# unstructured data

Published articles for unstructured data.

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## Doing your homework with Neo4j & AI

DevFeed: [Doing your homework with Neo4j & AI](<https://devfeed.tech/articles/doing-your-homework-with-neo4j-ai-50084.md>)

Original publisher: [Read original article](<https://neo4j.com/blog/genai/doing-your-homework-with-neo4j-ai/>)

Author: Jocelyn Hoppa

Published: 2026-09-03T06:59:00Z

Content type: tutorial

Language: en

Sources: [Graph Database & Technology | Neo4j Blog](<https://devfeed.tech/sources/graph-database-technology-neo4j-blog.md>)

Topics: [Neo4j](<https://devfeed.tech/topics/neo4j.md>), [Graphs](<https://devfeed.tech/topics/graphs.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [pdf](<https://devfeed.tech/topics/pdf.md>), [API keys](<https://devfeed.tech/topics/api-keys.md>), [OpenAI](<https://devfeed.tech/topics/openai.md>)

Tags: [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [developer](<https://devfeed.tech/tags/developer.md>), [genai](<https://devfeed.tech/tags/genai.md>), [graphs](<https://devfeed.tech/tags/graphs.md>), [llm](<https://devfeed.tech/tags/llm.md>), [neo4j](<https://devfeed.tech/tags/neo4j.md>), [openai](<https://devfeed.tech/tags/openai.md>), [pdf](<https://devfeed.tech/tags/pdf.md>), [unstructured-data](<https://devfeed.tech/tags/unstructured-data.md>)

### AI overview

A tutorial describes using Neo4j and AI to build structured graphs from homework documents and generate study questions. It contrasts this approach with prompting an LLM on an entire document and discusses evaluating responses with free or API-based options.

### Source excerpt

Building graphs from unstructured documentsTools are usually (well, at least sometimes) very good at doing what they are designed to do. It is hard to argue that a screwdriver isn't good at tightening loose screws (at least if you use... Read more ->

## What is Vector Similarity? Understanding its Role in AI Applications.

DevFeed: [What is Vector Similarity? Understanding its Role in AI Applications.](<https://devfeed.tech/articles/what-is-vector-similarity-understanding-its-role-in-ai-applications-46759.md>)

Original publisher: [Read original article](<https://qdrant.tech/blog/what-is-vector-similarity/>)

Author: info@qdrant.tech (Andrey Vasnetsov)

Published: 2024-02-24T08:00:00Z

Content type: article

Language: en

Sources: [Qdrant Blog on Qdrant - Vector Search Engine](<https://devfeed.tech/sources/qdrant-blog-on-qdrant-vector-search-engine.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [AI Development](<https://devfeed.tech/topics/ai-development.md>), [Database](<https://devfeed.tech/topics/database.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Deep learning](<https://devfeed.tech/topics/deep-learning.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-applications](<https://devfeed.tech/tags/ai-applications.md>), [approximate-nearest-neighbor-search](<https://devfeed.tech/tags/approximate-nearest-neighbor-search.md>), [bert](<https://devfeed.tech/tags/bert.md>), [database](<https://devfeed.tech/tags/database.md>), [deep-learning](<https://devfeed.tech/tags/deep-learning.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [fasttext](<https://devfeed.tech/tags/fasttext.md>), [hnsw](<https://devfeed.tech/tags/hnsw.md>), [image-search](<https://devfeed.tech/tags/image-search.md>), [knn-algorithm](<https://devfeed.tech/tags/knn-algorithm.md>), [matching](<https://devfeed.tech/tags/matching.md>), [neural-network](<https://devfeed.tech/tags/neural-network.md>), [qdrant](<https://devfeed.tech/tags/qdrant.md>), [recommender-system](<https://devfeed.tech/tags/recommender-system.md>), [saas](<https://devfeed.tech/tags/saas.md>), [search](<https://devfeed.tech/tags/search.md>), [simaes-networks](<https://devfeed.tech/tags/simaes-networks.md>), [similarity](<https://devfeed.tech/tags/similarity.md>), [transformer](<https://devfeed.tech/tags/transformer.md>), [unstructured-data](<https://devfeed.tech/tags/unstructured-data.md>), [vector](<https://devfeed.tech/tags/vector.md>), [vector-search](<https://devfeed.tech/tags/vector-search.md>), [vector-search-engine](<https://devfeed.tech/tags/vector-search-engine.md>), [vectors](<https://devfeed.tech/tags/vectors.md>), [word2vec](<https://devfeed.tech/tags/word2vec.md>)

### AI overview

This article explains vector similarity and its role in AI applications. It describes converting unstructured data and queries into vectors with deep-learning models, storing them in vector databases, and retrieving related results through similarity metrics.

### Source excerpt

Understanding Vector Similarity: Powering Next-Gen AI Applications A core function of a wide range of AI applications is to first understand the meaning behind a user query, and then provide relevant answers to the questions that the user is asking. With increasingly advanced interfaces and applications, this query can be in the form of language, or an image, an audio, video, or other forms of unstructured data. On an ecommerce platform, a user can, for instance, try to find 'clothing for a trek', when they actually want results around 'waterproof jackets', or 'winter socks'. Keyword, or full-text, or even synonym search would fail to provide any response to such a query. Similarly, on a music app, a user might be looking for songs that sound similar to an audio clip they have heard. Or, they might want to look up furniture that has a similar look as the one they saw on a trip.