mistralai[patch]: 16k token batching logic embed (#17136)

libs/partners/mistralai/langchain_mistralai/embeddings.py

@@ -1,5 +1,6 @@
+import asyncio
 import logging
-from typing import Dict, List, Optional
+from typing import Dict, Iterable, List, Optional
 
 from langchain_core.embeddings import Embeddings
 from langchain_core.pydantic_v1 import (
@@ -16,9 +17,12 @@ from mistralai.constants import (
     ENDPOINT as DEFAULT_MISTRAL_ENDPOINT,
 )
 from mistralai.exceptions import MistralException
+from tokenizers import Tokenizer  # type: ignore
 
 logger = logging.getLogger(__name__)
 
+MAX_TOKENS = 16_000
+
 
 class MistralAIEmbeddings(BaseModel, Embeddings):
     """MistralAI embedding models.
@@ -43,6 +47,7 @@ class MistralAIEmbeddings(BaseModel, Embeddings):
     max_retries: int = 5
     timeout: int = 120
     max_concurrent_requests: int = 64
+    tokenizer: Tokenizer = Field(default=None)
 
     model: str = "mistral-embed"
 
@@ -72,8 +77,33 @@ class MistralAIEmbeddings(BaseModel, Embeddings):
             timeout=values["timeout"],
             max_concurrent_requests=values["max_concurrent_requests"],
         )
+        if values["tokenizer"] is None:
+            values["tokenizer"] = Tokenizer.from_pretrained(
+                "mistralai/Mixtral-8x7B-v0.1"
+            )
         return values
 
+    def _get_batches(self, texts: List[str]) -> Iterable[List[str]]:
+        """Split a list of texts into batches of less than 16k tokens
+        for Mistral API."""
+        batch: List[str] = []
+        batch_tokens = 0
+
+        text_token_lengths = [
+            len(encoded) for encoded in self.tokenizer.encode_batch(texts)
+        ]
+
+        for text, text_tokens in zip(texts, text_token_lengths):
+            if batch_tokens + text_tokens > MAX_TOKENS:
+                yield batch
+                batch = [text]
+                batch_tokens = text_tokens
+            else:
+                batch.append(text)
+                batch_tokens += text_tokens
+        if batch:
+            yield batch
+
     def embed_documents(self, texts: List[str]) -> List[List[float]]:
         """Embed a list of document texts.
 
@@ -84,13 +114,17 @@ class MistralAIEmbeddings(BaseModel, Embeddings):
             List of embeddings, one for each text.
         """
         try:
-            embeddings_batch_response = self.client.embeddings(
-                model=self.model,
-                input=texts,
+            batch_responses = (
+                self.client.embeddings(
+                    model=self.model,
+                    input=batch,
+                )
+                for batch in self._get_batches(texts)
             )
             return [
                 list(map(float, embedding_obj.embedding))
-                for embedding_obj in embeddings_batch_response.data
+                for response in batch_responses
+                for embedding_obj in response.data
             ]
         except MistralException as e:
             logger.error(f"An error occurred with MistralAI: {e}")
@@ -106,13 +140,19 @@ class MistralAIEmbeddings(BaseModel, Embeddings):
             List of embeddings, one for each text.
         """
         try:
-            embeddings_batch_response = await self.async_client.embeddings(
-                model=self.model,
-                input=texts,
+            batch_responses = await asyncio.gather(
+                *[
+                    self.async_client.embeddings(
+                        model=self.model,
+                        input=batch,
+                    )
+                    for batch in self._get_batches(texts)
+                ]
             )
             return [
                 list(map(float, embedding_obj.embedding))
-                for embedding_obj in embeddings_batch_response.data
+                for response in batch_responses
+                for embedding_obj in response.data
             ]
         except MistralException as e:
             logger.error(f"An error occurred with MistralAI: {e}")