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48 lines
1.0 KiB
Markdown
48 lines
1.0 KiB
Markdown
---
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title: Ollama
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weight: 2600
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---
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# Using Ollama with Qdrant
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Ollama provides specialized embeddings for niche applications. Ollama supports a variety of embedding models, making it possible to build retrieval augmented generation (RAG) applications that combine text prompts with existing documents or other data in specialized areas.
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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 ollama
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```
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## Integration Example
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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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from ollama import Ollama
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# Initialize Ollama model
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model = Ollama("ollama-unique")
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# Generate embeddings for niche applications
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text = "Ollama excels in niche applications with specific embeddings."
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embeddings = model.embed(text)
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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="NicheApplications",
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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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