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from typing import Any, Dict, List
from fastrag.rankers import QuantizedBiEncoderRanker
from fastrag.retrievers import QuantizedBiEncoderRetriever
from haystack import Pipeline
from haystack.document_stores import InMemoryDocumentStore
from haystack.schema import Document
class EndpointHandler:
def __init__(self, path=""):
EXAMPLES = [
"There is a blue house on Oxford Street.",
"Paris is the capital of France.",
"The first commit in fastRAG was in 2022",
]
document_store = InMemoryDocumentStore(use_gpu=False, use_bm25=False, embedding_dim=384, return_embedding=True)
documents = []
for i, d in enumerate(EXAMPLES):
documents.append(Document(content=d, id=i))
document_store.write_documents(documents)
model_id = "Intel/bge-small-en-v1.5-rag-int8-static"
retriever = QuantizedBiEncoderRetriever(document_store=document_store, embedding_model=model_id)
document_store.update_embeddings(retriever=retriever)
ranker = QuantizedBiEncoderRanker("Intel/bge-large-en-v1.5-rag-int8-static")
self.pipe = Pipeline()
self.pipe.add_node(component=retriever, name="retriever", inputs=["Query"])
self.pipe.add_node(component=ranker, name="ranker", inputs=["retriever"])
def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
query = data.pop("inputs", data)
results = self.pipe.run(query=query)
return results
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