From f0bab76570820f79b0f1b003d25da15bc5f69448 Mon Sep 17 00:00:00 2001 From: Sam Partee Date: Tue, 21 Nov 2023 18:26:21 -0800 Subject: [PATCH] Update Version, add cli notebook, add versions --- Makefile | 6 + docs/_static/gallery-images/arxiv-search.png | Bin 0 -> 359900 bytes docs/_static/gallery-images/bytewax-redis.png | Bin 0 -> 95338 bytes docs/_static/gallery.yaml | 13 +- docs/_static/js/sidebar.js | 13 +- docs/_static/version_names.json | 21 +- docs/api/vectorizer.rst | 2 +- docs/concepts/distance.md | 49 --- docs/concepts/hybrid.md | 55 ---- docs/concepts/llm.md | 55 ---- docs/concepts/vector_index.md | 41 --- docs/concepts/vectordb.md | 27 -- docs/concepts/vectors.md | 45 --- docs/conf.py | 9 +- docs/overview/installation.md | 59 +++- docs/user_guide/cli.ipynb | 287 ++++++++++++++++++ docs/user_guide/getting_started_01.ipynb | 4 +- docs/user_guide/hybrid_queries_02.ipynb | 2 +- docs/versions.rst | 6 - redisvl/version.py | 2 +- setup.py | 2 +- 21 files changed, 384 insertions(+), 314 deletions(-) create mode 100644 docs/_static/gallery-images/arxiv-search.png create mode 100644 docs/_static/gallery-images/bytewax-redis.png delete mode 100644 docs/concepts/distance.md delete mode 100644 docs/concepts/hybrid.md delete mode 100644 docs/concepts/llm.md delete mode 100644 docs/concepts/vector_index.md delete mode 100644 docs/concepts/vectordb.md delete mode 100644 docs/concepts/vectors.md create mode 100644 docs/user_guide/cli.ipynb delete mode 100644 docs/versions.rst diff --git a/Makefile b/Makefile index a42ebc88..98787ac0 100644 --- a/Makefile +++ b/Makefile @@ -94,3 +94,9 @@ test-cov: cov: @coverage html @echo if data was present, coverage report is in ./htmlcov/index.html + + +# help: test-notebooks - Run all notebooks +.PHONY: test-notebooks +test-notebooks: + @cd docs/ && treon -v \ No newline at end of file diff --git a/docs/_static/gallery-images/arxiv-search.png b/docs/_static/gallery-images/arxiv-search.png new file mode 100644 index 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E|MqtFzIrwnZ5}P32E!aV;0Y-`J6=filt;m74(fY={Gp>y8Szf1djVqyi-Idzdk#>eM)pu+27J= z$2QC@YHJFF)^>WfLgoz23QxWG><#OYcntgnnTlAt%Dv0WRM7;;gj?}|%vRF2<*V?z zViXYVK6{Ex_^J``U=42RZJrFKJNg-9KJ9_p@O(~F+bn_41SB7&+nfvJ6VNZWD|{~> z3rB8lX!q)%ZKVaOP#XZGn^{^{=cj7e*mz2$v0nW_on~=002ovPDHLk FV1ngfa{d4S literal 0 HcmV?d00001 diff --git a/docs/_static/gallery.yaml b/docs/_static/gallery.yaml index ee619e93..1206a088 100644 --- a/docs/_static/gallery.yaml +++ b/docs/_static/gallery.yaml @@ -1,11 +1,10 @@ -# Metadata to create our example gallery. This YAML file does two things: -# -# 1. Used by `generate_gallery_images.py` to take snapshots of each item and place an image in `gallery`. -# 2. Used by our Gallery Grid directive to create the grid on our `examples/gallery.md` page. -# -# This file should only be modified by maintainer, contributors should add their project -# to the list of `gallery.md`. +- title: Arxiv Paper Search + website: https://docsearch.redisventures.com + img-bottom: ../_static/gallery-images/arxiv-search.png +- title: Real-Time Embeddings with Redis and Bytewax + website: https://github.com/awmatheson/real-time-embeddings + img-bottom: ../_static/gallery-images/bytewax-redis.png - title: Question and Answer with OpenAI website: https://www.redisvl.com/docs/html/examples/openai_qna.html img-bottom: ../_static/gallery-images/RedisOpenAI-QnA.png diff --git a/docs/_static/js/sidebar.js b/docs/_static/js/sidebar.js index b2d8353e..0188300b 100644 --- a/docs/_static/js/sidebar.js +++ b/docs/_static/js/sidebar.js @@ -2,8 +2,7 @@ const toc = [ { header: "Overview", toc: [ { title: "RedisVL", path: "/index.html" }, { title: "Install", path: "/overview/installation.html" }, - { title: "Quickstart", path: "/overview/quickstart.html" }, - { title: "CLI", path: "/overview/cli.html" }, + { title: "CLI", path: "/user_guide/cli.html" }, ]}, { header: "User Guide", toc: [ { title: "Getting Started", path: "/user_guide/getting_started_01.html" }, @@ -20,11 +19,11 @@ const toc = [ { title: "Vectorizers", path: "/api/vectorizer.html" }, { title: "LLMCache", path: "/api/cache.html" } ]}, - { header: "Concepts", toc: [ - { title: "Vector Embeddings", path: "/concepts/vectors.html" }, - { title: "HNSW vs Flat", path: "/concepts/vector_index.html" }, - { title: "Distance Metrics", path: "/concepts/distance.html" } -]} +// { header: "Concepts", toc: [ +// { title: "Vector Embeddings", path: "/concepts/vectors.html" }, +// { title: "HNSW vs Flat", path: "/concepts/vector_index.html" }, +// { title: "Distance Metrics", path: "/concepts/distance.html" } +//]} ]; document.addEventListener('DOMContentLoaded', function() { diff --git a/docs/_static/version_names.json b/docs/_static/version_names.json index 256c9ff4..b7a8e221 100644 --- a/docs/_static/version_names.json +++ b/docs/_static/version_names.json @@ -1,8 +1,13 @@ -{ - "version_names":[ - "0.1.0" - ], - "version_urls": [ - "https://www.craylabs.org/develop/overview.html", - ] -} +[ + { + "name": "0.0.5 (stable)", + "version": "0.0.5", + "url": "/", + "preferred": true + }, + { + "name": "0.0.4", + "version": "0.0.4", + "url": "/0.0.4/" + } +] \ No newline at end of file diff --git a/docs/api/vectorizer.rst b/docs/api/vectorizer.rst index 64177e00..848ccb09 100644 --- a/docs/api/vectorizer.rst +++ b/docs/api/vectorizer.rst @@ -44,7 +44,7 @@ OpenAITextVectorizer VertexAITextVectorizer -================ +====================== .. _vertexaitextvectorizer_api: diff --git a/docs/concepts/distance.md b/docs/concepts/distance.md deleted file mode 100644 index bb2b5f61..00000000 --- a/docs/concepts/distance.md +++ /dev/null @@ -1,49 +0,0 @@ ---- -id: metric.md -summary: Redis supports a variety of