Feat/milvus support (#671)
Co-authored-by: StyleZhang <jasonapring2015@outlook.com> Co-authored-by: JzoNg <jzongcode@gmail.com>pull/672/head
Jyong
3 years ago
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GitHub
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import numpy as np
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import sklearn.decomposition
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import pickle
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import time
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# Apply 'Algorithm 1' to the ada-002 embeddings to make them isotropic, taken from the paper:
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# ALL-BUT-THE-TOP: SIMPLE AND EFFECTIVE POST- PROCESSING FOR WORD REPRESENTATIONS
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# Jiaqi Mu, Pramod Viswanath
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# This uses Principal Component Analysis (PCA) to 'evenly distribute' the embedding vectors (make them isotropic)
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# For more information on PCA, see https://jamesmccaffrey.wordpress.com/2021/07/16/computing-pca-using-numpy-without-scikit/
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# get the file pointer of the pickle containing the embeddings
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fp = open('/path/to/your/data/Embedding-Latest.pkl', 'rb')
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# the embedding data here is a dict consisting of key / value pairs
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# the key is the hash of the message (SHA3-256), the value is the embedding from ada-002 (array of dimension 1536)
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# the hash can be used to lookup the orignal text in a database
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E = pickle.load(fp) # load the data into memory
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# seperate the keys (hashes) and values (embeddings) into seperate vectors
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K = list(E.keys()) # vector of all the hash values
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X = np.array(list(E.values())) # vector of all the embeddings, converted to numpy arrays
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# list the total number of embeddings
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# this can be truncated if there are too many embeddings to do PCA on
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print(f"Total number of embeddings: {len(X)}")
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# get dimension of embeddings, used later
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Dim = len(X[0])
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# flash out the first few embeddings
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print("First two embeddings are: ")
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print(X[0])
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print(f"First embedding length: {len(X[0])}")
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print(X[1])
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print(f"Second embedding length: {len(X[1])}")
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# compute the mean of all the embeddings, and flash the result
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mu = np.mean(X, axis=0) # same as mu in paper
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print(f"Mean embedding vector: {mu}")
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print(f"Mean embedding vector length: {len(mu)}")
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# subtract the mean vector from each embedding vector ... vectorized in numpy
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X_tilde = X - mu # same as v_tilde(w) in paper
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# do the heavy lifting of extracting the principal components
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# note that this is a function of the embeddings you currently have here, and this set may grow over time
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# therefore the PCA basis vectors may change over time, and your final isotropic embeddings may drift over time
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# but the drift should stabilize after you have extracted enough embedding data to characterize the nature of the embedding engine
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print(f"Performing PCA on the normalized embeddings ...")
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pca = sklearn.decomposition.PCA() # new object
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TICK = time.time() # start timer
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pca.fit(X_tilde) # do the heavy lifting!
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TOCK = time.time() # end timer
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DELTA = TOCK - TICK
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print(f"PCA finished in {DELTA} seconds ...")
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# dimensional reduction stage (the only hyperparameter)
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# pick max dimension of PCA components to express embddings
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# in general this is some integer less than or equal to the dimension of your embeddings
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# it could be set as a high percentile, say 95th percentile of pca.explained_variance_ratio_
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# but just hardcoding a constant here
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D = 15 # hyperparameter on dimension (out of 1536 for ada-002), paper recommeds D = Dim/100
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# form the set of v_prime(w), which is the final embedding
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# this could be vectorized in numpy to speed it up, but coding it directly here in a double for-loop to avoid errors and to be transparent
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E_prime = dict() # output dict of the new embeddings
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N = len(X_tilde)
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N10 = round(N/10)
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U = pca.components_ # set of PCA basis vectors, sorted by most significant to least significant
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print(f"Shape of full set of PCA componenents {U.shape}")
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U = U[0:D,:] # take the top D dimensions (or take them all if D is the size of the embedding vector)
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print(f"Shape of downselected PCA componenents {U.shape}")
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for ii in range(N):
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v_tilde = X_tilde[ii]
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v = X[ii]
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v_projection = np.zeros(Dim) # start to build the projection
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# project the original embedding onto the PCA basis vectors, use only first D dimensions
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for jj in range(D):
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u_jj = U[jj,:] # vector
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v_jj = np.dot(u_jj,v) # scaler
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v_projection += v_jj*u_jj # vector
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v_prime = v_tilde - v_projection # final embedding vector
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v_prime = v_prime/np.linalg.norm(v_prime) # create unit vector
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E_prime[K[ii]] = v_prime
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if (ii%N10 == 0) or (ii == N-1):
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print(f"Finished with {ii+1} embeddings out of {N} ({round(100*ii/N)}% done)")
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# save as new pickle
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print("Saving new pickle ...")
