pip install endee-llamaindex[hybrid]` | Dense + sparse hybrid search | + FastEmbed (CPU) |
| `pip install endee-llamaindex[hybrid-gpu]` | GPU-accelerated hybrid search | + FastEmbed (GPU) |
| `pip install endee-llamaindex[all]` | All features | All optional deps |

---

## Testing locally

From the project root:

```bash
python -m venv env && source env/bin/activate   # optional
pip install -e .
pip install pytest sentence-transformers huggingface-hub
export ENDEE_API_TOKEN="your-endee-api-token"   # or set in endee_llamaindex/test_cases/setup_class.py

cd endee_llamaindex/test_cases && PYTHONPATH=.. python -m pytest . -v
```

See [TESTING.md](TESTING.md) for more options and single-test runs.

---

## 2. Setting up Endee and OpenAI credentials

Configure your API credentials for Endee and OpenAI.

```python
import os
from llama_index.embeddings.openai import OpenAIEmbedding

# Set API keys
os.environ["OPENAI_API_KEY"] = "your-openai-api-key"
endee_api_token = "your-endee-api-token"
```

> **Tip:** Store your API keys in environment variables for production use.

---

## 3. Creating Sample Documents

Create documents with metadata for filtering and organization.

```python
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="Endee is a vector database that provides secure and private vector search capabilities.",
        metadata={"category": "database", "type": "vector", "product": "endee", "difficulty": "intermediate"}
    )
]

print(f"Created {len(documents)} sample documents")
```

**Output:**
```
Created 6 sample documents
```

---

## 4. Setting up Endee with LlamaIndex

Initialize the Endee vector store and connect it to LlamaIndex.

```python
from endee_llamaindex import EndeeVectorStore
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 Endee vector store
vector_store = EndeeVectorStore.from_params(
    api_token=endee_api_token,
    index_name=index_name,
    dimension=dimension,
    space_type="cosine",  # Can be "cosine", "l2", or "ip"
    precision="float16"  # Options: "binary", "float16", "float32", "int16d", "int8d" (default: "float16")
)

# Create storage context with our vector store
storage_context = StorageContext.from_defaults(vector_store=vector_store)

print(f"Initialized Endee vector store with index: {index_name}")
```

### Configuration Options

| Parameter | Description | Options |
|-----------|-------------|---------|
| `space_type` | Distance metric for similarity | `cosine`, `l2`, `ip` |
| `dimension` | Vector dimension | Must match embedding model |
| `precision` | Index precision setting | `"binary"`, `"float16"` (default), `"float32"`, `"int16d"`, `"int8d"` |
| `batch_size` | Vectors per API call | Default: `100` |
| `hybrid` | Enable hybrid search (dense + sparse) | Default: `False` |
| `M` | Optional HNSW M parameter (bi-directional links) | Optional (backend default if not specified) |
| `ef_con` | Optional HNSW ef_construction parameter | Optional (backend default if not specified) |

### Hybrid Search and Sparse Models

When you enable hybrid search by providing a positive `sparse_dim` and a `model_name`, the vector store automatically computes sparse (bag-of-words‑style) vectors in addition to dense vectors.

- **Sparse dimension (`sparse_dim`)**:
  - For the built-in SPLADE models, the recommended `sparse_dim` is **30522** (matching the model vocabulary size).
  - For dense‑only search, omit `sparse_dim` (or set it to `0`).
- **Supported sparse models (`model_name`)**:
  - `"splade_pp"` → `prithivida/Splade_PP_en_v1` (SPLADE++)
  - `"splade_cocondenser"` → `naver/splade-cocondenser-ensembledistil`

Example hybrid configuration:

```python
vector_store = EndeeVectorStore.from_params(
    api_token=endee_api_token,
    index_name=index_name,
    dimension=dimension,        # dense dimension (e.g., 1536 for OpenAI)
    space_type="cosine",
    precision="float16",
    hybrid=True,
    sparse_dim=30522,           # sparse dimension for SPLADE models
    model_name="splade_pp",     # or "splade_cocondenser"
)
```

---

## 5. Creating a Vector Index from Documents

Build a searchable vector index from your documents.

```python
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")
```

**Output:**
```
Vector index created successfully
```

---

## 6. Basic Retrieval with Query Engine

Create a query engine and perform semantic search.

```python
# 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)
```

**Example Output:**
```
Query: What is Python?
Response:
Python is a high-level, interpreted programming language known for its readability and simplicity.
```

---

## 7. Using Metadata Filters

Filter search results based on document metadata.

```python
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)
```

### Available Filter Operators

| Operator | Description | Backend Symbol | Example |
|----------|-------------|----------------|---------|
| `FilterOperator.EQ` | Equal to | `$eq` | `rating == 5` |
| `FilterOperator.IN` | In list | `$in` | `category in ["ai", "ml"]` |


> **Important Notes:**
> - Currently, the Endee LlamaIndex integration only supports **EQ** and **IN** metadata filters.
> - Range-style operators (LT, LTE, GT, GTE) are **not** supported in this adapter.

