wifi-densepose/vendor/sublinear-time-solver/plans/psycho-symbolic-wasm-api.md

Psycho-Symbolic Reasoner WASM API Plan

Project: psycho-symbolic-reasoner-wasm

Version: 0.1.0 License: MIT/Apache-2.0 Target: OpenAI-compatible completion API via WASM

---

🎯 Executive Summary

Transform the TypeScript psycho-symbolic reasoning engine into a high-performance Rust crate compiled to WASM, exposing an OpenAI-compatible API for seamless integration with existing LLM infrastructure.

Key Goals:

• 10x performance improvement over JavaScript implementation
• OpenAI API compatibility for drop-in replacement
• Sub-millisecond reasoning for cached queries
• Memory-efficient graph operations in Rust
• Streaming completions support

---

📐 Architecture

Core Components

// crate structure
psycho-symbolic-reasoner/
├── Cargo.toml
├── src/
│   ├── lib.rs                 // WASM entry points
│   ├── api/
│   │   ├── mod.rs             // OpenAI API handlers
│   │   ├── completions.rs     // /v1/completions endpoint
│   │   ├── chat.rs            // /v1/chat/completions endpoint
│   │   └── embeddings.rs      // /v1/embeddings endpoint
│   ├── reasoning/
│   │   ├── mod.rs             // Core reasoning engine
│   │   ├── knowledge_graph.rs // Triple-based knowledge
│   │   ├── bfs_traversal.rs   // Graph traversal algorithms
│   │   ├── inference.rs       // Logical inference chains
│   │   └── patterns.rs        // Cognitive pattern recognition
│   ├── cache/
│   │   ├── mod.rs             // High-performance cache
│   │   ├── similarity.rs      // Jaccard similarity matching
│   │   └── eviction.rs        // LRU eviction strategy
│   └── wasm/
│       ├── mod.rs             // WASM bindings
│       └── memory.rs          // Memory management
├── benches/
│   └── reasoning_bench.rs     // Performance benchmarks
└── tests/
    └── integration_tests.rs   // API compatibility tests

---

🔧 Implementation Plan

Phase 1: Core Data Structures (Week 1)

// src/reasoning/knowledge_graph.rs

use serde::{Deserialize, Serialize};
use std::collections::{HashMap, HashSet};

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Triple {
    pub subject: String,
    pub predicate: String,
    pub object: String,
    pub confidence: f32,
    pub timestamp: u64,
}

#[derive(Debug)]
pub struct KnowledgeGraph {
    triples: HashMap<String, Triple>,
    subject_index: HashMap<String, HashSet<String>>,
    object_index: HashMap<String, HashSet<String>>,
    predicate_index: HashMap<String, HashSet<String>>,
}

impl KnowledgeGraph {
    pub fn new() -> Self {
        Self {
            triples: HashMap::new(),
            subject_index: HashMap::new(),
            object_index: HashMap::new(),
            predicate_index: HashMap::new(),
        }
    }

    pub fn add_triple(&mut self, triple: Triple) -> String {
        let id = Self::generate_id(&triple);

        // Update indices for O(1) lookups
        self.subject_index
            .entry(triple.subject.clone())
            .or_insert_with(HashSet::new)
            .insert(id.clone());

        self.object_index
            .entry(triple.object.clone())
            .or_insert_with(HashSet::new)
            .insert(id.clone());

        self.predicate_index
            .entry(triple.predicate.clone())
            .or_insert_with(HashSet::new)
            .insert(id.clone());

        self.triples.insert(id.clone(), triple);
        id
    }

    pub fn bfs_traverse(&self, start: &str, max_depth: usize) -> Vec<Vec<String>> {
        // Sublinear BFS implementation
        let mut visited = HashSet::new();
        let mut queue = std::collections::VecDeque::new();
        let mut paths = Vec::new();

        queue.push_back((start.to_string(), 0, vec![start.to_string()]));

        while let Some((node, depth, path)) = queue.pop_front() {
            if depth >= max_depth || visited.contains(&node) {
                continue;
            }

            visited.insert(node.clone());
            paths.push(path.clone());

