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50 lines
1.4 KiB
Markdown
50 lines
1.4 KiB
Markdown
---
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title: OCI (Oracle Cloud Infrastructure)
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weight: 2500
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---
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# Using OCI (Oracle Cloud Infrastructure) with Qdrant
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OCI provides robust cloud-based embeddings for various media types. The Generative AI Embedding Models convert textual input - ranging from phrases and sentences to entire paragraphs - into a structured format known as embeddings. Each piece of text input is transformed into a numerical array consisting of 1024 distinct numbers.
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## Installation
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You can install the required package using the following pip command:
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```bash
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pip install oci
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```
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## Code Example
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Below is an example of how to obtain embeddings using OCI (Oracle Cloud Infrastructure)'s API and store them in a Qdrant collection:
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```python
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import qdrant_client
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from qdrant_client.models import Batch
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import oci
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# Initialize OCI client
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config = oci.config.from_file()
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ai_client = oci.ai_language.AIServiceLanguageClient(config)
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# Generate embeddings using OCI's AI service
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text = "OCI provides cloud-based AI services."
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response = ai_client.batch_detect_language_entities(text)
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embeddings = response.data[0].entities[0].embedding
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# Initialize Qdrant client
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qdrant_client = qdrant_client.QdrantClient(host="localhost", port=6333)
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# Upsert the embedding into Qdrant
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qdrant_client.upsert(
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collection_name="CloudAI",
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points=Batch(
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ids=[1],
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vectors=[embeddings],
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)
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)
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```
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