similarity metrics, including Euclidean distance, inner product. -weight: 15 -math: true -description: > ---- - -# Distance Metrics - -In the context of a Vector Database, a Distance Metric refers to a mathematical function that takes two vectors as an input parameter and computes a distance value representing their similarity or dissimilarity. The primary purpose of these metrics is to determine how close or distant the two vector embeddings are from each other. - -## What underlies the presence of multiple distance metrics? - -The dimensionality of [Vector Embeddings](VectorEmbeddings.md) used in Machine Learning models can vary widely, ranging from around 100 dimensions to even thousands of dimensions. - -Calculating the distance between two vectors becomes increasingly time-consuming as the number of dimensions increases. Additionally, certain distance metrics require more computational resources than others. This can pose a challenge when dealing with high-dimensional vectors. - -To address this challenge, various distance metrics have been developed to strike a balance between speed and accuracy in calculating distances between vectors. These metrics aim to provide efficient and reliable methods for measuring the similarity or dissimilarity between vectors, particularly in scenarios involving high-dimensional data. - -Redis employs three distance measures to gauge the similarity of vectors. Selecting an effective distance measure considerably enhances classification and clustering performance. - -## Cosine similarity - -Cosine similarity is a measure that determines the angle between two vectors in a multi-dimensional space. It operates on the premise that vectors pointing in similar directions are considered similar. This metric finds significant applications in Natural Language Processing (NLP). It enables the assessment of document similarity regardless of their magnitude. This is advantageous because even if two documents are far apart in terms of Euclidean distance, they can still be considered similar if their angle is small. - -The formula for Cosine Similarity is as follows: - -![cosine_similarity](../_static/concept-images/cosine_similarity.png "Cosine Similarity.") - -Cosine Similarity is the preferred metric for measuring the distance between vectors. This distance metric is widely used due to its suitability for continuous data and its significant utility in various applications. - -The following 3D graph illustrates the visualization of distance metrics in vector spaces - -![cosine_similarity](../_static/concept-images/cosine_similarity3d.png "Cosine Similarity.") - -## Euclidean distance (L2) - -Euclidean distance essentially counts the length of a line between two points. - -The formula for Euclidean distance is as follows: - -![euclidean](../_static/concept-images/euclidean_metric.png "Euclidean distance.") - -where **x** = (x1, x2,..., xn) and **y** = (y1, y2,..., yn) are two points in n-dimensional Euclidean space - -The following 3D graph illustrates the visualization of distance metrics in vector spaces - -![euclidean](../_static/concept-images/euclidean3d.png "Euclidean distance.") diff --git a/docs/concepts/hybrid.md b/docs/concepts/hybrid.md deleted file mode 100644 index cf926682..00000000 --- a/docs/concepts/hybrid.md +++ /dev/null @@ -1,55 +0,0 @@ -# Hybrid Filtering - -When developing vector search applications, it's common to incorporate additional filtering capabilities beyond vector similarity operations. This includes filtering specific fields within a record using simple filters. This combined approach of filtering on fields and applying vector similarity operations is known as hybrid filtering or metadata filtering. - -## Applying Simple Filters - -Hybrid filtering involves leveraging simple filters to narrow down search results based on specific field values. This allows for more targeted and precise retrieval of records that meet certain criteria. By combining these traditional filtering techniques with vector similarity operations, developers can enhance the search functionality and improve the relevance of the results. - -## Filtering in Redis for E-commerce Recommendation Systems - -Redis provides various filtering options that can be leveraged in e-commerce recommendation systems to retrieve relevant products based on specific criteria. Let's explore three types of filters: Tag filters, Numeric filters, and Text (match) filters. - -### Tag Filters - -Tag filters are useful when you want to filter documents based on specific tags associated with them. In RediSearch, you can use the tag field type for this purpose. Tags can have multiple words or include punctuation marks, and they are enclosed in curly braces. - -Example: - -```bash -@category:{ Electronics | Mobile Phones } -``` - -Multiple tag filters in the same query create a union of documents containing any of the specified tags. To create an intersection of documents containing all tags, you can repeat the tag filter for each tag. - -```bash -# Return all documents containing both tags 'Electronics' and 'Mobile Phones' -@category:{ Electronics } @category:{ Mobile Phones } -``` - -### Numeric Filters - -Numeric filters are handy when you want to filter documents based on numerical ranges, such as price or rating. In RediSearch, if a field in the schema is defined as NUMERIC, you can use the filter argument in Redis requests or specify filtering rules in the query. - -The syntax for a numeric filter is `@field:[{min} {max}]`, where `min` and `max` represent the range values. -Numeric filters can be inclusive, and you can use special symbols like -inf, inf, and +inf to represent negative and positive infinity. Exclusive