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docs: Use local inference rapid-rag-optimization-with-qdrant-and-quotient.md (#1601)

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* docs: Use local inference rapid-rag-optimization-with-qdrant-and-quotient.md

Signed-off-by: Anush008 <anushshetty90@gmail.com>

* Update qdrant-landing/content/articles/rapid-rag-optimization-with-qdrant-and-quotient.md

---------

Signed-off-by: Anush008 <anushshetty90@gmail.com>
Co-authored-by: George <george.panchuk@qdrant.tech>
This commit is contained in:
Anush
2025-05-15 19:33:44 +05:30
committed by GitHub
co-authored by George
parent 9aab01a353
commit 9577b88aac
1 changed files with 28 additions and 37 deletions
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qdrant-landing/content/articles/rapid-rag-optimization-with-qdrant-and-quotient.md
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@@ -65,19 +65,10 @@ We will then assess our RAG application’s ability to answer questions about Qd
To prepare our knowledge store we will use Qdrant, which can be leveraged in 3 different ways as below :
```python
##Uncomment to initialise qdrant client in memory
#client = qdrant_client.QdrantClient(
# location=":memory:",
#)
##Uncomment below to connect to Qdrant Cloud
client = qdrant_client.QdrantClient(
os.environ.get("QDRANT_URL"),
api_key=os.environ.get("QDRANT_API_KEY"),
)
## Uncomment below to connect to local Qdrant
#client = qdrant_client.QdrantClient("http://localhost:6333")
```
We will be using [Qdrant Cloud](https://cloud.qdrant.io/login) so it is a good idea to provide the `QDRANT_URL` and `QDRANT_API_KEY` as environment variables for easier access.
@@ -90,26 +81,7 @@ COLLECTION_NAME = "qdrant-docs-quotient"
In this case , we may need to create different collections based on the experiments we conduct.
To help us provide seamless embedding creations throughout the experiment, we will use Qdrant’s native embedding provider [Fastembed](https://qdrant.github.io/fastembed/) which supports [many different models](https://qdrant.github.io/fastembed/examples/Supported_Models/) including dense as well as sparse vector models.
We can initialize and switch the embedding model of our choice as below :
```python
## Declaring the intended Embedding Model with Fastembed
from fastembed.embedding import TextEmbedding
## General Fastembed specific operations
##Initilising embedding model
## Using Default Model - BAAI/bge-small-en-v1.5
embedding_model = TextEmbedding()
## For custom model supported by Fastembed
#embedding_model = TextEmbedding(model_name="BAAI/bge-small-en", max_length=512)
#embedding_model = TextEmbedding(model_name="sentence-transformers/all-MiniLM-L6-v2", max_length=384)
## Verify the chosen Embedding model
embedding_model.model_name
```
To help us provide seamless embedding creations throughout the experiment, we will use Qdrant’s own embeddings library [Fastembed](https://qdrant.github.io/fastembed/) which supports [many different models](https://qdrant.github.io/fastembed/examples/Supported_Models/) including dense as well as sparse vector models.
Before implementing RAG, we need to prepare and index our data in Qdrant.
@@ -134,7 +106,7 @@ len(langchain_docs)
#240
```
You can preview documents in the dataset as below :
You can preview documents in the dataset as below :
```python
## Here's an example of what a document in our dataset looks like
@@ -168,6 +140,10 @@ Following the ingestion of data in Qdrant, we proceed to retrieve pertinent docu
Next we define methods to take care of logistics with respect to adding documents to Qdrant
```python
import uuid
from qdrant_client import models
def add_documents(client, collection_name, chunk_size, chunk_overlap, embedding_model_name):
"""
This function adds documents to the desired Qdrant collection given the specified RAG parameters.
@@ -201,9 +177,23 @@ def add_documents(client, collection_name, chunk_size, chunk_overlap, embedding_
print("content: ", len(docs_contents))
print("metadata: ", len(docs_metadatas))
## Adding documents to Qdrant using desired embedding model
client.set_model(embedding_model_name=embedding_model_name)
client.add(collection_name=collection_name, metadata=docs_metadatas, documents=docs_contents)
if not client.collection_exists(collection_name):
client.create_collection(
collection_name=collection_name,
vectors_config=models.VectorParams(size=384, distance=models.Distance.COSINE),
)
client.upsert(
collection_name=collection_name,
points=[
models.PointStruct(
id=uuid.uuid4().hex,
vector=models.Document(text=content, model=embedding_model_name),
payload={"metadata": metadata, "document": content},
)
for metadata, content in zip(docs_metadatas, docs_contents)
],
)
```
and retrieving documents from Qdrant during our RAG Pipeline assessment.
@@ -214,12 +204,13 @@ def get_documents(collection_name, query, num_documents=3):
This function retrieves the desired number of documents from the Qdrant collection given a query.
It returns a list of the retrieved documents.
"""
search_results = client.query(
search_results = client.query_points(
collection_name=collection_name,
query_text=query,
query=models.Document(text=query, model=embedding_model_name),
limit=num_documents,
)
results = [r.metadata["document"] for r in search_results]
).points
results = [r.payload["document"] for r in search_results]
return results
```