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57 lines
1.6 KiB
Markdown
57 lines
1.6 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](https://ollama.com) provides specialized embeddings for niche applications. Ollama supports a [variety of embedding models](https://ollama.com/search?c=embedding), 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 packages using the following pip command:
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```bash
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pip install ollama qdrant-client
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```
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## Integration Example
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The following code assumes Ollama is accessible at port `11434` and Qdrant at port `6334`.
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```python
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from qdrant_client import QdrantClient, models
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import ollama
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COLLECTION_NAME = "NicheApplications"
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# Initialize Ollama client
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oclient = ollama.Client(host="localhost")
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# Initialize Qdrant client
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qclient = QdrantClient(host="localhost", port=6333)
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# Text to embed
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text = "Ollama excels in niche applications with specific embeddings"
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# Generate embeddings
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response = oclient.embeddings(model="llama3.2", prompt=text)
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embeddings = response["embedding"]
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# Create a collection if it doesn't already exist
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if not qclient.collection_exists(COLLECTION_NAME):
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qclient.create_collection(
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collection_name=COLLECTION_NAME,
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vectors_config=models.VectorParams(
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size=len(embeddings), distance=models.Distance.COSINE
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),
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)
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# Upload the vectors to the collection along with the original text as payload
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qclient.upsert(
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collection_name=COLLECTION_NAME,
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points=[models.PointStruct(id=1, vector=embeddings, payload={"text": text})],
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)
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```
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