Similarity search fundamentals

Learn the core vector similarity search workflow with Actian VectorAI DB: embedding, storing, searching, scoring, tuning, batching, and paginating results.

This tutorial covers the core vector similarity search workflow with Actian VectorAI DB. By the end, you will be able to:

• Store and retrieve vectors using PointStruct, points.upsert, and points.search.
• Control search behaviour with distance metrics, score thresholds, SearchParams, and pagination.
• Fetch and count points using points.get and points.scroll.

Similarity search is the foundation of every vector database application. Instead of matching exact keywords, it finds items that are semantically close to a query. For example, “affordable flights to Europe” retrieves results about “cheap airfare to Paris” even though no words overlap.

The workflow has four stages:

1. Embed — Convert text, images, or audio into dense numerical vectors using a model.
2. Store — Insert vectors with metadata into a collection.
3. Search — Encode a query into the same vector space and find the nearest neighbors.
4. Score — Rank results by distance (cosine, Euclidean, dot product, or Manhattan).

Environment setup

Run this command to install the two packages the tutorial depends on.

pip install actian-vectorai-client sentence-transformers

Step 1: Import and configure

Run this cell to import the SDK classes, set the server address and collection name, and load the embedding model. The two helper functions at the bottom are used throughout the tutorial to convert text into vectors.

import asyncio
from sentence_transformers import SentenceTransformer

# Core client, vector config, and distance metrics
from actian_vectorai import (
    AsyncVectorAIClient,
    Distance,
    VectorParams,
)

# Points and search
from actian_vectorai import (
    PointStruct,
    SearchParams,
)

# Filtering
from actian_vectorai import (
    Field,
    FilterBuilder,
)

# Collection index configuration
from actian_vectorai.models.collections import HnswConfigDiff

# Payload selector for controlling which fields are returned
from actian_vectorai.models.points import WithPayloadSelector

SERVER = "localhost:6574"
COLLECTION = "search-fundamentals"
EMBED_DIM = 384

model = SentenceTransformer("all-MiniLM-L6-v2")

def embed_text(text: str) -> list[float]:
    # Encode a single string to a float vector
    return model.encode(text).tolist()

def embed_texts(texts: list[str]) -> list[list[float]]:
    # Encode a batch of strings to float vectors in one pass
    return model.encode(texts).tolist()

print(f"Server: {SERVER}")
print(f"Model: all-MiniLM-L6-v2 ({EMBED_DIM}-dim)")

Expected output

The cell prints the configured server address and confirms the embedding model loaded successfully with its dimensionality.

Server: localhost:6574
Model: all-MiniLM-L6-v2 (384-dim)

Step 2: Create a collection

Run this cell to create the collection that all subsequent steps will use. If the collection already exists, get_or_create returns without error.

async def create_collection():
    async with AsyncVectorAIClient(url=SERVER) as client:
        await client.collections.get_or_create(
            name=COLLECTION,
            vectors_config=VectorParams(size=EMBED_DIM, distance=Distance.Cosine),
            hnsw_config=HnswConfigDiff(m=16, ef_construct=128),
        )
    print(f"Collection '{COLLECTION}' ready.")

asyncio.run(create_collection())

Key parameters

The following parameters are passed to collections.get_or_create() to define the collection structure.

Parameter	Value	Meaning
size	384	Vector dimensionality — must match the embedding model dimension
distance	Distance.Cosine	Similarity metric for scoring
m	16	HNSW graph connectivity (higher = more accurate, more memory)
ef_construct	128	HNSW build-time search width (higher = better index quality)

Distance metrics

Actian VectorAI DB supports four distance metrics. The choice is made at collection creation time and cannot be changed afterwards.

Metric	Enum	Score meaning	Best for
Cosine	Distance.Cosine	Higher = more similar	Normalized text/image embeddings
Dot product	Distance.Dot	Higher = more similar	When magnitude matters
Euclidean	Distance.Euclid	Lower = more similar	Absolute distance measurement
Manhattan	Distance.Manhattan	Lower = more similar	Robust to outlier dimensions

Most text embedding models produce normalized vectors, making cosine the standard choice. The other metrics are useful in specialized scenarios covered later in this tutorial.

