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139 lines
4.3 KiB
Python
139 lines
4.3 KiB
Python
import logging
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import time
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from typing import List
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import numpy as np
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from flask import current_app
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from langchain.embeddings import OpenAIEmbeddings
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from langchain.embeddings.base import Embeddings
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from langchain.schema import Document
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from sklearn.manifold import TSNE
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from core.embedding.cached_embedding import CacheEmbedding
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from core.index.vector_index.vector_index import VectorIndex
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from core.llm.llm_builder import LLMBuilder
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from extensions.ext_database import db
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from models.account import Account
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from models.dataset import Dataset, DocumentSegment, DatasetQuery
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class HitTestingService:
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@classmethod
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def retrieve(cls, dataset: Dataset, query: str, account: Account, limit: int = 10) -> dict:
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if dataset.available_document_count == 0 or dataset.available_document_count == 0:
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return {
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"query": {
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"content": query,
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"tsne_position": {'x': 0, 'y': 0},
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},
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"records": []
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}
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model_credentials = LLMBuilder.get_model_credentials(
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tenant_id=dataset.tenant_id,
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model_provider=LLMBuilder.get_default_provider(dataset.tenant_id),
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model_name='text-embedding-ada-002'
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)
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embeddings = CacheEmbedding(OpenAIEmbeddings(
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**model_credentials
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))
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vector_index = VectorIndex(
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dataset=dataset,
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config=current_app.config,
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embeddings=embeddings
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)
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start = time.perf_counter()
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documents = vector_index.search(
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query,
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search_type='similarity_score_threshold',
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search_kwargs={
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'k': 10
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}
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)
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end = time.perf_counter()
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logging.debug(f"Hit testing retrieve in {end - start:0.4f} seconds")
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dataset_query = DatasetQuery(
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dataset_id=dataset.id,
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content=query,
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source='hit_testing',
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created_by_role='account',
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created_by=account.id
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)
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db.session.add(dataset_query)
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db.session.commit()
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return cls.compact_retrieve_response(dataset, embeddings, query, documents)
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@classmethod
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def compact_retrieve_response(cls, dataset: Dataset, embeddings: Embeddings, query: str, documents: List[Document]):
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text_embeddings = [
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embeddings.embed_query(query)
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]
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text_embeddings.extend(embeddings.embed_documents([document.page_content for document in documents]))
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tsne_position_data = cls.get_tsne_positions_from_embeddings(text_embeddings)
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query_position = tsne_position_data.pop(0)
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i = 0
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records = []
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for document in documents:
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index_node_id = document.metadata['doc_id']
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segment = db.session.query(DocumentSegment).filter(
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DocumentSegment.dataset_id == dataset.id,
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DocumentSegment.enabled == True,
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DocumentSegment.status == 'completed',
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DocumentSegment.index_node_id == index_node_id
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).first()
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if not segment:
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i += 1
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continue
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record = {
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"segment": segment,
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"score": document.metadata['score'],
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"tsne_position": tsne_position_data[i]
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}
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records.append(record)
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i += 1
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return {
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"query": {
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"content": query,
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"tsne_position": query_position,
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},
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"records": records
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}
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@classmethod
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def get_tsne_positions_from_embeddings(cls, embeddings: list):
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embedding_length = len(embeddings)
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if embedding_length <= 1:
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return [{'x': 0, 'y': 0}]
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concatenate_data = np.array(embeddings).reshape(embedding_length, -1)
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# concatenate_data = np.concatenate(embeddings)
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perplexity = embedding_length / 2 + 1
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if perplexity >= embedding_length:
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perplexity = max(embedding_length - 1, 1)
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tsne = TSNE(n_components=2, perplexity=perplexity, early_exaggeration=12.0)
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data_tsne = tsne.fit_transform(concatenate_data)
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tsne_position_data = []
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for i in range(len(data_tsne)):
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tsne_position_data.append({'x': float(data_tsne[i][0]), 'y': float(data_tsne[i][1])})
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return tsne_position_data
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