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| from pathlib import Path | |
| from pydantic import Field | |
| from pydantic_settings import BaseSettings, SettingsConfigDict | |
| class Settings(BaseSettings): | |
| model_config = SettingsConfigDict( | |
| env_file=Path(__file__).resolve().parents[1] / ".env", extra="ignore" | |
| ) | |
| elastic_url: str = "http://localhost:9200" | |
| elastic_api_key: str = "" | |
| elastic_index: str = "studies" | |
| elastic_local: bool = False | |
| openai_api_key: str = "" | |
| openalex_snapshot_manifest: str = ( | |
| "https://openalex.s3.amazonaws.com/data/parquet/works/manifest.json" | |
| ) | |
| hf_token: str = "" | |
| embedding_model: str = "sentence-transformers/all-MiniLM-L6-v2" | |
| embedding_dimensions: int = 384 | |
| embedding_device: str = "cpu" | |
| embeddings_enabled: bool = True | |
| reference_min_similarity: float = Field(default=0.55, ge=0, le=1) | |
| classifier_model_path: str = "" | |
| small_model: str = "gpt-4.1-mini" | |
| narration_model: str = "gpt-4.1" | |
| extraction_limit: int = Field(default=100, ge=0, le=200) | |
| llm_concurrency: int = Field(default=5, ge=1, le=20) | |
| # USD per million tokens: configurable assumptions, never claimed as a bill. | |
| small_input_price: float = 0.4 | |
| small_cached_price: float = 0.1 | |
| small_output_price: float = 1.6 | |
| narration_input_price: float = 2.0 | |
| narration_cached_price: float = 0.5 | |
| narration_output_price: float = 8.0 | |
| ttc_api_key: str = "" | |
| ttc_model: str = "bear-2" | |
| compression_enabled: bool = False | |
| compression_validated: bool = False | |
| ttc_input_price: float = 0.0 | |
| request_timeout: float = 45.0 | |
| extraction_cache_version: str = "v4-result-direction" | |
| # Query-time full text from Europe PMC for papers with a PMCID; abstract otherwise. | |
| fulltext_enabled: bool = True | |
| europepmc_url: str = "https://www.ebi.ac.uk/europepmc/webservices/rest" | |
| fulltext_timeout: float = 20.0 | |
| fulltext_max_lines: int = Field(default=160, ge=20, le=600) | |
| # Gap map: the studies nearest the question, fetched by kNN and clustered per | |
| # question. A few hundred nodes is what a reader can take in and what a force | |
| # layout can settle in a second; the full-index scan below only runs when | |
| # there is no question to center on. | |
| gapmap_neighborhood: int = Field(default=800, ge=50, le=5000) | |
| # Whole-index build (no question): every embedded study, scanned in pages. | |
| # The region count is a resolution, and what counts as an empty band depends on | |
| # it: measured on 6000 documents of the live index, 24 regions leave the corpus | |
| # continuous (no band under the occupancy threshold) while 80 keep a median of | |
| # 64 primary attempts per region and still expose bands. | |
| gapmap_regions: int = Field(default=80, ge=2, le=200) | |
| gapmap_seed: int = 0 | |
| # A cap on how much of the embedded corpus one build reads; 0 is no cap, and | |
| # the coverage line and the warning always report what was actually read. | |
| # Measured on the live index (2,078,515 embedded studies, 384 dimensions): | |
| # the scan runs at ~11k documents/s and the vectors weigh ~3.0 GiB, so an | |
| # uncapped build is ~3 minutes and needs a host with several GB to spare. | |
| gapmap_scan_limit: int = Field(default=0, ge=0) | |
| # Page size and concurrent point-in-time slices for that scan. Sixteen slices | |
| # measured ~11k docs/s against ~3.2k for a single unsliced walk. | |
| gapmap_batch: int = Field(default=2000, ge=100, le=10000) | |
| gapmap_slices: int = Field(default=16, ge=1, le=64) | |
| # How many of the clustered studies the canvas draws. Every study shapes the | |
| # regions; a browser cannot paint two million marks, so the drawn subset is | |
| # thinned by a hash of the document id. | |
| gapmap_points: int = Field(default=6000, ge=100, le=50000) | |
| # Edges the canvas draws between drawn studies: each study joins its most | |
| # similar few, and only when the cosine reaches the floor. The cap is what | |
| # keeps a dense literature from becoming one hub; the floor is what leaves an | |
| # isolated paper isolated instead of tethered to its nearest stranger. | |
| gapmap_edge_neighbors: int = Field(default=3, ge=1, le=12) | |
| gapmap_edge_floor: float = Field(default=0.8, ge=-1.0, le=1.0) | |
| # Rows the 2-D projection is fitted on. The basis is a drawing choice, and a | |
| # full-corpus SVD would stall the build for minutes to move points by pixels. | |
| # Nothing can be drawn before it exists, so it is fitted on the first pages | |
| # rather than after a long silent read. | |
| gapmap_projection_sample: int = Field(default=1000, ge=1000, le=500000) | |
| # Bound on the full-corpus k-means. Measured at 200k documents and 80 | |
| # regions: 10 iterations ~3.2 s, 40 ~7.7 s; the assignment usually settles | |
| # first and the loop stops when it does. | |
| gapmap_iterations: int = Field(default=25, ge=1, le=200) | |
| # Minimum seconds between two streamed frames while the corpus is scanning. | |
| gapmap_tick_seconds: float = Field(default=0.15, ge=0.0, le=10.0) | |
| max_concurrent_searches: int = 4 | |
| frontend_dist: str = str(Path(__file__).resolve().parents[2] / "dist") | |
| cors_origins: list[str] = ["http://localhost:5173"] | |
| settings = Settings() | |