Add TypeScript examples for Documentation & Quickstart (#376)

* Add TS examples for Documentation/Concepts * Minor FAQ improvements: spelling & formatting (#318) * Minor spelling and formatting improvements in FAQ * Add unused RAM is wasted RAM quote, improve memory text * Add TypeScript version of Quickstart (#378) * Minor FAQ improvements: spelling & formatting (#318) * Minor spelling and formatting improvements in FAQ * Add unused RAM is wasted RAM quote, improve memory text * Add TS version of quickstart --------- Co-authored-by: Tim Visée <tim@visee.me> * Add TS examples to Documentation / Guides * Fix terminology * Add TS examples to Documentation / Cloud * Add TS version of bulk upload tutorial * Format tutorials with black * Move author data to [params.author] (#385) * Add TS examples for Documentation/Concepts * Add TypeScript version of Quickstart (#378) * Minor FAQ improvements: spelling & formatting (#318) * Minor spelling and formatting improvements in FAQ * Add unused RAM is wasted RAM quote, improve memory text * Add TS version of quickstart --------- Co-authored-by: Tim Visée <tim@visee.me> * Add TS examples to Documentation / Guides * Fix terminology * Add TS examples to Documentation / Cloud * Add TS version of bulk upload tutorial * Format tutorials with black --------- Co-authored-by: Tim Visée <tim@visee.me>
2026-09-30 16:38:31 +02:00 · 2023-10-31 11:45:05 +01:00
parent c79061a2ef
commit a389d78bd2
24 changed files with 2225 additions and 425 deletions
@@ -61,7 +61,7 @@ from aleph_alpha_client import (
    AsyncClient,
    SemanticEmbeddingRequest,
    SemanticRepresentation,
-    ImagePrompt
+    ImagePrompt,
 )

 from glob import glob
@@ -69,7 +69,7 @@ from glob import glob
 ids, vectors, payloads = [], [], []
 async with AsyncClient(token=aa_token) as client:
    for i, image_path in enumerate(glob("./val2017/*.jpg")):
-        # Convert the JPEG file into the embedding by calling 
+        # Convert the JPEG file into the embedding by calling
        # Aleph Alpha API
        prompt = ImagePrompt.from_file(image_path)
        prompt = Prompt.from_image(prompt)
@@ -79,9 +79,7 @@ async with AsyncClient(token=aa_token) as client:
            "compress_to_size": 128,
        }
        query_request = SemanticEmbeddingRequest(**query_params)
-        query_response = await client.semantic_embed(
-            request=query_request, model=model
-        )
+        query_response = await client.semantic_embed(request=query_request, model=model)

        # Finally store the id, vector and the payload
        ids.append(i)
@@ -99,7 +97,7 @@ from qdrant_client.http.models import Batch, VectorParams, Distance

 qdrant_client = qdrant_client.Qdrant.Client()
 qdrant_client.recreate_collection(
-    collection_name="COCO"
+    collection_name="COCO",
    vector_params=VectorParams(
        size=len(vectors[0]),
        distance=Distance.COSINE,
@@ -135,9 +133,7 @@ async with AsyncCliet(token=aa_token) as client:
        "compress_to_size": 128,
    }
    query_request = SemanticEmbeddingRequest(**query_params)
-    query_response = await client.semantic_embed(
-        request=query_request, model=model
-    )
+    query_response = await client.semantic_embed(request=query_request, model=model)

    results = qdrant.search(
        collection_name="COCO",
@@ -155,7 +151,7 @@ Here are the results:
 and Spanish. Your search is not only multimodal, but also multilingual, without any need for translations.

 ```python
-text= "Surfing"
+text = "Surfing"

 async with AsyncClient(token=aa_token) as client:
    query_params = {
@@ -164,9 +160,7 @@ async with AsyncClient(token=aa_token) as client:
        "compres_to_size": 128,
    }
    query_request = SemanticEmbeddingRequest(**query_params)
-    query_response = await client.semantic_embed(
-        request=query_request, model=model
-    )
+    query_response = await client.semantic_embed(request=query_request, model=model)

    results = qdrant.search(
        collection_name="COCO",
@@ -41,15 +41,16 @@ from qdrant_client import models
 import qdrant_client
 import asyncio

+
 async def main():
    client = qdrant_client.AsyncQdrantClient("localhost")
-    
+
    # Create a collection
    await client.create_collection(
        collection_name="my_collection",
        vectors_config=models.VectorParams(size=4, distance=models.Distance.COSINE),
    )
-    
+
    # Insert a vector
    await client.upsert(
        collection_name="my_collection",
@@ -61,19 +62,20 @@ async def main():
                },
                vector=[0.9, 0.1, 0.1, 0.5],
            ),
-        ]
+        ],
    )
-    
+
    # Search for nearest neighbors
    points = await client.search(
        collection_name="my_collection",
        query_vector=[0.9, 0.1, 0.1, 0.5],
        limit=2,
    )
-    
+
    # Your async code using AsyncQdrantClient might be put here
    # ...

+
 asyncio.run(main())
 ```

