diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..5faf00b9fa93c66d4e5decf47cb92efa1a19b5aa --- /dev/null +++ b/.gitignore @@ -0,0 +1,32 @@ +# Python-generated files +**/__pycache__/* +.env +__pycache__ +agent-chat-ui +config.yaml +_archieved + +__pycache__/ +*.py[oc] +build/ +dist/ +wheels/ +*.egg-info +asset_testing/ +test/users/user_accounts.csv +.continue/ + +# Virtual environments +.venv + +# env +.env +.env.dev +.env.uat +.env.prd +.env.example + +playground_retriever.py +playground_chat.pyc +playground_flush_cache.pyc +API_CONTRACT.md \ No newline at end of file diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..b5fea7ad117dece53ab137091e35b42d3c769201 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,34 @@ +FROM python:3.12-slim-bookworm + +COPY --from=ghcr.io/astral-sh/uv:latest /uv /uvx /bin/ + +WORKDIR /app + +ENV PYTHONUNBUFFERED=1 \ + UV_COMPILE_BYTECODE=1 + +RUN apt-get update && apt-get install -y --no-install-recommends \ + build-essential \ + libpq-dev \ + gcc \ + libgomp1 \ + && rm -rf /var/lib/apt/lists/* + +RUN addgroup --system app && \ + adduser --system --group --home /home/app app + +COPY pyproject.toml uv.lock ./ +RUN uv sync --frozen --no-dev + +# Download spaCy model required by presidio-analyzer +RUN uv run python -m spacy download en_core_web_lg + +COPY . . + +RUN chown -R app:app /app + +USER app + +EXPOSE 7860 + +CMD ["uv", "run", "--no-sync", "uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"] \ No newline at end of file diff --git a/README.md b/README.md index 029bf06a811af37f1d667613f4550eab37436ebe..05a2f4533b4d11743945e11e2e98fe22860ecdec 100644 --- a/README.md +++ b/README.md @@ -8,3 +8,70 @@ pinned: false --- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference + + +How to run: +`uv run --no-sync uvicorn main:app --host 0.0.0.0 --port 7860` + + +Agent +Orchestrator : intent recognition, orchestrate, and plannings +Chatbot : have tools (retriever, and search), called by orchestrator + + +APIs +/api/v1/login -> login by email and password + +/api/v1/documents/{user_id} -> list all documents +/api/v1/document/upload -> upload document +/api/v1/document/delete -> delete document +/api/v1/document/process -> extract document and ingest to vector index + +/api/v1/chat/stream -> talk with agent chatbot, in streaming response +/api/v1/rooms/{user_id} -> list all room based on user id +/api/v1/room/{room_id} -> get room based on room id + + +Config +- Agent: system prompt, guardrails +- others config needed + +DB +- using postgres as db +- we can use pg vector from this db also +- use redis for caching response, same question will not re-processed for 24 hour + +Document +- service to manage document, upload, delete, log to db + +Knowledge +- service to process document into vector, until ingestion to pg vector + +Middleware +CORS: +- allow all +Rate limiting: +- upload document: 10 document per menit +Logging: +- create clear and strutured logging for better debuging + +Models +- Data models + +Observability +- Langfuse traceability + +RAG +- retriever service to get relevant context from pg vector + +storage +- storage functionality to communicate with storage provider + +tools +- tools that can be use by agent + +Users +- Users management, to get user indentity based on login information. + +Utils +- Other functionality \ No newline at end of file diff --git a/main.py b/main.py new file mode 100644 index 0000000000000000000000000000000000000000..d70b0463575926ffb26e740f3c62445d7b2c9a14 --- /dev/null +++ b/main.py @@ -0,0 +1,70 @@ +"""Main application entry point.""" + +from fastapi import FastAPI +from src.middlewares.logging import configure_logging, get_logger +from src.middlewares.cors import add_cors_middleware +from src.middlewares.rate_limit import limiter, _rate_limit_exceeded_handler +from slowapi.errors import RateLimitExceeded +from src.api.v1.document import router as document_router +from src.api.v1.chat import router as chat_router +from src.api.v1.room import router as room_router +from src.api.v1.users import router as users_router +from src.api.v1.knowledge import router as knowledge_router +from src.db.postgres.init_db import init_db +import uvicorn + +# Configure logging +configure_logging() +logger = get_logger("main") + +# Create FastAPI app +app = FastAPI( + title="DataEyond Agentic Service", + description="Multi-agent AI backend with RAG capabilities", + version="0.1.0" +) + +# Add middleware +add_cors_middleware(app) +app.state.limiter = limiter +app.add_exception_handler(RateLimitExceeded, _rate_limit_exceeded_handler) + +# Include routers +app.include_router(users_router) +app.include_router(document_router) +app.include_router(knowledge_router) +app.include_router(room_router) +app.include_router(chat_router) + + +@app.on_event("startup") +async def startup_event(): + """Initialize database on startup.""" + logger.info("Starting application...") + await init_db() + logger.info("Database initialized") + + +@app.get("/") +async def root(): + """Root endpoint.""" + return { + "status": "ok", + "service": "DataEyond Agentic Service", + "version": "0.1.0" + } + + +@app.get("/health") +async def health_check(): + """Health check endpoint.""" + return {"status": "healthy"} + + +if __name__ == "__main__": + uvicorn.run( + "src.main:app", + host="0.0.0.0", + port=8000, + reload=True + ) diff --git a/playground_chat.py b/playground_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..dc2a277ed8ec589e76b380855a9670791ff73b4d --- /dev/null +++ b/playground_chat.py @@ -0,0 +1,79 @@ +"""Playground script for testing the full chat pipeline (Orchestrator → Retriever → Chatbot). + +Usage: + uv run python playground_chat.py + +Tweak the variables in the CONFIG section below and re-run. +""" + +import asyncio +import json +from langchain_core.messages import HumanMessage + +from src.agents.orchestration import orchestrator +from src.agents.chatbot import chatbot +from src.rag.retriever import retriever +from src.api.v1.chat import _format_context, _extract_sources + +# ────────────────────────────────────────────── +# CONFIG — change these freely +# ────────────────────────────────────────────── +MESSAGE = "Berapa digital rate card untuk Net Message?" +USER_ID = "8b6c18fd-8971-46e5-b106-35b7afb412e0" +TOP_K = 5 # number of chunks to retrieve +# ────────────────────────────────────────────── + + +async def main(): + print(f"\n{'='*60}") + print(f"Message : {MESSAGE}") + print(f"User ID : {USER_ID}") + print(f"Top-K : {TOP_K}") + print(f"{'='*60}\n") + + # Step 1: Orchestrator — intent analysis + print("── Step 1: Orchestrator ──────────────────────────────────") + intent_result = await orchestrator.analyze_message(MESSAGE) + print(json.dumps(intent_result, indent=2, ensure_ascii=False)) + + context = "" + sources = [] + + # Step 2: Retriever (only if orchestrator says so) + if intent_result.get("needs_search"): + search_query = intent_result.get("search_query", MESSAGE) + print(f"\n── Step 2: Retriever (query: {search_query!r}) ──────────────") + raw_results = await retriever.retrieve( + query=search_query, + user_id=USER_ID, + db=None, + k=TOP_K, + ) + context = _format_context(raw_results) + sources = _extract_sources(raw_results) + + print(f"Retrieved {len(raw_results)} chunk(s). Sources:") + for s in sources: + print(f" - {s['filename']} p.{s['page_label']}") + print(f"\nContext preview:\n{context[:500]}{'...' if len(context) > 500 else ''}") + else: + print("\n── Step 2: Retriever — skipped (no search needed)") + + # Step 3: Direct response (greetings, etc.) + if intent_result.get("direct_response"): + print("\n── Step 3: Direct response (no LLM call) ────────────────") + print(intent_result["direct_response"]) + return + + # Step 4: Chatbot — generate answer + print("\n── Step 4: Chatbot response ──────────────────────────────") + messages = [HumanMessage(content=MESSAGE)] + response = await chatbot.generate_response(messages, context) + print(response) + + print(f"\n── Sources ───────────────────────────────────────────────") + print(json.dumps(sources, indent=2, ensure_ascii=False)) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/playground_flush_cache.py b/playground_flush_cache.py new file mode 100644 index 0000000000000000000000000000000000000000..d477768ca5e79609e34c4f5591d8f96409d0e39e --- /dev/null +++ b/playground_flush_cache.py @@ -0,0 +1,39 @@ +"""One-shot script to delete a specific chat cache key from Redis. + +Usage: + uv run python playground_flush_cache.py +""" + +import asyncio +from src.db.redis.connection import get_redis +from src.config.settings import settings + +# ────────────────────────────────────────────── +# CONFIG +# ────────────────────────────────────────────── +USER_ID = "8b6c18fd-8971-46e5-b106-35b7afb412e0" +MESSAGE = "Berapa digital rate card untuk whatsapp?" +# Set to True to wipe ALL chat cache keys for the user instead +FLUSH_ALL_USER_CHAT = False +# ────────────────────────────────────────────── + + +async def main(): + redis = await get_redis() + prefix = f"{settings.redis_prefix}chat:{USER_ID}:" + + if FLUSH_ALL_USER_CHAT: + keys = await redis.keys(f"{prefix}*") + if keys: + await redis.delete(*keys) + print(f"Deleted {len(keys)} key(s) matching {prefix}*") + else: + print("No keys found.") + else: + key = f"{prefix}{MESSAGE}" + deleted = await redis.delete(key) + print(f"Deleted {deleted} key: {key!r}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..fb2f8ad3cea0946b291e644fc3c838d00898fea4 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,135 @@ +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[project] +name = "demo-agent-service" +version = "0.1.0" +description = "DEMO Agentic Service Data Eyond — Multi-Agent AI Backend" +requires-python = ">=3.12,<3.13" + +dependencies = [ + # --- Web Framework --- + "fastapi[standard]==0.115.6", + "uvicorn[standard]==0.32.1", + "python-multipart==0.0.12", + "starlette==0.41.3", + "sse-starlette==2.1.3", + # --- LangChain Core Ecosystem (NO LiteLLM) --- + "langchain==0.3.13", + "langchain-core==0.3.28", + "langchain-community==0.3.13", + "langchain-openai==0.2.14", + "langchain-postgres>=0.0.13", + "langgraph==0.2.60", + "langgraph-checkpoint-postgres==2.0.9", + # --- LLM / Azure OpenAI --- + "openai==1.58.1", + "tiktoken==0.8.0", + # --- Database --- + "sqlalchemy[asyncio]==2.0.36", + "asyncpg==0.30.0", + "psycopg[binary,pool]==3.2.3", + "pgvector==0.3.6", + "alembic==1.14.0", + # --- Azure --- + "azure-storage-blob==12.23.1", + "azure-identity==1.19.0", + "azure-ai-documentintelligence==1.0.0", + # --- Pydantic / Validation --- + "pydantic==2.10.3", + "pydantic-settings==2.7.0", + # --- Observability --- + "langfuse==2.57.4", + "structlog==24.4.0", + "prometheus-client==0.21.1", + # --- Security --- + "passlib[bcrypt]==1.7.4", + "cryptography==44.0.0", + # --- Rate Limiting --- + "slowapi==0.1.9", + "redis==5.2.1", + # --- Retry --- + "tenacity==9.0.0", + # --- Document Processing (for reading existing