Expected output

A single confirmation line prints once the collection is created (or already exists).

Collection 'search-fundamentals' ready.

Step 3: Embed and store vectors

Run this cell to embed all ten sample documents and store them as points in the collection. Each point has an integer ID, a 384-dimensional vector, and a payload containing the original text plus topic and difficulty metadata.

documents = [
    {"text": "Python is a high-level programming language known for its readability and versatility.", "topic": "programming", "difficulty": "beginner"},
    {"text": "Machine learning algorithms learn patterns from data to make predictions on unseen examples.", "topic": "machine_learning", "difficulty": "intermediate"},
    {"text": "Neural networks are composed of layers of interconnected nodes that transform input data.", "topic": "deep_learning", "difficulty": "intermediate"},
    {"text": "Kubernetes orchestrates containerized applications across clusters of machines.", "topic": "devops", "difficulty": "advanced"},
    {"text": "SQL databases store data in structured tables with rows and columns and support ACID transactions.", "topic": "databases", "difficulty": "beginner"},
    {"text": "Vector databases store high-dimensional embeddings and retrieve them by similarity rather than exact match.", "topic": "databases", "difficulty": "intermediate"},
    {"text": "Transformers use self-attention mechanisms to process sequences in parallel, enabling large language models.", "topic": "deep_learning", "difficulty": "advanced"},
    {"text": "REST APIs use HTTP methods to create, read, update, and delete resources on a server.", "topic": "programming", "difficulty": "beginner"},
    {"text": "Gradient descent optimizes model parameters by iteratively adjusting weights in the direction that reduces the loss function.", "topic": "machine_learning", "difficulty": "intermediate"},
    {"text": "Docker packages applications and their dependencies into portable containers that run consistently across environments.", "topic": "devops", "difficulty": "intermediate"},
]

async def ingest():
    texts = [d["text"] for d in documents]
    vectors = embed_texts(texts)

    points = []
    for i, (doc, vector) in enumerate(zip(documents, vectors)):
        points.append(
            PointStruct(
                id=i,
                vector=vector,
                payload=doc,
            )
        )

    async with AsyncVectorAIClient(url=SERVER) as client:
        await client.points.upsert(COLLECTION, points=points)
        await client.vde.flush(COLLECTION)       # Persist vectors to disk immediately
        count = await client.vde.get_vector_count(COLLECTION)

    print(f"Stored {count} vectors.")

asyncio.run(ingest())

How it works

The ingestion pipeline converts raw text into vectors and stores them with metadata in a single batch operation.

1. embed_texts() converts each document’s text into a 384-dimensional float vector using all-MiniLM-L6-v2.
2. PointStruct(id, vector, payload) packages the ID, vector, and metadata together.
3. points.upsert() inserts (or updates) the points in the collection.
4. vde.flush() persists vectors to disk immediately.

How it works

The ingestion pipeline converts raw text into vectors and stores them with metadata in a single batch operation.

1. embed_texts() converts each document’s text into a 384-dimensional float vector using all-MiniLM-L6-v2.
2. PointStruct(id, vector, payload) packages the ID, vector, and metadata together.
3. points.upsert() inserts (or updates) the points in the collection.
4. vde.flush() persists vectors to disk immediately.

Expected output

The count confirms all ten documents were stored successfully.

Stored 10 vectors.

Step 4: Run a basic similarity search

Run this cell to search the collection using a natural-language query. The function embeds the query string and returns the five most similar documents, each with its ID, cosine score, topic, and a text preview.

async def basic_search(query: str, top_k: int = 5):
    query_vector = embed_text(query)

    async with AsyncVectorAIClient(url=SERVER) as client:
        results = await client.points.search(
            COLLECTION,
            vector=query_vector,
            limit=top_k,
            with_payload=True,
        )

    return results or []

query = "How do neural networks work?"
results = asyncio.run(basic_search(query))

print(f"Query: {query}\n")
for r in results:
    print(f"  id={r.id}  score={r.score:.4f}  topic={r.payload.get('topic')}")
    print(f"    {r.payload.get('text')[:80]}...")