min or max values can be expressed using parentheses. - -```bash -@price:[100 200] - -# Price greater than 100 -@price:[(100 inf] - -# Price between 100 and 200 (exclusive) -@price:[(100 (200] -``` - -### Text (Match) Filters - -Text filters, also known as match filters, are used to search for documents that match specific keywords or phrases. In RediSearch, you can use the text field type for this purpose. Text filters can be applied to fields like product titles, descriptions, or any other textual data. - -```bash -@title:redis @description:"in-memory database" -``` - -In the above example, the query filters documents based on the presence of the word "redis" in the title field and the phrase "in-memory database" in the description field. diff --git a/docs/concepts/llm.md b/docs/concepts/llm.md deleted file mode 100644 index 89160f29..00000000 --- a/docs/concepts/llm.md +++ /dev/null @@ -1,55 +0,0 @@ -# Large Language Models - -Large language models (LLMs) are advanced deep-learning models that have been specifically developed to process and analyze human languages. These models showcase remarkable abilities and have found extensive applications in various fields. A large language model operates as a highly potent deep-learning model, possessing the capability to comprehend and generate text similar to how humans do. At its core, this model utilizes a large-scale transformer model to achieve its impressive performance. - -This article delves into the structure and functioning of large language models, with a focus on the following key areas: - -1. Understanding the Transformer Model -2. How the Transformer Model Predicts Text -3. The Human-like Text Generation by Large Language Models - -## Understanding the Transformer Model - -When humans read text, we perceive it as a sequence of words, sentences, and larger units like documents. However, computers view text differently, considering it as a mere sequence of characters. A possible way to make machines understand text is by creating a model using recurrent neural networks. This model processes text incrementally, one word or character at a time, and generates output once the entire input has been processed. While this model is effective, it has a tendency to lose track of the initial information as it progresses through the sequence. - -In 2017, [Vaswani et al](https://arxiv.org/abs/1706.03762). introduced the transformer model in their paper titled *"Attention is All You Need."* This model is built on the attention mechanism, which allows it to process entire sentences or paragraphs at once rather than individual words. This unique capability enhances the transformer model's understanding of the contextual relationships between words. Many cutting-edge language processing models are now built upon the foundation of transformers. - -To process text using a transformer model, the input text is first tokenized, breaking it down into a sequence of words. These tokens are then converted into numerical representations and transformed into embeddings, which are vector representations that retain the meaning of the tokens. The transformer encoder then processes the embeddings of all the tokens, generating a context vector that encapsulates the essence of the entire input text. - -Here's an example demonstrating the tokenization and vector embedding process: - -**Input Text:** -"In the moonlit night, he strolled along the shoreline. The ocean's rhythmic melody soothed his soul, and with each step, his worries washed away." - -**Tokenization:** -['In', ' the', ' moonlit', ' night', ',', ' he', ' strolled', ' along', ' the', ' shoreline', '.', 'The', ' ocean', "'s", ' rhythmic', ' melody', ' soothed', ' his', ' soul', ',', ' and', ' with', ' each', ' step', ',', ' his', ' worries', ' washed', ' away', '.'] - -**Vector Embedding:** -[0.42, 0.22, 2.49, 1.6, 0.14, 2.26, 0.61, 2.58, 0.36, 2.87, 2.65, 1.89, 2.87, 0.16, 3.34, 1.71, 3.77, 0.42, 4.15, 1.6, 4.53, 0.36, 4.91, 1.71, 5.29, 0.16, 5.77, 1.89, 6.15] - -The context vector captures the essence of the entire input text, serving as a representation of its contextual information. By leveraging this vector, the transformer decoder is able to generate output based on the provided context cues. For instance, by using the original input as a prompt, the transformer decoder can predict the subsequent word that naturally follows. This iterative process can be repeated to generate an entire paragraph, starting from an initial sentence. - -The transformer model adopts an autoregressive generation process, generating text word by word. Similarly, a large language model follows a similar approach but utilizes a transformer model that can handle longer input texts, complex concepts, and includes multiple layers in its encoder and decoder structures. - -### What enables the Transformer to make text predictions? - -While recurrent neural networks have shown reasonable performance in predicting the next word in a text, they often struggle with capturing long-range dependencies and understanding context. The transformer model addresses these limitations by incorporating an attention mechanism, which enables it to consider the entire input sequence's context. This attention mechanism assigns varying weights to different words in the sequence based on their relevance to each other. By attending to important words, the transformer model can effectively capture dependencies and make more accurate predictions. - -Additionally, the transformer model utilizes self-attention, allowing it to consider the relationships between different words within the same input sequence. This mechanism enables the model to assign higher weights to words