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1c223a0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 | from __future__ import annotations
import numpy as np
import pandas as pd
from sklearn.decomposition import PCA
from rag_chunk_visualizer.core.config import settings
from rag_chunk_visualizer.core.logging import get_logger
logger = get_logger(__name__)
class ProjectionError(RuntimeError):
pass
def validate_projection_inputs(matrix: np.ndarray | None, method: str) -> None:
if matrix is None:
raise ProjectionError("No embedding matrix available for projection.")
if matrix.ndim != 2:
raise ProjectionError(f"Expected a 2D embedding matrix, but got shape {matrix.shape}.")
if matrix.shape[0] == 0:
raise ProjectionError("Embedding matrix has no rows.")
if method not in {"pca", "umap"}:
raise ProjectionError(
f"Unsupported projection method '{method}'. Expected 'pca' or 'umap'."
)
def project_single_point() -> tuple[np.ndarray, dict, dict]:
coordinates = np.array([[0.0, 0.0]], dtype=np.float32)
summary = {
"method": "single-point",
"point_count": 1,
"x_label": "X",
"y_label": "Y",
"note": "Only one chunk available, so the point is placed at the origin.",
}
projection_model = {"kind": "single-point"}
return coordinates, summary, projection_model
def fit_pca_projector(
matrix: np.ndarray,
random_state: int,
) -> tuple[np.ndarray, dict, PCA]:
projector = PCA(n_components=2, random_state=random_state)
coordinates = projector.fit_transform(matrix).astype(np.float32)
explained = projector.explained_variance_ratio_.tolist()
summary = {
"method": "pca",
"point_count": int(matrix.shape[0]),
"x_label": "PC1",
"y_label": "PC2",
"explained_variance_ratio_pc1": round(float(explained[0]), 6),
"explained_variance_ratio_pc2": round(float(explained[1]), 6),
}
return coordinates, summary, projector
def fit_umap_projector(
matrix: np.ndarray,
random_state: int,
n_neighbors: int,
min_dist: float,
):
try:
import umap
except ImportError as exc:
raise ProjectionError(
"UMAP is not installed. Install 'umap-learn' or switch to PCA."
) from exc
effective_n_neighbors = min(n_neighbors, max(2, matrix.shape[0] - 1))
reducer = umap.UMAP(
n_components=2,
n_neighbors=effective_n_neighbors,
min_dist=min_dist,
metric="cosine",
random_state=random_state,
)
coordinates = reducer.fit_transform(matrix).astype(np.float32)
summary = {
"method": "umap",
"point_count": int(matrix.shape[0]),
"x_label": "UMAP-1",
"y_label": "UMAP-2",
"umap_n_neighbors": int(effective_n_neighbors),
"umap_min_dist": float(min_dist),
"umap_metric": "cosine",
}
return coordinates, summary, reducer
def fit_projection_model(
matrix: np.ndarray,
method: str | None = None,
random_state: int | None = None,
umap_n_neighbors: int | None = None,
umap_min_dist: float | None = None,
):
resolved_method = method or settings.projection_method
resolved_random_state = (
settings.projection_random_state if random_state is None else random_state
)
resolved_umap_n_neighbors = (
settings.umap_n_neighbors if umap_n_neighbors is None else umap_n_neighbors
)
resolved_umap_min_dist = settings.umap_min_dist if umap_min_dist is None else umap_min_dist
validate_projection_inputs(matrix, resolved_method)
if matrix.shape[0] == 1:
return project_single_point()
if resolved_method == "umap" and matrix.shape[0] < 4:
logger.info("Falling back to PCA because the dataset is too small for UMAP.")
coordinates, summary, projector = fit_pca_projector(
matrix=matrix,
random_state=resolved_random_state,
)
summary["fallback_from"] = "umap"
summary["fallback_reason"] = "too_few_points"
return coordinates, summary, projector
if resolved_method == "pca":
coordinates, summary, projector = fit_pca_projector(
matrix=matrix,
random_state=resolved_random_state,
)
else:
coordinates, summary, projector = fit_umap_projector(
matrix=matrix,
random_state=resolved_random_state,
n_neighbors=resolved_umap_n_neighbors,
min_dist=resolved_umap_min_dist,
)
logger.info(
"Projected embedding matrix | method=%s | points=%s",
summary["method"],
summary["point_count"],
)
return coordinates, summary, projector
def transform_query_vector(
query_vector: np.ndarray,
projection_model,
projection_summary: dict,
) -> dict:
query_vector = np.asarray(query_vector, dtype=np.float32)
if query_vector.ndim != 1:
raise ProjectionError(f"Expected a 1D query vector, but got shape {query_vector.shape}.")
method = projection_summary.get("method")
if method == "single-point":
return {
"x": 0.0,
"y": 0.0,
}
if projection_model is None:
raise ProjectionError("No fitted projection model available for query projection.")
try:
coordinates = projection_model.transform(query_vector.reshape(1, -1))
except Exception as exc:
raise ProjectionError("Failed to project query vector into the existing 2D space.") from exc
coordinates = np.asarray(coordinates, dtype=np.float32)
return {
"x": round(float(coordinates[0, 0]), 8),
"y": round(float(coordinates[0, 1]), 8),
}
def build_projection_dataframe(
chunks: list[dict],
coordinates: np.ndarray,
) -> pd.DataFrame:
if coordinates.ndim != 2 or coordinates.shape[1] != 2:
raise ProjectionError(
f"Expected 2D coordinates with shape (n, 2), got {coordinates.shape}."
)
if len(chunks) != coordinates.shape[0]:
raise ProjectionError("Chunk count does not match projected coordinate count.")
rows = []
for plot_index, (chunk, coord) in enumerate(zip(chunks, coordinates, strict=True)):
rows.append(
{
"plot_index": plot_index,
"chunk_id": chunk["chunk_id"],
"doc_id": chunk["doc_id"],
"filename": chunk["filename"],
"chunk_index": chunk["chunk_index"],
"char_count": chunk["char_count"],
"word_count": chunk["word_count"],
"preview": chunk["preview"],
"text": chunk["text"],
"x": round(float(coord[0]), 8),
"y": round(float(coord[1]), 8),
}
)
return pd.DataFrame(rows)
def project_chunks(
chunks: list[dict],
embedding_matrix: np.ndarray,
method: str | None = None,
):
if not chunks:
raise ProjectionError("No chunks available for projection.")
coordinates, summary, projection_model = fit_projection_model(
matrix=embedding_matrix,
method=method,
)
projection_df = build_projection_dataframe(
chunks=chunks,
coordinates=coordinates,
)
return projection_df, summary, projection_model
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