Faiss python example github. The memory usage is (d * 4 + M * 2 * 4) bytes per vector.

Faiss python example github RUN apt-get install -y libopenblas-dev python-numpy python-dev swig git python-pip curl: RUN pip install matplotlib: COPY . 3 and above) IndexBinaryHash: A classical method is to extract a hash from the binary vectors and to use that to split the dataset in buckets. IndexFlatL2(top_docs_embeddings[0]. The examples will most often be in the form of Python notebooks, but as usual translation to C++ should be Faiss is a library for efficient similarity search and clustering of dense vectors. 3 min on 1 Kepler-class K40m GPU Faiss is a library for efficient similarity search and clustering of dense vectors. - facebookresearch/faiss In the AutoGPT tutorial with FAISS, no actual documents are being added, as once you initialize FAISS, I guess it is temporarily storing in memory the results of the internet searches the agent is doing. ANN can index the existent vectors. How to build a semantic search engine with Transformers and Faiss; How to deploy a machine learning model on AWS Elastic Beanstalk with Streamlit and Docker; Check out the blogs if you want to learn how to create a semantic search engine with Sentence Transformers and Faiss. Fast and customizable framework for automatic and quick Causal Inference in Python. python opencv faiss fastapi Updated An advanced environmental science chatbot powered by cutting-edge technologies like Langchain, Llama2, Chatlit, FAISS, and RAG, providing insightful answers to environmental queries - Smit1400/EcoMed-Expert-llama There is an efficient 4-bit PQ implementation in Faiss. Inspired by YouTube Video from Prompt Engineer. # You need the Cohere Python In this page, we reference example use cases for Faiss, with some explanations. py --help for more information on possible settings. It is intended to facilitate the index_ivf = faiss. For a higher level API without explicit resource allocation, a few easy wrappers are defined:. FAISS_ENABLE_GPU: Setting this variable to ON builds faiss-gpu package. Reload to refresh your session. This is of course the case when the train set is the same as the added vectors. For most application cases it performs worse than PQ in the tradeoffs between memory vs. Summary Platform OS: Ubuntu 20. index_path-> Destination path of the created index. Built on Langchain, OpenAI, FAISS, Streamlit. It also contains supporting code for evaluation and parameter tuning. It that exports all of $ python client_sample. The website ann-benchmarks. Platform Python 3. Example Dockerfile for faiss. py search 10 # search by specified id, get numer of neighbors given value python client. - facebookresearch/faiss SWIG parses the Faiss header files and generates classes in Python for all the C++ classes it finds. FederView - render and interaction. The 4 <= M <= 64 is the number of links per vector, higher is more accurate but uses more RAM. The hash value is the first b bits of the binary vector. shape[0]) # This example shows how to use Cohere binary embeddings to get a 32x reduction in memory # and up to a 40x faster search speed. py search-by-key a2 --host localhost:50051 --count 2 $ python client_sample. Faiss is an efficient and powerful library developed by Facebook AI Research (FAIR) for similarity search and clustering of dense vectors. The index_factory function interprets a string to produce a composite Faiss index. For faiss-gpu, the nvidia channel is required for CUDA, which is not published in the main Faiss is a library for efficient similarity search and clustering of dense vectors. ipynb. index_cpu_gpu_list: same, but in addition takes a list of gpu ids There is an efficient 4-bit PQ implementation in Faiss. This is much faster than scipy. 7 crash on calling search functionality in basic example. We provide code examples in C++ and Python. py import data/embeds. Naive RAG implementation using LangChain + OpenAI GPT 3. Faiss itself is internally threaded in a couple of different ways. See python/faiss. They do not store vector ids, since in many cases sequential numbering is enough. The SWIG module is called swigfaiss in Python, this is the low-lever wrapper. 7 OS: macOS 11. py heatbeat # search by query, get numer of neighbors given value (query is auto generated in command as identity vector) python client. 2 Installed from: pip install faiss-cpu --no-cache Faiss compilation options: Running on: CPU GP An introductory talk about faiss by its core devs can be found on YouTube, and a high-level intro is also in a FB engineering blogpost. sql Since most Faiss indexes do encode the vectors they store, the codec API just uses plain indexes as codecs. Still, I have some issues concerning the querying, as it seems that, after the merging, no result was provided, as I get that the output of the given query provides -1 indices for each query vector. Have you optimized this calculation? Thank you! Faiss version: <1. Faiss is a library for efficient similarity search and clustering of dense vectors. (a-b)²=a²+b²-2ab. shard = True. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. Threading is done through OpenMP, and a multithreaded BLAS implementation. py. 3 introduces two new fields, which allow to perform the calls to ProductQuantizer::compute_code() faster:::transposed_centroids which stores the coordinates I encountered some problems while running the python example CaydynMacbookPro:faiss caydyn$ python python/demo_auto_tune. py load data load GT prepare criterion Traceback (most recent call last): File "python/demo_auto_tune. py --plottype recall/time --latex --scatter --outputdir website/. 6. - aaronkazah/python-vector-search More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects. 2, . These sets are de-duplicated. (Faiss 1. See python run. For example, for an IndexIVF, one query vector may be run with nprobe=10 and another with nprobe=20. The script utilizes the LangChain library for text processing and vector storage, incorporating multithreading for parallel execution. So first I need to get the related value in index=faiss. USearch is compact and broadly compatible without sacrificing performance, primarily focusing on user-defined metrics and fewer dependencies. Optional GPU support is provided via CUDA or AMD ROCm, and the Python interface is also optional. This page explains how to change this to arbitrary ids. py search-by-embedding 1 --host localhost:50051 --count 2 For example, using an embedding framework, We are going to build a prototype in python, and any libraries that need to be installed are mentioned in step 0. Code Issues Quicker ADC is an implementation of fast distance computation techniques for nearest neighbor search in large-scale databases of high-dimensional vectors. 4, . 0> Installed from: Faiss compilation options: cmake -B build . index_infos_path-> Destination path of the index infos. For example, the file Index_c. A basic example on how to build an similar image search web service with Python, OpenCV, FAISS and GitHub is where people build software. IndexHNSWFlat(d,32). It is based on open-sourced Faiss 1. BM25 and FAISS hybrid search example. Faiss is written in C++ with complete wrappers for Python (versions 2 and 3). For example to obtain a HNSW coarse quantizer and inverted lists on GPU, use index_cpu_to_gpu on the index, since that will not convert the HNSW coarse quantizer to GPU. The memory usage is (d * 4 + M * 2 * 4) bytes per vector. They do not inherit directly from IndexPQ and IndexIVFPQ because the codes are "packed" in batches of bbs=32 (64 and 96 are supported as well but there are few operating points where they are competitive). they do support efficient direct vector access (with reconstruct and reconstruct_n). You signed in with another tab or window. py example, I managed to create one. All 302 Python 170 Jupyter Notebook 70 C++ 12 JavaScript 9 Go 5 HTML 5 Java 4 Rust 4 Shell Facebook's Faiss CPU example with Dockerfile ready and tested for Deepnote so you don't have to try and fail like I did 😎 More than 100 million people use GitHub to discover, fork, and contribute to over 330 million projects. - Azure/azureml-examples GitHub is where people build software. FederLayout - layout calculations. I was wondering if Spherical is set to True whether the embeddings would still need to be normalized before? Or does Spherical only normalize the centroids? For example, the normalization step would no longer be needed:. Summary Platform OS: Faiss version: Faiss compilation options: Running on: CPU GPU Interface: C++ Python Reproduction instructions Hi everyone whether there is a way to save cluster/index into a local file? For example: ncentroids = 1024 niter = 20 verbose = true d = x. By default Faiss assigns a sequential id to vectors added to the indexes. - facebookresearch/faiss At Loopio, we use Facebook AI Similarity Search (FAISS) to efficiently search for similar text. python opencv faiss fastapi Updated Dec 27, 2019; The distribution is estimated on a sample provided at train time, that should be representative of the data that is indexed. py search-by-id 0 10 faiss serving :). When adding data and searching, Faiss checks only whether the dimensionality of the data is correct (and this only in the Python wrappers). Create a new database in Azure SQL DB or use an existing one, then create and import a sample of Wikipedia data using script sql/import-wikipedia. Also, I guess range_search may be more memory efficient than search, but I'm not sure. The 4-bit PQ implementation of Faiss is heavily inspired by SCANN. Build a FAISS model store it in MSSQL. The