app.py

from haystack.document_stores.faiss import FAISSDocumentStore
from haystack.nodes.retriever import EmbeddingRetriever
from haystack.nodes.ranker import BaseRanker
from haystack.pipelines import Pipeline

from haystack.document_stores.base import BaseDocumentStore
from haystack.schema import Document

from typing import Optional, List

# from huggingface_hub import get_inference_endpoint
from datasets import load_dataset
from time import perf_counter
import gradio as gr
import numpy as np
import requests
import os

TOP_K = 2
BATCH_SIZE = 16

HF_TOKEN = os.getenv("HF_TOKEN")
RANKER_URL = os.getenv("RANKER_URL")
RETRIEVER_URL = os.getenv("RETRIEVER_URL")

# RETRIEVER_IE = get_inference_endpoint(
#     "fastrag-retriever", namespace="optimum-intel", token=HF_TOKEN
# )
# RANKER_IE = get_inference_endpoint(
#     "fastrag-ranker", namespace="optimum-intel", token=HF_TOKEN
# )

# if RETRIEVER_IE.status != "running":
#     RETRIEVER_IE.resume()
#     RETRIEVER_IE.wait()

# if RANKER_IE.status != "running":
#     RANKER_IE.resume()
#     RANKER_IE.wait()


def post(url, payload):
    response = requests.post(
        url,
        json=payload,
        headers={"Authorization": f"Bearer {HF_TOKEN}"},
    )
    return response.json()


def method_timer(method):
    def timed(self, *args, **kw):
        start_time = perf_counter()
        result = method(self, *args, **kw)
        end_time = perf_counter()
        print(
            f"{self.__class__.__name__}.{method.__name__} took {end_time - start_time} seconds"
        )
        return result

    return timed


class Retriever(EmbeddingRetriever):
    def __init__(
        self,
        document_store: Optional[BaseDocumentStore] = None,
        top_k: int = 10,
        batch_size: int = 32,
        scale_score: bool = True,
    ):
        self.document_store = document_store
        self.top_k = top_k
        self.batch_size = batch_size
        self.scale_score = scale_score

    @method_timer
    def embed_queries(self, queries: List[str]) -> np.ndarray:
        payload = {"queries": queries, "inputs": ""}
        response = post(RETRIEVER_URL, payload)

        if "error" in response:
            raise gr.Error(response["error"])

        arrays = np.array(response)
        return arrays

    @method_timer
    def embed_documents(self, documents: List[Document]) -> np.ndarray:
        documents = [d.to_dict() for d in documents]
        for doc in documents:
            doc["embedding"] = None

        payload = {"documents": documents, "inputs": ""}
        response = post(RETRIEVER_URL, payload)

        if "error" in response:
            raise gr.Error(response["error"])

        arrays = np.array(response)
        return arrays


class Ranker(BaseRanker):
    @method_timer
    def predict(
        self, query: str, documents: List[Document], top_k: Optional[int] = None
    ) -> List[Document]:
        documents = [d.to_dict() for d in documents]
        for doc in documents:
            doc["embedding"] = None

        payload = {"query": query, "documents": documents, "top_k": top_k, "inputs": ""}
        response = post(RANKER_URL, payload)

        if "error" in response:
            raise gr.Error(response["error"])

        return [Document.from_dict(d) for d in response]