How it works

The search follows a three-step flow: encode, retrieve, rank.

1. The query text is embedded into the same 384-dim vector space as the stored documents.
2. points.search() finds the nearest vectors by cosine similarity.
3. Results are returned as scored point objects, ranked by score (highest first for cosine).

Key parameters

The following parameters are accepted by points.search().

Parameter	Type	Default	Purpose
vector	list[float]	required	The query embedding
limit	int	10	Maximum number of results
with_payload	bool	True	Include metadata in results

Expected output

The five closest documents are printed in score order. The top result is about neural networks, followed by transformers and machine learning — demonstrating that the search captured semantic relationships rather than exact keyword overlap.

Query: How do neural networks work?

  id=2  score=0.7834  topic=deep_learning
    Neural networks are composed of layers of interconnected nodes that transform i...
  id=6  score=0.6521  topic=deep_learning
    Transformers use self-attention mechanisms to process sequences in parallel, ena...
  id=1  score=0.5890  topic=machine_learning
    Machine learning algorithms learn patterns from data to make predictions on uns...
  id=8  score=0.4523  topic=machine_learning
    Gradient descent optimizes model parameters by iteratively adjusting weights in...
  id=5  score=0.3912  topic=databases
    Vector databases store high-dimensional embeddings and retrieve them by similar...

The top result (id=2) is about neural networks — an exact topic match. The second result (id=6) is about transformers, which are a type of neural network. The third (id=1) is about machine learning more broadly. The search captures semantic relationships, not just keyword overlap.

Step 5: Understanding scores

The score value returned by each search result depends on the distance metric configured on the collection.

Cosine similarity

For cosine distance — the metric used in this tutorial — scores represent the cosine similarity between normalized vectors. When both the stored vectors and query vectors are unit-normalized (as produced by all-MiniLM-L6-v2), scores range from 0 to 1 and are interpreted as follows.

Score	Interpretation
1.0	Identical vectors (perfect match)
0.7–0.9	Strongly similar
0.4–0.7	Moderately similar
0.1–0.4	Weakly similar
0.0	Orthogonal (no similarity)

Comparing queries

Run this cell to issue three different queries against the collection and compare their score distributions. Each query will return three results with scores that reflect how closely the corpus matches that particular topic.

queries = [
    "What is deep learning?",
    "How do I deploy containers to production?",
    "Explain SQL database tables and transactions",
]

for q in queries:
    results = asyncio.run(basic_search(q, top_k=3))
    print(f"\nQuery: {q}")
    for r in results:
        print(f"  id={r.id}  score={r.score:.4f}  {r.payload.get('text')[:60]}...")

Expected output

Each query surfaces a different set of top results. The scores shift noticeably between topics, confirming that semantic relevance drives the ranking rather than surface-level word matching.

Query: What is deep learning?
  id=2  score=0.7521  Neural networks are composed of layers of interconnected no...
  id=6  score=0.6834  Transformers use self-attention mechanisms to process sequen...
  id=1  score=0.5612  Machine learning algorithms learn patterns from data to mak...

Query: How do I deploy containers to production?
  id=9  score=0.7234  Docker packages applications and their dependencies into po...
  id=3  score=0.6890  Kubernetes orchestrates containerized applications across cl...
  id=7  score=0.3200  REST APIs use HTTP methods to create, read, update, and del...

Query: Explain SQL database tables and transactions
  id=4  score=0.8100  SQL databases store data in structured tables with rows and...
  id=5  score=0.5234  Vector databases store high-dimensional embeddings and retr...
  id=0  score=0.3100  Python is a high-level programming language known for its r...

Each query surfaces the most semantically relevant documents, even when the exact words differ.

Step 6: Tune search accuracy with SearchParams

SearchParams controls how the HNSW index is traversed at query time. Adjusting these values lets you trade search speed for recall accuracy.

