From fea37c8107184a1af175806644422ca42c4cee92 Mon Sep 17 00:00:00 2001 From: PrasanKumar Date: Thu, 25 Jan 2024 17:18:54 +0530 Subject: [PATCH 1/6] triggers tutorial - code comments added --- .../index-triggers-and-functions.mdx | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/docs/howtos/solutions/triggers-and-functions/getting-started-tf/index-triggers-and-functions.mdx b/docs/howtos/solutions/triggers-and-functions/getting-started-tf/index-triggers-and-functions.mdx index 7f21d276a0..87fc28fb9d 100644 --- a/docs/howtos/solutions/triggers-and-functions/getting-started-tf/index-triggers-and-functions.mdx +++ b/docs/howtos/solutions/triggers-and-functions/getting-started-tf/index-triggers-and-functions.mdx @@ -444,13 +444,6 @@ redis.registerStreamTrigger( 'calculateStats', // trigger name 'TRANSACTION_STREAM', // Detects new data added to the stream function (client, data) { - // data = JSON.stringify( - // data, - // (key, value) => (typeof value === "bigint" ? value.toString() : value) //id conversion - // ); - - // data = JSON.parse(data); - var streamEntry = {}; for (let i = 0; i < data.record?.length; i++) { streamEntry[data.record[i][0]] = data.record[i][1]; @@ -528,6 +521,13 @@ redis.registerStreamTrigger( ); ``` +In above `calculateStats` function, we are listening to `TRANSACTION_STREAM` and updating different sales statistics like + +- `statsTotalPurchaseAmount` variable stores total purchase amount +- `statsProductPurchaseQtySet` is a sorted set which tracks trending products based on highest purchase quantity +- `statsCategoryPurchaseAmountSet` is a sorted set which tracks category wise purchase interest +- `statsBrandPurchaseAmountSet` is a sorted set which tracks largest brand purchases + ### Adding the function to Redis We can add functions to Redis using various methods: @@ -609,7 +609,7 @@ A sample command to add details to the stream: "XADD" "TRANSACTION_STREAM" "*" "action" "PAYMENT_PROCESSED" "userId" "USR_f0f00a86-7131" "orderDetails" "{'orderId':'bc438c5d-117e-41bd-97fa-943c03be0b1c','products':[],'paymentId':'clrrl8yp50007pf851m7f92u2'}" "transactionPipeline" "['PAYMENT_PROCESSED']" ``` -The `calculateStats` function listens to `TRANSACTION_STREAM` stream and updates the sales statistics accordingly. +The `calculateStats` function listens to `TRANSACTION_STREAM` stream for `PAYMENT_PROCESSED` action and updates the sales statistics accordingly. Check different stats variable values in RedisInsight which were used in trigger function `calculateStats`. ![stream-trigger-test-ri](./images/stream-trigger-test-ri.png) From 26875e57c42f22069f0a3257b3f96c37125afdfd Mon Sep 17 00:00:00 2001 From: William Johnston Date: Mon, 29 Jan 2024 12:04:41 -0800 Subject: [PATCH 2/6] adding video qa to sidebar, adjusting title --- docs/howtos/solutions/vector/video-qa/index-video-qa.mdx | 2 +- sidebars.js | 1 + 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/docs/howtos/solutions/vector/video-qa/index-video-qa.mdx b/docs/howtos/solutions/vector/video-qa/index-video-qa.mdx index aa45b6744d..187cae1d0f 100644 --- a/docs/howtos/solutions/vector/video-qa/index-video-qa.mdx +++ b/docs/howtos/solutions/vector/video-qa/index-video-qa.mdx @@ -2,7 +2,7 @@ id: index-video-qa title: Building an AI-Powered Video Q&A Application with Redis and LangChain sidebar_label: Building an AI-Powered Video Q&A Application with Redis and LangChain -slug: /howtos/solutions/vector/building-an-ai-powered-video-qa-application-with-redis-and-langchain +slug: /howtos/solutions/vector/ai-qa-videos-langchain-redis-openai-google authors: [prasan, will] --- diff --git a/sidebars.js b/sidebars.js index 833ae24d6e..dec6749179 100644 --- a/sidebars.js +++ b/sidebars.js @@ -398,6 +398,7 @@ module.exports = { 'howtos/solutions/vector/gen-ai-chatbot/index-solutions-gen-ai-chatbot', 'howtos/solutions/vector/semantic-text-search/index-semantic-text-search', 'howtos/solutions/vector/image-summary-search/index-image-summary-search', + 'howtos/solutions/vector/video-qa/index-video-qa' ], }, { From 69d548c9c29ef05ed91282cd8a4dc1081f40d629 Mon Sep 17 00:00:00 2001 From: William Johnston Date: Mon, 29 Jan 2024 12:05:53 -0800 Subject: [PATCH 3/6] adding video qa tile link --- docs/howtos/solutions/index-solutions.mdx | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/docs/howtos/solutions/index-solutions.mdx b/docs/howtos/solutions/index-solutions.mdx index 37e98dc625..e93e8742c2 100644 --- a/docs/howtos/solutions/index-solutions.mdx +++ b/docs/howtos/solutions/index-solutions.mdx @@ -192,6 +192,14 @@ Learn how to easily build, test and deploy code for common microservice and cach /> +
+ +