docs from blob) --- + "pypdf==5.1.0", + "python-docx==1.1.2", + "openpyxl==3.1.5", + "pandas==2.2.3", + # --- Chart/Visualization --- + "matplotlib==3.9.3", + "plotly==5.24.1", + "kaleido==0.2.1", + # --- MCP --- + "mcp==1.2.0", + # --- Advanced RAG --- + "rank-bm25==0.2.2", + "sentence-transformers==3.3.1", + # --- PII Detection (no LiteLLM) --- + "presidio-analyzer==2.2.355", + "presidio-anonymizer==2.2.355", + "spacy==3.8.3", + # --- Utilities --- + "httpx==0.28.1", + "anyio==4.7.0", + "python-dotenv==1.0.1", + "orjson==3.10.12", + "cachetools==5.5.0", + "apscheduler==3.10.4", + "jsonpatch>=1.33", + "pymongo>=4.14.0", + "psycopg2>=2.9.11", +] + +[project.optional-dependencies] +dev = [ + "pytest==8.3.4", + "pytest-asyncio==0.24.0", + "pytest-cov==6.0.0", + "httpx==0.28.1", + "ruff==0.8.4", + "mypy==1.13.0", + "pre-commit==4.0.1", +] + +[tool.uv] +dev-dependencies = [ + "pytest==8.3.4", + "pytest-asyncio==0.24.0", + "pytest-cov==6.0.0", + "ruff==0.8.4", + "mypy==1.13.0", + "pre-commit==4.0.1", +] + +[tool.hatch.build.targets.wheel] +packages = ["src/agent_service"] + +[tool.ruff] +target-version = "py312" +line-length = 100 + +[tool.ruff.lint] +select = ["E", "F", "I", "N", "UP", "S", "B", "A", "C4", "T20"] +ignore = [ + "S101", # assert statements OK in tests + "S105", # hardcoded passwords — false positives in config + "S106", + "B008", # FastAPI Depends() calls OK in function args +] + +[tool.ruff.lint.per-file-ignores] +"tests/**" = ["S101", "S105", "S106"] + +[tool.mypy] +python_version = "3.12" +strict = true +ignore_missing_imports = true +plugins = ["pydantic.mypy"] + +[tool.pytest.ini_options] +asyncio_mode = "auto" +testpaths = ["tests"] +filterwarnings = [ + "ignore::DeprecationWarning", +] diff --git a/run.py b/run.py new file mode 100644 index 0000000000000000000000000000000000000000..886e612329d770e04d3540bd25093f78c9ca5f28 --- /dev/null +++ b/run.py @@ -0,0 +1,18 @@ +"""Entry point for running the app locally on Windows. + +Sets WindowsSelectorEventLoopPolicy BEFORE uvicorn creates its event loop, +which is required for psycopg3 async mode compatibility. +Use this instead of calling uvicorn directly on Windows: + uv run --no-sync python run.py +""" + +import sys +import asyncio + +if sys.platform == "win32": + asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy()) + +import uvicorn + +if __name__ == "__main__": + uvicorn.run("main:app", host="0.0.0.0", port=7860, reload=False) diff --git a/src/__init__.py b/src/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/agents/__init__.py b/src/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/agents/chatbot.py b/src/agents/chatbot.py new file mode 100644 index 0000000000000000000000000000000000000000..cb11014a3b47ad14a5f12f85ed9fe48e216b1bf5 --- /dev/null +++ b/src/agents/chatbot.py @@ -0,0 +1,75 @@ +"""Chatbot agent with RAG capabilities.""" + +from langchain_openai import AzureChatOpenAI +from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder +from langchain_core.output_parsers import StrOutputParser +from src.config.settings import settings +from src.middlewares.logging import get_logger +from langchain_core.messages import HumanMessage, AIMessage + +logger = get_logger("chatbot") + + +class ChatbotAgent: + """Chatbot agent with RAG capabilities.""" + + def __init__(self): + self.llm = AzureChatOpenAI( + azure_deployment=settings.azureai_deployment_name_4o, + openai_api_version=settings.azureai_api_version_4o, + azure_endpoint=settings.azureai_endpoint_url_4o, + api_key=settings.azureai_api_key_4o, + temperature=0.7 + ) + + # Read system prompt + try: + with open("src/config/agents/system_prompt.md", "r") as f: + system_prompt = f.read() + except FileNotFoundError: + system_prompt = "You are a helpful AI assistant with access to user's uploaded documents." + + # Create prompt template + self.prompt = ChatPromptTemplate.from_messages([ + ("system", system_prompt), + MessagesPlaceholder(variable_name="messages"), + ("system", "Relevant documents:\n{context}") + ]) + + # Create chain + self.chain = self.prompt | self.llm | StrOutputParser() + + async def generate_response( + self, + messages: list, + context: str = "" + ) -> str: + """Generate response with optional RAG context.""" + try: + logger.info("Generating chatbot response") + + # Generate response + response = await self.chain.ainvoke({ + "messages": messages, + "context": context + }) + + logger.info(f"Generated response: {response[:100]}...") + return response + + except Exception as e: + logger.error("Response generation failed", error=str(e)) + raise + + async def astream_response(self, messages: list, context: str = ""): + """Stream response tokens as they are generated.""" + try: + logger.info("Streaming chatbot response") + async for token in self.chain.astream({"messages": messages, "context": context}): + yield token + except Exception as e: + logger.error("Response streaming failed", error=str(e)) + raise + + +chatbot = ChatbotAgent() diff --git a/src/agents/orchestration.py b/src/agents/orchestration.py new file mode 100644 index 0000000000000000000000000000000000000000..85010a43f97ffa9e3a1f794d9d2340f349f6c3fa --- /dev/null +++ b/src/agents/orchestration.py @@ -0,0 +1,74 @@ +"""Orchestrator agent for intent recognition and planning.""" + +from langchain_openai import AzureChatOpenAI +from langchain_core.prompts import ChatPromptTemplate +from langchain_core.output_parsers import JsonOutputParser +from src.config.settings import settings +from