@@ -40,7 +40,7 @@ from qdrant_client import QdrantClient, models

 client = QdrantClient("localhost", port=6333)

-client.recreate_collection(
+client.create_collection(
    collection_name="{collection_name}",
    vectors_config=models.VectorParams(size=768, distance=models.Distance.COSINE),
    optimizers_config=models.OptimizersConfigDiff(
@@ -49,6 +49,22 @@ client.recreate_collection(
 )
 ```

+```typescript
+import { QdrantClient } from "@qdrant/js-client-rest";
+
+const client = new QdrantClient({ host: "localhost", port: 6333 });
+
+client.createCollection("{collection_name}", {
+  vectors: {
+    size: 768,
+    distance: "Cosine",
+  },
+  optimizers_config: {
+    indexing_threshold: 0,
+  },
+});
+```
+
 After upload is done, you can enable indexing by setting `indexing_threshold` to a desired value (default is 20000):

 ```http
@@ -68,12 +84,22 @@ client = QdrantClient("localhost", port=6333)

 client.update_collection(
    collection_name="{collection_name}",
-    optimizer_config=models.OptimizersConfigDiff(
-        indexing_threshold=20000
-    )
+    optimizer_config=models.OptimizersConfigDiff(indexing_threshold=20000),
 )
 ```

+```typescript
+import { QdrantClient } from "@qdrant/js-client-rest";
+
+const client = new QdrantClient({ host: "localhost", port: 6333 });
+
+client.updateCollection("{collection_name}", {
+  optimizers_config: {
+    indexing_threshold: 20000,
+  },
+});
+```
+
 ## Upload directly to disk

 When the vectors you upload do not all fit in RAM, you likely want to use
@@ -117,9 +143,23 @@ from qdrant_client import QdrantClient, models

 client = QdrantClient("localhost", port=6333)

-client.recreate_collection(
+client.create_collection(
    collection_name="{collection_name}",
    vectors_config=models.VectorParams(size=768, distance=models.Distance.COSINE),
    shard_number=2,
 )
+```
+
+```typescript
+import { QdrantClient } from "@qdrant/js-client-rest";
+
+const client = new QdrantClient({ host: "localhost", port: 6333 });
+
+client.createCollection("{collection_name}", {
+  vectors: {
+    size: 768,
+    distance: "Cosine",
+  },
+  shard_number: 2,
+});
 ```
@@ -92,12 +92,10 @@ client.recreate_collection(
    collection_name=collection_name,
    vectors_config={
        "image": models.VectorParams(
-            size=image_embeddings.shape[1], 
-            distance=models.Distance.COSINE
+            size=image_embeddings.shape[1], distance=models.Distance.COSINE
        ),
        "text": models.VectorParams(
-            size=text_embeddings.shape[1], 
-            distance=models.Distance.COSINE
+            size=text_embeddings.shape[1], distance=models.Distance.COSINE
        ),
    },
 )
@@ -136,7 +134,6 @@ As a response, we should see a similar output:

 ```python
 name='LAION-5B-1217055918586176-2023-07-04-11-51-24.snapshot' creation_time='2023-07-04T11:51:25' size=74202112
-
 ```

 ## List all snapshots
@@ -150,7 +147,13 @@ print(snapshots)
 This endpoint exposes all the snapshots in the same format as before:

 ```python
-[SnapshotDescription(name='LAION-5B-1217055918586176-2023-07-04-11-51-24.snapshot', creation_time='2023-07-04T11:51:25', size=74202112)]
+[
+    SnapshotDescription(
+        name="LAION-5B-1217055918586176-2023-07-04-11-51-24.snapshot",
+        creation_time="2023-07-04T11:51:25",
+        size=74202112,
+    )
+]
 ```

 We can use the same naming convention to create the URL to download it.
@@ -11,7 +11,7 @@ weight: 2
 This tutorial shows you how to build and deploy your own neural search service to look through descriptions of companies from [startups-list.com](https://www.startups-list.com/) and pick the most similar ones to your query.
 The website contains the company names, descriptions, locations, and a picture for each entry. 

