feat: FastEmbed embedding provider (#13109)

## Description:
This PR intends to add
[Qdrant/FastEmbed](https://qdrant.github.io/fastembed/) as a local
embeddings provider, associated tests and documentation.

**Documentation preview:**
https://langchain-git-fork-anush008-master-langchain.vercel.app/docs/integrations/text_embedding/fastembed

---------

Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>

libs/langchain/langchain/embeddings/__init__.py

@@ -32,6 +32,7 @@ from langchain.embeddings.elasticsearch import ElasticsearchEmbeddings
 from langchain.embeddings.embaas import EmbaasEmbeddings
 from langchain.embeddings.ernie import ErnieEmbeddings
 from langchain.embeddings.fake import DeterministicFakeEmbedding, FakeEmbeddings
+from langchain.embeddings.fastembed import FastEmbedEmbeddings
 from langchain.embeddings.google_palm import GooglePalmEmbeddings
 from langchain.embeddings.gpt4all import GPT4AllEmbeddings
 from langchain.embeddings.gradient_ai import GradientEmbeddings
@@ -77,6 +78,7 @@ __all__ = [
     "ClarifaiEmbeddings",
     "CohereEmbeddings",
     "ElasticsearchEmbeddings",
+    "FastEmbedEmbeddings",
     "HuggingFaceEmbeddings",
     "HuggingFaceInferenceAPIEmbeddings",
     "GradientEmbeddings",

libs/langchain/langchain/embeddings/fastembed.py

@@ -0,0 +1,108 @@
+from typing import Any, Dict, List, Literal, Optional
+
+import numpy as np
+
+from langchain.pydantic_v1 import BaseModel, Extra, root_validator
+from langchain.schema.embeddings import Embeddings
+
+
+class FastEmbedEmbeddings(BaseModel, Embeddings):
+    """Qdrant FastEmbedding models.
+    FastEmbed is a lightweight, fast, Python library built for embedding generation.
+    See more documentation at:
+    * https://github.com/qdrant/fastembed/
+    * https://qdrant.github.io/fastembed/
+
+    To use this class, you must install the `fastembed` Python package.
+
+    `pip install fastembed`
+    Example:
+        from langchain.embeddings import FastEmbedEmbeddings
+        fastembed = FastEmbedEmbeddings()
+    """
+
+    model_name: str = "BAAI/bge-small-en-v1.5"
+    """Name of the FastEmbedding model to use
+    Defaults to "BAAI/bge-small-en-v1.5"
+    Find the list of supported models at
+    https://qdrant.github.io/fastembed/examples/Supported_Models/
+    """
+
+    max_length: int = 512
+    """The maximum number of tokens. Defaults to 512.
+    Unknown behavior for values > 512.
+    """
+
+    cache_dir: Optional[str]
+    """The path to the cache directory.
+    Defaults to `local_cache` in the parent directory
+    """
+
+    threads: Optional[int]
+    """The number of threads single onnxruntime session can use.
+    Defaults to None
+    """
+
+    doc_embed_type: Literal["default", "passage"] = "default"
+    """Type of embedding to use for documents
+    "default": Uses FastEmbed's default embedding method
+    "passage": Prefixes the text with "passage" before embedding.
+    """
+
+    _model: Any  # : :meta private:
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.forbid
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that FastEmbed has been installed."""
+        try:
+            from fastembed.embedding import FlagEmbedding
+
+            model_name = values.get("model_name")
+            max_length = values.get("max_length")
+            cache_dir = values.get("cache_dir")
+            threads = values.get("threads")
+            values["_model"] = FlagEmbedding(
+                model_name=model_name,
+                max_length=max_length,
+                cache_dir=cache_dir,
+                threads=threads,
+            )
+        except ImportError as ie:
+            raise ImportError(
+                "Could not import 'fastembed' Python package. "
+                "Please install it with `pip install fastembed`."
+            ) from ie
+        return values
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Generate embeddings for documents using FastEmbed.
+
+        Args:
+            texts: The list of texts to embed.
+
+        Returns:
+            List of embeddings, one for each text.
+        """
+        embeddings: List[np.ndarray]
+        if self.doc_embed_type == "passage":
+            embeddings = self._model.passage_embed(texts)
+        else:
+            embeddings = self._model.embed(texts)
+        return [e.tolist() for e in embeddings]
+
+    def embed_query(self, text: str) -> List[float]:
+        """Generate query embeddings using FastEmbed.
+
+        Args:
+            text: The text to embed.
+
+        Returns:
+            Embeddings for the text.
+        """
+        query_embeddings: np.ndarray = next(self._model.query_embed(text))
+        return query_embeddings.tolist()

