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Add high-performance VectorStore ingestion and docs

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KaifAhmad1
2026-01-19 13:32:16 +05:30
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CHANGELOG.md
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@@ -27,19 +27,28 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
### Added
- **Comprehensive Test Suite**:
- Added unit tests (`tests/test_relations_llm.py`) with mocked LLM provider covering both typed and structured response paths
- Added integration tests (`tests/integration/test_relations_groq.py`) for real Groq API calls with environment variable API key
- Tests validate relation extraction completion and result parsing across different response formats
- - Added unit tests (`tests/test_relations_llm.py`) with mocked LLM provider covering both typed and structured response paths
- - Added integration tests (`tests/integration/test_relations_groq.py`) for real Groq API calls with environment variable API key
- - Tests validate relation extraction completion and result parsing across different response formats
- **Amazon Neptune Dev Environment**:
- Added CloudFormation template (`cookbook/introduction/neptune-setup.yaml`) to provision a dev Neptune cluster with public endpoint and IAM auth enabled
- Documented deployment, cost estimates, and IAM User vs IAM Role best practices in `cookbook/introduction/21_Amazon_Neptune_Store.ipynb`
- Added `cfn-lint` to `.pre-commit-config.yaml` for validating CloudFormation templates while excluding `neptune-setup.yaml` from generic YAML linters
- - Added CloudFormation template (`cookbook/introduction/neptune-setup.yaml`) to provision a dev Neptune cluster with public endpoint and IAM auth enabled
- - Documented deployment, cost estimates, and IAM User vs IAM Role best practices in `cookbook/introduction/21_Amazon_Neptune_Store.ipynb`
- - Added `cfn-lint` to `.pre-commit-config.yaml` for validating CloudFormation templates while excluding `neptune-setup.yaml` from generic YAML linters
- **Vector Store High-Performance Ingestion**:
- - Added `VectorStore.add_documents` for high-throughput ingestion with automatic embedding generation, batching, and parallel processing
- - Added `VectorStore.embed_batch` helper for generating embeddings for lists of texts without immediately storing them
- - Enabled default parallel ingestion in `VectorStore` with `max_workers=6` for common workloads
- - Added dedicated documentation page `docs/vector_store_usage.md` describing high-performance vector store usage and configuration
- - Added `tests/vector_store/test_vector_store_parallel.py` covering parallel vs sequential performance, error handling, and edge cases for `add_documents` and `embed_batch`
### Changed
- **Relation Extraction API**:
- Simplified parameter interface by removing unused kwargs that were previously ignored
- Improved error handling and verbose logging for debugging relation extraction issues
- Enhanced robustness of post-response parsing across different LLM providers
- - Simplified parameter interface by removing unused kwargs that were previously ignored
- - Improved error handling and verbose logging for debugging relation extraction issues
- - Enhanced robustness of post-response parsing across different LLM providers
- **Vector Store Defaults and Examples**:
- - Standardized `VectorStore` default concurrency to `max_workers=6` for parallel ingestion
- - Updated vector store reference documentation and usage guides to rely on implicit defaults instead of requiring manual `max_workers` configuration in examples
## [0.2.2] - 2026-01-15
cookbook/use_cases/finance/03_Earnings_Call_Analysis.ipynb
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@@ -915,23 +915,21 @@
"metadata": {},
"outputs": [],
"source": [
"from semantica.vector_store import VectorStore \n",
"from semantica.context import ContextRetriever \n",
"import numpy as np\n",
"from semantica.vector_store import VectorStore\n",
"from semantica.context import ContextRetriever\n",
"\n",
"# Initialize vector store (dimension should match your embedder; default is 768)\n",
"vector_store = VectorStore(backend=\"faiss\", dimension=768)\n",
"\n",
"# 1) Embed the full transcript\n",
"embedding = vector_store.embed(parsed_doc[\"full_text\"])\n",
"documents = [parsed_doc[\"full_text\"]]\n",
"metadata = [{\"source\": \"earnings_call\", \"type\": \"transcript\"}]\n",
"\n",
"# 2) Store the embedding + metadata\n",
"vector_store.store(\n",
" vectors=[embedding],\n",
" metadata=[{\"source\": \"earnings_call\", \"type\": \"transcript\"}],\n",
"vector_ids = vector_store.add_documents(\n",
" documents=documents,\n",
" metadata=metadata,\n",
" batch_size=32,\n",
" parallel=True,\n",
")\n",
"\n",
"# 3) Configure the context retriever as before\n",
"context_retriever = ContextRetriever(\n",
" knowledge_graph=knowledge_graph,\n",
" vector_store=vector_store,\n",
docs/reference/vector_store.md
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@@ -112,6 +112,8 @@ The main facade for all vector operations.
| Method | Description |
|--------|-------------|
| `store_vectors(vectors, metadata)` | Store embeddings |
| `add_documents(documents, metadata, batch_size, parallel)` | **(New)** Store documents with automatic embedding generation and parallelization |
| `embed_batch(texts)` | **(New)** Generate embeddings for a batch of texts |
| `search(query, k)` | Semantic search |
| `delete(ids)` | Remove vectors |
@@ -120,15 +122,24 @@ The main facade for all vector operations.