-Alternatvely, you cna use such datasources as [Crunchbase](https://www.crunchbase.com/), but that would require obtaining an API key from them.
+Alternatively, you can use datasources such as [Crunchbase](https://www.crunchbase.com/), but that would require obtaining an API key from them.

 Our neural search service will use [Fastembed](https://github.com/qdrant/fastembed) package to generate embeddings of text descriptions and [FastAPI](https://fastapi.tiangolo.com/) to serve the search API.
 Fastembed natively integrates with Qdrant client, so you can easily upload the data into Qdrant and perform search queries.
@@ -106,7 +106,7 @@ Now you need to write a script to upload all startup data and vectors into the s
 # Import client library
 from qdrant_client import QdrantClient

-qdrant_client = QdrantClient('http://localhost:6333')
+qdrant_client = QdrantClient("http://localhost:6333")
 ```

 3. Select model to encode your data.
@@ -122,7 +122,7 @@ qdrant_client.set_model("sentence-transformers/all-MiniLM-L6-v2")

 ```python
 qdrant_client.recreate_collection(
-    collection_name='startups',
+    collection_name="startups",
    vectors_config=qdrant_client.get_fastembed_vector_params(),
 )
 ```
@@ -137,14 +137,14 @@ Additionally, you can specify extended configuration for our vectors, like `quan
 5. Read data from the file.

 ```python
-payload_path = os.path.join(DATA_DIR, 'startups_demo.json')
+payload_path = os.path.join(DATA_DIR, "startups_demo.json")
 metadata = []
 documents = []

 with open(payload_path) as fd:
    for line in fd:
        obj = json.loads(line)
-        documents.append(obj.pop('description'))
+        documents.append(obj.pop("description"))
        metadata.append(obj)
 ```

@@ -157,10 +157,10 @@ We will use `documents` to encode the data into vectors.

 ```python
 client.add(
-    collection_name='startups',
+    collection_name="startups",
    documents=documents,
    metadata=metadata,
-    parallel=0, # Use all available CPU cores to encode data
+    parallel=0,  # Use all available CPU cores to encode data
 )
 ```

@@ -178,10 +178,10 @@ You can monitor the progress of the encoding by passing tqdm progress bar to the
 from tqdm import tqdm

 client.add(
-    collection_name='startups',
+    collection_name="startups",
    documents=documents,
    metadata=metadata,
-    ids=tqdm(range(len(documents)))
+    ids=tqdm(range(len(documents))),
 )
 ```

@@ -201,19 +201,19 @@ Fastembed integration into qdrant client combines encoding and uploading into a
 ```python
 from qdrant_client import QdrantClient

-class NeuralSearcher:

+class NeuralSearcher:
    def __init__(self, collection_name):
        self.collection_name = collection_name
        # initialize Qdrant client
-        self.qdrant_client = QdrantClient('http://localhost:6333')
-        self.qdrant_client.set_model('sentence-transformers/all-MiniLM-L6-v2')
+        self.qdrant_client = QdrantClient("http://localhost:6333")
+        self.qdrant_client.set_model("sentence-transformers/all-MiniLM-L6-v2")
 ```

 2. Write the search function.

 ```python
-    def search(self, text: str):
+def search(self, text: str):
        search_result = self.qdrant_client.query(
            collection_name=self.collection_name,
            query_text=text,
@@ -255,7 +255,6 @@ from qdrant_client.models import Filter
        limit=5
    )
    ...
-
 ```

 You have now created a class for neural search queries. Now wrap it up into a service.
@@ -279,7 +278,7 @@ Create a file named `service.py` and specify the following.
 The service will have only one API endpoint and will look like this: 

 ```python
- from fastapi import FastAPI
+from fastapi import FastAPI

 # The file where NeuralSearcher is stored
 from neural_searcher import NeuralSearcher
@@ -299,7 +298,6 @@ def search_startup(q: str):
 if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)
-
 ```

