Feat/milvus support (#671)

Co-authored-by: StyleZhang <jasonapring2015@outlook.com>
Co-authored-by: JzoNg <jzongcode@gmail.com>

api/core/index/vector_index/test-embedding.py

@@ -1,123 +0,0 @@
-import numpy as np
-import sklearn.decomposition
-import pickle
-import time
-
-
-# Apply 'Algorithm 1' to the ada-002 embeddings to make them isotropic, taken from the paper:
-# ALL-BUT-THE-TOP: SIMPLE AND EFFECTIVE POST- PROCESSING FOR WORD REPRESENTATIONS
-# Jiaqi Mu, Pramod Viswanath
-
-# This uses Principal Component Analysis (PCA) to 'evenly distribute' the embedding vectors (make them isotropic)
-# For more information on PCA, see https://jamesmccaffrey.wordpress.com/2021/07/16/computing-pca-using-numpy-without-scikit/
-
-
-# get the file pointer of the pickle containing the embeddings
-fp = open('/path/to/your/data/Embedding-Latest.pkl', 'rb')
-
-
-# the embedding data here is a dict consisting of key / value pairs
-# the key is the hash of the message (SHA3-256), the value is the embedding from ada-002 (array of dimension 1536)
-# the hash can be used to lookup the orignal text in a database
-E = pickle.load(fp) # load the data into memory
-
-# seperate the keys (hashes) and values (embeddings) into seperate vectors
-K = list(E.keys()) # vector of all the hash values
-X = np.array(list(E.values())) # vector of all the embeddings, converted to numpy arrays
-
-
-# list the total number of embeddings
-# this can be truncated if there are too many embeddings to do PCA on
-print(f"Total number of embeddings: {len(X)}")
-
-# get dimension of embeddings, used later
-Dim = len(X[0])
-
-# flash out the first few embeddings
-print("First two embeddings are: ")
-print(X[0])
-print(f"First embedding length: {len(X[0])}")
-print(X[1])
-print(f"Second embedding length: {len(X[1])}")
-
-
-# compute the mean of all the embeddings, and flash the result
-mu = np.mean(X, axis=0) # same as mu in paper
-print(f"Mean embedding vector: {mu}")
-print(f"Mean embedding vector length: {len(mu)}")
-
-
-# subtract the mean vector from each embedding vector ... vectorized in numpy
-X_tilde = X - mu # same as v_tilde(w) in paper
-
-
-
-# do the heavy lifting of extracting the principal components
-# note that this is a function of the embeddings you currently have here, and this set may grow over time
-# therefore the PCA basis vectors may change over time, and your final isotropic embeddings may drift over time
-# but the drift should stabilize after you have extracted enough embedding data to characterize the nature of the embedding engine
-print(f"Performing PCA on the normalized embeddings ...")
-pca = sklearn.decomposition.PCA()  # new object
-TICK = time.time() # start timer
-pca.fit(X_tilde) # do the heavy lifting!
-TOCK = time.time() # end timer
-DELTA = TOCK - TICK
-
-print(f"PCA finished in {DELTA} seconds ...")
-
-# dimensional reduction stage (the only hyperparameter)
-# pick max dimension of PCA components to express embddings
-# in general this is some integer less than or equal to the dimension of your embeddings
-# it could be set as a high percentile, say 95th percentile of pca.explained_variance_ratio_
-# but just hardcoding a constant here
-D = 15 # hyperparameter on dimension (out of 1536 for ada-002), paper recommeds D = Dim/100
-
-
-# form the set of v_prime(w), which is the final embedding
-# 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
-E_prime = dict() # output dict of the new embeddings
-N = len(X_tilde)
-N10 = round(N/10)
-U = pca.components_ # set of PCA basis vectors, sorted by most significant to least significant
-print(f"Shape of full set of PCA componenents {U.shape}")
-U = U[0:D,:] # take the top D dimensions (or take them all if D is the size of the embedding vector)
-print(f"Shape of downselected PCA componenents {U.shape}")
-for ii in range(N):
-    v_tilde = X_tilde[ii]
-    v = X[ii]
-    v_projection = np.zeros(Dim) # start to build the projection
-    # project the original embedding onto the PCA basis vectors, use only first D dimensions
-    for jj in range(D):
-        u_jj = U[jj,:] # vector
-        v_jj = np.dot(u_jj,v) # scaler
-        v_projection += v_jj*u_jj # vector
-    v_prime = v_tilde - v_projection # final embedding vector
-    v_prime = v_prime/np.linalg.norm(v_prime) # create unit vector
-    E_prime[K[ii]] = v_prime
-
-    if (ii%N10 == 0) or (ii == N-1):
-        print(f"Finished with {ii+1} embeddings out of {N} ({round(100*ii/N)}% done)")
-
-
-# save as new pickle
-print("Saving new pickle ...")
-embeddingName = '/path/to/your/data/Embedding-Latest-Isotropic.pkl'
-with open(embeddingName, 'wb') as f:  # Python 3: open(..., 'wb')
-    pickle.dump([E_prime,mu,U], f)
-    print(embeddingName)
-
-print("Done!")
-
-# When working with live data with a new embedding from ada-002, be sure to tranform it first with this function before comparing it
-#
-def projectEmbedding(v,mu,U):
-    v = np.array(v)
-    v_tilde = v - mu
-    v_projection = np.zeros(len(v)) # start to build the projection
-    # project the original embedding onto the PCA basis vectors, use only first D dimensions
-    for u in U:
-        v_jj = np.dot(u,v) # scaler
-        v_projection += v_jj*u # vector
-    v_prime = v_tilde - v_projection # final embedding vector
-    v_prime = v_prime/np.linalg.norm(v_prime) # create unit vector
-    return v_prime