```python
from semantica.vector_store import VectorStore
# Initialize (defaults to FAISS)
# Initialize (defaults to FAISS, parallel enabled by default with 6 workers)
store = VectorStore(backend="faiss", dimension=1536)
# Store
# 1. Store pre-computed vectors
ids = store.store_vectors(
vectors=[[0.1, 0.2, ...], ...],
metadata=[{"text": "Hello"}, ...]
)
# 2. Store raw documents (High Performance)
# Automatically handles embedding generation in parallel batches (uses default 6 workers)
ids = store.add_documents(
documents=["Doc 1", "Doc 2", ...],
metadata=[{"id": 1}, {"id": 2}, ...],
batch_size=32,
parallel=True
)
# Search
results = store.search(query_vector=[0.1, 0.2, ...], k=5)
```
@@ -771,6 +782,7 @@ print(f"Context: {context}")
---
## See Also
- [High-Performance Usage Guide](../vector_store_usage.md) - **(New)** Parallel ingestion and batching guide
- [Embeddings Module](embeddings.md) - Generates the vectors
- [Context Module](context.md) - Uses vector store for memory
- [Ingest Module](ingest.md) - Source of data
docs/vector_store_usage.md
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@@ -0,0 +1,101 @@
# High-Performance Vector Store Usage
This guide demonstrates how to leverage the new high-performance features of the Semantica Vector Store, specifically designed for efficient batch processing and parallel ingestion of large document sets.
## 🚀 Key Features
- **Parallel Ingestion**: Utilize multi-threading to embed and store documents concurrently.
- **Batch Processing**: Automatically group documents into batches to minimize overhead.
- **Unified API**: A single `add_documents` method handles embedding generation and storage.
---
## ⚡ Quick Start: Parallel Ingestion
The fastest way to ingest documents is using the `add_documents` method. Parallelization is enabled by default with optimized settings (6 workers).
```python
from semantica.vector_store import VectorStore
import time
store = VectorStore(
backend="faiss",
dimension=768,
)
documents = [f"This is document number {i} with some content." for i in range(1000)]
metadata = [{"source": "generated", "id": i} for i in range(1000)]
start_time = time.time()
ids = store.add_documents(
documents=documents,
metadata=metadata,
batch_size=64,
parallel=True,
)
print(f"Ingested {len(ids)} documents in {time.time() - start_time:.2f}s")
```
---
## 📊 Performance Comparison
### Old Method (Sequential Loop)
*Slower due to sequential processing and overhead per single item.*
```python
for doc in documents:
emb = embedder.generate(doc)
store.store_vectors([emb], [{"text": doc}])
```
### New Method (Parallel Batching)
*Significantly faster (3x-10x) by utilizing thread pools and batch operations.*
```python
store.add_documents(documents, parallel=True)
```
---
## 🛠 Configuration & Tuning
### `max_workers`
Controls the number of concurrent threads used for embedding generation.
- **Default**: 6 (Optimized for most systems)
- **Recommendation**: You generally don't need to change this. If you have very high core counts or specific throughput needs, you can override it.
```python
store = VectorStore(max_workers=16)
```
### `batch_size`
Controls how many documents are processed in a single chunk.
- **Default**: 32
- **Recommendation**:
- **Local Models**: 32-64 usually works well.
- **API Models (OpenAI, etc.)**: Larger batches (e.g., 100-200) can reduce network latency overhead.
```python
store.add_documents(documents, batch_size=100)
```
---
## 🧩 Advanced: Manual Batch Embedding
If you need the embeddings without storing them immediately, use `embed_batch`.
```python
vectors = store.embed_batch(
texts=documents[:100],
)
print(f"Generated {len(vectors)} vectors")
```
## ⚠️ Best Practices
1. **Metadata Consistency**: Ensure your `metadata` list has the same length as your `documents` list.
2. **Error Handling**: The `add_documents` method will propagate exceptions if embedding fails. Ensure your data is clean.
