This tutorial focuses on building a Q&A answer engine for video content. It will cover the following topics:
OpenAI, Google Gemini, and LangChain to summarize video content and generate vector embeddingsRedis to store and search vector embeddingsRedis as a semantic vector search cacheBelow is a command to the clone the source code for the application used in this tutorial
git clone https://github.com/redis-developer/video-qa-semantic-vector-cachingBefore we dive into the details of this tutorial, let's go over a few concepts that are important to understand when building generative AI applications.
Our application leverages these technologies to create a unique Q&A platform based on video content. Users can upload YouTube video URLs or IDs, and the application utilizes generative AI to summarize these videos, formulate potential questions, and create a searchable database. This database can then be queried to find answers to user-submitted questions, drawing directly from the video content.
Here's how our application uses AI and semantic vector search to answer user questions based on video content:
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. However, when you run the application you can adjust it to cover your own set of videos.2. Video processing and AI interaction: Using the Youtube Data API, the application obtains video titles, descriptions, and thumbnails. It also uses 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.
An example summary and sample questions generated by the LLM are shown below:
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.
Example Questions and Answers:
Q1: What is Redis Stack and what role does it play in the application?
Q2: How is the stock watch list stored and managed within the application?
Q3: What type of data does the application store using time series capabilities of
Redis Stack?
Q4: Can you explain the use of the Top-K filter in the application?
Q5: What methods are used to update the front end with real-time information in
the application?
Q6: How does the application sync the watch list with the streaming service?
Q7: What frontend technologies are mentioned for building the UI of the application?
Q8: How does Redis Insight help in managing the application data?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.
4. Search and answer retrieval: The frontend, built with Next.js, allows users to ask questions. The application then searches the Redis database using semantic vector similarity to find relevant video content. It further uses the LLM to formulate answers, prioritizing information from video transcripts.
5. Presentation of results: The app displays the most relevant videos along with the AI-generated answers, offering a comprehensive and interactive user experience. It also displays cached results from previous queries using semantic vector caching for faster response times.
To get started with our AI-powered video Q&A application, you'll first need to set up your development environment. We'll follow the instructions outlined in the project's README.md file.
Redis is used as our database to store and retrieve data efficiently. You can start quickly with a cloud-hosted Redis instance by signing up at redis.com/try-free. This is ideal for both development and testing purposes. You can easily store the data for this application within the limitations of the Redis free tier.
First, clone the repository containing our project:
git clone https://github.com/redis-developer/video-qa-semantic-vector-cachingAfter setting up your Node.js environment, you'll need to install the necessary packages. Navigate to the root of your project directory and run the following command:
npm installThis command will install all the dependencies listed in the package.json file, ensuring you have everything needed to run the application.
Before running the application, make sure to configure the environment variables. There is a script to automatically generate the .env files for you. Run the following command:
npm run setupThis will generate the following files:
app/.env - This file contains the environment variables for the Next.js application.app/.env.docker - This file contains overrides for the environment variables when running in Docker.services/video-search/.env - This file contains the environment variables for the video search service.services/video-search/.env.docker - This file contains overrides for the environment variables when running in Docker.By default, you should not need to touch the environment files in the app. However, you will need to configure the environment files in the services/video-search directory.
The services/video-search/.env looks like this:
USE=<HF|OPENAI>
REDIS_URL=<redis[s]://[[username][:password]@][host][:port][/db-number]>
SEARCHAPI_API_KEY=<https://www.searchapi.io/>
YOUTUBE_TRANSCRIPT_PREFIX=<redis-transcript-prefix>
YOUTUBE_VIDEO_INFO_PREFIX=<redis-video-info-prefix>
GOOGLE_API_KEY=<https://console.cloud.google.com/apis/credentials>
GOOGLE_EMBEDDING_MODEL=<https://ai.google.dev/models/gemini#model_variations>
GOOGLE_SUMMARY_MODEL=<https://ai.google.dev/models/gemini#model_variations>
OPENAI_API_KEY=<https://platform.openai.com/api-keys>
OPENAI_ORGANIZATION=<https://platform.openai.com/account/organization>
OPENAI_EMBEDDING_MODEL=<https://platform.openai.com/account/limits>
OPENAI_SUMMARY_MODEL=<https://platform.openai.com/account/limits>For Gemini models, you can use the following if you are not sure what to do:
GOOGLE_EMBEDDING_MODEL=embedding-001
GOOGLE_SUMMARY_MODEL=gemini-proFor OpenAI models, you can use the following if you are not sure what to do:
OPENAI_EMBEDDING_MODEL=text-embedding-ada-002
OPENAI_SUMMARY_MODEL=gpt-4-1106-previewNOTE: Depending on your OpenAI tier you may have to use a different summary model. gpt-3.5 models will be okay.
