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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 |
|---|---|
| LlamaIndex | 200 |
LlamaIndex (GPT Index)
LlamaIndex (formerly GPT Index) acts as an interface between your external data and Large Language Models. So you can bring your private data and augment LLMs with it. LlamaIndex simplifies data ingestion and indexing, integrating Qdrant as a vector index.
Installing LlamaIndex is straightforward if we use pip as a package manager:
pip install llama-index
LlamaIndex requires providing an instance of QdrantClient, so it can interact with Qdrant server.
from llama_index import GPTQdrantIndex
import qdrant_client
client = qdrant_client.QdrantClient(
"<qdrant-url>",
api_key="<qdrant-api-key>", # For Qdrant Cloud, None for local instance
)
index = GPTQdrantIndex.from_documents(documents, client=client, collection_name="documents")
The library comes with a notebook that shows an end-to-end example of how to use Qdrant within LlamaIndex.