oxirs_vec/
tensorflow.rs

1//! TensorFlow integration for embedding generation and model serving
2
3use crate::real_time_embedding_pipeline::traits::{
4    ContentItem, EmbeddingGenerator, GeneratorStatistics, ProcessingResult, ProcessingStatus,
5};
6use crate::Vector;
7use anyhow::{anyhow, Result};
8use scirs2_core::random::Random;
9use serde::{Deserialize, Serialize};
10use std::collections::HashMap;
11use std::path::PathBuf;
12use std::time::{Duration, Instant};
13
14/// TensorFlow model configuration
15#[derive(Debug, Clone, Serialize, Deserialize)]
16pub struct TensorFlowConfig {
17    pub model_path: PathBuf,
18    pub input_name: String,
19    pub output_name: String,
20    pub device: TensorFlowDevice,
21    pub batch_size: usize,
22    pub max_sequence_length: usize,
23    pub optimization_level: OptimizationLevel,
24    pub use_mixed_precision: bool,
25    pub session_config: SessionConfig,
26}
27
28/// TensorFlow device configuration
29#[derive(Debug, Clone, Serialize, Deserialize)]
30pub enum TensorFlowDevice {
31    Cpu { num_threads: Option<usize> },
32    Gpu { device_id: i32, memory_growth: bool },
33    Tpu { worker: String },
34}
35
36/// TensorFlow optimization levels
37#[derive(Debug, Clone, Serialize, Deserialize)]
38pub enum OptimizationLevel {
39    None,
40    Basic,
41    Extended,
42    Aggressive,
43}
44
45/// TensorFlow session configuration
46#[derive(Debug, Clone, Serialize, Deserialize)]
47pub struct SessionConfig {
48    pub inter_op_parallelism_threads: Option<usize>,
49    pub intra_op_parallelism_threads: Option<usize>,
50    pub allow_soft_placement: bool,
51    pub log_device_placement: bool,
52}
53
54impl Default for TensorFlowConfig {
55    fn default() -> Self {
56        Self {
57            model_path: PathBuf::from("./models/universal-sentence-encoder"),
58            input_name: "inputs".to_string(),
59            output_name: "outputs".to_string(),
60            device: TensorFlowDevice::Cpu { num_threads: None },
61            batch_size: 32,
62            max_sequence_length: 512,
63            optimization_level: OptimizationLevel::Basic,
64            use_mixed_precision: false,
65            session_config: SessionConfig::default(),
66        }
67    }
68}
69
70impl Default for SessionConfig {
71    fn default() -> Self {
72        Self {
73            inter_op_parallelism_threads: None,
74            intra_op_parallelism_threads: None,
75            allow_soft_placement: true,
76            log_device_placement: false,
77        }
78    }
79}
80
81/// TensorFlow model metadata
82#[derive(Debug, Clone)]
83pub struct TensorFlowModelInfo {
84    pub model_path: PathBuf,
85    pub input_signature: Vec<TensorSpec>,
86    pub output_signature: Vec<TensorSpec>,
87    pub model_version: String,
88    pub dimensions: usize,
89    pub preprocessing_required: bool,
90}
91
92/// TensorFlow tensor specification
93#[derive(Debug, Clone)]
94pub struct TensorSpec {
95    pub name: String,
96    pub dtype: TensorDataType,
97    pub shape: Vec<Option<i64>>,
98}
99
100/// TensorFlow data types
101#[derive(Debug, Clone)]
102pub enum TensorDataType {
103    Float32,
104    Float64,
105    Int32,
106    Int64,
107    String,
108    Bool,
109}
110
111/// TensorFlow embedder for generating embeddings
112#[derive(Debug)]
113pub struct TensorFlowEmbedder {
114    config: TensorFlowConfig,
115    model_info: Option<TensorFlowModelInfo>,
116    session_initialized: bool,
117    preprocessing_pipeline: PreprocessingPipeline,
118}
119
120/// Text preprocessing pipeline for TensorFlow models
121#[derive(Debug)]
122pub struct PreprocessingPipeline {
123    pub lowercase: bool,
124    pub remove_punctuation: bool,
125    pub tokenizer: Option<String>,
126    pub vocabulary: Option<HashMap<String, i32>>,
127}
128
129impl Default for PreprocessingPipeline {
130    fn default() -> Self {
131        Self {
132            lowercase: true,
133            remove_punctuation: false,
134            tokenizer: None,
135            vocabulary: None,
136        }
137    }
138}
139
140impl TensorFlowEmbedder {
141    /// Create a new TensorFlow embedder
142    pub fn new(config: TensorFlowConfig) -> Result<Self> {
143        Ok(Self {
144            config,
145            model_info: None,
146            session_initialized: false,
147            preprocessing_pipeline: PreprocessingPipeline::default(),
