Software Alternatives, Accelerators & Startups

Qdrant VS Case Law Access Project

Compare Qdrant VS Case Law Access Project and see what are their differences

Qdrant logo Qdrant

Qdrant is a high-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/

Case Law Access Project logo Case Law Access Project

6.5 million state and federal cases dating back to 1600s
  • Qdrant Landing page
    Landing page //
    2023-12-20

Qdrant is a leading open-source high-performance Vector Database written in Rust with extended metadata filtering support and advanced features. It deploys as an API service providing a search for the nearest high-dimensional vectors. With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications. Powering vector similarity search solutions of any scale due to a flexible architecture and low-level optimization. Qdrant is trusted and high-rated by Machine Learning and Data Science teams of top-tier companies worldwide.

  • Case Law Access Project Landing page
    Landing page //
    2021-10-06

Qdrant

$ Details
freemium
Platforms
Linux Windows Kubernetes Docker
Release Date
2021 May

Case Law Access Project

Website
case.law
Pricing URL
-
$ Details
-
Platforms
-
Release Date
-

Qdrant features and specs

  • Advanced Filtering: Yes
  • On-disc Storage: Yes
  • Scalar Quantization: Yes
  • Product Quantization: Yes
  • Binary Quantization: Yes
  • Sparse Vectors: Yes
  • Hybrid Search: Yes
  • Discovery API: Yes
  • Recommendation API: Yes

Case Law Access Project features and specs

No features have been listed yet.

Category Popularity

0-100% (relative to Qdrant and Case Law Access Project)
Search Engine
100 100%
0% 0
eCommerce
0 0%
100% 100
Databases
100 100%
0% 0
Developer Tools
0 0%
100% 100

Questions and Answers

As answered by people managing Qdrant and Case Law Access Project.

Why should a person choose your product over its competitors?

Qdrant's answer

Advanced Features, Performance, Scalability, Developer Experience, and Resources Saving.

What makes your product unique?

Qdrant's answer

Highest performance https://qdrant.tech/benchmarks/, scalability and ease of use.

Which are the primary technologies used for building your product?

Qdrant's answer

Qdrant is written completely in Rust. SDKs available for all popular languages Python, Go, Rust, Java, .NET, etc.

User comments

Share your experience with using Qdrant and Case Law Access Project. For example, how are they different and which one is better?
Log in or Post with

Social recommendations and mentions

Based on our record, Qdrant should be more popular than Case Law Access Project. It has been mentiond 40 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Qdrant mentions (40)

  • WizSearch: ๐Ÿ† Winning My First AI Hackathon ๐Ÿš€
    Vector Databases: Qdrant for efficient data storage and retrieval. - Source: dev.to / 4 days ago
  • How to Build a Chat App with Your Postgres Data using Agent Cloud
    AgentCloud uses Qdrant as the vector store to efficiently store and manage large sets of vector embeddings. For a given user query the RAG application fetches relevant documents from vector store by analyzing how similar their vector representation is compared to the query vector. - Source: dev.to / about 1 month ago
  • Hindi-Language AI Chatbot for Enterprises Using Qdrant, MLFlow, and LangChain
    Great. Now that we have the embeddings, we need to store them in a vector database. We will be using Qdrant for this purpose. Qdrant is an open-source vector database that allows you to store and query high-dimensional vectors. The easiest way to get started with the Qdrant database is using the docker. - Source: dev.to / about 1 month ago
  • Boost Your Code's Efficiency: Introducing Semantic Cache with Qdrant
    I took Qdrant for this project. The reason was that Qdrant stands for high-performance vector search, the best choice against use cases like finding similar function calls based on semantic similarity. Qdrant is not only powerful but also scalable to support a variety of advanced search features that are greatly useful to nuanced caching mechanisms like ours. - Source: dev.to / about 2 months ago
  • Ask HN: Has Anyone Trained a personal LLM using their personal notes?
    I'm currently looking to implement locally, using QDrant [1] for instance. I'm just playing around, but it makes sense to have a runnable example for our users at work too :) [2]. [1]. https://qdrant.tech/. - Source: Hacker News / 2 months ago
View more

Case Law Access Project mentions (9)

  • Ask HN: Who is hiring? (July 2023)
    Harvard Library Innovation Lab | Multiple roles | Full time | Cambridge, MA (hybrid schedule, REMOTE possible) The Harvard Library Innovation Lab explores the future of libraries by building tools and communities for open knowledge. We build long term services like https://perma.cc, https://opencasebook.org, and https://case.law, and we host fellows like Molly White, creator of Web3 is Going Just Great. We are a... - Source: Hacker News / 12 months ago
  • U.S. Army sergeant who shot Austin protester Garrett Foster found guilty of murder. One member of my local party is quitting the chapter over this, the rest of us feel justice was served. What do y'all think?
    This stuff has been in the case law for centuries. You maybe oughta go read the statutes and cases, okay? It ain't like Harvard Law School hasn't done their damnedest to make the case.law freely available to everyone. Source: about 1 year ago
  • Ask HN: Who is hiring? (January 2023)
    Harvard Library Innovation Lab | Multiple roles | Full time | Onsite Cambridge, MA (hybrid schedule) The Harvard Library Innovation Lab explores the future of libraries by building tools and communities for open knowledge. We build long term services like https://perma.cc, https://opencasebook.org, and https://case.law, and we host fellows and technologists-in-residence like Molly White, creator of Web3 is Going... - Source: Hacker News / over 1 year ago
  • Does the US really not have anything like Canlii for searching text of cases? GLIN seems to be a private very small collection only up until 2010, LII seems to just search the Criminal Code? I must be missing something major, right?
    Use the Caselaw Access Project from Harvard: https://case.law/. It doesn't have everything but it has decent coverage of American case law. Source: over 1 year ago
  • Ask HN: Freelancer? Seeking freelancer? (March 2022)
    SEEKING FREELANCER | Remote, must be US employment authorized The Harvard Library Innovation Lab builds open source websites to democratize access to information. We are seeking Vue + Django developers to add features to our projects: https://perma.cc, https://opencasebook.org, https://case.law Please send experience and hourly rates for public-interest open source work to the harvard.edu address in my profile. - Source: Hacker News / over 2 years ago
View more

What are some alternatives?

When comparing Qdrant and Case Law Access Project, you can also consider the following products

Milvus - Vector database built for scalable similarity search Open-source, highly scalable, and blazing fast.

Depict.ai - Amazon-quality product recommendations for any online store

Weaviate - Welcome to Weaviate

Collie CLI - One project on AWS, two on Azure, and might there be something on GCP too ๐Ÿ˜ต?

pgvecto.rs - Scalable, Low-latency and Hybrid-enabled Vector Search in Postgres. Revolutionize Vector Search, not Database. - tensorchord/pgvecto.rs

Google Scholar - Google Scholar is a freely accessible web search engine that indexes the full text of scholarly...