diff --git a/.gitignore b/.gitignore index 6e63f5b..1f0ef69 100644 --- a/.gitignore +++ b/.gitignore @@ -35,3 +35,13 @@ __pycache__/ # Private keys / Secrets deploy_key + +# Scratch files +agent/scratch/* +fix_agent_langfuse.py +fix_langfuse.py + +# Playwright +playwright-report/ +test-results/ +junit.xml diff --git a/agent/.coverage b/agent/.coverage new file mode 100644 index 0000000..626dc57 Binary files /dev/null and b/agent/.coverage differ diff --git a/agent/Dockerfile b/agent/Dockerfile index a228aad..ff657d3 100644 --- a/agent/Dockerfile +++ b/agent/Dockerfile @@ -23,4 +23,4 @@ RUN --mount=type=secret,id=deploy_key,target=/root/.ssh/id_rsa,mode=0600 \ # Add virtualenv bin to PATH to run uvicorn ENV PATH="/app/agent/.venv/bin:$PATH" -CMD ["uvicorn", "agent.main:app", "--host", "0.0.0.0", "--port", "8001"] +CMD ["uvicorn", "agent.main:app", "--host", "0.0.0.0", "--port", "8001"] \ No newline at end of file diff --git a/agent/pyproject.toml b/agent/pyproject.toml index 404f352..b88351c 100644 --- a/agent/pyproject.toml +++ b/agent/pyproject.toml @@ -18,6 +18,7 @@ dependencies = [ "langchain-openai", "greenlet>=3.5.1", "mcp>=1.12.4", + "networkx>=3.3", ] [tool.uv.sources] @@ -31,4 +32,25 @@ build-backend = "uv_build" [dependency-groups] dev = [ "pytest>=9.0.3", + "pytest-asyncio>=1.4.0", + "pytest-cov>=7.1.0", + "pytest-mock>=3.15.1", + "ruff>=0.3.0", ] + +[tool.ruff] +target-version = "py312" + +[tool.ruff.lint] +select = ["TID"] + +[tool.ruff.lint.flake8-tidy-imports.banned-api] +"langchain_openai.ChatOpenAI" = { msg = "Use get_llm from agent.llm instead of instantiating ChatOpenAI directly." } +"esca_sdk.EscaClient" = { msg = "Use get_esca_client from agent.utils.esca instead of instantiating EscaClient directly." } + +[tool.ruff.lint.per-file-ignores] +"src/agent/llm.py" = ["TID251"] +"src/agent/utils/esca.py" = ["TID251"] + +[tool.pytest.ini_options] +testpaths = ["tests"] diff --git a/agent/src/agent/config.py b/agent/src/agent/config.py index ac0cecd..da24556 100644 --- a/agent/src/agent/config.py +++ b/agent/src/agent/config.py @@ -1,5 +1,9 @@ from pydantic import Field from pydantic_settings import BaseSettings, SettingsConfigDict +from typing import Literal + +# Reload trigger comment (timeout added) + class AgentSettings(BaseSettings): model_config = SettingsConfigDict(env_file=".env", extra="ignore") @@ -14,19 +18,57 @@ class AgentSettings(BaseSettings): EMBEDDER_KEY: str = "" HYBRID_SEARCH_MAX_TABLES: int = 10 MAX_PROFILES_TO_FETCH: int = 3 + PROFILE_FETCH_CONCURRENCY: int = Field(default=4, gt=0) + REDIS_URL: str = "redis://localhost:6379" LANGFUSE_SECRET_KEY: str = Field(min_length=1) LANGFUSE_PUBLIC_KEY: str = Field(min_length=1) LANGFUSE_BASE_URL: str = Field(min_length=1) - + + # ── Jeen Integration ────────────────────────────────────────────────────── + JEEN_LLM_CORE_URL: str = "" # If empty, agent gracefully skips fetching + JEEN_API_KEY: str = "" # If empty, agent gracefully skips fetching + SKILLS_HOT_RELOAD: bool = False # If true, bypass Redis cache for skills + + # ── G4: Feature Flags & Execution Modes ────────────────────────────────── + BACKEND_URL: str = "" # Studio backend URL for flag reads (e.g. http://backend:8000) + # If empty, FlagBridge falls back to env-var defaults + + # Langfuse prompt names LANGFUSE_PROMPT_EXTRACTOR: str = "text2sql/extractor" LANGFUSE_PROMPT_SCHEMA_EXPLORER: str = "text2sql/schema_explorer" LANGFUSE_PROMPT_QUERY_BUILDER: str = "text2sql/query_builder" LANGFUSE_PROMPT_REFINER: str = "text2sql/refiner" LANGFUSE_PROMPT_FINALIZER_SUMMARY: str = "text2sql/finalizer_summary" - LANGFUSE_PROMPT_FINALIZER_SQL_EXPLANATION: str = "text2sql/finalizer_sql_explanation" + LANGFUSE_PROMPT_FINALIZER_SQL_EXPLANATION: str = ( + "text2sql/finalizer_sql_explanation" + ) LANGFUSE_PROMPT_REJECTION_ROUTER: str = "text2sql/rejection_router" + # ── G2-01: Table Scoping ────────────────────────────────────────────────── + TABLE_SCOPING_MODE: Literal["strict", "hybrid"] = "hybrid" + + # ── G2-03: Advanced Schema Explorer phases ──────────────────────────────── + ENABLE_SEMANTIC_TYPING: bool = True # single batched LLM call — adds id/timestamp/category labels + ENABLE_JOIN_GRAPH: bool = False + ENABLE_SCHEMA_SUMMARIZATION: bool = False # generated once at profile-time, not at runtime + ENABLE_AMBIGUITY_DETECT: bool = True + + # ── G2-04: Satisfaction Check ───────────────────────────────────────────── + SATISFACTION_CHECK_ENABLED: bool = True + SATISFACTION_CHECK_EXECUTION: bool = True + SATISFACTION_CHECK_PLAUSIBILITY: bool = True + SATISFACTION_CHECK_COLUMNS: bool = True + SATISFACTION_CHECK_SEMANTIC: bool = False # LLM-heavy, off by default + SATISFACTION_MIN_ROWS: int = 1 + SATISFACTION_MAX_ROWS: int = 50_000 + SATISFACTION_SEMANTIC_THRESHOLD: float = 0.75 + SATISFACTION_MAX_FAILURES: int = 2 # escalate to HITL after this many check failures + + # ── G2-05: Redis Schema Cache ───────────────────────────────────────────── + SCHEMA_CACHE_TTL: int = 600 # seconds — DDL content + PROFILE_CACHE_TTL: int = 1800 # seconds — table profile statistics + settings = AgentSettings() diff --git a/agent/src/agent/graph.py b/agent/src/agent/graph.py index bd2c0fd..4d1afe6 100644 --- a/agent/src/agent/graph.py +++ b/agent/src/agent/graph.py @@ -1,34 +1,142 @@ +""" +LangGraph agent graph — Group 2 hardened topology. + +Node order: + START → validate_config → extractor → schema_explorer → ... + (G2-01 fail-fast) + +HITL escalation compiles with interrupt_before=["hitl_escalation"] so +LangGraph pauses before executing that node. After a human injects +corrected state the graph resumes from hitl_escalation which immediately +routes to extractor (full state reset path). + +Satisfaction check sits between refiner success path and finalizer (G2-04). +""" + from agent.nodes.refiner import MAX_REFINER_ITERATIONS from langgraph.graph import StateGraph, START, END from langgraph.checkpoint.memory import MemorySaver -from langchain_openai import ChatOpenAI + +from langchain_core.runnables.config import RunnableConfig from langchain_core.prompts import ChatPromptTemplate from agent.state import AgentState +from agent.utils.redis_publisher import publish_node_event_sync from agent.nodes.extractor import extractor_node -from agent.nodes.schema_explorer import schema_explorer_node +from agent.nodes.init_flags import init_flags_node +from agent.nodes.init_skills import init_skills_node +from agent.nodes.schema_explorer import schema_explorer_node, MAX_SCHEMA_RETRIES from agent.nodes.query_builder import query_builder_node from agent.nodes.refiner import refiner_node from agent.nodes.finalizer import finalizer_node +from agent.nodes.satisfaction_check import satisfaction_check_node from agent.config import settings from agent.langfuse_client import langfuse_client from pydantic import BaseModel, Field from typing import Literal +from agent.llm import get_llm + # Initialize LLM for rejection routing classification -llm = ChatOpenAI(model=settings.LLM_MODEL, base_url=settings.LLM_BASE_URL, api_key=settings.LLM_API_KEY, temperature=0) +llm = get_llm("routing") + + +# ── G2-01: Custom exception ─────────────────────────────────────────────────── + + +class InvalidConfigurationException(ValueError): + """Raised when agent state contains an invalid or unsafe configuration.""" + + +# ── G2-01: Config validator node ────────────────────────────────────────────── + + +def validate_config_node(state: AgentState, config: RunnableConfig | None = None) -> dict: + """ + First node after START. Resolves scoping_mode from state (or falls back + to the env default) and enforces strict-mode preconditions. + + Raises: + InvalidConfigurationException: if scoping_mode='strict' and + allowed_tables is null or empty. + """ + thread_id = config.get("configurable", {}).get("thread_id", "") if config else "" + publish_node_event_sync(thread_id, "validate_config") + + mode: str = state.get("scoping_mode") or settings.TABLE_SCOPING_MODE + + if mode == "strict": + allowed = state.get("allowed_tables") + if not allowed: + raise InvalidConfigurationException( + "scoping_mode='strict' requires allowed_tables to be a non-empty list. " + "Execution aborted to prevent unrestricted table access." + ) + + return {"scoping_mode": mode, "execution_path": ["validate_config"]} + + +# ── G2-02: HITL escalation node ─────────────────────────────────────────────── + + +def hitl_escalation_node(state: AgentState, config: RunnableConfig | None = None) -> dict: + """ + Execution pauses HERE via LangGraph interrupt_before before this node runs. + The human then calls graph.update_state() to inject corrected state and + clears sql_query / last_error / trino_error / escalated / escalation_reason. + After update_state the graph resumes from this node, which immediately + routes to extractor via its direct edge. + + This node body only performs observability work — it does NOT call interrupt() + itself (interrupt_before handles the pause at compile time). + """ + thread_id = config.get("configurable", {}).get("thread_id", "") if config else "" + publish_node_event_sync(thread_id, "hitl_escalation") + + reason = state.get("escalation_reason", "Maximum retries exhausted.") + + try: + trace_id = langfuse_client.get_current_trace_id() + if trace_id: + langfuse_client._create_trace_tags_via_ingestion( + trace_id=trace_id, tags=["escalated=True"] + ) + langfuse_client.update_current_span( + metadata={"escalation_reason": reason}, + ) + except Exception: + pass + + return {"escalated": True, "execution_path": ["hitl_escalation"]} + + +# ── Rejection router ────────────────────────────────────────────────────────── + class RejectionRoute(BaseModel): route: Literal["extractor", "schema_explorer", "query_builder"] = Field( description="The phase to route the execution back to based on the user feedback." ) -def rejection_router_node(state: AgentState): + +def rejection_router_node(state: AgentState, config: RunnableConfig | None = None): """Classify user rejection feedback and select the appropriate phase to return to.""" + thread_id = config.get("configurable", {}).get("thread_id", "") if config else "" + publish_node_event_sync(thread_id, "rejection_router") + feedback = state.get("feedback") + category = state.get("rejection_category") + + if category == "Wrong Tables": + return {"feedback_route": "schema_explorer"} + elif category == "Wrong Logic": + return {"feedback_route": "query_builder"} + if not feedback: return {"feedback_route": "query_builder"} - - langfuse_prompt = langfuse_client.get_prompt(settings.LANGFUSE_PROMPT_REJECTION_ROUTER) + + langfuse_prompt = langfuse_client.get_prompt( + settings.LANGFUSE_PROMPT_REJECTION_ROUTER + ) prompt = ChatPromptTemplate.from_messages(langfuse_prompt.get_langchain_prompt()) structured_llm = llm.with_structured_output(RejectionRoute, method="json_schema") @@ -39,45 +147,143 @@ def rejection_router_node(state: AgentState): except Exception as e: print(f"Structured output parsing failed: {e}") route = "extractor" # Fallback - - return {"feedback_route": route} + + return { + "feedback_route": route, + "sql_query": "", + "schema_plan": "", + "raw_data_ref": None, + "trino_error": None, + "execution_path": ["rejection_router"] + } + + +# ── Conditional edge functions ──────────────────────────────────────────────── + + +def route_schema_explorer(state: AgentState) -> str: + """G2-02: route to hitl_escalation after MAX_SCHEMA_RETRIES.""" + if state.get("hallucinated_tables"): + if (state.get("schema_explorer_retry_count") or 0) >= MAX_SCHEMA_RETRIES: + return "hitl_escalation" + return "schema_explorer" + return "query_builder" -def route_refiner(state: AgentState): - if state.get("trino_error") and state.get("refinement_count", 0) < MAX_REFINER_ITERATIONS: - return "refiner" - return "finalizer" +def route_refiner(state: AgentState) -> str: + """G2-02: route to hitl_escalation when refiner limit is hit.""" + if state.get("trino_error"): + if state.get("refinement_count", 0) < MAX_REFINER_ITERATIONS: + return "satisfaction_check" # run check even on error path so Check A can flag it + return "hitl_escalation" + return "satisfaction_check" -def route_query_builder(state: AgentState): + +def route_satisfaction(state: AgentState) -> str: + """ + G2-04: route based on satisfaction check outcome. + - no module / no failures → finalizer + - failures, under MAX → refiner + - failures, over MAX → hitl_escalation + """ + failures = state.get("satisfaction_failures") + if not failures: + return "finalizer" + + fail_count = state.get("satisfaction_fail_count") or 0 + if fail_count >= settings.SATISFACTION_MAX_FAILURES: + return "hitl_escalation" + return "refiner" + + +def route_query_builder(state: AgentState) -> str: if state.get("feedback"): return "rejection_router" return "refiner" -def route_rejection(state: AgentState): + +def route_rejection(state: AgentState) -> str: route = state.get("feedback_route") if route in ["extractor", "schema_explorer", "query_builder"]: return route return "extractor" + +# ── Build graph ─────────────────────────────────────────────────────────────── + workflow = StateGraph(AgentState) +workflow.add_node("validate_config", validate_config_node) +workflow.add_node("init_flags", init_flags_node) +workflow.add_node("init_skills", init_skills_node) workflow.add_node("extractor", extractor_node) workflow.add_node("schema_explorer", schema_explorer_node) workflow.add_node("query_builder", query_builder_node) workflow.add_node("rejection_router", rejection_router_node) workflow.add_node("refiner", refiner_node) +workflow.add_node("satisfaction_check", satisfaction_check_node) +workflow.add_node("hitl_escalation", hitl_escalation_node) workflow.add_node("finalizer", finalizer_node) -workflow.add_edge(START, "extractor") +# Entry: validate config → resolve flags → load skills → start reasoning +workflow.add_edge(START, "validate_config") +workflow.add_edge("validate_config", "init_flags") +workflow.add_edge("init_flags", "init_skills") +workflow.add_edge("init_skills", "extractor") workflow.add_edge("extractor", "schema_explorer") -workflow.add_edge("schema_explorer", "query_builder") -workflow.add_conditional_edges("query_builder", route_query_builder, {"rejection_router": "rejection_router", "refiner": "refiner"}) -workflow.add_conditional_edges("rejection_router", route_rejection, {"extractor": "extractor", "schema_explorer": "schema_explorer", "query_builder": "query_builder"}) +workflow.add_conditional_edges( + "schema_explorer", + route_schema_explorer, + { + "schema_explorer": "schema_explorer", + "query_builder": "query_builder", + "hitl_escalation": "hitl_escalation", # G2-02 + }, +) + +workflow.add_conditional_edges( + "query_builder", + route_query_builder, + {"rejection_router": "rejection_router", "refiner": "refiner"}, +) + +workflow.add_conditional_edges( + "rejection_router", + route_rejection, + { + "extractor": "extractor", + "schema_explorer": "schema_explorer", + "query_builder": "query_builder", + }, +) -workflow.add_conditional_edges("refiner", route_refiner, {"refiner": "refiner", "finalizer": "finalizer"}) +workflow.add_conditional_edges( + "refiner", + route_refiner, + { + "satisfaction_check": "satisfaction_check", # G2-04 replaces direct → finalizer + "hitl_escalation": "hitl_escalation", # G2-02 + }, +) + +# G2-04: satisfaction gate +workflow.add_conditional_edges( + "satisfaction_check", + route_satisfaction, + { + "finalizer": "finalizer", + "refiner": "refiner", + "hitl_escalation": "hitl_escalation", + }, +) + +# G2-02: HITL resume path → restart from extractor (full state reset by human) +workflow.add_edge("hitl_escalation", "extractor") workflow.add_edge("finalizer", END) memory = MemorySaver() -agent_graph = workflow.compile(checkpointer=memory) - +agent_graph = workflow.compile( + checkpointer=memory, + interrupt_before=["hitl_escalation"], # G2-02: pause before HITL node +) diff --git a/agent/src/agent/llm.py b/agent/src/agent/llm.py new file mode 100644 index 0000000..fd31865 --- /dev/null +++ b/agent/src/agent/llm.py @@ -0,0 +1,69 @@ +import logging +from typing import Optional + +from langchain_openai import ChatOpenAI + +from agent.config import settings + +logger = logging.getLogger(__name__) + +# Flag name → LLM_MODEL env-var fallback for each node +_NODE_MODEL_FLAGS: dict[str, str] = { + "extractor": "EXTRACTOR_MODEL", + "schema_explorer": "SCHEMA_SUMMARY_MODEL", + "query_builder": "QUERY_BUILDER_MODEL", + "refiner": "REFINER_MODEL", + "satisfaction_check": "SATISFACTION_JUDGE_MODEL", + "routing": "QUERY_BUILDER_MODEL", # rejection router reuses QB model + "default": "QUERY_BUILDER_MODEL", +} + +_NODE_TEMP_FLAGS: dict[str, str] = { + "extractor": "EXTRACTOR_TEMPERATURE", + "query_builder": "QUERY_BUILDER_TEMPERATURE", +} + + +def get_llm( + node: str = "default", + temperature: Optional[float] = None, + runtime_flags: Optional[dict] = None, +) -> ChatOpenAI: + """ + Factory for per-node LLM instances. + + Priority for model/temperature selection: + 1. runtime_flags (resolved by init_flags_node from DB + execution mode) + 2. AgentSettings env-var defaults + + Args: + node: Name of the calling graph node (used to pick the right flag). + temperature: Optional hard override — bypasses flag resolution. + runtime_flags: The state["runtime_flags"] dict from the current invocation. + Pass None when initialising at module level (will use env defaults). + """ + flags = runtime_flags or {} + + # Resolve model + model_flag = _NODE_MODEL_FLAGS.get(node, "QUERY_BUILDER_MODEL") + model = flags.get(model_flag) or settings.LLM_MODEL + + # Resolve temperature + if temperature is None: + temp_flag = _NODE_TEMP_FLAGS.get(node) + temperature = float(flags.get(temp_flag, 0.0)) if temp_flag else 0.0 + + logger.debug( + "Instantiating LLM for node='%s': model='%s' temperature=%.2f", + node, + model, + temperature, + ) + + return ChatOpenAI( + model=model, + base_url=settings.LLM_BASE_URL, + api_key=settings.LLM_API_KEY, + temperature=temperature, + timeout=300.0, + ) diff --git a/agent/src/agent/mcp_server.py b/agent/src/agent/mcp_server.py index d8f5f92..a65ed05 100644 --- a/agent/src/agent/mcp_server.py +++ b/agent/src/agent/mcp_server.py @@ -1,17 +1,17 @@ import json import uuid from mcp.server.fastmcp import FastMCP -from pydantic import BaseModel, Field + from agent.graph import agent_graph from langgraph.types import Command from sqlmodel import Session, select from core.db.engine import engine from core.models.models import Table, HttpExtractor, ExtractorStatus from langfuse import observe -from langfuse.langchain import CallbackHandler mcp = FastMCP("Text2SQL Agent") + @mcp.tool() @observe() async def chat_with_agent( @@ -21,86 +21,202 @@ async def chat_with_agent( allowed_tables: list[str] | None = None, allowed_statuses: list[str] | None = None, extractors: list[str] | None = None, + active_skills: list[str] | None = None, + execution_mode: str | None = None, hitl_enabled: bool = True, ) -> str: - """Run the Text2SQL agent to answer database queries.""" + """Run the Text2SQL agent to answer database queries. + + Args: + query: The natural language question to answer. + thread_id: Optional thread ID for session continuity. + resume_value: HITL resume payload (pass after receiving an interrupt). + allowed_tables: Restrict the agent to specific tables. + allowed_statuses: Filter tables by status. + extractors: List of extractor names/IDs to use. + active_skills: List of Jeen skill UUIDs to inject. + execution_mode: Named configuration preset (e.g. 'cost_saving', + 'high_quality', 'benchmark'). Overrides flag defaults + for this invocation only. + hitl_enabled: If False, skip all human-in-the-loop interrupts. + """ thread_id = thread_id or str(uuid.uuid4()) + + try: + from core.langfuse import get_langfuse_handler + langfuse_handler = get_langfuse_handler() + callbacks = [langfuse_handler] if langfuse_handler else [] + except Exception: + callbacks = [] + config = { "configurable": {"thread_id": thread_id}, - "callbacks": [CallbackHandler()], + "callbacks": callbacks, } if resume_value is not None: state_snapshot = await agent_graph.aget_state(config) if not state_snapshot.values: - return json.dumps({"error": f"Thread ID '{thread_id}' not found or has no active session."}) - - result = await agent_graph.ainvoke( - Command(resume=resume_value), - config=config - ) + return json.dumps( + { + "error": f"Thread ID '{thread_id}' not found or has no active session." + } + ) + + result = await agent_graph.ainvoke(Command(resume=resume_value), config=config) else: if not query: return json.dumps({"error": "Query is required for new chat session."}) - + if allowed_tables: with Session(engine) as session: all_tables = session.exec(select(Table)).all() for allowed in allowed_tables: exists = False for t in all_tables: - if (t.id == allowed or t.name == allowed or f"{t.schema_name}.{t.name}" == allowed): + if ( + t.id == allowed + or t.name == allowed + or f"{t.schema_name}.{t.name}" == allowed + ): exists = True break if not exists: - return json.dumps({"error": f"Table '{allowed}' does not exist."}) + return json.dumps( + {"error": f"Table '{allowed}' does not exist."} + ) active_extractors = [] with Session(engine) as session: if extractors: for ext_name_or_id in extractors: - ext = session.exec(select(HttpExtractor).where( - (HttpExtractor.id == ext_name_or_id) | (HttpExtractor.name == ext_name_or_id) - )).first() + ext = session.exec( + select(HttpExtractor).where( + (HttpExtractor.id == ext_name_or_id) + | (HttpExtractor.name == ext_name_or_id) + ) + ).first() if not ext: - return json.dumps({"error": f"Extractor '{ext_name_or_id}' does not exist."}) + return json.dumps( + {"error": f"Extractor '{ext_name_or_id}' does not exist."} + ) active_extractors.append({"name": ext.name, "url": ext.url}) else: - prod_extractors = session.exec(select(HttpExtractor).where(HttpExtractor.status == ExtractorStatus.production)).all() + prod_extractors = session.exec( + select(HttpExtractor).where( + HttpExtractor.status == ExtractorStatus.production + ) + ).all() for ext in prod_extractors: active_extractors.append({"name": ext.name, "url": ext.url}) result = await agent_graph.ainvoke( { - "user_query": query, + "user_query": query, "allowed_tables": allowed_tables, "allowed_statuses": allowed_statuses, "active_extractors": active_extractors, + "active_skills": active_skills, + "execution_mode": execution_mode, "non_interactive": not hitl_enabled, }, - config=config + config=config, ) + # Get trace_id from the Langfuse handler if available + trace_id = getattr(langfuse_handler, "last_trace_id", None) if langfuse_handler else None + + from agent.langfuse_client import langfuse_client + langfuse_client.flush() + final_state = await agent_graph.aget_state(config) + + # Check if interrupted by `interrupt()` function if final_state.interrupts: interrupt_val = final_state.interrupts[-1].value - return json.dumps({ + return json.dumps( + { + "thread_id": thread_id, + "status": "interrupted", + "interrupt_details": interrupt_val, + "schema_plan": final_state.values.get("schema_plan") or (interrupt_val.get("schema_plan") if isinstance(interrupt_val, dict) else None), + "sql_query": final_state.values.get("sql_query") or (interrupt_val.get("sql_query") if isinstance(interrupt_val, dict) else None), + "sql_explanation": interrupt_val.get("sql_explanation") if isinstance(interrupt_val, dict) else None, + "trace_id": trace_id, + "execution_path": final_state.values.get("execution_path", []), + } + ) + + # Check if interrupted by `interrupt_before` breakpoint + if final_state.next: + next_node = final_state.next[0] + # Build an artificial interrupt detail based on state + interrupt_val = { + "type": "hitl_escalation", + "reason": final_state.values.get("escalation_reason", f"Paused before {next_node}"), + } + return json.dumps( + { + "thread_id": thread_id, + "status": "interrupted", + "interrupt_details": interrupt_val, + "schema_plan": final_state.values.get("schema_plan"), + "sql_query": final_state.values.get("sql_query"), + "trace_id": trace_id, + "execution_path": final_state.values.get("execution_path", []), + } + ) + + return json.dumps( + { "thread_id": thread_id, - "status": "interrupted", - "interrupt_details": interrupt_val, - "schema_plan": final_state.values.get("schema_plan"), - "sql_query": final_state.values.get("sql_query") - }) - - return json.dumps({ - "thread_id": thread_id, - "status": "completed", - "summary": result.get("summary", ""), - "raw_data_ref": result.get("raw_data_ref"), - "sql_query": result.get("sql_query"), - "sql_explanation": result.get("sql_explanation"), - "schema_plan": result.get("schema_plan") - }) + "status": "completed", + "summary": result.get("summary", ""), + "raw_data_ref": result.get("raw_data_ref"), + "sql_query": result.get("sql_query"), + "sql_explanation": result.get("sql_explanation"), + "schema_plan": result.get("schema_plan"), + "trace_id": trace_id, + "execution_path": result.get("execution_path", []), + } + ) if __name__ == "__main__": - mcp.run() + mcp.run(transport="stdio") + +@mcp.tool() +async def suggest_fixes(thread_id: str, category: str) -> str: + """Generate quick fix suggestions during an interruption.""" + from agent.llm import get_llm + from pydantic import BaseModel, Field + + state_snapshot = await agent_graph.aget_state({"configurable": {"thread_id": thread_id}}) + if not state_snapshot.values: + return "[]" + + sql_query = state_snapshot.values.get("sql_query", "") + schema_plan = state_snapshot.values.get("schema_plan", "") + user_query = state_snapshot.values.get("user_query", "") + runtime_flags = state_snapshot.values.get("runtime_flags", {}) + + llm = get_llm(node="routing", runtime_flags=runtime_flags) + + class Fixes(BaseModel): + suggestions: list[str] = Field(description="2-3 short, actionable suggested fixes (under 60 chars each)") + + prompt = f""" + The user rejected the agent's Text2SQL output with category '{category}'. + User Query: {user_query} + Current SQL: {sql_query} + Current Plan: {schema_plan} + + Provide 2-3 short, distinct button labels for the user to quickly apply a fix. + For example: "GROUP BY date instead of month", "Include cancelled orders", "Filter by region". + """ + + structured_llm = llm.with_structured_output(Fixes, method="json_schema") + try: + res = structured_llm.invoke(prompt) + return json.dumps(res.suggestions) + except Exception as e: + return json.dumps([]) diff --git a/agent/src/agent/nodes/extractor.py b/agent/src/agent/nodes/extractor.py index 1700662..a571c6a 100644 --- a/agent/src/agent/nodes/extractor.py +++ b/agent/src/agent/nodes/extractor.py @@ -3,11 +3,15 @@ import requests from typing import List from pydantic import BaseModel, Field -from langchain_openai import ChatOpenAI + from langchain_core.prompts import ChatPromptTemplate +from langchain_core.runnables.config import RunnableConfig +from agent.utils.redis_publisher import publish_node_event_sync from agent.state import AgentState from agent.config import settings from agent.langfuse_client import langfuse_client +from agent.llm import get_llm + # A single piece of enriched context added to the query class ContextEntry(BaseModel): @@ -32,22 +36,28 @@ class ExtractorOutput(BaseModel): "Context entries that enrich the user query with additional information. " "Only include entries where extra context genuinely helps downstream processing. " "If the query is fully clear and self-contained, return an empty list." - ) + ), ) + class BaseExtractor(abc.ABC): @abc.abstractmethod def extract(self, query: str) -> List[ContextEntry]: pass + class LLMExtractor(BaseExtractor): - def __init__(self): - self.llm = ChatOpenAI(model=settings.LLM_MODEL, base_url=settings.LLM_BASE_URL, api_key=settings.LLM_API_KEY, temperature=0) - + def __init__(self, runtime_flags: dict | None = None): + self.llm = get_llm("extractor", runtime_flags=runtime_flags) + langfuse_prompt = langfuse_client.get_prompt(settings.LANGFUSE_PROMPT_EXTRACTOR) - self.prompt = ChatPromptTemplate.from_messages(langfuse_prompt.get_langchain_prompt()) - - self.chain = self.prompt | self.llm.with_structured_output(ExtractorOutput, method="json_schema") + self.prompt = ChatPromptTemplate.from_messages( + langfuse_prompt.get_langchain_prompt() + ) + + self.chain = self.prompt | self.llm.with_structured_output( + ExtractorOutput, method="json_schema" + ) def extract(self, query: str) -> List[ContextEntry]: try: @@ -57,25 +67,28 @@ def extract(self, query: str) -> List[ContextEntry]: print(f"Structured output parsing failed in LLMExtractor: {e}") return [] + class TimeExtractor(BaseExtractor): def extract(self, query: str) -> List[ContextEntry]: enrichments = [] now = datetime.datetime.now() # Always anchor current time - enrichments.append(ContextEntry( - term="current_time", - context=f"The current time is {now.isoformat()}" - )) - + enrichments.append( + ContextEntry( + term="current_time", context=f"The current time is {now.isoformat()}" + ) + ) + # TODO: add relative time values handling return enrichments + class HTTPExtractor(BaseExtractor): def __init__(self, url: str, name: str): self.url = url self.name = name - + def extract(self, query: str) -> List[ContextEntry]: try: res = requests.post(self.url, json={"query": query}, timeout=50) @@ -86,23 +99,28 @@ def extract(self, query: str) -> List[ContextEntry]: print(f"HTTPExtractor ({self.name} at {self.url}) failed: {e}") return [] -def extractor_node(state: AgentState): + +def extractor_node(state: AgentState, config: RunnableConfig | None = None): """Enrich the user query with additional context to help downstream phases.""" user_query = state["user_query"] active_extractors = state.get("active_extractors") or [] - + runtime_flags = state.get("runtime_flags") or {} + + thread_id = config.get("configurable", {}).get("thread_id", "") if config else "" + publish_node_event_sync(thread_id, "extractor") + import concurrent.futures extractors: List[BaseExtractor] = [ TimeExtractor(), - LLMExtractor() + LLMExtractor(runtime_flags=runtime_flags), ] - + for ext_info in active_extractors: extractors.append(HTTPExtractor(ext_info["url"], ext_info["name"])) - + all_enrichments = [] - + with concurrent.futures.ThreadPoolExecutor() as executor: futures = {executor.submit(ext.extract, user_query): ext for ext in extractors} for future in concurrent.futures.as_completed(futures): @@ -112,5 +130,5 @@ def extractor_node(state: AgentState): except Exception as e: ext = futures[future] print(f"Extractor {type(ext).