148        })
149    }
150
151    /// Load and initialize the TensorFlow model
152    pub fn load_model(&mut self) -> Result<()> {
153        if !self.config.model_path.exists() {
154            return Err(anyhow!(
155                "Model path does not exist: {:?}",
156                self.config.model_path
157            ));
158        }
159
160        // Mock model loading - in a real implementation, this would use tensorflow-rust
161        let model_info = TensorFlowModelInfo {
162            model_path: self.config.model_path.clone(),
163            input_signature: vec![TensorSpec {
164                name: self.config.input_name.clone(),
165                dtype: TensorDataType::String,
166                shape: vec![None, None], // batch_size, sequence_length
167            }],
168            output_signature: vec![TensorSpec {
169                name: self.config.output_name.clone(),
170                dtype: TensorDataType::Float32,
171                shape: vec![None, Some(512)], // batch_size, embedding_dim
172            }],
173            model_version: "1.0.0".to_string(),
174            dimensions: 512,
175            preprocessing_required: true,
176        };
177
178        self.model_info = Some(model_info);
179        self.session_initialized = true;
180        Ok(())
181    }
182
183    /// Generate embeddings for text content
184    pub fn embed_text(&self, text: &str) -> Result<Vector> {
185        if !self.session_initialized {
186            return Err(anyhow!("Model not loaded. Call load_model() first."));
187        }
188
189        let preprocessed_text = self.preprocess_text(text)?;
190        let embedding = self.run_inference(&preprocessed_text)?;
191        Ok(Vector::new(embedding))
192    }
193
194    /// Generate embeddings for multiple texts
195    pub fn embed_batch(&self, texts: &[String]) -> Result<Vec<Vector>> {
196        if !self.session_initialized {
197            return Err(anyhow!("Model not loaded. Call load_model() first."));
198        }
199
200        let mut results = Vec::new();
201        for text in texts {
202            let embedding = self.embed_text(text)?;
203            results.push(embedding);
204        }
205        Ok(results)
206    }
207
208    /// Preprocess text according to model requirements
209    fn preprocess_text(&self, text: &str) -> Result<String> {
210        let mut processed = text.to_string();
211
212        if self.preprocessing_pipeline.lowercase {
213            processed = processed.to_lowercase();
214        }
215
216        if self.preprocessing_pipeline.remove_punctuation {
217            processed = processed
218                .chars()
219                .filter(|c| c.is_alphanumeric() || c.is_whitespace())
220                .collect();
221        }
222
223        // Truncate to max sequence length
224        if processed.len() > self.config.max_sequence_length {
225            processed.truncate(self.config.max_sequence_length);
226        }
227
228        Ok(processed)
229    }
230
231    /// Run TensorFlow inference (mock implementation)
232    fn run_inference(&self, text: &str) -> Result<Vec<f32>> {
233        let model_info = self
234            .model_info
235            .as_ref()
236            .ok_or_else(|| anyhow!("Model info not available"))?;
237
238        // Mock inference - generate random embeddings
239        let mut rng = Random::seed(text.len() as u64);
240        use scirs2_core::random::Rng;
241
242        let mut embedding = vec![0.0f32; model_info.dimensions];
243        for value in &mut embedding {
244            *value = rng.gen_range(-1.0..1.0);
245        }
246
247        // Normalize embedding
248        let norm = embedding.iter().map(|x| x * x).sum::<f32>().sqrt();
249        if norm > 0.0 {
250            for x in &mut embedding {
251                *x /= norm;
252            }
253        }
254
255        Ok(embedding)
256    }
257
258    /// Get model information
259    pub fn get_model_info(&self) -> Option<&TensorFlowModelInfo> {
260        self.model_info.as_ref()
261    }
262
263    /// Get output dimensions
264    pub fn get_dimensions(&self) -> Option<usize> {
265        self.model_info.as_ref().map(|info| info.dimensions)
266    }
267
268    /// Update preprocessing pipeline
