integrate weaviate kb with company retrieval and performance analysis…

src/hitachione/tools/company_filtering_tool/test.py

@@ -13,6 +13,7 @@
 # Load .env file (project root is 5 levels up from this file)
 load_dotenv(Path(__file__).resolve().parents[4] / ".env")
 
+import os
 from tool import (
     find_relevant_symbols,
     find_relevant_sp500_symbols,
@@ -34,7 +35,8 @@ def test_symbol_extraction():
     print_section("Testing Symbol Extraction from Weaviate")
 
     print("Extracting all unique tickers from Weaviate collection...")
-    print("(Iterates through Hitachi_finance_news collection)\n")
+    collection = os.getenv('WEAVIATE_COLLECTION_NAME', 'hitachi-finance-news')
+    print(f"(Iterates through {collection} collection)\n")
 
     try:
         import time

src/hitachione/tools/company_filtering_tool/tool.py

@@ -1,7 +1,7 @@
 """
 Tool for finding relevant stock symbols from the Weaviate financial news knowledge base.
 
-This tool queries the Weaviate `Hitachi_finance_news` collection to retrieve unique
+This tool queries the Weaviate financial news collection to retrieve unique
 stock tickers and uses an LLM to filter them based on user queries.
 """
 
@@ -28,8 +28,8 @@
 _cached_companies: dict[str, str] | None = None  # ticker -> company name
 _client_manager = None
 
-# Weaviate collection name
-WEAVIATE_COLLECTION = "Hitachi_finance_news"
+# Weaviate collection name (from WEAVIATE_COLLECTION_NAME env var)
+WEAVIATE_COLLECTION = os.getenv("WEAVIATE_COLLECTION_NAME", "Hitachi_finance_news")
 
 
 def get_client_manager() -> AsyncClientManager:
@@ -71,7 +71,7 @@ def get_all_symbols() -> List[str]:
     """
     Get all unique stock tickers from the Weaviate knowledge base.
 
-    Iterates through the Hitachi_finance_news collection and collects
+    Iterates through the Weaviate collection and collects
     unique ticker symbols and their corresponding company names.
 
     Returns:
@@ -254,7 +254,7 @@ def find_relevant_symbols(query: str, use_llm_filter: bool = True) -> List[str]:
         "name": "find_relevant_symbols",
         "description": (
             "Find relevant stock symbols from the Weaviate financial news knowledge base "
-            "(Hitachi_finance_news collection). The tool uses an LLM internally to filter "
+            "The tool uses an LLM internally to filter "
             "symbols based on the query, returning only symbols that match the specified "
             "criteria (sector, industry, time period, ranking, etc.)."
         ),