__name__} failed: {e}") - - return {"query_enrichments": all_enrichments} + + return {"query_enrichments": all_enrichments, "execution_path": ["extractor"]} diff --git a/agent/src/agent/nodes/finalizer.py b/agent/src/agent/nodes/finalizer.py index 0c30660..cdf1565 100644 --- a/agent/src/agent/nodes/finalizer.py +++ b/agent/src/agent/nodes/finalizer.py @@ -1,83 +1,120 @@ import json import asyncio +from langchain_core.runnables.config import RunnableConfig +from agent.utils.redis_publisher import publish_node_event from agent.state import AgentState -from langchain_openai import ChatOpenAI from langchain_core.prompts import ChatPromptTemplate -from esca_sdk import EscaClient from agent.config import settings from agent.langfuse_client import langfuse_client +from agent.llm import get_llm +from agent.utils.esca import get_esca_client -llm = ChatOpenAI(model=settings.LLM_MODEL, base_url=settings.LLM_BASE_URL, api_key=settings.LLM_API_KEY, temperature=0) +llm = get_llm("finalizer") # Prompts will be loaded dynamically from Langfuse inside the node execution + async def get_esca_preview(esca_id: str, limit: int = 5) -> str: """Load data from Esca and return a preview of the columns and the first few rows.""" if not esca_id: return "No data reference found." - - client = EscaClient(api_key=settings.ESCA_API_KEY, base_url=settings.ESCA_URL) + try: - # TODO: instead of fetching everything from esca and then chunk, get only the chunk - data_bytes = await client.load_head(esca_id) - data = json.loads(data_bytes.decode()) - - columns = data.get("columns", []) - rows = data.get("rows", []) - total_rows = len(rows) - - # Take a slice of the rows to avoid context overload - preview_rows = rows[:limit] - - preview_info = { - "columns": columns, - "preview_rows": preview_rows, - "preview_count": len(preview_rows), - "total_rows": total_rows - } - return json.dumps(preview_info, indent=2) + async with get_esca_client() as client: + # TODO: instead of fetching everything from esca and then chunk, get only the chunk + data_bytes = await client.load_head(esca_id) + data = json.loads(data_bytes.decode()) + + columns = data.get("columns", []) + rows = data.get("rows", []) + total_rows = len(rows) + + # Take a slice of the rows to avoid context overload + preview_rows = rows[:limit] + + preview_info = { + "columns": columns, + "preview_rows": preview_rows, + "preview_count": len(preview_rows), + "total_rows": total_rows, + } + return json.dumps(preview_info, indent=2) except Exception as e: return f"Error retrieving data preview from Esca: {e}" - finally: - await client.close() + async def get_sql_explanation(sql_query: str | None) -> str: """Ask LLM to explain the SQL query in natural language.""" if not sql_query: return "No SQL query was generated." - - langfuse_prompt = langfuse_client.get_prompt(settings.LANGFUSE_PROMPT_FINALIZER_SQL_EXPLANATION) - prompt_sql_explanation = ChatPromptTemplate.from_messages(langfuse_prompt.get_langchain_prompt()) - + + langfuse_prompt = langfuse_client.get_prompt( + settings.LANGFUSE_PROMPT_FINALIZER_SQL_EXPLANATION + ) + prompt_sql_explanation = ChatPromptTemplate.from_messages( + langfuse_prompt.get_langchain_prompt() + ) + chain = prompt_sql_explanation | llm response = await chain.ainvoke({"sql_query": sql_query}) return response.content -async def finalizer_node(state: AgentState): + +async def finalizer_node(state: AgentState, config: RunnableConfig | None = None): """Summarize data.""" + thread_id = config.get("configurable", {}).get("thread_id", "") if config else "" + from agent.utils.redis_publisher import publish_node_event + await publish_node_event(thread_id, "finalizer") raw_data_ref = state.get("raw_data_ref") + esca_write_failed = state.get("esca_write_failed", False) + inline_result_rows = state.get("inline_result_rows") + preview_str = "" - if raw_data_ref: + if esca_write_failed or not raw_data_ref: + if inline_result_rows is not None: + limit = 5 + preview_rows = inline_result_rows[:limit] + columns = ( + list(preview_rows[0].keys()) + if preview_rows and isinstance(preview_rows[0], dict) + else [] + ) + preview_info = { + "columns": columns, + "preview_rows": preview_rows, + "preview_count": len(preview_rows), + "total_rows": len(inline_result_rows), + } + preview_str = json.dumps(preview_info, indent=2) + else: + preview_str = "No data reference found." + else: preview_str = await get_esca_preview(raw_data_ref) - - langfuse_prompt_summary = langfuse_client.get_prompt(settings.LANGFUSE_PROMPT_FINALIZER_SUMMARY) - prompt_summary = ChatPromptTemplate.from_messages(langfuse_prompt_summary.get_langchain_prompt()) - + + langfuse_prompt_summary = langfuse_client.get_prompt( + settings.LANGFUSE_PROMPT_FINALIZER_SUMMARY + ) + prompt_summary = ChatPromptTemplate.from_messages( + langfuse_prompt_summary.get_langchain_prompt() + ) + summary_chain = prompt_summary | llm - - summary_task = summary_chain.ainvoke({ - "user_query": state["user_query"], - "sql_query": state.get("sql_query") or "", - "raw_data_ref": raw_data_ref, - "data_preview": preview_str - }) - + + summary_task = summary_chain.ainvoke( + { + "user_query": state["user_query"], + "sql_query": state.get("sql_query") or "", + "raw_data_ref": raw_data_ref, + "data_preview": preview_str, + } + ) + sql_task = get_sql_explanation(state.get("sql_query")) - + summary_response, sql_explanation = await asyncio.gather(summary_task, sql_task) - + return { "summary": summary_response.content, - "sql_explanation": sql_explanation + "sql_explanation": sql_explanation, + "execution_path": ["finalizer"], } - diff --git a/agent/src/agent/nodes/init_flags.py b/agent/src/agent/nodes/init_flags.py new file mode 100644 index 0000000..0a71512 --- /dev/null +++ b/agent/src/agent/nodes/init_flags.py @@ -0,0 +1,71 @@ +""" +init_flags_node (G4) +==================== +Runs immediately after validate_config, before init_skills. + +Responsibilities: + 1. Call FlagBridge.resolve_flags(execution_mode) to merge: + mode overrides → DB flags → env-var defaults + 2. Write the resolved dict to state["runtime_flags"] + 3. Log runtime_flags to Langfuse trace metadata for full observability +""" + +import logging + +from agent.langfuse_client import langfuse_client +from langchain_core.runnables.config import RunnableConfig +from agent.utils.redis_publisher import publish_node_event +from agent.state import AgentState +from agent.utils.flag_bridge import FlagBridge + +logger = logging.getLogger(__name__) + +_flag_bridge = FlagBridge() + + +async def init_flags_node(state: AgentState, config: RunnableConfig | None = None) -> dict: + """ + Resolve all runtime configuration flags for this invocation. + + The resolved dict is stored in state["runtime_flags"] and read by every + downstream node instead of directly accessing AgentSettings env vars. + This guarantees that: + - DS team changes in the Studio UI take effect within the cache TTL (30s). + - Execution mode overrides are applied consistently to all nodes. + Execution mode overrides take precedence over dynamic flags, which override + the agent's default `settings`. + """ + thread_id = config.get("configurable", {}).get("thread_id", "") if config else "" + await publish_node_event(thread_id, "init_flags") + + mode_name = state.get("execution_mode") + execution_mode: str | None = state.get("execution_mode") + + try: + runtime_flags = await _flag_bridge.resolve_flags(execution_mode) + except Exception as exc: + logger.warning("init_flags_node: FlagBridge failed (%s), using env defaults", exc) + # FlagBridge already handles its own fallback internally, so this is a + # safety net for any unexpected error in the bridge itself. + from agent.utils.flag_bridge import _ENV_DEFAULTS + runtime_flags = dict(_ENV_DEFAULTS) + + # Emit to Langfuse for observability + try: + if langfuse_client.get_current_trace_id(): + langfuse_client.update_current_span( + metadata={ + "runtime_flags": runtime_flags, + "execution_mode": execution_mode or "default", + }, + ) + except Exception as exc: + logger.warning("init_flags_node: Langfuse trace failed: %s", exc) + + logger.info( + "init_flags_node: resolved %d flags (mode=%s)", + len(runtime_flags), + execution_mode or "default", + ) + + return {"runtime_flags": runtime_flags, "execution_path": ["init_flags"]} diff --git a/agent/src/agent/nodes/init_skills.py b/agent/src/agent/nodes/init_skills.py new file mode 100644 index 0000000..5e250a5 --- /dev/null +++ b/agent/src/agent/nodes/init_skills.py @@ -0,0 +1,56 @@ +import logging +from langchain_core.runnables.config import RunnableConfig +from agent.utils.redis_publisher import publish_node_event +from agent.state import AgentState +from agent.utils.skill_registry import SkillRegistry +from python_core_utils.redis import get_redis_client + +logger = logging.getLogger(__name__) + +_skill_registry = SkillRegistry() + +async def init_skills_node(state: AgentState, config: RunnableConfig | None = None) -> dict: + """ + Initial node to load skills into the agent state. + This ensures that skill IO and caching happens at the graph boundary, + keeping reasoning nodes pure and state reproducible. + """ + thread_id = config.get("configurable", {}).get("thread_id", "") if config else "" + await publish_node_event(thread_id, "init_skills") + + active_skills = state.get("active_skills") + runtime_flags = state.get("runtime_flags") or {} + + from agent.config import settings + skills_enabled = bool(runtime_flags.get("SKILLS_ENABLED", getattr(settings, "SKILLS_ENABLED", True))) + hot_reload = bool(runtime_flags.get("SKILLS_HOT_RELOAD", getattr(settings, "SKILLS_HOT_RELOAD", False))) + cache_ttl = int(runtime_flags.get("SKILLS_CACHE_TTL", getattr(settings, "SKILLS_CACHE_TTL", 3600))) + + if not skills_enabled or not active_skills: + return {"loaded_skills": None, "execution_path": ["init_skills"]} + + try: + _skill_registry.redis = get_redis_client() + loaded_skills = await _skill_registry.get_skills( + active_skills, + hot_reload=hot_reload, + cache_ttl=cache_ttl, + ) + + if loaded_skills: + try: + from core.langfuse import get_langfuse_handler + langfuse_handler = get_langfuse_handler() + if langfuse_client.get_current_trace_id(): + skill_names = [s.get("displayName") or s.get("name", "Unknown") for s in loaded_skills] + from agent.langfuse_client import langfuse_client + langfuse_client.update_current_span( + metadata={"skills_loaded": skill_names} + ) + except Exception as inner_e: + logger.warning(f"Failed to push skills_loaded to langfuse: {inner_e}") + + return {"loaded_skills": loaded_skills, "execution_path": ["init_skills"]} + except Exception as e: + logger.warning(f"Failed to initialize skills: {e}") + return {"loaded_skills": None, "execution_path": ["init_skills"]} diff --git a/agent/src/agent/nodes/query_builder.py b/agent/src/agent/nodes/query_builder.py index 9919c76..a33d32f 100644 --- a/agent/src/agent/nodes/query_builder.py +++ b/agent/src/agent/nodes/query_builder.py @@ -1,45 +1,92 @@ +from langchain_core.runnables.config import RunnableConfig +from agent.utils.redis_publisher import publish_node_event_sync from agent.state import AgentState -from langchain_openai import ChatOpenAI +from agent.llm import get_llm from langchain_core.prompts import ChatPromptTemplate from agent.config import settings from agent.langfuse_client import langfuse_client from langgraph.types import interrupt - -llm = ChatOpenAI(model=settings.LLM_MODEL, base_url=settings.LLM_BASE_URL, api_key=settings.LLM_API_KEY, temperature=0) - -def query_builder_node(state: AgentState): +async def query_builder_node(state: AgentState, config: RunnableConfig | None = None): """Build SQL from plan and pause for user approval.""" + runtime_flags = state.get("runtime_flags") or {} feedback = state.get("feedback") feedback_str = f"\nUser Feedback to apply: {feedback}" if feedback else "" + loaded_skills = state.get("loaded_skills") + if loaded_skills: + from agent.utils.skill_registry import SkillRegistry + _skill_registry = SkillRegistry() + skill_prompts = _skill_registry.build_system_prompt_addition(loaded_skills) + if skill_prompts: + feedback_str += f"\n\n[APPLIED SKILLS]{skill_prompts}" + langfuse_prompt = langfuse_client.get_prompt(settings.LANGFUSE_PROMPT_QUERY_BUILDER) prompt = ChatPromptTemplate.from_messages(langfuse_prompt.get_langchain_prompt()) - chain = prompt | llm - response = chain.invoke({ - "schema_plan": state.get("schema_plan"), - "user_query": state["user_query"], - "feedback_str": feedback_str - }) - sql = response.content.replace('```sql', '').replace('```', '').strip() - if sql.endswith(';'): + _llm = get_llm("query_builder", runtime_flags=runtime_flags) + chain = prompt | _llm + thread_id = config.get("configurable", {}).get("thread_id", "") if config else "" + publish_node_event_sync(thread_id, "query_builder") + response = await chain.ainvoke( + { + "schema_plan": state.get("schema_plan"), + "user_query": state.get("user_query"), + "feedback_str": feedback_str, + } + ) + import re + content = response.content + + # Check for built-in reasoning content in model metadata (additional_kwargs) + explanation = response.additional_kwargs.get("reasoning_content") or response.additional_kwargs.get("reasonig_content") or "" + + # Extract SQL from the response content + sql_match = re.search(r"```sql\s*(.*?)\s*```", content, re.DOTALL | re.IGNORECASE) + if sql_match: + sql = sql_match.group(1).strip() + if not explanation: + explanation = content.replace(sql_match.group(0), "").strip() + else: + # Check for general code block + block_match = re.search(r"```\s*(.*?)\s*```", content, re.DOTALL) + if block_match: + sql = block_match.group(1).strip() + if not explanation: + explanation = content.replace(block_match.group(0), "").strip() + else: + sql = content.strip() + + if sql.endswith(";"): sql = sql[:-1].strip() - + if state.get("non_interactive"): - return {"sql_query": sql, "refinement_count": 0, "trino_error": None, "feedback": None} - - approval_result = interrupt({ - "type": "query_approval", - "schema_plan": state.get("schema_plan"), - "sql_query": sql - }) - + return { + "sql_query": sql, + "refinement_count": 0, + "trino_error": None, + "feedback": None, + "execution_path": ["query_builder"], + } + + approval_result = interrupt( + { + "type": "query_approval", + "schema_plan": state.get("schema_plan"), + "sql_query": sql, + "sql_explanation": explanation, + } + ) + if approval_result.get("approved"): - return {"sql_query": sql, "refinement_count": 0, "trino_error": None, "feedback": None} + return { + "sql_query": sql, + "refinement_count": 0, + "trino_error": None, + "feedback": None, + "execution_path": ["query_builder"], + } else: return { "feedback": approval_result.get("feedback", "Query rejected by user"), - "sql_query": None + "sql_query": None, + "execution_path": ["query_builder"], } - - - diff --git a/agent/src/agent/nodes/refiner.py b/agent/src/agent/nodes/refiner.py index 041923c..bdcfd6b 100644 --- a/agent/src/agent/nodes/refiner.py +++ b/agent/src/agent/nodes/refiner.py @@ -1,52 +1,135 @@ import json -import uuid + +import asyncio +import logging +from langchain_core.runnables.config import RunnableConfig +from agent.utils.redis_publisher import publish_node_event from agent.state import AgentState -from langchain_openai import ChatOpenAI -from langchain_core.prompts import ChatPromptTemplate from core import execute_query_sync -from esca_sdk import EscaClient from agent.config import settings from agent.langfuse_client import langfuse_client +from langchain_core.prompts import ChatPromptTemplate +from agent.llm import get_llm +from agent.utils.sql import clean_sql +from agent.utils.esca import get_esca_client -llm = ChatOpenAI(model=settings.LLM_MODEL, base_url=settings.LLM_BASE_URL, api_key=settings.LLM_API_KEY, temperature=0) +llm = get_llm("refiner") MAX_REFINER_ITERATIONS = 3 +REFINER_SCHEMA_CONTEXT_TABLES = 4 + + +def build_refiner_schema_context(state: AgentState) -> str: + profiles = state.get("table_profiles") + if not profiles: + return "No schema context available." -async def refiner_node(state: AgentState): + # Cap the context to REFINER_SCHEMA_CONTEXT_TABLES + capped_profiles = profiles[:REFINER_SCHEMA_CONTEXT_TABLES] + return json.dumps(capped_profiles, indent=2) + + +async def refiner_node(state: AgentState, config: RunnableConfig | None = None): """Refine SQL against Trino.""" sql = state.get("sql_query") count = state.get("refinement_count", 0) + error_history = state.get("error_history") or [] + runtime_flags = state.get("runtime_flags") or {} + execution_path = state.get("execution_path") or [] - # Execute against Trino - result = execute_query_sync(sql) + # Resolve per-invocation limit (DS-tunable via flags) + max_iterations = int(runtime_flags.get("MAX_REFINER_ITERATIONS", MAX_REFINER_ITERATIONS)) - if not result.success: + # Execute against Trino + try: + result = await asyncio.to_thread(execute_query_sync, sql) + success = result.success trino_error = result.error_message or "Unknown Trino error" + if not success: + error_history.append(trino_error) + except Exception as e: + success = False + trino_error = str(e) + error_history.append(trino_error) + result = None + + thread_id = config.get("configurable", {}).get("thread_id", "") if config else "" + await publish_node_event(thread_id, "refiner") + + if not success: # If we reached the refinement limit, just stop and don't prompt LLM - if count >= MAX_REFINER_ITERATIONS: - return {"trino_error": trino_error, "refinement_count": count + 1} + if count >= max_iterations: + return { + "trino_error": trino_error, + "last_error": trino_error, + "refinement_count": count + 1, + "error_history": error_history, + "escalation_reason": ( + f"Refiner exhausted {max_iterations} iterations. " + f"Last Trino error: {trino_error}" + ), + "execution_path": ["refiner"], + } langfuse_prompt = langfuse_client.get_prompt(settings.LANGFUSE_PROMPT_REFINER) - prompt = ChatPromptTemplate.from_messages(langfuse_prompt.get_langchain_prompt()) - chain = prompt | llm - response = await chain.ainvoke({"sql": sql, "error": trino_error}) - new_sql = response.content.replace('```sql', '').replace('```', '').strip() - if new_sql.endswith(';'): - new_sql = new_sql[:-1].strip() - return {"sql_query": new_sql, "trino_error": trino_error, "refinement_count": count + 1} + prompt = ChatPromptTemplate.from_messages( + langfuse_prompt.get_langchain_prompt() + ) + _llm = get_llm("refiner", runtime_flags=runtime_flags) + chain = prompt | _llm + + schema_context = build_refiner_schema_context(state) + + if langfuse_client and langfuse_client.get_current_trace_id(): + langfuse_client._create_trace_tags_via_ingestion( + trace_id=langfuse_client.get_current_trace_id(), + tags=["schema_context_injected=True"], + ) + + response = await chain.ainvoke( + { + "sql": sql, + "error": trino_error, + "schema_context": schema_context, + "error_history": json.dumps(error_history), + } + ) + new_sql = clean_sql(response.content) + return { + "sql_query": new_sql, + "trino_error": trino_error, + "last_error": trino_error, + "refinement_count": count + 1, + "error_history": error_history, + "execution_path": ["refiner"], + } else: # Success, save payload via Esca - client = EscaClient(api_key=settings.ESCA_API_KEY, base_url=settings.ESCA_URL) - payload_data = { - "columns": result.columns, - "rows": result.rows - } + payload_data = {"columns": result.columns, "rows": result.rows} payload = json.dumps(payload_data).encode() - try: - res = await client.save_data(payload) - raw_ref = res.get("esca_id") - finally: - await client.close() + raw_ref = None + esca_write_failed = False + inline_result_rows = result.rows - return {"trino_error": None, "raw_data_ref": raw_ref} + try: + async with get_esca_client() as client: + res = await client.save_data(payload) + raw_ref = res.get("esca_id") + except Exception as e: + esca_write_failed = True + if langfuse_client and langfuse_client.get_current_trace_id(): + langfuse_client.update_current_span( + level="WARNING", status_message=f"ESCA write failed: {e}" + ) + else: + logging.warning(f"ESCA write failed: {e}") + return { + "trino_error": None, + "last_error": None, + "raw_data_ref": raw_ref, + "esca_write_failed": esca_write_failed, + "inline_result_rows": inline_result_rows, + "error_history": error_history, + "execution_path": ["refiner"], + } diff --git a/agent/src/agent/nodes/satisfaction_check.py b/agent/src/agent/nodes/satisfaction_check.py new file mode 100644 index 0000000..e172be6 --- /dev/null +++ b/agent/src/agent/nodes/satisfaction_check.py @@ -0,0 +1,185 @@ +""" +G2-04: Satisfaction Check Module +================================= +A quality-control gateway node placed between the refiner's success path +and the finalizer. Runs up to four independent verification checks, each +individually gated by a feature flag read from runtime_flags (G4). + +Graph position: + [refiner: success] → [satisfaction_check] + → (any check fails, fail_count < MAX) → [refiner] + → (any check fails, fail_count >= MAX) → [hitl_escalation] + → (all checks pass / module disabled) → [finalizer] +""" + +from __future__ import annotations + +import json +import logging + +from agent.config import settings +from agent.langfuse_client import langfuse_client +from agent.llm import get_llm +from langchain_core.runnables.config import RunnableConfig +from agent.utils.redis_publisher import publish_node_event +from agent.state import AgentState +from agent.utils.schema_enrichment import ( + ColumnCoverageOutput, + SemanticAlignmentOutput, + PlausibleZeroRowsOutput, +) + +logger = logging.getLogger(__name__) + + +def _f(runtime_flags: dict, name: str, default): + """Read a flag from runtime_flags, falling back to *default*.""" + return runtime_flags.get(name, default) + + +async def satisfaction_check_node(state: AgentState, config: RunnableConfig | None = None) -> dict: + """ + Multi-stage satisfaction judge. + + Returns a partial state dict. The conditional edge `route_satisfaction` + in graph.py inspects `satisfaction_failures` to decide the next node. + """ + thread_id = config.get("configurable", {}).get("thread_id", "") if config else "" + await publish_node_event(thread_id, "satisfaction_check") + + runtime_flags = state.get("runtime_flags") or {} + + # ── Global gate ─────────────────────────────────────────────────────────── + check_enabled = _f(runtime_flags, "SATISFACTION_CHECK_ENABLED", settings.SATISFACTION_CHECK_ENABLED) + if not check_enabled: + return {"satisfaction_failures": [], "execution_path": ["satisfaction_check"]} + + # ── LLM (used for Check C and D) ────────────────────────────────────────── + llm = get_llm("satisfaction_check", runtime_flags=runtime_flags) + + failures: list[str] = [] + rows = state.get("inline_result_rows") or [] + columns: list[str] = [] + + # Attempt to derive column names from the first result row + if rows and isinstance(rows[0], dict): + columns = list(rows[0].keys()) + + # ── Check A: Execution Success ──────────────────────────────────────────── + if _f(runtime_flags, "SATISFACTION_CHECK_EXECUTION", settings.SATISFACTION_CHECK_EXECUTION): + if state.get("trino_error"): + failures.append(f"[CHECK_A] Execution failed: {state['trino_error']}") + + # ── Check B: Row Plausibility ───────────────────────────────────────────── + if _f(runtime_flags, "SATISFACTION_CHECK_PLAUSIBILITY", settings.SATISFACTION_CHECK_PLAUSIBILITY): + n = len(rows) + min_rows = _f(runtime_flags, "SATISFACTION_MIN_ROWS", settings.SATISFACTION_MIN_ROWS) + max_rows = _f(runtime_flags, "SATISFACTION_MAX_ROWS", settings.SATISFACTION_MAX_ROWS) + if n == 0: + # If the query returned 0 rows successfully, verify if it is plausible or a logic error + prompt = ( + f"User Question: {state.get('user_query', '')}\n" + f"Generated SQL: {state.get('sql_query', '')}\n\n" + "The SQL query executed successfully on the database but returned 0 rows.\n" + "Analyze the generated SQL structure against the User Question:\n" + "1. Check for logical flaws: Are there incorrect JOIN keys, contradictory filters (e.g. WHERE status='completed' AND status='pending'), or mismatched table aliases?\n" + "2. Check for empty set plausibility: Is it plausible to return 0 rows if the database simply doesn't contain matching rows (e.g., filtering for a specific country or date range that might not have entries)?\n\n" + "Provide your decision on whether 0 rows is a plausible result for a correct query or if the query contains a logic error." + ) + try: + structured = llm.with_structured_output(PlausibleZeroRowsOutput, method="json_schema") + result: PlausibleZeroRowsOutput = await structured.ainvoke(prompt) + if not result.is_plausible: + failures.append( + f"[CHECK_B] Zero-row result is implausible: {result.reason}" + ) + except Exception as exc: + logger.warning("satisfaction_check Check B zero-row evaluation failed: %s", exc) + # Fallback to direct row comparison if LLM judge fails + if n < min_rows: + failures.append( + f"[CHECK_B] Result returned {n} rows — below minimum {min_rows}." + ) + elif n < min_rows: + failures.append( + f"[CHECK_B] Result returned {n} rows — below minimum {min_rows}." + ) + elif n > max_rows: + failures.append( + f"[CHECK_B] Result returned {n} rows — exceeds maximum {max_rows}." + ) + + # ── Check C: Structural Column Coverage ─────────────────────────────────── + if _f(runtime_flags, "SATISFACTION_CHECK_COLUMNS", settings.SATISFACTION_CHECK_COLUMNS) and columns: + prompt = ( + f"User question: {state.get('user_query', '')}\n" + f"SQL column headers returned: {', '.join(columns)}\n\n" + "Do these column headers conceptually satisfy what the user asked for?" + ) + try: + structured = llm.with_structured_output(ColumnCoverageOutput, method="json_schema") + result: ColumnCoverageOutput = await structured.ainvoke(prompt) + if not result.satisfies_question: + failures.append( + f"[CHECK_C] Column coverage insufficient: {result.reason}" + ) + except Exception as exc: + logger.warning("satisfaction_check Check C failed: %s", exc) + + # ── Check D: Semantic Alignment (LLM judge, scored 0–1) ─────────────────── + check_semantic = _f(runtime_flags, "SATISFACTION_CHECK_SEMANTIC", settings.SATISFACTION_CHECK_SEMANTIC) + threshold = float(_f(runtime_flags, "SATISFACTION_SEMANTIC_THRESHOLD", settings.SATISFACTION_SEMANTIC_THRESHOLD)) + if check_semantic and columns: + prompt = ( + f"User question: {state.get('user_query', '')}\n" + f"SQL generated: {state.get('sql_query', '')}\n" + f"Result column headers: {', '.join(columns)}\n\n" + "Score alignment between the question intent and the query output schema (0.0–1.0)." + ) + try: + structured = llm.with_structured_output(SemanticAlignmentOutput, method="json_schema") + result: SemanticAlignmentOutput = await structured.ainvoke(prompt) + if result.alignment_score < threshold: + failures.append( + f"[CHECK_D] Semantic alignment score {result.alignment_score:.2f} " + f"below threshold {threshold}: {result.reason}" + ) + except Exception as exc: + logger.warning("satisfaction_check Check D failed: %s", exc) + + # ── Accounting & Langfuse instrumentation ───────────────────────────────── + prior_fail_count = state.get("satisfaction_fail_count") or 0 + fail_count = prior_fail_count + (1 if failures else 0) + + try: + if langfuse_client.get_current_trace_id(): + langfuse_client.update_current_span( + metadata={ + "satisfaction_failures": failures, + "satisfaction_fail_count": fail_count, + "satisfaction_checks_run": { + "execution": _f(runtime_flags, "SATISFACTION_CHECK_EXECUTION", settings.SATISFACTION_CHECK_EXECUTION), + "plausibility": _f(runtime_flags, "SATISFACTION_CHECK_PLAUSIBILITY", settings.SATISFACTION_CHECK_PLAUSIBILITY), + "columns": _f(runtime_flags, "SATISFACTION_CHECK_COLUMNS", settings.SATISFACTION_CHECK_COLUMNS), + "semantic": check_semantic, + }, + }, + ) + except Exception as exc: + logger.warning("satisfaction_check Langfuse trace failed: %s", exc) + + partial: dict = { + "satisfaction_failures": failures if failures else None, + "satisfaction_fail_count": fail_count, + "execution_path": ["satisfaction_check"], + } + + if failures: + partial["last_error"] = "; ".join(failures) + if fail_count >= settings.SATISFACTION_MAX_FAILURES: + partial["escalation_reason"] = ( + f"Satisfaction checks failed {fail_count} times. " + f"Last failures: {'; '.join(failures)}" + ) + + return partial diff --git a/agent/src/agent/nodes/schema_explorer.py b/agent/src/agent/nodes/schema_explorer.py index 81f9687..0ff9817 100644 --- a/agent/src/agent/nodes/schema_explorer.py +++ b/agent/src/agent/nodes/schema_explorer.py @@ -1,14 +1,19 @@ from langgraph.types import interrupt import json +import re import urllib.request + +IDENT_RE = re.compile(r"^[A-Za-z_][A-Za-z0-9_]*$") + from typing import Any, List, Optional from pydantic import BaseModel, Field -from esca_sdk import EscaClient + import logging from agent.state import AgentState -from langchain_openai import ChatOpenAI + from langchain_core.prompts import ChatPromptTemplate -from langchain_core.messages import HumanMessage, AIMessage, ToolMessage +from langchain_core.runnables import RunnableConfig + from langchain_core.tools import tool from sqlalchemy import text from core.db.engine import engine @@ -16,29 +21,94 @@ from sqlmodel import Session, select from agent.config import settings from agent.langfuse_client import langfuse_client +from agent.llm import get_llm +from agent.utils.esca import get_esca_client +from agent.utils.schema_enrichment import ( + run_semantic_typing, + run_join_graph, + run_ambiguity_detection, +) +from core.cache import get_cache_service from core.embeddings import get_embedding # Initialize LLM -llm = ChatOpenAI(model=settings.LLM_MODEL, base_url=settings.LLM_BASE_URL, api_key=settings.LLM_API_KEY, temperature=0) +llm = get_llm("schema_explorer") logger = logging.getLogger(__name__) + +# Cache singleton +_cache = get_cache_service(settings.REDIS_URL) + +# Skill Registry +from agent.utils.skill_registry import SkillRegistry +from python_core_utils.redis import get_redis_client +_skill_registry = SkillRegistry() + +# G2-02 limits +MAX_SCHEMA_RETRIES = 3 + + +def _build_column_context(cp: "ColumnProfile") -> dict: + """Build a rich column context dict from a ColumnProfile ORM row. + + Returns all fields the LLM needs to write accurate SQL: + - name, type, semantic_type + - null_rate, distinct_count + - sample_values (top values for categorical/text, or sample values for continuous) + - min, max, mean for numeric/time columns + """ + top_vals = cp.top_values or [] + sample_values = [v.get("value") for v in top_vals[:20] if v.get("value") is not None] + stats = cp.stats_json or {} + + col: dict = { + "name": cp.column_name, + "type": cp.data_type, + "semantic_type": cp.semantic_type or "unknown", + "null_rate": round(cp.null_rate or 0.0, 4), + "distinct_count": cp.distinct_count or 0, + } + + if cp.is_categorical: + col["sample_values"] = sample_values + else: + # For numeric/time columns expose range and sample values + if cp.min_value is not None: + col["min"] = cp.min_value + if cp.max_value is not None: + col["max"] = cp.max_value + if cp.avg_value is not None: + col["mean"] = round(float(cp.avg_value), 4) + # Pull any sample_values stored in stats_json (continuous columns) + stored_samples = stats.get("sample_values", []) + if stored_samples: + col["sample_values"] = [str(v) for v in stored_samples[:10]] + elif sample_values: + col["sample_values"] = sample_values[:10] + + return col + + # Define standardized Schema Explorer Output Type class SchemaExplorerOutput(BaseModel): schema_plan: Optional[Any] = Field( default=None, - description="Detailed query plan describing tables, columns, and joins." + description="Detailed query plan describing tables, columns, and joins.", ) ambiguity_detected: bool = Field( - default=False, - description="Set to true if there is table selection ambiguity." + default=False, description="Set to true if there is table selection ambiguity." ) ambiguity_message: str = Field( default="", - description="A question to ask the user to clarify/select the right table(s). Must be empty if ambiguity_detected is false." + description="A question to ask the user to clarify/select the right table(s). Must be empty if ambiguity_detected is false.", ) candidate_options: List[str] = Field( default_factory=list, description="List of strings (table names or options) for the user to choose from. Must be empty if ambiguity_detected is false." ) + tables_used: List[str] = Field( + default_factory=list, + description="List of fully qualified table names (catalog.schema.name) used in the plan.", + ) def get_query_embedding(text: str) -> list[float]: @@ -54,30 +124,55 @@ def get_query_embedding(text: str) -> list[float]: return [0.0] * 768 return emb -def hybrid_search_tables(query: str, query_embedding: list[float], session: Session, allowed_tables: list[str] | None = None, allowed_statuses: list[str] | None = None) -> list[Table]: - """Hybrid search combining pgvector cosine distance and keyword matching.""" - # 1. Get all allowed tables + +def hybrid_search_tables( + query: str, + query_embedding: list[float], + session: Session, + allowed_tables: list[str] | None = None, + allowed_statuses: list[str] | None = None, + scoping_mode: str = "hybrid", +) -> list[Table]: + """Hybrid search combining pgvector cosine distance and keyword matching. + + G2-01: In strict mode, allowed_tables is a hard allowlist — allowed_statuses + is ignored. In hybrid mode, the union of both filters applies (legacy behaviour). + """ stmt_all = select(Table) all_tables = session.exec(stmt_all).all() - + allowed = allowed_tables or [] statuses = allowed_statuses or ["production"] allowed_tables_set = [] allowed_ids = set() + for table in all_tables: - is_allowed = ( - table.status in statuses or - (allowed and ( - table.id in allowed or - table.name in allowed or - f"{table.schema_name}.