motorhead
pgvector
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motorhead | pgvector | |
---|---|---|
10 | 78 | |
822 | 9,211 | |
2.6% | 10.4% | |
8.0 | 9.9 | |
9 days ago | 3 days ago | |
Rust | 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.
motorhead
- Motorhead is a memory and information retrieval server for LLMs
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Comparison of Vector Databases
Metal [1] is another one on my radar. Their API looks super simple.
Disclosures: None
[1] https://getmetal.io
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Any Alternatives to Langchain?
Any alternatives? I found this Rust based project that might be interesting: https://github.com/getmetal/motorhead
- RasaGPT: First headless LLM chatbot built on top of Rasa, Langchain and FastAPI
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Langchain question and answer without openai
you could run motorhead on docker https://github.com/getmetal/motorhead
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How to use Enum with Vec to parse the mixed data vector from RedisSearch
The code is found using GitHub search FT.SEARCH inside https://github.com/getmetal/motorhead/blob/main/src/models.rs and adapted.
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Memory in production
All the examples that Langchain gives are for persisting memory locally which won't work in a serverless (statelesss) environment, and the one solution documented for stateless applications, getmetal/motorhead, is a containerized, Rust-based service we would have to run ourselves.
- Show HN: Motörhead, LLM Memory Server Built in Rust
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OpenAI Embeddings API alternative?
I've only just signed up and haven't had a chance to build anything with it yet, but this might be something to consider https://getmetal.io/
- Motörhead – memory and information retrieval server for LLMs
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?
lmql - A language for constraint-guided and efficient LLM programming.
Milvus - A cloud-native vector database, storage for next generation AI applications
NeMo-Guardrails - NeMo Guardrails is an open-source toolkit for easily adding programmable guardrails to LLM-based conversational systems.
faiss - A library for efficient similarity search and clustering of dense vectors.
RasaGPT - 💬 RasaGPT is the first headless LLM chatbot platform built on top of Rasa and Langchain. Built w/ Rasa, FastAPI, Langchain, LlamaIndex, SQLModel, pgvector, ngrok, telegram
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​.
kor - LLM(😽)
Elasticsearch - Free and Open, Distributed, RESTful Search Engine
Abstract Feature Branch - abstract_feature_branch is a Ruby gem that provides a variation on the Branch by Abstraction Pattern by Paul Hammant and the Feature Toggles Pattern by Martin Fowler (aka Feature Flags) to enable Continuous Integration and Trunk-Based Development.
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
rasa-haystack
ann-benchmarks - Benchmarks of approximate nearest neighbor libraries in Python