that are more closely related, enhancing its understanding of context and facilitating more precise predictions. - -Furthermore, the transformer model benefits from its ability to learn from extensive training data, which enables it to capture and generalize patterns effectively. By training on large-scale datasets, the model develops a broad understanding of language, enabling it to generate coherent and contextually relevant text. - -### The Human-like Text Generation by Large Language Models - -Large language models have showcased remarkable abilities in generating text that resembles human-like writing. By leveraging the autoregressive generation process of the transformer model, these models can produce text gradually, word by word, taking into account the context and preceding words in the sequence. - -The text generated by large language models often demonstrates qualities such as fluency, coherence, and semantic understanding. These models can generate diverse responses, adapt their writing style based on prompts, and emulate various writing genres. However, it is crucial to acknowledge that large language models are trained on extensive datasets and do not possess genuine comprehension or consciousness. - -While large language models have found utility in applications like language translation, question answering, and text completion, it is essential to approach their outputs with critical evaluation. The generated text may not always be entirely accurate or factually correct, and biases present in the training data can manifest in hallucinated responses. - ---- - -In conclusion, large language models powered by transformer models have revolutionized natural language processing tasks. They can predict text with improved accuracy by considering the entire context and dependencies between words. -A vector database enhances LLMs by serving as a semantic cache, enabling context retrieval for RAG (Retrieval Augmented Generation) and providing conversational adaptive long-term memory through external storage of precomputed representations of questions. - ---- diff --git a/docs/concepts/vector_index.md b/docs/concepts/vector_index.md deleted file mode 100644 index 49e715d0..00000000 --- a/docs/concepts/vector_index.md +++ /dev/null @@ -1,41 +0,0 @@ -# Vector Indexing - -Vector indexing is a technique used to organize and retrieve data based on vector representations. Instead of storing data in traditional tabular or document formats, vector indices represent data objects as vectors in a multi-dimensional space. - -## Why use vectors for data indexing? - -The main reason for indexing data as vectors is to capture the semantic similarity or contextual relationships between data objects. By assigning vector values that reflect the similarity or relatedness of objects, the resulting vectors contain meaningful information about the object's meaning and its connections to other data. - -To illustrate this concept, consider a two-dimensional space where words can be represented as vectors. Algorithms trained on a large corpus, such as [GloVe](https://www.researchgate.net/publication/284576917_Glove_Global_Vectors_for_Word_Representation), can learn the relationships between words and assign vector coordinates to represent their similarity. In this vector space, words with similar meanings or co-occurrence patterns are located close to each other, while dissimilar words are farther apart. This approach extends beyond textual data and can be applied to various types of unstructured data, including images, videos, DNA sequences, and more. - -## Choosing the right vector index type - -The choice of vector index type depends on the data size, the nature of the data, and the specific use case or business requirements. Choosing the right vector index type is crucial for efficient data retrieval. Different vector index types offer varying trade-offs in terms of query performance, indexing speed, and scalability. Redis supports multiple vector index types, including HNSW and FLAT, giving users the flexibility to choose the most suitable option based on their requirements. - -## FLAT Index - -The flat indexing method is a straightforward and efficient approach to vector indexing that is commonly used for similarity searches. In this method, data objects or vectors are directly stored in a flat array or list, with each object associated with a unique identifier or index. - -Data insertion in the flat indexing method is uncomplicated, as new data objects can be easily appended to the end of the flat array. Similarly, when conducting a search operation, the entire flat array is sequentially scanned to calculate the similarity between the search query vector and all the vectors in the array. The most relevant vectors are then identified based on the similarity metric, and their corresponding indices are returned as search results. Here are two subsections highlighting when it makes sense to use FLAT: - -1. Fast and Efficient Search: FLAT is well-suited for applications requiring fast and efficient similarity search. It excels when the focus is on retrieving the most relevant vectors based on similarity metrics, such as cosine similarity or Euclidean distance. FLAT's indexing structure optimizes search performance, making it ideal for use cases where quick retrieval of similar vectors is crucial. - -2. Limited Indexing Space: FLAT is a suitable choice when there are limitations on the available indexing space. It provides a compact and memory-efficient index structure, enabling efficient storage and retrieval of vectors even in scenarios with constrained resources. If the application demands a small memory footprint without compromising