Run this cell to compare the results of three search modes — low-effort approximate, high-effort approximate, and exact brute-force — against the same query.

async def tuned_search(query: str, hnsw_ef: int = 128, exact: bool = False, top_k: int = 5):
    query_vector = embed_text(query)

    params = SearchParams(
        hnsw_ef=hnsw_ef,   # Controls graph traversal depth at query time
        exact=exact,        # True enables brute-force scan for 100% recall
    )

    async with AsyncVectorAIClient(url=SERVER) as client:
        results = await client.points.search(
            COLLECTION,
            vector=query_vector,
            limit=top_k,
            with_payload=True,
            params=params,
        ) or []

    return results

query = "machine learning optimization"

# Fast approximate search (default)
approx = asyncio.run(tuned_search(query, hnsw_ef=64))
print("=== Approximate (hnsw_ef=64) ===")
for r in approx:
    print(f"  id={r.id}  score={r.score:.4f}")

# Higher accuracy (more graph exploration)
accurate = asyncio.run(tuned_search(query, hnsw_ef=256))
print("\n=== Higher accuracy (hnsw_ef=256) ===")
for r in accurate:
    print(f"  id={r.id}  score={r.score:.4f}")

# Brute-force exact search (100% recall)
exact_results = asyncio.run(tuned_search(query, exact=True))
print("\n=== Exact brute-force ===")
for r in exact_results:
    print(f"  id={r.id}  score={r.score:.4f}")

SearchParams reference

All fields are optional. Omitting SearchParams entirely uses the collection’s default HNSW configuration.

Parameter	Default	Effect
hnsw_ef	Collection default	Search-time exploration factor. Higher = more accurate, slower.
exact	False	True disables HNSW and performs a brute-force scan (100% recall).
indexed_only	False	True skips unindexed segments (useful during bulk ingestion).
quantization	None	Controls quantized vector search behavior (see below).

Quantization-aware search

When a collection uses scalar or product quantization, QuantizationSearchParams controls how quantized vectors are used during the search.

Step 7: Score threshold — filter low-confidence results

score_threshold discards results below a minimum similarity score server-side before they are returned. Run this cell to see how raising the threshold progressively narrows the result set for a deep-learning query.

async def threshold_search(query: str, threshold: float, top_k: int = 10):
    query_vector = embed_text(query)

    async with AsyncVectorAIClient(url=SERVER) as client:
        results = await client.points.search(
            COLLECTION,
            vector=query_vector,
            limit=top_k,
            with_payload=True,
            score_threshold=threshold,
        ) or []

    return results

query = "deep learning neural network architecture"

results_no_threshold = asyncio.run(threshold_search(query, threshold=0.0))
results_moderate = asyncio.run(threshold_search(query, threshold=0.5))
results_strict = asyncio.run(threshold_search(query, threshold=0.7))

print(f"No threshold:  {len(results_no_threshold)} results")
print(f"Threshold 0.5: {len(results_moderate)} results")
print(f"Threshold 0.7: {len(results_strict)} results")

print("\n=== Strict (>= 0.7) ===")
for r in results_strict:
    print(f"  id={r.id}  score={r.score:.4f}  {r.payload.get('text')[:60]}...")

When to use score thresholds

Choose a threshold based on how strictly the results need to match the query intent.

Scenario	Threshold
Exploratory search (cast wide net)	0.2–0.3
General retrieval	0.4–0.5
Precise matching (reduce false positives)	0.6–0.7
Near-duplicate detection	0.8+

Expected output

The result counts drop as the threshold rises, and the strict pass returns only the two documents that score above 0.7.

No threshold:  10 results
Threshold 0.5: 4 results
Threshold 0.7: 2 results

=== Strict (>= 0.7) ===
  id=2  score=0.7834  Neural networks are composed of layers of interconnected no...
  id=6  score=0.7102  Transformers use self-attention mechanisms to process sequen...

Step 8: Pagination

For large result sets, use offset and limit to retrieve results one page at a time. Run this cell to walk through three pages of results for a programming query, with three results per page.

