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Cosine Similarity to Retrieve Top K Chunks in Python
Compute cosine similarity between a query vector and a list of chunk vectors, then return the indices and scores of the top k most similar chunks.
import numpy as np
from numpy.linalg import norm
def cosine_similarity(vec1, vec2):
return np.dot(vec1, vec2) / (norm(vec1) * norm(vec2))
def retrieve_top_k(query_vec, chunk_vectors, k=3):
similarities = [cosine_similarity(query_vec, vec) for vec in chunk_vectors]
top_indices = sorted(range(len(similarit…How to Batch Embed a List of Strings in Python
Batch embed a list of strings into deterministic pseudo-random vectors using a mock encoder class.
class MockEncoder:
def __init__(self, dim=8, seed=42):
self.dim = dim
self.seed = seed
def embed(self, text):
# Deterministic pseudo-random embedding based on text content
hash_val = hash(text)
import random
rng = random.Random(hash_val + self.seed)
retu…How to Build an In-Memory Vector Store in Python
Build a lightweight in-memory vector store using a Python dict and cosine similarity for fast nearest-neighbor searches.
import math
from typing import Dict, List, Optional
class InMemoryVectorStore:
def __init__(self) -> None:
self.vectors: Dict[str, List[float]] = {}
self.index: Dict[str, List[str]] = {} # query -> list of ids sorted by similarity
def add(self, vector_id: str, vector: List[float]) -> None:
…How to Create a Mock Text Embedding with Hash in Python
Generate deterministic mock text embeddings using SHA-256 hashing and numpy, producing normalized vectors for similarity testing without an LLM.
import hashlib
import numpy as np
def mock_embed(text: str, dim: int = 10, seed: int = 42) -> np.ndarray:
"""Generate a deterministic mock embedding using a hash function.
Args:
text: Input text to embed
dim: Dimension of the output vector
seed: Seed for reproducibility
R…How to Expand Related Resources with a Mock Embed in Python
Simulate API response embedding by attaching mock embedded data to each related resource in a list using a simple Python class.
import json
class EmbedMock:
def __init__(self, resources):
self.resources = resources
def expand(self):
for resource in self.resources:
resource["embedded"] = self._generate_embed()
def _generate_embed(self):
return {
"id": 1,
"type": "mock",
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Samples vs tutorials and challenges
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