            // Find connected nodes via subject/object indices
            if let Some(triple_ids) = self.subject_index.get(&node) {
                for id in triple_ids {
                    if let Some(triple) = self.triples.get(id) {
                        let mut new_path = path.clone();
                        new_path.push(triple.object.clone());
                        queue.push_back((triple.object.clone(), depth + 1, new_path));
                    }
                }
            }
        }

        paths
    }

    fn generate_id(triple: &Triple) -> String {
        use sha2::{Sha256, Digest};
        let mut hasher = Sha256::new();
        hasher.update(format!("{}{}{}", triple.subject, triple.predicate, triple.object));
        format!("{:x}", hasher.finalize())
    }
}

Phase 2: OpenAI API Implementation (Week 2)

// src/api/completions.rs

use serde::{Deserialize, Serialize};
use wasm_bindgen::prelude::*;

#[derive(Deserialize)]
#[serde(rename_all = "snake_case")]
pub struct CompletionRequest {
    pub model: String,
    pub prompt: String,
    #[serde(default = "default_max_tokens")]
    pub max_tokens: u32,
    #[serde(default = "default_temperature")]
    pub temperature: f32,
    #[serde(default)]
    pub top_p: Option<f32>,
    #[serde(default)]
    pub n: Option<u32>,
    #[serde(default)]
    pub stream: bool,
    #[serde(default)]
    pub stop: Option<Vec<String>>,
}

#[derive(Serialize)]
pub struct CompletionResponse {
    pub id: String,
    pub object: String,
    pub created: u64,
    pub model: String,
    pub choices: Vec<CompletionChoice>,
    pub usage: Usage,
}

#[derive(Serialize)]
pub struct CompletionChoice {
    pub text: String,
    pub index: u32,
    pub logprobs: Option<LogProbs>,
    pub finish_reason: String,
}

#[derive(Serialize)]
pub struct Usage {
    pub prompt_tokens: u32,
    pub completion_tokens: u32,
    pub total_tokens: u32,
}

#[wasm_bindgen]
pub async fn complete(request: JsValue) -> Result<JsValue, JsValue> {
    let req: CompletionRequest = serde_wasm_bindgen::from_value(request)?;

    // Initialize reasoning engine
    let mut reasoner = PsychoSymbolicReasoner::new();

    // Perform reasoning with cache check
    let result = reasoner.reason(&req.prompt, req.max_tokens as usize).await?;

    // Format as OpenAI response
    let response = CompletionResponse {
        id: format!("cmpl-{}", uuid::Uuid::new_v4()),
        object: "text_completion".to_string(),
        created: std::time::SystemTime::now()
            .duration_since(std::time::UNIX_EPOCH)
            .unwrap()
            .as_secs(),
        model: req.model,
        choices: vec![CompletionChoice {
            text: result.answer,
            index: 0,
            logprobs: None,
            finish_reason: "stop".to_string(),
        }],
        usage: Usage {
            prompt_tokens: estimate_tokens(&req.prompt),
            completion_tokens: estimate_tokens(&result.answer),
            total_tokens: estimate_tokens(&req.prompt) + estimate_tokens(&result.answer),
        },
    };

    Ok(serde_wasm_bindgen::to_value(&response)?)
}

fn estimate_tokens(text: &str) -> u32 {
    // Rough estimation: 4 chars per token
    (text.len() / 4) as u32
}

Phase 3: Reasoning Engine (Week 3)

// src/reasoning/mod.rs

use std::collections::{HashMap, HashSet, VecDeque};
use crate::cache::ReasoningCache;

pub struct PsychoSymbolicReasoner {
    knowledge_graph: KnowledgeGraph,
    cache: ReasoningCache,
    patterns: PatternRecognizer,
}

impl PsychoSymbolicReasoner {
    pub fn new() -> Self {
        let mut kg = KnowledgeGraph::new();
        Self::initialize_knowledge(&mut kg);

        Self {
            knowledge_graph: kg,
            cache: ReasoningCache::new(10000),
            patterns: PatternRecognizer::new(),
        }
    }

    pub async fn reason(&mut self, query: &str, max_depth: usize) -> Result<ReasoningResult, String> {
        // Check cache first (O(1) lookup)
        if let Some(cached) = self.cache.get(query) {
            return Ok(cached);
        }

        let start = std::time::Instant::now();

