IPEX embedder compatibility with Haystack v2.4+ (#74)

* Updated IPEX embedder to work with new Haystack version (2.7)

Signed-off-by: gadmarkovits <[email protected]>

* Updated after running pre-commit

Signed-off-by: gadmarkovits <[email protected]>

---------

Signed-off-by: gadmarkovits <[email protected]>

Author: gadmarkovits

fastrag/embedders/ipex_embedder.py

@@ -1,5 +1,6 @@
-from typing import Dict, List, Optional, Tuple, Union
+from typing import Any, Dict, List, Optional, Tuple, Union
 
+import torch
 from haystack.components.embedders import (
     SentenceTransformersDocumentEmbedder,
     SentenceTransformersTextEmbedder,
@@ -33,7 +34,7 @@ def __init__(
         import sentence_transformers
 
         class _IPEXSTTransformers(sentence_transformers.models.Transformer):
-            def _load_model(self, model_name_or_path, config, cache_dir, **model_args):
+            def _load_model(self, model_name_or_path, config, cache_dir, backend, **model_args):
                 print("Loading IPEX ST Transformer model")
                 optimized_intel_import.check()
                 self.auto_model = IPEXModel.from_pretrained(
@@ -89,23 +90,39 @@ def _load_auto_model(
                 cache_folder: Optional[str],
                 revision: Optional[str] = None,
                 trust_remote_code: bool = False,
+                local_files_only: bool = False,
+                model_kwargs: Optional[Dict[str, Any]] = None,
+                tokenizer_kwargs: Optional[Dict[str, Any]] = None,
+                config_kwargs: Optional[Dict[str, Any]] = None,
             ):
                 """
                 Creates a simple Transformer + Mean Pooling model and returns the modules
                 """
+
+                shared_kwargs = {
+                    "token": token,
+                    "trust_remote_code": trust_remote_code,
+                    "revision": revision,
+                    "local_files_only": local_files_only,
+                }
+                model_kwargs = (
+                    shared_kwargs if model_kwargs is None else {**shared_kwargs, **model_kwargs}
+                )
+                tokenizer_kwargs = (
+                    shared_kwargs
+                    if tokenizer_kwargs is None
+                    else {**shared_kwargs, **tokenizer_kwargs}
+                )
+                config_kwargs = (
+                    shared_kwargs if config_kwargs is None else {**shared_kwargs, **config_kwargs}
+                )
+
                 transformer_model = _IPEXSTTransformers(
                     model_name_or_path,
                     cache_dir=cache_folder,
-                    model_args={
-                        "token": token,
-                        "trust_remote_code": trust_remote_code,
-                        "revision": revision,
-                    },
-                    tokenizer_args={
-                        "token": token,
-                        "trust_remote_code": trust_remote_code,
-                        "revision": revision,
-                    },
+                    model_args=model_kwargs,
+                    tokenizer_args=tokenizer_kwargs,
+                    config_args=config_kwargs,
                 )
                 pooling_model = sentence_transformers.models.Pooling(
                     transformer_model.get_word_embedding_dimension(), "mean"
@@ -114,7 +131,7 @@ def _load_auto_model(
 
             @property
             def device(self):
-                return "cpu"
+                return torch.device("cpu")
 
         self.model = _IPEXSentenceTransformer(
             model_name_or_path=model,
@@ -132,7 +149,7 @@ def ipex_model_warm_up(self):
     """
     Initializes the component.
     """
-    if not hasattr(self, "embedding_backend"):
+    if not getattr(self, "embedding_backend", None):
         self.embedding_backend = _IPEXSentenceTransformersEmbeddingBackend(
             model=self.model,
             device=self.device.to_torch_str(),

fastrag/rankers/bi_encoder_ranker.py

@@ -110,7 +110,7 @@ def run(
         scores = torch.tensor(query_vector) @ doc_vectors.T  ## perhaps need to break it into chunks
         scores = scores.reshape(len(documents))
         # Store scores in documents_with_vectors
-        for doc, score in zip(documents_with_vectors, scores.tolist()):            
+        for doc, score in zip(documents_with_vectors, scores.tolist()):
             doc.score = score
 
         indices = scores.cpu().sort(descending=True).indices