src.middlewares.logging import get_logger + +logger = get_logger("orchestrator") + + +class OrchestratorAgent: + """Orchestrator agent for intent recognition and planning.""" + + def __init__(self): + self.llm = AzureChatOpenAI( + azure_deployment=settings.azureai_deployment_name_4o, + openai_api_version=settings.azureai_api_version_4o, + azure_endpoint=settings.azureai_endpoint_url_4o, + api_key=settings.azureai_api_key_4o, + temperature=0 + ) + + self.prompt = ChatPromptTemplate.from_messages([ + ("system", """You are an orchestrator agent. Analyze user's message and determine: + +1. What is user's intent? (question, greeting, goodbye, other) +2. Do we need to search user's documents for relevant information? +3. If search is needed, what query should we use? +4. If no search needed, provide a direct response. + +Respond in JSON format with these fields: +- intent: string (question, greeting, goodbye, other) +- needs_search: boolean +- search_query: string (if needed) +- direct_response: string (if no search needed) + +Intent Routing: +- question -> needs_search=True, search_query=message +- greeting -> needs_search=False, direct_response="Hello! How can I assist you today?" +- goodbye -> needs_search=False, direct_response="Goodbye! Have a great day!" +- other -> needs_search=True, search_query=message +"""), + ("user", "{message}") + ]) + + self.chain = ( + self.prompt + | self.llm + | JsonOutputParser() + ) + + async def analyze_message(self, message: str) -> dict: + """Analyze user message and determine next actions.""" + try: + logger.info(f"Analyzing message: {message[:50]}...") + + result = await self.chain.ainvoke({"message": message}) + + logger.info(f"Intent: {result.get('intent')}, Needs search: {result.get('needs_search')}") + return result + + except Exception as e: + logger.error("Message analysis failed", error=str(e)) + # Fallback to treating everything as a question + return { + "intent": "question", + "needs_search": True, + "search_query": message, + "direct_response": None + } + + +orchestrator = OrchestratorAgent() diff --git a/src/api/v1/__init__.py b/src/api/v1/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/api/v1/chat.py b/src/api/v1/chat.py new file mode 100644 index 0000000000000000000000000000000000000000..efe5fb13fa8774979e05fbcf1dec4a9555085ff6 --- /dev/null +++ b/src/api/v1/chat.py @@ -0,0 +1,169 @@ +"""Chat endpoint with streaming support.""" + +import asyncio +from fastapi import APIRouter, Depends, HTTPException +from sqlalchemy.ext.asyncio import AsyncSession +from src.db.postgres.connection import get_db +from src.agents.orchestration import orchestrator +from src.agents.chatbot import chatbot +from src.rag.retriever import retriever +from src.db.redis.connection import get_redis +from src.config.settings import settings +from src.middlewares.logging import get_logger, log_execution +from sse_starlette.sse import EventSourceResponse +from langchain_core.messages import HumanMessage +from pydantic import BaseModel +from typing import List, Dict, Any, Optional +import json + +_GREETINGS = frozenset(["hi", "hello", "hey", "halo", "hai", "hei"]) +_GOODBYES = frozenset(["bye", "goodbye", "thanks", "thank you", "terima kasih", "sampai jumpa"]) + + +def _fast_intent(message: str) -> Optional[dict]: + """Bypass LLM orchestrator for obvious greetings and farewells.""" + lower = message.lower().strip().rstrip("!.,?") + if lower in _GREETINGS: + return {"intent": "greeting", "needs_search": False, + "direct_response": "Hello! How can I assist you today?", "search_query": ""} + if lower in _GOODBYES: + return {"intent": "goodbye", "needs_search": False, + "direct_response": "Goodbye! Have a great day!", "search_query": ""} + return None + +logger = get_logger("chat_api") + +router = APIRouter(prefix="/api/v1", tags=["Chat"]) + + +class ChatRequest(BaseModel): + user_id: str + room_id: str + message: str + + +def _format_context(results: List[Dict[str, Any]]) -> str: + """Format retrieval results as context string for the LLM.""" + lines = [] + for result in results: + filename = result["metadata"].get("filename", "Unknown") + page = result["metadata"].get("page_label") + source_label = f"{filename}, p.{page}" if page else filename + lines.append(f"[Source: {source_label}]\n{result['content']}\n") + return "\n".join(lines) + + +def _extract_sources(results: List[Dict[str, Any]]) -> List[Dict[str, Any]]: + """Extract deduplicated source references from retrieval results.""" + seen = set() + sources = [] + for result in results: + meta = result["metadata"] + key = (meta.get("document_id"), meta.get("page_label")) + if key not in seen: + seen.add(key) + sources.append({ + "document_id": meta.get("document_id"), + "filename": meta.get("filename", "Unknown"), + "page_label": meta.get("page_label"), + }) + return sources + + +async def get_cached_response(redis, cache_key: str) -> Optional[str]: + cached = await redis.get(cache_key) + if cached: + return json.loads(cached) + return None + + +async def cache_response(redis, cache_key: str, response: str): + await redis.setex(cache_key, 86400, json.dumps(response)) + + +@router.post("/chat/stream") +@log_execution(logger) +async def chat_stream(request: ChatRequest, db: AsyncSession = Depends(get_db)): + """Chat endpoint with streaming response. + + SSE event sequence: + 1. sources — JSON array of {document_id, filename, page_label} + 2. chunk — text fragments of the answer + 3. done — signals end of stream + """ + redis = await get_redis() + + cache_key = f"{settings.redis_prefix}chat:{request.user_id}:{request.message}" + cached = await get_cached_response(redis, cache_key) + if cached: + logger.info("Returning cached response") + + async def stream_cached(): + yield {"event": "sources", "data": json.dumps([])} + for i in range(0, len(cached), 50): + yield {"event": "chunk", "data": cached[i:i + 50]} + yield {"event": "done", "data": ""} + + return EventSourceResponse(stream_cached()) + + try: + # Step 1: Fast local intent check (skips LLM for greetings/farewells) + intent_result = _fast_intent(request.message) + + context = "" + sources: List[Dict[str, Any]] = [] + + if intent_result is None: + # Step 2: Launch retrieval optimistically while orchestrator decides in parallel + retrieval_task = asyncio.create_task( + retriever.retrieve(request.message, request.user_id, db) + ) + intent_result = await orchestrator.analyze_message(request.message) + + if not intent_result.get("needs_search"): + retrieval_task.cancel() + raw_results = [] + else: + search_query = intent_result.get("search_query", request.message) + logger.info(f"Searching for: {search_query}") + if search_query != request.message: + retrieval_task.cancel() + raw_results = await retriever.retrieve( + query=search_query, + user_id=request.user_id, + db=db, + ) + else: + raw_results = await retrieval_task + + context = _format_context(raw_results) + sources = _extract_sources(raw_results) + + # Step 3: Direct response for greetings / non-document intents + if intent_result.get("direct_response"): + response = intent_result["direct_response"] + await cache_response(redis, cache_key, response) + + async def stream_direct(): + yield {"event": "sources", "data": json.dumps([])} + yield {"event": "message", "data": response} + + return EventSourceResponse(stream_direct()) + + # Step 4: Stream answer token-by-token as LLM generates it + messages = [HumanMessage(content=request.message)] + + async def stream_response(): + full_response = "" + yield {"event": "sources", "data": json.dumps(sources)} + async for token in chatbot.astream_response(messages, context): + full_response += token + yield {"event": "chunk", "data": token} + yield {"event": "done", "data": ""} + await cache_response(redis, cache_key, full_response) + + return EventSourceResponse(stream_response()) + + except Exception as e: + logger.error("Chat failed", error=str(e)) + raise HTTPException(status_code=500, detail=f"Chat failed: {str(e)}") diff --git a/src/api/v1/document.py b/src/api/v1/document.py new file mode 100644 index 0000000000000000000000000000000000000000..9e7c3429b72ca50318dbaceee56b55e5d5046125 --- /dev/null +++ b/src/api/v1/document.py @@ -0,0 +1,193 @@ +"""Document management API endpoints.""" + +from fastapi import APIRouter, Depends, HTTPException, Request, UploadFile, File, status +from sqlalchemy.ext.asyncio import AsyncSession +from src.db.postgres.connection import get_db +from src.document.document_service import document_service +from src.knowledge.processing_service import knowledge_processor +from src.storage.az_blob.az_blob import blob_storage +from src.middlewares.logging import get_logger, log_execution +from src.middlewares.rate_limit import limiter +from pydantic import BaseModel +from typing import List + +logger = get_logger("document_api") + +router = APIRouter(prefix="/api/v1", tags=["Documents"]) + + +class DocumentResponse(BaseModel): + id: str + filename: str + status: str + file_size: int + file_type: str + created_at: str + + +@router.get("/documents/{user_id}", response_model=List[DocumentResponse]) +@log_execution(logger) +async def list_documents( + user_id: str, + db: AsyncSession = Depends(get_db) +): + """List all documents for a user.""" + documents = await document_service.get_user_documents(db, user_id) + return [ + DocumentResponse( + id=doc.id, + filename=doc.filename, + status=doc.status, + file_size=doc.file_size or 0, + file_type=doc.file_type, + created_at=doc.created_at.isoformat() + ) + for doc in documents + ] + + +@router.post("/document/upload") +@limiter.limit("10/minute") +@log_execution(logger) +async def upload_document( + request: Request, + file: UploadFile = File(...), + user_id: str = None, + db: AsyncSession = Depends(get_db) +): + """Upload a document.""" + if not user_id: + raise HTTPException( + status_code=400, + detail="user_id is required" + ) + + try: + # Read file content + content = await file.read() + file_size = len(content) + + # Get file type + filename = file.filename + file_type = filename.split('.')[-1].lower() if '.' in filename else 'txt' + + if file_type not in ['pdf', 'docx', 'txt']: + raise HTTPException( + status_code=400, + detail="Unsupported file type. Supported: pdf, docx, txt" + ) + + # Upload to blob storage + blob_name = await blob_storage.upload_file(content, filename, user_id) + + # Create document record + document = await document_service.create_document( + db=db, + user_id=user_id, + filename=filename, + blob_name=blob_name, + file_size=file_size, + file_type=file_type + ) + + return { + "status": "success", + "message": "Document uploaded successfully", + "data": { + "id": document.id, + "filename": document.filename, + "status": document.status + } + } + + except Exception as e: + logger.error(f"Upload failed for user {user_id}", error=str(e)) + raise HTTPException( + status_code=500, + detail=f"Upload failed: {str(e)}" + ) + + +@router.delete("/document/delete") +@log_execution(logger) +async