community: OCI GenAI embedding batch size (#22986)

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- [x] **PR title**: "community: OCI GenAI embedding batch size"



- [x] **PR message**:
    - **Issue:** #22985 


- [ ] **Add tests and docs**: N/A


- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/

Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.

If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.

---------

Signed-off-by: Anders Swanson <anders.swanson@oracle.com>
Co-authored-by: Chester Curme <chester.curme@gmail.com>

libs/community/langchain_community/embeddings/oci_generative_ai.py

@@ -1,5 +1,5 @@
 from enum import Enum
-from typing import Any, Dict, List, Mapping, Optional
+from typing import Any, Dict, Iterator, List, Mapping, Optional
 
 from langchain_core.embeddings import Embeddings
 from langchain_core.pydantic_v1 import BaseModel, Extra, root_validator
@@ -80,6 +80,10 @@ class OCIGenAIEmbeddings(BaseModel, Embeddings):
     truncate: Optional[str] = "END"
     """Truncate embeddings that are too long from start or end ("NONE"|"START"|"END")"""
 
+    batch_size: int = 96
+    """Batch size of OCI GenAI embedding requests. OCI GenAI may handle up to 96 texts
+     per request"""
+
     class Config:
         """Configuration for this pydantic object."""
 
@@ -182,16 +186,23 @@ class OCIGenAIEmbeddings(BaseModel, Embeddings):
         else:
             serving_mode = models.OnDemandServingMode(model_id=self.model_id)
 
+        embeddings = []
+
+        def split_texts() -> Iterator[List[str]]:
+            for i in range(0, len(texts), self.batch_size):
+                yield texts[i : i + self.batch_size]
+
+        for chunk in split_texts():
             invocation_obj = models.EmbedTextDetails(
                 serving_mode=serving_mode,
                 compartment_id=self.compartment_id,
                 truncate=self.truncate,
-            inputs=texts,
+                inputs=chunk,
             )
-
             response = self.client.embed_text(invocation_obj)
+            embeddings.extend(response.data.embeddings)
 
-        return response.data.embeddings
+        return embeddings
 
     def embed_query(self, text: str) -> List[float]:
         """Call out to OCIGenAI's embedding endpoint.