krzysztof/landing_page
1
0
Fork 0
mirror of https://github.com/qdrant/landing_page.git synced 2026-09-27 15:08:30 +02:00
Code Issues Packages Projects Releases Wiki Activity

Improved Nested Docs and Link Grouping Support (#147)

Browse Source 
* 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>
This commit is contained in:
trean
2023-05-29 15:11:40 +02:00
committed by GitHub
co-authored by Andrey Vasnetsov David Sertic
parent b9874e5c56
commit 3215985d1d
60 changed files with 734 additions and 421 deletions
Download Patch File Download Diff File Expand all files Collapse all files
qdrant-landing/content/documentation/integrations/langchain.md
+53
View File
@@ -0,0 +1,53 @@
---
title: LangChain
weight: 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:
```bash
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:
```python
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](/articles/langchain-integration/). Some more information
might also be found in the [LangChain documentation](https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/qdrant.html).