        // Step 1: Pattern recognition
        let patterns = self.patterns.identify(query);

        // Step 2: Entity extraction
        let entities = self.extract_entities(query);

        // Step 3: Knowledge graph traversal (sublinear BFS)
        let mut insights = HashSet::new();
        for entity in &entities {
            let paths = self.knowledge_graph.bfs_traverse(entity, max_depth);
            for path in paths {
                if path.len() >= 2 {
                    let insight = self.generate_insight(&path);
                    insights.insert(insight);
                }
            }
        }

        // Step 4: Inference chain building
        let inferences = self.build_inference_chain(&entities, &patterns);

        // Step 5: Synthesis
        let answer = self.synthesize_answer(query, &insights, &inferences, &patterns);

        let result = ReasoningResult {
            answer,
            confidence: self.calculate_confidence(&insights, &inferences),
            insights: insights.into_iter().collect(),
            patterns: patterns.clone(),
            compute_time_ms: start.elapsed().as_millis() as u32,
        };

        // Cache the result
        self.cache.set(query, result.clone());

        Ok(result)
    }

    fn initialize_knowledge(kg: &mut KnowledgeGraph) {
        // Pre-load domain knowledge
        kg.add_triple(Triple {
            subject: "jwt".to_string(),
            predicate: "vulnerable_to".to_string(),
            object: "timing_attacks".to_string(),
            confidence: 0.85,
            timestamp: 0,
        });

        kg.add_triple(Triple {
            subject: "cache_collision".to_string(),
            predicate: "enables".to_string(),
            object: "privilege_escalation".to_string(),
            confidence: 0.92,
            timestamp: 0,
        });

        // Add more domain knowledge...
    }

    fn extract_entities(&self, query: &str) -> Vec<String> {
        // Fast entity extraction using regex and keyword matching
        let mut entities = Vec::new();
        let keywords = ["api", "jwt", "cache", "security", "user", "auth"];

        for keyword in &keywords {
            if query.to_lowercase().contains(keyword) {
                entities.push(keyword.to_string());
            }
        }

        entities
    }

    fn generate_insight(&self, path: &[String]) -> String {
        format!("{} implies {}", path.first().unwrap(), path.last().unwrap())
    }

    fn build_inference_chain(&self, entities: &[String], patterns: &[String]) -> Vec<String> {
        let mut inferences = Vec::new();

        // Apply logical rules based on patterns
        if patterns.contains(&"causal".to_string()) {
            for entity in entities {
                inferences.push(format!("{} causes downstream effects", entity));
            }
        }

        if patterns.contains(&"lateral".to_string()) {
            inferences.push("Consider unconventional approaches".to_string());
        }

        inferences
    }

    fn synthesize_answer(
        &self,
        query: &str,
        insights: &HashSet<String>,
        inferences: &[String],
        patterns: &[String],
    ) -> String {
        let mut answer = String::new();

        if patterns.contains(&"exploratory".to_string()) {
            answer.push_str("Analysis reveals: ");
        } else if patterns.contains(&"systems".to_string()) {
            answer.push_str("From a systems perspective: ");
        }

        // Add top insights
        for (i, insight) in insights.iter().take(3).enumerate() {
            if i > 0 {
                answer.push_str(". ");
            }
            answer.push_str(insight);
        }

        answer
    }

    fn calculate_confidence(&self, insights: &HashSet<String>, inferences: &[String]) -> f32 {
        let base = 0.5;
        let insight_boost = (insights.len() as f32) * 0.05;
        let inference_boost = (inferences.len() as f32) * 0.03;

        (base + insight_boost + inference_boost).min(1.0)
    }
}

Phase 4: High-Performance Cache (Week 4)

// src/cache/mod.rs

use std::collections::{HashMap, LinkedList};
use std::sync::{Arc, RwLock};

#[derive(Clone)]
pub struct ReasoningCache {
    cache: Arc<RwLock<HashMap<u64, CacheEntry>>>,
    lru: Arc<RwLock<LinkedList<u64>>>,
    max_size: usize,
}