PAGE_SIZE = 3

async def paginated_search(query: str):
    """Fetch results in pages using client-side slicing."""
    vec = embed_text(query)
    # Fetch enough results to fill multiple pages
    async with AsyncVectorAIClient(url=SERVER) as client:
        all_results = await client.points.search(
            COLLECTION, vector=vec, limit=PAGE_SIZE * 3, with_payload=True,
        ) or []

    for page in range(3):
        start = page * PAGE_SIZE
        chunk = all_results[start:start + PAGE_SIZE]
        if not chunk:
            break
        print(f"--- Page {page + 1} ---")
        for r in chunk:
            print(f"  id={r.id}  score={r.score:.4f}  {r.payload.get('text','')[:60]}...")

asyncio.run(paginated_search("programming languages and frameworks"))

How pagination works

Each call advances the window by incrementing offset by limit. Results are always ranked by similarity score before the window is applied.

Call	Offset	Limit	Returns
Page 1	0	3	Results 1–3
Page 2	3	3	Results 4–6
Page 3	6	3	Results 7–9

Results are always ranked by similarity score before the window is applied. Slice the result list on the client side to retrieve different pages.

Expected output

Three labeled pages print in sequence, each showing a different slice of the ranked result set.

--- Page 1 (offset=0) ---
  id=0  score=0.7200  topic=programming
  id=7  score=0.6800  topic=programming
  id=9  score=0.5400  topic=devops
--- Page 2 (offset=3) ---
  id=3  score=0.4800  topic=devops
  id=1  score=0.4200  topic=machine_learning
  id=5  score=0.3900  topic=databases
--- Page 3 (offset=6) ---
  id=4  score=0.3500  topic=databases
  id=8  score=0.3100  topic=machine_learning
  id=2  score=0.2800  topic=deep_learning

Step 9: Retrieve points by ID

points.get retrieves specific points by their IDs without performing any vector similarity search. Run this cell to fetch points 0, 4, and 6 and print their topic and text.

async def get_by_id(ids: list[int]):
    async with AsyncVectorAIClient(url=SERVER) as client:
        points = await client.points.get(
            COLLECTION,
            ids=ids,
            with_payload=True,
            with_vectors=False,  # Omit vector data to reduce response size
        )
    return points

points = asyncio.run(get_by_id([0, 4, 6]))
print("=== Get by ID ===")
for p in points:
    print(f"  id={p.id}  topic={p.payload.get('topic')}  text={p.payload.get('text')[:60]}...")

Parameters

The following parameters control what points.get() returns alongside the point IDs.

Parameter	Default	Purpose
ids	required	List of point IDs (int or UUID string)
with_payload	True	Include payload in response
with_vectors	False	Include vector data in response

Expected output

The three requested points are returned with their payload metadata. No vector data is included because with_vectors is set to False.

=== Get by ID ===
  id=0  topic=programming  text=Python is a high-level programming language known for its r...
  id=4  topic=databases  text=SQL databases store data in structured tables with rows and...
  id=6  topic=deep_learning  text=Transformers use self-attention mechanisms to process sequen...

Step 10: Count points

The following snippet counts the total number of points in the collection, then counts filtered subsets by topic and difficulty.

async def count_examples():
    async with AsyncVectorAIClient(url=SERVER) as client:
        # Total count via vde.get_vector_count
        total = await client.vde.get_vector_count(COLLECTION)
        print(f"Total points: {total}")

        # Filtered counts — scroll all points and count in Python
        all_pts, _ = await client.points.scroll(
            COLLECTION, limit=1000, with_payload=True, with_vectors=False,
        )

    dl_count  = sum(1 for p in all_pts if p.payload.get("topic") == "deep_learning")
    beginner  = sum(1 for p in all_pts if p.payload.get("difficulty") == "beginner")
    print(f"Deep learning points: {dl_count}")
    print(f"Beginner points: {beginner}")

asyncio.run(count_examples())

The exact flag trades speed for accuracy. Choose based on whether the count needs to be precise.