### Filter Examples

Here are practical examples showing how to use the supported filter operators:

```python
from llama_index.core.vector_stores.types import MetadataFilters, MetadataFilter, FilterOperator

# Example 1: Equal to (EQ)
# Find documents with rating equal to 5
rating_filter = MetadataFilter(key="rating", value=5, operator=FilterOperator.EQ)
filters = MetadataFilters(filters=[rating_filter])
# Backend: {"rating": {"$eq": 5}}

# Example 2: In list (IN)
# Find documents in AI or ML categories
category_filter = MetadataFilter(key="category", value=["ai", "ml"], operator=FilterOperator.IN)
filters = MetadataFilters(filters=[category_filter])
# Backend: {"category": {"$in": ["ai", "ml"]}}

# Example 3: Combined filters (AND logic)
# Find AI documents with rating equal to 5
filters = MetadataFilters(filters=[
    MetadataFilter(key="category", value="ai", operator=FilterOperator.EQ),
    MetadataFilter(key="rating", value=5, operator=FilterOperator.EQ)
])
# Backend: [{"category": {"$eq": "ai"}}, {"rating": {"$eq": 5}}]

# Create a query engine with filters
filtered_engine = index.as_query_engine(filters=filters)
response = filtered_engine.query("What is machine learning?")
```

---

## 8. Advanced Filtering with Multiple Conditions

Combine multiple metadata filters for precise results.

```python
# 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)
```

> **Note:** Multiple filters are combined with AND logic by default.

---

## 9. Custom Retriever Setup

Create a custom retriever for fine-grained control over the retrieval process.

```python
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}")
```

**Example Output:**
```
Retrieved 2 nodes for query: 'What is deep learning?' (with AI category filter)

Node 1:
Text: Deep learning is part of a broader family of machine learning methods...
Metadata: {'category': 'ai', 'field': 'deep_learning', 'difficulty': 'advanced'}
Score: 0.8934

Node 2:
Text: Machine learning is a subset of artificial intelligence...
Metadata: {'category': 'ai', 'field': 'machine_learning', 'difficulty': 'advanced'}
Score: 0.7821
```

---

## 10. Using a Custom Retriever with a Query Engine

Combine your custom retriever with a query engine for enhanced control.

```python
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

Query the Endee vector store directly, bypassing the LlamaIndex query engine.

```python
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}")
```

> **Tip:** Direct querying is useful when you need raw results without LLM processing.

---

## 12. Saving and Loading Indexes

Reconnect to your index in future sessions. Your vectors are stored in the cloud.

```python
# To reconnect to an existing index in a future session:
def reconnect_to_index(api_token, index_name):
    # Initialize the vector store with existing index
    vector_store = EndeeVectorStore.from_params(
        api_token=api_token,
        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
reconnected_index = reconnect_to_index(endee_api_token, index_name)
query_engine = reconnected_index.as_query_engine()
response = query_engine.query("What is Endee?")
print(response)

print(f"To reconnect to this index in the future, use:\n")
print(f"API Token: {endee_api_token}")
print(f"Index Name: {index_name}")
```

> **Important:** Save your `index_name` to reconnect to your data later.

---

## 13. Cleanup

Delete the index when you're done to free up resources.

```python
# Uncomment to delete your index
# endee.delete_index(index_name)
# print(f"Index {index_name} deleted")
```

> **Warning:** Deleting an index permanently removes all stored vectors and cannot be undone.

---

## Quick Reference

### EndeeVectorStore Parameters

| Parameter | Type | Description | Default |
|-----------|------|-------------|---------|
| `api_token` | `str` | Your Endee API token | Required |
| `index_name` | `str` | Name of the index | Required |
| `dimension` | `int` | Vector dimension | Required |
| `space_type` | `str` | Distance metric (`"cosine"`, `"l2"`, `"ip"`) | `"cosine"` |
| `precision` | `str` | Index precision (`"binary"`, `"float16"`, `"float32"`, `"int16d"`, `"int8d"`) | `"float16"` |
| `batch_size` | `int` | Vectors per API call | `100` |
| `hybrid` | `bool` | Enable hybrid search (dense + sparse vectors) | `False` |
| `sparse_dim` | `int` | Sparse dimension for hybrid index | `None` |
| `model_name` | `str` | Model name for sparse embeddings (e.g., `'splade_pp'`, `'bert_base'`) | `None` |
| `M` | `int` | Optional HNSW M parameter (bi-directional links per node) | `None` (backend default) |
| `ef_con` | `int` | Optional HNSW ef_construction parameter | `None` (backend default) |

### Distance Metrics

| Metric | Best For |
|--------|----------|
| `cosine` | Text embeddings, normalized vectors |
| `l2` | Image features, spatial data |
| `ip` | Recommendation systems, dot product similarity |

### Precision Settings

The `precision` parameter controls the vector storage format and affects memory usage and search performance:

| Precision | Description | Use Case |
|-----------|-------------|----------|
| `"float32"` | Full precision floating point | Maximum accuracy, higher memory usage |
| `"float16"` | Half precision floating point | Balanced accuracy and memory (default) |
| `"binary"` | Binary vectors | Extremely compact, best for binary embeddings |
| `"int8d"` | 8-bit integer quantization | High compression, good accuracy |
| `"int16d"` | 16-bit integer quantization | Better accuracy than int8d, moderate compression |

### HNSW Parameters (Optional)

HNSW (Hierarchical Navigable Small World) parameters control index construction and search quality. These are **optional** - if not provided, the Endee backend uses optimized defaults.

| Parameter | Description | Impact |
|-----------|-------------|--------|
| `M` | Number of bi-directional links per node | Higher M = better recall, more memory |
| `ef_con` | Size of dynamic candidate list during construction | Higher ef_con = better quality, slower indexing |

**Example with custom HNSW parameters:**

```python
vector_store = EndeeVectorStore.from_params(
    api_token="your-token",
    index_name="custom_index",
    dimension=384,
    space_type="cosine",
    M=32,           # Optional: custom M value
    ef_con=256      # Optional: custom ef_construction
)
```

**Note:** Only specify M and ef_con if you need to fine-tune performance. The backend defaults work well for most use cases.

---