Vector Search

Provides patterns for vector-based similarity search.

Embedding Generation

import requests
from typing import List

def get_embeddings(texts: List[str], api_url: str, api_key: str) -> List[List[float]]:
    """
    Generate embeddings for texts using embedding API.

    Returns list of embedding vectors.
    """
    headers = {
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json"
    }

    payload = {
        "model": "text-embedding-ada-002",
        "input": texts
    }

    response = requests.post(api_url, headers=headers, json=payload)
    response.raise_for_status()

    data = response.json()
    return [item["embedding"] for item in data["data"]]

Similarity Calculation

import math
from typing import List

def cosine_similarity(vec1: List[float], vec2: List[float]) -> float:
    """Calculate cosine similarity between two vectors."""
    dot_product = sum(a * b for a, b in zip(vec1, vec2))
    norm1 = math.sqrt(sum(a * a for a in vec1))
    norm2 = math.sqrt(sum(b * b for b in vec2))
    return dot_product / (norm1 * norm2) if norm1 and norm2 else 0

def find_similar(query_vec: List[float], vectors: List[List[float]],
                 top_k: int = 10) -> List[tuple]:
    """Find most similar vectors to query."""
    similarities = [
        (i, cosine_similarity(query_vec, vec))
        for i, vec in enumerate(vectors)
    ]
    return sorted(similarities, key=lambda x: x[1], reverse=True)[:top_k]

Note

Vector search is for advanced semantic similarity use cases. For basic aggregation tasks involving search APIs, use the search-api and content-aggregation skills instead.