community: Add Laser Embedding Integration (#18111)

- **Description:** Added Integration with Meta AI's LASER
Language-Agnostic SEntence Representations embedding library, which
supports multilingual embedding for any of the languages listed here:
https://github.com/facebookresearch/flores/blob/main/flores200/README.md#languages-in-flores-200,
including several low resource languages
- **Dependencies:** laser_encoders

libs/community/langchain_community/embeddings/laser.py

@@ -0,0 +1,89 @@
+from typing import Any, Dict, List, Optional
+
+import numpy as np
+from langchain_core.embeddings import Embeddings
+from langchain_core.pydantic_v1 import BaseModel, Extra, root_validator
+
+LASER_MULTILINGUAL_MODEL: str = "laser2"
+
+
+class LaserEmbeddings(BaseModel, Embeddings):
+    """LASER Language-Agnostic SEntence Representations.
+    LASER is a Python library developed by the Meta AI Research team
+    and used for creating multilingual sentence embeddings for over 147 languages
+    as of 2/25/2024
+    See more documentation at:
+    * https://github.com/facebookresearch/LASER/
+    * https://github.com/facebookresearch/LASER/tree/main/laser_encoders
+    * https://arxiv.org/abs/2205.12654
+
+    To use this class, you must install the `laser_encoders` Python package.
+
+    `pip install laser_encoders`
+    Example:
+        from laser_encoders import LaserEncoderPipeline
+        encoder = LaserEncoderPipeline(lang="eng_Latn")
+        embeddings = encoder.encode_sentences(["Hello", "World"])
+    """
+
+    lang: Optional[str]
+    """The language or language code you'd like to use
+    If empty, this implementation will default
+    to using a multilingual earlier LASER encoder model (called laser2)
+    Find the list of supported languages at
+    https://github.com/facebookresearch/flores/blob/main/flores200/README.md#languages-in-flores-200
+    """
+
+    _encoder_pipeline: Any  # : :meta private:
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.forbid
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that laser_encoders has been installed."""
+        try:
+            from laser_encoders import LaserEncoderPipeline
+
+            lang = values.get("lang")
+            if lang:
+                encoder_pipeline = LaserEncoderPipeline(lang=lang)
+            else:
+                encoder_pipeline = LaserEncoderPipeline(laser=LASER_MULTILINGUAL_MODEL)
+            values["_encoder_pipeline"] = encoder_pipeline
+
+        except ImportError as e:
+            raise ImportError(
+                "Could not import 'laser_encoders' Python package. "
+                "Please install it with `pip install laser_encoders`."
+            ) from e
+        return values
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Generate embeddings for documents using LASER.
+
+        Args:
+            texts: The list of texts to embed.
+
+        Returns:
+            List of embeddings, one for each text.
+        """
+        embeddings: np.ndarray
+        embeddings = self._encoder_pipeline.encode_sentences(texts)
+
+        return embeddings.tolist()
+
+    def embed_query(self, text: str) -> List[float]:
+        """Generate single query text embeddings using LASER.
+
+        Args:
+            text: The text to embed.
+
+        Returns:
+            Embeddings for the text.
+        """
+        query_embeddings: np.ndarray
+        query_embeddings = self._encoder_pipeline.encode_sentences([text])
+        return query_embeddings.tolist()[0]