new layout not only improves the cache hit I would think this also automatically handles the cloner - co = faiss. Kmeans(d, ncentroids, niter, verbo For example, I can put indexing on gpu 1 with gpu_index = faiss. - Running on GPUs · facebookresearch/faiss Wiki A library for efficient similarity search and clustering of dense vectors. Go straight to the example code! A common procedure used in information retrieval and # FAISS search on the top documents: sub_index = faiss. metric_type-> Similarity distance for the queries. py) demonstrating the integration of LangChain for processing data from URLs, extracting text content, and constructing a FAISS (Facebook AI Similarity Search) vector store. - facebookresearch/faiss fast, high performance efficient vector search implemented in python using Faiss & pickle for persistent storage. Since most Faiss indexes do encode the vectors they store, the codec API just uses plain indexes as codecs. - facebookresearch/faiss FAISS is a widely recognized standard for high-performance vector search engines. import faiss import numpy as np D = 2 N = 3 X = np. index_cpu_gpu_list: same, but in addition takes a list of gpu ids Faiss is a library for efficient similarity search and clustering of dense vectors. py install) The first command builds the python bindings for Faiss, while the second one generates and installs the python package. csv data/keys. Note that this shrinks You signed in with another tab or window. More code examples are available on the faiss GitHub repository. Just adding example if noob like me came here to find how to calculate the Cosine similarity from scratch. 1. 3 introduces two new fields, which allow to perform the calls to ProductQuantizer::compute_code() faster:::transposed_centroids which stores the coordinates A library for efficient similarity search and clustering of dense vectors. For example,I want to achieve the search in python in my own code. whl files for MacOS + Linux of the Facebook FAISS library - onfido/faiss_prebuilt A library for efficient similarity search and clustering of dense vectors. import faiss dataSetI = [. 7. All 521 Python 307 Jupyter Notebook 135 C++ 15 JavaScript 12 HTML 8 Go 6 TypeScript 5 Java 4 Rust 4 Shell 3. The reason why we don't support more platforms is because it is a lot of work to make sure Faiss runs in the supported configurations: building the conda packages for a new release of Faiss always surfaces compatibility issues. Functions are declared with the faiss_ prefix (e. User can upload a pdf file and the app will allow for queries against it. index_cpu_to_all_gpus: clones a CPU index to all available GPUs or to a number of GPUs specified with ngpu=3. Creating a FAISS index in 🤗 Datasets is simple — we use the Dataset. For CPU Faiss, the three basic operations on indexes (training, adding, searching) are internally multithreaded. The available encodings are (from least to strongest compression): no encoding at all (IndexFlat): the vectors are stored without compression;16-bit float encoding (IndexScalarQuantizer with QT_fp16): the vectors are compressed to 16-bit floats, which may cause some loss of precision;8/6/4-bit integer encoding (IndexScalarQuantizer with QT_8bit/QT_6bit/QT_4bit): The reason why we don't support more platforms is because it is a lot of work to make sure Faiss runs in the supported configurations: building the conda packages for a new release of Faiss always surfaces compatibility issues. As there was no equivalent to the demo_ondisk_ivf. In this example, we use FAISS with an inverse flat index (IndexIVFFlat). The faiss module is an additional level of wrapping above swigfaiss. A library for efficient similarity search and clustering of dense vectors. py test TestGPUKmeans. An example call: python create_website. For example std::vector<float>'s internal pointer is given by the data() method. However, it can be useful to set these parameters separately per query. Llama3-8B-Finetune-and-RAG, code base for sample, implemented in Kaggle platform. , it might not perfectly find all top-k nearest neighbors. GitHub Gist: instantly share code, notes, and snippets. Can automatically save and load vector when needed. 1, . 4 Installed from: pip install Faiss compilation options: no Running on: CPU GPU Interface: C++ Python Reproduction instructions I've run into this bug twice In Python Pr Faiss is a library for efficient similarity search and clustering of dense vectors. index_factory() The following attributes defined the way the index is created: train_num - if specified, sets the number of samples are used for the index training. What would be the case then when using Chroma instead of FAISS in this particular tutorial? Running on: CPU; GPU; Interface: C++; Python; Reproduction instructions. h, where «name» is the respective name from the C++ API. 5 + Sentence_Transformer + FAISS . The functions and class methods can be called transparently from Python. It is based upon Quick ADC but provides (i) AVX512 support, (ii) new optimized product quantizers, (iii) Faiss is a library for efficient similarity search and clustering of dense vectors. Set this variable if faiss is built with GPU support. Faiss 1. - Related projects · facebookresearch/faiss Wiki Faiss is a library for efficient similarity search and clustering of dense vectors. 