search capabilities, FLAT can be a favorable option. - -## HNSW Index (Hierarchical Navigable Small World) - -HNSW (Hierarchical Navigable Small World) is a vector index type that has gained popularity for its impressive query times in similarity searches. In scenarios where finding nearest neighbors is crucial, HNSW provides an efficient solution by organizing data objects into hierarchical layers. - -The core concept of HNSW lies in creating a multilayered graph structure, where each layer represents a different level of data coarseness. The lowest layer, known as "layer 0," initially contains all data objects, densely connected to enable fast local search operations. On top of layer 0, there are higher layers with progressively fewer data points represented in each layer. These higher layers are constructed in a way that maintains the relative neighborhood relationships of the data, facilitating efficient exploration of the search space. - -When a search query is presented, HNSW initiates the search process from the highest layer (top layer). The algorithm quickly identifies a small number of candidate data points that are closest to the query in this layer. This step significantly reduces the exploration space and allows for fast retrieval of potential nearest neighbors. - -To further refine the search, HNSW proceeds to lower layers, exploring neighbors from the candidate points found in the previous layer. This iterative process continues until the algorithm reaches the lowest layer, where it ultimately identifies the actual closest data object to the query. By organizing data objects hierarchically, HNSW minimizes the number of hops required to traverse the search space, resulting in exceptionally fast query times. - -One of the key advantages of HNSW is its memory efficiency. Unlike other exhaustive indexing methods that require storing all data points, HNSW keeps only the highest layer in cache, dramatically reducing memory requirements. When a higher layer requests neighboring data points, they are loaded on-demand, ensuring that only a small amount of memory is reserved for the active search process. - ---- - -In summary, vector indexing offers a way to represent and organize data objects as vectors, capturing semantic relationships and enabling efficient retrieval. Choosing the right vector index type is crucial to optimize query performance, indexing speed, and scalability. Multiple vector indexing allows for the selection and utilization of different index types based on specific requirements and priorities. - ---- diff --git a/docs/concepts/vectordb.md b/docs/concepts/vectordb.md deleted file mode 100644 index bb2eef50..00000000 --- a/docs/concepts/vectordb.md +++ /dev/null @@ -1,27 +0,0 @@ -# Vector Databases - -Vector databases are specifically designed to store, retrieve and search over dense numerical vectors in an efficient manner. While traditional databases typically organize data in rows and columns, Vector databases cater to applications leveraging image recognition, natural language processing, and recommendation systems, where data is represented as [Vector Embeddings](VectorEmbeddings.md) in a multi-dimensional space. - -They are designed to efficiently handle the storage and retrieval of these dense numerical Vectors. They leverage specialized data structures and indexing techniques, such as [hierarchical navigable small world (HNSW)](VectorIndex.md) and product quantization, to enable swift similarity and semantic searches. These databases enable users to find Vectors that are most similar to a given query vector based on a chosen [Distance Metric](DistanceMetrics.md), such as euclidean distance, cosine similarity, or dot product. - -In summary, vector databases are purpose-built databases that excel at storing and querying over dense numerical vectors efficiently. They enable fast similarity searches, accommodate complex data types, and offer advantages in various domains where similarity search and real-time analytics on unstructured are critical. - -## How does a vector database function? - -The operation of a vector database differs significantly from that of traditional databases, which primarily store scalar data in rows and columns. In contrast, vector databases operate on vectors and are optimized and queried in a distinct manner. - -A vector database utilizes various algorithms to enable Approximate Nearest Neighbor (ANN) search. These algorithms are carefully selected and combined to enhance the efficiency of the search process. Techniques such as hashing, quantization, and graph-based search are employed to optimize the retrieval of neighboring vectors. - -They employ various algorithms organized into a pipeline to efficiently locate the nearest neighbors to a given query vector. While it is true that ANN approaches within vector databases prioritize speed over exactness, it is important to note that this does not apply to all vector databases. In the case of a K-nearest neighbor (KNN) approach like FLAT, exactness is guaranteed but at the cost of additional latency. However, through thoughtful system design, it is possible to strike a balance and achieve near-perfect accuracy while still delivering ultra-fast search capabilities in vector databases. By optimizing the pipeline and considering factors such as indexing techniques, distance metrics, and query processing methods, vector databases can provide both speed and accuracy tailored to specific use cases. - -Let's delve into a typical architecture for a vector database: - -![VDB Architecture](../../assets/vdb_architect.png) - -Indexing: The process of [indexing vectors](VectorIndex.md) in a vector database involves