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Add references to DigitalOcean and diagram

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Kacper Łukawski
2024-04-15 11:36:43 +02:00
parent eaf2dc5eef
commit 87b8212bbe
2 changed files with 19 additions and 5 deletions
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qdrant-landing/content/documentation/examples/hybrid-search-llamaindex-jinaai.md
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@@ -19,11 +19,14 @@ We'll cover the essential steps required to build your system, including data in
## Components
- **Embeddings:** Jina Embeddings, served via the [Jina Embeddings API](https://jina.ai/embeddings/#apiform)
- **Database:** [Qdrant Hybrid Cloud](/documentation/hybrid-cloud/), deployed in an environment of your own choice
- **Database:** [Qdrant Hybrid Cloud](/documentation/hybrid-cloud/), deployed in a managed Kubernetes cluster on [DigitalOcean
(DOKS)](https://www.digitalocean.com/products/kubernetes)
- **LLM:** [Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) language model on HuggingFace
- **Framework:** [LlamaIndex](https://www.llamaindex.ai/) for extended RAG functionality and [Hybrid Search support](https://docs.llamaindex.ai/en/stable/examples/vector_stores/qdrant_hybrid/).
- **Parser:** [LlamaParse](https://github.com/run-llama/llama_parse) as a way to parse complex documents with embedded objects such as tables and figures.
![Architecture diagram](/documentation/examples/hybrid-search-llamaindex-jinaai/architecture-diagram.png)
### Procedure
Retrieval Augmented Generation (RAG) combines search with language generation. An external information retrieval system is used to identify documents likely to provide information relevant to the user's query. These documents, along with the user's request, are then passed on to a text-generating language model, producing a natural response.
@@ -31,7 +34,18 @@ Retrieval Augmented Generation (RAG) combines search with language generation. A
This method enables a language model to respond to questions and access information from a much larger set of documents than it could see otherwise. The language model only looks at a few relevant sections of the documents when generating responses, which also helps to reduce inexplicable errors.
## Prerequisites
First, install all dependencies:
### Qdrant cluster
Qdrant Hybrid Cloud is a flexible offer that gives you the effortless experience of a managed solution while keeping the
data on your premises. It might be launched on your Kubernetes cluster, such as DigitalOcean DOKS. A [detailed
description of running Qdrant Hybrid Cloud on DigitalOcean might be found in our
documentation](http://localhost:1313/documentation/hybrid-cloud/platform-deployment-options/#digital-ocean). Once it's
deployed, you should have a running Qdrant cluster with an API key.
### Development environment
Then, install all dependencies:
```python
!pip install -U \
@@ -132,8 +146,8 @@ from llama_index.vector_stores.qdrant import QdrantVectorStore
import qdrant_client
client = qdrant_client.QdrantClient(
url = os.getenv("QDRANT_HOST"),
api_key = os.getenv("QDRANT_API_KEY")
url=os.getenv("QDRANT_HOST"),
api_key=os.getenv("QDRANT_API_KEY")
)
vector_store = QdrantVectorStore(
@@ -214,7 +228,7 @@ print(result.response)
**Answer**
```python
```text
The water temperature is set to 70 ˚C during the Eco Drum Clean cycle. You cannot change the water temperature. However, the temperature for other cycles is not specified in the context.
```