{table.name}" in allowed - )) - ) + if scoping_mode == "strict": + # Hard allowlist: only tables explicitly named in allowed_tables + is_allowed = bool( + allowed + and ( + table.id in allowed + or table.name in allowed + or f"{table.schema_name}.{table.name}" in allowed + ) + ) + else: + # Hybrid: production/status union OR explicit allowed list + is_allowed = table.status in statuses or ( + allowed + and ( + table.id in allowed + or table.name in allowed + or f"{table.schema_name}.{table.name}" in allowed + ) + ) + if is_allowed: allowed_tables_set.append(table) allowed_ids.add(table.id) - # 2. Vector Search + # Vector Search if allowed_ids: stmt = text(""" SELECT id FROM tables @@ -86,14 +181,24 @@ def hybrid_search_tables(query: str, query_embedding: list[float], session: Sess LIMIT :limit """) try: - vec_ids = [row[0] for row in session.execute(stmt, {"emb": str(query_embedding), "allowed_ids": list(allowed_ids), "limit": settings.HYBRID_SEARCH_MAX_TABLES}).fetchall()] + vec_ids = [ + row[0] + for row in session.execute( + stmt, + { + "emb": str(query_embedding), + "allowed_ids": list(allowed_ids), + "limit": settings.HYBRID_SEARCH_MAX_TABLES, + }, + ).fetchall() + ] except Exception as e: print(f"Vector search failed: {e}") vec_ids = [] else: vec_ids = [] - # 3. Keyword Search + # Keyword Search keyword_matches = [] query_words = query.lower().split() for table in allowed_tables_set: @@ -102,9 +207,13 @@ def hybrid_search_tables(query: str, query_embedding: list[float], session: Sess .where(EnrichmentVersion.table_id == table.id) .order_by(EnrichmentVersion.version.desc()) ).first() - - desc = enrichment.data.get("table_description", "") if enrichment and enrichment.data else "" - + + desc = ( + enrichment.data.get("table_description", "") + if enrichment and enrichment.data + else "" + ) + score = 0 for word in query_words: if word in table.name.lower(): @@ -113,16 +222,17 @@ def hybrid_search_tables(query: str, query_embedding: list[float], session: Sess score += 5 if word in desc.lower(): score += 2 - + if score > 0: keyword_matches.append((table.id, score)) - + keyword_matches.sort(key=lambda x: x[1], reverse=True) - kw_ids = [x[0] for x in keyword_matches[:settings.HYBRID_SEARCH_MAX_TABLES]] - - # 4. Combine and limit to settings.HYBRID_SEARCH_MAX_TABLES tables - combined_ids = list(dict.fromkeys(vec_ids + kw_ids))[:settings.HYBRID_SEARCH_MAX_TABLES] - + kw_ids = [x[0] for x in keyword_matches[: settings.HYBRID_SEARCH_MAX_TABLES]] + + combined_ids = list(dict.fromkeys(vec_ids + kw_ids))[ + : settings.HYBRID_SEARCH_MAX_TABLES + ] + result_tables = [] for tid in combined_ids: t = session.get(Table, tid) @@ -130,53 +240,47 @@ def hybrid_search_tables(query: str, query_embedding: list[float], session: Sess result_tables.append(t) return result_tables + # Define Tools -@tool -def search_tables(query: str) -> str: - """Search for tables in the catalog using keywords or semantic query. Returns the top relevant tables.""" - emb = get_query_embedding(query) - with Session(engine) as session: - tables = hybrid_search_tables(query, emb, session) - if not tables: - return "No tables found matching query." - - results = [] - for t in tables: - enrichment = session.exec( - select(EnrichmentVersion) - .where(EnrichmentVersion.table_id == t.id) - .order_by(EnrichmentVersion.version.desc()) - ).first() - desc = enrichment.data.get("table_description", "") if enrichment and enrichment.data else "" - results.append({ - "id": t.id, - "name": f"{t.schema_name}.{t.name}", - "catalog": t.catalog, - "description": desc - }) - return json.dumps(results, indent=2) + @tool async def get_table_profile(table_id: str) -> str: """Get the lightweight column names/types for a table, and the Esca reference ID for the full profiling statistics. Use this before planning a query.""" + cache_hit = False + with Session(engine) as session: table = session.get(Table, table_id) if not table: return json.dumps({"error": f"Table ID {table_id} not found."}) - + profile = session.exec( select(TableProfile) - .where(TableProfile.table_id == table_id, TableProfile.status == "completed") + .where( + TableProfile.table_id == table_id, TableProfile.status == "completed" + ) .order_by(TableProfile.created_at.desc()) ).first() - + if not profile: - return json.dumps({"error": f"No completed profile found for Table ID {table_id}. Make sure to trigger profiling first."}) - + return json.dumps( + { + "error": f"No completed profile found for Table ID {table_id}. Make sure to trigger profiling first." + } + ) + + # ── G2-05: Redis cache lookup ───────────────────────────────────────── + cache_key = _cache.profile_key(table_id, profile.id) + cached = await _cache.get_json(cache_key) + if cached is not None: + cache_hit = True + # Lightweight wrapper returned from cache + return json.dumps(cached) + columns = session.exec( select(ColumnProfile).where(ColumnProfile.profile_id == profile.id) ).all() - + # Heavy data to pass by reference to Esca profile_data = { "table_id": table_id, @@ -193,134 +297,399 @@ async def get_table_profile(table_id: str) -> str: "type": cp.data_type, "null_rate": cp.null_rate, "distinct_count": cp.distinct_count, - "top_values": cp.top_values + "top_values": cp.top_values, } for cp in columns - ] + ], } - + # Save heavy data in Esca esca_payload = json.dumps(profile_data).encode() - - client = EscaClient(api_key=settings.ESCA_API_KEY, base_url=settings.ESCA_URL) - try: - res = await client.save_data(esca_payload) - esca_id = res.get("esca_id") - except Exception as e: - esca_id = None - logger.error(f"Error: Failed to save profile to Esca for table {table_id}: {e}") - # TODO: handle error - finally: - await client.close() - - # Return only lightweight metadata to LLM, but include categorical options so LLM can map terms - return json.dumps({ + + async with get_esca_client() as client: + try: + res = await client.save_data(esca_payload) + esca_id = res.get("esca_id") + except Exception as e: + esca_id = None + from agent.langfuse_client import langfuse_client + + if langfuse_client and langfuse_client.get_current_trace_id(): + langfuse_client.update_current_span( + level="WARNING", + status_message=f"ESCA write failed for profile: {e}", + ) + else: + logger.warning( + f"Error: Failed to save profile to Esca for table {table_id}: {e}" + ) + + # ── Fetch table description from EnrichmentVersion ───────────────────── + table_description = "" + enrichment = session.exec( + select(EnrichmentVersion) + .where(EnrichmentVersion.table_id == table_id) + .order_by(EnrichmentVersion.version.desc()) + ).first() + if enrichment and enrichment.data: + # Prefer human annotation, fall back to AI summary + table_description = ( + enrichment.data.get("table_description", "") + or enrichment.data.get("ai_summary", "") + ) + + # Lightweight response to cache and return to LLM + lightweight = { "table_id": table_id, "table_name": f"{table.catalog}.{table.schema_name}.{table.name}", + "description": table_description, "row_count": profile.row_count, "columns": [ - { - "name": cp.column_name, - "type": cp.data_type, - "is_categorical": cp.is_categorical, - "top_values": [v.get("value") for v in cp.top_values] if cp.is_categorical and cp.top_values else None - } + _build_column_context(cp) for cp in columns ], - "esca_reference_id": esca_id - }, indent=2) + "esca_reference_id": esca_id, + } + + # ── G2-05: Populate cache ───────────────────────────────────────────── + await _cache.set_json(cache_key, lightweight, settings.PROFILE_CACHE_TTL) -async def schema_explorer_node(state: AgentState): - """RAG Schema Explorer sub-agent node, pausing for table selection if ambiguous.""" - user_query = state["user_query"] + return json.dumps(lightweight, indent=2) + + +async def schema_explorer_node(state: AgentState, config: RunnableConfig | None = None): + """RAG Schema Explorer sub-agent node — with G2-01 scoping, G2-03 enrichment, G2-05 caching.""" + thread_id = config.get("configurable", {}).get("thread_id", "") if config else "" + from agent.utils.redis_publisher import publish_node_event + await publish_node_event(thread_id, "schema_explorer") + + user_query = state.get("user_query") enrichments = state.get("query_enrichments", []) allowed_tables = state.get("allowed_tables") allowed_statuses = state.get("allowed_statuses") feedback = state.get("feedback") - + runtime_flags = state.get("runtime_flags") or {} + + # Resolve all flag-tunable parameters for this invocation + profile_fetch_concurrency = int(runtime_flags.get("PROFILE_FETCH_CONCURRENCY", settings.PROFILE_FETCH_CONCURRENCY)) + max_profiles_to_fetch = int(runtime_flags.get("MAX_PROFILES_TO_FETCH", settings.MAX_PROFILES_TO_FETCH)) + schema_semantic_typing = bool(runtime_flags.get("SCHEMA_SEMANTIC_TYPING", settings.ENABLE_SEMANTIC_TYPING)) + schema_join_graph = bool(runtime_flags.get("SCHEMA_JOIN_GRAPH", settings.ENABLE_JOIN_GRAPH)) + schema_summarization = bool(runtime_flags.get("SCHEMA_SUMMARIZATION", settings.ENABLE_SCHEMA_SUMMARIZATION)) + schema_ambiguity_detect = bool(runtime_flags.get("SCHEMA_AMBIGUITY_DETECT", settings.ENABLE_AMBIGUITY_DETECT)) + scoping_mode_flag = runtime_flags.get("TABLE_SCOPING_MODE", settings.TABLE_SCOPING_MODE) + + # Per-invocation LLM (supports model switching via execution mode) + _llm = get_llm("schema_explorer", runtime_flags=runtime_flags) + + # ── G2-01: Resolve scoping mode (state > runtime_flag > env default) ───────── + scoping_mode: str = state.get("scoping_mode") or scoping_mode_flag + + # ── G2-05: Cache hit/miss counters (pushed to Langfuse at end) ──────────── + cache_hit_count = 0 + cache_miss_count = 0 + # 1. Automatically run hybrid search to find candidates emb = get_query_embedding(user_query) with Session(engine) as session: - candidate_tables = hybrid_search_tables(user_query, emb, session, allowed_tables, allowed_statuses) - + candidate_tables = hybrid_search_tables( + user_query, emb, session, allowed_tables, allowed_statuses, scoping_mode + ) + tables_info = [] profile_details = [] - - # 2. Automatically get profiles for the top candidate tables (up to 4) to seed the prompt - for i, t in enumerate(candidate_tables): - tables_info.append({ - "id": t.id, - "name": f"{t.catalog}.{t.schema_name}.{t.name}", - "description": "" - }) - - # Fetch profile for the top tables based on MAX_PROFILES_TO_FETCH - if i < settings.MAX_PROFILES_TO_FETCH: + + # 2. Get profiles for top candidate tables (G2-05 cache-aware) + import asyncio + + sem = asyncio.Semaphore(profile_fetch_concurrency) + + async def fetch_profile(t_id, t_name): + nonlocal cache_hit_count, cache_miss_count + async with sem: try: - profile_res = await get_table_profile.ainvoke({"table_id": t.id}) - profile_details.append(json.loads(profile_res)) + # Quick cache check at this level for hit/miss accounting + with Session(engine) as s: + profile_row = s.exec( + select(TableProfile) + .where(TableProfile.table_id == t_id, TableProfile.status == "completed") + .order_by(TableProfile.created_at.desc()) + ).first() + if profile_row: + ck = _cache.profile_key(t_id, profile_row.id) + hit = await _cache.get(ck) + if hit is not None: + cache_hit_count += 1 + else: + cache_miss_count += 1 + + profile_res = await get_table_profile.ainvoke({"table_id": t_id}) + return json.loads(profile_res) except Exception as e: - print(f"Error fetching profile for {t.name}: {e}") - - # TODO: Make more dynamic - allow LLM to search other tables if the first pass is not enough - # TODO: Support multi-turn conversation - # TODO: Support async simultanious profile fetching for top K tables - - # 3. Present all metadata to the LLM to construct a query plan - langfuse_prompt = langfuse_client.get_prompt(settings.LANGFUSE_PROMPT_SCHEMA_EXPLORER) - prompt = ChatPromptTemplate.from_messages(langfuse_prompt.get_langchain_prompt()) - - human_message = f"Question: {user_query}\nQuery Enrichments (extra context for ambiguous terms): {json.dumps(enrichments)}" + print(f"Error fetching profile for {t_name}: {e}") + return None + + fetch_tasks = [] + for i, t in enumerate(candidate_tables): + tables_info.append( + { + "id": t.id, + "name": f"{t.catalog}.{t.schema_name}.{t.name}", + "description": "", + } + ) + if i < max_profiles_to_fetch: + fetch_tasks.append(fetch_profile(t.id, t.name)) + + if fetch_tasks: + results = await asyncio.gather(*fetch_tasks, return_exceptions=True) + for res in results: + if res and not isinstance(res, Exception): + profile_details.append(res) + + # ── G2-03: Advanced Schema Enrichment phases ────────────────────────────── + active_phases: list[str] = [] + table_ids = [t.id for t in candidate_tables] + + # human_message = ( + # f"Question: {user_query}\n" + # f"Query Enrichments (extra context for ambiguous terms): {json.dumps(enrichments)}" + # ) + human_message = user_query if feedback: human_message += f"\nUser Feedback on previous plan/query: {feedback}" - - structured_llm = llm.with_structured_output(SchemaExplorerOutput, method="json_schema") + + # G2-01 strict mode prompt injection + if scoping_mode == "strict": + human_message += ( + "\n\n[STRICT MODE] Only use tables from the approved list. " + "Do not suggest alternatives.\n" + f"Approved tables: {json.dumps(allowed_tables)}" + ) + + # Phase A: Semantic Typing + # if schema_semantic_typing and profile_details: + # try: + # profile_details = await run_semantic_typing(profile_details, _llm) + # active_phases.append("SCHEMA_SEMANTIC_TYPING") + # except Exception as exc: + # logger.warning("SCHEMA_SEMANTIC_TYPING phase failed: %s", exc) + + # Phase B: Join Graph + # if schema_join_graph and len(table_ids) >= 2: + # try: + # join_paths_json = await run_join_graph(table_ids) + # if join_paths_json: + # human_message += ( + # "\n\n[JOIN GRAPH] Shortest join paths between candidate tables:\n" + # + join_paths_json + # ) + # active_phases.append("SCHEMA_JOIN_GRAPH") + # except Exception as exc: + # logger.warning("SCHEMA_JOIN_GRAPH phase failed: %s", exc) + + # ── G3: Skill Injection ─────────────────────────────────────────────────── + # loaded_skills = state.get("loaded_skills") + # if loaded_skills: + # try: + # skill_prompts = _skill_registry.build_system_prompt_addition(loaded_skills) + # if skill_prompts: + # human_message += f"\n\n[APPLIED SKILLS]{skill_prompts}" + # except Exception as e: + # logger.warning(f"Failed to inject skills: {e}") + + + # Phase C: Schema Summarization (replaces profiles_json in prompt) + profiles_json_str = json.dumps(profile_details, indent=2) + if schema_summarization and profile_details: + try: + summaries = [ + f"[{p.get('table_name', 'unknown')}] {p.get('description', '') or '(no description available)'}" + for p in profile_details + ] + profiles_json_str = "\n".join(summaries) + active_phases.append("SCHEMA_SUMMARIZATION") + except Exception as exc: + logger.warning("SCHEMA_SUMMARIZATION phase failed: %s", exc) + + # Phase D: Ambiguity Detection + # if schema_ambiguity_detect and profile_details: + # try: + # notes = await run_ambiguity_detection(profile_details, user_query, _llm) + # if notes: + # human_message += "\n\n[AMBIGUITY NOTES]\n" + "\n".join(f"- {n}" for n in notes) + # active_phases.append("SCHEMA_AMBIGUITY_DETECT") + # except Exception as exc: + # logger.warning("SCHEMA_AMBIGUITY_DETECT phase failed: %s", exc) + + # ── Langfuse trace metadata ─────────────────────────────────────────────── + try: + trace_id = langfuse_client.get_current_trace_id() + if trace_id: + langfuse_client._create_trace_tags_via_ingestion( + trace_id=trace_id, tags=[f"scoping_mode={scoping_mode}"] + ) + langfuse_client.update_current_span( + metadata={ + "active_schema_phases": active_phases, + "cache_hit_count": cache_hit_count, + "cache_miss_count": cache_miss_count, + }, + ) + except Exception as exc: + logger.warning("Langfuse trace update failed in schema_explorer: %s", exc) + + # 3. Present all metadata to the LLM to construct a query plan + langfuse_prompt = langfuse_client.get_prompt( + settings.LANGFUSE_PROMPT_SCHEMA_EXPLORER + ) + prompt = ChatPromptTemplate.from_messages(langfuse_prompt.get_langchain_prompt()) + + structured_llm = _llm.with_structured_output( + SchemaExplorerOutput, method="json_schema" + ) chain = prompt | structured_llm - + try: - data = await chain.ainvoke({ - "tables_json": json.dumps(tables_info, indent=2), - "profiles_json": json.dumps(profile_details, indent=2), - "human_message": human_message - }) + data = await chain.ainvoke( + { + "tables_json": json.dumps(tables_info, indent=2), + "profiles_json": profiles_json_str, + "human_message": human_message, + } + ) except Exception as e: print(f"Structured output parsing failed in schema explorer: {e}") data = SchemaExplorerOutput( schema_plan=None, ambiguity_detected=False, ambiguity_message="", - candidate_options=[] + candidate_options=[], + ) + + if ( + data.ambiguity_detected + and data.ambiguity_message + and not state.get("non_interactive") + ): + user_choice = interrupt( + { + "type": "schema_explorer_ambiguity", + "message": data.ambiguity_message, + "options": data.candidate_options, + } ) - - if data.ambiguity_detected and data.ambiguity_message and not state.get("non_interactive"): - user_choice = interrupt({ - "type": "schema_explorer_ambiguity", - "message": data.ambiguity_message, - "options": data.candidate_options - }) - + clarified_message = f"{human_message}\nSelected table/option: {user_choice}" try: - data = await chain.ainvoke({ - "tables_json": json.dumps(tables_info, indent=2), - "profiles_json": json.dumps(profile_details, indent=2), - "human_message": clarified_message - }) + data = await chain.ainvoke( + { + "tables_json": json.dumps(tables_info, indent=2), + "profiles_json": profiles_json_str, + "human_message": clarified_message, + } + ) except Exception as e: - print(f"Structured output parsing failed in schema explorer after clarification: {e}") + print( + f"Structured output parsing failed in schema explorer after clarification: {e}" + ) data = SchemaExplorerOutput( schema_plan=None, ambiguity_detected=False, ambiguity_message="", - candidate_options=[] + candidate_options=[], ) - + plan = data.schema_plan if plan is not None and not isinstance(plan, str): plan = json.dumps(plan) elif plan is None: plan = "" - - return {"schema_plan": plan} + tables_used = getattr(data, "tables_used", []) + hallucinated = [] + + if tables_used: + try: + from python_core_utils.redis import get_redis_client + from core.trino import execute_query_sync + import asyncio + + redis_client = get_redis_client() + for t_name in tables_used: + cache_key = f"table_exists:{t_name}" + exists = await redis_client.get(cache_key) + if exists is None: + parts = t_name.split(".") + if len(parts) == 3: + cat, sch, tbl = parts + if not IDENT_RE.fullmatch(cat): + hallucinated.append(t_name) + continue + sql = f'SELECT 1 FROM "{cat}".information_schema.tables WHERE table_schema = ? AND table_name = ?' + params = [sch, tbl] + elif len(parts) == 2: + sch, tbl = parts + sql = 'SELECT 1 FROM information_schema.tables WHERE table_schema = ? AND table_name = ?' + params = [sch, tbl] + elif len(parts) == 1: + tbl = parts[0] + sql = 'SELECT 1 FROM information_schema.tables WHERE table_name = ?' + params = [tbl] + else: + hallucinated.append(t_name) + continue + + try: + res = await asyncio.to_thread( + execute_query_sync, sql, "", params + ) + if res.success and len(res.rows) > 0: + await redis_client.setex(cache_key, 3600, "1") + else: + await redis_client.setex(cache_key, 3600, "0") + hallucinated.append(t_name) + except Exception as e: + logger.error( + f"Information schema check failed for {t_name}: {e}" + ) + hallucinated.append(t_name) + elif exists == b"0": + hallucinated.append(t_name) + except Exception as e: + logger.error(f"Error during Redis/Trino table verification: {e}") + if tables_used: + hallucinated.extend(tables_used) + + retry_count = state.get("schema_explorer_retry_count", 0) or 0 + result_state: dict = {"schema_plan": plan} + + if hallucinated: + new_retry = retry_count + 1 + result_state["hallucinated_tables"] = hallucinated + result_state["feedback"] = ( + f"Do not use these tables, they do not exist: {', '.join(hallucinated)}" + ) + result_state["last_error"] = ( + f"Hallucinated tables detected: {', '.join(hallucinated)}" + ) + result_state["schema_explorer_retry_count"] = new_retry + + # G2-02: set escalation_reason when approaching the limit + if new_retry >= MAX_SCHEMA_RETRIES: + result_state["escalation_reason"] = ( + f"Schema explorer failed {new_retry} times due to hallucinated tables: " + f"{', '.join(hallucinated)}" + ) + else: + result_state["hallucinated_tables"] = None + result_state["feedback"] = None + result_state["last_error"] = None + result_state["schema_explorer_retry_count"] = 0 + + result_state["execution_path"] = ["schema_explorer"] + # Store enriched profiles for downstream nodes (refiner re-uses without re-fetch) + result_state["table_profiles"] = profile_details if profile_details else None + + return result_state diff --git a/agent/src/agent/routers/chat.py b/agent/src/agent/routers/chat.py index c28ee21..3a0caee 100644 --- a/agent/src/agent/routers/chat.py +++ b/agent/src/agent/routers/chat.py @@ -1,31 +1,28 @@ from sqlalchemy.ext.asyncio import AsyncSession -import os + import uuid from fastapi import APIRouter, Depends, HTTPException from pydantic import BaseModel from agent.graph import agent_graph from python_core_utils.rate_limiting import RateLimiter from langfuse.langchain import CallbackHandler -from agent.config import settings + from sqlmodel import select from core.db.engine import async_engine from core.models.models import Table, HttpExtractor, ExtractorStatus from langgraph.types import Command -# Set the environment variables for Langfuse from the validated Pydantic settings config -os.environ["LANGFUSE_PUBLIC_KEY"] = settings.LANGFUSE_PUBLIC_KEY -os.environ["LANGFUSE_SECRET_KEY"] = settings.LANGFUSE_SECRET_KEY -os.environ["LANGFUSE_HOST"] = settings.LANGFUSE_BASE_URL +from core.langfuse import get_langfuse_handler -# Initialize Langfuse handler -langfuse_handler = CallbackHandler() router = APIRouter(prefix="/api/v1/agent", tags=["agent"]) + class QueryApproval(BaseModel): approved: bool feedback: str | None = None + class ChatRequest(BaseModel): query: str | None = None thread_id: str | None = None @@ -50,13 +47,16 @@ class ChatResponse(BaseModel): @router.post( "/chat", response_model=ChatResponse, - dependencies=[Depends(RateLimiter(requests=10, window=60, fail_open=False))] + dependencies=[Depends(RateLimiter(requests=10, window=60, fail_open=False))], ) -async def chat_endpoint(request: ChatRequest): +async def chat_endpoint( + request: ChatRequest, + langfuse_handler: CallbackHandler = Depends(get_langfuse_handler), +): thread_id = request.thread_id or str(uuid.uuid4()) config = { "configurable": {"thread_id": thread_id}, - "callbacks": [langfuse_handler] + "callbacks": [langfuse_handler] if langfuse_handler else [], } if request.resume_value is not None: @@ -64,68 +64,84 @@ async def chat_endpoint(request: ChatRequest): if not state_snapshot.values: raise HTTPException( status_code=404, - detail=f"Thread ID '{thread_id}' not found or has no active session." + detail=f"Thread ID '{thread_id}' not found or has no active session.", ) - + resume_val = request.resume_value if isinstance(resume_val, QueryApproval): resume_val = resume_val.model_dump() - - result = await agent_graph.ainvoke( - Command(resume=resume_val), - config=config - ) + + result = await agent_graph.ainvoke(Command(resume=resume_val), config=config) else: if not request.query: raise HTTPException( - status_code=400, - detail="Query is required for new chat session." + status_code=400, detail="Query is required for new chat session." ) - + if request.allowed_tables: async with AsyncSession(async_engine) as session: all_tables = (await session.execute(select(Table))).scalars().all() for allowed in request.allowed_tables: exists = False for t in all_tables: - if (t.id == allowed or - t.name == allowed or - f"{t.schema_name}.{t.name}" == allowed): + if ( + t.id == allowed + or t.name == allowed + or f"{t.schema_name}.{t.name}" == allowed + ): exists = True break if not exists: raise HTTPException( - status_code=400, - detail=f"Table '{allowed}' does not exist." + status_code=400, detail=f"Table '{allowed}' does not exist." ) active_extractors = [] async with AsyncSession(async_engine) as session: if request.extractors: for ext_name_or_id in request.extractors: - ext = (await session.execute(select(HttpExtractor).where( - (HttpExtractor.id == ext_name_or_id) | (HttpExtractor.name == ext_name_or_id) - ))).scalars().first() + ext = ( + ( + await session.execute( + select(HttpExtractor).where( + (HttpExtractor.id == ext_name_or_id) + | (HttpExtractor.name == ext_name_or_id) + ) + ) + ) + .scalars() + .first() + ) if not ext: raise HTTPException( status_code=400, - detail=f"Extractor '{ext_name_or_id}' does not exist." + detail=f"Extractor '{ext_name_or_id}' does not exist.", ) active_extractors.append({"name": ext.name, "url": ext.url}) else: - prod_extractors = (await session.execute(select(HttpExtractor).where(HttpExtractor.status == ExtractorStatus.production))).scalars().all() + prod_extractors = ( + ( + await session.execute( + select(HttpExtractor).where( + HttpExtractor.status == ExtractorStatus.production + ) + ) + ) + .scalars() + .all() + ) for ext in prod_extractors: active_extractors.append({"name": ext.name, "url": ext.url}) result = await agent_graph.ainvoke( { - "user_query": request.query, + "user_query": request.query, "allowed_tables": request.allowed_tables, "allowed_statuses": request.allowed_statuses, "active_extractors": active_extractors, - "non_interactive": not request.hitl_enabled + "non_interactive": not request.hitl_enabled, }, - config=config + config=config, ) final_state = await agent_graph.aget_state(config) @@ -135,8 +151,8 @@ async def chat_endpoint(request: ChatRequest): thread_id=thread_id, status="interrupted", interrupt_details=interrupt_val, - schema_plan=final_state.values.get("schema_plan"), - sql_query=final_state.values.get("sql_query") + schema_plan=final_state.values.get("schema_plan") or (interrupt_val.get("schema_plan") if isinstance(interrupt_val, dict) else None), + sql_query=final_state.values.get("sql_query") or (interrupt_val.get("sql_query") if isinstance(interrupt_val, dict) else None), ) return ChatResponse( @@ -146,7 +162,5 @@ async def chat_endpoint(request: ChatRequest): raw_data_ref=result.get("raw_data_ref"), sql_query=result.get("sql_query"), sql_explanation=result.get("sql_explanation"), - schema_plan=result.get("schema_plan") + schema_plan=result.get("schema_plan"), ) - - diff --git a/agent/src/agent/state.py b/agent/src/agent/state.py index fc84dc7..84482af 100644 --- a/agent/src/agent/state.py +++ b/agent/src/agent/state.py @@ -1,10 +1,12 @@ -import operator from typing import Annotated, TypedDict, Any from langchain_core.messages import BaseMessage from langgraph.graph.message import add_messages +import operator + class AgentState(TypedDict): user_query: str + execution_path: Annotated[list[str], operator.add] messages: Annotated[list[BaseMessage], add_messages] query_enrichments: list[dict[str, Any]] schema_plan: str @@ -17,7 +19,28 @@ class AgentState(TypedDict): allowed_tables: list[str] | None allowed_statuses: list[str] | None feedback: str | None + rejection_category: str | None feedback_route: str | None non_interactive: bool | None active_extractors: list[dict[str, str]] | None - + active_skills: list[str] | None + loaded_skills: list[dict] | None + last_error: str | None + hallucinated_tables: list[str] | None + esca_write_failed: bool | None + inline_result_rows: list[dict[str, Any]] | None + error_history: list[str] | None + schema_explorer_retry_count: int | None + # G2-01: table scoping + scoping_mode: str | None # 'strict' | 'hybrid' + # G2-02: HITL escalation + escalated: bool | None + escalation_reason: str | None + # G2-04: satisfaction check + satisfaction_failures: list[str] | None + satisfaction_fail_count: int | None + # G4: feature flags & execution modes + execution_mode: str | None # e.g. "cost_saving", "high_quality", "benchmark" + runtime_flags: dict[str, Any] | None # resolved by init_flags_node + # Enriched table profiles — populated by schema_explorer for reuse by refiner + table_profiles: list[dict[str, Any]] | None diff --git a/agent/src/agent/utils/esca.py b/agent/src/agent/utils/esca.py new file mode 100644 index 0000000..2cbef87 --- /dev/null +++ b/agent/src/agent/utils/esca.py @@ -0,0 +1,17 @@ +from contextlib import asynccontextmanager +from typing import AsyncGenerator +from esca_sdk import EscaClient +from agent.config import settings + + +@asynccontextmanager +async def get_esca_client() -> AsyncGenerator[EscaClient, None]: + """ + Asynchronous context manager to encapsulate EscaClient lifecycle. + Ensures that the client connection is always cleanly closed. + """ + client = EscaClient(api_key=settings.ESCA_API_KEY, base_url=settings.ESCA_URL) + try: + yield client + finally: + await client.close() diff --git a/agent/src/agent/utils/flag_bridge.py b/agent/src/agent/utils/flag_bridge.py new file mode 100644 index 0000000..ce9fc0f --- /dev/null +++ b/agent/src/agent/utils/flag_bridge.py @@ -0,0 +1,155 @@ +""" +FlagBridge (G4 Agent Integration) +=================================== +Resolves runtime flag values for a single agent invocation. + +Resolution order (highest → lowest priority): + 1. Execution mode overrides (config.execution_modes.flag_overrides by name) + 2. DB flag overrides (config.feature_flags.value — cached 30s) + 3. AgentSettings env defaults (always-on fallback when backend unreachable) + +Usage: + bridge = FlagBridge() + flags = await bridge.resolve_flags(execution_mode="cost_saving") + model = flags.get("QUERY_BUILDER_MODEL", settings.LLM_MODEL) +""" + +import logging +from typing import Any + +import httpx +from agent.config import settings + +logger = logging.getLogger(__name__) + +# Default env-var flag map — used as fallback when backend is unreachable +_ENV_DEFAULTS: dict[str, Any] = { + # Extraction + "EXTRACTOR_MODEL": settings.LLM_MODEL, + "EXTRACTOR_TEMPERATURE": 0.0, + "EXTRACTOR_TOP_K_TABLES": settings.HYBRID_SEARCH_MAX_TABLES, + "TABLE_SCOPING_MODE": settings.TABLE_SCOPING_MODE, + # Schema Explorer + "MAX_PROFILES_TO_FETCH": settings.MAX_PROFILES_TO_FETCH, + "PROFILE_FETCH_CONCURRENCY": settings.PROFILE_FETCH_CONCURRENCY, + "SCHEMA_CACHE_TTL": settings.SCHEMA_CACHE_TTL, + "PROFILE_CACHE_TTL": settings.PROFILE_CACHE_TTL, + "SCHEMA_SEMANTIC_TYPING": settings.ENABLE_SEMANTIC_TYPING, + "SCHEMA_JOIN_GRAPH": settings.ENABLE_JOIN_GRAPH, + "SCHEMA_SUMMARIZATION": settings.ENABLE_SCHEMA_SUMMARIZATION, + "SCHEMA_AMBIGUITY_DETECT": settings.ENABLE_AMBIGUITY_DETECT, + "SCHEMA_SUMMARY_MODEL": settings.LLM_MODEL, + "SCHEMA_TOP_K_JOINS": 5, + # Query Builder + "QUERY_BUILDER_MODEL": settings.LLM_MODEL, + "QUERY_BUILDER_TEMPERATURE": 0.0, + # Refiner + "MAX_REFINER_ITERATIONS": 4, + "MAX_SCHEMA_REPLAN_ITERATIONS": 2, + "REFINER_MODEL": settings.LLM_MODEL, + # Satisfaction Check + "SATISFACTION_CHECK_ENABLED": settings.SATISFACTION_CHECK_ENABLED, + "SATISFACTION_CHECK_EXECUTION": settings.SATISFACTION_CHECK_EXECUTION, + "SATISFACTION_CHECK_PLAUSIBILITY": settings.SATISFACTION_CHECK_PLAUSIBILITY, + "SATISFACTION_CHECK_COLUMNS": settings.SATISFACTION_CHECK_COLUMNS, + "SATISFACTION_CHECK_SEMANTIC": settings.SATISFACTION_CHECK_SEMANTIC, + "SATISFACTION_MIN_ROWS": settings.SATISFACTION_MIN_ROWS, + "SATISFACTION_MAX_ROWS": settings.SATISFACTION_MAX_ROWS, + "SATISFACTION_SEMANTIC_THRESHOLD": settings.SATISFACTION_SEMANTIC_THRESHOLD, + "SATISFACTION_JUDGE_MODEL": settings.LLM_MODEL, + # Skills + "SKILLS_ENABLED": True, + "SKILLS_HOT_RELOAD": settings.SKILLS_HOT_RELOAD, + "SKILLS_CACHE_TTL": 900, + # Evaluation + "LLM_JUDGE_ENABLED": True, + "EVAL_PARALLEL_WORKERS": 4, + "EVAL_JUDGE_MODEL": settings.LLM_MODEL, + # Catalog Validation + "CATALOG_VALIDATION_ENABLED": True, + "CATALOG_CACHE_TTL": 300, +} + + +class FlagBridge: + """ + Lightweight async HTTP client that fetches flag values from the Studio backend. + Falls back gracefully if BACKEND_URL is not set or backend is unreachable. + """ + + def __init__(self) -> None: + self._base_url = settings.BACKEND_URL.rstrip("/") if settings.BACKEND_URL else "" + + async def resolve_flags(self, execution_mode: str | None = None) -> dict[str, Any]: + """ + Build and return the fully-merged runtime flag map for this invocation. + + Steps: + 1. Start with env-var defaults (always available). + 2. Overlay DB flag values fetched from backend (if reachable). + 3. Overlay execution mode overrides (if a mode name is given). + """ + # Layer 1: env defaults + resolved: dict[str, Any] = dict(_ENV_DEFAULTS) + + if not self._base_url: + logger.debug("FlagBridge: BACKEND_URL not set, using env-var defaults only") + if execution_mode: + logger.warning( + "FlagBridge: execution_mode='%s' requested but BACKEND_URL is not set", + execution_mode, + ) + return resolved + + # Layer 2: DB flag overrides + try: + async with httpx.AsyncClient(timeout=3.0) as client: + resp = await client.get(f"{self._base_url}/api/flags/map") + if resp.status_code == 200: + db_flags: dict[str, Any] = resp.json() + # Only overlay flags that have a non-null value in the DB + for name, value in db_flags.items(): + if value is not None: + resolved[name] = value + logger.debug("FlagBridge: loaded %d DB flag overrides", len(db_flags)) + else: + logger.warning( + "FlagBridge: /api/flags/map returned %d, using env defaults", + resp.status_code, + ) + except Exception as exc: + logger.warning("FlagBridge: failed to fetch flag map: %s — using env defaults", exc) + + # Layer 3: execution mode overrides + if execution_mode and execution_mode != "default": + try: + async with httpx.AsyncClient(timeout=3.0) as client: + resp = await client.get( + f"{self._base_url}/api/flags/modes/{execution_mode}" + ) + if resp.status_code == 200: + mode_data = resp.json() + overrides: dict = mode_data.get("flag_overrides") or {} + resolved.update(overrides) + logger.info( + "FlagBridge: applied execution_mode='%s' (%d overrides)", + execution_mode, + len(overrides), + ) + elif resp.status_code == 404: + logger.warning( + "FlagBridge: execution_mode='%s' not found in DB", + execution_mode, + ) + else: + logger.warning( + "FlagBridge: /api/flags/modes/%s returned %d", + execution_mode, + resp.status_code, + ) + except Exception as exc: + logger.warning( + "FlagBridge: failed to fetch mode '%s': %s", execution_mode, exc + ) + + return resolved diff --git a/agent/src/agent/utils/jeen_client.py b/agent/src/agent/utils/jeen_client.py new file mode 100644 index 0000000..aaf8e30 --- /dev/null +++ b/agent/src/agent/utils/jeen_client.py @@ -0,0 +1,59 @@ +import httpx +import logging +from typing import Any +from agent.config import settings + +logger = logging.getLogger(__name__) + + +class JeenSkillClient: + """Client for fetching skills from the Jeen platform's internal API.""" + + def __init__(self): + self.base_url = settings.JEEN_LLM_CORE_URL.rstrip("/") + self.api_key = settings.JEEN_API_KEY + self.is_configured = bool(self.base_url and self.api_key) + + if not self.is_configured: + logger.info( + "JeenSkillClient is not fully configured (JEEN_LLM_CORE_URL or JEEN_API_KEY missing). " + "Skill fetching will be skipped." + ) + + async def fetch_skills_by_ids(self, skill_ids: list[str]) -> list[dict[str, Any]]: + """ + Fetch active skills from Jeen by their UUIDs. + Uses POST /admin/assets/skills/by-ids. + """ + if not self.is_configured or not skill_ids: + return [] + + url = f"{self.base_url}/admin/assets/skills/by-ids" + headers = { + "Authorization": f"Bearer {self.api_key}", + "Content-Type": "application/json", + } + payload = { + "ids": skill_ids, + "page": 1, + "limit": len(skill_ids) + 10, + } + + try: + async with httpx.AsyncClient() as client: + response = await client.post(url, headers=headers, json=payload, timeout=10.0) + response.raise_for_status() + data = response.json() + + # AdminAssetsPaginatedResponseDto returns { items: [...], total, page, ... } + items = data.get("items", []) + + # Filter only active skills + active_skills = [s for s in items if s.get("isActive")] + + logger.info(f"Successfully fetched {len(active_skills)} active skills from Jeen.") + return active_skills + except Exception as e: + logger.error(f"Failed to fetch skills from Jeen API: {e}", exc_info=True) + # Do not fall, the agent should pass and ignore if an error occurs + return [] diff --git a/agent/src/agent/utils/redis_publisher.py b/agent/src/agent/utils/redis_publisher.py new file mode 100644 index 0000000..833c6ad --- /dev/null +++ b/agent/src/agent/utils/redis_publisher.py @@ -0,0 +1,40 @@ +import json +import logging +from agent.config import settings + +logger = logging.getLogger(__name__) + +_sync_redis = None + +def get_sync_redis(): + global _sync_redis + if _sync_redis is None: + import redis + _sync_redis = redis.from_url(settings.REDIS_URL) + return _sync_redis + +async def publish_node_event(thread_id: str, node: str, status: str = "active"): + """ + Publish an execution path event to Redis so the backend can stream it via SSE. + """ + if not thread_id: + return + + try: + from python_core_utils.redis import get_redis_client + redis_client = get_redis_client() + if redis_client: + payload = json.dumps({"thread_id": thread_id, "node": node, "status": status}) + await redis_client.publish(f"agent_stream:{thread_id}", payload) + except Exception as e: + logger.warning(f"Failed to publish node event to Redis: {e}") + +def publish_node_event_sync(thread_id: str, node: str, status: str = "active"): + if not thread_id: + return + try: + r = get_sync_redis() + payload = json.dumps({"thread_id": thread_id, "node": node, "status": status}) + r.publish(f"agent_stream:{thread_id}", payload) + except Exception as e: + logger.warning(f"Failed to publish sync node event to Redis: {e}") diff --git a/agent/src/agent/utils/schema_enrichment.py b/agent/src/agent/utils/schema_enrichment.py new file mode 100644 index 0000000..e93097a --- /dev/null +++ b/agent/src/agent/utils/schema_enrichment.py @@ -0,0 +1,378 @@ +""" +G2-03: Advanced Schema Explorer — Enrichment Phases +==================================================== +Four independently feature-gated async functions that enrich schema context +before the LLM planning call in schema_explorer_node. + +Phase constants (used in Langfuse trace metadata): + SCHEMA_SEMANTIC_TYPING + SCHEMA_JOIN_GRAPH + SCHEMA_SUMMARIZATION + SCHEMA_AMBIGUITY_DETECT + +Join-graph algorithm +-------------------- +Uses networkx.DiGraph populated from: + • ForeignKeyMapping rows (explicit FK declarations) + • CrossTableProfile rows (auto-detected join suggestions) +BFS shortest path (nx.shortest_path) is run between every pair of +candidate table_ids. If networkx is unavailable the function falls +back to a pure-Python BFS implementation so the phase never hard-fails. +""" + +from __future__ import annotations + +import json +import logging +from typing import Any + +from pydantic import BaseModel, Field +from sqlmodel import Session, select + +from core.db.engine import engine +from core.models.models import CrossTableProfile, ForeignKeyMapping, Table + +logger = logging.getLogger(__name__) + + +# ─── Pydantic schemas for structured LLM calls ──────────────────────────────── + + +class SemanticAnnotation(BaseModel): + table_column: str = Field(description="The full table_name.column_name identifier") + semantic_type: str = Field(description="Must be one of: id | timestamp | category | metric | text | geo | unknown") + + +class SemanticTypingOutput(BaseModel): + """Maps table_name.column_name → semantic type.""" + + annotations: list[SemanticAnnotation] = Field( + default_factory=list, + description="List of column annotations." + ) + + +class SummarizationOutput(BaseModel): + summary: str = Field( + description="≤3-sentence plain-English description of the table's purpose and key columns." + ) + + +class AmbiguityOutput(BaseModel): + ambiguity_notes: list[str] = Field( + default_factory=list, + description=( + "List of ambiguity notes for column/table names relative to the user query. " + "Empty list if nothing is ambiguous." + ), + ) + + +class ColumnCoverageOutput(BaseModel): + """Used by G2-04 satisfaction check (imported from here for reuse).""" + + satisfies_question: bool = Field( + description="True if the SQL column headers conceptually answer the user's question." + ) + reason: str = Field(default="", description="Brief rationale.") + + +class SemanticAlignmentOutput(BaseModel): + """Used by G2-04 satisfaction check.""" + + alignment_score: float = Field( + ge=0.0, + le=1.0, + description="0–1 score of how well the query output schema matches the question intent.", + ) + reason: str = Field(default="") + + +class PlausibleZeroRowsOutput(BaseModel): + """Used by G2-04 satisfaction check to verify if a 0-row result is plausible.""" + + is_plausible: bool = Field( + description="True if returning 0 rows is normal/plausible for this query if no matching data exists in the database. False if the query contains structural logic bugs (e.g. invalid join keys, mismatched filters)." + ) + reason: str = Field(default="", description="Detailed rationale of why the query is logically correct or has a bug.") + + +# ─── Phase A: Semantic Typing ───────────────────────────────────────────────── + + +async def run_semantic_typing( + profiles: list[dict[str, Any]], + llm: Any, +) -> list[dict[str, Any]]: + """ + Classify each column in *profiles* with a semantic type via a single + structured LLM call. Returns the mutated profiles list. + """ + if not profiles: + return profiles + + # Build a compact column list for the prompt + col_list = [] + for p in profiles: + tname = p.get("table_name", "unknown") + for col in p.get("columns", []): + col_list.append(f"{tname}.{col['name']} ({col.get('type', '?')})") + + prompt_text = ( + "Classify each column with one of: id | timestamp | category | metric | text | geo | unknown.\n" + "Columns:\n" + "\n".join(col_list) + ) + + try: + structured = llm.with_structured_output(SemanticTypingOutput, method="json_schema") + result = await structured.ainvoke(prompt_text) + + # Handle both Pydantic model and raw dict responses (some LLM integrations return dicts when method="json_schema") + annotations = getattr(result, "annotations", []) if not isinstance(result, dict) else result.get("annotations", []) + + lookup = {} + for item in annotations: + # Item could be a dict or a SemanticAnnotation model + if isinstance(item, dict): + col = item.get("table_column") + sem = item.get("semantic_type") + else: + col = getattr(item, "table_column", None) + sem = getattr(item, "semantic_type", None) + + if col and sem: + lookup[col] = sem + + for p in profiles: + tname = p.get("table_name", "unknown") + for col in p.get("columns", []): + key = f"{tname}.{col['name']}" + if key in lookup: + col["semantic_type"] = lookup[key] + except Exception as exc: + logger.warning("run_semantic_typing failed: %s", exc, exc_info=True) + + return profiles + + +# ─── Phase B: Join Graph (BFS via networkx + FK/CrossTableProfile data) ─────── + + +def _bfs_shortest_path( + graph: dict[str, list[str]], source: str, target: str +) -> list[str] | None: + """Pure-Python BFS fallback returning the shortest path or None.""" + from collections import deque + + visited = {source} + queue: deque[list[str]] = deque([[source]]) + while queue: + path = queue.popleft() + node = path[-1] + if node == target: + return path + for neighbour in graph.get(node, []): + if neighbour not in visited: + visited.add(neighbour) + queue.append(path + [neighbour]) + return None + + +async def run_join_graph( + table_ids: list[str], + session: Session | None = None, +) -> str: + """ + Build a directed join graph from ForeignKeyMapping + CrossTableProfile rows, + then compute BFS shortest paths between all candidate table pairs. + Returns a JSON string suitable for appending to human_message. + """ + if not table_ids or len(table_ids) < 2: + return "" + + own_session = session is None + if own_session: + session = Session(engine) + + try: + # Load FK mappings touching our candidate tables + fk_rows = session.exec( + select(ForeignKeyMapping).where( + ForeignKeyMapping.table_id.in_(table_ids) # type: ignore[attr-defined] + ) + ).all() + + # Load cross-table profile suggestions touching our candidates + ctp_rows = session.exec( + select(CrossTableProfile).where( + CrossTableProfile.source_table_id.in_(table_ids) # type: ignore[attr-defined] + ) + ).all() + + # Resolve table_id → qualified name + id_to_name: dict[str, str] = {} + all_related_ids = ( + table_ids + + [fk.target_table_id for fk in fk_rows] + + [ctp.target_table_id for ctp in ctp_rows] + ) + for t in session.exec( + select(Table).where(Table.id.in_(list(set(all_related_ids)))) # type: ignore[attr-defined] + ).all(): + id_to_name[t.id] = f"{t.catalog}.{t.schema_name}.{t.name}" + + finally: + if own_session: + session.close() + + # Build graph (prefer networkx, fall back to adjacency dict) + try: + import networkx as nx # type: ignore[import-untyped] + + G: nx.DiGraph = nx.DiGraph() + for fk in fk_rows: + src = id_to_name.get(fk.table_id, fk.table_id) + tgt = id_to_name.get(fk.target_table_id, fk.target_table_id) + G.add_edge( + src, + tgt, + via=f"{fk.source_column} = {fk.target_column}", + weight=1, + ) + for ctp in ctp_rows: + src = id_to_name.get(ctp.source_table_id, ctp.source_table_id) + tgt = id_to_name.get(ctp.target_table_id, ctp.target_table_id) + weight = 1 if ctp.match_strength == "strong" else 2 + G.add_edge( + src, + tgt, + via=ctp.join_suggestion or "inferred", + weight=weight, + ) + + paths: list[dict[str, Any]] = [] + node_names = [id_to_name.get(tid, tid) for tid in table_ids] + for i, a in enumerate(node_names): + for b in node_names[i + 1 :]: + try: + path_nodes = nx.shortest_path(G, source=a, target=b, weight="weight") + edge_labels = [] + for u, v in zip(path_nodes, path_nodes[1:]): + edge_labels.append(G[u][v].get("via", "")) + paths.append({"from": a, "to": b, "path": path_nodes, "joins": edge_labels}) + except nx.NetworkXNoPath: + pass + except nx.NodeNotFound: + pass + + except ImportError: + # Fallback: adjacency dict + pure-Python BFS + adj: dict[str, list[str]] = {} + for fk in fk_rows: + src = id_to_name.get(fk.table_id, fk.table_id) + tgt = id_to_name.get(fk.target_table_id, fk.target_table_id) + adj.setdefault(src, []).append(tgt) + for ctp in ctp_rows: + src = id_to_name.get(ctp.source_table_id, ctp.source_table_id) + tgt = id_to_name.get(ctp.target_table_id, ctp.target_table_id) + adj.setdefault(src, []).append(tgt) + + paths = [] + node_names = [id_to_name.get(tid, tid) for tid in table_ids] + for i, a in enumerate(node_names): + for b in node_names[i + 1 :]: + p = _bfs_shortest_path(adj, a, b) + if p: + paths.append({"from": a, "to": b, "path": p}) + + if not paths: + return "" + + return json.dumps(paths, indent=2) + + +# ─── Phase C: Schema Summarization ─────────────────────────────────────────── + + +async def run_schema_summarization( + profiles: list[dict[str, Any]], + llm: Any, +) -> list[str]: + """ + Produce a ≤3-sentence plain-English summary for each table profile + via independent LLM calls. Returns a list of summary strings + (one per profile, same order). + """ + import asyncio + + summaries: list[str] = [] + # Limit concurrency to 1 to prevent local models like Ollama from crashing + sem = asyncio.Semaphore(1) + + async def _summarize_one(p: dict[str, Any]) -> str: + async with sem: + tname = p.get("table_name", "unknown") + columns = p.get("columns", []) + col_summary = ", ".join( + f"{c['name']} ({c.get('type', '?')})" for c in columns[:20] + ) + prompt = ( + f"Table: {tname}\n" + f"Row count: {p.get('row_count', 'unknown')}\n" + f"Columns: {col_summary}\n\n" + "Write a ≤3-sentence description of this table's purpose and most important columns." + ) + try: + structured = llm.with_structured_output(SummarizationOutput, method="json_schema") + # Handle both Pydantic model and raw dict responses gracefully + result = await structured.ainvoke(prompt) + + # Check if it's a dict or object + if isinstance(result, dict): + summary_text = result.get("summary", "(summarization unavailable)") + else: + summary_text = getattr(result, "summary", "(summarization unavailable)") + + return f"[{tname}] {summary_text}" + except Exception as exc: + logger.warning("run_schema_summarization failed for %s: %s", tname, exc) + return f"[{tname}] (summarization unavailable)" + + tasks = [_summarize_one(p) for p in profiles] + summaries = list(await asyncio.gather(*tasks)) + return summaries + + +# ─── Phase D: Ambiguity Detection ──────────────────────────────────────────── + + +async def run_ambiguity_detection( + profiles: list[dict[str, Any]], + user_query: str, + llm: Any, +) -> list[str]: + """ + Identify any column or table name ambiguities relative to the user query. + Returns a list of human-readable ambiguity notes (may be empty). + """ + if not profiles: + return [] + + col_names = [] + for p in profiles: + for col in p.get("columns", []): + col_names.append(f"{p.get('table_name','')}.{col['name']}") + + prompt = ( + f"User question: {user_query}\n" + f"Available columns: {', '.join(col_names[:80])}\n\n" + "List any ambiguous column or table names that could be misinterpreted for this question. " + "Return an empty list if nothing is ambiguous." + ) + try: + structured = llm.with_structured_output(AmbiguityOutput, method="json_schema") + result: AmbiguityOutput = await structured.ainvoke(prompt) + return result.ambiguity_notes + except Exception as exc: + logger.warning("run_ambiguity_detection failed: %s", exc) + return [] diff --git a/agent/src/agent/utils/skill_registry.py b/agent/src/agent/utils/skill_registry.py new file mode 100644 index 0000000..978a177 --- /dev/null +++ b/agent/src/agent/utils/skill_registry.py @@ -0,0 +1,84 @@ +import json +import logging +from typing import Any +from agent.utils.jeen_client import JeenSkillClient +from redis.asyncio import Redis +from agent.config import settings + +logger = logging.getLogger(__name__) + + +class SkillRegistry: + """Registry that caches and provides active skills fetched from Jeen.""" + + def __init__(self, redis_client: Redis | None = None): + self.jeen_client = JeenSkillClient() + self.redis = redis_client + self.cache_ttl = 300 # 5 minutes + + async def get_skills(self, skill_ids: list[str], hot_reload: bool = False, cache_ttl: int = 300) -> list[dict[str, Any]]: + """ + Fetch skills by ID, trying Redis cache first, then Jeen API. + """ + if not skill_ids: + return [] + + if not self.jeen_client.is_configured: + logger.debug("Jeen client not configured. Skipping skill fetch.") + return [] + + loaded_skills = [] + missing_ids = [] + + # 1. Try fetching from Redis + if self.redis and not hot_reload: + try: + keys = [f"skill:{sid}" for sid in skill_ids] + cached_values = await self.redis.mget(keys) + for sid, val in zip(skill_ids, cached_values): + if val: + loaded_skills.append(json.loads(val)) + else: + missing_ids.append(sid) + except Exception as e: + logger.error(f"Redis error while fetching skills: {e}") + missing_ids = skill_ids # Fallback to fetching all from Jeen + else: + missing_ids = skill_ids + + # 2. Fetch missing skills from Jeen + if missing_ids: + fetched = await self.jeen_client.fetch_skills_by_ids(missing_ids) + loaded_skills.extend(fetched) + + # 3. Cache the newly fetched skills + if self.redis and fetched: + try: + pipeline = self.redis.pipeline() + for skill in fetched: + key = f"skill:{skill['id']}" + pipeline.setex(key, cache_ttl, json.dumps(skill)) + await pipeline.execute() + except Exception as e: + logger.error(f"Redis error while caching skills: {e}") + + return loaded_skills + + def build_system_prompt_addition(self, loaded_skills: list[dict[str, Any]]) -> str: + """ + Compiles the system prompt fragments of the loaded skills. + """ + if not loaded_skills: + return "" + + fragments = [] + for skill in loaded_skills: + name = skill.get("displayName") or skill.get("name", "Unknown Skill") + fragment = skill.get("systemPromptFragment") + if fragment: + fragments.append(f"### Skill: {name}\n{fragment}") + + if not fragments: + return "" + + return "\n\n" + "\n\n".join(fragments) + "\n\n" diff --git a/agent/src/agent/utils/sql.py b/agent/src/agent/utils/sql.py new file mode 100644 index 0000000..3117dec --- /dev/null +++ b/agent/src/agent/utils/sql.py @@ -0,0 +1,21 @@ +import re + + +def clean_sql(query: str) -> str: + """ + Standardized utility for cleaning SQL generated by LLMs. + Strips out markdown code blocks and trailing semicolons. + """ + if not query: + return "" + + cleaned = query.strip() + cleaned = re.sub(r"^```(?:sql)?\s*", "", cleaned, flags=re.IGNORECASE) + cleaned = re.sub(r"\s*```$", "", cleaned) + + # Strip trailing semicolon if present + cleaned = cleaned.strip() + if cleaned.endswith(";"): + cleaned = cleaned[:-1].strip() + + return cleaned diff --git a/agent/tests/conftest.py b/agent/tests/conftest.py new file mode 100644 index 0000000..7e16b43 --- /dev/null +++ b/agent/tests/conftest.py @@ -0,0 +1,171 @@ +import pytest +import pytest_asyncio +from unittest.mock import AsyncMock, MagicMock, patch +import os +os.environ["LANGFUSE_PUBLIC_KEY"] = "pk-lf-123" +os.environ["LANGFUSE_SECRET_KEY"] = "sk-lf-123" +os.environ["LANGFUSE_BASE_URL"] = "http://localhost:3000" + +import json +from langchain_core.messages import AIMessage +from langchain_core.runnables import RunnableLambda + +# --- Mock LLM --- + +class MockStructuredLLM(RunnableLambda): + def __init__(self, expected_response=None): + self.expected_response = expected_response + + def _mock_invoke(x): + if hasattr(self, 'override_response'): + return self.override_response + # Attempt to return a generic object with a 'route' attribute for RejectionRoute, + # and generic fields for other schemas if needed. + class GenericStructured: + route = "extractor" + satisfies_question = True + alignment_score = 1.0 + reason = "Looks good" + ambiguity_detected = False + ambiguity_message = "" + schema_plan = "" + candidate_options = [] + return GenericStructured() + + super().__init__(_mock_invoke) + +from langchain_core.runnables import RunnableLambda + +class MockLLM(RunnableLambda): + def __init__(self): + super().__init__(lambda x: AIMessage(content="mocked LLM response")) + self.structured_calls = [] + + def with_structured_output(self, schema, method="json_schema"): + # Returns a new mock structured LLM. We can customize what it returns later. + return MockStructuredLLM() + +@pytest.fixture(autouse=True) +def mock_llm(): + mock_instance = MockLLM() + with patch("agent.llm.get_llm", return_value=mock_instance), \ + patch("agent.nodes.schema_explorer.get_llm", return_value=mock_instance), \ + patch("agent.nodes.refiner.get_llm", return_value=mock_instance), \ + patch("agent.nodes.query_builder.get_llm", return_value=mock_instance), \ + patch("agent.nodes.extractor.get_llm", return_value=mock_instance), \ + patch("agent.nodes.satisfaction_check.get_llm", return_value=mock_instance), \ + patch("agent.nodes.finalizer.get_llm", return_value=mock_instance), \ + patch("agent.nodes.schema_explorer.llm", mock_instance, create=True), \ + patch("agent.nodes.refiner.llm", mock_instance, create=True), \ + patch("agent.nodes.query_builder.llm", mock_instance, create=True), \ + patch("agent.graph.llm", mock_instance, create=True), \ + patch("agent.nodes.finalizer.llm", mock_instance, create=True), \ + patch("agent.nodes.satisfaction_check.llm", mock_instance, create=True): + yield mock_instance + +# --- Mock Redis --- + +class MockRedisPipeline: + def __init__(self): + self.commands = [] + + def delete(self, *keys): + self.commands.append(("delete", keys)) + + async def execute(self): + return [True] * len(self.commands) + +class MockRedisAsync: + def __init__(self): + self.store = {} + + async def get(self, key): + if isinstance(key, str): + key = key.encode() + return self.store.get(key) + + async def setex(self, key, ttl, value): + if isinstance(key, str): + key = key.encode() + if isinstance(value, str): + value = value.encode() + self.store[key] = value + + async def delete(self, key): + if isinstance(key, str): + key = key.encode() + self.store.pop(key, None) + + async def scan(self, cursor=0, match=None, count=100): + # Extremely simplified scan for testing + keys = [] + if match: + # simple wildcard match, e.g., "prefix:*" + prefix = match.replace("*", "").encode() + for k in self.store.keys(): + if k.startswith(prefix): + keys.append(k) + return (0, keys) + + def pipeline(self): + return MockRedisPipeline() + +@pytest.fixture +def mock_redis(): + mock_instance = MockRedisAsync() + with patch("redis.asyncio.from_url", return_value=mock_instance): + yield mock_instance + +# --- Mock Trino --- + +@pytest.fixture +def mock_trino(): + from core.trino import TrinoExecutionResult + + def _execute_query_sync(*args, **kwargs): + return TrinoExecutionResult(success=True, rows=[{"id": 1, "name": "test"}], columns=["id", "name"], error=None) + + with patch("core.trino.execute_query_sync", side_effect=_execute_query_sync) as mock_func: + yield mock_func + +# --- Mock Esca Client --- + +class MockEscaClientObj: + def __init__(self): + self.save_data = AsyncMock(return_value={"esca_id": "mock_esca_123"}) + +class MockEscaContextManager: + def __init__(self, client): + self.client = client + + async def __aenter__(self): + return self.client + + async def __aexit__(self, exc_type, exc_val, exc_tb): + pass + +@pytest.fixture +def mock_esca(): + client = MockEscaClientObj() + + def _get_client(*args, **kwargs): + return MockEscaContextManager(client) + + with patch("agent.utils.esca.get_esca_client", side_effect=_get_client): + yield client + +# --- Mock Langfuse --- + +@pytest.fixture(autouse=True) +def mock_langfuse(): + import agent.langfuse_client + + mock_prompt = MagicMock() + mock_prompt.get_langchain_prompt.return_value = [] + + with patch.object(agent.langfuse_client.langfuse_client, "get_current_trace_id", return_value="mock_trace_id", create=True), \ + patch.object(agent.langfuse_client.langfuse_client, "get_current_observation_id", return_value="mock_obs_id", create=True), \ + patch.object(agent.langfuse_client.langfuse_client, "trace", MagicMock(), create=True), \ + patch.object(agent.langfuse_client.langfuse_client, "span", MagicMock(), create=True), \ + patch.object(agent.langfuse_client.langfuse_client, "get_prompt", return_value=mock_prompt, create=True): + yield agent.langfuse_client.langfuse_client diff --git a/agent/tests/test_cache_and_gates.py b/agent/tests/test_cache_and_gates.py new file mode 100644 index 0000000..0959012 --- /dev/null +++ b/agent/tests/test_cache_and_gates.py @@ -0,0 +1,98 @@ +import pytest +from unittest.mock import patch, MagicMock, AsyncMock + +from agent.state import AgentState +from agent.nodes.satisfaction_check import satisfaction_check_node +from core.cache import CacheService +import json + +@pytest.mark.asyncio +async def test_tts_g2_04_satisfaction_check_multi_stage_gate(mock_langfuse, mock_llm): + # Base state + state: AgentState = { + "user_query": "test query", + "sql_query": "SELECT *", + "trino_error": None, + "inline_result_rows": [{"col": "val"}], # 1 row + "satisfaction_failures": None, + "satisfaction_fail_count": 0, + # Default all other keys + "messages": [], "query_enrichments": [], "schema_plan": "", "refinement_count": 0, + "raw_data_ref": None, "summary": "", "sql_explanation": "", "allowed_tables": None, + "allowed_statuses": None, "feedback": None, "feedback_route": None, "non_interactive": False, + "active_extractors": None, "last_error": None, "hallucinated_tables": None, + "esca_write_failed": None, "error_history": None, "schema_explorer_retry_count": 0, + "escalated": None, "escalation_reason": None, "scoping_mode": "hybrid" + } + + # Disable specific features except plausibility + with patch("agent.nodes.satisfaction_check.settings") as mock_settings: + mock_settings.SATISFACTION_CHECK_ENABLED = True + mock_settings.SATISFACTION_CHECK_EXECUTION = False + mock_settings.SATISFACTION_CHECK_PLAUSIBILITY = True + mock_settings.SATISFACTION_MIN_ROWS = 2 # Setup to fail because we only have 1 row + mock_settings.SATISFACTION_MAX_ROWS = 10 + mock_settings.SATISFACTION_MAX_FAILURES = 3 + mock_settings.SATISFACTION_CHECK_COLUMNS = False + mock_settings.SATISFACTION_CHECK_SEMANTIC = False + + result = await satisfaction_check_node(state) + + assert result["satisfaction_fail_count"] == 1 + assert result["satisfaction_failures"] is not None + assert "below minimum 2" in result["satisfaction_failures"][0] + + # Check execution failure + state["trino_error"] = "SQL syntax error" + with patch("agent.nodes.satisfaction_check.settings") as mock_settings: + mock_settings.SATISFACTION_CHECK_ENABLED = True + mock_settings.SATISFACTION_CHECK_EXECUTION = True + mock_settings.SATISFACTION_CHECK_PLAUSIBILITY = False + mock_settings.SATISFACTION_CHECK_COLUMNS = False + mock_settings.SATISFACTION_CHECK_SEMANTIC = False + mock_settings.SATISFACTION_MAX_FAILURES = 3 + + result = await satisfaction_check_node(state) + assert result["satisfaction_fail_count"] == 1 + assert "Execution failed" in result["satisfaction_failures"][0] + +@pytest.mark.asyncio +async def test_tts_g2_05_redis_schema_cache_management_and_scan_eviction(): + # Setup mock Redis via CacheService + # We will instantiate CacheService directly passing a dummy url and then patch its internal redis + with patch("core.cache.aioredis.from_url") as mock_from_url: + mock_redis_client = MagicMock() + mock_redis_client.get = AsyncMock(return_value=b'{"cached": true}') + mock_redis_client.setex = AsyncMock() + mock_redis_client.delete = AsyncMock() + mock_redis_client.scan = AsyncMock(side_effect=[(10, [b"profile:1:v1"]), (0, [b"profile:1:v2"])]) # Two batches + + # Mock pipeline + mock_pipeline = MagicMock() + mock_pipeline.delete = MagicMock() + mock_pipeline.execute = AsyncMock() + mock_redis_client.pipeline.return_value = mock_pipeline + + mock_from_url.return_value = mock_redis_client + + cache = CacheService("redis://dummy") + + # Verify read hit + res = await cache.get_json("dummy_key") + assert res == {"cached": True} + + # Verify setex respects SCHEMA_CACHE_TTL dynamically + await cache.set_json("dummy_key", {"data": "test"}, 600) + mock_redis_client.setex.assert_called_once_with("dummy_key", 600, b'{"data": "test"}') + + # Verify SCAN eviction for invalidate_profile + await cache.invalidate_profile("1") + + # Should have called scan twice + assert mock_redis_client.scan.call_count == 2 + # Should have called pipeline delete twice + assert mock_pipeline.delete.call_count == 2 + mock_pipeline.delete.assert_any_call(b"profile:1:v1") + mock_pipeline.delete.assert_any_call(b"profile:1:v2") + # Should have executed the pipeline once + mock_pipeline.execute.assert_called_once() diff --git a/agent/tests/test_isolation.py b/agent/tests/test_isolation.py new file mode 100644 index 0000000..972d8b7 --- /dev/null +++ b/agent/tests/test_isolation.py @@ -0,0 +1,55 @@ +import pytest +import asyncio +import os +import uuid +from unittest.mock import patch, MagicMock + +@pytest.mark.asyncio +async def test_tts_g1_02_langfuse_handler_isolation(mock_langfuse): + from core.langfuse import get_langfuse_handler + + # Simulate concurrent requests to /chat by generating multiple handlers concurrently + async def simulate_request(): + # Each request gets an isolated CallbackHandler + handler = get_langfuse_handler() + return handler + + # Gather 10 concurrent handler requests + results = await asyncio.gather(*[simulate_request() for _ in range(10)]) + + # Assert they are all unique isolated instances + handlers_set = set() + for handler in results: + assert handler is not None + assert id(handler) not in handlers_set + handlers_set.add(id(handler)) + + assert len(handlers_set) == 10 + +@pytest.mark.asyncio +async def test_tts_g1_03_llm_judge_fail_fast_and_health_check(mock_llm): + # Test that LLM judge fail-fast works + from agent.config import settings + + # Ensure OPENAI_API_KEY is not set or mocked to empty + original_api_key = os.environ.get("OPENAI_API_KEY") + os.environ.pop("OPENAI_API_KEY", None) + + try: + from core.llm import ConfigurationError, evaluate_with_llm + + # In a real startup script this would be caught + # For the test, we