3] dataSetII = [. Perhaps you want to find Now, let's dive into a hands-on example to demonstrate how Faiss can be effectively utilized in Python for similarity search tasks. python mindsdb streamlit-webapp langchain-python faiss-vector-database Updated Code Issues Pull requests Gemma2(9B), Llama3-8B-Finetune-and-RAG, code base for sample, implemented in Kaggle Faiss is a library for efficient similarity search and clustering of dense vectors. - GPU k means example · facebookresearch/faiss Wiki KNN Implementation for FAISS. shape[1] kmeans = faiss. Faiss does not Saved searches Use saved searches to filter your results more quickly GitHub is where people build software. This is problematic when the searches are called from different threads. /configure: The Faiss kmeans implementation is fairly efficient. - facebookresearch/faiss To evaluate our choice of an index, we work on a sample of 50M queries and 50M database vectors. A basic example on how to build an similar image search web service with Python, OpenCV, FAISS and FastAPI. Prebuilt . You switched accounts on another tab or window. To process the results, either use python plot. downloading datasets/query sets used to benchmark the index to data/; run 30-NN queries on the index for each query in the query set using a couple of different hyperparameters, A library for efficient similarity search and clustering of dense vectors. example of github actions: If you want to add your class to faiss, see this; Nearest neighbor search (CPU) The most basic nearest neighbor search by L2 distance. - Azure/azureml-examples Faiss is a library for efficient similarity search and clustering of dense vectors. accuracy and/or speed vs. - facebookresearch/faiss A library for efficient similarity search and clustering of dense vectors. We compare the Faiss fast-scan implementation with Google's SCANN, version 1. NOTE: The results are not going to be sorted by cosine similarity. The data layout is tuned to be efficient with AVX instructions, see simulate_kernels_PQ4. Thank you very much for your answer, I would however like to bring a slight precision that I personally had a The basic idea behind FAISS is to create a special data structure called an index that allows one to find which embeddings are similar to an input embedding. cd examples # show usage of client example python client. The Langchain library is used to process URLs and sitemaps, while MongoDB and FAISS handle data persistence and vector storage. Feder consists of three components:. Showcase of FAISS. Some Index classes implement a add_with_ids method, where 64-bit vector ids can be About. Clustering n=1M points in d=256 dimensions to k=20000 centroids (niter=25 EM iterations) is a brute-force operation that costs n * d * k * niter multiply-add operations, 128 Tflop in this case. 5 LTS Faiss version: v1. add_faiss_index() function and specify which column of our dataset we’d like to index: A library for efficient similarity search and clustering of dense vectors. It consumes a lot of computational resources. accuracy. Pull requests are welcome. serialize_index, faiss. It requires a lot of memory. More than 100 million people use GitHub to discover, fork, and contribute to over 330 million projects. - wolfmib/alinex-faiss A library for efficient similarity search and clustering of dense vectors. - Rmnesia/FAISS-example A library for efficient similarity search and clustering of dense vectors. HNSW does only support sequential adds Added easy-to-use serialization functions for indexes to byte arrays in Python (faiss. Finding items that are similar is commonplace in many applications. 