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* added a table of content * wide layout for docs, styles for the table of content * fixes for docs layout * wide footer at the docs section * added support for nested docs, added toggling groups of links, delimiters, external links * external link icon * added active state for nested links, styles for the external link icon * styles fix * remove doc sync * update directory structure and doc titles * fix outstanding links * fix more links for merge * Revert "fix more links for merge" This reverts commit 46c9ccaf1b7765f2cda8dc85d625fa6b4e3f5436. * Revert "fix outstanding links" This reverts commit 28e6380b74f1ab74690c8184551f186656d6d4e9. * fix remaining broken links * move how-to tutorials in the different page * split tutorials * fix link * upd github edit link * skip empty index pages --------- Co-authored-by: Andrey Vasnetsov <andrey@vasnetsov.com> Co-authored-by: David Sertic <62056091+davidmyriel@users.noreply.github.com>
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title, weight
| title | weight |
|---|---|
| LangChain | 100 |
LangChain
LangChain is a library that makes developing Large Language Models based applications much easier. It unifies the interfaces to different libraries, including major embedding providers and Qdrant. Using LangChain, you can focus on the business value instead of writing the boilerplate.
Langchain comes with the Qdrant integration by default. It might be installed with pip:
pip install langchain
Qdrant acts as a vector index that may store the embeddings with the documents used to generate them. There are various ways
how to use it, but calling Qdrant.from_texts is probably the most straightforward way how to get started:
from langchain.vectorstores import Qdrant
from langchain.embeddings import HuggingFaceEmbeddings
embeddings = HuggingFaceEmbeddings(
model_name="sentence-transformers/all-mpnet-base-v2"
)
doc_store = Qdrant.from_texts(
texts, embeddings, url="<qdrant-url>", api_key="<qdrant-api-key>", collection_name="texts"
)
Calling Qdrant.from_documents or Qdrant.from_texts will always recreate the collection and remove all the existing points.
That's fine for some experiments, but you'll prefer not to start from scratch every single time in a real-world scenario.
If you prefer reusing an existing collection, you can create an instance of Qdrant on your own:
import qdrant_client
client = qdrant_client.QdrantClient(
"<qdrant-url>",
api_key="<qdrant-api-key>", # For Qdrant Cloud, None for local instance
)
doc_store = Qdrant(
client=client, collection_name="texts",
embedding_function=embeddings.embed_query,
)
If you'd like to know more about running Qdrant in a LangChain-based application, please read our article Question Answering with LangChain and Qdrant without boilerplate. Some more information might also be found in the LangChain documentation.