LNSP Vec2Text Integration PRD
Trent Carter
7/27/2025
Product Requirements Document: Latent Neurolese Vector-to-Text Translator
Document Version: 1.0 Date: July 27, 2025 Author: Trent Carter Project: LNSP-Vec2Text IntegrationExecutive Summary
This PRD outlines the integration of Vec2Text technology into the Latent Neurolese Semantic Processor (LNSP) to create a bidirectional vector-text translation system. The integration will enable LNSP to decode its 384D compressed semantic vectors back to human-readable text, completing the vector-native processing pipeline and enabling real-world deployment scenarios.
Key Benefits:1. Problem Statement
Current State
Target State
2. Product Objectives
Primary Objectives
Secondary Objectives
3. Technical Requirements
3.1 Functional Requirements
FR1: Vec2Text Model Integration3.2 Non-Functional Requirements
NFR1: Architecture Integration4. System Architecture
4.1 High-Level Architecture
┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ Text │───▶│ LNSP │───▶│ Processed │───▶│ Vec2Text │
│ Input │ │ Processing │ │ Vectors │ │ Decoder │
└─────────────┘ └─────────────┘ └─────────────┘ └─────────────┘
│ │
▼ ▼
┌─────────────┐ ┌─────────────┐
│ Vector │ │ Text │
│ Output │ │ Output │
└─────────────┘ └─────────────┘
4.2 Component Architecture
Core Components:5. Implementation Plan
Phase 1: Core Integration (Weeks 1-2)
Phase 2: Performance Optimization (Weeks 3-4)
Phase 3: Advanced Features (Weeks 5-6)
Phase 4: Production Readiness (Weeks 7-8)
6. Success Metrics
6.1 Technical Metrics
6.2 Development Metrics
7. Risk Assessment
High Risk
Medium Risk
Low Risk
8. Dependencies
External Dependencies
Internal Dependencies
9. Future Considerations
9.1 Enhanced Capabilities
9.2 Integration Opportunities
10. Conclusion
Integrating Vec2Text into LNSP represents a critical milestone toward full vector-native language processing. This integration will complete the bidirectional pipeline, enable interpretability, and provide the foundation for advanced Latent Neurolese applications while maintaining LNSP's performance advantages.
The phased implementation approach ensures technical risk mitigation while delivering incremental value. Success will be measured through both technical performance metrics and developer experience improvements, positioning LNSP as a complete solution for vector-native semantic processing.
Psudo Code:
LNSP Vec2Text Integration Implementation
Bidirectional Vector-Text Translation for Latent Neurolese Semantic Processor
import torch
import torch.nn as nn
from typing import List, Dict, Optional, Union, Tuple
import numpy as np
from sentence_transformers import SentenceTransformer
import logging
from dataclasses import dataclass
from pathlib import Path
import json
import time
Vec2Text imports (install with: pip install vec2text)
try:
import vec2text
VEC2TEXT_AVAILABLE = True
except ImportError:
VEC2TEXT_AVAILABLE = False
logging.warning("Vec2Text not available. Install with: pip install vec2text")
@dataclass
class Vec2TextConfig:
"""Configuration for Vec2Text integration with LNSP"""
model_name: str = "jxm/vec2text__openai_ada002__msmarco__msl128__hypothesizer"
corrector_name: str = "jxm/vec2text__openai_ada002__msmarco__msl128__corrector"
max_tokens: int = 32
num_steps: int = 20
device: str = "auto"
batch_size: int = 8
cache_size: int = 1000
temperature: float = 1.0
class LNSPVec2TextProcessor:
"""
Main processor class that integrates Vec2Text with LNSP
Provides bidirectional vector-text translation capabilities
"""
def __init__(self,
lnsp_model_path: str,
teacher_model_name: str = "sentence-transformers/all-MiniLM-L6-v2",
vec2text_config: Optional[Vec2TextConfig] = None):
self.config = vec2text_config or Vec2TextConfig()
self.device = self._setup_device()
# Load LNSP components
self.teacher_model = SentenceTransformer(teacher_model_name)
self.lnsp_model = self._load_lnsp_model(lnsp_model_path)
# Initialize Vec2Text components
self.vec2text_adapter = None
if VEC2TEXT_AVAILABLE:
self.vec2text_adapter = Vec2TextAdapter(self.config, self.device)
# Validation and caching
self.validator = RoundTripValidator(self.teacher_model)
self.cache = VectorTextCache(max_size=self.config.cache_size)
logging.info(f"LNSP Vec2Text Processor initialized on {self.device}")
def _setup_device(self) -> torch.device:
"""Setup optimal device for processing"""
if self.config.device == "auto":
if torch.cuda.is_available():
return torch.device("cuda")
elif torch.backends.mps.is_available():
return torch.device("mps")
else:
return torch.device("cpu")
return torch.device(self.config.device)