utilizing algorithms such as PQ (Product Quantization), LSH (Locality-Sensitive Hashing), HNSW or FLAT. This step essentially maps the vectors to a data structure that facilitates faster searching. - -Pre-filtering: Prior to vector search, pre-filtering techniques can be applied to narrow down the dataset. This may involve performing filtering operations, range queries, or set intersections to refine the dataset and focus on relevant vectors. - -Querying: When it comes to querying, the vector database compares the query vector, which has been indexed, with the vectors stored in the dataset to determine the nearest neighbors. This comparison involves applying the specific similarity metric used by the index. - -Post Processing: In certain cases, after identifying the nearest neighbors, the vector database retrieves them from the dataset and carries out additional processing steps like post-filtering, aggregations, or re-ranking using a Vector Database diff --git a/docs/concepts/vectors.md b/docs/concepts/vectors.md deleted file mode 100644 index 678b561b..00000000 --- a/docs/concepts/vectors.md +++ /dev/null @@ -1,45 +0,0 @@ -# Vector Embeddings - -Vector embeddings are numerical representations of unstructured data, such as text, images, or audio, in the form of vectors (lists of numbers). These embeddings capture the semantic similarity of objects by mapping them to points in a vector space, where similar objects are represented by vectors that are close to each other. - -## Why vector embeddings? - -Vector embeddings have various applications in machine learning. They can be used for tasks like clustering, recommendation systems, and classification. In clustering, objects with similar characteristics are grouped together based on the proximity of their embeddings. Recommendation systems utilize vector embeddings to find similar objects and provide personalized recommendations. In classification, the labels of unseen objects can be determined by considering the labels of the most similar objects based on their embeddings. - -## Creating Vector Embeddings - -There are different approaches to creating vector embeddings. One method to generate vector embeddings involves feature engineering, where domain knowledge is utilized to define the vector values based on specific features of the objects. For example, in image vectors, pixel values can be used, or in explicit feature vectors, specific feature values are defined. However, this approach can be time-consuming and not scalable. Alternatively, newer techniques involve training Language Models (LLMs) to generate vector embeddings for objects using deep neural networks. State-of-the-art models like [Word2Vec](https://code.google.com/archive/p/word2vec/), [GLoVE](https://nlp.stanford.edu/projects/glove/), [BERT](https://github.com/google-research/bert), OpenAI's [GPT-3](https://openai.com/research/gpt-3), Cohere's [Sentence Transformers](https://cohere.ai/), and Hugging Face's [Transformers](https://huggingface.co/models) have been widely used for text-to-vector embedding transformations. - -These models offer advanced techniques to generate high-dimensional vector embeddings for text data, enabling more accurate representation and understanding of semantic relationships between sentences. - -Let's create a vector embedding example with a few sentences plotted on a 3D plot. Again, we will use the Universal Sentence Encoder (USE) to generate sentence embeddings. The USE model converts each sentence into a fixed-length vector representation. - -Here are three example sentences and their corresponding embeddings: -``` -Sentence: "I enjoy playing sports." -Embedding: [0.2, 0.8, 0.6] - -Sentence: "Reading books is my favorite hobby." -Embedding: [0.9, 0.5, 0.3] - -Sentence: "I love to travel and explore new places." -Embedding: [0.7, 0.2, 0.4] -``` - -![embeddings-redis](../_static/concept-images/embeddings-redis.png) - -Now, let's plot these sentence embeddings on a 3D plot: - -![vector_embeddings](../_static/concept-images/embeddings.png "Vector Embeddings") - -## References - -Vector embeddings have a wide range of practical uses and applications. One of the key applications is similarity search, where algorithms like K-nearest neighbors (KNN) or approximate nearest neighbors (ANN) are employed to measure the distances between embeddings. By determining the similarity based on these distances, various tasks can be accomplished, including de-duplication (identifying and eliminating duplicates), recommendations (finding similar items or content), anomaly detection (identifying outliers or unusual patterns), and reverse image search (finding similar images based on their embeddings). - -In addition to their applications in similarity search, vector embeddings are also utilized internally in many machine learning models and methods. For instance, encoder-decoder architectures used in tasks like machine translation and caption generation rely on vector embeddings. These architectures involve transforming input data into meaningful and compact representations (embeddings) using an encoder, which is then decoded to generate the desired output. By leveraging vector embeddings, these models can capture and understand the underlying structure and relationships within the data, enabling effective translation and captioning capabilities. - ---- - -In summary, vector embeddings provide a powerful way to represent and compare objects in machine learning tasks, allowing for the quantification of semantic similarity and supporting a wide range of applications across different domains. - ---- diff --git a/docs/conf.py b/docs/conf.py index 2eb94835..0c88c6d4 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -82,8 +82,8 @@ "doc_path": "docs", } - - +import redisvl +version = redisvl.