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* chore(fastembed.md): update image reference in the article

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NirantK
2023-10-10 19:57:13 +05:30
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qdrant-landing/content/articles/fastembed.md
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@@ -93,7 +93,7 @@ FastEmbed is fast because of a lot of small things we've taken care of for you:
1. **Quantized Models**: We quantize the models for CPU (and Mac Metal) – giving you the best buck for your compute model. Our models are so small, you can run this in AWS Lambda if you'd like!
2. **1.5x Throughput**: This is the fastest CPU model which beats OpenAI Embedding model as well. And we do so while being 1.5x faster than the Open Source implementation.
![](/articles_data/fastembed/image1.png "FastEmbed is 1.5x faster than the PyTorch implementation")
![](/articles_data/fastembed/image4.png "FastEmbed is 1.5x faster than the PyTorch implementation")
### Retaining Accuracy and Recall
@@ -155,7 +155,6 @@ documents: List[str] = [
"passage: This is an example passage.",
"fastembed is supported by and maintained by Qdrant."
]
```
In this list called `documents`, we define four text strings that we want to convert into embeddings.
@@ -230,7 +229,6 @@ client = QdrantClient(":memory:") # or QdrantClient(path="path/to/db")
Once the client is initialized, prepare the text documents you wish to embed, along with any associated metadata and unique IDs:
```python
docs = ["Qdrant has Langchain integrations", "Qdrant also has Llama Index integrations"]
metadata = [
@@ -252,24 +250,17 @@ docs = ["Qdrant has Langchain integrations", "Qdrant also has Llama Index integr
With your documents, metadata, and IDs ready, you can proceed to add these to a specified collection within Qdrant using the `add` method:
```python
client.add(
collection_name="demo_collection",
documents=docs,
metadata=metadata,
ids=ids
)
```
Behind the scenes, Qdrant is using FastEmbed to make the text embedding, generate ids if they're missing and then adding them to the index with metadata.
![INDEX TIME: Sequence Diagram for Qdrant and FastEmbed](/articles_data/fastembed/image3.png "Sequence Diagram for Qdrant and FastEmbed")
![INDEX TIME: Sequence Diagram for Qdrant and FastEmbed](/articles_data/fastembed/image2.png "Sequence Diagram for Qdrant and FastEmbed")
### Performing Queries
@@ -277,29 +268,22 @@ Behind the scenes, Qdrant is using FastEmbed to make the text embedding, generat
Finally, you can perform queries on your stored documents. Qdrant offers a robust querying capability, and the query results can be easily retrieved as follows:
```python
search_result = client.query(
collection_name="demo_collection",
query_text="This is a query document"
)
print(search_result)
```
Behind the scenes, we first convert the `query_text` to the embedding and use that to query the vector index.
![QUERY TIME: Sequence Diagram for Qdrant and FastEmbed integration](/articles_data/fastembed/image4.png "Sequence Diagram for Qdrant and FastEmbed integration")
![QUERY TIME: Sequence Diagram for Qdrant and FastEmbed integration](/articles_data/fastembed/image3.png "Sequence Diagram for Qdrant and FastEmbed integration")
By following these steps, you effectively utilize the combined capabilities of FastEmbed and Qdrant, thereby streamlining your embedding generation and retrieval tasks.
Qdrant is designed to handle large-scale datasets with billions of data points. Its architecture employs techniques like binary and scalar quantization for efficient storage and retrieval. When you inject FastEmbed's CPU-first design and lightweight nature into this equation, you end up with a system that can scale
seamlessly while maintaining low latency.
Qdrant is designed to handle large-scale datasets with billions of data points. Its architecture employs techniques like binary and scalar quantization for efficient storage and retrieval. When you inject FastEmbed's CPU-first design and lightweight nature into this equation, you end up with a system that can scale seamlessly while maintaining low latency.
## Open Source Contributions and Support