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doc: Fix ollama example (#1264)

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Signed-off-by: Anush008 <anushshetty90@gmail.com>
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Anush
2024-11-05 06:53:02 +05:30
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qdrant-landing/content/documentation/embeddings/ollama.md
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@@ -3,45 +3,54 @@ title: Ollama
weight: 2600
---
# Using Ollama with Qdrant
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.
# Using Ollama with Qdrant
[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.
## Installation
You can install the required package using the following pip command:
You can install the required packages using the following pip command:
```bash
pip install ollama
pip install ollama qdrant-client
```
## Integration Example
The following code assumes Ollama is accessible at port `11434` and Qdrant at port `6334`.
```python
import qdrant_client
from qdrant_client.models import Batch
from ollama import Ollama
from qdrant_client import QdrantClient, models
import ollama
# Initialize Ollama model
model = Ollama("ollama-unique")
COLLECTION_NAME = "NicheApplications"
# Generate embeddings for niche applications
text = "Ollama excels in niche applications with specific embeddings."
embeddings = model.embed(text)
# Initialize Ollama client
oclient = ollama.Client(host="localhost")
# Initialize Qdrant client
qdrant_client = qdrant_client.QdrantClient(host="localhost", port=6333)
qclient = QdrantClient(host="localhost", port=6333)
# Upsert the embedding into Qdrant
qdrant_client.upsert(
collection_name="NicheApplications",
points=Batch(
ids=[1],
vectors=[embeddings],
# Text to embed
text = "Ollama excels in niche applications with specific embeddings"
# Generate embeddings
response = oclient.embeddings(model="llama3.2", prompt=text)
embeddings = response["embedding"]
# Create a collection if it doesn't already exist
if not qclient.collection_exists(COLLECTION_NAME):
qclient.create_collection(
collection_name=COLLECTION_NAME,
vectors_config=models.VectorParams(
size=len(embeddings), distance=models.Distance.COSINE
),
)
# Upload the vectors to the collection along with the original text as payload
qclient.upsert(
collection_name=COLLECTION_NAME,
points=[models.PointStruct(id=1, vector=embeddings, payload={"text": text})],
)
```