269    pub fn set_preprocessing_pipeline(&mut self, pipeline: PreprocessingPipeline) {
270        self.preprocessing_pipeline = pipeline;
271    }
272}
273
274/// TensorFlow model server for serving multiple models
275#[derive(Debug)]
276pub struct TensorFlowModelServer {
277    models: HashMap<String, TensorFlowEmbedder>,
278    default_model: String,
279    server_config: ServerConfig,
280}
281
282/// Server configuration for TensorFlow model serving
283#[derive(Debug, Clone)]
284pub struct ServerConfig {
285    pub model_warming: bool,
286    pub request_batching: bool,
287    pub max_batch_size: usize,
288    pub batch_timeout_ms: u64,
289    pub model_versions: HashMap<String, String>,
290}
291
292impl Default for ServerConfig {
293    fn default() -> Self {
294        Self {
295            model_warming: true,
296            request_batching: true,
297            max_batch_size: 64,
298            batch_timeout_ms: 10,
299            model_versions: HashMap::new(),
300        }
301    }
302}
303
304impl TensorFlowModelServer {
305    /// Create a new TensorFlow model server
306    pub fn new(default_model: String, config: ServerConfig) -> Self {
307        Self {
308            models: HashMap::new(),
309            default_model,
310            server_config: config,
311        }
312    }
313
314    /// Register a model with the server
315    pub fn register_model(&mut self, name: String, embedder: TensorFlowEmbedder) -> Result<()> {
316        self.models.insert(name.clone(), embedder);
317
318        if self.server_config.model_warming {
319            if let Some(model) = self.models.get(&name) {
320                // Warm up the model with a test embedding
321                let _ = model.embed_text("warmup text");
322            }
323        }
324
325        Ok(())
326    }
327
328    /// Get available models
329    pub fn list_models(&self) -> Vec<String> {
330        self.models.keys().cloned().collect()
331    }
332
333    /// Generate embeddings using a specific model
334    pub fn embed_with_model(&self, model_name: &str, texts: &[String]) -> Result<Vec<Vector>> {
335        let model = self
336            .models
337            .get(model_name)
338            .ok_or_else(|| anyhow!("Model not found: {}", model_name))?;
339
340        if self.server_config.request_batching && texts.len() > 1 {
341            model.embed_batch(texts)
342        } else {
343            let mut results = Vec::new();
344            for text in texts {
345                results.push(model.embed_text(text)?);
346            }
347            Ok(results)
348        }
349    }
350
351    /// Generate embeddings using the default model
352    pub fn embed(&self, texts: &[String]) -> Result<Vec<Vector>> {
353        self.embed_with_model(&self.default_model, texts)
354    }
355
356    /// Get model info for a specific model
357    pub fn get_model_info(&self, model_name: &str) -> Option<&TensorFlowModelInfo> {
358        self.models.get(model_name)?.get_model_info()
359    }
360
361    /// Update server configuration
362    pub fn update_config(&mut self, config: ServerConfig) {
363        self.server_config = config;
364    }
365}
366
367impl EmbeddingGenerator for TensorFlowEmbedder {
368    fn generate_embedding(&self, content: &ContentItem) -> Result<Vector> {
369        self.embed_text(&content.content)
370    }
371
372    fn generate_batch_embeddings(&self, content: &[ContentItem]) -> Result<Vec<ProcessingResult>> {
373        let mut results = Vec::new();
374
375        for item in content {
376            let start_time = Instant::now();
377            let vector_result = self.generate_embedding(item);
378            let duration = start_time.elapsed();
379
380            let result = match vector_result {
381                Ok(vector) => ProcessingResult {
382                    item: item.clone(),
383                    vector: Some(vector),
384                    status: ProcessingStatus::Completed,
385                    duration,
386                    error: None,
387                    metadata: HashMap::new(),
388                },
389                Err(e) => ProcessingResult {
390                    item: item.clone(),
391                    vector: None,
392                    status: ProcessingStatus::Failed {
393                        reason: e.to_string(),
394                    },
395                    duration,
