feat: support nomic ai model (#12)

* feat: add semantic chunking to eval script; add wrapper for minilm

* fix: gaps in semantic chunking

* feat: add option to pass custom model for chunking

* feat: support nomic ai model

* chore: remove resolved todo comment

chunked_pooling/wrappers.py

@@ -4,6 +4,16 @@
 import torch.nn as nn
 from sentence_transformers import SentenceTransformer
 from transformers import AutoModel
+from transformers.modeling_outputs import BaseModelOutputWithPooling
+
+
+def construct_document(doc):
+    if isinstance(doc, str):
+        return doc
+    elif 'title' in doc:
+        return f'{doc["title"]} {doc["text"].strip()}'
+    else:
+        return doc['text'].strip()
 
 
 class JinaEmbeddingsV3Wrapper(nn.Module):
@@ -31,7 +41,7 @@ def encode_corpus(
         *args,
         **kwargs,
     ):
-        _sentences = [self._construct_document(sentence) for sentence in sentences]
+        _sentences = [construct_document(sentence) for sentence in sentences]
         return self._model.encode(_sentences, *args, task=self.tasks[1], **kwargs)
 
     def get_instructions(self):
@@ -45,13 +55,56 @@ def forward(self, *args, **kwargs):
         )
         return self._model.forward(*args, adapter_mask=adapter_mask, **kwargs)
 
-    def _construct_document(self, doc):
-        if isinstance(doc, str):
-            return doc
-        elif 'title' in doc:
-            return f'{doc["title"]} {doc["text"].strip()}'
-        else:
-            return doc['text'].strip()
+    @property
+    def device(self):
+        return self._model.device
+
+    @staticmethod
+    def has_instructions():
+        return True
+
+
+class NomicAIWrapper(nn.Module):
+    def __init__(self, model_name, **model_kwargs):
+        super().__init__()
+        self._model = SentenceTransformer(
+            model_name, trust_remote_code=True, **model_kwargs
+        )
+        self.instructions = ['search_query: ', 'search_document: ']
+
+    def get_instructions(self):
+        return self.instructions
+
+    def forward(self, *args, **kwargs):
+        model_output = self._model.forward(kwargs)
+        base_model_output = BaseModelOutputWithPooling(
+            last_hidden_state=model_output['token_embeddings'],
+            pooler_output=model_output['sentence_embedding'],
+            attentions=model_output['attention_mask'],
+        )
+        return base_model_output
+
+    def encode_queries(
+        self,
+        sentences: Union[str, List[str]],
+        *args,
+        **kwargs,
+    ):
+        return self._model.encode(
+            [self.instructions[0] + s for s in sentences], *args, **kwargs
+        )
+
+    def encode_corpus(
+        self,
+        sentences: Union[str, List[str]],
+        *args,
+        **kwargs,
+    ):
+        return self._model.encode(
+            [self.instructions[1] + construct_document(s) for s in sentences],
+            *args,
+            **kwargs,
+        )
 
     @property
     def device(self):
@@ -65,7 +118,9 @@ def has_instructions():
 MODEL_WRAPPERS = {
     'jinaai/jina-embeddings-v3': JinaEmbeddingsV3Wrapper,
     'sentence-transformers/all-MiniLM-L6-v2': SentenceTransformer,
+    'nomic-ai/nomic-embed-text-v1': NomicAIWrapper,
 }
+
 MODELS_WITHOUT_PROMPT_NAME_ARG = [
     'jinaai/jina-embeddings-v2-small-en',
     'jinaai/jina-embeddings-v2-base-en',