libs/langchain/tests/integration_tests/embeddings/test_fastembed.py

@@ -0,0 +1,76 @@
+"""Test FastEmbed embeddings."""
+import pytest
+
+from langchain.embeddings.fastembed import FastEmbedEmbeddings
+
+
+@pytest.mark.parametrize(
+    "model_name", ["sentence-transformers/all-MiniLM-L6-v2", "BAAI/bge-small-en-v1.5"]
+)
+@pytest.mark.parametrize("max_length", [50, 512])
+@pytest.mark.parametrize("doc_embed_type", ["default", "passage"])
+@pytest.mark.parametrize("threads", [0, 10])
+def test_fastembed_embedding_documents(
+    model_name: str, max_length: int, doc_embed_type: str, threads: int
+) -> None:
+    """Test fastembed embeddings for documents."""
+    documents = ["foo bar", "bar foo"]
+    embedding = FastEmbedEmbeddings(
+        model_name=model_name,
+        max_length=max_length,
+        doc_embed_type=doc_embed_type,
+        threads=threads,
+    )
+    output = embedding.embed_documents(documents)
+    assert len(output) == 2
+    assert len(output[0]) == 384
+
+
+@pytest.mark.parametrize(
+    "model_name", ["sentence-transformers/all-MiniLM-L6-v2", "BAAI/bge-small-en-v1.5"]
+)
+@pytest.mark.parametrize("max_length", [50, 512])
+def test_fastembed_embedding_query(model_name: str, max_length: int) -> None:
+    """Test fastembed embeddings for query."""
+    document = "foo bar"
+    embedding = FastEmbedEmbeddings(model_name=model_name, max_length=max_length)
+    output = embedding.embed_query(document)
+    assert len(output) == 384
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    "model_name", ["sentence-transformers/all-MiniLM-L6-v2", "BAAI/bge-small-en-v1.5"]
+)
+@pytest.mark.parametrize("max_length", [50, 512])
+@pytest.mark.parametrize("doc_embed_type", ["default", "passage"])
+@pytest.mark.parametrize("threads", [0, 10])
+async def test_fastembed_async_embedding_documents(
+    model_name: str, max_length: int, doc_embed_type: str, threads: int
+) -> None:
+    """Test fastembed embeddings for documents."""
+    documents = ["foo bar", "bar foo"]
+    embedding = FastEmbedEmbeddings(
+        model_name=model_name,
+        max_length=max_length,
+        doc_embed_type=doc_embed_type,
+        threads=threads,
+    )
+    output = await embedding.aembed_documents(documents)
+    assert len(output) == 2
+    assert len(output[0]) == 384
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    "model_name", ["sentence-transformers/all-MiniLM-L6-v2", "BAAI/bge-small-en-v1.5"]
+)
+@pytest.mark.parametrize("max_length", [50, 512])
+async def test_fastembed_async_embedding_query(
+    model_name: str, max_length: int
+) -> None:
+    """Test fastembed embeddings for query."""
+    document = "foo bar"
+    embedding = FastEmbedEmbeddings(model_name=model_name, max_length=max_length)
+    output = await embedding.aembed_query(document)
+    assert len(output) == 384

libs/langchain/tests/unit_tests/embeddings/test_imports.py

@@ -7,6 +7,7 @@ EXPECTED_ALL = [
     "ClarifaiEmbeddings",
     "CohereEmbeddings",
     "ElasticsearchEmbeddings",
+    "FastEmbedEmbeddings",
     "HuggingFaceEmbeddings",
     "HuggingFaceInferenceAPIEmbeddings",
     "GradientEmbeddings",