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embeddingName = '/path/to/your/data/Embedding-Latest-Isotropic.pkl'
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with open(embeddingName, 'wb') as f: # Python 3: open(..., 'wb')
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pickle.dump([E_prime,mu,U], f)
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print(embeddingName)
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print("Done!")
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# When working with live data with a new embedding from ada-002, be sure to tranform it first with this function before comparing it
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#
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def projectEmbedding(v,mu,U):
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v = np.array(v)
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v_tilde = v - mu
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v_projection = np.zeros(len(v)) # start to build the projection
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# project the original embedding onto the PCA basis vectors, use only first D dimensions
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for u in U:
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v_jj = np.dot(u,v) # scaler
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v_projection += v_jj*u # vector
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v_prime = v_tilde - v_projection # final embedding vector
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v_prime = v_prime/np.linalg.norm(v_prime) # create unit vector
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return v_prime
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@ -0,0 +1,48 @@
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from events.dataset_event import dataset_was_deleted
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from events.event_handlers.document_index_event import document_index_created
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from tasks.clean_dataset_task import clean_dataset_task
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import datetime
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import logging
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import time
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import click
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from celery import shared_task
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from werkzeug.exceptions import NotFound
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from core.indexing_runner import IndexingRunner, DocumentIsPausedException
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from extensions.ext_database import db
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from models.dataset import Document
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@document_index_created.connect
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def handle(sender, **kwargs):
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dataset_id = sender
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document_ids = kwargs.get('document_ids', None)
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documents = []
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start_at = time.perf_counter()
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for document_id in document_ids:
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logging.info(click.style('Start process document: {}'.format(document_id), fg='green'))
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document = db.session.query(Document).filter(
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Document.id == document_id,
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Document.dataset_id == dataset_id
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).first()
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if not document:
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raise NotFound('Document not found')
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document.indexing_status = 'parsing'
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document.processing_started_at = datetime.datetime.utcnow()
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documents.append(document)
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db.session.add(document)
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db.session.commit()
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try:
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indexing_runner = IndexingRunner()
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indexing_runner.run(documents)
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end_at = time.perf_counter()
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logging.info(click.style('Processed dataset: {} latency: {}'.format(dataset_id, end_at - start_at), fg='green'))
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except DocumentIsPausedException as ex:
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logging.info(click.style(str(ex), fg='yellow'))
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except Exception:
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pass
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@ -0,0 +1,4 @@
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from blinker import signal
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# sender: document
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document_index_created = signal('document-index-created')
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@ -1,24 +0,0 @@
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import logging
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import time
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import click
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import requests
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from celery import shared_task
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from core.generator.llm_generator import LLMGenerator
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@shared_task
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def generate_test_task():
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logging.info(click.style('Start generate test', fg='green'))
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start_at = time.perf_counter()
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try:
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#res = requests.post('https://api.openai.com/v1/chat/completions')
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answer = LLMGenerator.generate_conversation_name('84b2202c-c359-46b7-a810-bce50feaa4d1', 'avb', 'ccc')
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print(f'answer: {answer}')
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end_at = time.perf_counter()
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logging.info(click.style('Conversation test, latency: {}'.format(end_at - start_at), fg='green'))
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except Exception:
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logging.exception("generate test failed")
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