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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>
54 lines
2.0 KiB
Markdown
54 lines
2.0 KiB
Markdown
---
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title: LangChain
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weight: 100
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---
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# LangChain
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LangChain is a library that makes developing Large Language Models based applications much easier. It unifies the interfaces
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to different libraries, including major embedding providers and Qdrant. Using LangChain, you can focus on the business value
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instead of writing the boilerplate.
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Langchain comes with the Qdrant integration by default. It might be installed with pip:
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```bash
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pip install langchain
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```
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Qdrant acts as a vector index that may store the embeddings with the documents used to generate them. There are various ways
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how to use it, but calling `Qdrant.from_texts` is probably the most straightforward way how to get started:
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```python
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from langchain.vectorstores import Qdrant
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from langchain.embeddings import HuggingFaceEmbeddings
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embeddings = HuggingFaceEmbeddings(
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model_name="sentence-transformers/all-mpnet-base-v2"
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)
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doc_store = Qdrant.from_texts(
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texts, embeddings, url="<qdrant-url>", api_key="<qdrant-api-key>", collection_name="texts"
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)
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```
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Calling `Qdrant.from_documents` or `Qdrant.from_texts` will always recreate the collection and remove all the existing points.
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That's fine for some experiments, but you'll prefer not to start from scratch every single time in a real-world scenario.
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If you prefer reusing an existing collection, you can create an instance of Qdrant on your own:
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```
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import qdrant_client
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client = qdrant_client.QdrantClient(
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"<qdrant-url>",
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api_key="<qdrant-api-key>", # For Qdrant Cloud, None for local instance
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)
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doc_store = Qdrant(
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client=client, collection_name="texts",
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embedding_function=embeddings.embed_query,
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)
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```
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If you'd like to know more about running Qdrant in a LangChain-based application, please read our article
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[Question Answering with LangChain and Qdrant without boilerplate](/articles/langchain-integration/). Some more information
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might also be found in the [LangChain documentation](https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/qdrant.html).
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