The _PREFIX environment variables are used to prefix the keys in Redis. This is useful if you want to use the same Redis instance for multiple applications. They have the following defaults:
YOUTUBE_TRANSCRIPT_PREFIX=transcripts:
YOUTUBE_VIDEO_INFO_PREFIX=yt-videos:If you're satisfied with the defaults, you can delete these values from the .env file.
Lastly, the services/video-search/.env.docker file contains overrides for the Redis URL when used in Docker. By default this app sets up a local Redis instance in Docker. If you are using a cloud instance, you can simply add the URL to your .env and delete the override in the .env.docker file.
After installing and configuring the application, run the following command to build the Docker images and run containers:
npm run devThis command builds the app and the video service, and deploys them to Docker. It is all setup for hot reloading, so if you make changes to the code, it will automatically restart the servers.
Once the containers are up and running, the application will be accessible via your web browser:
This setup allows you to interact with the client-side application through your browser and make requests to the video search service hosted on a separate port.
The video search service doesn't publish a client application. Instead, it exposes a REST API that can be used to interact with the service. You can validate that it is running by checking Docker or by visiting the following URL:
You should be up and running now! The rest of this tutorial is focused on how the application works and how to use it, with code examples.
The backend is set up to handle YouTube video links or IDs. The relevant code snippet from the project demonstrates how these inputs are processed.
export type VideoDocument = Document<{
id: string;
link: string;
title: string;
description: string;
thumbnail: string;
}>;
export async function load(videos: string[] = config.youtube.VIDEOS) {
// Parse the video URLs to get a list of video IDs
const videosToLoad: string[] = videos.map(parseVideoUrl).filter((video) => {
return typeof video === 'string';
}) as string[];
// Get video title, description, and thumbnail from YouTube API v3
const videoInfo = await getVideoInfo(videosToLoad);
// Get video transcripts from SearchAPI.io, join the video info
const transcripts = await mapAsyncInOrder(videosToLoad, async (video) => {
return await getTranscript(video, videoInfo[video]);
});
// Return the videos as documents with metadata, and pageContent being the transcript
return transcripts.filter(
(transcript) => typeof transcript !== 'undefined',
) as VideoDocument[];
}In the same file you will see two caches:
const cache = cacheAside(config.youtube.TRANSCRIPT_PREFIX);
const videoCache = jsonCacheAside<VideoInfo>(config.youtube.VIDEO_INFO_PREFIX);These caches are used to store the transcripts (as a string) and video metadata (as JSON) in Redis. The cache functions are helper functions that use Redis to store and retrieve data. They looks like this:
export function cacheAside(prefix: string) {
return {
get: async (key: string) => {
return await client.get(`${prefix}${key}`);
},
set: async (key: string, value: string) => {
return await client.set(`${prefix}${key}`, value);
},
};
}
export function jsonCacheAside<T>(prefix: string) {
return {
get: async (key: string): Promise<T | undefined> => {
return client.json.get(`${prefix}${key}`) as T;
},
set: async (key: string, value: RedisJSON) => {
return await client.json.set(`${prefix}${key}`, '$', value);
},
};
}You will see these functions used elsewhere in the app. They are used to prevent unnecessary API calls, in this case to SearchAPI.io and the YouTube API.
