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langchain[minor], community[minor]: add CrossEncoderReranker with HuggingFaceCrossEncoder and SagemakerEndpointCrossEncoder (#13687)

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- **Description:** Support reranking based on cross encoder models
available from HuggingFace.
      - Added `CrossEncoder` schema
- Implemented `HuggingFaceCrossEncoder` and
`SagemakerEndpointCrossEncoder`
- Implemented `CrossEncoderReranker` that performs similar functionality
to `CohereRerank`
- Added `cross-encoder-reranker.ipynb` to demonstrate how to use it.
Please let me know if anything else needs to be done to make it visible
on the table-of-contents navigation bar on the left, or on the card list
on [retrievers documentation
page](https://python.langchain.com/docs/integrations/retrievers).
  - **Issue:** N/A
  - **Dependencies:** None other than the existing ones.

---------

Co-authored-by: Kenny Choe <kchoe@amazon.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
This commit is contained in:
Kenneth Choe
2024-03-31 15:51:31 -05:00
committed by GitHub
parent 3f7da03dd8
commit f98d7f7494
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{
"cells": [
{
"cell_type": "markdown",
"id": "fc0db1bc",
"metadata": {},
"source": [
"# Cross Encoder Reranker\n",
"\n",
"This notebook shows how to implement reranker in a retriever with your own cross encoder from [HuggingFace cross encoder models](https://huggingface.co/cross-encoder) or HuggingFace models that implements cross encoder function ([example: BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base)). `SagemakerEndpointCrossEncoder` enables you to use these HuggingFace models loaded on Sagemaker.\n",
"\n",
"This builds on top of ideas in the [ContextualCompressionRetriever](/docs/modules/data_connection/retrievers/contextual_compression/). Overall structure of this document came from [Cohere Reranker documentation](/docs/integrations/retrievers/cohere-reranker.ipynb).\n",
"\n",
"For more about why cross encoder can be used as reranking mechanism in conjunction with embeddings for better retrieval, refer to [HuggingFace Cross-Encoders documentation](https://www.sbert.net/examples/applications/cross-encoder/README.html)."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b37bd138-4f3c-4d2c-bc4b-be705ce27a09",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"#!pip install faiss sentence_transformers\n",
"\n",
"# OR (depending on Python version)\n",
"\n",
"#!pip install faiss-cpu sentence_transformers"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "28e8dc12",
"metadata": {},
"outputs": [],
"source": [
"# Helper function for printing docs\n",
"\n",
"\n",
"def pretty_print_docs(docs):\n",
" print(\n",
" f\"\\n{'-' * 100}\\n\".join(\n",
" [f\"Document {i+1}:\\n\\n\" + d.page_content for i, d in enumerate(docs)]\n",
" )\n",
" )"
]
},
{
"cell_type": "markdown",
"id": "6fa3d916",
"metadata": {
"tags": []
},
"source": [
"## Set up the base vector store retriever\n",
"Let's start by initializing a simple vector store retriever and storing the 2023 State of the Union speech (in chunks). We can set up the retriever to retrieve a high number (20) of docs."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9fbcc58f",
"metadata": {},
"outputs": [],
"source": [
"from langchain.document_loaders import TextLoader\n",
"from langchain_community.embeddings import HuggingFaceEmbeddings\n",
"from langchain_community.vectorstores import FAISS\n",
"from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
"\n",
"documents = TextLoader(\"../../modules/state_of_the_union.txt\").load()\n",
"text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)\n",
"texts = text_splitter.split_documents(documents)\n",
"embeddingsModel = HuggingFaceEmbeddings(\n",
" model_name=\"sentence-transformers/msmarco-distilbert-dot-v5\"\n",
")\n",
"retriever = FAISS.from_documents(texts, embeddingsModel).as_retriever(\n",
" search_kwargs={\"k\": 20}\n",
")\n",
"\n",
"query = \"What is the plan for the economy?\"\n",
"docs = retriever.get_relevant_documents(query)\n",
"pretty_print_docs(docs)"
]
},
{
"cell_type": "markdown",
"id": "b7648612",
"metadata": {},
"source": [
"## Doing reranking with CrossEncoderReranker\n",
"Now let's wrap our base retriever with a `ContextualCompressionRetriever`. `CrossEncoderReranker` uses `HuggingFaceCrossEncoder` to rerank the returned results."
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "9a658023",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Document 1:\n",
"\n",
"More infrastructure and innovation in America. \n",
"\n",
"More goods moving faster and cheaper in America. \n",
"\n",
"More jobs where you can earn a good living in America. \n",
"\n",
