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43 lines
1.6 KiB
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43 lines
1.6 KiB
Markdown
---
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title: LlamaIndex
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aliases:
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- ../integrations/llama-index/
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- /documentation/overview/integrations/llama-index/
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---
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# LlamaIndex
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Llama Index acts as an interface between your external data and Large Language Models. So you can bring your
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private data and augment LLMs with it. LlamaIndex simplifies data ingestion and indexing, integrating Qdrant as a vector index.
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Installing Llama Index is straightforward if we use pip as a package manager. Qdrant is not installed by default, so we need to
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install it separately. The integration of both tools also comes as another package.
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```bash
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pip install llama-index llama-index-vector-stores-qdrant
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```
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Llama Index requires providing an instance of `QdrantClient`, so it can interact with Qdrant server.
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```python
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from llama_index.core.indices.vector_store.base import VectorStoreIndex
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from llama_index.vector_stores.qdrant import QdrantVectorStore
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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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vector_store = QdrantVectorStore(client=client, collection_name="documents")
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index = VectorStoreIndex.from_vector_store(vector_store=vector_store)
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```
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## Further Reading
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- [LlamaIndex Documentation](https://docs.llamaindex.ai/en/stable/examples/vector_stores/QdrantIndexDemo/)
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- [Example Notebook](https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/docs/examples/vector_stores/QdrantIndexDemo.ipynb)
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- [Source Code](https://github.com/run-llama/llama_index/tree/main/llama-index-integrations/vector_stores/llama-index-vector-stores-qdrant)
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