def _load_lnsp_model(self, model_path: str) -> nn.Module:
"""Load the trained LNSP model"""
model = torch.load(model_path, map_location=self.device)
model.eval()
return model
def text_to_vectors(self, texts: Union[str, List[str]]) -> torch.Tensor:
"""Convert text to LNSP input vectors using teacher model"""
if isinstance(texts, str):
texts = [texts]
with torch.no_grad():
embeddings = self.teacher_model.encode(texts, convert_to_tensor=True)
return embeddings.to(self.device)
def process_vectors(self, vectors: torch.Tensor) -> torch.Tensor:
"""Process vectors through LNSP model"""
with torch.no_grad():
if vectors.dim() == 1:
vectors = vectors.unsqueeze(0)
processed = self.lnsp_model(vectors)
return processed
def vectors_to_text(self,
vectors: torch.Tensor,
use_cache: bool = True,
return_metadata: bool = False) -> Union[List[str], Tuple[List[str], Dict]]:
"""
Convert vectors back to text using Vec2Text
Args:
vectors: Tensor of shape (batch_size, 384) or (384,)
use_cache: Whether to use vector-text cache
return_metadata: Whether to return generation metadata
Returns:
List of generated texts or (texts, metadata) tuple
"""
if not self.vec2text_adapter:
raise RuntimeError("Vec2Text not available. Install with: pip install vec2text")
# Ensure proper tensor shape
if vectors.dim() == 1:
vectors = vectors.unsqueeze(0)
# Check cache first
if use_cache:
cached_results = self.cache.get_batch(vectors)
if len(cached_results) == len(vectors):
return cached_results if not return_metadata else (cached_results, {})
# Generate text using Vec2Text
start_time = time.time()
texts, metadata = self.vec2text_adapter.decode_vectors(vectors)
generation_time = time.time() - start_time
# Update cache
if use_cache:
self.cache.update_batch(vectors, texts)
if return_metadata:
metadata.update({
'generation_time': generation_time,
'vectors_processed': len(vectors),
'cache_hits': len(cached_results) if use_cache else 0
})
return texts, metadata
return texts
def round_trip_process(self,
texts: Union[str, List[str]],
return_all_stages: bool = False) -> Union[List[str], Dict]:
"""
Complete round-trip: Text -> LNSP Processing -> Text
Args:
texts: Input text(s)
return_all_stages: Return intermediate vectors and metadata
Returns:
Generated texts or complete processing pipeline results
"""
if isinstance(texts, str):
texts = [texts]
# Stage 1: Text to vectors
input_vectors = self.text_to_vectors(texts)
# Stage 2: LNSP processing
processed_vectors = self.process_vectors(input_vectors)
# Stage 3: Vectors to text
output_texts, metadata = self.vectors_to_text(processed_vectors, return_metadata=True)
if return_all_stages:
return {
'input_texts': texts,
'input_vectors': input_vectors.cpu().numpy(),
'processed_vectors': processed_vectors.cpu().numpy(),
'output_texts': output_texts,
'metadata': metadata,
'validation': self.validator.validate_round_trip(texts, output_texts)
}
return output_texts
def debug_concept_processing(self, text: str, top_k: int = 5) -> Dict:
"""
Debug tool: Analyze how LNSP processes specific concepts
Args:
text: Input text to analyze
top_k: Number of nearest neighbors to show
Returns:
Debug information including vector analysis and concept mappings
"""
# Get input vector
input_vector = self.text_to_vectors(text)
processed_vector = self.process_vectors(input_vector)
# Generate text representation
output_text = self.vectors_to_text(processed_vector)[0]
# Analyze vector changes
vector_delta = processed_vector - input_vector
delta_magnitude = torch.norm(vector_delta).item()
# Validation metrics
validation = self.validator.validate_round_trip([text], [output_text])
return {
'input_text': text,
'output_text': output_text,
'vector_delta_magnitude': delta_magnitude,
'semantic_similarity': validation['similarities'][0],
'processing_analysis': {
'input_norm': torch.norm(input_vector).item(),
'output_norm': torch.norm(processed_vector).item(),
'dominant_changes': self._analyze_dominant_dimensions(vector_delta, top_k)
},
'validation_metrics': validation
}
def _analyze_dominant_dimensions(self, delta_vector: torch.Tensor, top_k: int) -> List[Dict]:
"""Analyze which vector dimensions changed most during processing"""
abs_changes = torch.abs(delta_vector.flatten())
top_indices = torch.topk(abs_changes, top_k).indices
changes = []
for idx in top_indices:
changes.append({
'dimension': idx.item(),
'delta': delta_vector.flatten()[idx].item(),
'abs_delta': abs_changes[idx].item()
})
return changes
class Vec2TextAdapter:
"""Adapter class to integrate Vec2Text models with LNSP vectors"""
def __init__(self, config: Vec2TextConfig, device: torch.device):
