Specialized References and Implementation Guides
1. Vector Search Implementation
A. Vector Database Optimization
1. Facebook AI Similarity Search (FAISS) Implementation Guide
Description: Detailed guide for FAISS implementation with performance optimization
URL: https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index
Support: Vector search optimization
Key Implementation Notes:
```python
# FAISS Index Selection Guide
def select_faiss_index(vector_dimension: int, dataset_size: int) -> str:
if dataset_size < 1_000_000:
return "IndexFlatL2" # Exact search for small datasets
elif dataset_size < 10_000_000:
return "IndexIVFFlat" # Approximate search with good accuracy
else:
return "IndexIVFPQ" # Compressed vectors for large scale
-
ANN Benchmark Analysis Description: Comparative analysis of Approximate Nearest Neighbor algorithms URL: https://github.com/erikbern/ann-benchmarks Support: Vector search algorithm selection
Performance Comparison Matrix:
| Algorithm | Query Time | Memory Usage | Accuracy | Scale |
|---------------|------------|--------------|----------|--------|
| Flat L2 | Slow | Low | 100% | Small |
| IVF Flat | Medium | Medium | 95-99% | Medium |
| IVF PQ | Fast | Low | 90-95% | Large |
| HNSW | Very Fast | High | 95-99% | Medium |
B. Sentence Transformers Optimization
1. Sentence Transformers Model Selection
Description: Guide for selecting and optimizing transformer models
URL: https://www.sbert.net/docs/pretrained_models.html
Support: Embedding generation
Implementation Example:
```python
from sentence_transformers import SentenceTransformer
class EmbeddingService:
def __init__(self):
self.model = SentenceTransformer('all-mpnet-base-v2')
# Cache frequent embeddings
self.cache = LRUCache(maxsize=10000)
async def generate_embedding(
self,
text: str,
cache: bool = True
) -> np.ndarray:
if cache and text in self.cache:
return self.cache[text]
embedding = self.model.encode(
text,
normalize_embeddings=True # L2 normalization
)
if cache:
self.cache[text] = embedding
return embedding
Context
The current situation requires a decision because:
- Requirement 1
- Constraint 2
- Need 3
Status
Accepted | YYYY-MM-DD
2. PostgreSQL Performance Tuning
A. Database Configuration
1. PostgreSQL Configuration Guide
Description: Comprehensive guide for PostgreSQL optimization
URL: https://postgresqlco.nf/doc/en/param/
Support: Database tuning
Example Configuration:
```ini
# Memory Configuration
shared_buffers = '8GB' # 25% of RAM
effective_cache_size = '24GB' # 75% of RAM
maintenance_work_mem = '2GB' # For maintenance operations
work_mem = '128MB' # Per-operation memory
# Checkpoint Configuration
checkpoint_timeout = '15min'
checkpoint_completion_target = 0.9
max_wal_size = '16GB'
# Query Planning
random_page_cost = 1.1 # For SSD storage
effective_io_concurrency = 200 # For SSD storage
default_statistics_target = 100 # For complex queries
-
pgvector Performance Optimization Description: Specific optimizations for vector operations URL: https://github.com/pgvector/pgvector/blob/master/README.md#performance Support: Vector search tuning
Implementation Guide:
-- Create optimized index
CREATE INDEX ON chunks USING ivfflat (embedding vector_cosine_ops)
WITH (lists = 100);
-- Analyze for optimal performance
ANALYZE chunks;
-- Monitor index usage
CREATE VIEW vector_index_stats AS
SELECT
schemaname,
tablename,
indexname,
idx_scan,
idx_tup_read,
idx_tup_fetch
FROM pg_stat_user_indexes
WHERE indexname LIKE '%embedding%';
3. Chunking Strategy Implementation
A. Intelligent Document Chunking
1. Natural Language Processing for Chunking
Description: NLP-based document segmentation strategies
URL: https://spacy.io/api/doc#sents
Support: Document processing
Implementation Example:
```python
class DocumentChunker:
def __init__(
self,
chunk_size: int = 1000,
overlap: float = 0.1
):
self.nlp = spacy.load("en_core_web_sm")
self.chunk_size = chunk_size
self.overlap = overlap
def create_chunks(
self,
document: str
) -> List[DocumentChunk]:
doc = self.nlp(document)
chunks = []
current_chunk = []
current_length = 0
for sent in doc.sents:
# Check if adding sentence exceeds chunk size
if current_length + len(sent) > self.chunk_size:
# Create chunk with context
chunk_text = self._create_chunk_with_context(
current_chunk,
document
)
chunks.append(chunk_text)
# Start new chunk with overlap
overlap_start = max(
0,
len(current_chunk) - int(self.chunk_size * self.overlap)
)
current_chunk = current_chunk[overlap_start:]
current_length = sum(len(s) for s in current_chunk)
current_chunk.append(sent)
current_length += len(sent)
# Add final chunk
if current_chunk:
chunks.append(self._create_chunk_with_context(
current_chunk,
document
))
return chunks
def _create_chunk_with_context(
self,
sentences: List[Span],
document: str
) -> DocumentChunk:
# Create chunk with metadata
text = " ".join(str(s) for s in sentences)
start_idx = sentences[0].start_char
end_idx = sentences[-1].end_char
return DocumentChunk(
content=text,
start_offset=start_idx,
end_offset=end_idx,
metadata=self._extract_metadata(
text,
document[
max(0, start_idx - 100):
min(len(document), end_idx + 100)
]
)
)
4. Cache Strategy Implementation
A. Multi-Level Caching
1. Redis Caching Patterns
Description: Advanced Redis caching strategies
URL: https://redis.io/topics/patterns
Support: Cache implementation
Implementation Example:
```python
class CacheService:
def __init__(
self,
redis_client: Redis,
local_cache_size: int = 1000
):
self.redis = redis_client
self.local_cache = LRUCache(maxsize=local_cache_size)
async def get_or_set(
self,
key: str,
generator: Callable,
ttl: int = 3600,
local_ttl: int = 300
) -> Any:
# Check local cache first
if key in self.local_cache:
return self.local_cache[key]
# Check Redis cache
value = await self.redis.get(key)
if value is not None:
self.local_cache[key] = value
return value
# Generate value
value = await generator()
# Store in both caches
await self.redis.setex(key, ttl, value)
self.local_cache[key] = value
return value
async def invalidate(self, pattern: str):
"""Invalidate cache entries matching pattern"""
# Clear local cache entries
self.local_cache.clear()
# Clear Redis entries
keys = await self.redis.keys(pattern)
if keys:
await self.redis.delete(*keys)
Would you like me to continue with:
- More specialized implementations?
- Additional optimization strategies?
- Specific component guides?
- Performance tuning examples?