Extend langchain embedding API (#735)

* extend langchain embeddings

Signed-off-by: yuwenzho <yuwen.zhou@intel.com>

intel_extension_for_transformers/langchain/__init__.py

@@ -0,0 +1,16 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2023 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.

intel_extension_for_transformers/langchain/embeddings/__init__.py

@@ -0,0 +1,22 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2023 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from .embeddings import (
+    HuggingFaceEmbeddings,
+    HuggingFaceBgeEmbeddings,
+    HuggingFaceInstructEmbeddings
+)

intel_extension_for_transformers/langchain/embeddings/embeddings.py

@@ -0,0 +1,305 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2023 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import logging
+from typing import Any, Dict, List, Optional
+from .optimized_instructor_embedding import OptimizedInstructor
+from .optimized_sentence_transformers import OptimizedSentenceTransformer
+from intel_extension_for_transformers.transformers.utils.utility import LazyImport
+
+langchain_core = LazyImport("langchain_core")
+
+DEFAULT_MODEL_NAME = "sentence-transformers/all-mpnet-base-v2"
+DEFAULT_INSTRUCT_MODEL = "hkunlp/instructor-large"
+DEFAULT_BGE_MODEL = "BAAI/bge-large-en"
+DEFAULT_EMBED_INSTRUCTION = "Represent the document for retrieval: "
+DEFAULT_QUERY_INSTRUCTION = (
+    "Represent the question for retrieving supporting documents: "
+)
+DEFAULT_QUERY_BGE_INSTRUCTION_EN = (
+    "Represent this question for searching relevant passages: "
+)
+DEFAULT_QUERY_BGE_INSTRUCTION_ZH = "为这个句子生成表示以用于检索相关文章："
+
+logger = logging.getLogger(__name__)
+
+
+class HuggingFaceEmbeddings(langchain_core.pydantic_v1.BaseModel, langchain_core.embeddings.Embeddings):
+    """HuggingFace sentence_transformers embedding models.
+
+    To use, you should have the ``sentence_transformers`` python package installed.
+
+    Example:
+        .. code-block:: python
+
+            from intel_extension_for_transformers.langchain.embeddings import HuggingFaceEmbeddings
+
+            model_name = "sentence-transformers/all-mpnet-base-v2"
+            model_kwargs = {'device': 'cpu'}
+            encode_kwargs = {'normalize_embeddings': False}
+            hf = HuggingFaceEmbeddings(
+                model_name=model_name,
+                model_kwargs=model_kwargs,
+                encode_kwargs=encode_kwargs
+            )
+    """
+
+    client: Any  #: :meta private:
+    model_name: str = DEFAULT_MODEL_NAME
+    """Model name to use."""
+    cache_folder: Optional[str] = None
+    """Path to store models. 
+    Can be also set by SENTENCE_TRANSFORMERS_HOME environment variable."""
+    model_kwargs: Dict[str, Any] = langchain_core.pydantic_v1.Field(default_factory=dict)
+    """Keyword arguments to pass to the model."""
+    encode_kwargs: Dict[str, Any] = langchain_core.pydantic_v1.Field(default_factory=dict)
+    """Keyword arguments to pass when calling the `encode` method of the model."""
+    multi_process: bool = False
+    """Run encode() on multiple GPUs."""
+
+    def __init__(self, **kwargs: Any):
+        """Initialize the sentence_transformer."""
+        super().__init__(**kwargs)
+        try:
+            import sentence_transformers
+
+        except ImportError as exc:
+            raise ImportError(
+                "Could not import sentence_transformers python package. "
+                "Please install it with `pip install sentence-transformers`."
+            ) from exc
+
+        self.client = OptimizedSentenceTransformer(
+            self.model_name, cache_folder=self.cache_folder, **self.model_kwargs
+        )
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = langchain_core.pydantic_v1.Extra.forbid
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Compute doc embeddings using a HuggingFace transformer model.
+
+        Args:
+            texts: The list of texts to embed.
+
+        Returns:
+            List of embeddings, one for each text.
+        """
+        import sentence_transformers
+
+        texts = list(map(lambda x: x.replace("\n", " "), texts))
+        if self.multi_process:
+            pool = self.client.start_multi_process_pool()
+            embeddings = self.client.encode_multi_process(texts, pool)
+            sentence_transformers.SentenceTransformer.stop_multi_process_pool(pool)
+        else:
+            embeddings = self.client.encode(texts, **self.encode_kwargs)
+
+        return embeddings.tolist()
+
+    def embed_query(self, text: str) -> List[float]: 
+        """Compute query embeddings using a HuggingFace transformer model.
+
+        Args:
+            text: The text to embed.
+
+        Returns:
+            Embeddings for the text.
+        """
+        return self.embed_documents([text])[0]
+
+
+class HuggingFaceBgeEmbeddings(langchain_core.pydantic_v1.BaseModel, langchain_core.embeddings.Embeddings):
+    """HuggingFace BGE sentence_transformers embedding models.
+
+    To use, you should have the ``sentence_transformers`` python package installed.
+
+    Example:
+        .. code-block:: python
+
+            from intel_extension_for_transformers.langchain.embeddings import HuggingFaceBgeEmbeddings
+
+            model_name = "BAAI/bge-large-en"
+            model_kwargs = {'device': 'cpu'}