__version__ # This allows us to use ::: to denote directives, useful for admonitions myst_enable_extensions = ["colon_fence"] myst_heading_anchors = 3 @@ -100,6 +100,11 @@ "navigation_depth": 5, "navbar_align": "content", # [left, content, right] For testing that the navbar items align properly "secondary_sidebar_items": ["page-toc", "edit-this-page", "sourcelink"], + "switcher": { + "json_url": "/_static/version_names.json", + "version_match": version, + }, + "navbar_start": ["navbar-logo", "version-switcher"], "icon_links": [ { "name": "GitHub", diff --git a/docs/overview/installation.md b/docs/overview/installation.md index 3875a734..301ae153 100644 --- a/docs/overview/installation.md +++ b/docs/overview/installation.md @@ -5,28 +5,75 @@ myst: Installation instructions for RedisVL --- - ## Install RedisVL +There are a few ways to install RedisVL. The easiest way is to use pip. + Install `redisvl` into your Python (>=3.8) environment using `pip`: ```bash -pip install redisvl +$ pip install redisvl ``` +RedisVL comes with a few dependencies that are automatically installed, however, a few dependencies +are optional and can be installed separately if needed: + +```bash +$ pip install redisvl[all] # install vectorizer dependencies +$ pip install redisvl[dev] # install dev dependencies +``` + +If you use ZSH, remember to escape the brackets: + +```bash +$ pip install redisvl\[all\] +``` + + +## Install RedisVL from Source + +To install RedisVL from source, clone the repository and install the package using `pip`: + +```bash +$ git clone https://github.com/redisventures/redisvl.git && cd redisvl +$ pip install . + +# or for an editable installation (for developers of RedisVL) +$ pip install -e . +``` + + +## Installing Redis + +RedisVL requires a distribution of Redis that supports the [Search and Query](https://redis.com/modules/redis-search/) capability of which there are 3: + +offering +1. [Redis Cloud](https://redis.com/try-free), a fully managed cloud offering +2. [Redis Stack](https://redis.io/docs/getting-started/install-stack/docker/), a local docker image for testing and development +3. [Redis Enterprise](https://redis.com/redis-enterprise/), a commercial self-hosted -## Choosing a Redis Instance ### Redis Cloud -Then make sure to have [Redis](https://redis.io) accessible with Search & Query features enabled on [Redis Cloud](https://redis.com/try-free) or locally in docker with [Redis Stack](https://redis.io/docs/getting-started/install-stack/docker/): +Redis Cloud is the easiest way to get started with RedisVL. You can sign up for a free account [here](https://redis.com/try-free). Make sure to have the `Search and Query` +capability enabled when creating your database. + -### Redis Stack (local) +### Redis Stack (local development) +For local development and testing, Redis-Stack and be used. We recommend running Redis +in a docker container. To do so, run the following command: ```bash docker run -d --name redis-stack -p 6379:6379 -p 8001:8001 redis/redis-stack:latest ``` -This will also spin up the [Redis Insight GUI](https://redis.com/redis-enterprise/redis-insight/) at `http://localhost:8001`. \ No newline at end of file +This will also spin up the [Redis Insight GUI](https://redis.com/redis-enterprise/redis-insight/) at `http://localhost:8001`. + + +### Redis Enterprise (self-hosted) + +Redis Enterprise is a commercial offering that can be self-hosted. You can download the latest version [here](https://redis.com/redis-enterprise-software/download-center/software/). + +If you are considering a self-hosted Redis Enterprise deployment on Kubernetes, there is the [Redis Enterprise Operator](https://docs.redis.com/latest/kubernetes/) for Kubernetes. This will allow you to easily deploy and manage a Redis Enterprise cluster on Kubernetes. \ No newline at end of file diff --git a/docs/user_guide/cli.ipynb b/docs/user_guide/cli.ipynb new file mode 100644 index 00000000..a073abbd --- /dev/null +++ b/docs/user_guide/cli.ipynb @@ -0,0 +1,287 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Redis Vector Library CLI (``rvl``)\n", + "\n", + "RedisVL is a Python library with a dedicated CLI to help load and create vector search indices within Redis.\n", + "\n", + "This notebook will walk through how to use the Redis Vector Library CLI (``rvl``).\n", + "\n", + "Before running this notebook, be sure to\n", + "1. Have installed ``redisvl`` and have that environment active for this notebook.\n", + "2. Have a running Redis instance with the Search and Query capability" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[32m17:33:28\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m RedisVL version 0.0.4\n" + ] + } + ], + "source": [ + "# First, see if the rvl tool is installed\n", + "!rvl version" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Index\n", + "\n", + "The ``rvl index`` command is can be used for a number of tasks related to creating and managing vector indices. Wether you are working in Python or another language, this cli tool can still be useful for managing and inspecting your indices.\n", + "\n", + "first, we will create an index from a yaml schema that looks like the following\n", + "\n", + "```yaml\n", + "index:\n", + " name: providers\n", + " prefix: rvl\n", + " storage_type: hash\n", + " key_separator: ':'\n", + "\n", + "fields:\n", + " text:\n", + " - name: sentence\n", + " vector:\n", + " - name: embedding\n", + " dims: 768\n", + " algorithm: flat\n", + " distance_metric: cosine\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index already exists, not overwriting.