+ ## Triggers and Functions From 07d73bfbeab1454d8c90e05b35f9d31f8a522378 Mon Sep 17 00:00:00 2001 From: William Johnston Date: Mon, 29 Jan 2024 15:51:32 -0800 Subject: [PATCH 4/6] adding screenshots and data snippets --- .../vector/video-qa/images/ask-question.png | Bin 0 -> 11546 bytes .../video-qa/images/redisinsight-keys.png | Bin 0 -> 24971 bytes .../vector/video-qa/images/upload-videos.png | Bin 0 -> 11150 bytes .../images/video-qa-existing-answer.png | Bin 382979 -> 154755 bytes .../images/video-qa-supporting-videos.png | Bin 1258938 -> 468172 bytes .../vector/video-qa/index-video-qa.mdx | 52 ++++++++++++++++-- 6 files changed, 48 insertions(+), 4 deletions(-) create mode 100644 docs/howtos/solutions/vector/video-qa/images/ask-question.png create mode 100644 docs/howtos/solutions/vector/video-qa/images/redisinsight-keys.png create mode 100644 docs/howtos/solutions/vector/video-qa/images/upload-videos.png diff --git a/docs/howtos/solutions/vector/video-qa/images/ask-question.png 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--git a/docs/howtos/solutions/vector/video-qa/index-video-qa.mdx b/docs/howtos/solutions/vector/video-qa/index-video-qa.mdx index d2d2eb38f3..127ec4eb26 100644 --- a/docs/howtos/solutions/vector/video-qa/index-video-qa.mdx +++ b/docs/howtos/solutions/vector/video-qa/index-video-qa.mdx @@ -52,33 +52,66 @@ Here's how our application uses AI and semantic vector search to answer user que An example summary and sample questions generated by the LLM are shown below: -```markdown title="https://www.youtube.com/watch?v=LaiQFZ5bXaM" -### Summary: +```text title="https://www.youtube.com/watch?v=LaiQFZ5bXaM" +Summary: +The video provides a walkthrough of building a real-time stock tracking application +using Redis Stack, demonstrating its capability to handle multiple data models and +act as a message broker in a single integrated database. The application maintains +a watch list of stock symbols, along with real-time trading information and a chart +updated with live data from the Alpaca API. The presenter uses Redis Stack features +such as sets, JSON documents, time series, Pub/Sub, and Top-K filter to store and +manage different types of data. An architecture diagram is provided, explaining the +interconnection between the front end, API service, and streaming service within +the application. Code snippets highlight key aspects of the API and streaming +service written in Python, highlighting the use of Redis Bloom, Redis JSON, Redis +Time Series, and Redis Search for managing data. The video concludes with a +demonstration of how data structures are visualized and managed in RedisInsight, +emphasizing how Redis Stack can simplify the building of a complex real-time +application by replacing multiple traditional technologies with one solution. -The video demonstrates how Redis Stack can be utilized to build complex real-time applications by turning Redis into a multi-model database. This capability is showcased through the development of a stock watchlist application, which utilizes various data types and models supported by Redis Stack, such as JSON documents, time series data, and probabilistic data structures. Key features of the app include real-time updates on stock prices, trending stocks based on trade frequency, and news updates, all powered by Redis Stack in combination with the Alpaca API for stock data. The application's architecture is explained, highlighting the use of Redis Stack for data storage, Pub/Sub for inter-service communication, and websockets for real-time front-end updates. Some coding aspects are touched upon, emphasizing how Redis Stack simplifies the handling of complex data structures and real-time data flow. +Example Questions and Answers: -### Example Questions and Answers: +Q1: What is Redis Stack and what role does it play in the application? +A1: Redis Stack is an extension to Redis that adds additional modules, turning it +into a multi-model database. In the application, it is used for storing various +types of data and managing real-time communication between microservices. -**Q1: How does Redis Stack enhance Redis to support real-time applications?** -A1: Redis Stack transforms Redis into a multi-model database, adding support for various data types like JSON, time series, and probabilistic data structures, facilitating complex data management and inter-service communication in real-time applications. +Q2: How is the stock watch list stored and managed within the application? +A2: The watch list is stored as a Redis set which helps automatically prevent +duplicate stock symbols. In addition, further information about each stock is +stored in JSON documents within Redis Stack. -**Q2: Can you explain how the sample stock watchlist application uses Redis Stack?** -A2: The stock watchlist application leverages Redis Stack as its primary database and message broker. It uses Redis for storing different data types, such as JSON documents for stock metadata and time series data for stock prices. It also utilizes Pub/Sub for real-time updates between microservices, enhancing its ability to track and display stock information dynamically. +Q3: What type of data does the application store using time series capabilities of +Redis Stack? +A3: The application uses time series data to store and retrieve the price movements +of the stocks, making it easy to query over a date range and to visualize chart +data with time on the x-axis. -**Q3: What are some key features of the stock watchlist application demonstrated in the video?** -A3: Key features include the ability to add and remove stocks from a watchlist, view real-time trading data, check trending stocks based on trade frequency, access up-to-date news for each stock, and display detailed charts for stock prices over time, all updated in real-time thanks to Redis Stack and the Alpaca API. +Q4: Can you explain the use of the Top-K filter in the application? +A4: The Top-K filter is a feature of Redis Bloom that is used to maintain a +leaderboard of the most frequently traded stocks on the watch list, updating every +minute with the number of trades that happen. -**Q4: How does the application's architecture support its real-time functionality?