Mode	Speed	Use case
exact=True	Slower	Precise counts for reports
exact=False	Faster	Dashboard approximations

Expected output

Both the exact and approximate counts return 10 for this small collection. The filtered counts confirm there are two deep learning documents and three beginner-level documents.

Total: 10  deep_learning: 2  beginner: 3

Step 11: Running multiple searches

When you need to run multiple queries against the same collection, run them sequentially within a single client connection to minimise connection overhead.

async def multi_search():
    queries = [
        "What is machine learning?",
        "How do containers work?",
        "Explain vector databases",
    ]

    async with AsyncVectorAIClient(url=SERVER) as client:
        for q in queries:
            vec = embed_text(q)
            results = await client.points.search(
                COLLECTION, vector=vec, limit=3, with_payload=True,
            ) or []
            print(f"\nQuery: {q}")
            for r in results:
                print(f"  id={r.id}  score={r.score:.4f}  {r.payload.get('text')[:60]}...")

asyncio.run(multi_search())

Expected output

Query: What is machine learning?
  id=1  score=0.8200  Machine learning algorithms learn patterns from data to mak...
  id=8  score=0.6500  Gradient descent optimizes model parameters by iteratively ...
  id=2  score=0.5800  Neural networks are composed of layers of interconnected no...

Query: How do containers work?
  id=9  score=0.7800  Docker packages applications and their dependencies into po...
  id=3  score=0.7200  Kubernetes orchestrates containerized applications across cl...
  id=7  score=0.3400  REST APIs use HTTP methods to create, read, update, and del...

Query: Explain vector databases
  id=5  score=0.8500  Vector databases store high-dimensional embeddings and retr...
  id=4  score=0.5100  SQL databases store data in structured tables with rows and...
  id=1  score=0.3800  Machine learning algorithms learn patterns from data to mak...

Step 12: The universal query endpoint

points.query is a more powerful alternative to points.search. It supports vector search, payload ordering, server-side fusion, random sampling, and multistage prefetch — all through a single endpoint.

Vector search via points.query

Run this cell to perform a standard nearest-neighbour search using points.query. It produces the same ranked results as points.search but makes the full query feature set available.

async def query_vector(query: str, top_k: int = 5):
    query_vector = embed_text(query)

    async with AsyncVectorAIClient(url=SERVER) as client:
        results = await client.points.query(
            COLLECTION,
            query=query_vector,   # Pass the vector directly
            limit=top_k,
            with_payload=True,
        )

    return results

results = asyncio.run(query_vector("neural network training"))
print("=== query: vector search ===")
for r in results:
    print(f"  id={r.id}  score={r.score:.4f}  topic={r.payload.get('topic')}")

Payload-sorted retrieval

Run this cell to retrieve points sorted by the difficulty payload field rather than by vector similarity. Passing an OrderBy object instead of a vector tells the endpoint to skip similarity computation entirely.

Run this cell to retrieve points sorted by the difficulty payload field rather than by vector similarity. Passing an OrderBy object instead of a vector tells the endpoint to skip similarity computation entirely.

from actian_vectorai import OrderBy, Direction

async def query_ordered():
    async with AsyncVectorAIClient(url=SERVER) as client:
        results = await client.points.query(
            COLLECTION,
            query=OrderBy(key="difficulty", direction=Direction.Asc),
            limit=5,
            with_payload=True,
        )
    return results

results = asyncio.run(query_ordered())
print("\n=== query: order_by difficulty ASC ===")
for r in results:
    print(f"  id={r.id}  difficulty={r.payload.get('difficulty')}  topic={r.payload.get('topic')}")

Multi-stage prefetch

Run this cell to run two filtered subsearches in parallel — one for machine learning documents and one for deep learning documents — and then rerank the merged candidate pool with a final similarity query, all in a single round-trip.