src/hitachione/tools/performance_analysis_tool/test.py

@@ -0,0 +1,289 @@
+"""
+Test harness for the Performance Analysis Tool (Weaviate-backed).
+
+Run:
+    cd src/hitachione/tools/performance_analysis_tool
+    python3 test.py all          # full suite
+    python3 test.py data         # data retrieval only (no LLM)
+    python3 test.py analyse      # full analysis (requires LLM key)
+    python3 test.py schema       # show tool schema
+    python3 test.py interactive  # interactive ticker input
+"""
+
+import json
+import os
+import sys
+import time
+from pathlib import Path
+
+from dotenv import load_dotenv
+
+# Load .env from project root
+load_dotenv(Path(__file__).resolve().parents[4] / ".env")
+
+from tool import (
+    TOOL_SCHEMA,
+    analyse_stock_performance,
+    get_ticker_data,
+)
+
+# ── Tickers known to exist in the Weaviate collection ──
+KNOWN_TICKERS = ["AAPL", "AMZN", "GOOGL", "JPM", "META", "MSFT", "NVDA", "TSLA", "V", "WMT"]
+UNKNOWN_TICKER = "ZZZZZ"
+
+
+# ──────────────────────────────────────────────────────────────────────────
+# Helpers
+# ──────────────────────────────────────────────────────────────────────────
+
+def _section(title: str) -> None:
+    print("\n" + "=" * 80)
+    print(f"  {title}")
+    print("=" * 80 + "\n")
+
+
+def _pass(msg: str) -> None:
+    print(f"  ✓ {msg}")
+
+
+def _fail(msg: str) -> None:
+    print(f"  ✗ {msg}")
+
+
+# ──────────────────────────────────────────────────────────────────────────
+# Tests
+# ──────────────────────────────────────────────────────────────────────────
+
+def test_tool_schema() -> None:
+    """Validate the tool schema structure."""
+    _section("Tool Schema")
+    print(json.dumps(TOOL_SCHEMA, indent=2))
+
+    assert TOOL_SCHEMA["type"] == "function"
+    fn = TOOL_SCHEMA["function"]
+    assert fn["name"] == "analyse_stock_performance"
+    assert "ticker" in fn["parameters"]["properties"]
+    assert "ticker" in fn["parameters"]["required"]
+    _pass("Schema is valid")
+
+
+def test_data_retrieval_known_ticker() -> None:
+    """Retrieve data for known tickers and verify structure."""
+    _section("Data Retrieval — Known Tickers")
+
+    for ticker in ["AAPL", "TSLA", "JPM"]:
+        t0 = time.time()
+        data = get_ticker_data(ticker)
+        elapsed = time.time() - t0
+
+        assert isinstance(data, dict), f"Expected dict, got {type(data)}"
+        for key in ("price_data", "earnings", "news"):
+            assert key in data, f"Missing key '{key}' for {ticker}"
+
+        total = sum(len(v) for v in data.values())
+        _pass(
+            f"{ticker}: {len(data['price_data'])} price, "
+            f"{len(data['earnings'])} earnings, "
+            f"{len(data['news'])} news  ({elapsed:.2f}s, {total} total)"
+        )
+
+        # At least one data source should have records
+        assert total > 0, f"No data returned for known ticker {ticker}"
+
+
+def test_data_retrieval_unknown_ticker() -> None:
+    """Verify graceful handling of an unknown ticker."""
+    _section("Data Retrieval — Unknown Ticker")
+
+    data = get_ticker_data(UNKNOWN_TICKER)
+    total = sum(len(v) for v in data.values())
+    assert total == 0, f"Expected 0 records for {UNKNOWN_TICKER}, got {total}"
+    _pass(f"{UNKNOWN_TICKER}: 0 records as expected")
+
+
+def test_data_retrieval_case_insensitive() -> None:
+    """Verify ticker is uppercased automatically."""
+    _section("Data Retrieval — Case Insensitivity")
+
+    data_upper = get_ticker_data("AAPL")
+    data_lower = get_ticker_data("aapl")
+
+    assert len(data_upper["price_data"]) == len(data_lower["price_data"]), \
+        "Price data count differs between 'AAPL' and 'aapl'"
+    _pass("'AAPL' and 'aapl' return same results")
+
+
+def test_price_data_fields() -> None:
+    """Verify price records contain expected fields."""
+    _section("Price Data — Field Validation")
+
+    data = get_ticker_data("TSLA")
+    if not data["price_data"]:
+        _fail("No price data for TSLA")
+        return
+
+    expected_fields = {"date", "open", "high", "low", "close"}
+    for i, rec in enumerate(data["price_data"][:3]):
+        present = set(rec.keys()) & expected_fields
+        assert present == expected_fields, (
+            f"Record {i} missing fields: {expected_fields - present}"
+        )
+    _pass(f"First {min(3, len(data['price_data']))} records have all OHLC fields")
+
+
+def test_price_data_sorted() -> None:
+    """Verify price records are sorted by date."""
+    _section("Price Data — Sort Order")
+
+    data = get_ticker_data("GOOGL")
+    dates = [r["date"] for r in data["price_data"] if "date" in r]
+    assert dates == sorted(dates), "Price data is not sorted by date"
+    _pass(f"GOOGL: {len(dates)} price records sorted correctly")
+
+
+def test_analyse_unknown_ticker() -> None:
+    """Full analysis on unknown ticker returns None score."""