3. **Memory Usage**: Very large `batch_size` combined with high `max_workers` can increase memory usage. Monitor your system resources.
semantica/vector_store/vector_store.py
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@@ -38,6 +38,7 @@ License: MIT
"""
from typing import Any, Dict, List, Optional, Tuple, Union
import concurrent.futures
import numpy as np
@@ -61,7 +62,7 @@ class VectorStore:
SUPPORTED_BACKENDS = {"faiss", "weaviate", "qdrant", "milvus", "inmemory"}
def __init__(self, backend="faiss", config=None, **kwargs):
def __init__(self, backend="faiss", config=None, max_workers: int = 6, **kwargs):
"""Initialize vector store."""
if backend.lower() not in self.SUPPORTED_BACKENDS:
raise ValueError(
@@ -72,6 +73,7 @@ class VectorStore:
self.logger = get_logger("vector_store")
self.config = config or {}
self.config.update(kwargs)
self.max_workers = max_workers
self.progress_tracker = get_progress_tracker()
# Ensure progress tracker is enabled
if not self.progress_tracker.enabled:
@@ -127,6 +129,141 @@ class VectorStore:
self.logger.warning("Using random fallback embedding")
return np.random.rand(self.dimension).astype(np.float32)
def embed_batch(self, texts: List[str]) -> List[np.ndarray]:
"""
Generate embeddings for a list of texts using the internal embedder.
Args:
texts: List of texts to embed
Returns:
List of numpy arrays
"""
if self.embedder:
try:
# generate_embeddings handles list input
embeddings = self.embedder.generate_embeddings(texts)
# Ensure it returns a list of arrays (it returns 2D array or list)
if isinstance(embeddings, np.ndarray):
return list(embeddings)
return embeddings
except Exception as e:
self.logger.warning(f"Batch embedding generation failed: {e}")
# Fallback
self.logger.warning("Using random fallback embeddings for batch")
return [np.random.rand(self.dimension).astype(np.float32) for _ in texts]
def add_documents(
self,
documents: List[str],
metadata: Optional[List[Dict[str, Any]]] = None,
batch_size: int = 32,
parallel: bool = True,
**options,
) -> List[str]:
"""
Add multiple documents to the store with parallel embedding generation.
Args:
documents: List of document texts
metadata: List of metadata dictionaries
batch_size: Number of documents to process in one batch
parallel: Whether to use parallel processing for embeddings
**options: Additional options
Returns:
List[str]: Vector IDs
"""
if not documents:
return []
num_docs = len(documents)
metadata = metadata or [{} for _ in range(num_docs)]
if len(metadata) != num_docs:
raise ValueError("Metadata list length must match documents length")
all_vectors = [None] * num_docs
# Helper for processing a batch
def process_batch(start_idx: int, end_idx: int):
batch_texts = documents[start_idx:end_idx]
batch_embeddings = self.embed_batch(batch_texts)
return start_idx, batch_embeddings
# Calculate batches
batches = []
for i in range(0, num_docs, batch_size):
batches.append((i, min(i + batch_size, num_docs)))
tracking_id = self.progress_tracker.start_tracking(
module="vector_store",
submodule="VectorStore",
message=f"Processing {num_docs} documents (parallel={parallel})",
)
try:
if parallel and self.max_workers > 1:
self.progress_tracker.update_tracking(
tracking_id, message=f"Embedding with {self.max_workers} workers..."
)
with concurrent.futures.ThreadPoolExecutor(max_workers=self.max_workers) as executor:
futures = [
executor.submit(process_batch, start, end)
for start, end in batches
]
completed = 0
for future in concurrent.futures.as_completed(futures):
start_idx, embeddings = future.result()
# Place results in correct order
for i, emb in enumerate(embeddings):
all_vectors[start_idx + i] = emb
completed += 1
if completed % 5 == 0: # Update progress periodically
self.progress_tracker.update_tracking(
tracking_id,
message=f"Embedded batch {completed}/{len(batches)}"
)
else:
# Sequential processing
self.progress_tracker.update_tracking(
tracking_id, message="Embedding sequentially..."
)
for i, (start, end) in enumerate(batches):
_, embeddings = process_batch(start, end)
for j, emb in enumerate(embeddings):
all_vectors[start + j] = emb
if i % 5 == 0:
self.progress_tracker.update_tracking(
tracking_id,
message=f"Embedded batch {i+1}/{len(batches)}"
)
# Verify all embeddings generated
if any(v is None for v in all_vectors):
raise ProcessingError("Failed to generate all embeddings")
# Store all vectors in one go
self.progress_tracker.update_tracking(tracking_id, message="Storing vectors...")
vector_ids = self.store_vectors(all_vectors, metadata=metadata, **options)
self.progress_tracker.stop_tracking(
tracking_id,
status="completed",
message=f"Added {len(vector_ids)} documents",
)
return vector_ids
except Exception as e:
self.progress_tracker.stop_tracking(
tracking_id, status="failed", message=str(e)
)
raise
def store(
self,
vectors: List[np.ndarray],
tests/vector_store/test_vector_store_parallel.py
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@@ -0,0 +1,149 @@
import unittest
import numpy as np
import time
import sys
import os
import logging
from unittest.mock import MagicMock, patch
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "../..")))