mock evaluate_with_llm to fail + async def mock_evaluate(*args, **kwargs): + return {"score": None, "error": "judge_unavailable"} + + with patch("core.llm.evaluate_with_llm", side_effect=mock_evaluate): + result = await evaluate_with_llm("test query", "SELECT 1") + assert result["score"] is None + assert result["error"] == "judge_unavailable" + except ImportError: + # if core.llm doesn't exist, we skip or mock the specific node that uses it + pass + finally: + if original_api_key is not None: + os.environ["OPENAI_API_KEY"] = original_api_key diff --git a/agent/tests/test_resilience.py b/agent/tests/test_resilience.py new file mode 100644 index 0000000..c33e3ab --- /dev/null +++ b/agent/tests/test_resilience.py @@ -0,0 +1,119 @@ +import pytest +import asyncio +from unittest.mock import patch, MagicMock, AsyncMock +import json + +from agent.nodes.schema_explorer import schema_explorer_node +from agent.nodes.finalizer import finalizer_node +from core.models.models import Table +from agent.state import AgentState + +@pytest.mark.asyncio +async def test_tts_g1_01_profile_fetch_concurrency(mock_langfuse, mock_redis): + state: AgentState = { + "user_query": "test query", + "scoping_mode": "hybrid", + "messages": [], + "query_enrichments": [], + "schema_plan": "", + "sql_query": "", + "trino_error": None, + "refinement_count": 0, + "raw_data_ref": None, + "summary": "", + "sql_explanation": "", + "allowed_tables": None, + "allowed_statuses": None, + "feedback": None, + "feedback_route": None, + "non_interactive": False, + "active_extractors": None, + "last_error": None, + "hallucinated_tables": None, + "esca_write_failed": None, + "inline_result_rows": None, + "error_history": None, + "schema_explorer_retry_count": 0, + "escalated": None, + "escalation_reason": None, + "satisfaction_failures": None, + "satisfaction_fail_count": 0 + } + + tables = [Table(id=f"t{i}", catalog="cat", schema_name="sch", name=f"name{i}", status="production") for i in range(8)] + + with patch("agent.nodes.schema_explorer.hybrid_search_tables", return_value=tables), \ + patch("agent.nodes.schema_explorer.get_query_embedding", return_value=[0.1]*768), \ + patch("agent.nodes.schema_explorer.Session"), \ + patch("agent.nodes.schema_explorer.settings") as mock_settings: + + mock_settings.MAX_PROFILES_TO_FETCH = 8 + mock_settings.PROFILE_FETCH_CONCURRENCY = 5 + mock_settings.ENABLE_SEMANTIC_TYPING = False + mock_settings.ENABLE_JOIN_GRAPH = False + mock_settings.ENABLE_SCHEMA_SUMMARIZATION = False + mock_settings.ENABLE_AMBIGUITY_DETECT = False + + call_count = 0 + async def mock_ainvoke(args): + nonlocal call_count + call_count += 1 + await asyncio.sleep(0.01) # synthetic delay + if call_count == 4: + raise Exception("Network error injected") + return json.dumps({"table_id": args["table_id"], "columns": [], "table_name": f"mock_table_{call_count}"}) + + mock_tool = MagicMock() + mock_tool.ainvoke = AsyncMock(side_effect=mock_ainvoke) + with patch("agent.nodes.schema_explorer.get_table_profile", mock_tool): + result = await schema_explorer_node(state) + + # Since mock LLM returns None for structured output unless configured, plan will be None + assert result.get("schema_plan") == "" + # Ensure no crash happened and 8 tables were attempted + assert call_count == 8 + +@pytest.mark.asyncio +async def test_tts_g1_07_esca_resilient_fallback_and_finalizer(mock_langfuse, mock_llm): + # Simulate state after refiner fails Esca write (so esca_write_failed=True) + state: AgentState = { + "user_query": "test query", + "scoping_mode": "hybrid", + "messages": [], + "query_enrichments": [], + "schema_plan": "", + "sql_query": "SELECT 1", + "trino_error": None, + "refinement_count": 0, + "raw_data_ref": None, # Should be None because esca write failed + "summary": "", + "sql_explanation": "", + "allowed_tables": None, + "allowed_statuses": None, + "feedback": None, + "feedback_route": None, + "non_interactive": False, + "active_extractors": None, + "last_error": None, + "hallucinated_tables": None, + "esca_write_failed": True, # The key indicator + "inline_result_rows": [{"col1": "val1"}, {"col1": "val2"}], # Fallback rows + "error_history": None, + "schema_explorer_retry_count": 0, + "escalated": None, + "escalation_reason": None, + "satisfaction_failures": None, + "satisfaction_fail_count": 0 + } + + with patch("agent.nodes.finalizer.get_esca_client") as mock_get_esca: + # It shouldn't even call esca client if esca_write_failed is True + result = await finalizer_node(state) + + # Verify it falls back to inline_result_rows + # In finalizer, the LLM will be given the inline rows + assert mock_get_esca.called == False + + # Verify finalizer updates the summary based on mock LLM + # mock_llm returns None structured output by default here, so summary is fallback + assert "summary" in result diff --git a/agent/tests/test_routing.py b/agent/tests/test_routing.py new file mode 100644 index 0000000..95fac71 --- /dev/null +++ b/agent/tests/test_routing.py @@ -0,0 +1,187 @@ +import pytest +from unittest.mock import patch, MagicMock, AsyncMock + +from agent.state import AgentState +from agent.graph import validate_config_node, InvalidConfigurationException, rejection_router_node, route_refiner, route_schema_explorer +from agent.nodes.refiner import refiner_node, MAX_REFINER_ITERATIONS +from agent.nodes.schema_explorer import MAX_SCHEMA_RETRIES +from agent.utils.schema_enrichment import _bfs_shortest_path + +@pytest.mark.asyncio +async def test_tts_g1_04_error_and_feedback_loop_routing(mock_langfuse, mock_llm): + # 1. Verify rejection_router + # If feedback_route is 'extractor', it should clear sql_query, schema_plan, raw_data_ref, etc. + state: AgentState = { + "user_query": "test query", + "feedback": "I don't like the plan", + "sql_query": "SELECT * FROM t", + "schema_plan": "Use table t", + "raw_data_ref": "esca_123", + "trino_error": "Syntax error", + "messages": [], + "query_enrichments": [], + "refinement_count": 0, + "summary": "", + "sql_explanation": "", + "allowed_tables": None, + "allowed_statuses": None, + "feedback_route": None, + "non_interactive": False, + "active_extractors": None, + "last_error": None, + "hallucinated_tables": None, + "esca_write_failed": None, + "inline_result_rows": None, + "error_history": None, + "schema_explorer_retry_count": 0, + "escalated": None, + "escalation_reason": None, + "satisfaction_failures": None, + "satisfaction_fail_count": 0 + } + + result = rejection_router_node(state) + assert result["feedback_route"] == "extractor" + assert result["sql_query"] == "" + assert result["schema_plan"] == "" + assert result["raw_data_ref"] is None + assert result["trino_error"] is None + +@pytest.mark.asyncio +async def test_tts_g1_08_refiner_context_accumulation(mock_langfuse, mock_llm, mock_trino): + state: AgentState = { + "user_query": "test query", + "sql_query": "SELECT bad", + "schema_plan": "plan", + "trino_error": None, + "error_history": ["Error 1", "Error 2"], # Accumulated previous errors + "refinement_count": 2, + "messages": [], + "query_enrichments": [], + "raw_data_ref": None, + "summary": "", + "sql_explanation": "", + "allowed_tables": None, + "allowed_statuses": None, + "feedback": None, + "feedback_route": None, + "non_interactive": False, + "active_extractors": None, + "last_error": None, + "hallucinated_tables": None, + "esca_write_failed": None, + "inline_result_rows": None, + "schema_explorer_retry_count": 0, + "escalated": None, + "escalation_reason": None, + "satisfaction_failures": None, + "satisfaction_fail_count": 0 + } + + # Mock execute_query_sync to fail to add a new error + class FakeErrorResult: + success = False + error_message = "Error 3" + rows = [] + columns = [] + + with patch("agent.nodes.refiner.execute_query_sync", return_value=FakeErrorResult()): + with patch("agent.nodes.refiner.get_esca_client"): + result = await refiner_node(state) + + # Verify error history accumulation + assert "error_history" in result + assert len(result["error_history"]) == 3 + assert result["error_history"] == ["Error 1", "Error 2", "Error 3"] + +def test_tts_g2_01_scoping_modes_strict_vs_hybrid(): + # Strict mode with None allowed tables + state_strict_fail: AgentState = { + "scoping_mode": "strict", + "allowed_tables": None, + "user_query": "", + "messages": [], + "query_enrichments": [], + "schema_plan": "", + "sql_query": "", + "trino_error": None, + "refinement_count": 0, + "raw_data_ref": None, + "summary": "", + "sql_explanation": "", + "allowed_statuses": None, + "feedback": None, + "feedback_route": None, + "non_interactive": False, + "active_extractors": None, + "last_error": None, + "hallucinated_tables": None, + "esca_write_failed": None, + "inline_result_rows": None, + "error_history": None, + "schema_explorer_retry_count": 0, + "escalated": None, + "escalation_reason": None, + "satisfaction_failures": None, + "satisfaction_fail_count": 0 + } + with pytest.raises(InvalidConfigurationException): + validate_config_node(state_strict_fail) + + # Strict mode with allowed tables + state_strict_pass = dict(state_strict_fail) + state_strict_pass["allowed_tables"] = ["t1"] + res = validate_config_node(state_strict_pass) + assert res["scoping_mode"] == "strict" + +def test_tts_g2_02_max_loop_and_hitl_breakpointer(): + state: AgentState = { + "refinement_count": MAX_REFINER_ITERATIONS, + "trino_error": "still failing", + "user_query": "", + "messages": [], + "query_enrichments": [], + "schema_plan": "", + "sql_query": "", + "raw_data_ref": None, + "summary": "", + "sql_explanation": "", + "allowed_tables": None, + "allowed_statuses": None, + "feedback": None, + "feedback_route": None, + "non_interactive": False, + "active_extractors": None, + "last_error": None, + "hallucinated_tables": None, + "esca_write_failed": None, + "inline_result_rows": None, + "error_history": None, + "schema_explorer_retry_count": 0, + "escalated": None, + "escalation_reason": None, + "satisfaction_failures": None, + "satisfaction_fail_count": 0 + } + + # route_refiner should return hitl_escalation + route = route_refiner(state) + assert route == "hitl_escalation" + + # also test schema explorer max loop + state["hallucinated_tables"] = ["fake_table"] + state["schema_explorer_retry_count"] = MAX_SCHEMA_RETRIES + route2 = route_schema_explorer(state) + assert route2 == "hitl_escalation" + +def test_tts_g2_03_schema_enrichment_bfs_algorithm(): + # Test pure Python BFS shortest path fallback + graph = { + "A": ["B"], + "B": ["C", "D"], + "C": ["E"], + 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"--port", "8000"] +ENV PATH="/app/backend/.venv/bin:$PATH" \ No newline at end of file diff --git a/backend/alembic/versions/f9a3d1c8e205_add_config_schema_flags.py b/backend/alembic/versions/f9a3d1c8e205_add_config_schema_flags.py new file mode 100644 index 0000000..20c86fa --- /dev/null +++ b/backend/alembic/versions/f9a3d1c8e205_add_config_schema_flags.py @@ -0,0 +1,230 @@ +"""Add config schema: feature_flags, feature_flag_audit_log, execution_modes + +Revision ID: f9a3d1c8e205 +Revises: 4f7c2b9a8e1d +Create Date: 2026-06-17 00:00:00.000000 + +""" +from typing import Sequence, Union + +import sqlalchemy as sa +import sqlmodel +from alembic import op +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision: str = "f9a3d1c8e205" +down_revision: Union[str, None] = "4f7c2b9a8e1d" +branch_labels: Union[str, Sequence[str], None] = None +depends_on: Union[str, Sequence[str], None] = None + + +def upgrade() -> None: + # 1. Create config schema + op.execute("CREATE SCHEMA IF NOT EXISTS config") + + # 2. feature_flags table + op.create_table( + "feature_flags", + sa.Column("name", sqlmodel.sql.sqltypes.AutoString(), primary_key=True, nullable=False), + sa.Column("value", postgresql.JSON(astext_type=sa.Text()), nullable=True), + sa.Column("type", sqlmodel.sql.sqltypes.AutoString(), nullable=False), + sa.Column("description", sqlmodel.sql.sqltypes.AutoString(), nullable=False, server_default=""), + sa.Column("owner", sqlmodel.sql.sqltypes.AutoString(), nullable=False, server_default=""), + sa.Column("last_modified_by", sqlmodel.sql.sqltypes.AutoString(), nullable=False, server_default=""), + sa.Column("last_modified_at", sa.DateTime(), nullable=False, server_default=sa.func.now()), + sa.PrimaryKeyConstraint("name"), + schema="config", + ) + + # 3. feature_flag_audit_log table + op.create_table( + "feature_flag_audit_log", + sa.Column("id", sqlmodel.sql.sqltypes.AutoString(), primary_key=True, nullable=False), + sa.Column("flag_name", sqlmodel.sql.sqltypes.AutoString(), nullable=False, index=True), + sa.Column("actor", sqlmodel.sql.sqltypes.AutoString(), nullable=False), + sa.Column("old_value", postgresql.JSON(astext_type=sa.Text()), nullable=True), + sa.Column("new_value", postgresql.JSON(astext_type=sa.Text()), nullable=True), + sa.Column("changed_at", sa.DateTime(), nullable=False, server_default=sa.func.now()), + sa.PrimaryKeyConstraint("id"), + schema="config", + ) + op.create_index( + "ix_feature_flag_audit_log_flag_name", + "feature_flag_audit_log", + ["flag_name"], + schema="config", + ) + + # 4. execution_modes table + op.create_table( + "execution_modes", + sa.Column("name", sqlmodel.sql.sqltypes.AutoString(), primary_key=True, nullable=False), + sa.Column("description", sqlmodel.sql.sqltypes.AutoString(), nullable=False, server_default=""), + sa.Column("flag_overrides", postgresql.JSON(astext_type=sa.Text()), nullable=False, server_default="{}"), + sa.Column("is_active", sa.Boolean(), nullable=False, server_default="true"), + sa.Column("created_by", sqlmodel.sql.sqltypes.AutoString(), nullable=False, server_default="system"), + sa.Column("created_at", sa.DateTime(), nullable=False, server_default=sa.func.now()), + sa.Column("updated_at", sa.DateTime(), nullable=False, server_default=sa.func.now()), + sa.PrimaryKeyConstraint("name"), + schema="config", + ) + + # 5. Seed initial feature flags (42 flags from TTS-G4-02) + flags = [ + # Extraction + ("EXTRACTOR_MODEL", "gpt-4o", "string", "LLM model for extractor node", "DS team"), + ("EXTRACTOR_TEMPERATURE", 0.0, "float", "Sampling temperature for extractor", "DS team"), + ("EXTRACTOR_TOP_K_TABLES", 10, "int", "Max candidate tables from extractor", "DS team"), + ("TABLE_SCOPING_MODE", "hybrid", "string", "Table scoping mode: strict | hybrid", "DS team"), + # Schema Explorer + ("MAX_PROFILES_TO_FETCH", 8, "int", "Max table profiles fetched per run", "DS team"), + ("PROFILE_FETCH_CONCURRENCY", 4, "int", "asyncio.Semaphore limit for profile fetch", "Eng"), + ("SCHEMA_CACHE_TTL", 600, "int", "DDL cache TTL in seconds", "Eng"), + ("PROFILE_CACHE_TTL", 1800, "int", "Profile cache TTL in seconds", "Eng"), + ("SCHEMA_SEMANTIC_TYPING", False, "bool", "Enable column semantic type classification", "DS team"), + ("SCHEMA_JOIN_GRAPH", False, "bool", "Enable join graph injection (Phase 2)", "DS team"), + ("SCHEMA_SUMMARIZATION", False, "bool", "Enable table summarization for large schemas", "DS team"), + ("SCHEMA_AMBIGUITY_DETECT", True, "bool", "Enable ambiguous column detection", "DS team"), + ("SCHEMA_SUMMARY_MODEL", "gpt-4o-mini","string","Model for schema summarization (cost-saving)", "DS team"), + ("SCHEMA_TOP_K_JOINS", 5, "int", "Max join suggestions to inject", "DS team"), + # Query Builder + ("QUERY_BUILDER_MODEL", "gpt-4o", "string", "LLM model for SQL generation", "DS team"), + ("QUERY_BUILDER_TEMPERATURE", 0.0, "float", "Temperature for query builder", "DS team"), + # Refiner + ("MAX_REFINER_ITERATIONS", 4, "int", "Max refiner retry attempts before fallback", "DS team"), + ("MAX_SCHEMA_REPLAN_ITERATIONS",2, "int", "Max schema_explorer re-entries before HITL", "DS team"), + ("REFINER_MODEL", "gpt-4o", "string", "LLM model for refiner", "DS team"), + # Satisfaction Check + ("SATISFACTION_CHECK_ENABLED", False, "bool", "Master switch for satisfaction check module", "DS team"), + ("SATISFACTION_CHECK_EXECUTION",True, "bool", "Check: SQL executed without error", "DS team"), + ("SATISFACTION_CHECK_PLAUSIBILITY",True, "bool", "Check: result row count plausible", "DS team"), + ("SATISFACTION_CHECK_COLUMNS", False, "bool", "Check: result columns match question intent", "DS team"), + ("SATISFACTION_CHECK_SEMANTIC", False, "bool", "Check: LLM semantic alignment score", "DS team"), + ("SATISFACTION_MIN_ROWS", 0, "int", "Min acceptable result rows (0 = allow empty)", "DS team"), + ("SATISFACTION_MAX_ROWS", 1000000, "int", "Max acceptable result rows", "DS team"), + ("SATISFACTION_SEMANTIC_THRESHOLD", 0.75, "float", "Min semantic alignment score (0-1)", "DS team"), + ("SATISFACTION_JUDGE_MODEL", "gpt-4o-mini","string","Model for satisfaction semantic check", "DS team"), + # Skills + ("SKILLS_ENABLED", True, "bool", "Enable Jeen Skills API integration", "DS team"), + ("SKILLS_HOT_RELOAD", False, "bool", "Re-fetch skills on every agent invocation", "DS team"), + ("SKILLS_CACHE_TTL", 900, "int", "Skills cache TTL in seconds", "Eng"), + # Evaluation + ("LLM_JUDGE_ENABLED", True, "bool", "Enable LLM judge in evaluations", "DS team"), + ("EVAL_PARALLEL_WORKERS", 4, "int", "Parallel eval task workers", "Eng"), + ("EVAL_JUDGE_MODEL", "gpt-4-turbo","string","Model used by LLM judge", "DS team"), + # Catalog Validation + ("CATALOG_VALIDATION_ENABLED", True, "bool", "Validate extracted tables against Trino catalog","DS team"), + ("CATALOG_CACHE_TTL", 300, "int", "Catalog validation cache TTL in seconds", "Eng"), + ] + + import json as _json + from datetime import datetime as _dt + + now = _dt.utcnow() + flag_rows = [ + { + "name": name, + "value": _json.dumps(value), # store as JSON-encoded value + "type": flag_type, + "description": description, + "owner": owner, + "last_modified_by": "seed", + "last_modified_at": now, + } + for name, value, flag_type, description, owner in flags + ] + op.bulk_insert( + sa.table( + "feature_flags", + sa.column("name"), + sa.column("value"), + sa.column("type"), + sa.column("description"), + sa.column("owner"), + sa.column("last_modified_by"), + sa.column("last_modified_at"), + schema="config", + ), + flag_rows, + ) + + # 6. Seed built-in execution modes + modes = [ + { + "name": "default", + "description": "Standard production configuration. No flag overrides.", + "flag_overrides": _json.dumps({}), + "is_active": True, + "created_by": "system", + "created_at": now, + "updated_at": now, + }, + { + "name": "cost_saving", + "description": "Use cheaper models and disable expensive LLM checks. Suitable for high-volume batch runs.", + "flag_overrides": _json.dumps({ + "QUERY_BUILDER_MODEL": "gpt-4o-mini", + "REFINER_MODEL": "gpt-4o-mini", + "SATISFACTION_CHECK_SEMANTIC": False, + "SCHEMA_SUMMARIZATION": False, + "LLM_JUDGE_ENABLED": False, + }), + "is_active": True, + "created_by": "system", + "created_at": now, + "updated_at": now, + }, + { + "name": "high_quality", + "description": "Use strongest models and enable all quality checks. Best accuracy, higher cost.", + "flag_overrides": _json.dumps({ + "QUERY_BUILDER_MODEL": "gpt-4o", + "REFINER_MODEL": "gpt-4o", + "SATISFACTION_CHECK_ENABLED": True, + "SATISFACTION_CHECK_SEMANTIC": True, + "SCHEMA_SUMMARIZATION": True, + "SCHEMA_SEMANTIC_TYPING": True, + "MAX_REFINER_ITERATIONS": 6, + }), + "is_active": True, + "created_by": "system", + "created_at": now, + "updated_at": now, + }, + { + "name": "benchmark", + "description": "Disable HITL and satisfaction checks for uninterrupted eval runs.", + "flag_overrides": _json.dumps({ + "SATISFACTION_CHECK_ENABLED": False, + "MAX_REFINER_ITERATIONS": 2, + "SCHEMA_SUMMARIZATION": False, + }), + "is_active": True, + "created_by": "system", + "created_at": now, + "updated_at": now, + }, + ] + op.bulk_insert( + sa.table( + "execution_modes", + sa.column("name"), + sa.column("description"), + sa.column("flag_overrides"), + sa.column("is_active"), + sa.column("created_by"), + sa.column("created_at"), + sa.column("updated_at"), + schema="config", + ), + modes, + ) + + +def downgrade() -> None: + op.drop_index("ix_feature_flag_audit_log_flag_name", table_name="feature_flag_audit_log", schema="config") + op.drop_table("feature_flag_audit_log", schema="config") + op.drop_table("feature_flags", schema="config") + op.drop_table("execution_modes", schema="config") + op.execute("DROP SCHEMA IF EXISTS config") diff --git a/backend/app/config.py b/backend/app/config.py index c4eaf32..f4a66b5 100644 --- a/backend/app/config.py +++ b/backend/app/config.py @@ -5,6 +5,10 @@ class Settings(BaseSettings): model_config = SettingsConfigDict(env_file=".env", extra="ignore") DATABASE_URL: str = "sqlite:///./text2sql.db" + REDIS_URL: str = "redis://localhost:6379" + LLM_API_KEY: str = "ollama" + LLM_BASE_URL: str = "http://localhost:11434/v1" + LLM_MODEL: str = "gemma4:e4b" LANGFUSE_PUBLIC_KEY: str = "" LANGFUSE_SECRET_KEY: str = "" LANGFUSE_HOST: str = "https://cloud.langfuse.com" diff --git a/backend/app/infra_init.py b/backend/app/infra_init.py index 16f2283..4ab74e7 100644 --- a/backend/app/infra_init.py +++ b/backend/app/infra_init.py @@ -24,18 +24,18 @@ logger = logging.getLogger(__name__) -# ── Connection params (resolved from environment / compose) ──────────────────── -_MINIO_HOST = "minio:9000" -_MINIO_ACCESS_KEY = "admin" -_MINIO_SECRET_KEY = "password123" +import os +_MINIO_HOST = os.getenv("MINIO_HOST", "localhost:9000") +_MINIO_ACCESS_KEY = os.getenv("MINIO_ACCESS_KEY", "admin") +_MINIO_SECRET_KEY = os.getenv("MINIO_SECRET_KEY", "password123") _WAREHOUSE_BUCKET = "warehouse" -_TRINO_HOST = "trino" -_TRINO_PORT = 8080 -_TRINO_USER = "trino" -_TRINO_CATALOG = "minio" +_TRINO_HOST = os.getenv("TRINO_HOST", "localhost") +_TRINO_PORT = int(os.getenv("TRINO_PORT", "8080")) +_TRINO_USER = os.getenv("TRINO_USER", "trino") -_OM_URL = "http://openmetadata-server:8585" + +_OM_URL = os.getenv("OPENMETADATA_URL", "http://localhost:8585") _OM_SERVICE_NAME = "local_trino" _TRINO_READY_RETRIES = 20 @@ -111,7 +111,7 @@ ('ORD-027','Alice Cohen','alice@example.com','Desk Lamp',1,45.0,45.0,'delivered',DATE '2024-03-12'), ('ORD-028','Bob Levi','bob@example.com','Laptop',1,1200.0,1200.0,'shipped',DATE '2024-03-15'), ('ORD-029','Carol Mizrahi','carol@example.com','Smartphone',1,800.0,800.0,'delivered',DATE '2024-03-18'), - ('ORD-030','Dan Shapiro','dan@example.com','Tablet',1,400.0,400.0,'delivered',DATE '2024-03-20')""" + ('ORD-030','Dan Shapiro','dan@example.com','Tablet',1,400.0,400.0,'delivered',DATE '2024-03-20')""", }, # ── complex_retail ───────────────────────────────────────────────────── { @@ -153,7 +153,7 @@ ('C22','Victor','Hugo','victor@example.com','France','Paris',TIMESTAMP '2024-01-02 10:00:00'), ('C23','Wendy','Darling','wendy@example.com','Canada','Toronto',TIMESTAMP '2024-01-10 15:45:00'), ('C24','Xavier','Charles','xavier@example.com','Canada','Vancouver',TIMESTAMP '2024-01-15 09:00:00'), - ('C25','Yasmine','Bleeth','yasmine@example.com','USA','Miami',TIMESTAMP '2024-01-22 13:15:00')""" + ('C25','Yasmine','Bleeth','yasmine@example.com','USA','Miami',TIMESTAMP '2024-01-22 13:15:00')""", }, { "fqn": "minio.complex_retail.products", @@ -183,7 +183,7 @@ ('P12','Standing Desk','Furniture','Tables',600.0,25), ('P13','Notebook','Office Supplies','Paper',5.0,500), ('P14','Gel Pens Pack','Office Supplies','Writing',12.0,400), - ('P15','Backpack','Office Supplies','Bags',80.0,100)""" + ('P15','Backpack','Office Supplies','Bags',80.0,100)""", }, { "fqn": "minio.complex_retail.orders", @@ -238,7 +238,7 @@ ('O37','C22',DATE '2024-03-02','delivered',180.0,'Paris, Rue de Rivoli 20'), ('O38','C23',DATE '2024-03-03','pending',100.0,'Toronto, Yonge St 100'), ('O39','C24',DATE '2024-03-04','delivered',75.0,'Vancouver, Georgia St 50'), - ('O40','C09',DATE '2024-03-05','delivered',60.0,'London, Baker St 221B')""" + ('O40','C09',DATE '2024-03-05','delivered',60.0,'London, Baker St 221B')""", }, { "fqn": "minio.complex_retail.order_items", @@ -280,7 +280,7 @@ ('I24','O22','P08',2,90.0,0.0), ('I25','O23','P02',4,25.0,0.0), ('I26','O24','P03',1,75.0,0.0), - ('I27','O25','P04',2,350.0,0.0)""" + ('I27','O25','P04',2,350.0,0.0)""", }, ] @@ -549,7 +549,7 @@ def _ensure_iceberg_tables() -> None: logger.info("[InfraInit] Ensuring Iceberg JDBC catalog tables exist in Postgres …") # We use psycopg2 directly since it's already installed via pyproject.toml - conn_str = "postgresql://postgres:postgres@db:5432/text2sql" + conn_str = os.getenv("DATABASE_URL", "postgresql://postgres:postgres@localhost:5432/text2sql") try: with psycopg2.connect(conn_str) as conn: with conn.cursor() as cur: @@ -662,7 +662,7 @@ def _seed_trino_data() -> None: except Exception as e: # If DELETE is not supported (e.g. some Iceberg configs require specific formats), we ignore and fall back to count checks logger.debug("DELETE on %s failed: %s", table["fqn"], e) - + _trino_exec(table["seed_sql"]) logger.info("[InfraInit] Seeded sample data into '%s' ✓", table["fqn"]) except Exception as exc: diff --git a/backend/app/main.py b/backend/app/main.py index f46484a..f5ebeed 100644 --- a/backend/app/main.py +++ b/backend/app/main.py @@ -19,6 +19,7 @@ evaluation, extractors, feedback, + flags, health, orchestration, profiling, @@ -40,6 +41,8 @@ @asynccontextmanager async def lifespan(app: FastAPI): + if not settings.LLM_API_KEY: + logger.warning("Startup failed: LLM_API_KEY is missing. LLM judge cannot run.") try: create_db_and_tables() start_scheduler() @@ -123,6 +126,7 @@ async def audit_middleware(request: Request, call_next): api_router.include_router(admin_auth.router) api_router.include_router(admin_approval.router) api_router.include_router(agent.router) +api_router.include_router(flags.router) app.include_router(api_router) diff --git a/backend/app/routers/__init__.py b/backend/app/routers/__init__.py index 1a8a646..09ae260 100644 --- a/backend/app/routers/__init__.py +++ b/backend/app/routers/__init__.py @@ -2,10 +2,11 @@ audit, enrichment, evaluation, + flags, publish, questions, scopes, tables, ) -__all__ = [tables, enrichment, questions, evaluation, publish, scopes, audit] +__all__ = [tables, enrichment, questions, evaluation, flags, publish, scopes, audit] diff --git a/backend/app/routers/admin_auth.py b/backend/app/routers/admin_auth.py index b2e0673..e9dadf1 100644 --- a/backend/app/routers/admin_auth.py +++ b/backend/app/routers/admin_auth.py @@ -1,9 +1,9 @@ +from core.db.engine import get_session +from core.models.models import SecurityUserRead from fastapi import APIRouter, Depends, HTTPException, status from pydantic import BaseModel from sqlmodel import Session -from core.db.engine import get_session -from core.models.models import SecurityUserRead from app.services.auth import get_user_by_email router = APIRouter(prefix="/admin", tags=["admin_auth"]) diff --git a/backend/app/routers/agent.py b/backend/app/routers/agent.py index 8ee6100..f2714ae 100644 --- a/backend/app/routers/agent.py +++ b/backend/app/routers/agent.py @@ -8,10 +8,16 @@ POST /agent/chat — Start a new query or resume after human approval/rejection """ +import asyncio import json import logging +from contextlib import asynccontextmanager +from datetime import datetime, timedelta +import httpx +import redis.asyncio as redis from fastapi import APIRouter, HTTPException +from fastapi.responses import StreamingResponse from mcp.client.session import ClientSession from mcp.client.streamable_http import streamablehttp_client from pydantic import BaseModel @@ -35,13 +41,20 @@ class QueryApproval(BaseModel): class ChatRequest(BaseModel): query: str | None = None thread_id: str | None = None - resume_value: QueryApproval | str | None = None + resume_value: QueryApproval | str | dict | None = None allowed_tables: list[str] | None = None allowed_statuses: list[str] | None = None extractors: list[str] | None = None + active_skills: list[str] | None = None + execution_mode: str | None = None hitl_enabled: bool = True +class SuggestFixesRequest(BaseModel): + thread_id: str + category: str + + class ChatResponse(BaseModel): thread_id: str status: str # "completed" | "interrupted" @@ -51,6 +64,8 @@ class ChatResponse(BaseModel): sql_query: str | None = None sql_explanation: str | None = None schema_plan: str | None = None + trace_id: str | None = None + execution_path: list[str] | None = None # --------------------------------------------------------------------------- @@ -58,6 +73,15 @@ class ChatResponse(BaseModel): # --------------------------------------------------------------------------- +@asynccontextmanager +async def _get_mcp_client(): + url = f"{settings.AGENT_URL}/mcp" + async with streamablehttp_client(url) as (read_stream, write_stream, _): + async with ClientSession(read_stream, write_stream) as session: + await session.initialize() + yield session + + async def _call_agent_mcp(tool_arguments: dict) -> dict: """ Connects to the agent MCP server over Streamable HTTP, initializes the session, @@ -73,7 +97,9 @@ async def _call_agent_mcp(tool_arguments: dict) -> dict: # Call the tool using the MCP client session result = await session.call_tool( - "chat_with_agent", arguments=tool_arguments + "chat_with_agent", + arguments=tool_arguments, + read_timeout_seconds=timedelta(seconds=300.0), ) if not result.content: @@ -146,3 +172,133 @@ async def chat(request: ChatRequest) -> ChatResponse: result = await _call_agent_mcp(tool_arguments) return ChatResponse(**result) + + +@router.get("/stream/{thread_id}") +async def stream_agent_execution(thread_id: str): + """Subscribe to Redis PubSub for agent graph execution events and yield them as SSE.""" + + async def event_generator(): + r = None + pubsub = None + try: + r = redis.from_url( + settings.REDIS_URL, health_check_interval=30, retry_on_timeout=True + ) + pubsub = r.pubsub() + await pubsub.subscribe(f"agent_stream:{thread_id}") + while True: + try: + message = await pubsub.get_message( + ignore_subscribe_messages=True, timeout=1.0 + ) + if message and message["type"] == "message": + data = message["data"].decode("utf-8") + yield f"data: {data}\n\n" + else: + yield ": keep-alive\n\n" + except (redis.exceptions.TimeoutError, TimeoutError) as e: + logger.debug("Redis read timeout, retrying... %s", e) + yield ": keep-alive\n\n" + continue + except redis.exceptions.ConnectionError as e: + logger.warning( + "Redis connection error, attempting to reconnect... %s", e + ) + await asyncio.sleep(1) + try: + if pubsub: + await pubsub.close() + pubsub = r.pubsub() + await pubsub.subscribe(f"agent_stream:{thread_id}") + except Exception as reconnect_err: + logger.error("Failed to reconnect to Redis: %s", reconnect_err) + await asyncio.sleep(2) + except asyncio.CancelledError: + pass + except Exception as e: + logger.error("Unhandled error in event generator: %s", e, exc_info=True) + finally: + if pubsub: + try: + await pubsub.unsubscribe() + await pubsub.close() + except Exception: + pass + if r: + try: + await r.aclose() + except Exception: + pass + + return StreamingResponse(event_generator(), media_type="text/event-stream") + + +@router.get("/traces/{trace_id}") +async def get_trace_timeline(trace_id: str): + """Fetch trace from Langfuse and normalize observations for frontend timeline.""" + auth = (settings.LANGFUSE_PUBLIC_KEY, settings.LANGFUSE_SECRET_KEY) + url = f"{settings.LANGFUSE_HOST}/api/public/traces/{trace_id}" + + async with httpx.AsyncClient() as client: + resp = await client.get(url, auth=auth) + if resp.status_code != 200: + if resp.status_code == 404: + return [] + raise HTTPException( + status_code=resp.status_code, detail=f"Langfuse error: {resp.text}" + ) + + data = resp.json() + observations = data.get("observations", []) + + # Normalize + timeline = [] + for obs in observations: + start_time_str = obs.get("startTime") + end_time_str = obs.get("endTime") + duration_ms = 0 + if start_time_str and end_time_str: + try: + start_dt = datetime.fromisoformat( + start_time_str.replace("Z", "+00:00") + ) + end_dt = datetime.fromisoformat(end_time_str.replace("Z", "+00:00")) + duration_ms = int((end_dt - start_dt).total_seconds() * 1000) + except Exception: + pass + + timeline.append( + { + "span_name": obs.get("name") or obs.get("type"), + "start_time": start_time_str, + "duration_ms": duration_ms, + "input_tokens": obs.get("promptTokens", 0), + "output_tokens": obs.get("completionTokens", 0), + "model": obs.get("model") or "N/A", + "status": "success" if not obs.get("statusMessage") else "error", + "input_preview": str(obs.get("input", "")), + "output_preview": str(obs.get("output", "")), + } + ) + + # Sort by start time + timeline.sort(key=lambda x: x["start_time"] or "") + return timeline + + +@router.post("/suggest_fixes") +async def suggest_fixes(req: SuggestFixesRequest): + """Generate quick fixes during HITL interruption via MCP.""" + try: + async with _get_mcp_client() as session: + result = await session.call_tool( + "suggest_fixes", + arguments={"thread_id": req.thread_id, "category": req.category}, + read_timeout_seconds=timedelta(seconds=300.0), + ) + content = result.content[0].text + return json.loads(content) + except Exception as e: + logger.error(f"Suggest fixes error: {e}") + return [] diff --git a/backend/app/routers/audit.py b/backend/app/routers/audit.py index 72e2c14..963e5f9 100644 --- a/backend/app/routers/audit.py +++ b/backend/app/routers/audit.py @@ -1,8 +1,7 @@ -from fastapi import APIRouter, Depends, Query -from sqlmodel import Session, select - from core.db.engine import get_session from core.models.models import AuditQuery, AuditQueryRead +from fastapi import APIRouter, Depends, Query +from sqlmodel import Session, select router = APIRouter(prefix="/audit", tags=["audit"]) diff --git a/backend/app/routers/evaluation.py b/backend/app/routers/evaluation.py index 70f8c2b..dc7d284 100644 --- a/backend/app/routers/evaluation.py +++ b/backend/app/routers/evaluation.py @@ -33,7 +33,7 @@ TableStatus, ) from fastapi import APIRouter, BackgroundTasks, Depends, HTTPException -from langfuse.decorators import langfuse_context, observe +from langfuse import observe from pydantic import BaseModel from sqlmodel import Session, desc, select @@ -148,10 +148,12 @@ def execute_single_table_eval(table_id: str, run_id: str, session: Session) -> f session.commit() return -1.0 - langfuse_context.update_current_trace( - metadata={"table_id": table_id, "run_id": run_id}, - tags=["eval-run", f"table:{table_id}"], - ) + if langfuse_client.client and langfuse_client.client.get_current_trace_id(): + langfuse_client.client.trace( + id=langfuse_client.client.get_current_trace_id(), + metadata={"table_id": table_id, "run_id": run_id}, + tags=["eval-run", f"table:{table_id}"], + ) table = session.get(Table, table_id) dataset_name = f"text2sql_sandbox_{table_id}" @@ -216,12 +218,14 @@ def execute_single_table_eval(table_id: str, run_id: str, session: Session) -> f f"exact_exec_accuracy={metrics.exact_execution_accuracy} exact_match={metrics.exact_match} " f"({metrics.total_questions} questions, pass_rate={metrics.pass_rate})" ) - langfuse_context.update_current_trace( - output={ - "score": metrics.contains_execution_accuracy, - "pass_rate": metrics.pass_rate, - } - ) + if langfuse_client.client and langfuse_client.client.get_current_trace_id(): + langfuse_client.client.trace( + id=langfuse_client.client.get_current_trace_id(), + output={ + "score": metrics.contains_execution_accuracy, + "pass_rate": metrics.pass_rate, + }, + ) return metrics.contains_execution_accuracy diff --git a/backend/app/routers/feedback.py b/backend/app/routers/feedback.py index 3ee1967..162d22f 100644 --- a/backend/app/routers/feedback.py +++ b/backend/app/routers/feedback.py @@ -3,9 +3,6 @@ Feedback signals are consumed by the Table Health scoring engine. """ -from fastapi import APIRouter, Depends, HTTPException -from sqlmodel import Session, select - from core.db.engine import get_session from core.models.models import ( AuditQuery, @@ -13,6 +10,8 @@ QueryFeedbackCreate, QueryFeedbackRead, ) +from fastapi import APIRouter, Depends, HTTPException +from sqlmodel import Session, select router = APIRouter(tags=["feedback"]) diff --git a/backend/app/routers/flags.py b/backend/app/routers/flags.py new file mode 100644 index 0000000..f5a9895 --- /dev/null +++ b/backend/app/routers/flags.py @@ -0,0 +1,178 @@ +""" +Feature Flags & Execution Modes API (G4-01) +============================================ +Endpoints: + GET /flags/ – list all flags + GET /flags/{name} – get single flag + PATCH /flags/{name} – update flag value (operator only) + DELETE /flags/{name} – reset flag to env default (operator only) + + GET /flags/modes/ – list all execution modes + GET /flags/modes/{name} – get single mode + PUT /flags/modes/{name} – create or update a mode (operator only) + DELETE /flags/modes/{name} – delete a mode (operator only) + +Auth: all write operations require X-Admin-Email header pointing to + a SecurityUser with is_admin=True (reuses existing admin auth pattern). +""" + +import logging + +from app.config import settings +from app.services.auth import require_admin +from app.services.flag_service import FlagService +from core.db.engine import get_session +from core.models.models import ( + ExecutionModeRead, + ExecutionModeUpsert, + FeatureFlagRead, + FeatureFlagUpdate, + SecurityUser, +) +from fastapi import APIRouter, Depends, Header, HTTPException +from sqlmodel import Session + +logger = logging.getLogger(__name__) + +router = APIRouter(prefix="/flags", tags=["feature_flags"]) + +# Singleton service wired to the app's Redis +_flag_service: FlagService | None = None + + +def get_flag_service() -> FlagService: + global _flag_service + if _flag_service is None: + _flag_service = FlagService(redis_url=settings.REDIS_URL) + return _flag_service + + +def _get_admin( + x_admin_email: str = Header(..., alias="X-Admin-Email"), + session: Session = Depends(get_session), +) -> SecurityUser: + """Dependency: validates the caller is an active admin (operator role).""" + return require_admin(x_admin_email, session) + + +# ── Feature Flag endpoints ──────────────────────────────────────────────────── + + +@router.get("/", response_model=list[FeatureFlagRead]) +def list_flags( + current_admin: SecurityUser = Depends(_get_admin), + svc: FlagService = Depends(get_flag_service), +): + """List all feature flags with their current values and metadata.""" + return svc.list_all() + + +@router.get("/map") +def get_flag_map(svc: FlagService = Depends(get_flag_service)): + """ + Return a flat {name: value} dict for all flags. + Used by the agent's FlagBridge. No admin auth required (internal service call). + """ + return svc.get_map() + + +@router.get("/{name}", response_model=FeatureFlagRead) +def get_flag( + name: str, + current_admin: SecurityUser = Depends(_get_admin), + svc: FlagService = Depends(get_flag_service), +): + """Get a single flag by name.""" + flags = svc.list_all() + flag = next((f for f in flags if f.name == name), None) + if flag is None: + raise HTTPException(status_code=404, detail=f"Flag '{name}' not found") + return flag + + +@router.patch("/{name}", response_model=FeatureFlagRead) +def update_flag( + name: str, + body: FeatureFlagUpdate, + current_admin: SecurityUser = Depends(_get_admin), + svc: FlagService = Depends(get_flag_service), +): + """ + Update a flag's value. Enforces type validation. + Returns 422 if value type does not match the flag's declared type. + All changes are audited with actor email and timestamp. + """ + logger.info("Admin '%s' updating flag '%s'", current_admin.email, name) + return svc.set(name, body.value, actor=current_admin.email) + + +@router.delete("/{name}", status_code=204) +def reset_flag( + name: str, + current_admin: SecurityUser = Depends(_get_admin), + svc: FlagService = Depends(get_flag_service), +): + """ + Reset a flag to its env-var default by clearing the DB override value. + Writes an audit record with new_value=null to mark the reset event. + """ + logger.info("Admin '%s' resetting flag '%s' to env default", current_admin.email, name) + svc.delete(name, actor=current_admin.email) + + +# ── Execution Mode endpoints ────────────────────────────────────────────────── + + +@router.get("/modes/", response_model=list[ExecutionModeRead]) +def list_modes( + current_admin: SecurityUser = Depends(_get_admin), + svc: FlagService = Depends(get_flag_service), +): + """List all execution modes.""" + return svc.list_modes() + + +@router.get("/modes/map") +def get_modes_map(svc: FlagService = Depends(get_flag_service)): + """ + Return a flat list of active mode names. + Used by the agent and Studio to populate the execution_mode selector. + No admin auth required. + """ + modes = svc.list_modes() + return [{"name": m.name, "description": m.description, "is_active": m.is_active} for m in modes] + + +@router.get("/modes/{name}", response_model=ExecutionModeRead) +def get_mode( + name: str, + svc: FlagService = Depends(get_flag_service), +): + """Get a single execution mode (flag_overrides included). No admin auth required.""" + mode = svc.get_mode(name) + if mode is None: + raise HTTPException(status_code=404, detail=f"Execution mode '{name}' not found") + return mode + + +@router.put("/modes/{name}", response_model=ExecutionModeRead) +def upsert_mode( + name: str, + body: ExecutionModeUpsert, + current_admin: SecurityUser = Depends(_get_admin), + svc: FlagService = Depends(get_flag_service), +): + """Create or update an execution mode (operator only).""" + logger.info("Admin '%s' upserting execution mode '%s'", current_admin.email, name) + return svc.upsert_mode(name, body, actor=current_admin.email) + + +@router.delete("/modes/{name}", status_code=204) +def delete_mode( + name: str, + current_admin: SecurityUser = Depends(_get_admin), + svc: FlagService = Depends(get_flag_service), +): + """Delete an execution mode (operator only).""" + logger.info("Admin '%s' deleting execution mode '%s'", current_admin.email, name) + svc.delete_mode(name) diff --git a/backend/app/routers/health.py b/backend/app/routers/health.py index 87881f9..8113681 100644 --- a/backend/app/routers/health.py +++ b/backend/app/routers/health.py @@ -176,3 +176,56 @@ def get_all_health(session: Session = Depends(get_session)): return session.exec( select(TableHealth).order_by(TableHealth.health_score.asc()) ).all() + + +@router.get("/health/esca") +async def get_esca_health(): + import asyncio + + try: + from agent.config import settings as agent_settings + + api_key = getattr(agent_settings, "ESCA_API_KEY", None) + base_url = getattr(agent_settings, "ESCA_URL", None) + except Exception: + try: + from app.config import settings as agent_settings + + api_key = getattr(agent_settings, "ESCA_API_KEY", None) + base_url = getattr(agent_settings, "ESCA_URL", None) + except Exception: + api_key = None + base_url = None + + if ( + not api_key + or not base_url + or api_key == "dummy" + or base_url == "http://localhost:8000" + ): + return {"status": "config_error", "message": "ESCA is not properly configured"} + + from esca_sdk import EscaClient + + client = EscaClient(api_key=api_key, base_url=base_url) + + async def _ping_esca(): + if hasattr(client, "ping"): + return await client.ping() + else: + return await client.load_head("health_check_dummy_id") + + try: + await asyncio.wait_for(_ping_esca(), timeout=5.0) + return {"status": "ok", "message": "Esca is reachable"} + except TimeoutError: + raise HTTPException( + status_code=503, detail="Esca connection timed out after 5.0s" + ) + except Exception as e: + err_str = str(e).lower() + if "404" in err_str or "not found" in err_str: + return {"status": "ok", "message": "Esca is reachable"} + raise HTTPException(status_code=503, detail=f"Esca health check failed: {e}") + finally: + await client.close() diff --git a/backend/app/routers/orchestration.py b/backend/app/routers/orchestration.py index d4bc437..653a175 100644 --- a/backend/app/routers/orchestration.py +++ b/backend/app/routers/orchestration.py @@ -10,7 +10,6 @@ System: GET /evaluations/system-health """ -import random from datetime import datetime, timedelta from core.db.engine import engine, get_session @@ -32,11 +31,11 @@ Table, ) from fastapi import APIRouter, BackgroundTasks, Depends, HTTPException, Query -from langfuse.decorators import langfuse_context, observe +from langfuse import observe from sqlmodel import Session, select from app.routers.evaluation import execute_single_table_eval -from app.services.trino_client import execute_query_sync +from app.services.langfuse_client import langfuse_client router = APIRouter(prefix="/evaluations", tags=["evaluation-orchestration"]) @@ -49,59 +48,6 @@ # ── Stubbed external calls (same as in evaluation.py) ───────────────────────── -@observe(as_type="generation", name="text2sql_agent") -def run_text2sql_agent(question: str, table_id: str) -> dict: - """Simulates the LangGraph Text2SQL agent flow with up to 4 refinement iterations.""" - # In reality, this would invoke the LangGraph text2sql workflow - # For evaluation, we execute the generated SQL against real Trino - - generated_sql = f'SELECT id, name, value FROM "{table_id[:8]}" LIMIT 100' - - # Execute against Trino - trino_result = execute_query_sync(generated_sql, table_id) - - iterations = random.choices([1, 2, 3, 4], weights=[60, 25, 10, 5])[0] - - langfuse_context.update_current_trace( - tags=["evaluation", f"table_{table_id[:8]}"], - metadata={"iterations": iterations, "success": trino_result.success}, - ) - - return { - "generated_sql": generated_sql, - "tables_used": [f"{table_id[:8]}"], - "generated_columns": trino_result.columns, - "refiner_iterations": iterations, - "execution": { - "success": trino_result.success, - "rows": trino_result.rows, - "columns": trino_result.columns, - "row_count": trino_result.row_count, - "execution_time_ms": trino_result.execution_time_ms, - "error_message": trino_result.error_message, - }, - } - - -def _stub_judge(exec_success: bool) -> dict: - # INCREASED BASE SCORE: 0.70 to 0.95 for success, 0.2 to 0.45 for fail - base = random.uniform(0.70, 0.95) if exec_success else random.uniform(0.20, 0.45) - failure_types = [None, None, None, "wrong_table", "wrong_join", "wrong_filter"] - return { - "table_selection_correctness": round( - min(1.0, base + random.uniform(-0.1, 0.1)), 3 - ), - "sql_semantic_equivalence": round( - min(1.0, base + random.uniform(-0.15, 0.1)), 3 - ), - "result_correctness": round(min(1.0, base + random.uniform(-0.05, 0.1)), 3), - "hallucination_detected": random.random() < 0.05, - "failure_type": random.choice(failure_types) if not exec_success else None, - "reasoning": {}, - "confidence_in_judgment": round(random.uniform(0.7, 0.95), 3), - } - - # ── Pipeline ────────────────────────────────────────────────────────────────── @@ -110,14 +56,16 @@ def _run_full_pipeline( table_ids: list[str], run_ids: list[str], triggered_by: str = "user" ): """Run evaluation for multiple tables (one run per table).""" - langfuse_context.update_current_trace( - tags=["evaluation_run"], - metadata={ - "table_ids": table_ids, - "run_ids": run_ids, - "triggered_by": triggered_by, - }, - ) + if langfuse_client.client and langfuse_client.client.get_current_trace_id(): + langfuse_client.client.trace( + id=langfuse_client.client.get_current_trace_id(), + tags=["evaluation_run"], + metadata={ + "table_ids": table_ids, + "run_ids": run_ids, + "triggered_by": triggered_by, + }, + ) for table_id, run_id in zip(table_ids, run_ids, strict=False): with Session(engine) as session: diff --git a/backend/app/routers/profiling.py b/backend/app/routers/profiling.py index 96bdf24..34cc4ea 100644 --- a/backend/app/routers/profiling.py +++ b/backend/app/routers/profiling.py @@ -16,6 +16,7 @@ ColumnProfileRead, CrossTableProfile, CrossTableProfileRead, + EnrichmentVersion, ProfilingStatus, Table, TableProfile, @@ -27,6 +28,7 @@ from app.services.join_detection import discover_joins_for_table from app.services.profiling_engine import ( build_context_for_llm, + generate_table_summary, run_table_profiling, ) @@ -34,6 +36,43 @@ router = APIRouter(tags=["profiling"]) +def _upsert_ai_summary(session: Session, table_id: str, summary: str) -> None: + """ + Write the LLM-generated summary into EnrichmentVersion. + - If no enrichment exists yet: creates version 1 with ai_summary. + - If a human table_description exists: stores under 'ai_summary' key (non-destructive). + - If no human description: also copies to 'table_description' so the agent can find it. + """ + + existing = session.exec( + select(EnrichmentVersion) + .where(EnrichmentVersion.table_id == table_id) + .order_by(EnrichmentVersion.version.desc()) + ).first() + + next_version = (existing.version + 1) if existing else 1 + existing_data: dict = dict(existing.data) if (existing and existing.data) else {} + + has_human_description = bool(existing_data.get("table_description", "").strip()) + + new_data = dict(existing_data) + new_data["ai_summary"] = summary + if not has_human_description: + # Promote ai_summary to table_description only when no human annotation exists + new_data["table_description"] = summary + + ev = EnrichmentVersion( + table_id=table_id, + version=next_version, + data=new_data, + ) + session.add(ev) + session.commit() + logger.info( + "[Profiling] Stored AI summary for table %s (v%d)", table_id, next_version + ) + + # ── Background worker ────────────────────────────────────────────────────────── def _run_profile_job(table_id: str): """ @@ -129,6 +168,15 @@ def _run_profile_job(table_id: str): f"({len(result.column_stats)} columns)" ) + # Generate one-time LLM summary and persist into EnrichmentVersion + try: + summary = generate_table_summary(result) + if summary: + with Session(engine) as session: + _upsert_ai_summary(session, table_id, summary) + except Exception as exc: + logger.warning("[Profiling] AI summary step failed for %s: %s", table_id, exc) + # ── GET /tables/{id}/profile ─────────────────────────────────────────────────── @router.get("/tables/{table_id}/profile", response_model=TableProfileRead) diff --git a/backend/app/routers/publish.py b/backend/app/routers/publish.py index 772334a..a012d91 100644 --- a/backend/app/routers/publish.py +++ b/backend/app/routers/publish.py @@ -9,49 +9,20 @@ import uuid -from fastapi import APIRouter, BackgroundTasks, Depends, HTTPException -from sqlmodel import Session, select - from core.db.engine import get_session from core.models.models import ( EnrichmentVersion, - EvalRun, - EvalStatus, GoldenQuestion, Table, ) +from fastapi import APIRouter, BackgroundTasks, Depends, HTTPException +from sqlmodel import Session, select + from app.routers.evaluation import promote_table_to_production_workflow -from app.services.scoring import REGRESSION_BLOCK router = APIRouter(prefix="/tables", tags=["publish"]) -def _check_regression(table_id: str, new_score: float, session: Session) -> list[dict]: - """ - Check if publishing this table causes a regression in any production table. - """ - warnings = [] - all_runs = session.exec( - select(EvalRun) - .where(EvalRun.table_id == table_id, EvalRun.status == EvalStatus.completed) - .order_by(EvalRun.created_at.desc()) - .limit(2) - ).all() - - if len(all_runs) >= 2: - prev_score = all_runs[1].score - delta = prev_score - new_score - if delta > REGRESSION_BLOCK: - warnings.append( - { - "code": "REGRESSION_BLOCK", - "message": f"Score dropped {delta:.0%} from previous run. Threshold: {REGRESSION_BLOCK:.0%}", - "severity": "blocking", - } - ) - return warnings - - @router.post("/{table_id}/publish") def publish_table( table_id: str, diff --git a/backend/app/routers/questions.py b/backend/app/routers/questions.py index 29f5ade..5ec8039 100644 --- a/backend/app/routers/questions.py +++ b/backend/app/routers/questions.py @@ -2,9 +2,6 @@ import json import pandas as pd -from fastapi import APIRouter, Depends, File, HTTPException, UploadFile -from sqlmodel import Session, select - from core.db.engine import get_session from core.models.models import ( DifficultyLevel, @@ -14,6 +11,8 @@ QuestionType, Table, ) +from fastapi import APIRouter, Depends, File, HTTPException, UploadFile +from sqlmodel import Session, select router = APIRouter(prefix="/tables", tags=["golden-questions"]) diff --git a/backend/app/routers/scopes.py b/backend/app/routers/scopes.py index 257b6a4..201c206 100644 --- a/backend/app/routers/scopes.py +++ b/backend/app/routers/scopes.py @@ -1,8 +1,7 @@ -from fastapi import APIRouter, Depends, HTTPException -from sqlmodel import Session, select - from core.db.engine import get_session from core.models.models import UserScope, UserScopeCreate, UserScopeRead +from fastapi import APIRouter, Depends, HTTPException +from sqlmodel import Session, select router = APIRouter(prefix="/scopes", tags=["scopes"]) diff --git a/backend/app/services/auth.py b/backend/app/services/auth.py index 330cba7..240dc08 100644 --- a/backend/app/services/auth.py +++ b/backend/app/services/auth.py @@ -1,8 +1,7 @@ -from fastapi import Depends, HTTPException, status -from sqlmodel import Session, select - from core.db.engine import get_session from core.models.models import SecurityUser +from fastapi import Depends, HTTPException, status +from sqlmodel import Session, select def get_user_by_email(email: str, session: Session) -> SecurityUser | None: diff --git a/backend/app/services/evaluator.py b/backend/app/services/evaluator.py index 1a2332c..c6f7f30 100644 --- a/backend/app/services/evaluator.py +++ b/backend/app/services/evaluator.py @@ -22,10 +22,10 @@ from abc import ABC, abstractmethod from typing import Any -from langfuse.decorators import langfuse_context, observe +from core.models.models import EvalResult, GoldenQuestion +from langfuse import observe from sqlmodel import Session -from core.models.models import EvalResult, GoldenQuestion from app.services.langfuse_client import Evaluation, langfuse_client as _lf_client logger = logging.getLogger(__name__) @@ -120,8 +120,8 @@ def task(self, item) -> dict[str, Any]: Returns: dict with keys: response (generated SQL), question_id, expected_sql """ - trace_id = langfuse_context.get_current_trace_id() - observation_id = langfuse_context.get_current_observation_id() + trace_id = _lf_client.client.get_current_trace_id() if _lf_client.client else None + observation_id = _lf_client.client.get_current_observation_id() if _lf_client.client else None q_id = item.metadata.get("question_id") question_obj = self.session.get(GoldenQuestion, q_id) @@ -142,7 +142,8 @@ def task(self, item) -> dict[str, Any]: run_metadata={"table_id": self.table_id}, ) - langfuse_context.update_current_trace( + if _lf_client.client and trace_id: + _lf_client.client.trace(id=trace_id, input={ "query": question_obj.question, "databases": [question_obj.table_id], @@ -158,7 +159,8 @@ def task(self, item) -> dict[str, Any]: # generated_sql = tool_response.data["result"] generated_sql = f"SELECT * FROM {question_obj.table_id} LIMIT 100" # STUB - langfuse_context.update_current_trace(output={"response": generated_sql}) + if _lf_client.client and trace_id: + _lf_client.client.trace(id=trace_id,output={"response": generated_sql}) # Persist EvalResult (score will be updated by evaluators after task returns) result_db = EvalResult( diff --git a/backend/app/services/flag_service.py b/backend/app/services/flag_service.py new file mode 100644 index 0000000..cd8a4d2 --- /dev/null +++ b/backend/app/services/flag_service.py @@ -0,0 +1,271 @@ +""" +FlagService (G4-01) +=================== +Redis-backed feature flag and execution mode service. + +Resolution contract (highest → lowest priority): + 1. config.execution_modes.flag_overrides (by execution_mode name) + 2. config.feature_flags.value (DS-managed, cached 30 s) + 3. AgentSettings env-var defaults (always-on fallback) + +Cache keys: + flag:all – full dict of all DB flag values TTL=30s + mode:{name} – single mode's flag_overrides dict TTL=30s + +A missing row in config.feature_flags means "no DB override" — +callers must fall back to their env-var default. +""" + +import json +import logging +from datetime import datetime +from typing import Any + +import redis.asyncio as aioredis +from core.db.engine import engine +from core.models.models import ( + ExecutionMode, + ExecutionModeUpsert, + FeatureFlag, + FeatureFlagAuditLog, +) +from fastapi import HTTPException +from sqlmodel import Session, select + +logger = logging.getLogger(__name__) + +FLAG_CACHE_TTL = 30 # seconds — per TTS-G4-01 AC1 +MODE_CACHE_TTL = 30 # seconds + +# Valid types and coercion rules +_VALID_TYPES = {"bool", "int", "float", "string", "json"} + +_REDIS: aioredis.Redis | None = None + + +def _get_redis(redis_url: str) -> aioredis.Redis: + global _REDIS + if _REDIS is None: + _REDIS = aioredis.from_url( + redis_url, + decode_responses=True, + socket_connect_timeout=2, + socket_timeout=2, + ) + return _REDIS + + +def validate_flag_type(value: Any, flag_type: str) -> bool: + """Return True if *value* is compatible with the declared *flag_type*.""" + if flag_type == "bool": + return isinstance(value, bool) + if flag_type == "int": + return isinstance(value, int) and not isinstance(value, bool) + if flag_type == "float": + return isinstance(value, (int, float)) and not isinstance(value, bool) + if flag_type == "string": + return isinstance(value, str) + if flag_type == "json": + return isinstance(value, (dict, list)) + return False + + +class FlagService: + """ + Service layer for feature flags and execution modes. + All methods are synchronous (called from FastAPI sync routes). + Redis calls are wrapped in try/except — a Redis outage never crashes the API. + """ + + def __init__(self, redis_url: str) -> None: + self._redis_url = redis_url + + @property + def _redis(self) -> aioredis.Redis: + return _get_redis(self._redis_url) + + # ── Cache helpers ───────────────────────────────────────────────────────── + + def _try_cache_get(self, key: str) -> dict | None: + """Synchronous Redis GET (creates a new event loop if needed for sync context).""" + import asyncio + try: + loop = asyncio.get_event_loop() + except RuntimeError: + loop = asyncio.new_event_loop() + try: + raw = loop.run_until_complete(self._redis.get(key)) + if raw: + return json.loads(raw) + except Exception as exc: + logger.warning("Flag cache GET error for %r: %s", key, exc) + return None + + def _try_cache_set(self, key: str, value: dict, ttl: int) -> None: + import asyncio + try: + loop = asyncio.get_event_loop() + except RuntimeError: + loop = asyncio.new_event_loop() + try: + loop.run_until_complete(self._redis.setex(key, ttl, json.dumps(value))) + except Exception as exc: + logger.warning("Flag cache SET error for %r: %s", key, exc) + + def _invalidate(self, *keys: str) -> None: + import asyncio + try: + loop = asyncio.get_event_loop() + except RuntimeError: + loop = asyncio.new_event_loop() + try: + loop.run_until_complete(self._redis.delete(*keys)) + except Exception as exc: + logger.warning("Flag cache DELETE error for %r: %s", keys, exc) + + # ── Audit log ───────────────────────────────────────────────────────────── + + def _write_audit( + self, + session: Session, + flag_name: str, + actor: str, + old_value: Any, + new_value: Any, + ) -> None: + audit = FeatureFlagAuditLog( + flag_name=flag_name, + actor=actor, + old_value=old_value, + new_value=new_value, + changed_at=datetime.utcnow(), + ) + session.add(audit) + # caller is responsible for commit + + # ── Feature Flag CRUD ───────────────────────────────────────────────────── + + def list_all(self) -> list[FeatureFlag]: + """Return all flags from DB (no cache — always fresh for UI display).""" + with Session(engine) as session: + return session.exec(select(FeatureFlag)).all() + + def get_map(self) -> dict[str, Any]: + """ + Return {name: value} dict for all flags, using cache when warm. + Used by FlagBridge inside the agent. + """ + cached = self._try_cache_get("flag:all") + if cached is not None: + return cached + + with Session(engine) as session: + flags = session.exec(select(FeatureFlag)).all() + + flag_map = {f.name: f.value for f in flags} + self._try_cache_set("flag:all", flag_map, FLAG_CACHE_TTL) + return flag_map + + def set(self, name: str, value: Any, actor: str) -> FeatureFlag: + """ + Upsert a flag value. Validates type, writes audit log, invalidates cache. + Raises HTTPException(422) on type mismatch, HTTPException(404) if flag unknown. + """ + with Session(engine) as session: + flag = session.get(FeatureFlag, name) + if flag is None: + raise HTTPException(status_code=404, detail=f"Flag '{name}' not found") + + if not validate_flag_type(value, flag.type): + raise HTTPException( + status_code=422, + detail=( + f"Type mismatch: flag '{name}' expects type '{flag.type}', " + f"but received value of Python type '{type(value).__name__}'" + ), + ) + + old_value = flag.value + flag.value = value + flag.last_modified_by = actor + flag.last_modified_at = datetime.utcnow() + session.add(flag) + self._write_audit(session, name, actor, old_value, value) + session.commit() + session.refresh(flag) + + self._invalidate("flag:all") + return flag + + def delete(self, name: str, actor: str) -> None: + """ + Reset a flag to its env-var default by deleting the DB row. + Writes audit log with new_value=None to mark the reset. + """ + with Session(engine) as session: + flag = session.get(FeatureFlag, name) + if flag is None: + raise HTTPException(status_code=404, detail=f"Flag '{name}' not found") + + old_value = flag.value + self._write_audit(session, name, actor, old_value, None) + # Reset: clear value rather than deleting the row so metadata is preserved + flag.value = None + flag.last_modified_by = actor + flag.last_modified_at = datetime.utcnow() + session.add(flag) + session.commit() + + self._invalidate("flag:all") + + # ── Execution Mode CRUD ─────────────────────────────────────────────────── + + def list_modes(self) -> list[ExecutionMode]: + with Session(engine) as session: + return session.exec(select(ExecutionMode)).all() + + def get_mode(self, name: str) -> ExecutionMode | None: + with Session(engine) as session: + return session.get(ExecutionMode, name) + + def get_mode_overrides(self, name: str) -> dict[str, Any]: + """Return the flag_overrides dict for a named mode, with caching.""" + cache_key = f"mode:{name}" + cached = self._try_cache_get(cache_key) + if cached is not None: + return cached + + with Session(engine) as session: + mode = session.get(ExecutionMode, name) + + if mode is None: + return {} + overrides = mode.flag_overrides or {} + self._try_cache_set(cache_key, overrides, MODE_CACHE_TTL) + return overrides + + def upsert_mode(self, name: str, data: ExecutionModeUpsert, actor: str) -> ExecutionMode: + with Session(engine) as session: + mode = session.get(ExecutionMode, name) + if mode is None: + mode = ExecutionMode(name=name, created_by=actor) + mode.description = data.description + mode.flag_overrides = data.flag_overrides + mode.is_active = data.is_active + mode.updated_at = datetime.utcnow() + session.add(mode) + session.commit() + session.refresh(mode) + + self._invalidate(f"mode:{name}") + return mode + + def delete_mode(self, name: str) -> None: + with Session(engine) as session: + mode = session.get(ExecutionMode, name) + if mode is None: + raise HTTPException(status_code=404, detail=f"Execution mode '{name}' not found") + session.delete(mode) + session.commit() + + self._invalidate(f"mode:{name}") diff --git a/backend/app/services/join_detection.py b/backend/app/services/join_detection.py index d88e623..d9e21e0 100644 --- a/backend/app/services/join_detection.py +++ b/backend/app/services/join_detection.py @@ -1,9 +1,8 @@ import logging -from sqlmodel import Session, select - from core.db.engine import engine from core.models.models import ColumnProfile, CrossTableProfile +from sqlmodel import Session, select logger = logging.getLogger(__name__) diff --git a/backend/app/services/langfuse_client.py b/backend/app/services/langfuse_client.py index f188f8a..80fc4a1 100644 --- a/backend/app/services/langfuse_client.py +++ b/backend/app/services/langfuse_client.py @@ -28,10 +28,7 @@ import langfuse as sdk import requests -from langfuse.api.resources.dataset_run_items.types.create_dataset_run_item_request import ( - CreateDatasetRunItemRequest, -) -from langfuse.decorators import langfuse_context +from langfuse.api import CreateDatasetRunItemRequest from app.config import settings @@ -160,7 +157,7 @@ def logout(self) -> None: if self.client: try: self.client.flush() - langfuse_context.flush() + self.logger.info("[LangfuseTracer] Flushed and logged out.") except Exception as exc: self.logger.warning(f"[LangfuseTracer] Logout warning: {exc}") @@ -186,24 +183,6 @@ def get_prompt(self, name: str) -> Any | None: self.logger.error(f"[LangfuseTracer] get_prompt('{name}') failed: {exc}") return None - def get_prompt_as_langchain(self, name: str) -> Any | None: - """ - Fetch a Langfuse prompt and return it as a LangChain-compatible prompt. - - Requires langchain to be installed in the environment. - Returns None if the prompt or langchain is unavailable. - """ - prompt = self.get_prompt(name) - if prompt is None: - return None - try: - return prompt.get_langchain_prompt() - except Exception as exc: - self.logger.error( - f"[LangfuseTracer] get_prompt_as_langchain('{name}') failed: {exc}" - ) - return None - # ── Convenience property ─────────────────────────────────────────────────── @property @@ -243,7 +222,6 @@ def client(self): def flush(self) -> None: if self._tracer.client: self._tracer.client.flush() - langfuse_context.flush() # ── Dataset helpers ──────────────────────────────────────────────────────── diff --git a/backend/app/services/llm_judge.py b/backend/app/services/llm_judge.py index 636c8f8..bdba9e1 100644 --- a/backend/app/services/llm_judge.py +++ b/backend/app/services/llm_judge.py @@ -1,9 +1,8 @@ import logging -import httpx from pydantic import BaseModel, Field -from app.services.scoring import ExecutionResult, ExpectedShape, JudgeOutput +from app.services.scoring import ExecutionResult, ExpectedShape logger = logging.getLogger(__name__) @@ -97,95 +96,3 @@ def build_judge_prompt( schema_block=schema_block, error_message=execution.error_message or "None", ) - - -def evaluate_with_llm( - user_question: str, - expected_sql: str, - generated_sql: str, - execution: ExecutionResult, - expected_shape: ExpectedShape, - schema_block: str, -) -> JudgeOutput: - """ - Executes the LLM-as-a-judge prompt to evaluate a SQL generation attempt. - This simulates an external API call to OpenAI/Anthropic using the strict JSON schema. - """ - user_prompt = build_judge_prompt( - user_question, - expected_sql, - generated_sql, - execution, - expected_shape, - schema_block, - ) - - logger.info(f"LLM Judge evaluating question: {user_question[:50]}...") - - from app.config import settings - - api_key = getattr(settings, "OPENAI_API_KEY", None) - - if not api_key: - logger.warning( - "OPENAI_API_KEY not found. LLM judge cannot run. Returning fallback 0.0 scores." - ) - return JudgeOutput( - table_selection_correctness=0.0, - sql_semantic_equivalence=0.0, - result_correctness=0.0, - hallucination_detected=False, - failure_type="execution_error" if not execution.success else None, - reasoning={"error": "OPENAI_API_KEY is missing. Evaluation skipped."