docarray VS haystack

Compare docarray vs haystack and see what are their differences.

haystack

:mag: LLM orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots. (by deepset-ai)
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docarray haystack
32 55
2,748 13,633
1.5% 2.5%
9.2 9.9
3 days ago 7 days ago
Python Python
Apache License 2.0 Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.

docarray

Posts with mentions or reviews of docarray. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-03-19.

haystack

Posts with mentions or reviews of haystack. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-04-28.

What are some alternatives?

When comparing docarray and haystack you can also consider the following projects:

Milvus - A cloud-native vector database, storage for next generation AI applications

langchain - 🦜🔗 Build context-aware reasoning applications

transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]

bootcamp - Dealing with all unstructured data, such as reverse image search, audio search, molecular search, video analysis, question and answer systems, NLP, etc.

gpt-neo - An implementation of model parallel GPT-2 and GPT-3-style models using the mesh-tensorflow library.

kaggle-environments

BentoML - The most flexible way to serve AI/ML models in production - Build Model Inference Service, LLM APIs, Inference Graph/Pipelines, Compound AI systems, Multi-Modal, RAG as a Service, and more!

imodels - Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).

label-studio - Label Studio is a multi-type data labeling and annotation tool with standardized output format

discoart - 🪩 Create Disco Diffusion artworks in one line

jina - ☁️ Build multimodal AI applications with cloud-native stack