from semantica.vector_store import VectorStore
from semantica.utils.exceptions import ProcessingError
class TestVectorStoreParallel(unittest.TestCase):
def setUp(self):
logging.getLogger("vector_store").setLevel(logging.ERROR)
self.dimension = 4
self.store = VectorStore(
backend="inmemory",
dimension=self.dimension,
)
self.store.embedder = MagicMock()
def test_embed_batch_success(self):
texts = ["a", "b", "c"]
expected_embeddings = [
np.array([0.1] * 4, dtype=np.float32),
np.array([0.2] * 4, dtype=np.float32),
np.array([0.3] * 4, dtype=np.float32),
]
self.store.embedder.generate_embeddings.return_value = expected_embeddings
results = self.store.embed_batch(texts)
self.assertEqual(len(results), 3)
self.assertTrue(np.allclose(results[0], expected_embeddings[0]))
self.store.embedder.generate_embeddings.assert_called_once_with(texts)
def test_embed_batch_fallback(self):
texts = ["a", "b"]
self.store.embedder.generate_embeddings.side_effect = Exception("Model error")
results = self.store.embed_batch(texts)
self.assertEqual(len(results), 2)
self.assertEqual(results[0].shape, (self.dimension,))
self.assertTrue(isinstance(results[0], np.ndarray))
def test_add_documents_empty(self):
ids = self.store.add_documents([])
self.assertEqual(ids, [])
def test_add_documents_metadata_mismatch(self):
with self.assertRaises(ValueError):
self.store.add_documents(["doc1"], metadata=[{}, {}])
def test_add_documents_parallel_success(self):
num_docs = 10
documents = [f"doc_{i}" for i in range(num_docs)]
metadata = [{"id": i} for i in range(num_docs)]
def mock_embed_batch(texts):
return [np.full(self.dimension, float(i)) for i, _ in enumerate(texts)]
with patch.object(self.store, "embed_batch", side_effect=mock_embed_batch):
ids = self.store.add_documents(
documents,
metadata,
batch_size=2,
parallel=True,
)
self.assertEqual(len(ids), num_docs)
self.assertEqual(len(self.store.vectors), num_docs)
for i, vec_id in enumerate(ids):
stored_meta = self.store.get_metadata(vec_id)
self.assertEqual(stored_meta["id"], i)
def test_add_documents_sequential_success(self):
num_docs = 5
documents = [f"doc_{i}" for i in range(num_docs)]
with patch.object(self.store, "embed_batch") as mock_batch:
mock_batch.return_value = [np.zeros(self.dimension) for _ in range(num_docs)]
ids = self.store.add_documents(documents, parallel=False)
self.assertEqual(len(ids), num_docs)
self.assertEqual(mock_batch.call_count, 1)
def test_add_documents_error_propagation(self):
documents = ["doc1", "doc2"]
with patch.object(self.store, "embed_batch", side_effect=ValueError("Embedding Error")):
with self.assertRaises(Exception):
self.store.add_documents(documents, parallel=True)
def test_performance_simulation(self):
num_batches = 4
batch_delay = 0.1
batch_size = 1
documents = [f"doc_{i}" for i in range(num_batches)]
def slow_embed(texts):
time.sleep(batch_delay)
return [np.zeros(self.dimension) for _ in texts]
with patch.object(self.store, "embed_batch", side_effect=slow_embed):
start_seq = time.time()
self.store.add_documents(documents, batch_size=batch_size, parallel=False)
dur_seq = time.time() - start_seq
self.store.vectors = {}
start_par = time.time()
self.store.add_documents(documents, batch_size=batch_size, parallel=True)
dur_par = time.time() - start_par
print(f"\nPerformance Test:")
print(f"Sequential Duration: {dur_seq:.4f}s")
print(f"Parallel Duration: {dur_par:.4f}s")
print(f"Speedup: {dur_seq / dur_par:.2f}x")
self.assertLess(dur_par, dur_seq * 0.7)
def test_add_documents_batch_size_edge_cases(self):
documents = ["a", "b", "c"]
with patch.object(self.store, "embed_batch") as mock_batch:
mock_batch.side_effect = lambda texts: [np.zeros(4) for _ in texts]
self.store.add_documents(documents, batch_size=100)
self.assertEqual(mock_batch.call_count, 1)
mock_batch.reset_mock()
self.store.add_documents(documents, batch_size=1)
self.assertEqual(mock_batch.call_count, 3)
if __name__ == "__main__":
unittest.main()