import numpy as np
import sys
from LlamaRetriever import LlamaRetriever
sys.path.append("../..")
from retriever.retriever_pipeline import CustomPipeline
from api.embeddingsServiceCaller import EmbeddingServiceCaller
from haystack.nodes import PreProcessor
from custom_evaluation import eval, eval_llama

caller= EmbeddingServiceCaller()
doc_index = "stupo_eval_docs_llama"
label_index = "stupo_eval_labels_llama"
pipeline= CustomPipeline(label_index=label_index, doc_index=doc_index)

retriever=  LlamaRetriever(document_store=pipeline.vector_doc_store_llama)
open_domain=True
if not open_domain:
    preprocessor = PreProcessor(
        split_by="word",
        split_length=100,
        split_overlap=0,
        split_respect_sentence_boundary=False,
        clean_empty_lines=False,
        clean_whitespace=False,
    )
    # emb_query = np.array(caller.get_embeddings(query))
    # results = pipeline.query_by_emb(index=index, emb=emb_query)
    pipeline.doc_store_mpnet.add_eval_data(
            filename="squad_format.json",
            doc_index=doc_index,
            label_index=label_index,
            preprocessor=preprocessor,
        )
    # pipeline.vector_doc_store.update_embeddings(retriever,index=doc_index )
index= "stupo" if open_domain else doc_index
retriever_eval_results= eval_llama(label_index=label_index, doc_index=index, top_k=30, document_store= pipeline.doc_store_mpnet, vector_store= pipeline.vector_doc_store_llama, retriever= retriever, reRanker=None, open_domain=open_domain)
print(retriever_eval_results)