ragas
pgvector
ragas | pgvector | |
---|---|---|
10 | 79 | |
4,874 | 9,612 | |
17.7% | 9.5% | |
9.6 | 9.9 | |
7 days ago | 7 days ago | |
Python | C | |
Apache License 2.0 | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
ragas
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Show HN: Ragas – the de facto open-source standard for evaluating RAG pipelines
congrats on launching! i think my continuing struggle with looking at Ragas as a company rather than an oss library is that the core of it is like 8 metrics (https://github.com/explodinggradients/ragas/tree/main/src/ra...) that are each 1-200 LOC. i can inline that easily in my app and retain full control, or model that in langchain or haystack or whatever.
why is Ragas a library and a company, rather than an overall "standard" or philosophy (eg like Heroku's 12 Factor Apps) that could maybe be more robust?
(just giving an opp to pitch some underappreciated benefits of using this library)
- FLaNK 04 March 2024
- FLaNK Stack 05 Feb 2024
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SuperDuperDB - how to use it to talk to your documents locally using llama 7B or Mistral 7B?
Also, at some point you'll need to get serious about evaluation (trust me, you will). You may be interested in https://github.com/explodinggradients/ragas
- Ragas – Framework for RAG Evaluation
- Ragas: Open-source Evaluation framework for RAG pipelines
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Building a customer support chatbot using GPT-3.5 and lLamaIndex🚀
The problem becomes worse if you want to inspect outputs from not just one, but several different queries. Luckily, there are several free open source packages such as ragas and DeepEval that can help evaluate your chatbot so you don't have to manually do it 😌
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Patterns for Building LLM-Based Systems and Products
We have build RAGAS framework for this https://github.com/explodinggradients/ragas
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[R] All about evaluating Large language models
Hi u/thecuteturtle, I am building open-source projects for evaluating LLM-based applications. Check it out https://github.com/explodinggradients/ragas and if you like to collaborate let me know :)
pgvector
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Integrate txtai with Postgres
# Install Postgres and pgvector !apt-get update && apt install postgresql postgresql-server-dev-14 !git clone --branch v0.6.2 https://github.com/pgvector/pgvector.git !cd pgvector && make && make install # Start database !service postgresql start !sudo -u postgres psql -U postgres -c "ALTER USER postgres PASSWORD 'pass';"
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Vector Database solutions on AWS
When talking about Vector Databases, in the market we can find the specialized ones and multi-model, most of the major database providers like Oracle, PostgreSQL or MongoDB, for mention some of them, have integrated a specific solution to retrieve vector data.
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Using pgvector To Locate Similarities In Enterprise Data
For this example, I wanted to focus on how pgvector – an open-source vector similarity search for Postgres – can be used to identify data similarities that exist in enterprise data.
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pgvector vs. pgvecto.rs in 2024: A Comprehensive Comparison for Vector Search in PostgreSQL
pgvector supports dense vector search well, but it does not have plan to support sparse vector.
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Pg_vectorize: The simplest way to do vector search and RAG on Postgres
There's an issue in the pgvector repo about someone having several ~10-20million row tables and getting acceptable performance with the right hardware and some performance tuning: https://github.com/pgvector/pgvector/issues/455
I'm in the early stages of evaluating pgvector myself. but having used pinecone I currently am liking pgvector better because of it being open source. The indexing algorithm is clear, one can understand and modify the parameters. Furthermore the database is postgresql, not a proprietary document store. When the other data in the problem is stored relationally, it is very convenient to have the vectors stored like this as well. And postgresql has good observability and metrics. I think when it comes to flexibility for specialized applications, pgvector seems like the clear winner. But I can definitely see pinecone's appeal if vector search is not a core component of the problem/business, as it is very easy to use and scales very easily
- FLaNK 04 March 2024
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Vector Database and Spring IA
The Spring AI project aims to streamline the development of applications that incorporate artificial intelligence functionality without unnecessary complexity. On this example we use features like: Embedding, Prompts, ETL and save all embedding on PGvector(Postgres Vector database)
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Use pgvector for searching images on Azure Cosmos DB for PostgreSQL
Official GitHub repository of the pgvector extension
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pgvector 0.6.0: 30x faster with parallel index builds
pgvector 0.6.0 was just released and will be available on Supabase projects soon. Again, a special shout out to Andrew Kane and everyone else who worked on parallel index builds.
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Store embeddings in Azure Cosmos DB for PostgreSQL with pgvector
The pgvector extension adds vector similarity search capabilities to your PostgreSQL database. To use the extension, you have to first create it in your database. You can install the extension, by connecting to your database and running the CREATE EXTENSION command from the psql command prompt:
What are some alternatives?
deepeval - The LLM Evaluation Framework
Milvus - A cloud-native vector database, storage for next generation AI applications
chameleon-llm - Codes for "Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models".
faiss - A library for efficient similarity search and clustering of dense vectors.
Local-LLM-Langchain - Load local LLMs effortlessly in a Jupyter notebook for testing purposes alongside Langchain or other agents. Contains Oobagooga and KoboldAI versions of the langchain notebooks with examples.
Weaviate - Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database.
FastLoRAChat - Instruct-tune LLaMA on consumer hardware with shareGPT data
Elasticsearch - Free and Open, Distributed, RESTful Search Engine
agenta - The all-in-one LLM developer platform: prompt management, evaluation, human feedback, and deployment all in one place.
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
text-generation-webui-colab - A colab gradio web UI for running Large Language Models
ann-benchmarks - Benchmarks of approximate nearest neighbor libraries in Python