After obtaining the video transcripts and metadata, the transcripts are then summarized using LangChain and the LLMs, both Gemini and ChatGPT. There are a few interesting pieces of code to understand here:
prompt used to ask the LLM to summarize the video transcript and generate sample questionsrefinement chain used to obtain the summarized video and sample questionsvector embedding chain that uses the LLM to generate text embeddings and store them in RedisThe LLM summary prompt is split into two parts. This is done to allow analyzing videos where the transcript length is larger than the LLM's accepted context.
import { PromptTemplate } from 'langchain/prompts';
const summaryTemplate = `
You are an expert in summarizing YouTube videos.
Your goal is to create a summary of a video.
Below you find the transcript of a video:
--------
{text}
--------
The transcript of the video will also be used as the basis for a question and answer bot.
Provide some examples questions and answers that could be asked about the video. Make these questions very specific.
Total output will be a summary of the video and a list of example questions the user could ask of the video.
SUMMARY AND QUESTIONS:
`;
export const SUMMARY_PROMPT = PromptTemplate.fromTemplate(summaryTemplate);
const summaryRefineTemplate = `
You are an expert in summarizing YouTube videos.
Your goal is to create a summary of a video.
We have provided an existing summary up to a certain point: {existing_answer}
Below you find the transcript of a video:
--------
{text}
--------
Given the new context, refine the summary and example questions.
The transcript of the video will also be used as the basis for a question and answer bot.
Provide some examples questions and answers that could be asked about the video. Make
these questions very specific.
If the context isn't useful, return the original summary and questions.
Total output will be a summary of the video and a list of example questions the user could ask of the video.
SUMMARY AND QUESTIONS:
`;
export const SUMMARY_REFINE_PROMPT = PromptTemplate.fromTemplate(
summaryRefineTemplate,
);The summary prompts are used to create a refinement chain with LangChain. LangChain will automatically handle splitting the video transcript document(s) and calling the LLM accordingly.
const videoSummarizeChain = loadSummarizationChain(llm, {
type: 'refine',
questionPrompt: SUMMARY_PROMPT,
refinePrompt: SUMMARY_REFINE_PROMPT,
});
const summaryCache = cacheAside(`${prefix}-${config.redis.SUMMARY_PREFIX}`);
async function summarizeVideos(videos: VideoDocument[]) {
const summarizedDocs: VideoDocument[] = [];
for (const video of videos) {
log.debug(`Summarizing ${video.metadata.link}`, {
...video.metadata,
location: `${prefix}.summarize.docs`,
});
const existingSummary = await summaryCache.get(video.metadata.id);
if (typeof existingSummary === 'string') {
summarizedDocs.push(
new Document({
metadata: video.metadata,
pageContent: existingSummary,
}),
);
continue;
}
const splitter = new TokenTextSplitter({
chunkSize: 10000,
chunkOverlap: 250,
});
const docsSummary = await splitter.splitDocuments([video]);
const summary = await videoSummarizeChain.run(docsSummary);
log.debug(`Summarized ${video.metadata.link}:\n ${summary}`, {
summary,
location: `${prefix}.summarize.docs`,
});
await summaryCache.set(video.metadata.id, summary);
summarizedDocs.push(
new Document({
metadata: video.metadata,
pageContent: summary,
}),
);
}
return summarizedDocs;
}Notice the summaryCache is used to first ask Redis if the video has already been summarized. If it has, it will return the summary and skip the LLM. This is a great example of how Redis can be used to cache data and avoid unnecessary API calls. Below is an example video summary with questions.
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.
Example Questions and Answers:
Q1: What is Redis Stack and what role does it play in the application?
Q2: How is the stock watch list stored and managed within the application?
Q3: What type of data does the application store using time series capabilities of
Redis Stack?
Q4: Can you explain the use of the Top-K filter in the application?
Q5: What methods are used to update the front end with real-time information in
the application?
Q6: How does the application sync the watch list with the streaming service?
Q7: What frontend technologies are mentioned for building the UI of the application?