** -A4: The application's architecture includes a front end for displaying data, an API service for communication between the front end and Redis Stack, and a streaming service handling real-time data from the Alpaca API. Redis Stack's capabilities for data storage, retrieval, and real-time updates, along with its Pub/Sub system, enable efficient real-time functionality across the application. +Q5: What methods are used to update the front end with real-time information in +the application? +A5: WebSockets are used to receive real-time updates for trending stocks, trades, +and stock bars from the API service, which, in turn, receives the information from +Redis Pub/Sub messages generated by the streaming service. -**Q5: Can you provide details on how trending stocks are determined and updated in the application?** -A5: Trending stocks are determined using Redis Stack's Top K feature, which automatically builds a leaderboard of the most frequently traded stocks in the watchlist. This list is updated every minute based on new trade data received from the Alpaca API. Changes to the leaderboard are published via Pub/Sub, prompting the front end to display the latest trending stocks in real time. +Q6: How does the application sync the watch list with the streaming service? +A6: The application listens to the watch list key space in Redis for updates. When +a stock is added or removed from the watch list on the front end, the API +communicates this to Redis, and the streaming service then subscribes or +unsubscribes to updates from the Alpaca API for that stock. -**Q6: What technologies were used in the front end of the application?** -A6: The front end of the application was developed using Next.js, with Tailwind CSS for styling and Chart.js for rendering stock price charts. It connects to the backend using websockets to receive real-time updates on stock data. +Q7: What frontend technologies are mentioned for building the UI of the application? +A7: The UI service for the front end is built using Tailwind CSS, Chart.js, and +Next.js, which is a typical tech stack for creating a modern web application. -**Q7: How does Redis Stack enable querying of time series data in the application?** -A7: Redis Stack provides time series capabilities, allowing for efficient storage and querying of time-based data. In the application, time series data is used to store stock prices, which can be queried over a specific date range or used to obtain the latest trade price, facilitating the dynamic update of stock charts and price information in real time. +Q8: How does Redis Insight help in managing the application data? +A8: Redis Insight provides a visual interface to see and manage the data structures +used in Redis Stack, including JSON documents, sets, and time series data related +to the stock information in the application. ``` 3. **Data storage with Redis**: All generated data, including video summaries, potential questions, and vector embeddings, are stored in Redis. The app utilizes Redis's diverse data types for efficient data handling, caching, and quick retrieval. From 1a3dbdb302b1dfed90216319cb53465b922eae5f Mon Sep 17 00:00:00 2001 From: William Johnston Date: Mon, 29 Jan 2024 16:00:13 -0800 Subject: [PATCH 6/6] adding Redis YouTube link --- docs/howtos/solutions/vector/video-qa/index-video-qa.mdx | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/docs/howtos/solutions/vector/video-qa/index-video-qa.mdx b/docs/howtos/solutions/vector/video-qa/index-video-qa.mdx index 127ec4eb26..77d97ffd28 100644 --- a/docs/howtos/solutions/vector/video-qa/index-video-qa.mdx +++ b/docs/howtos/solutions/vector/video-qa/index-video-qa.mdx @@ -45,8 +45,9 @@ Our application leverages these technologies to create a unique Q&A platform bas Here's how our application uses AI and semantic vector search to answer user questions based on video content: -1. **Uploading videos**: Users can upload YouTube videos either via links (e.g. `https://www.youtube.com/watch?v=LaiQFZ5bXaM`) or video IDs (e.g. `LaiQFZ5bXaM`). The application processes these inputs to retrieve necessary video information. -![Upload videos screenshot](./images/upload-videos.png) +1. **Uploading videos**: Users can upload YouTube videos either via links (e.g. `https://www.youtube.com/watch?v=LaiQFZ5bXaM`) or video IDs (e.g. `LaiQFZ5bXaM`). The application processes these inputs to retrieve necessary video information. For the purposes of this tutorial, the app is pre-seeded with a collection of videos from the [Redis YouTube channel](https://www.youtube.com/@Redisinc). + +![Upload videos screenshot](./images/upload-videos.png). 2. **Video processing and AI interaction**: Using the [Youtube Data API](https://developers.google.com/youtube/v3), the application obtains video `titles`, `descriptions`, and `thumbnails`. It also uses [SearchAPI.io](https://searchapi.io) to retrieve video transcripts. These transcripts are then passed to a large language model (LLM) - either Google Gemini or OpenAI's ChatGPT - for summarization and sample question generation. The LLM also generates vector embeddings for these summaries.

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