 3. Run the service.
@@ -67,20 +67,22 @@ This is a performance-optimized sentence embedding model and you can read more a
 4. Download and create a pre-trained sentence encoder.

 ```python
-model = SentenceTransformer('all-MiniLM-L6-v2', device="cuda") # or device="cpu" if you don't have a GPU
+model = SentenceTransformer(
+    "all-MiniLM-L6-v2", device="cuda"
+)  # or device="cpu" if you don't have a GPU
 ```
 5. Read the raw data file.

 ```python
-df = pd.read_json('./startups_demo.json', lines=True)
+df = pd.read_json("./startups_demo.json", lines=True)
 ```
 6. Encode all startup descriptions to create an embedding vector for each. Internally, the `encode` function will split the input into batches, which will significantly speed up the process.

 ```python
-vectors = model.encode([
-    row.alt + ". " + row.description
-    for row in df.itertuples()
-], show_progress_bar=True)
+vectors = model.encode(
+    [row.alt + ". " + row.description for row in df.itertuples()],
+    show_progress_bar=True,
+)
 ```
 All of the descriptions are now converted into vectors. There are 40474 vectors of 384 dimensions. The output layer of the model has this dimension

@@ -92,7 +94,7 @@ vectors.shape
 7. Download the saved vectors into a new file named `startup_vectors.npy`

 ```python
-np.save('startup_vectors.npy', vectors, allow_pickle=False)
+np.save("startup_vectors.npy", vectors, allow_pickle=False)
 ```

 ## Run Qdrant in Docker
@@ -144,14 +146,14 @@ Now you need to write a script to upload all startup data and vectors into the s
 from qdrant_client import QdrantClient
 from qdrant_client.models import VectorParams, Distance

-qdrant_client = QdrantClient('http://localhost:6333')
+qdrant_client = QdrantClient("http://localhost:6333")
 ```

 3. Related vectors need to be added to a collection. Create a new collection for your startup vectors.

 ```python
 qdrant_client.recreate_collection(
-    collection_name='startups', 
+    collection_name="startups",
    vectors_config=VectorParams(size=384, distance=Distance.COSINE),
 )
 ```
@@ -171,25 +173,25 @@ The Qdrant client library defines a special function that allows you to load dat
 However, since there may be too much data to fit a single computer memory, the function takes an iterator over the data as input.

 ```python
-fd = open('./startups_demo.json')
+fd = open("./startups_demo.json")

 # payload is now an iterator over startup data
 payload = map(json.loads, fd)

 # Load all vectors into memory, numpy array works as iterable for itself.
 # Other option would be to use Mmap, if you don't want to load all data into RAM
-vectors = np.load('./startup_vectors.npy')
+vectors = np.load("./startup_vectors.npy")
 ```

 5. Upload the data

 ```python
 qdrant_client.upload_collection(
-    collection_name='startups',
+    collection_name="startups",
    vectors=vectors,
    payload=payload,
    ids=None,  # Vector ids will be assigned automatically
-    batch_size=256  # How many vectors will be uploaded in a single request?
+    batch_size=256,  # How many vectors will be uploaded in a single request?
 )
 ```

@@ -209,19 +211,18 @@ from sentence_transformers import SentenceTransformer


 class NeuralSearcher:
-
    def __init__(self, collection_name):
        self.collection_name = collection_name
        # Initialize encoder model
-        self.model = SentenceTransformer('all-MiniLM-L6-v2', device='cpu')
+        self.model = SentenceTransformer("all-MiniLM-L6-v2", device="cpu")
        # initialize Qdrant client
-        self.qdrant_client = QdrantClient('http://localhost:6333')
+        self.qdrant_client = QdrantClient("http://localhost:6333")
 ```

 2. Write the search function.

 ```python
-    def search(self, text: str):
+def search(self, text: str):
        # Convert text query into vector
        vector = self.model.encode(text).tolist()

@@ -267,7 +268,6 @@ from qdrant_client.models import Filter
        limit=5
    )
    ...
-
 ```

 You have now created a class for neural search queries. Now wrap it up into a service.
@@ -291,7 +291,7 @@ Create a file named `service.py` and specify the following.
 The service will have only one API endpoint and will look like this: 

 ```python
- from fastapi import FastAPI
+from fastapi import FastAPI

 # The file where NeuralSearcher is stored
 from neural_searcher import NeuralSearcher
@@ -311,7 +311,6 @@ def search_startup(q: str):
 if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)
-
 ```