Q8: How does Redis Insight help in managing the application data?The vector embedding chain is used to generate vector embeddings for the video summaries. This is done by asking the LLM to generate text embeddings for the summary. The vector embedding chain is defined as follows:
const vectorStore = new RedisVectorStore(embeddings, {
redisClient: client,
indexName: `${prefix}-${config.redis.VIDEO_INDEX_NAME}`,
keyPrefix: `${prefix}-${config.redis.VIDEO_PREFIX}`,
indexOptions: {
ALGORITHM: VectorAlgorithms.HNSW,
DISTANCE_METRIC: 'IP',
},
});The vector store uses the RedisVectorStore class from LangChain. This class is a wrapper around Redis that allows you to store and search vector embeddings. We are using the HNSW algorithm and the IP distance metric. For more information on the supported algorithms and distance metrics, see the Redis vector store documentation. We pass the embeddings object to the RedisVectorStore constructor. This object is defined as follows:
new GoogleGenerativeAIEmbeddings({
apiKey: config.google.API_KEY,
modelName: modelName ?? config.google.EMBEDDING_MODEL,
taskType: TaskType.SEMANTIC_SIMILARITY,
});Or for OpenAI:
new OpenAIEmbeddings({
openAIApiKey: config.openai.API_KEY,
modelName: modelName ?? config.openai.EMBEDDING_MODEL,
configuration: {
organization: config.openai.ORGANIZATION,
},
});The embeddings object is used to generate vector embeddings for the video summaries. These embeddings are then stored in Redis using the vectorStore.
Notice that we first check if we have already generated a vector using the Redis Set VECTOR_SET. If we have, we skip the LLM and use the existing vector. This avoids unnecessary API calls and can speed things up.
One of the key features of our application is the ability to search through video content using AI-generated queries. This section will cover how the backend handles search requests and interacts with the AI models.
When a user submits a question through the frontend, the backend performs the following steps to obtain the answer to the question as well as supporting videos:
vectorStore to search for the most relevant videos based on the semantic question.To answer a question, we first generate a semantically similar question to the one being asked. This is done using the QUESTION_PROMPT defined below:
import { PromptTemplate } from 'langchain/prompts';
const questionTemplate = `
You are an expert in summarizing questions.
Your goal is to reduce a question down to its simplest form while still retaining the semantic meaning.
Below you find the question:
--------
{question}
--------
Total output will be a semantically similar question that will be used to search an existing dataset.
SEMANTIC QUESTION:
`;
export const QUESTION_PROMPT = PromptTemplate.fromTemplate(questionTemplate);Using this prompt, we generate the semantic question and use it to search for videos. We may also need to search using the original question if we don't find any videos with the semantic question. This is done using the ORIGINAL_QUESTION_PROMPT defined below:
async function getVideos(question: string) {
log.debug(
`Performing similarity search for videos that answer: ${question}`,
{
question,
location: `${prefix}.search.search`,
},
);
const KNN = config.searches.KNN;
/* Simple standalone search in the vector DB */
return await (vectorStore.similaritySearch(question, KNN) as Promise<
VideoDocument[]
>);
}
async function searchVideos(question: string) {
log.debug(`Original question: ${question}`, {
location: `${prefix}.search.search`,
});
const semanticQuestion = await prompt.getSemanticQuestion(question);
log.debug(`Semantic question: ${semanticQuestion}`, {
location: `${prefix}.search.search`,
});
let videos = await getVideos(semanticQuestion);
if (videos.length === 0) {
log.debug(
'No videos found for semantic question, trying with original question',
{
location: `${prefix}.search.search`,
},
);
videos = await getVideos(question);
}
log.debug(`Found ${videos.length} videos`, {
location: `${prefix}.search.search`,
});
const answerDocument = await prompt.answerQuestion(question, videos);
return [
{
...answerDocument.metadata,
question: answerDocument.pageContent,
isOriginal: true,
},
];
}The code above shows the whole process for getting answers from the LLM and returning them to the user. Once relevant videos are identified, the backend uses either Google Gemini or OpenAI's ChatGPT to generate answers. These answers are formulated based on the video transcripts stored in Redis, ensuring they are contextually relevant to the user's query. The ANSWER_PROMPT used to ask the LLM for answers is as follows:
import { PromptTemplate } from 'langchain/prompts';
const answerTemplate = `
You are an expert in answering questions about Redis and Redis Stack.