#[derive(Clone)]
struct CacheEntry {
    result: ReasoningResult,
    hit_count: u32,
    timestamp: u64,
}

impl ReasoningCache {
    pub fn new(max_size: usize) -> Self {
        Self {
            cache: Arc::new(RwLock::new(HashMap::new())),
            lru: Arc::new(RwLock::new(LinkedList::new())),
            max_size,
        }
    }

    pub fn get(&self, query: &str) -> Option<ReasoningResult> {
        let key = self.hash_query(query);

        let cache = self.cache.read().unwrap();
        if let Some(entry) = cache.get(&key) {
            // Update LRU
            let mut lru = self.lru.write().unwrap();
            lru.retain(|&k| k != key);
            lru.push_front(key);

            return Some(entry.result.clone());
        }

        None
    }

    pub fn set(&mut self, query: &str, result: ReasoningResult) {
        let key = self.hash_query(query);

        let mut cache = self.cache.write().unwrap();
        let mut lru = self.lru.write().unwrap();

        // Evict if necessary
        if cache.len() >= self.max_size {
            if let Some(&oldest) = lru.back() {
                cache.remove(&oldest);
                lru.pop_back();
            }
        }

        cache.insert(key, CacheEntry {
            result,
            hit_count: 0,
            timestamp: std::time::SystemTime::now()
                .duration_since(std::time::UNIX_EPOCH)
                .unwrap()
                .as_secs(),
        });

        lru.push_front(key);
    }

    fn hash_query(&self, query: &str) -> u64 {
        use std::collections::hash_map::DefaultHasher;
        use std::hash::{Hash, Hasher};

        let mut hasher = DefaultHasher::new();
        query.hash(&mut hasher);
        hasher.finish()
    }
}

Phase 5: WASM Bindings (Week 5)

// src/wasm/mod.rs

use wasm_bindgen::prelude::*;
use web_sys::console;

#[wasm_bindgen(start)]
pub fn init() {
    // Set panic hook for better error messages
    console_error_panic_hook::set_once();

    console::log_1(&"Psycho-Symbolic Reasoner WASM initialized".into());
}

#[wasm_bindgen]
pub struct WasmReasoner {
    inner: PsychoSymbolicReasoner,
}

#[wasm_bindgen]
impl WasmReasoner {
    #[wasm_bindgen(constructor)]
    pub fn new() -> Self {
        Self {
            inner: PsychoSymbolicReasoner::new(),
        }
    }

    #[wasm_bindgen]
    pub async fn complete(&mut self, request: JsValue) -> Result<JsValue, JsValue> {
        complete(request).await
    }

    #[wasm_bindgen]
    pub async fn chat(&mut self, request: JsValue) -> Result<JsValue, JsValue> {
        // Handle chat completion format
        let req: ChatCompletionRequest = serde_wasm_bindgen::from_value(request)?;

        // Extract the last user message
        let prompt = req.messages
            .iter()
            .rev()
            .find(|m| m.role == "user")
            .map(|m| m.content.clone())
            .ok_or_else(|| JsValue::from_str("No user message found"))?;

        // Convert to completion request and process
        let completion_req = CompletionRequest {
            model: req.model,
            prompt,
            max_tokens: req.max_tokens.unwrap_or(100),
            temperature: req.temperature.unwrap_or(0.7),
            top_p: req.top_p,
            n: req.n,
            stream: req.stream.unwrap_or(false),
            stop: req.stop,
        };

        complete(serde_wasm_bindgen::to_value(&completion_req)?).await
    }

    #[wasm_bindgen]
    pub fn get_cache_stats(&self) -> JsValue {
        let stats = CacheStats {
            size: self.inner.cache.size(),
            hit_ratio: self.inner.cache.hit_ratio(),
            avg_compute_time_ms: self.inner.cache.avg_compute_time(),
        };

        serde_wasm_bindgen::to_value(&stats).unwrap()
    }
}

---

📦 Cargo.toml Configuration

[package]
name = "psycho-symbolic-reasoner"
version = "0.1.0"
authors = ["rUv <github.com/ruvnet>"]
edition = "2021"
license = "MIT OR Apache-2.0"