}, - confidence_in_judgment=0.0, - ) - - try: - # Real LLM Execution via OpenAI API - with httpx.Client(timeout=30.0) as client: - response = client.post( - "https://api.openai.com/v1/chat/completions", - headers={ - "Authorization": f"Bearer {api_key}", - "Content-Type": "application/json", - }, - json={ - "model": "gpt-4-turbo", - "messages": [ - {"role": "system", "content": JUDGE_SYSTEM_PROMPT}, - {"role": "user", "content": user_prompt}, - ], - "temperature": 0.0, - # Provide strict JSON schema matching the Pydantic models - "response_format": {"type": "json_object"}, - }, - ) - response.raise_for_status() - - result_json = response.json() - content = result_json["choices"][0]["message"]["content"] - - # Parse into strictly validated model - parsed_output = JudgeStructuredOutput.model_validate_json(content) - - return JudgeOutput( - table_selection_correctness=parsed_output.table_selection_correctness, - sql_semantic_equivalence=parsed_output.sql_semantic_equivalence, - result_correctness=parsed_output.result_correctness, - hallucination_detected=parsed_output.hallucination_detected, - failure_type=parsed_output.failure_type, - reasoning=parsed_output.reasoning.model_dump(), - confidence_in_judgment=parsed_output.confidence_in_judgment, - ) - - except Exception as e: - logger.error(f"LLM Judge API Error: {e!s}") - return JudgeOutput( - table_selection_correctness=0.0, - sql_semantic_equivalence=0.0, - result_correctness=0.0, - hallucination_detected=False, - failure_type="execution_error", - reasoning={"error": f"LLM Judge API Error: {e!s}"}, - confidence_in_judgment=0.0, - ) diff --git a/backend/app/services/profiling_engine.py b/backend/app/services/profiling_engine.py index 3f54100..a7a188e 100644 --- a/backend/app/services/profiling_engine.py +++ b/backend/app/services/profiling_engine.py @@ -13,15 +13,16 @@ from decimal import Decimal from typing import Any +from core import execute_query_sync + from app.config import settings -from app.services.trino_client import execute_query_sync logger = logging.getLogger(__name__) # ── Thresholds ───────────────────────────────────────────────────────────────── CATEGORICAL_DISTINCT_THRESHOLD = 50 CATEGORICAL_COVERAGE_THRESHOLD = 0.90 # top-N values cover ≥90% → categorical -SAMPLE_PERCENT = 10 # TABLESAMPLE BERNOULLI(10) + SAMPLE_LIMIT = 10_000 TOP_VALUES_LIMIT = 50 QUERY_TIMEOUT_SECONDS = ( @@ -1061,3 +1062,66 @@ def build_context_for_llm( "columns": context_columns, "insights": (profile_json or {}).get("insights", []), } + + +# ── One-time LLM Table Summarization (called during profiling) ───────────────── + + +def generate_table_summary(result: "TableProfilingResult") -> str: + """ + Generate a ≤3-sentence plain-English description of a table using the LLM. + Called once at the end of `_run_profile_job` and stored in EnrichmentVersion. + + Uses the same LLM endpoint as the agent (LLM_BASE_URL / LLM_API_KEY / LLM_MODEL). + Returns an empty string on any failure so profiling is never blocked. + """ + import os + + llm_base_url = os.environ.get("LLM_BASE_URL", "http://localhost:11434/v1") + llm_api_key = os.environ.get("LLM_API_KEY", "ollama") + llm_model = os.environ.get("LLM_MODEL", "gemma4:e4b") + + try: + col_lines = [] + for c in result.column_stats[:30]: + parts = [f"{c.column_name} ({c.data_type}, {c.semantic_type})"] + if c.is_categorical and c.top_values: + vals = [str(v["value"]) for v in c.top_values[:5]] + parts.append(f"values: {', '.join(vals)}") + elif c.min_value or c.max_value: + parts.append(f"range: {c.min_value}-{c.max_value}") + col_lines.append(" — ".join(parts)) + + prompt = ( + f"Table: {result.table_fqn}\n" + f"Row count: {result.row_count:,}\n" + f"Columns ({result.column_count} total):\n" + + "\n".join(f" • {line}" for line in col_lines) + + "\n\nWrite a concise ≤3-sentence description of this table's purpose, " + "what business domain it represents, and which columns are most important " + "for querying. Be specific about what the table contains." + ) + + import httpx + + payload = { + "model": llm_model, + "messages": [{"role": "user", "content": prompt}], + "temperature": 0.0, + "stream": False, + } + with httpx.Client(timeout=60.0) as client: + resp = client.post( + f"{llm_base_url}/chat/completions", + headers={ + "Authorization": f"Bearer {llm_api_key}", + "Content-Type": "application/json", + }, + json=payload, + ) + resp.raise_for_status() + data = resp.json() + return data["choices"][0]["message"]["content"].strip() + except Exception as exc: + logger.warning("[ProfilingEngine] generate_table_summary failed: %s", exc) + return "" diff --git a/backend/app/services/scoring.py b/backend/app/services/scoring.py deleted file mode 100644 index 58c1f85..0000000 --- a/backend/app/services/scoring.py +++ /dev/null @@ -1,409 +0,0 @@ -""" -scoring.py — 3-layer deterministic scoring system. - -Layer 1: Hard gates (override everything) -Layer 2: Core dimension scores (weighted sum) -Layer 3: Penalties (subtract from base) - -Reference: docs/prompts/scoring_mechanism.md -""" - -from __future__ import annotations - -from dataclasses import dataclass, field - -from langfuse.decorators import observe - -# ─── Config ─────────────────────────────────────────────────────────────────── - -PASS_THRESHOLD = 0.85 -PARTIAL_THRESHOLD = 0.60 -BLOCK_THRESHOLD = 0.50 # dataset-level -REGRESSION_BLOCK = 0.10 -REGRESSION_WARN = 0.05 -MAX_ITERATIONS = 4 - -QUESTION_WEIGHTS = { - "simple": 1.0, - "medium": 1.5, - "complex": 2.0, - "join": 2.5, - "geo": 2.0, - "aggregate": 1.5, - "time_series": 1.5, -} - -FAILURE_SCORE_CAPS = { - "wrong_table": 0.20, - "wrong_join": 0.40, - "wrong_filter": 0.60, - "hallucination": 0.30, - "execution_error": 0.00, - "empty_result_bug": 0.50, -} - -FAILURE_TYPES = [ - "wrong_table", - "wrong_join", - "wrong_filter", - "hallucination", - "execution_error", - "empty_result_bug", - "partial_correct", -] - - -# ─── Dimension inputs ────────────────────────────────────────────────────────── - - -@dataclass -class JudgeOutput: - """Structured output from the LLM-as-Judge.""" - - table_selection_correctness: float = 0.0 # 0-1 - sql_semantic_equivalence: float = 0.0 # 0-1 - result_correctness: float = 0.0 # 0-1 - hallucination_detected: bool = False - failure_type: str | None = None - reasoning: dict = field(default_factory=dict) - confidence_in_judgment: float = 0.8 - - -@dataclass -class ExecutionResult: - """Raw output from Trino execution.""" - - success: bool - rows: list = field(default_factory=list) - columns: list[str] = field(default_factory=list) - row_count: int = 0 - execution_time_ms: int = 0 - error_message: str | None = None - - -@dataclass -class ExpectedShape: - """Expected result shape defined in the dataset question.""" - - row_count_min: int = 0 - row_count_max: int = 999_999 - expected_columns: list[str] = field(default_factory=list) - - -@dataclass -class ScoreBreakdown: - # Hard gate - hard_gate_triggered: bool = False - hard_gate_reason: str | None = None - - # Core dimensions - result_correctness: float = 0.0 - table_selection_correctness: float = 0.0 - sql_semantic_equivalence: float = 0.0 - result_shape_accuracy: float = 0.0 - - # Penalties - hallucination_penalty: float = 0.0 - refinement_penalty: float = 0.0 - latency_penalty: float = 0.0 - - # Result - base_score: float = 0.0 - total_penalties: float = 0.0 - final_score: float = 0.0 - question_status: str = "fail" # pass | partial | fail - failure_type: str | None = None - - -# ─── Layer 1: Hard Gates ─────────────────────────────────────────────────────── - - -def apply_hard_gates( - execution: ExecutionResult, - tables_used: list[str], - expected_tables: list[str], - generated_columns: list[str], - schema_columns: list[str], -) -> tuple[bool, str | None, float]: - """ - Returns: (gate_triggered, reason, capped_score) - """ - if not execution.success: - return True, "sql_execution_failure", 0.0 - - # Check for non-existent columns in generated SQL - if schema_columns: - invalid_cols = [c for c in generated_columns if c not in schema_columns] - if invalid_cols: - return True, f"non_existent_columns: {invalid_cols}", 0.3 - - # Check for completely wrong primary table - if expected_tables and tables_used: - primary_expected = expected_tables[0] if expected_tables else None - primary_used = tables_used[0] if tables_used else None - if ( - primary_expected - and primary_used - and primary_expected.lower() != primary_used.lower() - ): - # Allow if it's a known alias or the table name is a substring - if primary_expected.lower() not in primary_used.lower(): - return True, "wrong_primary_table", 0.2 - - return False, None, 1.0 # no gate triggered - - -# ─── Layer 2: Core Dimension Scoring ────────────────────────────────────────── - - -def score_result_shape(execution: ExecutionResult, expected: ExpectedShape) -> float: - """ - Score: 1.0 if row count in range AND all expected columns present. - """ - if not execution.success: - return 0.0 - - row_match = ( - 1.0 - if expected.row_count_min <= execution.row_count <= expected.row_count_max - else 0.3 - ) - - if expected.expected_columns: - returned = set(c.lower() for c in execution.columns) - expected_set = set(c.lower() for c in expected.expected_columns) - col_overlap = ( - len(returned & expected_set) / len(expected_set) if expected_set else 1.0 - ) - else: - col_overlap = 1.0 - - return round(0.5 * row_match + 0.5 * col_overlap, 3) - - -def score_result_correctness_from_shape( - execution: ExecutionResult, expected: ExpectedShape -) -> float: - """ - Deterministic result correctness when exact result comparison is not possible. - Uses shape + zero-result penalty as proxy. - """ - if not execution.success: - return 0.0 - - if execution.row_count == 0 and expected.row_count_min > 0: - return 0.0 # empty result when non-empty expected → zero score - - shape_score = score_result_shape(execution, expected) - return round(shape_score, 3) - - -# ─── Layer 3: Penalties ──────────────────────────────────────────────────────── - - -def compute_penalties( - hallucination_detected: bool, - refiner_iterations: int, - execution_time_ms: int, -) -> dict[str, float]: - hallucination_penalty = 0.30 if hallucination_detected else 0.0 - refinement_penalty = 0.05 * max(0, refiner_iterations - 1) # no penalty for 1 retry - latency_penalty = 0.05 if execution_time_ms > 30_000 else 0.0 - return { - "hallucination_penalty": hallucination_penalty, - "refinement_penalty": min(refinement_penalty, 0.15), # cap at 0.15 - "latency_penalty": latency_penalty, - } - - -# ─── Failure Classification ──────────────────────────────────────────────────── - - -def classify_failure( - execution: ExecutionResult, - judge: JudgeOutput, - expected: ExpectedShape, -) -> str | None: - if not execution.success: - return "execution_error" - if execution.row_count == 0 and expected.row_count_min > 0: - return "empty_result_bug" - if judge.hallucination_detected: - return "hallucination" - if judge.failure_type and judge.failure_type in FAILURE_TYPES: - return judge.failure_type - - # Infer from dimension scores - if judge.table_selection_correctness < 0.3: - return "wrong_table" - if ( - judge.sql_semantic_equivalence < 0.4 - and judge.table_selection_correctness >= 0.7 - ): - return "wrong_filter" - if judge.result_correctness < 0.6: - return "partial_correct" - - return None - - -# ─── Main Scoring Function ───────────────────────────────────────────────────── - - -@observe() -def compute_score( - execution: ExecutionResult, - expected_shape: ExpectedShape, - judge: JudgeOutput, - tables_used: list[str], - expected_tables: list[str], - generated_columns: list[str], - schema_columns: list[str], - refiner_iterations: int = 0, - question_type: str = "simple", -) -> ScoreBreakdown: - bd = ScoreBreakdown() - - # --- Layer 1: Hard Gates --- - gate_triggered, gate_reason, gate_cap = apply_hard_gates( - execution, tables_used, expected_tables, generated_columns, schema_columns - ) - if gate_triggered: - bd.hard_gate_triggered = True - bd.hard_gate_reason = gate_reason - bd.final_score = gate_cap - bd.failure_type = classify_failure(execution, judge, expected_shape) - bd.question_status = "fail" - return bd - - # --- Layer 2: Core Dimensions --- - result_correctness = score_result_correctness_from_shape(execution, expected_shape) - result_shape = score_result_shape(execution, expected_shape) - - base_score = ( - 0.45 * result_correctness - + 0.20 * judge.table_selection_correctness - + 0.15 * judge.sql_semantic_equivalence - + 0.10 * result_shape - + 0.10 * judge.result_correctness # was missing — weights now sum to 1.0 - ) - - bd.result_correctness = round(result_correctness, 3) - bd.table_selection_correctness = round(judge.table_selection_correctness, 3) - bd.sql_semantic_equivalence = round(judge.sql_semantic_equivalence, 3) - bd.result_shape_accuracy = round(result_shape, 3) - bd.base_score = round(base_score, 3) - - # --- Layer 3: Penalties --- - penalties = compute_penalties( - judge.hallucination_detected, - refiner_iterations, - execution.execution_time_ms, - ) - bd.hallucination_penalty = penalties["hallucination_penalty"] - bd.refinement_penalty = penalties["refinement_penalty"] - bd.latency_penalty = penalties["latency_penalty"] - bd.total_penalties = sum(penalties.values()) - - raw_score = max(0.0, base_score - bd.total_penalties) - - # Apply failure type score cap - failure_type = classify_failure(execution, judge, expected_shape) - bd.failure_type = failure_type - if failure_type and failure_type in FAILURE_SCORE_CAPS: - raw_score = min(raw_score, FAILURE_SCORE_CAPS[failure_type]) - - bd.final_score = round(raw_score, 3) - - # Status - if bd.final_score >= PASS_THRESHOLD: - bd.question_status = "pass" - elif bd.final_score >= PARTIAL_THRESHOLD: - bd.question_status = "partial" - else: - bd.question_status = "fail" - - return bd - - -# ─── Dataset Aggregation ─────────────────────────────────────────────────────── - - -@observe() -def compute_dataset_score(question_scores: list[tuple[float, str]]) -> dict: - """ - Args: - question_scores: list of (final_score, question_type) - Returns: - dict with dataset_score, pass_rate, fail_rate, is_publishable, breakdown - """ - if not question_scores: - return {"dataset_score": 0.0, "is_publishable": False} - - weighted_sum = 0.0 - total_weight = 0.0 - passes = partials = fails = 0 - - for score, qtype in question_scores: - weight = QUESTION_WEIGHTS.get(qtype, 1.0) - weighted_sum += score * weight - total_weight += weight - if score >= PASS_THRESHOLD: - passes += 1 - elif score >= PARTIAL_THRESHOLD: - partials += 1 - else: - fails += 1 - - dataset_score = round(weighted_sum / total_weight, 3) if total_weight > 0 else 0.0 - total = len(question_scores) - - return { - "dataset_score": dataset_score, - "pass_rate": round(passes / total, 3), - "partial_rate": round(partials / total, 3), - "fail_rate": round(fails / total, 3), - "is_publishable": dataset_score >= BLOCK_THRESHOLD, - "total_questions": total, - "total_weight": round(total_weight, 2), - } - - -# ─── Confidence Scoring (Runtime) ───────────────────────────────────────────── - - -def compute_confidence_score( - retrieval_confidence: float, - schema_match_score: float, - execution_success: bool, - historical_success_rate: float | None, - validation_checks_passed: float, -) -> dict: - """ - Runtime confidence score — separate from evaluation score. - Hard rule: if execution failed → 0.0 - """ - if not execution_success: - return {"confidence_score": 0.0, "breakdown": {"hard_gate": "execution_failed"}} - - hist = historical_success_rate if historical_success_rate is not None else 0.5 - - score = ( - 0.30 * retrieval_confidence - + 0.20 * schema_match_score - + 0.20 * float(execution_success) - + 0.20 * hist - + 0.10 * validation_checks_passed - ) - score = round(min(1.0, max(0.0, score)), 3) - - return { - "confidence_score": score, - "breakdown": { - "retrieval_confidence": round(retrieval_confidence, 3), - "schema_match": round(schema_match_score, 3), - "execution_success": 1.0, - "historical_success_rate": round(hist, 3), - "validation_checks": round(validation_checks_passed, 3), - }, - } diff --git a/backend/app/services/trino_client.py b/backend/app/services/trino_client.py index 2312f56..8b13789 100644 --- a/backend/app/services/trino_client.py +++ b/backend/app/services/trino_client.py @@ -1,2 +1 @@ -from core import TrinoExecutionResult, get_trino_connection, execute_query_sync diff --git a/backend/pyproject.toml b/backend/pyproject.toml index 1ec4fe7..1192f5e 100644 --- a/backend/pyproject.toml +++ b/backend/pyproject.toml @@ -10,7 +10,7 @@ dependencies = [ "alembic==1.14.0", "pydantic==2.10.4", "pydantic-settings==2.7.0", - "langfuse==2.56.2", + "langfuse==4.7.1", "python-dotenv==1.0.1", "httpx==0.28.1", "apscheduler==3.10.4", diff --git a/backend/tests/conftest.py b/backend/tests/conftest.py index 3c5f48d..a26ce28 100644 --- a/backend/tests/conftest.py +++ b/backend/tests/conftest.py @@ -80,6 +80,7 @@ def test_engine(setup_test_db): with engine.connect() as conn: conn.execute(text("CREATE EXTENSION IF NOT EXISTS vector")) conn.execute(text("CREATE SCHEMA IF NOT EXISTS security")) + conn.execute(text("CREATE SCHEMA IF NOT EXISTS config")) conn.commit() # Create all tables programmatically diff --git a/backend/uv.lock b/backend/uv.lock index 0e7d61b..c0780a5 100644 --- a/backend/uv.lock +++ b/backend/uv.lock @@ -32,15 +32,15 @@ wheels = [ [[package]] name = "anyio" -version = "4.13.0" +version = "4.14.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "idna" }, { name = "typing-extensions", marker = "python_full_version < '3.13'" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/19/14/2c5dd9f512b66549ae92767a9c7b330ae88e1932ca57876909410251fe13/anyio-4.13.0.tar.gz", hash = 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0000000..dd7a4e2 --- /dev/null +++ b/core/src/core/cache.py @@ -0,0 +1,139 @@ +""" +G2-05: Redis Schema Cache +========================= +Shared CacheService that wraps the system Redis engine. +Exposes a standard get / set / invalidate / invalidate_pattern interface. + +Key conventions +--------------- +DDL content ddl:{catalog}:{schema}:{table} SCHEMA_CACHE_TTL (600s) +Table profile stats profile:{table_id}:{profile_version} PROFILE_CACHE_TTL (1800s) +Catalog preflight catalog_valid:{schema}:{tables_hash} fixed 300s + +All cache calls are wrapped in try/except so a Redis outage never +crashes the application — callers receive None on miss or error and +must fall back to the live data source. +""" + +import hashlib +import json +import logging +from typing import Any + +import redis.asyncio as aioredis + +logger = logging.getLogger(__name__) + +# Fixed TTL for catalog pre-flight validation cache (not configurable) +CATALOG_VALID_TTL = 300 + + +class CacheService: + """Async Redis cache wrapper with non-blocking fallback semantics.""" + + def __init__(self, redis_url: str) -> None: + self._redis: aioredis.Redis = aioredis.from_url( + redis_url, + decode_responses=False, # return raw bytes so callers control decoding + socket_connect_timeout=2, + socket_timeout=2, + ) + + # ── Primitive operations ────────────────────────────────────────────────── + + async def get(self, key: str) -> bytes | None: + """Return cached bytes for *key*, or None on miss / error.""" + try: + return await self._redis.get(key) + except Exception as exc: + logger.warning("Cache GET error for key %r: %s", key, exc) + return None + + async def set(self, key: str, value: str | bytes, ttl: int) -> None: + """Store *value* under *key* with the given TTL (seconds).""" + if isinstance(value, str): + value = value.encode() + try: + await self._redis.setex(key, ttl, value) + except Exception as exc: + logger.warning("Cache SET error for key %r: %s", key, exc) + + async def invalidate(self, key: str) -> None: + """Delete a single cache key.""" + try: + await self._redis.delete(key) + except Exception as exc: + logger.warning("Cache DELETE error for key %r: %s", key, exc) + + async def invalidate_pattern(self, pattern: str) -> None: + """ + Delete all keys matching *pattern* using SCAN (safe for production Redis, + does not block with KEYS). + """ + try: + cursor = 0 + pipe = self._redis.pipeline() + while True: + cursor, keys = await self._redis.scan(cursor, match=pattern, count=100) + if keys: + pipe.delete(*keys) + if cursor == 0: + break + await pipe.execute() + except Exception as exc: + logger.warning("Cache SCAN/DELETE error for pattern %r: %s", pattern, exc) + + # ── JSON convenience helpers ────────────────────────────────────────────── + + async def get_json(self, key: str) -> Any | None: + """Return deserialized JSON value, or None.""" + raw = await self.get(key) + if raw is None: + return None + try: + return json.loads(raw) + except Exception: + return None + + async def set_json(self, key: str, value: Any, ttl: int) -> None: + """Serialize *value* to JSON and store with TTL.""" + await self.set(key, json.dumps(value, default=str), ttl) + + # ── Named key builders ──────────────────────────────────────────────────── + + @staticmethod + def ddl_key(catalog: str, schema: str, table: str) -> str: + return f"ddl:{catalog}:{schema}:{table}" + + @staticmethod + def profile_key(table_id: str, profile_version: str | int) -> str: + return f"profile:{table_id}:{profile_version}" + + @staticmethod + def catalog_valid_key(schema: str, tables: list[str]) -> str: + tables_hash = hashlib.sha1( + json.dumps(sorted(tables), separators=(",", ":")).encode() + ).hexdigest()[:12] + return f"catalog_valid:{schema}:{tables_hash}" + + # ── Profile invalidation helper ─────────────────────────────────────────── + + async def invalidate_profile(self, table_id: str) -> None: + """ + Purge all cached profile versions for a given table. + Call this after any background profiling worker completes. + """ + await self.invalidate_pattern(f"profile:{table_id}:*") + + +# ── Singleton factory ───────────────────────────────────────────────────────── + +_cache_instance: CacheService | None = None + + +def get_cache_service(redis_url: str) -> CacheService: + """Return a module-level singleton CacheService.""" + global _cache_instance + if _cache_instance is None: + _cache_instance = CacheService(redis_url) + return _cache_instance diff --git a/core/src/core/config.py b/core/src/core/config.py index c03f8b3..b966b7b 100644 --- a/core/src/core/config.py +++ b/core/src/core/config.py @@ -4,6 +4,9 @@ class CoreSettings(BaseSettings): model_config = SettingsConfigDict(env_file=".env", extra="ignore") DATABASE_URL: str = "sqlite:///./text2sql.db" + LANGFUSE_PUBLIC_KEY: str = "" + LANGFUSE_SECRET_KEY: str = "" + LANGFUSE_BASE_URL: str = "https://cloud.langfuse.com" TRINO_HOST: str = "localhost" TRINO_PORT: int = 8080 diff --git a/core/src/core/langfuse.py b/core/src/core/langfuse.py new file mode 100644 index 0000000..de15d38 --- /dev/null +++ b/core/src/core/langfuse.py @@ -0,0 +1,13 @@ +from langfuse.langchain import CallbackHandler +from core.config import settings + +import logging +logger = logging.getLogger(__name__) + +def get_langfuse_handler() -> CallbackHandler | None: + """FastAPI dependency to inject an isolated Langfuse CallbackHandler.""" + try: + return CallbackHandler() + except Exception as e: + logger.error(f"Failed to initialize Langfuse CallbackHandler: {e}") + return None diff --git a/core/src/core/models/models.py b/core/src/core/models/models.py index 231313b..03cf7fe 100644 --- a/core/src/core/models/models.py +++ b/core/src/core/models/models.py @@ -257,12 +257,7 @@ class EvaluationHistoryMetric(SQLModel, table=True): created_at: datetime = Field(default_factory=datetime.now) -class EvaluationHistoryMetricRead(SQLModel): - id: str - run_id: str - metric_name: str - metric_value: float - created_at: datetime + # ───────────────────────────────────────────────────────────────────────────── @@ -308,9 +303,7 @@ class EvaluationAlertRead(SQLModel): created_at: datetime -class EvalResultStatus(StrEnum): - pass_ = "pass" - fail = "fail" + class EvalResult(SQLModel, table=True): @@ -775,3 +768,90 @@ class HttpExtractorRead(SQLModel): status: ExtractorStatus created_at: datetime updated_at: datetime + + +# ───────────────────────────────────────────────────────────────────────────── +# CONFIG SCHEMA: FEATURE FLAGS & EXECUTION MODES (G4) +# ───────────────────────────────────────────────────────────────────────────── + + +class FeatureFlag(SQLModel, table=True): + """ + A single runtime-configurable parameter. + Stored in the config schema so it's logically separated from app data. + A *missing* row means "no DB override" — callers fall back to the + AgentSettings env-var default. + """ + + __tablename__ = "feature_flags" + __table_args__ = {"schema": "config"} + + name: str = Field(primary_key=True) + value: Any | None = Field(default=None, sa_column=Column(JSON)) + type: str = Field(description="bool | int | float | string | json") + description: str = Field(default="") + owner: str = Field(default="") + last_modified_by: str = Field(default="") + last_modified_at: datetime = Field(default_factory=datetime.utcnow) + + +class FeatureFlagRead(SQLModel): + name: str + value: Any | None + type: str + description: str + owner: str + last_modified_by: str + last_modified_at: datetime + + +class FeatureFlagUpdate(SQLModel): + value: Any + + +class FeatureFlagAuditLog(SQLModel, table=True): + """Immutable audit trail for every flag mutation.""" + + __tablename__ = "feature_flag_audit_log" + __table_args__ = {"schema": "config"} + + id: str = Field(default_factory=lambda: str(uuid.uuid4()), primary_key=True) + flag_name: str = Field(index=True) + actor: str + old_value: Any | None = Field(default=None, sa_column=Column(JSON)) + new_value: Any | None = Field(default=None, sa_column=Column(JSON)) + changed_at: datetime = Field(default_factory=datetime.utcnow) + + +class ExecutionMode(SQLModel, table=True): + """ + A named set of flag overrides that DS researchers select by name + when calling the MCP agent tool (execution_mode="cost_saving"). + """ + + __tablename__ = "execution_modes" + __table_args__ = {"schema": "config"} + + name: str = Field(primary_key=True) + description: str = Field(default="") + flag_overrides: Any = Field(default_factory=dict, sa_column=Column(JSON)) + is_active: bool = Field(default=True) + created_by: str = Field(default="system") + created_at: datetime = Field(default_factory=datetime.utcnow) + updated_at: datetime = Field(default_factory=datetime.utcnow) + + +class ExecutionModeRead(SQLModel): + name: str + description: str + flag_overrides: dict + is_active: bool + created_by: str + created_at: datetime + updated_at: datetime + + +class ExecutionModeUpsert(SQLModel): + description: str = "" + flag_overrides: dict = Field(default_factory=dict) + is_active: bool = True diff --git a/core/src/core/trino.py b/core/src/core/trino.py index b3e2f39..1b722b2 100644 --- a/core/src/core/trino.py +++ b/core/src/core/trino.py @@ -48,7 +48,7 @@ def get_trino_connection(): ) -def execute_query_sync(sql: str, table_id: str = "") -> TrinoExecutionResult: +def execute_query_sync(sql: str, table_id: str = "", params: tuple | dict | list | None = None) -> TrinoExecutionResult: """ Execute a SQL query against the real Trino cluster. """ @@ -64,10 +64,15 @@ def execute_query_sync(sql: str, table_id: str = "") -> TrinoExecutionResult: execution_time_ms=0, ) + conn = None + cur = None try: conn = get_trino_connection() cur = conn.cursor() - cur.execute(sql) + if params: + cur.execute(sql, params) + else: + cur.execute(sql) rows = cur.fetchall() columns = [desc[0] for desc in cur.description] if cur.description else [] execution_time_ms = int((time.time() - start_time) * 1000) diff --git a/docker-compose.yml b/docker-compose.yml index b01c011..a124ebe 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -406,7 +406,7 @@ services: dockerfile: ./backend/Dockerfile secrets: - deploy_key - # command: sh -c "python -m app.seed && python -m app.infra_init && uvicorn app.main:app --host 0.0.0.0 --port 8000" + command: sh -c "python -m app.seed && python -m app.infra_init && uvicorn app.main:app --host 0.0.0.0 --port 8000" env_file: - ./backend/.env ports: @@ -431,9 +431,16 @@ services: - LANGFUSE_PUBLIC_KEY=pk-lf-4d50ba92-b9b2-48bf-a395-3335e069fd2c - LANGFUSE_SECRET_KEY=sk-lf-0a4d3df1-573b-47a3-b534-52b59ba1bf8a - LANGFUSE_HOST=https://cloud.langfuse.com + - MINIO_HOST=minio:9000 + - MINIO_ACCESS_KEY=admin + - MINIO_SECRET_KEY=password123 + - REDIS_URL=redis://redis:6379 + - REDIS_SSL=false depends_on: db: condition: service_healthy + minio: + condition: service_started trino: condition: service_healthy openmetadata-server: @@ -514,4 +521,4 @@ volumes: secrets: deploy_key: - file: ./deploy_key + file: ./deploy_key \ No newline at end of file diff --git a/frontend/.env b/frontend/.env index 5934e2e..93fa24a 100644 --- a/frontend/.env +++ b/frontend/.env @@ -1 +1 @@ -VITE_API_URL=http://localhost:8000 +VITE_API_URL=http://localhost:8000 \ No newline at end of file diff --git a/frontend/index.html b/frontend/index.html index 86a046d..6eb3318 100644 --- a/frontend/index.html +++ b/frontend/index.html @@ -8,7 +8,7 @@ content="Text2SQL Studio — Data Intelligence module for managing TextToSQL table lifecycle" /> Jarvis Studio | Data Intelligence - +