"And instead of relying on foreign supply chains, let’s make it in America. \n",
"\n",
"Economists call it “increasing the productive capacity of our economy.” \n",
"\n",
"I call it building a better America. \n",
"\n",
"My plan to fight inflation will lower your costs and lower the deficit.\n",
"----------------------------------------------------------------------------------------------------\n",
"Document 2:\n",
"\n",
"Second – cut energy costs for families an average of $500 a year by combatting climate change. \n",
"\n",
"Let’s provide investments and tax credits to weatherize your homes and businesses to be energy efficient and you get a tax credit; double America’s clean energy production in solar, wind, and so much more; lower the price of electric vehicles, saving you another $80 a month because you’ll never have to pay at the gas pump again.\n",
"----------------------------------------------------------------------------------------------------\n",
"Document 3:\n",
"\n",
"Look at cars. \n",
"\n",
"Last year, there weren’t enough semiconductors to make all the cars that people wanted to buy. \n",
"\n",
"And guess what, prices of automobiles went up. \n",
"\n",
"So—we have a choice. \n",
"\n",
"One way to fight inflation is to drive down wages and make Americans poorer. \n",
"\n",
"I have a better plan to fight inflation. \n",
"\n",
"Lower your costs, not your wages. \n",
"\n",
"Make more cars and semiconductors in America. \n",
"\n",
"More infrastructure and innovation in America. \n",
"\n",
"More goods moving faster and cheaper in America.\n"
]
}
],
"source": [
"from langchain.retrievers import ContextualCompressionRetriever\n",
"from langchain.retrievers.document_compressors import CrossEncoderReranker\n",
"from langchain_community.cross_encoders import HuggingFaceCrossEncoder\n",
"\n",
"model = HuggingFaceCrossEncoder(model_name=\"BAAI/bge-reranker-base\")\n",
"compressor = CrossEncoderReranker(model=model, top_n=3)\n",
"compression_retriever = ContextualCompressionRetriever(\n",
" base_compressor=compressor, base_retriever=retriever\n",
")\n",
"\n",
"compressed_docs = compression_retriever.get_relevant_documents(\n",
" \"What is the plan for the economy?\"\n",
")\n",
"pretty_print_docs(compressed_docs)"
]
},
{
"cell_type": "markdown",
"id": "419a2bf3-de4b-4c4d-9a40-4336552f604c",
"metadata": {},
"source": [
"## Uploading HuggingFace model to SageMaker endpoint\n",
"\n",
"Refer to [this article](https://www.philschmid.de/custom-inference-huggingface-sagemaker) for general guideline. Here is a simple `inference.py` for creating an endpoint that works with `SagemakerEndpointCrossEncoder`.\n",
"\n",
"It downloads HuggingFace model on the fly, so you do not need to keep the model artifacts such as `pytorch_model.bin` in your `model.tar.gz`."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e579c743-40c3-432f-9483-0982e2808f9a",
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"import logging\n",
"from typing import List\n",
"\n",
"import torch\n",
"from sagemaker_inference import encoder\n",
"from transformers import AutoModelForSequenceClassification, AutoTokenizer\n",
"\n",
"PAIRS = \"pairs\"\n",
"SCORES = \"scores\"\n",
"\n",
"\n",
"class CrossEncoder:\n",
" def __init__(self) -> None:\n",
" self.device = (\n",
" torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
" )\n",
" logging.info(f\"Using device: {self.device}\")\n",
" model_name = \"BAAI/bge-reranker-base\"\n",
" self.tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
" self.model = AutoModelForSequenceClassification.from_pretrained(model_name)\n",
" self.model = self.model.to(self.device)\n",
"\n",
" def __call__(self, pairs: List[List[str]]) -> List[float]:\n",
" with torch.inference_mode():\n",
" inputs = self.tokenizer(\n",
" pairs,\n",
" padding=True,\n",
" truncation=True,\n",
" return_tensors=\"pt\",\n",
" max_length=512,\n",
" )\n",
" inputs = inputs.to(self.device)\n",
" scores = (\n",
" self.model(**inputs, return_dict=True)\n",
" .logits.view(\n",
" -1,\n",
" )\n",
" .float()\n",
" )\n",
"\n",
" return scores.detach().cpu().tolist()\n",
"\n",
"\n",
"def model_fn(model_dir: str) -> CrossEncoder:\n",
" try:\n",
" return CrossEncoder()\n",
" except Exception:\n",
" logging.exception(f\"Failed to load model from: {model_dir}\")\n",
" raise\n",
"\n",
"\n",
"def transform_fn(\n",
" cross_encoder: CrossEncoder, input_data: bytes, content_type: str, accept: str\n",
") -> bytes:\n",
" payload = json.loads(input_data)\n",
" model_output = cross_encoder(**payload)\n",
" output = {SCORES: model_output}\n",
" return encoder.encode(output, accept)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
}
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"nbformat": 4,
"nbformat_minor": 5
}