Your goal is to take a question and some relevant information extracted from videos and return the answer to the question.
- Try to mostly use the provided video info, but if you can't find the answer there you can use other resources.
- Make sure your answer is related to Redis. All questions are about Redis. For example, if a question is asking about strings, it is asking about Redis strings.
- The answer should be formatted as a reference document using markdown. Make all headings and links bold, and add new paragraphs around any code blocks.
- Your answer should include as much detail as possible and be no shorter than 500 words.
Here is some extracted video information relevant to the question: {data}
Below you find the question:
--------
{question}
--------
Total output will be the answer to the question.
ANSWER:
`;
export const ANSWER_PROMPT = PromptTemplate.fromTemplate(answerTemplate);That's it! The backend will now return the answer and supporting videos to the user.
The application we've built in this tutorial is a great starting point for exploring the possibilities of AI-powered video Q&A. However, there are many ways to improve the application and make it more efficient. One such improvement is to use Redis as a semantic vector cache.
Note in the previous section, we discussed making a call to the LLM to answer every question. There is a performance bottleneck during this step, because LLM response times vary, but can take several seconds. What if there was a way we could prevent unnecessary calls to the LLM? This is where semantic vector caching comes in.
Semantic vector caching happens when you take the results of a call to an LLM and cache them alongside the vector embedding for the prompt. In the case of our application, we could generate vector embeddings for the questions and store them in Redis with the answer from the LLM. This would allow us to avoid calling the LLM for similar questions that have already been answered.
You might ask why store the question as a vector? Why not just store the question as a string? The answer is that storing the question as a vector allows us to perform semantic vector similarity searches. So rather than relying on someone asking the exact same question, we can determine an acceptable similarity score and return answers for similar questions
If you're already familiar with storing vectors in Redis, which we have covered in this tutorial, semantic vector caching is an extension of that and operates in essentially the same way. The only difference is that we are storing the question as a vector, rather than the video summary. We are also using the cache aside pattern. The process is as follows:
In order to store the question vectors we need to create a new vector store. This will create an index specifically for the question and answer vector. The code looks like this:
const answerVectorStore = new RedisVectorStore(embeddings, {
redisClient: client,
indexName: `${prefix}-${config.redis.ANSWER_INDEX_NAME}`,
keyPrefix: `${prefix}-${config.redis.ANSWER_PREFIX}`,
indexOptions: {
ALGORITHM: VectorAlgorithms.FLAT,
DISTANCE_METRIC: 'L2',
},
});The answerVectorStore looks nearly identical to the vectorStore we defined earlier, but it uses a different algorithm and distance metric. This algorithm is better suited for similarity searches for our questions.
The following code demonstrates how to use the answerVectorStore to check if a similar question has already been answered.
async function checkAnswerCache(question: string) {
const haveAnswers = await answerVectorStore.checkIndexExists();
if (!(haveAnswers && config.searches.answerCache)) {
return;
}
log.debug(`Searching for closest answer to question: ${question}`, {
location: `${prefix}.search.getAnswer`,
question,
});
/**
* Scores will be between 0 and 1, where 0 is most accurate and 1 is least accurate
*/
let results = (await answerVectorStore.similaritySearchWithScore(
question,
config.searches.KNN,
)) as Array<[AnswerDocument, number]>;
if (Array.isArray(results) && results.length > 0) {
// Filter out results with too high similarity score
results = results.filter(
(result) => result[1] <= config.searches.maxSimilarityScore,
);
const inaccurateResults = results.filter(
(result) => result[1] > config.searches.maxSimilarityScore,
);
if (Array.isArray(inaccurateResults) && inaccurateResults.length > 0) {
log.debug(
`Rejected ${inaccurateResults.length} similar answers that have a score > ${config.searches.maxSimilarityScore}`,
{
location: `${prefix}.search.getAnswer`,
scores: inaccurateResults.map((result) => result[1]),
},
);
}
}
if (Array.isArray(results) && results.length > 0) {
log.debug(
`Accepted ${results.length} similar answers that have a score <= ${config.searches.maxSimilarityScore}`,
{
location: `${prefix}.search.getAnswer`,
scores: results.map((result) => result[1]),
},
);
return results.map((result) => {
return {
...result[0].metadata,
question: result[0].pageContent,
isOriginal: false,
};
});
}
}The similaritySearchWithScore will find similar questions to the one being asked. It ranks them from 0 to 1, where 0 is most similar or "closest". We then filter out any results that are too similar, as defined by the maxSimilarityScore environment variable. If we find any results, we return them to the user. Using a max score is crucial here, because we don't want to return inaccurate results.
