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140ba682f1
# Description
This PR makes it possible to use named vectors from Qdrant in Langchain.
That was requested multiple times, as people want to reuse externally
created collections in Langchain. It doesn't change anything for the
existing applications. The changes were covered with some integration
tests and included in the docs.
## Example
```python
Qdrant.from_documents(
docs,
embeddings,
location=":memory:",
collection_name="my_documents",
vector_name="custom_vector",
)
```
### Issue: #2594
Tagging @rlancemartin & @eyurtsev. I'd appreciate your review.
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| .. | ||
| agent | ||
| cache | ||
| callbacks | ||
| chains | ||
| chat_models | ||
| document_loaders | ||
| embeddings | ||
| examples | ||
| llms | ||
| memory | ||
| prompts | ||
| retrievers | ||
| utilities | ||
| vectorstores | ||
| __init__.py | ||
| .env.example | ||
| conftest.py | ||
| test_document_transformers.py | ||
| test_kuzu.py | ||
| test_nebulagraph.py | ||
| test_nlp_text_splitters.py | ||
| test_pdf_pagesplitter.py | ||
| test_schema.py | ||
| test_text_splitter.py | ||