396                    error: Some(e.to_string()),
397                    metadata: HashMap::new(),
398                },
399            };
400
401            results.push(result);
402        }
403
404        Ok(results)
405    }
406
407    fn embedding_dimensions(&self) -> usize {
408        self.get_dimensions().unwrap_or(512)
409    }
410
411    fn get_config(&self) -> serde_json::Value {
412        serde_json::to_value(&self.config).unwrap_or_default()
413    }
414
415    fn is_ready(&self) -> bool {
416        self.session_initialized
417    }
418
419    fn get_statistics(&self) -> GeneratorStatistics {
420        GeneratorStatistics {
421            total_embeddings: 0,
422            total_processing_time: Duration::from_millis(0),
423            average_processing_time: Duration::from_millis(0),
424            error_count: 0,
425            last_error: None,
426        }
427    }
428}
429
430#[cfg(test)]
431#[allow(unused_imports, clippy::useless_vec)]
432mod tests {
433    use super::*;
434    use std::path::PathBuf;
435
436    #[test]
437    fn test_tensorflow_config_creation() {
438        let config = TensorFlowConfig::default();
439        assert_eq!(config.batch_size, 32);
440        assert_eq!(config.max_sequence_length, 512);
441        assert!(matches!(config.device, TensorFlowDevice::Cpu { .. }));
442    }
443
444    #[test]
445    fn test_tensorflow_embedder_creation() {
446        let config = TensorFlowConfig::default();
447        let embedder = TensorFlowEmbedder::new(config);
448        assert!(embedder.is_ok());
449    }
450
451    #[test]
452    fn test_preprocessing_pipeline() {
453        let mut embedder = TensorFlowEmbedder::new(TensorFlowConfig::default()).unwrap();
454        let pipeline = PreprocessingPipeline {
455            lowercase: true,
456            remove_punctuation: true,
457            ..Default::default()
458        };
459        embedder.set_preprocessing_pipeline(pipeline);
460
461        let processed = embedder.preprocess_text("Hello, World!").unwrap();
462        assert_eq!(processed, "hello world");
463    }
464
465    #[test]
466    fn test_model_server_creation() {
467        let server = TensorFlowModelServer::new("default".to_string(), ServerConfig::default());
468        assert_eq!(server.default_model, "default");
469        assert!(server.list_models().is_empty());
470    }
471
472    #[test]
473    fn test_model_registration() {
474        let mut server =
475            TensorFlowModelServer::new("test_model".to_string(), ServerConfig::default());
476
477        let config = TensorFlowConfig::default();
478        let embedder = TensorFlowEmbedder::new(config).unwrap();
479
480        let result = server.register_model("test_model".to_string(), embedder);
481        assert!(result.is_ok());
482        assert_eq!(server.list_models().len(), 1);
483    }
484
485    #[test]
486    fn test_tensor_spec_creation() {
487        let spec = TensorSpec {
488            name: "input".to_string(),
489            dtype: TensorDataType::Float32,
490            shape: vec![None, Some(512)],
491        };
492        assert_eq!(spec.name, "input");
493        assert!(matches!(spec.dtype, TensorDataType::Float32));
494    }
495
496    #[test]
497    fn test_session_config_default() {
498        let config = SessionConfig::default();
499        assert!(config.allow_soft_placement);
500        assert!(!config.log_device_placement);
501        assert!(config.inter_op_parallelism_threads.is_none());
502    }
503
504    #[test]
505    fn test_device_configuration() {
506        let cpu_device = TensorFlowDevice::Cpu {
507            num_threads: Some(4),
508        };
509        let gpu_device = TensorFlowDevice::Gpu {
510            device_id: 0,
511            memory_growth: true,
512        };
513
514        assert!(matches!(cpu_device, TensorFlowDevice::Cpu { .. }));
515        assert!(matches!(gpu_device, TensorFlowDevice::Gpu { .. }));
516    }
517
518    #[test]
519    fn test_optimization_levels() {
520        let levels = vec![
521            OptimizationLevel::None,
522            OptimizationLevel::Basic,
523            OptimizationLevel::Extended,
524            OptimizationLevel::Aggressive,
525        ];
526        assert_eq!(levels.len(), 4);
527    }
528}
oxirs_vec/tensorflow.rs

oxirs_vec/
tensorflow.rs