self.config = config
self.device = device
# Load Vec2Text models
self.hypothesizer = vec2text.load_model(
config.model_name,
device=device
)
self.corrector = vec2text.load_model(
config.corrector_name,
device=device
)
logging.info(f"Vec2Text models loaded on {device}")
def decode_vectors(self, vectors: torch.Tensor) -> Tuple[List[str], Dict]:
"""
Decode vectors to text using Vec2Text iterative correction
Args:
vectors: Tensor of shape (batch_size, 384)
Returns:
Tuple of (generated_texts, metadata)
"""
batch_size = vectors.shape[0]
# Initial hypothesis generation
hypotheses = self._generate_hypotheses(vectors)
# Iterative correction
corrected_texts = self._iterative_correction(vectors, hypotheses)
metadata = {
'batch_size': batch_size,
'num_correction_steps': self.config.num_steps,
'model_names': {
'hypothesizer': self.config.model_name,
'corrector': self.config.corrector_name
}
}
return corrected_texts, metadata
def _generate_hypotheses(self, vectors: torch.Tensor) -> List[str]:
"""Generate initial text hypotheses from vectors"""
with torch.no_grad():
# Use hypothesizer model to generate initial guesses
hypotheses = vec2text.invert_embeddings(
embeddings=vectors,
model=self.hypothesizer,
num_steps=1 # Just initial hypothesis
)
return hypotheses
def _iterative_correction(self, vectors: torch.Tensor, hypotheses: List[str]) -> List[str]:
"""Apply iterative correction to improve text quality"""
current_texts = hypotheses
for step in range(self.config.num_steps):
with torch.no_grad():
# Use corrector model to refine texts
current_texts = vec2text.invert_embeddings(
embeddings=vectors,
model=self.corrector,
num_steps=1,
initial_text=current_texts
)
return current_texts
class RoundTripValidator:
"""Validates the quality of round-trip text processing"""
def __init__(self, teacher_model: SentenceTransformer):
self.teacher_model = teacher_model
def validate_round_trip(self,
input_texts: List[str],
output_texts: List[str]) -> Dict:
"""
Validate round-trip processing quality
Args:
input_texts: Original texts
output_texts: Reconstructed texts
Returns:
Validation metrics including similarity scores and BLEU
"""
# Semantic similarity
input_embeddings = self.teacher_model.encode(input_texts)
output_embeddings = self.teacher_model.encode(output_texts)
similarities = []
for i in range(len(input_texts)):
sim = np.dot(input_embeddings[i], output_embeddings[i]) / (
np.linalg.norm(input_embeddings[i]) np.linalg.norm(output_embeddings[i])
)
similarities.append(float(sim))
# BLEU score calculation (simplified)
bleu_scores = self._calculate_bleu_scores(input_texts, output_texts)
return {
'similarities': similarities,
'mean_similarity': np.mean(similarities),
'bleu_scores': bleu_scores,
'mean_bleu': np.mean(bleu_scores),
'pairs': list(zip(input_texts, output_texts))
}
def _calculate_bleu_scores(self, references: List[str], candidates: List[str]) -> List[float]:
"""Calculate BLEU scores for text pairs"""
try:
from nltk.translate.bleu_score import sentence_bleu
from nltk.tokenize import word_tokenize
scores = []
for ref, cand in zip(references, candidates):
ref_tokens = [word_tokenize(ref.lower())]
cand_tokens = word_tokenize(cand.lower())
score = sentence_bleu(ref_tokens, cand_tokens)
scores.append(score)
return scores
except ImportError:
# Fallback: simple word overlap scoring
scores = []
for ref, cand in zip(references, candidates):
ref_words = set(ref.lower().split())
cand_words = set(cand.lower().split())
if len(ref_words) == 0:
scores.append(0.0)
else:
overlap = len(ref_words.intersection(cand_words)) / len(ref_words)
scores.append(overlap)
return scores
class VectorTextCache:
"""Simple cache for vector-text mappings to improve performance"""
def __init__(self, max_size: int = 1000):
self.cache = {}
self.max_size = max_size
self.access_order = []
def _vector_key(self, vector: torch.Tensor) -> str:
"""Create a hashable key from vector"""
# Use first few dimensions and norm for approximate matching
key_vector = vector.flatten()[:10]
key_str = ",".join([f"{x:.4f}" for x in key_vector.tolist()])
return key_str
def get(self, vector: torch.Tensor) -> Optional[str]:
"""Get cached text for vector"""
key = self._vector_key(vector)
if key in self.cache:
# Move to end (most recently used)
self.access_order.remove(key)
self.access_order.append(key)
return self.cache[key]
return None
def get_batch(self, vectors: torch.Tensor) -> List[Optional[str]]:
"""Get cached texts for batch of vectors"""
results = []
for i in range(len(vectors)):
result = self.get(vectors[i])
results.append(result)
return [r for r in results if r is not None]