[lib]
crate-type = ["cdylib", "rlib"]

[dependencies]
wasm-bindgen = "0.2"
wasm-bindgen-futures = "0.4"
serde = { version = "1.0", features = ["derive"] }
serde-wasm-bindgen = "0.6"
serde_json = "1.0"
sha2 = "0.10"
uuid = { version = "1.0", features = ["v4", "wasm-bindgen"] }
console_error_panic_hook = "0.1"
web-sys = { version = "0.3", features = ["console"] }

[dev-dependencies]
wasm-bindgen-test = "0.3"
criterion = "0.5"

[profile.release]
opt-level = "z"     # Optimize for size
lto = true          # Enable Link Time Optimization
codegen-units = 1   # Single codegen unit for better optimization
strip = true        # Strip symbols
panic = "abort"     # Smaller binary size

[[bench]]
name = "reasoning"
harness = false

---

🚀 Build & Deployment

Build Commands

# Install dependencies
cargo install wasm-pack

# Build for web
wasm-pack build --target web --out-dir pkg

# Build for Node.js
wasm-pack build --target nodejs --out-dir pkg-node

# Build for bundlers (webpack, etc.)
wasm-pack build --target bundler --out-dir pkg-bundler

# Optimize WASM size
wasm-opt -Oz -o pkg/psycho_symbolic_reasoner_bg_opt.wasm pkg/psycho_symbolic_reasoner_bg.wasm

JavaScript Integration

// index.js - OpenAI-compatible API server
import { WasmReasoner } from './pkg/psycho_symbolic_reasoner.js';

const reasoner = new WasmReasoner();

// Express server setup
app.post('/v1/completions', async (req, res) => {
  try {
    const result = await reasoner.complete(req.body);
    res.json(result);
  } catch (error) {
    res.status(500).json({ error: error.message });
  }
});

app.post('/v1/chat/completions', async (req, res) => {
  try {
    const result = await reasoner.chat(req.body);
    res.json(result);
  } catch (error) {
    res.status(500).json({ error: error.message });
  }
});

// Cache statistics endpoint
app.get('/v1/cache/stats', (req, res) => {
  res.json(reasoner.get_cache_stats());
});

---

📊 Performance Targets

Benchmarks

Operation	JavaScript (v1.0.11)	Rust WASM (Target)	Improvement
Cold Start	1-2ms	0.1-0.2ms	10x
Cache Hit	0.03ms	0.003ms	10x
Graph Traversal	0.5ms	0.05ms	10x
Pattern Recognition	0.2ms	0.02ms	10x
Memory Usage	10MB	1MB	10x

Memory Optimizations

1. Compact Triple Storage: Use integer IDs instead of strings
2. Bit-packed Confidence: Store as u8 (0-255) instead of f32
3. Arena Allocator: Reduce allocation overhead
4. Zero-copy Deserialization: Minimize data copying

---

🧪 Testing Strategy

Unit Tests

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_knowledge_graph_traversal() {
        let mut kg = KnowledgeGraph::new();
        kg.add_triple(Triple {
            subject: "a".to_string(),
            predicate: "leads_to".to_string(),
            object: "b".to_string(),
            confidence: 0.9,
            timestamp: 0,
        });

        let paths = kg.bfs_traverse("a", 2);
        assert_eq!(paths.len(), 2);
    }

    #[test]
    fn test_cache_eviction() {
        let mut cache = ReasoningCache::new(2);
        cache.set("query1", result1());
        cache.set("query2", result2());
        cache.set("query3", result3()); // Should evict query1

        assert!(cache.get("query1").is_none());
        assert!(cache.get("query2").is_some());
        assert!(cache.get("query3").is_some());
    }
}

Integration Tests

#[wasm_bindgen_test]
async fn test_openai_api_compatibility() {
    let request = r#"{
        "model": "psycho-symbolic-v1",
        "prompt": "What are JWT security vulnerabilities?",
        "max_tokens": 100,
        "temperature": 0.7
    }"#;

    let response = complete(serde_json::from_str(request).unwrap()).await.unwrap();

    assert!(response.choices.len() > 0);
    assert!(response.usage.total_tokens > 0);
}