+            encode_kwargs = {'normalize_embeddings': True}
+            hf = HuggingFaceBgeEmbeddings(
+                model_name=model_name,
+                model_kwargs=model_kwargs,
+                encode_kwargs=encode_kwargs
+            )
+    """
+
+    client: Any  #: :meta private:
+    model_name: str = DEFAULT_BGE_MODEL
+    """Model name to use."""
+    cache_folder: Optional[str] = None
+    """Path to store models.
+    Can be also set by SENTENCE_TRANSFORMERS_HOME environment variable."""
+    model_kwargs: Dict[str, Any] = langchain_core.pydantic_v1.Field(default_factory=dict)
+    """Keyword arguments to pass to the model."""
+    encode_kwargs: Dict[str, Any] = langchain_core.pydantic_v1.Field(default_factory=dict)
+    """Keyword arguments to pass when calling the `encode` method of the model."""
+    query_instruction: str = DEFAULT_QUERY_BGE_INSTRUCTION_EN
+    """Instruction to use for embedding query."""
+
+    def __init__(self, **kwargs: Any):
+        """Initialize the sentence_transformer."""
+        super().__init__(**kwargs)
+        try:
+            import sentence_transformers
+
+        except ImportError as exc:
+            raise ImportError(
+                "Could not import sentence_transformers python package. "
+                "Please install it with `pip install sentence_transformers`."
+            ) from exc
+
+        self.client = OptimizedSentenceTransformer(
+            self.model_name, cache_folder=self.cache_folder, **self.model_kwargs
+        )
+        if "-zh" in self.model_name:
+            self.query_instruction = DEFAULT_QUERY_BGE_INSTRUCTION_ZH
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = langchain_core.pydantic_v1.Extra.forbid
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Compute doc embeddings using a HuggingFace transformer model.
+
+        Args:
+            texts: The list of texts to embed.
+
+        Returns:
+            List of embeddings, one for each text.
+        """
+        texts = [t.replace("\n", " ") for t in texts]
+        embeddings = self.client.encode(texts, **self.encode_kwargs)
+        return embeddings.tolist()
+
+    def embed_query(self, text: str) -> List[float]:
+        """Compute query embeddings using a HuggingFace transformer model.
+
+        Args:
+            text: The text to embed.
+
+        Returns:
+            Embeddings for the text.
+        """
+        text = text.replace("\n", " ")
+        embedding = self.client.encode(
+            self.query_instruction + text, **self.encode_kwargs
+        )
+        return embedding.tolist()
+
+class HuggingFaceInstructEmbeddings(langchain_core.pydantic_v1.BaseModel, langchain_core.embeddings.Embeddings):
+    """Wrapper around sentence_transformers embedding models.
+
+    To use, you should have the ``sentence_transformers``
+    and ``InstructorEmbedding`` python packages installed.
+
+    Example:
+        .. code-block:: python
+
+            from intel_extension_for_transformers.langchain.embeddings import HuggingFaceInstructEmbeddings
+
+            model_name = "hkunlp/instructor-large"
+            model_kwargs = {'device': 'cpu'}
+            encode_kwargs = {'normalize_embeddings': True}
+            hf = HuggingFaceInstructEmbeddings(
+                model_name=model_name,
+                model_kwargs=model_kwargs,
+                encode_kwargs=encode_kwargs
+            )
+    """
+
+    client: Any  #: :meta private:
+    model_name: str = DEFAULT_INSTRUCT_MODEL
+    """Model name to use."""
+    cache_folder: Optional[str] = None
+    """Path to store models. 
+    Can be also set by SENTENCE_TRANSFORMERS_HOME environment variable."""
+    model_kwargs: Dict[str, Any] = langchain_core.pydantic_v1.Field(default_factory=dict)
+    """Keyword arguments to pass to the model."""
+    encode_kwargs: Dict[str, Any] = langchain_core.pydantic_v1.Field(default_factory=dict)
+    """Keyword arguments to pass when calling the `encode` method of the model."""
+    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)
+
+        # check sentence_transformers python package
+        try:
+            import sentence_transformers
+
+        except ImportError as exc:
+            raise ImportError(
+                "Could not import sentence_transformers python package. "
+                "Please install it with `pip install sentence_transformers`."
+            ) from exc
+
+        # check InstructorEmbedding python package
+        try:
+            import InstructorEmbedding
+
+        except ImportError as exc:
+            raise ImportError(
+                "Could not import InstructorEmbedding python package. "
+                "Please install it with `pip install InstructorEmbedding`."
+            ) from exc
+
+        self.client = OptimizedInstructor(
+            self.model_name, cache_folder=self.cache_folder, **self.model_kwargs
+        )
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = langchain_core.pydantic_v1.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 = [[self.embed_instruction, text] for text in texts]
+        embeddings = self.client.encode(instruction_pairs, **self.encode_kwargs)
+        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], **self.encode_kwargs)[0]
+        return embedding.tolist()