def update(self, vector: torch.Tensor, text: str):
"""Update cache with new vector-text mapping"""
key = self._vector_key(vector)
# Remove oldest if at capacity
if len(self.cache) >= self.max_size and key not in self.cache:
oldest_key = self.access_order.pop(0)
del self.cache[oldest_key]
self.cache[key] = text
if key in self.access_order:
self.access_order.remove(key)
self.access_order.append(key)
def update_batch(self, vectors: torch.Tensor, texts: List[str]):
"""Update cache with batch of vector-text mappings"""
for vector, text in zip(vectors, texts):
self.update(vector, text)
class ConstellationTextifier:
"""Generate text descriptions of semantic constellations"""
def __init__(self, processor: LNSPVec2TextProcessor):
self.processor = processor
def describe_constellation(self,
center_vector: torch.Tensor,
radius: float = 0.1,
num_samples: int = 10) -> Dict:
"""
Generate text description of a semantic constellation
Args:
center_vector: Central vector of the constellation
radius: Radius for sampling nearby vectors
num_samples: Number of sample vectors to generate text for
Returns:
Description including center concept and neighborhood
"""
# Generate center text
center_text = self.processor.vectors_to_text(center_vector.unsqueeze(0))[0]
# Sample nearby vectors
nearby_vectors = self._sample_nearby_vectors(center_vector, radius, num_samples)
nearby_texts = self.processor.vectors_to_text(nearby_vectors)
# Analyze constellation properties
constellation_analysis = self._analyze_constellation(center_vector, nearby_vectors)
return {
'center_concept': center_text,
'nearby_concepts': nearby_texts,
'constellation_properties': constellation_analysis,
'description': self._generate_description(center_text, nearby_texts)
}
def _sample_nearby_vectors(self,
center: torch.Tensor,
radius: float,
num_samples: int) -> torch.Tensor:
"""Sample vectors in the neighborhood of center vector"""
# Add small random perturbations to create nearby vectors
noise = torch.randn(num_samples, center.shape[0]) radius
nearby_vectors = center.unsqueeze(0) + noise
# Normalize to unit vectors (like embeddings)
nearby_vectors = torch.nn.functional.normalize(nearby_vectors, dim=1)
return nearby_vectors
def _analyze_constellation(self,
center: torch.Tensor,
nearby: torch.Tensor) -> Dict:
"""Analyze properties of the semantic constellation"""
# Calculate distances
distances = torch.norm(nearby - center.unsqueeze(0), dim=1)
# Calculate similarities
similarities = torch.nn.functional.cosine_similarity(
center.unsqueeze(0), nearby, dim=1
)
return {
'mean_distance': float(distances.mean()),
'std_distance': float(distances.std()),
'mean_similarity': float(similarities.mean()),
'coherence_score': float(similarities.std()) # Lower std = more coherent
}
def _generate_description(self, center: str, nearby: List[str]) -> str:
"""Generate natural language description of constellation"""
description = f"The concept '{center}' forms a semantic constellation with related concepts including: "
description += ", ".join(nearby[:5]) # Show first 5 concepts
if len(nearby) > 5:
description += f" and {len(nearby) - 5} other related concepts"
return description
Example usage and testing functions
def example_usage():
"""Example of how to use the LNSP Vec2Text processor"""
# Initialize processor
processor = LNSPVec2TextProcessor(
lnsp_model_path="path/to/your/lnsp_model.pth",
teacher_model_name="sentence-transformers/all-MiniLM-L6-v2"
)
# Example 1: Round-trip processing
input_texts = [
"What is machine learning?",
"The impact of glucose on metabolism",
"Quantum computing algorithms"
]
output_texts = processor.round_trip_process(input_texts)
print("Round-trip results:")
for inp, out in zip(input_texts, output_texts):
print(f"Input: {inp}")
print(f"Output: {out}")
print()
# Example 2: Debug concept processing
debug_info = processor.debug_concept_processing("artificial intelligence")
print("Debug analysis:")
print(f"Semantic similarity: {debug_info['semantic_similarity']:.3f}")
print(f"Vector change magnitude: {debug_info['vector_delta_magnitude']:.3f}")
# Example 3: Constellation analysis
textifier = ConstellationTextifier(processor)
concept_vector = processor.text_to_vectors("machine learning")
constellation = textifier.describe_constellation(concept_vector[0])
print("Constellation description:")
print(constellation['description'])
if __name__ == "__main__":
# Run example if Vec2Text is available
if VEC2TEXT_AVAILABLE:
example_usage()
else:
print("Vec2Text not available. Install with: pip install vec2text")
print("This is the implementation structure that will work once installed.")