6] ChatGPT-like app for querying pdf files. so check out FAISS’ github wiki. 5 for natural language processing. - Compiling and developing for Faiss · facebookresearch/faiss Wiki A library for efficient similarity search and clustering of dense vectors. This server can be deployed on any cloud platform and is optimized for managing vector databases for AI applications. faiss_IndexFlat_new), whereas new types have the K-Means clustering of molecules with the FASS library from Facebook AI Research - PatWalters/faiss_kmeans LangChain Chatbot: A Flask-based web application that integrates a Chatbot leveraging OpenAI's GPT-3. random ((N, D)) In Python index_gpu_to_cpu, index_cpu_to_gpu and index_cpu_to_gpu_multiple are available. details Locality Sensitive Hashing (LSH) is an indexing method whose theoretical aspects have been studied extensively. At search time, the number of visited buckets is 1 + b + b * (b - GitHub is where people build software. The implementation is heavily inspired by Google's SCANN. For a new query vector, this index can be used to find the nearest neighbors. - ademarc/langchain-chat-website Also, I would like to ask if faiss kind of uses this calculation for L2 distance calculation? Since IntelMKL can't calculate L2 distance directly. h file corresponds to the base Index API. GpuMultipleClonerOptions() co. they support removal with remove. Below is an example for faiss built with avx2 option and OpenBLAS backend. Faiss is highly optimized for performance, supporting both CPU tl;dr: The faiss library allows to perform nearest neighbor search in an efficient way, scaling to several million dense vectors. - facebookresearch/faiss Faiss is a library for efficient similarity search and clustering of dense vectors. The code can be run by copy/pasting it or running it from the tutorial/ subdirectory of the Faiss distribution. The library is mostly implemented in C++, the only dependency is a BLAS implementation. And then implement the entire process of search in python. py -h # show heatbeat message python client. The Faiss implementation takes: 11 min on CPU. Faiss A library for efficient similarity search and clustering of dense vectors. Is there any demo? Running on: CPU; GPU; Interface: C++; Python; Reproduction instructions. Stable releases are pushed regularly to the pytorch conda channel, as well as pre-release nightly builds. If you have a lots of RAM or the dataset is small, HNSW is the best option, it is a very fast and accurate index. . Example app using facebookresearch/faiss inside web API for NMF based recommender system. FederIndex - parse the index file. Official community-driven Azure Machine Learning examples, tested with GitHub Actions. Faiss comes with precompiled libraries for Anaconda in Python, see faiss-cpu, faiss-gpu and faiss-gpu-cuvs. - facebookresearch/faiss Alternatively, if index_builder_type is not specified, one can set faiss_factory just like in FAISS API factory call faiss. py --dataset glove-100-angular or python create_website. USearch and FAISS both employ the same HNSW algorithm, but they differ significantly in their design principles. - facebookresearch/faiss This example uses plain arrays, because this is the lowest common denominator all C++ matrix libraries support. The codec API add three functions that are prefixed with sa_ (standalone):. py", line 73, Faiss indexes have their search-time parameters as object fields. Note that experiments can take a long time. Contribute to shankarpm/faiss_knn development by creating an account on GitHub. For FAISS also build a containerized REST service and expose FAISS via REST API that can be consumed by T-SQL. You signed out in another tab or window. index_key-> (optional) Describe the index to build. Sample requests included for learning and ease of use. Faiss is written in C++ with complete wrappers for Python/numpy. \-DFAISS_ENABLE_GPU=OFF -DFAISS_ENABLE_PYTHON=ON The available encodings are (from least to strongest compression): no encoding at all (IndexFlat): the vectors are stored without compression;16-bit float encoding (IndexScalarQuantizer with QT_fp16): the vectors are compressed to 16-bit floats, which may cause some loss of precision;8/6/4-bit integer encoding (IndexScalarQuantizer with QT_8bit/QT_6bit/QT_4bit): Faiss is a library for efficient similarity search and clustering of dense vectors. The data layout is A library for efficient similarity search and clustering of dense vectors. 04. 2->v1. 5, . Add a description, image, and links to the faiss topic page so that developers can The C API is composed of: A set of C header files comprising the main Faiss interfaces, converted for use in C. IndexIVFFlat(quantizer, d, nlist, faiss. linex-FAISS is a scalable, cloud-agnostic FAISS vector search server built using Flask and Python. csv data/ids. GitHub is where people build software. We compute the Faiss is a library for efficient similarity search and clustering of dense vectors. Fitting Faiss is a library for efficient similarity search and clustering of dense vectors. - facebookresearch/faiss Quick description of the autofaiss build_index command:. $ (cd build/faiss/python && python setup. This is 1/30,000 th the scale at which we will operate eventually. Flat indexes are similar to C++ vectors. The codec API add three Trained ProductQuantizer struct maintains a list of centroids in an 1D array field called ::centroids, its layout is (M, ksub, dsub). Contribute to ynqa/faiss-server development