Use langchain-qdrant for vector stores
Open
Nobody has claimed this yet.
code-review
- Dominant language
- Python
- Stars
- 570
- Forks
- 351
- Avg merge
- 2d 13h
- Merged PRs (30d)
- 65
Description
We currently use the Qdrant client directly. Using the LangChain integration would simplify the interface.
Docs: https://docs.langchain.com/oss/python/integrations/vectorstores/qdrant
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
The issue provides no file or test entry point. Start with the linked Qdrant integration documentation, then locate where the direct Qdrant client is used; done means those vector-store paths use the LangChain integration without changing their intended behavior.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- databases, machine-learning
- Issue type
- Refactor
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100