\n", + "\u001b[32m17:33:28\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m Index created successfully\n" + ] + } + ], + "source": [ + "# Create an index from a yaml schema\n", + "!rvl index create -s schema.yaml" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[32m17:33:28\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m Indices:\n", + "\u001b[32m17:33:28\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m 1. providers\n" + ] + } + ], + "source": [ + "# list the indices that are available\n", + "!rvl index listall" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Index Information:\n", + "╭──────────────┬────────────────┬────────────┬─────────────────┬────────────╮\n", + "│ Index Name │ Storage Type │ Prefixes │ Index Options │ Indexing │\n", + "├──────────────┼────────────────┼────────────┼─────────────────┼────────────┤\n", + "│ providers │ HASH │ ['rvl'] │ [] │ 0 │\n", + "╰──────────────┴────────────────┴────────────┴─────────────────┴────────────╯\n", + "Index Fields:\n", + "╭───────────┬─────────────┬────────┬────────────────┬────────────────╮\n", + "│ Name │ Attribute │ Type │ Field Option │ Option Value │\n", + "├───────────┼─────────────┼────────┼────────────────┼────────────────┤\n", + "│ sentence │ sentence │ TEXT │ WEIGHT │ 1 │\n", + "│ embedding │ embedding │ VECTOR │ │ │\n", + "╰───────────┴─────────────┴────────┴────────────────┴────────────────╯\n" + ] + } + ], + "source": [ + "# inspect the index fields\n", + "!rvl index info -i providers" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[32m17:33:29\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m Index deleted successfully\n" + ] + } + ], + "source": [ + "# delete an index without deleting the data within it\n", + "!rvl index delete -i providers" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[32m17:33:30\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m Indices:\n" + ] + } + ], + "source": [ + "# see the index\n", + "!rvl index listall" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Stats\n", + "\n", + "The ``rvl stats`` command will return some basic information about the index. This is useful for checking the status of an index, or for getting information about the index to use in other commands." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[32m17:33:30\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m Index created successfully\n" + ] + } + ], + "source": [ + "# create a new index with the same schema\n", + "!rvl index create -s schema.yaml" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[32m17:33:30\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m Indices:\n", + "\u001b[32m17:33:30\u001b[0m \u001b[34m[RedisVL]\u001b[0m \u001b[1;30mINFO\u001b[0m 1. providers\n" + ] + } + ], + "source": [ + "# list the indices that are available\n", + "!rvl index listall" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Statistics:\n", + "╭─────────────────────────────┬─────────╮\n", + "│ Stat Key │ Value │\n", + "├─────────────────────────────┼─────────┤\n", + "│ num_docs │ 0 │\n", + "│ num_terms │ 0 │\n", + "│ max_doc_id │ 0 │\n", + "│ num_records │ 0 │\n", + "│ percent_indexed │ 1 │\n", + "│ hash_indexing_failures │ 0 │\n", + "│ number_of_uses │ 2 │\n", + "│ bytes_per_record_avg │ nan │\n", + "│ doc_table_size_mb │ 0 │\n", + "│ inverted_sz_mb │ 0 │\n", + "│ key_table_size_mb │ 0 │\n", + "│ offset_bits_per_record_avg │ nan │\n", + "│ offset_vectors_sz_mb │ 0 │\n", + "│ offsets_per_term_avg │ nan │\n", + "│ records_per_doc_avg │ nan │\n", + "│ sortable_values_size_mb │ 0 │\n", + "│ total_indexing_time │ 0 │\n", + "│ total_inverted_index_blocks │ 0 │\n", + "│ vector_index_sz_mb │ 0 │\n", + "╰─────────────────────────────┴─────────╯\n" + ] + } + ], + "source": [ + "# see all the stats for the index\n", + "!rvl stats -i providers" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "rvldev", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/user_guide/getting_started_01.ipynb b/docs/user_guide/getting_started_01.ipynb index 6cc0a47c..71dad7ab 100644 --- a/docs/user_guide/getting_started_01.ipynb +++ b/docs/user_guide/getting_started_01.ipynb @@ -7,7 +7,7 @@ "source": [ "# Getting Started with RedisVL\n", "\n", - "RedisVL is a Python library with a dedicated CLI to help load and create vector search indices within Redis. While \n", + "RedisVL is a Python library with a dedicated CLI to help load and create vector search indices within Redis.\n", "\n", "This notebook will walk through\n", "1. Preparing a dataset with vectors.\n", @@ -781,7 +781,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.8.13" }, "orig_nbformat": 4, "vscode": { diff --git a/docs/user_guide/hybrid_queries_02.ipynb b/docs/user_guide/hybrid_queries_02.ipynb index 3daa48ef..324cd536 100644 --- a/docs/user_guide/hybrid_queries_02.ipynb +++ b/docs/user_guide/hybrid_queries_02.ipynb @@ -1178,7 +1178,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.8.13" }, "orig_nbformat": 4, "vscode": { diff --git a/docs/versions.rst b/docs/versions.rst deleted file mode 100644 index ad5e25c8..00000000 --- a/docs/versions.rst +++ /dev/null @@ -1,6 +0,0 @@ -******** -Versions -******** - -.. raw:: html - :file: _templates/versions.html \ No newline at end of file diff --git a/redisvl/version.py b/redisvl/version.py index 81f0fdec..b1a19e32 100644 --- a/redisvl/version.py +++ b/redisvl/version.py @@ -1 +1 @@ -__version__ = "0.0.4" +__version__ = "0.0.5" diff --git a/setup.py b/setup.py index 6d5ab9e4..a5f2b0d9 100644 --- a/setup.py +++ b/setup.py @@ -23,7 +23,7 @@ def read_dev_requirements(): setup( name="redisvl", - version="0.0.4", + version="0.0.5", python_requires=">=3.7", install_requires=read_requirements(), extras_require=extras_require,