by creating an account on GitHub. - facebookresearch/faiss The IndexPQFastScan and IndexIVFPQFastScan objects perform 4-bit PQ fast scan. To build original Faiss with no optimization, just follow the original build way, like: This feature changes the layout of PQ code in InvertedLists in IndexIVFPQ. The bottleneck fo K-Means clustering of molecules with the FASS library from Facebook AI Research - PatWalters/faiss_kmeans A library for efficient similarity search and clustering of dense vectors. ; In case of excessive amount of data, we support separating the computation part and running it on a node server. random. By following these step-by-step instructions, you The Faiss Python API serves as a bridge between the core Faiss C++ library and Python, enabling Python developers to easily leverage Faiss’s capabilities. Therefore: they don't support add_with_id (but they can be wrapped in an IndexIDMap to add that functionality). /opt/faiss: WORKDIR /opt/faiss: RUN . 3. g. Each file follows the format «name»_c. Faiss can accommodate any matrix library, provided it provides a pointer to the underlying data. 4 Faiss version: faiss-cpu 1. index_cpu_to_gpu(res, 1, index) but if I want to put on gpu 1,2,3 because I'm using gpu 0, how can I use index_cpu_to_gpu_multiple or index_cpu_to_gpu_multiple_py? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. The codec can be constructed using the index_factory and trained with the train method. This nearest neighbor search is not perfect, i. The speed-accuracy tradeoff is set via the efSearch parameter. This repository contains a Python script (url_data_loader. com contains the results of benchmarks run with different libraries for approximate nearest neighbors search FAISS may do something similar to this with its clustered indexing. FAISS_OPT_LEVEL: Faiss SIMD optimization, one of generic, sse4, avx2. For major changes, please open an issue first to discuss what Summary Python 3. The Python KMeans object can be used to use the GPU directly, just add gpu=True to the constuctor see gpu/test/test_gpu_index. - Storing IVF indexes on disk · facebookresearch/faiss Wiki 🦜🔗 Build context-aware reasoning applications. save_on_disk-> Save the index on the disk. py for more details. csv --host localhost:50051 $ python client_sample. For cosine similarity search, this idea might be modified for angular coordinates by doing PCA down to N dimensions and testing if cosine_similarity( PCA(embedding, Trained ProductQuantizer struct maintains a list of centroids in an 1D array field called ::centroids, its layout is (M, ksub, dsub). - facebookresearch/faiss The first command builds the python bindings for Faiss, while the second one generates and installs the python package. This allows to access the coordinates of the centroids directly. e. deserialize_index). I am trying to use the range search operation (only CPU support) for a scenario wherein the dataset contains ~200K high dimensional (16-32 D) points but there are only a small number of search queries to be made (<500). embeddings-> Source path of the embeddings in numpy. Contribute to langchain-ai/langchain development by creating an account on GitHub. At search time, all hashtable entries within nflip Hamming radius of the query vector's hash are visited. semantic cache kaggle finetuning rag faiss-vector-database llama3 python faiss streamlit langchain azure-openai A library for efficient similarity search and clustering of dense vectors. METRIC_L2) # here we specify METRIC_L2, by default it performs inner-product search # make it an IVF GPU index You signed in with another tab or window. Step 4: Installing the C++ library and headers (optional) $ make -C build install A library for efficient similarity search and clustering of dense vectors. 8. More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects. In Python index_gpu_to_cpu, index_cpu_to_gpu and index_cpu_to_gpu_multiple are available. It The supported way to install Faiss is through conda. docker cmake protobuf cpp grpc grpc-python faiss Updated Sep 26, 2023; Python; jorge-armando-navarro-flores / chat_with_your_docs Star 124. sa_code_size: returns the size in bytes of the codes generated by the codec; sa_encode: This is an optimized version of Faiss by Intel. py get-embedding 1 --host localhost:50051 $ python client_sample. Multiple GPU experiments Here we run the same experiment with 4 GPUs, and we keep only the options where the inverted lists are stored on GPU. takes care of. It is specifically designed to handle large-scale datasets and high-dimensional vector spaces, making it well-suited for applications in computer vision, natural language processing, and machine learning. The string is a comma-separated list of components. fhbmhv omfefch jhlzz puhk atlv rzkmck jckwdmy peqfcwv dxbzwks wwufsc