rfc: instruct embeddings (#811)

Co-authored-by: seanaedmiston <seane999@gmail.com>

docs/modules/utils/combine_docs_examples/embeddings.ipynb

@@ -255,10 +255,68 @@
     "query_result = embeddings.embed_query(text)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "59428e05",
+   "metadata": {},
+   "source": [
+    "## InstructEmbeddings\n",
+    "Let's load the HuggingFace instruct Embeddings class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "92c5b61e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings import HuggingFaceInstructEmbeddings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "062547b9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "load INSTRUCTOR_Transformer\n",
+      "max_seq_length  512\n"
+     ]
+    }
+   ],
+   "source": [
+    "embeddings = HuggingFaceInstructEmbeddings(query_instruction=\"Represent the query for retrieval: \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e1dcc4bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text = \"This is a test document.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "90f0db94",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query_result = embeddings.embed_query(text)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "90f0db94",
+   "id": "a961cdb5",
    "metadata": {},
    "outputs": [],
    "source": []

langchain/embeddings/__init__.py

@@ -3,7 +3,10 @@ import logging
 from typing import Any
 
 from langchain.embeddings.cohere import CohereEmbeddings
-from langchain.embeddings.huggingface import HuggingFaceEmbeddings
+from langchain.embeddings.huggingface import (
+    HuggingFaceEmbeddings,
+    HuggingFaceInstructEmbeddings,
+)
 from langchain.embeddings.huggingface_hub import HuggingFaceHubEmbeddings
 from langchain.embeddings.openai import OpenAIEmbeddings
 from langchain.embeddings.tensorflow_hub import TensorflowHubEmbeddings
@@ -16,6 +19,7 @@ __all__ = [
     "CohereEmbeddings",
     "HuggingFaceHubEmbeddings",
     "TensorflowHubEmbeddings",
+    "HuggingFaceInstructEmbeddings",
 ]
 
 

langchain/embeddings/huggingface.py

@@ -6,6 +6,11 @@ from pydantic import BaseModel, Extra
 from langchain.embeddings.base import Embeddings
 
 DEFAULT_MODEL_NAME = "sentence-transformers/all-mpnet-base-v2"
+DEFAULT_INSTRUCT_MODEL = "hkunlp/instructor-large"
+DEFAULT_EMBED_INSTRUCTION = "Represent the document for retrieval: "
+DEFAULT_QUERY_INSTRUCTION = (
+    "Represent the question for retrieving supporting documents: "
+)
 
 
 class HuggingFaceEmbeddings(BaseModel, Embeddings):
@@ -68,3 +73,68 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings):
         text = text.replace("\n", " ")
         embedding = self.client.encode(text)
         return embedding.tolist()
+
+
+class HuggingFaceInstructEmbeddings(BaseModel, Embeddings):
+    """Wrapper around sentence_transformers embedding models.
+
+    To use, you should have the ``sentence_transformers`` python package installed.
+
+    Example:
+        .. code-block:: python
+
+            from langchain.embeddings import HuggingFaceInstructEmbeddings
+            model_name = "hkunlp/instructor-large"
+            hf = HuggingFaceInstructEmbeddings(model_name=model_name)
+    """
+
+    client: Any  #: :meta private:
+    model_name: str = DEFAULT_INSTRUCT_MODEL
+    """Model name to use."""
+    embed_instruction: str = DEFAULT_EMBED_INSTRUCTION
+    """Instruction to use for embedding documents."""
+    query_instruction: str = DEFAULT_QUERY_INSTRUCTION
+    """Instruction to use for embedding query."""
+
+    def __init__(self, **kwargs: Any):
+        """Initialize the sentence_transformer."""
+        super().__init__(**kwargs)
+        try:
+            from InstructorEmbedding import INSTRUCTOR
+
+            self.client = INSTRUCTOR(self.model_name)
+        except ImportError as e:
+            raise ValueError("Dependencies for InstructorEmbedding not found.") from e
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.forbid
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Compute doc embeddings using a HuggingFace instruct model.
+
+        Args:
+            texts: The list of texts to embed.
+
+        Returns:
+            List of embeddings, one for each text.
+        """
+        instruction_pairs = []
+        for text in texts:
+            instruction_pairs.append([self.embed_instruction, text])
+        embeddings = self.client.encode(instruction_pairs)
+        return embeddings.tolist()
+
+    def embed_query(self, text: str) -> List[float]:
+        """Compute query embeddings using a HuggingFace instruct model.
+
+        Args:
+            text: The text to embed.
+
+        Returns:
+            Embeddings for the text.
+        """
+        instruction_pair = [self.query_instruction, text]
+        embedding = self.client.encode([instruction_pair])[0]
+        return embedding.tolist()

tests/integration_tests/embeddings/test_huggingface.py

@@ -1,7 +1,10 @@
 """Test huggingface embeddings."""
 import unittest
 
-from langchain.embeddings.huggingface import HuggingFaceEmbeddings
+from langchain.embeddings.huggingface import (
+    HuggingFaceEmbeddings,
+    HuggingFaceInstructEmbeddings,
+)
 
 
 @unittest.skip("This test causes a segfault.")
@@ -21,3 +24,20 @@ def test_huggingface_embedding_query() -> None:
     embedding = HuggingFaceEmbeddings()
     output = embedding.embed_query(document)
     assert len(output) == 768
+
+
+def test_huggingface_instructor_embedding_documents() -> None:
+    """Test huggingface embeddings."""
+    documents = ["foo bar"]
+    embedding = HuggingFaceInstructEmbeddings()
+    output = embedding.embed_documents(documents)
+    assert len(output) == 1
+    assert len(output[0]) == 768
+
+
+def test_huggingface_instructor_embedding_query() -> None:
+    """Test huggingface embeddings."""
+    query = "foo bar"
+    embedding = HuggingFaceInstructEmbeddings()
+    output = embedding.embed_query(query)
+    assert len(output) == 768