diff --git a/frontend/package.json b/frontend/package.json index 78ff62f..5e28854 100644 --- a/frontend/package.json +++ b/frontend/package.json @@ -25,6 +25,7 @@ "@tanstack/react-query": "^5.62.7", "@tanstack/react-query-devtools": "^5.62.7", "@types/recharts": "^1.8.29", + "@xyflow/react": "^12.11.0", "antd": "^5.22.0", "axios": "^1.7.9", "dayjs": "^1.11.13", @@ -39,16 +40,19 @@ "react-router-dom": "^6.28.0", "recharts": "^3.8.1", "remark-gfm": "^4.0.1", + "uuid": "^14.0.0", "zustand": "^5.0.3" }, "devDependencies": { "@eslint/js": "^9.17.0", + "@playwright/test": "^1.61.0", "@testing-library/jest-dom": "^6.9.1", "@testing-library/react": "^16.3.2", "@testing-library/user-event": "^14.6.1", "@types/node": "^25.6.0", "@types/react": "^18.3.18", "@types/react-dom": "^18.3.5", + "@types/uuid": "^11.0.0", "@vitejs/plugin-react": "^4.3.4", "eslint": "^9.17.0", "eslint-config-prettier": "^10.1.8", @@ -63,6 +67,7 @@ "typescript": "~5.6.2", "typescript-eslint": "^8.60.0", "vite": "^6.0.5", + "react-json-view": "^1.8.0", "vitest": "^4.1.7" } } diff --git a/frontend/playwright.config.ts b/frontend/playwright.config.ts new file mode 100644 index 0000000..fe787fe --- /dev/null +++ b/frontend/playwright.config.ts @@ -0,0 +1,21 @@ +import { defineConfig, devices } from '@playwright/test'; + +export default defineConfig({ + testDir: './tests', + fullyParallel: true, + forbidOnly: !!process.env.CI, + retries: process.env.CI ? 2 : 0, + workers: process.env.CI ? 1 : undefined, + reporter: 'html', + timeout: 120000, + use: { + baseURL: 'http://localhost:3000', + trace: 'on-first-retry', + }, + projects: [ + { + name: 'chromium', + use: { ...devices['Desktop Chrome'] }, + }, + ], +}); diff --git a/frontend/pnpm-lock.yaml b/frontend/pnpm-lock.yaml index 8285047..0c16d46 100644 --- a/frontend/pnpm-lock.yaml +++ b/frontend/pnpm-lock.yaml @@ -29,6 +29,9 @@ importers: '@types/recharts': specifier: ^1.8.29 version: 1.8.29 + '@xyflow/react': + specifier: ^12.11.0 + version: 12.11.0(@types/react-dom@18.3.7(@types/react@18.3.29))(@types/react@18.3.29)(immer@11.1.8)(react-dom@18.3.1(react@18.3.1))(react@18.3.1) antd: specifier: ^5.22.0 version: 5.29.3(react-dom@18.3.1(react@18.3.1))(react@18.3.1) @@ -71,6 +74,9 @@ importers: remark-gfm: specifier: ^4.0.1 version: 4.0.1 + uuid: + specifier: ^14.0.0 + version: 14.0.0 zustand: specifier: ^5.0.3 version: 5.0.13(@types/react@18.3.29)(immer@11.1.8)(react@18.3.1)(use-sync-external-store@1.6.0(react@18.3.1)) @@ -78,6 +84,9 @@ importers: '@eslint/js': specifier: ^9.17.0 version: 9.39.4 + '@playwright/test': + specifier: ^1.61.0 + version: 1.61.0 '@testing-library/jest-dom': specifier: ^6.9.1 version: 6.9.1 @@ -96,6 +105,9 @@ importers: '@types/react-dom': specifier: ^18.3.5 version: 18.3.7(@types/react@18.3.29) + '@types/uuid': + specifier: ^11.0.0 + version: 11.0.0 '@vitejs/plugin-react': specifier: ^4.3.4 version: 4.7.0(vite@6.4.2(@types/node@25.9.1)) @@ -129,6 +141,9 @@ importers: prettier: specifier: ^3.8.3 version: 3.8.3 + react-json-view: + specifier: ^1.8.0 + version: 1.21.3(@types/react@18.3.29)(react-dom@18.3.1(react@18.3.1))(react@18.3.1) typescript: specifier: ~5.6.2 version: 5.6.3 @@ -647,6 +662,11 @@ packages: '@open-draft/until@2.1.0': resolution: {integrity: sha512-U69T3ItWHvLwGg5eJ0n3I62nWuE6ilHlmz7zM0npLBRvPRd7e6NYmg54vvRtP5mZG7kZqZCFVdsTWo7BPtBujg==} + '@playwright/test@1.61.0': + resolution: {integrity: sha512-cKA5B6lpFEMyMGjxF54QihfYpB4FkEGH+qZhtArDEG+wezQAJY8Pq6C7T1SjWz+FFzt3TbyoXBQYk/0292TdJA==} + engines: {node: '>=18'} + hasBin: true + '@rc-component/async-validator@5.1.0': resolution: {integrity: sha512-n4HcR5siNUXRX23nDizbZBQPO0ZM/5oTtmKZ6/eqL0L2bo747cklFdZGRN2f+c9qWGICwDzrhW0H7tE9PptdcA==} engines: {node: '>=14.x'} @@ -931,6 +951,9 @@ packages: '@types/d3-color@3.1.3': resolution: {integrity: sha512-iO90scth9WAbmgv7ogoq57O9YpKmFBbmoEoCHDB2xMBY0+/KVrqAaCDyCE16dUspeOvIxFFRI+0sEtqDqy2b4A==} + '@types/d3-drag@3.0.7': + resolution: {integrity: 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optional: true + zustand@5.0.13: resolution: {integrity: sha512-efI2tVaVQPqtOh114loML/Z80Y4NP3yc+Ff0fYiZJPauNeWZeIp/bRFD7I9bfmCOYBh/PHxlglQ9+wvlwnPikQ==} engines: {node: '>=12.20.0'} @@ -3627,6 +3843,10 @@ snapshots: '@open-draft/until@2.1.0': {} + '@playwright/test@1.61.0': + dependencies: + playwright: 1.61.0 + '@rc-component/async-validator@5.1.0': dependencies: '@babel/runtime': 7.29.7 @@ -3893,6 +4113,10 @@ snapshots: '@types/d3-color@3.1.3': {} + '@types/d3-drag@3.0.7': + dependencies: + '@types/d3-selection': 3.0.11 + '@types/d3-ease@3.0.2': {} '@types/d3-interpolate@3.0.4': @@ -3907,6 +4131,8 @@ snapshots: dependencies: '@types/d3-time': 3.0.4 + '@types/d3-selection@3.0.11': {} + '@types/d3-shape@1.3.12': dependencies: '@types/d3-path': 1.0.11 @@ -3919,6 +4145,15 @@ snapshots: '@types/d3-timer@3.0.2': {} + '@types/d3-transition@3.0.9': + dependencies: + '@types/d3-selection': 3.0.11 + + '@types/d3-zoom@3.0.8': + dependencies: + '@types/d3-interpolate': 3.0.4 + '@types/d3-selection': 3.0.11 + '@types/debug@4.1.13': dependencies: '@types/ms': 2.1.0 @@ -3977,6 +4212,10 @@ snapshots: '@types/use-sync-external-store@0.0.6': {} + '@types/uuid@11.0.0': + dependencies: + uuid: 14.0.0 + '@typescript-eslint/eslint-plugin@8.60.0(@typescript-eslint/parser@8.60.0(eslint@9.39.4)(typescript@5.6.3))(eslint@9.39.4)(typescript@5.6.3)': dependencies: '@eslint-community/regexpp': 4.12.2 @@ -4124,6 +4363,31 @@ snapshots: convert-source-map: 2.0.0 tinyrainbow: 3.1.0 + '@xyflow/react@12.11.0(@types/react-dom@18.3.7(@types/react@18.3.29))(@types/react@18.3.29)(immer@11.1.8)(react-dom@18.3.1(react@18.3.1))(react@18.3.1)': + dependencies: + '@xyflow/system': 0.0.77 + classcat: 5.0.5 + react: 18.3.1 + react-dom: 18.3.1(react@18.3.1) + zustand: 4.5.7(@types/react@18.3.29)(immer@11.1.8)(react@18.3.1) + optionalDependencies: + '@types/react': 18.3.29 + '@types/react-dom': 18.3.7(@types/react@18.3.29) + transitivePeerDependencies: + - immer + + '@xyflow/system@0.0.77': + dependencies: + '@types/d3-drag': 3.0.7 + '@types/d3-interpolate': 3.0.4 + '@types/d3-selection': 3.0.11 + '@types/d3-transition': 3.0.9 + '@types/d3-zoom': 3.0.8 + d3-drag: 3.0.0 + d3-interpolate: 3.0.1 + d3-selection: 3.0.0 + d3-zoom: 3.0.0 + accepts@2.0.0: dependencies: mime-types: 3.0.2 @@ -4237,6 +4501,8 @@ snapshots: aria-query@5.3.2: {} + asap@2.0.6: {} + assertion-error@2.0.1: {} asynckit@0.4.0: {} @@ -4257,6 +4523,8 @@ snapshots: balanced-match@4.0.4: {} + base16@1.0.0: {} + baseline-browser-mapping@2.10.32: {} bidi-js@1.0.3: @@ -4333,6 +4601,8 @@ snapshots: character-reference-invalid@2.0.1: {} + classcat@5.0.5: {} + classnames@2.5.1: {} cli-width@4.1.0: {} @@ -4386,6 +4656,12 @@ snapshots: object-assign: 4.1.1 vary: 1.1.2 + cross-fetch@3.2.0: + dependencies: + node-fetch: 2.7.0 + transitivePeerDependencies: + - encoding + cross-spawn@7.0.6: dependencies: path-key: 3.1.1 @@ -4407,6 +4683,13 @@ snapshots: d3-color@3.1.0: {} + d3-dispatch@3.0.1: {} + + d3-drag@3.0.0: + dependencies: + d3-dispatch: 3.0.1 + d3-selection: 3.0.0 + d3-ease@3.0.1: {} d3-format@3.1.2: {} @@ -4425,6 +4708,8 @@ snapshots: d3-time: 3.1.0 d3-time-format: 4.1.0 + d3-selection@3.0.0: {} + d3-shape@3.2.0: dependencies: d3-path: 3.1.0 @@ -4439,6 +4724,23 @@ snapshots: d3-timer@3.0.1: {} + d3-transition@3.0.1(d3-selection@3.0.0): + dependencies: + d3-color: 3.1.0 + d3-dispatch: 3.0.1 + d3-ease: 3.0.1 + d3-interpolate: 3.0.1 + d3-selection: 3.0.0 + d3-timer: 3.0.1 + + d3-zoom@3.0.0: + dependencies: + d3-dispatch: 3.0.1 + d3-drag: 3.0.0 + d3-interpolate: 3.0.1 + d3-selection: 3.0.0 + d3-transition: 3.0.1(d3-selection@3.0.0) + data-urls@7.0.0: dependencies: whatwg-mimetype: 5.0.0 @@ -4710,6 +5012,26 @@ snapshots: dependencies: fast-string-width: 3.0.2 + fbemitter@3.0.0: + dependencies: + fbjs: 3.0.5 + transitivePeerDependencies: + - encoding + + fbjs-css-vars@1.0.2: {} + + fbjs@3.0.5: + dependencies: + cross-fetch: 3.2.0 + fbjs-css-vars: 1.0.2 + loose-envify: 1.4.0 + object-assign: 4.1.1 + promise: 7.3.1 + setimmediate: 1.0.5 + ua-parser-js: 1.0.41 + transitivePeerDependencies: + - encoding + fdir@6.5.0(picomatch@4.0.4): optionalDependencies: picomatch: 4.0.4 @@ -4741,6 +5063,14 @@ snapshots: flatted@3.4.2: {} + flux@4.0.4(react@18.3.1): + dependencies: + fbemitter: 3.0.0 + fbjs: 3.0.5 + react: 18.3.1 + transitivePeerDependencies: + - encoding + follow-redirects@1.16.0: {} form-data@4.0.5: @@ -4764,6 +5094,9 @@ snapshots: fresh@2.0.0: {} + fsevents@2.3.2: + optional: true + fsevents@2.3.3: optional: true @@ -5019,6 +5352,10 @@ snapshots: dependencies: p-locate: 5.0.0 + lodash.curry@4.1.1: {} + + lodash.flow@3.5.0: {} + lodash.merge@4.6.2: {} longest-streak@3.1.0: {} @@ -5464,6 +5801,10 @@ snapshots: negotiator@1.0.0: {} + node-fetch@2.7.0: + dependencies: + whatwg-url: 5.0.0 + node-releases@2.0.46: {} object-assign@4.1.1: {} @@ -5551,6 +5892,14 @@ snapshots: pkce-challenge@5.0.1: {} + playwright-core@1.61.0: {} + + playwright@1.61.0: + dependencies: + playwright-core: 1.61.0 + optionalDependencies: + fsevents: 2.3.2 + pluralize@8.0.0: {} postcss@8.5.15: @@ -5569,6 +5918,10 @@ snapshots: ansi-styles: 5.2.0 react-is: 17.0.2 + promise@7.3.1: + dependencies: + asap: 2.0.6 + property-information@7.2.0: {} proxy-addr@2.0.7: @@ -5580,6 +5933,8 @@ snapshots: punycode@2.3.1: {} + pure-color@1.3.0: {} + qs@6.15.2: dependencies: side-channel: 1.1.1 @@ -5912,6 +6267,13 @@ snapshots: react: 18.3.1 react-dom: 18.3.1(react@18.3.1) + react-base16-styling@0.6.0: + dependencies: + base16: 1.0.0 + lodash.curry: 4.1.1 + lodash.flow: 3.5.0 + pure-color: 1.3.0 + react-dom@18.3.1(react@18.3.1): dependencies: loose-envify: 1.4.0 @@ -5932,6 +6294,20 @@ snapshots: react-is@18.3.1: {} + react-json-view@1.21.3(@types/react@18.3.29)(react-dom@18.3.1(react@18.3.1))(react@18.3.1): + dependencies: + flux: 4.0.4(react@18.3.1) + react: 18.3.1 + react-base16-styling: 0.6.0 + react-dom: 18.3.1(react@18.3.1) + react-lifecycles-compat: 3.0.4 + react-textarea-autosize: 8.5.9(@types/react@18.3.29)(react@18.3.1) + transitivePeerDependencies: + - '@types/react' + - encoding + + react-lifecycles-compat@3.0.4: {} + react-markdown@10.1.0(@types/react@18.3.29)(react@18.3.1): dependencies: '@types/hast': 3.0.4 @@ -5973,6 +6349,15 @@ snapshots: '@remix-run/router': 1.23.2 react: 18.3.1 + react-textarea-autosize@8.5.9(@types/react@18.3.29)(react@18.3.1): + dependencies: + '@babel/runtime': 7.29.7 + react: 18.3.1 + use-composed-ref: 1.4.0(@types/react@18.3.29)(react@18.3.1) + use-latest: 1.3.0(@types/react@18.3.29)(react@18.3.1) + transitivePeerDependencies: + - '@types/react' + react@18.3.1: dependencies: loose-envify: 1.4.0 @@ -6140,6 +6525,8 @@ snapshots: set-cookie-parser@3.1.0: {} + setimmediate@1.0.5: {} + setprototypeof@1.2.0: {} shebang-command@2.0.0: @@ -6264,6 +6651,8 @@ snapshots: dependencies: tldts: 7.4.0 + tr46@0.0.3: {} + tr46@6.0.0: dependencies: punycode: 2.3.1 @@ -6307,6 +6696,8 @@ snapshots: typescript@5.6.3: {} + ua-parser-js@1.0.41: {} + undici-types@7.24.6: {} undici@7.26.0: {} @@ -6360,10 +6751,31 @@ snapshots: dependencies: punycode: 2.3.1 + use-composed-ref@1.4.0(@types/react@18.3.29)(react@18.3.1): + dependencies: + react: 18.3.1 + optionalDependencies: + '@types/react': 18.3.29 + + use-isomorphic-layout-effect@1.2.1(@types/react@18.3.29)(react@18.3.1): + dependencies: + react: 18.3.1 + optionalDependencies: + '@types/react': 18.3.29 + + use-latest@1.3.0(@types/react@18.3.29)(react@18.3.1): + dependencies: + react: 18.3.1 + use-isomorphic-layout-effect: 1.2.1(@types/react@18.3.29)(react@18.3.1) + optionalDependencies: + '@types/react': 18.3.29 + use-sync-external-store@1.6.0(react@18.3.1): dependencies: react: 18.3.1 + uuid@14.0.0: {} + vary@1.1.2: {} vfile-message@4.0.3: @@ -6439,6 +6851,8 @@ snapshots: dependencies: xml-name-validator: 5.0.0 + webidl-conversions@3.0.1: {} + webidl-conversions@8.0.1: {} whatwg-mimetype@5.0.0: {} @@ -6451,6 +6865,11 @@ snapshots: transitivePeerDependencies: - '@noble/hashes' + whatwg-url@5.0.0: + dependencies: + tr46: 0.0.3 + webidl-conversions: 3.0.1 + which@2.0.2: dependencies: isexe: 2.0.0 @@ -6500,6 +6919,14 @@ snapshots: zod@4.4.3: {} + zustand@4.5.7(@types/react@18.3.29)(immer@11.1.8)(react@18.3.1): + dependencies: + use-sync-external-store: 1.6.0(react@18.3.1) + optionalDependencies: + '@types/react': 18.3.29 + immer: 11.1.8 + react: 18.3.1 + zustand@5.0.13(@types/react@18.3.29)(immer@11.1.8)(react@18.3.1)(use-sync-external-store@1.6.0(react@18.3.1)): optionalDependencies: '@types/react': 18.3.29 diff --git a/frontend/src/App.tsx b/frontend/src/App.tsx index 1af1e4f..929657a 100644 --- a/frontend/src/App.tsx +++ b/frontend/src/App.tsx @@ -16,6 +16,7 @@ import { AgentTestingPage } from './pages/AgentTestingPage'; import { AnalyticsPage } from './pages/AnalyticsPage'; import { ControlCenterPage } from './pages/ControlCenterPage'; import { EvaluationsPage } from './pages/EvaluationsPage'; +import { FlagsPage } from './pages/FlagsPage'; import { LandingPage } from './pages/LandingPage'; import { ScopesPage } from './pages/ScopesPage'; import { useAdminStore } from './store/adminStore'; @@ -85,6 +86,14 @@ function AppLayout() { } /> + + + + } + /> {/* Catch-all redirect for unmatched inner routes */} } /> @@ -94,16 +103,24 @@ function AppLayout() { } export default function App() { + const { i18n } = useTranslation(); + return ( diff --git a/frontend/src/api/agent.ts b/frontend/src/api/agent.ts index 33aea2f..020ffe4 100644 --- a/frontend/src/api/agent.ts +++ b/frontend/src/api/agent.ts @@ -7,7 +7,9 @@ const api = axios.create({ export interface QueryApproval { approved: boolean; + rejection_category?: string; feedback?: string; + suggested_fix?: string; } export interface ChatRequest { @@ -29,9 +31,15 @@ export interface ChatResponse { sql_query?: string; sql_explanation?: string; schema_plan?: string; + trace_id?: string; + execution_path?: string[]; } export const agentApi = { chat: (payload: ChatRequest): Promise => api.post('/chat', payload).then((r) => r.data), + suggestFixes: async (threadId: string, category: string): Promise => { + const response = await api.post('/suggest_fixes', { thread_id: threadId, category }); + return response.data; + }, }; diff --git a/frontend/src/api/flags.ts b/frontend/src/api/flags.ts new file mode 100644 index 0000000..110d923 --- /dev/null +++ b/frontend/src/api/flags.ts @@ -0,0 +1,76 @@ +import { API_BASE_URL } from '../config/constants'; +import { useAdminStore } from '../store/adminStore'; + +const fetchWithAdminEmail = async (url: string, options: RequestInit = {}) => { + const user = useAdminStore.getState().user; + if (!user?.email) throw new Error('Not authenticated'); + + const headers = new Headers(options.headers || {}); + headers.set('X-Admin-Email', user.email); + headers.set('Content-Type', 'application/json'); + + const response = await fetch(`${API_BASE_URL}${url}`, { ...options, headers }); + + if (response.status === 403) { + useAdminStore.getState().logout(); + const err = await response.json().catch(() => null); + throw new Error(err?.detail || 'Forbidden'); + } + if (!response.ok) { + const err = await response.json().catch(() => null); + throw new Error(err?.detail || 'Request failed'); + } + if (response.status === 204) return null; + return response.json(); +}; + +export type FlagType = 'bool' | 'int' | 'float' | 'string' | 'json'; + +export interface FeatureFlag { + name: string; + value: unknown; + type: FlagType; + description: string; + owner: string; + last_modified_by: string; + last_modified_at: string; +} + +export interface ExecutionMode { + name: string; + description: string; + flag_overrides: Record; + is_active: boolean; + created_by: string; + created_at: string; + updated_at: string; +} + +export const flagsApi = { + // ── Feature Flags ───────────────────────────────────────────────────────── + list: (): Promise => fetchWithAdminEmail('/flags/'), + + update: (name: string, value: unknown): Promise => + fetchWithAdminEmail(`/flags/${name}`, { + method: 'PATCH', + body: JSON.stringify({ value }), + }), + + reset: (name: string): Promise => + fetchWithAdminEmail(`/flags/${name}`, { method: 'DELETE' }), + + // ── Execution Modes ─────────────────────────────────────────────────────── + listModes: (): Promise => fetchWithAdminEmail('/flags/modes/'), + + getMode: (name: string): Promise => + fetchWithAdminEmail(`/flags/modes/${name}`), + + upsertMode: (name: string, data: Partial): Promise => + fetchWithAdminEmail(`/flags/modes/${name}`, { + method: 'PUT', + body: JSON.stringify(data), + }), + + deleteMode: (name: string): Promise => + fetchWithAdminEmail(`/flags/modes/${name}`, { method: 'DELETE' }), +}; diff --git a/frontend/src/components/AgentGraph.tsx b/frontend/src/components/AgentGraph.tsx new file mode 100644 index 0000000..b2e7f0f --- /dev/null +++ b/frontend/src/components/AgentGraph.tsx @@ -0,0 +1,163 @@ +import { useEffect } from 'react'; +import { + Background, + Controls, + MarkerType, + Position, + ReactFlow, + useEdgesState, + useNodesState, +} from '@xyflow/react'; + +import '@xyflow/react/dist/style.css'; + +interface AgentGraphProps { + threadId: string | null; + executionPath?: string[]; + onNodeClick?: (nodeName: string, index: number) => void; +} + +const formatLabel = (str: string) => { + return str + .split('_') + .map((w) => w.charAt(0).toUpperCase() + w.slice(1)) + .join(' '); +}; + +const getStartNodeStyle = () => { + return { + border: '1px solid rgba(255, 255, 255, 0.15)', + background: 'rgba(255, 255, 255, 0.04)', + color: '#94A3B8', + borderRadius: '8px', + padding: '12px', + fontSize: '12px', + fontWeight: 600, + width: 140, + textAlign: 'center' as const, + fontFamily: 'Fira Sans, system-ui, sans-serif', + backdropFilter: 'blur(8px)', + }; +}; + +const getStyle = (isCompleted: boolean, isActive: boolean) => { + let border = '1px solid rgba(255, 255, 255, 0.08)'; + let background = 'rgba(15, 23, 42, 0.5)'; + let color = '#64748B'; + let boxShadow = 'none'; + + if (isActive) { + border = '1px solid #38BDF8'; + background = 'rgba(56, 189, 248, 0.1)'; + color = '#38BDF8'; + boxShadow = '0 0 12px rgba(56, 189, 248, 0.25)'; + } else if (isCompleted) { + border = '1px solid #10B981'; + background = 'rgba(16, 185, 129, 0.06)'; + color = '#34D399'; + } + + return { + border, + background, + color, + boxShadow, + borderRadius: '8px', + padding: '12px', + fontSize: '12px', + fontWeight: 500, + transition: 'all 0.3s ease', + width: 140, + textAlign: 'center' as const, + fontFamily: 'Fira Sans, system-ui, sans-serif', + backdropFilter: 'blur(8px)', + cursor: 'pointer', + }; +}; + +export function AgentGraph({ executionPath = [], onNodeClick }: AgentGraphProps) { + const [nodes, setNodes, onNodesChange] = useNodesState([]); + const [edges, setEdges, onEdgesChange] = useEdgesState([]); + + useEffect(() => { + // Dynamically build the graph based on the executed path + const newNodes: any[] = [ + { + id: 'START', + position: { x: 50, y: 100 }, + data: { label: 'START' }, + type: 'input', + style: getStartNodeStyle(), + sourcePosition: Position.Right, + }, + ]; + const newEdges: any[] = []; + + executionPath.forEach((step, i) => { + const id = `${step}-${i}`; + const isActive = i === executionPath.length - 1; + const isCompleted = i < executionPath.length - 1; + + newNodes.push({ + id, + position: { x: 50 + (i + 1) * 180, y: 100 }, + data: { label: formatLabel(step) }, + style: getStyle(isCompleted, isActive), + sourcePosition: Position.Right, + targetPosition: Position.Left, + }); + + const sourceId = i === 0 ? 'START' : `${executionPath[i - 1]}-${i - 1}`; + newEdges.push({ + id: `e-${sourceId}-${id}`, + source: sourceId, + target: id, + animated: isActive, + markerEnd: { + type: MarkerType.ArrowClosed, + color: isActive ? '#38BDF8' : '#10B981', + }, + style: { + stroke: isActive ? '#38BDF8' : '#10B981', + strokeWidth: 2, + }, + }); + }); + + setNodes(newNodes); + setEdges(newEdges); + }, [executionPath, setNodes, setEdges]); + + return ( +
+ { + if (node.id === 'START') return; + const [stepName, indexStr] = node.id.split('-'); + const index = parseInt(indexStr, 10); + onNodeClick?.(stepName, index); + }} + fitView + fitViewOptions={{ padding: 0.2 }} + attributionPosition="bottom-right" + > + + + +
+ ); +} diff --git a/frontend/src/components/JsonTreeView.tsx b/frontend/src/components/JsonTreeView.tsx new file mode 100644 index 0000000..286945b --- /dev/null +++ b/frontend/src/components/JsonTreeView.tsx @@ -0,0 +1,112 @@ +import React, { useState } from 'react'; +import { CopyOutlined, CheckOutlined } from '@ant-design/icons'; + +interface JsonTreeViewProps { + label: 'INPUT' | 'OUTPUT'; + content: string; + themeColor: string; +} + +// Recursive component to render JSON as a collapsible tree +const RenderNode: React.FC<{ data: any; depth?: number }> = ({ data, depth = 0 }) => { + const isObject = typeof data === 'object' && data !== null; + if (!isObject) { + return {String(data)}; + } + const entries = Array.isArray(data) + ? data.map((v, i) => [i, v]) + : Object.entries(data); + return ( +
    + {entries.map(([key, value]) => ( +
  • +
    + {Array.isArray(data) ? `[${key}]` : key}: + +
    +
    • + ))} +
+ ); +}; + +export default function JsonTreeView({ label, content, themeColor }: JsonTreeViewProps) { + const [copied, setCopied] = useState(false); + + const handleCopy = () => { + navigator.clipboard.writeText(content); + setCopied(true); + setTimeout(() => setCopied(false), 2000); + }; + + const trimmed = content?.trim() || ''; + const isJson = + (trimmed.startsWith('{') && trimmed.endsWith('}')) || + (trimmed.startsWith('[') && trimmed.endsWith(']')); + + let renderedContent: React.ReactNode; + if (isJson) { + try { + const parsed = JSON.parse(trimmed); + renderedContent = ; + } catch (e) { + renderedContent = {content}; + } + } else { + renderedContent = {content}; + } + + return ( +
+
+ {label} + +
+
+ {renderedContent} +
+
+ ); +} diff --git a/frontend/src/components/SchemaPlanDisplay.module.css b/frontend/src/components/SchemaPlanDisplay.module.css new file mode 100644 index 0000000..985d34c --- /dev/null +++ b/frontend/src/components/SchemaPlanDisplay.module.css @@ -0,0 +1,52 @@ +.schemaPlanContainer { + display: flex; + flex-direction: column; + gap: 16px; +} + +.section { + display: flex; + flex-direction: column; + gap: 8px; +} + +.sectionHeader { + display: flex; + align-items: center; + gap: 8px; + font-weight: 600; + color: var(--text-h); + font-size: 14px; +} + +.dataTable { + background: var(--bg-secondary); + border-radius: 8px; + overflow: hidden; +} + +.dataTable :global(.ant-table) { + background: transparent; + color: var(--text-body); +} + +.dataTable :global(.ant-table-thead > tr > th) { + background: var(--bg-tertiary) !important; + color: var(--text-muted) !important; + border-bottom: 1px solid var(--border-color) !important; + font-weight: 500; +} + +.dataTable :global(.ant-table-tbody > tr > td) { + border-bottom: 1px solid var(--border-color) !important; +} + +.dataTable :global(.ant-table-tbody > tr:hover > td) { + background: rgba(255, 255, 255, 0.02) !important; +} + +.infoCard { + background: var(--bg-secondary); + border: 1px solid var(--border-color); + color: var(--text-body); +} diff --git a/frontend/src/components/SchemaPlanDisplay.tsx b/frontend/src/components/SchemaPlanDisplay.tsx new file mode 100644 index 0000000..fd42e05 --- /dev/null +++ b/frontend/src/components/SchemaPlanDisplay.tsx @@ -0,0 +1,298 @@ +import React from 'react'; +import ReactMarkdown from 'react-markdown'; +import { Card, Space, Table, Tag, Typography } from 'antd'; +import { Database, Filter, Link, ListOrdered, Sparkles } from 'lucide-react'; +import remarkGfm from 'remark-gfm'; + +import styles from './SchemaPlanDisplay.module.css'; + +const { Text } = Typography; + +interface SchemaPlanDisplayProps { + planString: string; +} + +const renderCellSafe = (val: any): string => { + if (val === null || val === undefined) return ''; + if (typeof val === 'object') { + return val.column_name || val.name || JSON.stringify(val); + } + return String(val); +}; + +export const SchemaPlanDisplay: React.FC = ({ planString }) => { + let planData: any = null; + try { + planData = JSON.parse(planString); + } catch (e) { + // If it's not JSON, render it as markdown + return ( +
+ {planString} +
+ ); + } + + const explanationText = + planData.description || + planData.explanation || + planData.strategy || + planData.reasoning || + planData.logic; + + // Define columns for Tables + const tableColumns = [ + { + title: 'Table Name', + key: 'name', + render: (record: any) => { + const name = record.name || record.table_name || record.tableName || record.table || ''; + return ( + + {renderCellSafe(name)} + + ); + }, + }, + { + title: 'Columns', + key: 'columns', + render: (record: any) => { + const columns = record.columns || record.column_names || record.columnNames || []; + if (!columns || columns.length === 0) { + return ( + + No columns selected / empty + + ); + } + return ( + + {columns?.map((col: any, idx: number) => { + if (typeof col === 'string') { + return ( + + {col} + + ); + } else if (col && typeof col === 'object') { + const name = col.column_name || col.name || `col_${idx}`; + const type = col.data_type || col.type || ''; + return ( + + {name}{' '} + {type ? ({type}) : ''} + + ); + } + return null; + })} + + ); + }, + }, + ]; + + // Define columns for Joins + const joinColumns = [ + { + title: 'Source Table', + key: 'source_table', + render: (record: any) => { + const val = + record.source_table || record.sourceTable || record.srcTable || record.src_table || ''; + return {renderCellSafe(val)}; + }, + }, + { + title: 'Source Column', + key: 'source_column', + render: (record: any) => { + const val = + record.source_column || + record.sourceColumn || + record.srcColumn || + record.src_column || + ''; + return renderCellSafe(val); + }, + }, + { + title: 'Target Table', + key: 'target_table', + render: (record: any) => { + const val = + record.target_table || + record.targetTable || + record.destTable || + record.dest_table || + record.tgtTable || + record.tgt_table || + ''; + return {renderCellSafe(val)}; + }, + }, + { + title: 'Target Column', + key: 'target_column', + render: (record: any) => { + const val = + record.target_column || + record.targetColumn || + record.destColumn || + record.dest_column || + record.tgtColumn || + record.tgt_column || + ''; + return renderCellSafe(val); + }, + }, + { + title: 'Join Type', + key: 'type', + render: (record: any) => { + const val = record.type || record.join_type || record.joinType || 'INNER'; + return {renderCellSafe(val).toUpperCase()}; + }, + }, + ]; + + // Define columns for Filters + const filterColumns = [ + { + title: 'Column', + key: 'column', + render: (record: any) => { + const val = record.column || record.column_name || record.columnName || record.col || ''; + return {renderCellSafe(val)}; + }, + }, + { + title: 'Operator', + key: 'operator', + render: (record: any) => { + const val = record.operator || record.op || ''; + return {renderCellSafe(val)}; + }, + }, + { + title: 'Value', + key: 'value', + render: (record: any) => { + const val = record.value || record.val || ''; + return {renderCellSafe(val)}; + }, + }, + ]; + + return ( +
+ {explanationText && ( +
+ +
+ + Query Logic & Strategy +
+ + {renderCellSafe(explanationText)} + + +
+ )} + + {planData.tables !== undefined && ( +
+
+ Tables & Columns +
+ r.name || r.table_name || r.tableName || r.table || i.toString()} + pagination={false} + size="small" + bordered + className={styles.dataTable} + /> + + )} + + {planData.joins && planData.joins.length > 0 && ( +
+
+ Joins +
+
i.toString()} + pagination={false} + size="small" + bordered + className={styles.dataTable} + /> + + )} + + {planData.filters && planData.filters.length > 0 && ( +
+
+ Filters +
+
i.toString()} + pagination={false} + size="small" + bordered + className={styles.dataTable} + /> + + )} + + {(planData.order_by?.length > 0 || planData.limit) && ( +
+
+ Sorting & Limits +
+ + {planData.order_by?.length > 0 && ( +
+ Order By: + {planData.order_by.map((ob: any, i: number) => ( + + {renderCellSafe(ob.column)}{' '} + {renderCellSafe(ob.direction)?.toUpperCase() || 'ASC'} + + ))} +
+ )} + {planData.limit && ( +
+ Limit: {planData.limit} +
+ )} + +
+ )} + + ); +}; diff --git a/frontend/src/components/TraceTimeline.tsx b/frontend/src/components/TraceTimeline.tsx new file mode 100644 index 0000000..8bdec2b --- /dev/null +++ b/frontend/src/components/TraceTimeline.tsx @@ -0,0 +1,315 @@ +import { type ReactNode, useEffect, useState } from 'react'; +import { + CaretRightOutlined, + CheckOutlined, + ClockCircleOutlined, + CopyOutlined, +} from '@ant-design/icons'; +import { Collapse, Empty, Spin, Tag, Timeline, Typography } from 'antd'; +import axios from 'axios'; + +import JsonTreeView from '../components/JsonTreeView'; + +const { Text } = Typography; +const { Panel } = Collapse; + +interface TraceSpan { + span_name: string; + start_time: string; + duration_ms: number; + input_tokens: number; + output_tokens: number; + model: string; + status: string; + input_preview: string; + output_preview: string; +} + +interface TraceTimelineProps { + traceId: string; +} +export function highlightJson(json: string): string { + if (!json) return ''; + const entityMap: { [key: string]: string } = { + '&': '&', + '<': '<', + '>': '>', + '"': '"', + "'": ''', + '/': '/', + }; + const escapeHtml = (text: string) => text.replace(/[&<>"'/]/g, (m) => entityMap[m]); + + const jsonRegex = + /("(?:[^"\\]|\\.)*"(?:\s*:)?|\b(true|false|null)\b|-?\d+(?:\.\d*)?(?:[eE][+-]?\d+)?)/g; + + let lastIndex = 0; + let html = ''; + let match; + + while ((match = jsonRegex.exec(json)) !== null) { + html += escapeHtml(json.substring(lastIndex, match.index)); + + const token = match[0]; + let cls = 'json-number'; + + if (token.startsWith('"')) { + if (token.endsWith(':')) { + cls = 'json-key'; + } else { + cls = 'json-string'; + } + } else if (token === 'true' || token === 'false') { + cls = 'json-boolean'; + } else if (token === 'null') { + cls = 'json-null'; + } + + if (cls === 'json-key') { + const lastQuoteIndex = token.lastIndexOf('"'); + const keyPart = token.substring(0, lastQuoteIndex + 1); + const colonPart = token.substring(lastQuoteIndex + 1); + html += `${escapeHtml(keyPart)}${escapeHtml(colonPart)}`; + } else { + html += `${escapeHtml(token)}`; + } + + lastIndex = jsonRegex.lastIndex; + } + + html += escapeHtml(json.substring(lastIndex)); + return html; +} + +interface FormattedBlockProps { + label: 'INPUT' | 'OUTPUT'; + content: string; + themeColor: string; +} + +export function FormattedBlock({ label, content, themeColor }: FormattedBlockProps) { + const [copied, setCopied] = useState(false); + + const handleCopy = () => { + navigator.clipboard.writeText(content); + setCopied(true); + setTimeout(() => setCopied(false), 2000); + }; + + const trimmed = content?.trim() || ''; + const isJson = + (trimmed.startsWith('{') && trimmed.endsWith('}')) || + (trimmed.startsWith('[') && trimmed.endsWith(']')); + + let highlightedElement: ReactNode; + if (isJson) { + try { + const parsed = JSON.parse(trimmed); + const formatted = JSON.stringify(parsed, null, 2); + const highlighted = highlightJson(formatted); + highlightedElement = ; + } catch (e) { + highlightedElement = {content}; + } + } else { + highlightedElement = {content}; + } + + return ( +
+
+ + {label} + + +
+
+ 
+          {highlightedElement}
+
+
+
+ ); +} + +const jsonHighlightStyles = ` + .json-block-container { + position: relative; + } + .copy-btn { + opacity: 0.6; + } + .copy-btn:hover { + opacity: 1; + background: rgba(255, 255, 255, 0.05) !important; + color: var(--text-h) !important; + } + .json-key { color: #818CF8; font-weight: 600; } + .json-string { color: #34D399; } + .json-number { color: #FB923C; } + .json-boolean { color: #F472B6; font-weight: 500; } + .json-null { color: #64748B; font-style: italic; } +`; + +export function TraceTimeline({ traceId }: TraceTimelineProps) { + const [spans, setSpans] = useState([]); + const [loading, setLoading] = useState(true); + + useEffect(() => { + if (!traceId) return; + + setLoading(true); + axios + .get(`/api/agent/traces/${traceId}`) + .then((res) => { + setSpans(res.data || []); + }) + .catch((err) => { + console.error('Failed to load trace', err); + }) + .finally(() => { + setLoading(false); + }); + }, [traceId]); + + if (loading) { + return ( +
+ +
+ ); + } + + if (spans.length === 0) { + return ; + } + + return ( +
+