 3. Run the service.
@@ -42,7 +42,7 @@ from sentence_transformers import SentenceTransformer
 The [Sentence Transformers](https://www.sbert.net/index.html) framework contains many embedding models. However, [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) is the fastest encoder for this tutorial.

 ```python
-encoder = SentenceTransformer('all-MiniLM-L6-v2') 
+encoder = SentenceTransformer("all-MiniLM-L6-v2")
 ```

 ## 2. Add the dataset
@@ -51,19 +51,84 @@ encoder = SentenceTransformer('all-MiniLM-L6-v2')

 ```python
 documents = [
-{ "name": "The Time Machine", "description": "A man travels through time and witnesses the evolution of humanity.", "author": "H.G. Wells", "year": 1895 },
-{ "name": "Ender's Game", "description": "A young boy is trained to become a military leader in a war against an alien race.", "author": "Orson Scott Card", "year": 1985 },
-{ "name": "Brave New World", "description": "A dystopian society where people are genetically engineered and conditioned to conform to a strict social hierarchy.", "author": "Aldous Huxley", "year": 1932 },
-{ "name": "The Hitchhiker's Guide to the Galaxy", "description": "A comedic science fiction series following the misadventures of an unwitting human and his alien friend.", "author": "Douglas Adams", "year": 1979 },
-{ "name": "Dune", "description": "A desert planet is the site of political intrigue and power struggles.", "author": "Frank Herbert", "year": 1965 },
-{ "name": "Foundation", "description": "A mathematician develops a science to predict the future of humanity and works to save civilization from collapse.", "author": "Isaac Asimov", "year": 1951 },
-{ "name": "Snow Crash", "description": "A futuristic world where the internet has evolved into a virtual reality metaverse.", "author": "Neal Stephenson", "year": 1992 },
-{ "name": "Neuromancer", "description": "A hacker is hired to pull off a near-impossible hack and gets pulled into a web of intrigue.", "author": "William Gibson", "year": 1984 },
-{ "name": "The War of the Worlds", "description": "A Martian invasion of Earth throws humanity into chaos.", "author": "H.G. Wells", "year": 1898 },
-{ "name": "The Hunger Games", "description": "A dystopian society where teenagers are forced to fight to the death in a televised spectacle.", "author": "Suzanne Collins", "year": 2008 },
-{ "name": "The Andromeda Strain", "description": "A deadly virus from outer space threatens to wipe out humanity.", "author": "Michael Crichton", "year": 1969 },
-{ "name": "The Left Hand of Darkness", "description": "A human ambassador is sent to a planet where the inhabitants are genderless and can change gender at will.", "author": "Ursula K. Le Guin", "year": 1969 },
-{ "name": "The Three-Body Problem", "description": "Humans encounter an alien civilization that lives in a dying system.", "author": "Liu Cixin", "year": 2008 }
+    {
+        "name": "The Time Machine",
+        "description": "A man travels through time and witnesses the evolution of humanity.",
+        "author": "H.G. Wells",
+        "year": 1895,
+    },
+    {
+        "name": "Ender's Game",
+        "description": "A young boy is trained to become a military leader in a war against an alien race.",
+        "author": "Orson Scott Card",
+        "year": 1985,
+    },
+    {
+        "name": "Brave New World",
+        "description": "A dystopian society where people are genetically engineered and conditioned to conform to a strict social hierarchy.",
+        "author": "Aldous Huxley",
+        "year": 1932,
+    },
+    {
+        "name": "The Hitchhiker's Guide to the Galaxy",
+        "description": "A comedic science fiction series following the misadventures of an unwitting human and his alien friend.",
+        "author": "Douglas Adams",
+        "year": 1979,
+    },
+    {
+        "name": "Dune",
+        "description": "A desert planet is the site of political intrigue and power struggles.",
+        "author": "Frank Herbert",
+        "year": 1965,
+    },
+    {
+        "name": "Foundation",
+        "description": "A mathematician develops a science to predict the future of humanity and works to save civilization from collapse.",
+        "author": "Isaac Asimov",
+        "year": 1951,
+    },
+    {
+        "name": "Snow Crash",
+        "description": "A futuristic world where the internet has evolved into a virtual reality metaverse.",
+        "author": "Neal Stephenson",
+        "year": 1992,
+    },
+    {
+        "name": "Neuromancer",
+        "description": "A hacker is hired to pull off a near-impossible hack and gets pulled into a web of intrigue.",
+        "author": "William Gibson",
+        "year": 1984,
+    },
+    {
+        "name": "The War of the Worlds",
+        "description": "A Martian invasion of Earth throws humanity into chaos.",
+        "author": "H.G. Wells",
+        "year": 1898,
+    },
+    {
+        "name": "The Hunger Games",
+        "description": "A dystopian society where teenagers are forced to fight to the death in a televised spectacle.",
+        "author": "Suzanne Collins",
+        "year": 2008,
+    },
+    {
+        "name": "The Andromeda Strain",
+        "description": "A deadly virus from outer space threatens to wipe out humanity.",
+        "author": "Michael Crichton",
+        "year": 1969,
+    },
+    {
+        "name": "The Left Hand of Darkness",
+        "description": "A human ambassador is sent to a planet where the inhabitants are genderless and can change gender at will.",
+        "author": "Ursula K. Le Guin",
+        "year": 1969,
+    },
+    {
+        "name": "The Three-Body Problem",
+        "description": "Humans encounter an alien civilization that lives in a dying system.",
+        "author": "Liu Cixin",
+        "year": 2008,
+    },
 ]
 ```