To complete this process, we need to apply the cache aside pattern and store the question as a vector in Redis. This is done as follows:
async function searchVideos(
question: string,
{ useCache = config.searches.answerCache }: VideoSearchOptions = {},
) {
log.debug(`Original question: ${question}`, {
location: `${prefix}.search.search`,
});
if (useCache) {
const existingAnswer = await checkAnswerCache(question);
if (typeof existingAnswer !== 'undefined') {
return existingAnswer;
}
}
const semanticQuestion = await prompt.getSemanticQuestion(question);
log.debug(`Semantic question: ${semanticQuestion}`, {
location: `${prefix}.search.search`,
});
if (useCache) {
const existingAnswer = await checkAnswerCache(semanticQuestion);
if (typeof existingAnswer !== 'undefined') {
return existingAnswer;
}
}
let videos = await getVideos(semanticQuestion);
if (videos.length === 0) {
log.debug(
'No videos found for semantic question, trying with original question',
{
location: `${prefix}.search.search`,
},
);
videos = await getVideos(question);
}
log.debug(`Found ${videos.length} videos`, {
location: `${prefix}.search.search`,
});
const answerDocument = await prompt.answerQuestion(question, videos);
if (config.searches.answerCache) {
await answerVectorStore.addDocuments([answerDocument]);
}
return [
{
...answerDocument.metadata,
question: answerDocument.pageContent,
isOriginal: true,
},
];
}When a question is asked, we first check the answer cache. We check both the question and the generated semantic question. If we find an answer, we return it to the user. If we don't find an answer, we call the LLM to generate an answer. We then store the question as a vector in Redis, along with the answer from the LLM. It may look like we're doing more work here than we were without the cache, but keep in mind the LLM is the bottleneck. By doing this, we are avoiding unnecessary calls to the LLM.
Below are a couple screenshots from the application to see what it looks like when you find an existing answer to a question:
In this tutorial, we've explored how to build an AI-powered video Q&A application using Redis, LangChain, and various other technologies. We've covered setting up the environment, processing video uploads, and implementing search functionality. You also saw how to use Redis as a vector store and semantic vector cache.
NOTE: Not included in this tutorial is an overview of the frontend Next.js app. However, you can find the code in the GitHub repository in the app directory.
Remember, Redis offers an easy start with cloud-hosted instances, which you can sign up for at redis.com/try-free. This makes experimenting with AI and Redis more accessible than ever.
We hope this tutorial inspires you to explore the exciting possibilities of combining AI with powerful databases like Redis to create innovative applications.
async function storeVideoVectors(documents: VideoDocument[]) {
log.debug('Storing documents...', {
location: `${prefix}.store.store`,
});
const newDocuments: VideoDocument[] = [];
await Promise.all(
documents.map(async (doc) => {
const exists = await client.sIsMember(
`${prefix}-${config.redis.VECTOR_SET}`,
doc.metadata.id,
);
if (!exists) {
newDocuments.push(doc);
}
}),
);
log.debug(`Found ${newDocuments.length} new documents`, {
location: `${prefix}.store.store`,
});
if (newDocuments.length === 0) {
return;
}
await vectorStore.addDocuments(newDocuments);
await Promise.all(
newDocuments.map(async (doc) => {
await client.sAdd(
`${prefix}-${config.redis.VECTOR_SET}`,
doc.metadata.id,
);
}),
);
}