Our great sponsors
-
Github link Documentation Hackernews link
-
Cherche is compatible with large corpora like wikipedia for example and provides decent response times notebook. I will benchmark Jina and Haystack under the same conditions but there shouldn't be much difference as the responsibility falls on Elasticsearch or on Faiss via retrieve.Encoder. The strength of Cherche is the fancy pipelines composed of union and intersection operations between TfIdf BM25, Flash, multiple rankers... and this is less adapted to Wikipedia. It is more adapted to industrial or personal corpora (<= 100000 documents).
-
WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
-
I started developing this tool after using haystack. Pipelines are easier to build with cherche because of the operators. Also, cherche offers FlashText, Lunr.py retrievers that are not available in Haystack and that I needed for the project I wanted to solve. Haystack is clearly more complete but I think also more complex to use.
-
I started developing this tool after using haystack. Pipelines are easier to build with cherche because of the operators. Also, cherche offers FlashText, Lunr.py retrievers that are not available in Haystack and that I needed for the project I wanted to solve. Haystack is clearly more complete but I think also more complex to use.
Related posts
- [P] what is the most efficient way to pattern matching word-to-word?
- What is the most efficient way to find substrings in strings?
- How can I speed up thousands of re.subs()?
- My first NLP pipeline using SpaCy: detect news headlines with company acquisitions
- What tech do I need to learn to programmatically parse ingredients from a recipe?