@@ -72,7 +137,7 @@ documents = [
 You need to tell Qdrant where to store embeddings. This is a basic demo, so your local computer will use its memory as temporary storage.

 ```python
-qdrant = QdrantClient(":memory:") 
+qdrant = QdrantClient(":memory:")
 ```

 ## 4. Create a collection
@@ -81,11 +146,11 @@ All data in Qdrant is organized by collections. In this case, you are storing bo

 ```python
 qdrant.recreate_collection(
-	collection_name="my_books",
-	vectors_config=models.VectorParams(
-		size=encoder.get_sentence_embedding_dimension(), # Vector size is defined by used model
-		distance=models.Distance.COSINE
-	)
+    collection_name="my_books",
+    vectors_config=models.VectorParams(
+        size=encoder.get_sentence_embedding_dimension(),  # Vector size is defined by used model
+        distance=models.Distance.COSINE,
+    ),
 )
 ```

@@ -102,14 +167,13 @@ Tell the database to upload `documents` to the `my_books` collection. This will

 ```python
 qdrant.upload_records(
-	collection_name="my_books",
-	records=[
-		models.Record(
-			id=idx,
-			vector=encoder.encode(doc["description"]).tolist(),
-			payload=doc
-		) for idx, doc in enumerate(documents)
-	]
+    collection_name="my_books",
+    records=[
+        models.Record(
+            id=idx, vector=encoder.encode(doc["description"]).tolist(), payload=doc
+        )
+        for idx, doc in enumerate(documents)
+    ],
 )
 ```

@@ -119,12 +183,12 @@ Now that the data is stored in Qdrant, you can ask it questions and receive sema

 ```python
 hits = qdrant.search(
-	collection_name="my_books",
-	query_vector=encoder.encode("alien invasion").tolist(),
-	limit=3
+    collection_name="my_books",
+    query_vector=encoder.encode("alien invasion").tolist(),
+    limit=3,
 )
 for hit in hits:
-	print(hit.payload, "score:", hit.score)
+    print(hit.payload, "score:", hit.score)
 ```

 **Response:**
@@ -143,22 +207,15 @@ How about the most recent book from the early 2000s?

 ```python
 hits = qdrant.search(
-	collection_name="my_books",
-	query_vector=encoder.encode("alien invasion").tolist(),
-	query_filter=models.Filter(
-		must=[
-			models.FieldCondition(
-				key="year",
-				range=models.Range(
-					gte=2000
-				)
-			)
-		]
-	),
-	limit=1
+    collection_name="my_books",
+    query_vector=encoder.encode("alien invasion").tolist(),
+    query_filter=models.Filter(
+        must=[models.FieldCondition(key="year", range=models.Range(gte=2000))]
+    ),
+    limit=1,
 )
 for hit in hits:
-	print(hit.payload, "score:", hit.score)
+    print(hit.payload, "score:", hit.score)
 ```

 **Response:**