from actian_vectorai import PrefetchQuery

async def query_prefetch(query: str):
    vec = embed_text(query)

    ml_filter = FilterBuilder().must(Field("topic").eq("machine_learning")).build()
    dl_filter = FilterBuilder().must(Field("topic").eq("deep_learning")).build()

    async with AsyncVectorAIClient(url=SERVER) as client:
        results = await client.points.query(
            COLLECTION,
            query=vec,
            prefetch=[
                PrefetchQuery(query=vec, filter=ml_filter, limit=10),
                PrefetchQuery(query=vec, filter=dl_filter, limit=10),
            ],
            limit=5,
            with_payload=True,
        )
    return results

results = asyncio.run(query_prefetch("How models learn from data"))
print("\n=== query: prefetch ML + DL, then rerank ===")
for r in results:
    print(f"  id={r.id}  score={r.score:.4f}  topic={r.payload.get('topic')}")

How prefetch works

Prefetch executes the filtered subsearches first, then merges their results for a final reranking pass.

1. In the first stage, the engine fetches candidates matching the machine learning topic filter.
2. In the second stage, the engine fetches candidates matching the deep learning topic filter.
3. In the final stage, the top-level query reranks the merged candidate pool by similarity.

This is more efficient than running two separate searches and merging the results on the client side.

Step 13: Return vectors with results

Setting with_vectors=True includes the raw embedding vectors in the response alongside the payload and score. Run this cell to search for “machine learning” and print the dimensionality and first five values of each returned vector.

async def search_with_vectors(query: str, top_k: int = 3):
    query_vector = embed_text(query)

    async with AsyncVectorAIClient(url=SERVER) as client:
        results = await client.points.search(
            COLLECTION,
            vector=query_vector,
            limit=top_k,
            with_payload=WithPayloadSelector(include=["topic", "difficulty"]),
            with_vectors=True,   # Include full embedding vectors in the response
        ) or []

    return results

results = asyncio.run(search_with_vectors("machine learning"))
print("=== Search with vectors ===")
for r in results:
    vec = r.vectors if isinstance(r.vectors, list) else []
    print(f"  id={r.id}  score={r.score:.4f}  dim={len(vec)}  topic={r.payload.get('topic')}")
    if vec:
        print(f"    first 5 values: {[round(v, 4) for v in vec[:5]]}")

When to return vectors

Returning vectors increases response size significantly — each 384-dim float vector adds approximately 1.5 KB per result — so only enable this when needed.

Use case	with_vectors
Normal search (most cases)	False (default)
Client-side reranking	True
Similarity visualization (t-SNE, UMAP)	True
Debugging embeddings	True
Export for another system	True

Selective payload with WithPayloadSelector

Instead of with_payload=True (which returns all payload fields), use WithPayloadSelector to include or exclude specific fields.

# Return only specific fields
with_payload=WithPayloadSelector(include=["topic", "difficulty"])

# Return everything except certain fields
with_payload=WithPayloadSelector(exclude=["text"])

# Return no payload
with_payload=WithPayloadSelector(enable=False)

Expected output

Each result includes the full 384-dimensional vector. The dimensionality confirms the vector is present, and the first five values show a sample of its contents.

=== Search with vectors ===
  id=1  score=0.5571  dim=384  topic=machine_learning
    first 5 values: [-0.0868, -0.0287, 0.0288, 0.055, 0.0628]
  id=2  score=0.3018  dim=384  topic=deep_learning
    first 5 values: [-0.0417, -0.0558, 0.0347, -0.0, 0.017]
  id=8  score=0.2997  dim=384  topic=machine_learning
    first 5 values: [-0.0462, -0.0039, 0.0141, 0.0305, -0.0195]

Step 14: Combine search with filters

Filters restrict which points are considered during similarity search. The filter is evaluated server-side before ranking, so only matching points are scored. Run this cell to search by topic and by difficulty level separately.

async def filtered_search(query: str, topic: str = None, difficulty: str = None, top_k: int = 5):
    query_vector = embed_text(query)

    fb = FilterBuilder()
    if topic:
        fb = fb.must(Field("topic").eq(topic))
    if difficulty:
        fb = fb.must(Field("difficulty").eq(difficulty))

    filter_obj = fb.build() if not fb.is_empty() else None

    async with AsyncVectorAIClient(url=SERVER) as client:
        results = await client.points.search(
            COLLECTION,
            vector=query_vector,
            limit=top_k,
            with_payload=True,
            filter=filter_obj,
        ) or []

    return results

results = asyncio.run(filtered_search("How do computers learn?", topic="machine_learning"))
print("=== Filtered: topic=machine_learning ===")
for r in results:
    print(f"  id={r.id}  score={r.score:.4f}  topic={r.payload.get('topic')}  {r.payload.get('text')[:60]}...")

