d77fe12cfc
Source layer (5 court sources), processing pipeline (parse/extract/chunk/embed/graph), property graph with 8 node types, juror subgraph queries with 6 personas, orchestrator with bootstrap/watch/backfill/audit/process modes, 170 tests. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
78 lines
1.8 KiB
TOML
78 lines
1.8 KiB
TOML
# AuCourtIngest configuration
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# Copy to config.local.toml and fill in API keys — do not commit secrets.
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user_agent = "AuCourtIngest/0.1 (legal research; contact: TODO@TODO.com)"
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[sources.fedcourt]
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base_url = "https://www.judgments.fedcourt.gov.au"
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fetch_strategy = "rss_poll_then_docx_download"
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rss_feed = "https://www.judgments.fedcourt.gov.au/rss/fca-judgments"
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doc_formats = ["html", "docx", "pdf"]
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coverage_from = 1977
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[sources.fedcourt.rate_limit]
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rps = 1.0
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concurrent = 2
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retry_after = 60
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[sources.highcourt]
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base_url = "https://www.hcourt.gov.au"
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fetch_strategy = "index_crawl"
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doc_formats = ["html", "pdf"]
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coverage_from = 1994
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[sources.highcourt.rate_limit]
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rps = 0.5
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concurrent = 1
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retry_after = 120
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[sources.nsw_caselaw]
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base_url = "https://www.caselaw.nsw.gov.au"
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browse_url = "https://www.caselaw.nsw.gov.au/browse"
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fetch_strategy = "browse_pagination"
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doc_formats = ["html", "pdf"]
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coverage_from = 1988
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[sources.nsw_caselaw.rate_limit]
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rps = 1.0
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concurrent = 2
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retry_after = 60
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[sources.qld_judgments]
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base_url = "https://www.queenslandjudgments.com.au"
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fetch_strategy = "search_pagination"
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doc_formats = ["html", "pdf"]
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[sources.qld_judgments.rate_limit]
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rps = 0.5
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concurrent = 1
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retry_after = 120
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[sources.auslaw_mcp]
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base_url = "mcp://auslaw"
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fetch_strategy = "mcp_server"
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doc_formats = ["html", "pdf"]
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[sources.auslaw_mcp.rate_limit]
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rps = 0.3
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concurrent = 1
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retry_after = 180
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[storage]
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data_dir = "data"
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neo4j_uri = "bolt://localhost:7687"
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neo4j_user = "neo4j"
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neo4j_password = "password"
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neo4j_database = "au_legal"
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[llm]
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anthropic_api_key = "" # set via ANTHROPIC_API_KEY env or here
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openai_api_key = "" # set via OPENAI_API_KEY env or here
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extraction_model = "claude-haiku-4-5-20251001"
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embedding_model = "text-embedding-3-small"
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embedding_batch_size = 100
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[telegram]
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bot_token = ""
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chat_id = ""
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enabled = false
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