---

🔐 Security Considerations

1. Input Validation: Sanitize all queries to prevent injection
2. Rate Limiting: Built-in request throttling
3. Memory Limits: Prevent OOM attacks with bounded caches
4. Secure Random: Use getrandom for cryptographic operations

---

📈 Optimization Roadmap

Phase 6: Advanced Optimizations (Weeks 6-8)

1. SIMD Acceleration: Use WASM SIMD for vector operations
2. WebGPU Integration: Offload matrix operations to GPU
3. Streaming Responses: Implement Server-Sent Events
4. Multi-threading: Use Web Workers for parallel reasoning
5. Compression: LZ4 compression for cache entries

---

🌐 Deployment Options

1. Edge Functions (Cloudflare Workers)

export default {
  async fetch(request, env) {
    const reasoner = new WasmReasoner();
    const body = await request.json();
    const result = await reasoner.complete(body);
    return new Response(JSON.stringify(result), {
      headers: { 'Content-Type': 'application/json' },
    });
  },
};

2. Docker Container

FROM rust:1.75 as builder
WORKDIR /app
COPY . .
RUN cargo install wasm-pack
RUN wasm-pack build --target nodejs

FROM node:20-slim
WORKDIR /app
COPY --from=builder /app/pkg ./pkg
COPY server.js .
RUN npm install express
CMD ["node", "server.js"]

3. Native Binary with Embedded WASM

// native-server.rs
use wasmtime::*;

fn main() {
    let engine = Engine::default();
    let module = Module::from_file(&engine, "psycho_symbolic_reasoner.wasm").unwrap();
    // ... server implementation
}

---

📝 API Documentation

Endpoints

POST /v1/completions

{
  "model": "psycho-symbolic-v1",
  "prompt": "Analyze security vulnerabilities in JWT tokens",
  "max_tokens": 150,
  "temperature": 0.7,
  "top_p": 0.9,
  "stream": false
}

POST /v1/chat/completions

{
  "model": "psycho-symbolic-v1",
  "messages": [
    {"role": "user", "content": "What are hidden complexities in API design?"}
  ],
  "max_tokens": 200,
  "temperature": 0.8
}

Response Format

{
  "id": "cmpl-7abc123",
  "object": "text_completion",
  "created": 1699123456,
  "model": "psycho-symbolic-v1",
  "choices": [{
    "text": "Analysis reveals several hidden complexities...",
    "index": 0,
    "finish_reason": "stop"
  }],
  "usage": {
    "prompt_tokens": 12,
    "completion_tokens": 45,
    "total_tokens": 57
  }
}

---

🎯 Success Metrics

1. Performance: <0.1ms response time for cached queries
2. Accuracy: 95% relevance score on benchmark queries
3. Compatibility: 100% OpenAI API compatibility
4. Size: <500KB WASM binary
5. Memory: <1MB runtime memory usage

---

📅 Timeline

Week	Milestone	Deliverable
1	Core data structures	Knowledge graph implementation
2	OpenAI API	Completion endpoints
3	Reasoning engine	BFS traversal, inference chains
4	Caching system	LRU cache with similarity matching
5	WASM compilation	Working WASM module
6	Optimization	SIMD, compression, benchmarks
7	Testing	Integration tests, API validation
8	Deployment	Docker, edge function, documentation

---

🚀 Getting Started

# Clone the repository
git clone https://github.com/ruvnet/psycho-symbolic-reasoner-wasm
cd psycho-symbolic-reasoner-wasm

# Build the WASM module
wasm-pack build

# Run benchmarks
cargo bench

# Start the API server
npm start

# Test the API
curl -X POST http://localhost:3000/v1/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "psycho-symbolic-v1",
    "prompt": "What are JWT vulnerabilities?",
    "max_tokens": 100
  }'

---

📚 References

• OpenAI API Documentation
• WebAssembly Specification
• Rust WASM Book
• wasm-bindgen Guide

---

This plan provides a complete roadmap for creating a high-performance, OpenAI-compatible psycho-symbolic reasoning API in Rust/WASM with 10x performance improvements over the JavaScript implementation.