    @method_timer
    def predict_batch(
        self,
        queries: List[str],
        documents: List[List[Document]],
        batch_size: Optional[int] = None,
        top_k: Optional[int] = None,
    ) -> List[List[Document]]:
        documents = [[d.to_dict() for d in docs] for docs in documents]
        for docs in documents:
            for doc in docs:
                doc["embedding"] = None

        payload = {
            "queries": queries,
            "documents": documents,
            "batch_size": batch_size,
            "top_k": top_k,
            "inputs": "",
        }
        response = post(RANKER_URL, payload)

        if "error" in response:
            raise gr.Error(response["error"])

        return [[Document.from_dict(d) for d in docs] for docs in response]


if (
    os.path.exists("/data/faiss_document_store.db")
    and os.path.exists("/data/faiss_index.json")
    and os.path.exists("/data/faiss_index")
):
    document_store = FAISSDocumentStore.load("/data/faiss_index")
    retriever = Retriever(
        document_store=document_store, top_k=TOP_K, batch_size=BATCH_SIZE
    )
    document_store.save(index_path="/data/faiss_index")
else:
    for file in [
        "/data/faiss_document_store.db",
        "/data/faiss_index.json",
        "/data/faiss_index",
    ]:
        try:
            os.remove(file)
        except FileNotFoundError:
            pass

    document_store = FAISSDocumentStore(
        sql_url="sqlite:////data/faiss_document_store.db",
        return_embedding=True,
        embedding_dim=384,
    )
    document_store.write_documents(
        load_dataset("bilgeyucel/seven-wonders", split="train")
    )
    retriever = Retriever(
        document_store=document_store, top_k=TOP_K, batch_size=BATCH_SIZE
    )
    document_store.update_embeddings(retriever=retriever)
    document_store.save(index_path="/data/faiss_index")

ranker = Ranker()

pipe = Pipeline()
pipe.add_node(component=retriever, name="Retriever", inputs=["Query"])
pipe.add_node(component=ranker, name="Ranker", inputs=["Retriever"])


def run(query: str) -> dict:
    pipe_output = pipe.run(query=query)

    output = f"""<h2>Top {TOP_K} Documents</h2>"""

    for i, doc in enumerate(pipe_output["documents"]):
        output += f"""
        <h3>Document {i + 1}</h3>
        <p><strong>ID:</strong> {doc.id}</p>
        <p><strong>Score:</strong> {doc.score}</p>
        <p><strong>Content:</strong> {doc.content}</p>
        """

    return output


examples = [
    "Where is Gardens of Babylon?",
    "Why did people build Great Pyramid of Giza?",
    "What does Rhodes Statue look like?",
    "Why did people visit the Temple of Artemis?",
    "What is the importance of Colossus of Rhodes?",
    "What happened to the Tomb of Mausolus?",
    "How did Colossus of Rhodes collapse?",
]

input_text = gr.components.Textbox(
    label="Query", placeholder="Enter a query", value=examples[0], lines=1
)
output_html = gr.components.HTML(label="Documents")

gr.Interface(
    fn=run,
    inputs=input_text,
    outputs=output_html,
    examples=examples,
    cache_examples=False,
    allow_flagging="never",
    title="End-to-End Retrieval & Ranking with Hugging Face Inference Endpoints and Spaces",
    description="""## A [haystack](https://haystack.deepset.ai/) pipeline with the following components
- <strong>Document Store</strong>: A [FAISS document store](https://github.com/facebookresearch/faiss/tree/main) containing the [`seven-wonders` dataset](https://huggingface.co/datasets/bilgeyucel/seven-wonders), created on this Space's [persistent storage](https://huggingface.co/docs/hub/en/spaces-storage).
- <strong>Retriever</strong>: [Quantized FastRAG Retriever](https://huggingface.co/optimum-intel/fastrag-retriever) deployed on [Inference Endpoints](https://huggingface.co/docs/inference-endpoints/index) + Intel Sapphire Rapids CPU.
- <strong>Ranker</strong>: [Quantized FastRAG Retriever](https://huggingface.co/optimum-intel/fastrag-ranker) deployed on [Inference Endpoints](https://huggingface.co/docs/inference-endpoints/index) + Intel Sapphire Rapids CPU.

This Space is based on the optimizations demonstrated in the blog [CPU Optimized Embeddings with 🤗 Optimum Intel and fastRAG](https://huggingface.co/blog/intel-fast-embedding)
""",
).launch()