+    _section("Full Analysis — Unknown Ticker")
+
+    result = analyse_stock_performance(UNKNOWN_TICKER)
+    assert result["ticker"] == UNKNOWN_TICKER
+    assert result["performance_score"] is None
+    assert result["data_summary"]["price_records"] == 0
+    _pass(f"{UNKNOWN_TICKER}: score=None, outlook={result['outlook']}")
+
+
+def test_analyse_known_ticker() -> None:
+    """Full analysis on a known ticker returns valid structure."""
+    _section("Full Analysis — Known Tickers (LLM)")
+
+    api_key = (
+        os.getenv("OPENAI_API_KEY")
+        or os.getenv("GEMINI_API_KEY")
+        or os.getenv("GOOGLE_API_KEY")
+    )
+    if not api_key:
+        print("  ⚠️  No LLM API key — skipping LLM analysis tests")
+        return
+
+    for ticker in ["AAPL", "NVDA"]:
+        t0 = time.time()
+        result = analyse_stock_performance(ticker)
+        elapsed = time.time() - t0
+
+        assert isinstance(result, dict)
+        assert result["ticker"] == ticker
+        assert isinstance(result["performance_score"], int)
+        assert 1 <= result["performance_score"] <= 10
+        assert result["outlook"] in ("Bullish", "Bearish", "Volatile", "Sideways")
+        assert len(result["justification"]) > 20
+        assert result["data_summary"]["price_records"] > 0
+
+        _pass(
+            f"{ticker}: score={result['performance_score']}, "
+            f"outlook={result['outlook']}, {elapsed:.1f}s"
+        )
+        print(f"    Justification: {result['justification'][:120]}...")
+
+
+def test_analyse_multiple_tickers() -> None:
+    """Analyse several tickers to confirm consistency."""
+    _section("Full Analysis — All Known Tickers (LLM)")
+
+    api_key = (
+        os.getenv("OPENAI_API_KEY")
+        or os.getenv("GEMINI_API_KEY")
+        or os.getenv("GOOGLE_API_KEY")
+    )
+    if not api_key:
+        print("  ⚠️  No LLM API key — skipping")
+        return
+
+    for ticker in KNOWN_TICKERS:
+        result = analyse_stock_performance(ticker)
+        score = result["performance_score"]
+        outlook = result["outlook"]
+        ds = result["data_summary"]
+        _pass(
+            f"{ticker:5s}: score={score:>2}, outlook={outlook:8s}  "
+            f"(price={ds['price_records']}, earn={ds['earnings_records']}, "
+            f"news={ds['news_records']})"
+        )
+
+
+# ──────────────────────────────────────────────────────────────────────────
+# Interactive mode
+# ──────────────────────────────────────────────────────────────────────────
+
+def interactive() -> None:
+    _section("Interactive Mode")
+    print(f"Available tickers: {', '.join(KNOWN_TICKERS)}")
+    print("Enter a ticker (or 'quit' to exit)\n")
+
+    while True:
+        try:
+            ticker = input("Ticker> ").strip()
+            if ticker.lower() in ("quit", "exit", "q"):
+                break
+            if not ticker:
+                continue
+
+            print(f"\nAnalysing {ticker.upper()}...")
+            t0 = time.time()
+            result = analyse_stock_performance(ticker)
+            elapsed = time.time() - t0
+
+            print(json.dumps(result, indent=2))
+            print(f"({elapsed:.1f}s)\n")
+
+        except KeyboardInterrupt:
+            print("\n")
+            break
+        except Exception as e:
+            print(f"  ✗ Error: {e}\n")
+
+
+# ──────────────────────────────────────────────────────────────────────────
+# Main
+# ──────────────────────────────────────────────────────────────────────────
+
+def main() -> None:
+    print("\n" + "=" * 80)
+    print("  Performance Analysis Tool (Weaviate) — Test Harness")
+    print("=" * 80)
+
+    if len(sys.argv) > 1:
+        mode = sys.argv[1].lower()
+    else:
+        print("\nModes:")
+        print("  1. all         — Run all tests")
+        print("  2. data        — Data retrieval tests only (no LLM)")
+        print("  3. analyse     — Full analysis tests (requires LLM)")
+        print("  4. schema      — Display tool schema")
+        print("  5. interactive — Interactive ticker input")
+
+        choice = input("\nSelect (1-5) or press Enter for 'all': ").strip()
+        mode = {"1": "all", "2": "data", "3": "analyse", "4": "schema", "5": "interactive"}.get(choice, "all")
+
+    if mode in ("all", "schema"):
+        test_tool_schema()
+
+    if mode in ("all", "data"):
+        test_data_retrieval_known_ticker()
+        test_data_retrieval_unknown_ticker()
+        test_data_retrieval_case_insensitive()
+        test_price_data_fields()
+        test_price_data_sorted()
+
+    if mode in ("all", "analyse"):
+        test_analyse_unknown_ticker()
+        test_analyse_known_ticker()
+        test_analyse_multiple_tickers()
+
+    if mode == "interactive":
+        interactive()
+
+    _section("Test Harness Complete")
+
+
+if __name__ == "__main__":
+    main()