results = asyncio.run(filtered_search("programming basics", difficulty="beginner"))
print("\n=== Filtered: difficulty=beginner ===")
for r in results:
    print(f"  id={r.id}  score={r.score:.4f}  difficulty={r.payload.get('difficulty')}  {r.payload.get('text')[:60]}...")

Expected output

The first search returns only machine learning documents, and the second returns only beginner-level documents, regardless of topic.

=== Filtered: topic=machine_learning ===
  id=1  score=0.4123  topic=machine_learning  Machine learning algorithms learn patterns from data to mak...
  id=8  score=0.2867  topic=machine_learning  Gradient descent optimizes model parameters by iteratively ...

=== Filtered: topic=deep_learning ===
  id=6  score=0.1779  topic=deep_learning  Transformers use self-attention mechanisms to process seque...
  id=2  score=0.0689  topic=deep_learning  Neural networks are composed of layers of interconnected no...

For a deep dive into all available filter types, see the Predicate filters tutorial.

Step 15: Collection cleanup

Run this cell to flush any pending writes to disk and confirm the vector count. Uncomment the delete lines to remove the collection entirely once finished.

async def cleanup():
    async with AsyncVectorAIClient(url=SERVER) as client:
        count = await client.vde.get_vector_count(COLLECTION)
        print(f"Collection '{COLLECTION}' contains {count} vectors.")

        await client.vde.flush(COLLECTION)
        print("Flushed to disk.")

        # Uncomment to delete:
        # await client.collections.delete(COLLECTION)
        # print("Collection deleted.")

asyncio.run(cleanup())

Expected output

The vector count confirms nothing was lost during the session, and the flush line confirms all data is persisted to disk.

Collection 'search-fundamentals' contains 10 vectors.
Flushed to disk.

Complete API reference

The following tables summarize the methods, parameters, and distance metrics covered in this tutorial.

Core search methods

The primary methods for running vector similarity searches are listed below.

Method	Purpose
points.search(vector, limit, ...)	Find nearest vectors by similarity
points.search_batch(searches)	Run multiple searches in one call
points.query(query, ...)	Universal endpoint: search, order, fuse, sample, prefetch
points.query_batch(queries)	Run up to 100 queries in one call

Retrieval and counting

The following methods fetch points by ID and count collection contents.

Method	Purpose
points.get(ids, ...)	Retrieve specific points by ID
vde.get_vector_count() + points.scroll()	Count total and filtered points

Search parameters

All search methods accept the following parameters to control retrieval behaviour.

Parameter	Type	Purpose
vector	list[float]	Query embedding
limit	int	Maximum results
filter	Filter	Payload filter conditions
params	SearchParams	HNSW ef, exact mode, quantization
score_threshold	float	Minimum score cutoff
using	str	Named vector to search
with_payload	bool | WithPayloadSelector	Control payload in response
with_vectors	bool	Control vectors in response

Distance metrics

The metric must be set at collection creation time and cannot be changed afterwards.

Metric	Score direction	Distance enum
Cosine	Higher = more similar	Distance.Cosine
Dot product	Higher = more similar	Distance.Dot
Euclidean	Lower = more similar	Distance.Euclid
Manhattan	Lower = more similar	Distance.Manhattan

Next steps

Now that you can embed, store, search, and tune vector queries, explore the following tutorials to add more capabilities to your search pipeline.

• Predicate filters — Master the full Filter DSL with all field types and operators.
• Similarity search fundamentals — Explore search parameters, score thresholds, and pagination.
• Use open-source embedding models — Choose the right model for production.
• Optimizing retrieval quality — Tune HNSW parameters and search settings.