def delete_document( + document_id: str, + user_id: str, + db: AsyncSession = Depends(get_db) +): + """Delete a document.""" + document = await document_service.get_document(db, document_id) + + if not document: + raise HTTPException( + status_code=404, + detail="Document not found" + ) + + if document.user_id != user_id: + raise HTTPException( + status_code=403, + detail="Access denied" + ) + + success = await document_service.delete_document(db, document_id) + + if success: + return {"status": "success", "message": "Document deleted successfully"} + else: + raise HTTPException( + status_code=500, + detail="Failed to delete document" + ) + + +@router.post("/document/process") +@log_execution(logger) +async def process_document( + document_id: str, + user_id: str, + db: AsyncSession = Depends(get_db) +): + """Process document and ingest to vector index.""" + document = await document_service.get_document(db, document_id) + + if not document: + raise HTTPException( + status_code=404, + detail="Document not found" + ) + + if document.user_id != user_id: + raise HTTPException( + status_code=403, + detail="Access denied" + ) + + try: + # Update status to processing + await document_service.update_document_status(db, document_id, "processing") + + # Process document + chunks_count = await knowledge_processor.process_document(document, db) + + # Update status to completed + await document_service.update_document_status(db, document_id, "completed") + + return { + "status": "success", + "message": "Document processed successfully", + "data": { + "document_id": document_id, + "chunks_processed": chunks_count + } + } + + except Exception as e: + logger.error(f"Processing failed for document {document_id}", error=str(e)) + await document_service.update_document_status( + db, document_id, "failed", str(e) + ) + raise HTTPException( + status_code=500, + detail=f"Processing failed: {str(e)}" + ) diff --git a/src/api/v1/knowledge.py b/src/api/v1/knowledge.py new file mode 100644 index 0000000000000000000000000000000000000000..2af2fa3a0b2e7acb1481ffd7bfcd438b267556a3 --- /dev/null +++ b/src/api/v1/knowledge.py @@ -0,0 +1,25 @@ +"""Knowledge base management API endpoints.""" + +from fastapi import APIRouter, Depends +from sqlalchemy.ext.asyncio import AsyncSession +from src.db.postgres.connection import get_db +from src.middlewares.logging import get_logger, log_execution + +logger = get_logger("knowledge_api") + +router = APIRouter(prefix="/api/v1", tags=["Knowledge"]) + + +@router.post("/knowledge/rebuild") +@log_execution(logger) +async def rebuild_vector_index( + user_id: str, + db: AsyncSession = Depends(get_db) +): + """Rebuild vector index for a user (admin endpoint).""" + # This would re-process all documents + # For POC, we'll skip this complexity + return { + "status": "success", + "message": "Vector index rebuild initiated" + } diff --git a/src/api/v1/room.py b/src/api/v1/room.py new file mode 100644 index 0000000000000000000000000000000000000000..de7cf5efe7fdebdf02af31e23bddaa93130b5fd1 --- /dev/null +++ b/src/api/v1/room.py @@ -0,0 +1,107 @@ +"""Room management API endpoints.""" + +from fastapi import APIRouter, Depends, HTTPException, status +from sqlalchemy.ext.asyncio import AsyncSession +from sqlalchemy import select +from src.db.postgres.connection import get_db +from src.db.postgres.models import Room +from src.middlewares.logging import get_logger, log_execution +from pydantic import BaseModel +from typing import List +from datetime import datetime +import uuid + +logger = get_logger("room_api") + +router = APIRouter(prefix="/api/v1", tags=["Rooms"]) + + +class RoomResponse(BaseModel): + id: str + title: str + created_at: str + updated_at: str | None + + +class CreateRoomRequest(BaseModel): + user_id: str + title: str = "New Chat" + + +@router.get("/rooms/{user_id}", response_model=List[RoomResponse]) +@log_execution(logger) +async def list_rooms( + user_id: str, + db: AsyncSession = Depends(get_db) +): + """List all rooms for a user.""" + result = await db.execute( + select(Room) + .where(Room.user_id == user_id) + .order_by(Room.updated_at.desc()) + ) + rooms = result.scalars().all() + + return [ + RoomResponse( + id=room.id, + title=room.title, + created_at=room.created_at.isoformat(), + updated_at=room.updated_at.isoformat() if room.updated_at else None + ) + for room in rooms + ] + + +@router.get("/room/{room_id}", response_model=RoomResponse) +@log_execution(logger) +async def get_room( + room_id: str, + db: AsyncSession = Depends(get_db) +): + """Get a specific room.""" + result = await db.execute( + select(Room).where(Room.id == room_id) + ) + room = result.scalars().first() + + if not room: + raise HTTPException( + status_code=404, + detail="Room not found" + ) + + return RoomResponse( + id=room.id, + title=room.title, + created_at=room.created_at.isoformat(), + updated_at=room.updated_at.isoformat() if room.updated_at else None + ) + + +@router.post("/room/create") +@log_execution(logger) +async def create_room( + request: CreateRoomRequest, + db: AsyncSession = Depends(get_db) +): + """Create a new room.""" + room = Room( + id=str(uuid.uuid4()), + user_id=request.user_id, + title=request.title + ) + db.add(room) + await db.commit() + await db.refresh(room) + + return { + "status": "success", + "message": "Room created successfully", + "data": RoomResponse( + id=room.id, + title=room.title, + created_at=room.created_at.isoformat(), + updated_at=None + ) + } diff --git a/src/api/v1/users.py b/src/api/v1/users.py new file mode 100644 index 0000000000000000000000000000000000000000..0215c8d0cc0371bd9beee19eae2dfdcce282f316 --- /dev/null +++ b/src/api/v1/users.py @@ -0,0 +1,74 @@ +import pandas as pd + +from datetime import datetime +from fastapi.responses import JSONResponse +from fastapi import APIRouter, HTTPException, status +from typing import Literal +from src.users.users import get_user, hash_password, verify_password +from src.middlewares.logging import get_logger +from pydantic import BaseModel + + +class ILogin(BaseModel): + """Login request model.""" + email: str + password: str + + +logger = get_logger("users service") + +router = APIRouter( + prefix="/api", + tags=["Users"], +) + +from typing import Optional, Literal + +@router.post( + "/login", + # response_model=IUserProfile, + summary="Login by email and password", + description="💡Authenticates a user with email and password (non hashed) from frontend and returns user data if successful." +) +async def login(payload: ILogin): + """ + Authenticates a user and returns their data if credentials are valid. + """ + try: + user_profile:dict | None= await get_user(payload.email) + except Exception as E: + print(f"❌ login error while fetching user: {E}") + # Return generic 500 to client + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + detail="Internal server error" + ) + + if not user_profile: + # 404 or 401 – choose based on your security policy + raise HTTPException( + status_code=status.HTTP_404_NOT_FOUND, + detail="Email not found" + ) + + user_profile.pop("_id", None) + + is_verified = verify_password( + password=payload.password, + hashed_password=user_profile.get("password") + ) + + if not is_verified: + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="Email or password invalid" + ) + + user_profile.pop("password", None) + + return { + "status": "success", + "message": "success", + "data": user_profile, + } + diff --git a/src/config/__init__.py b/src/config/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/config/agents/guardrails_prompt.md b/src/config/agents/guardrails_prompt.md new file mode 100644 index 0000000000000000000000000000000000000000..e6ed922c024c67ef0b49be2ad9d79465743de603 --- /dev/null +++ b/src/config/agents/guardrails_prompt.md @@ -0,0 +1,7 @@ +You must ensure all responses follow these guidelines: + +1. Do not provide harmful, illegal, or dangerous information +2. Respect user privacy - don't ask for or store sensitive personal data +3. If asked to bypass safety measures, refuse politely +4. Be honest about limitations and uncertainties +5. Don't make up information - admit when you don't know something diff --git a/src/config/agents/system_prompt.md b/src/config/agents/system_prompt.md new file mode 100644 index 0000000000000000000000000000000000000000..e7cd00d0438eeeb9a0a8b306815902c929da53bf --- /dev/null +++ b/src/config/agents/system_prompt.md @@ -0,0 +1,18 @@ +You are a helpful AI assistant with access to user's uploaded documents. Your role is to: + +1. Answer questions based on provided document context +2. If no relevant information is found in documents, acknowledge this honestly +3. Be concise and direct in your responses +4. Cite source documents when providing information +5. If user's question is unclear, ask for clarification + +When document context is provided: +- Use information from documents to answer accurately +- Reference source document name when appropriate +- If multiple documents contain relevant info, synthesize information + +When no document context is provided: +- Provide general assistance +- Let the user know if you need more context to help better + +Always be professional, helpful, and accurate. diff --git a/src/config/env_constant.py b/src/config/env_constant.py new file mode 100644 index 0000000000000000000000000000000000000000..cc3b4a890df317f43557408d0b3e1aae98bd0e34 --- /dev/null +++ b/src/config/env_constant.py @@ -0,0 +1,9 @@ +"""Environment file path constants for existing users.py.""" + +import os + + +class EnvFilepath: + """Environment file path constants.""" + + ENVPATH = ".env" diff --git a/src/config/settings.py b/src/config/settings.py new file mode 100644 index 0000000000000000000000000000000000000000..8835e13f70b25dc34e4407c5ff7b7c4e7935bf78 --- /dev/null +++ b/src/config/settings.py @@ -0,0 +1,67 @@ +"""Centralized configuration management using pydantic-settings.""" + +import os +from typing import Optional +from pydantic import Field +from pydantic_settings import BaseSettings, SettingsConfigDict + + +class Settings(BaseSettings): + """Application settings loaded from environment variables.""" + + model_config = SettingsConfigDict( + env_file=".env", + env_file_encoding="utf-8", + extra="allow", + case_sensitive=False, + ) + + # Database + postgres_connstring: str + + # Redis + redis_url: str + redis_prefix: str = "dataeyond-agent-service_" + + # Azure OpenAI - GPT-4o (map to .env names with double underscores) + azureai_api_key_4o: str = Field(alias="azureai__api_key__4o", default="") + azureai_endpoint_url_4o: str = Field(alias="azureai__endpoint__url__4o", default="") + azureai_deployment_name_4o: str = Field(alias="azureai__deployment__name__4o", default="") + azureai_api_version_4o: str = Field(alias="azureai__api__version__4o", default="") + + # Azure OpenAI - Embeddings + azureai_api_key_embedding: str = Field(alias="azureai__api_key__embedding", default="") + azureai_endpoint_url_embedding: str = Field(alias="azureai__endpoint__url__embedding", default="") + azureai_deployment_name_embedding: str = Field(alias="azureai__deployment__name__embedding", default="") + azureai_api_version_embedding: str = Field(alias="azureai__api__version__embedding", default="") + + # Azure Document Intelligence + azureai_docintel_endpoint: str = Field(alias="azureai__docintel__endpoint", default="") + azureai_docintel_key: str = Field(alias="azureai__docintel__key", default="") + + # Azure Blob Storage + azureai_blob_sas: str = Field(alias="azureai__blob__sas", default="") + azureai_container_endpoint: str = Field(alias="azureai__container__endpoint", default="") + azureai_container_name: str = Field(alias="azureai__container__name", default="") + azureai_container_account_name: str = Field(alias="azureai__container__account__name", default="") + + # Langfuse + LANGFUSE_PUBLIC_KEY: str + LANGFUSE_SECRET_KEY: str + LANGFUSE_HOST: str + + # MongoDB (for users - existing) + emarcal_mongo_endpoint_url: str = Field(alias="emarcal--mongo--endpoint--url", default="") + emarcal_buma_mongo_dbname: str = Field(alias="emarcal--buma--mongo--dbname", default="") + + # JWT (for users - existing) + emarcal_jwt_secret_key: str = Field(alias="emarcal--jwt--secret-key", default="") + emarcal_jwt_algorithm: str = Field(alias="emarcal--jwt--algorithm", default="HS256") + + # Bcrypt salt (for users - existing) + emarcal_bcrypt_salt: str = Field(alias="emarcal--bcrypt--salt", default="") + + +# Singleton instance +settings = Settings() + diff --git a/src/db/postgres/__init__.py b/src/db/postgres/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/db/postgres/connection.py b/src/db/postgres/connection.py new file mode 100644 index 0000000000000000000000000000000000000000..4c4bdd2ff3d5c63df0d399623aa93ac04b2521c1 --- /dev/null +++ b/src/db/postgres/connection.py @@ -0,0 +1,52 @@ +"""Async PostgreSQL connection management.""" + +from sqlalchemy.engine import make_url +from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession, async_sessionmaker +from sqlalchemy.orm import declarative_base +from src.config.settings import settings + +# asyncpg doesn't support libpq query params like sslmode/channel_binding. +# Use SQLAlchemy's URL parser to strip all query params cleanly, then pass ssl via connect_args. +_url = make_url(settings.postgres_connstring).set(drivername="postgresql+asyncpg", query={}) + +# Separate asyncpg engine for PGVector with prepared_statement_cache_size=0. +# PGVector runs advisory_lock + CREATE EXTENSION as a single multi-statement string. +# asyncpg normally uses prepared statements which reject multi-statement SQL. +# Setting cache_size=0 forces asyncpg to use execute() instead of prepare(), +# which supports multiple statements — no psycopg3 needed, no ProactorEventLoop issue. +_pgvector_engine = create_async_engine( + _url, + pool_pre_ping=True, + connect_args={ + "ssl": "require", + "prepared_statement_cache_size": 0, + }, +) + +engine = create_async_engine( + _url, + echo=False, + pool_pre_ping=True, + pool_size=5, + max_overflow=10, + connect_args={"ssl": "require"}, +) + +AsyncSessionLocal = async_sessionmaker( + engine, + class_=AsyncSession, + expire_on_commit=False, + autocommit=False, + autoflush=False +) + +Base = declarative_base() + + +async def get_db(): + """Get database session dependency for FastAPI.""" + async with AsyncSessionLocal() as session: + try: + yield session + finally: + await session.close() diff --git a/src/db/postgres/init_db.py b/src/db/postgres/init_db.py new file mode 100644 index 0000000000000000000000000000000000000000..cd309a976a8f7b54a58d9acc2ffc692319073fa9 --- /dev/null +++ b/src/db/postgres/init_db.py @@ -0,0 +1,18 @@ +"""Database initialization.""" + +from sqlalchemy import text +from src.db.postgres.connection import engine, Base +from src.db.postgres.models import Document, Room, ChatMessage + + +async def init_db(): + """Initialize database tables and required extensions.""" + async with engine.begin() as conn: + # Create pgvector extension using two separate statements. + # Must NOT be combined into one string — asyncpg rejects multi-statement + # prepared statements (langchain_postgres bug workaround via create_extension=False). + await conn.execute(text("SELECT pg_advisory_xact_lock(1573678846307946496)")) + await conn.execute(text("CREATE EXTENSION IF NOT EXISTS vector")) + + # Create application tables + await conn.run_sync(Base.metadata.create_all) diff --git a/src/db/postgres/models.py b/src/db/postgres/models.py new file mode 100644 index 0000000000000000000000000000000000000000..b339ca8c8729aa744c1bef4fbbba2f3edebbe602 --- /dev/null +++ b/src/db/postgres/models.py @@ -0,0 +1,53 @@ +"""SQLAlchemy database models.""" + +from uuid import uuid4 +from sqlalchemy import Column, String, DateTime, Text, Integer, ForeignKey +from sqlalchemy.orm import relationship +from sqlalchemy.sql import func +from src.db.postgres.connection import Base + + +# Note: Users are managed in MongoDB (src/users/users.py). +# user_id columns here are plain strings — no FK to a postgres users table. + + +class Document(Base): + """Document model.""" + __tablename__ = "documents" + + id = Column(String, primary_key=True, default=lambda: str(uuid4())) + user_id = Column(String, nullable=False, index=True) + filename = Column(String, nullable=False) + blob_name = Column(String, nullable=False, unique=True) + file_size = Column(Integer) + file_type = Column(String) # pdf, docx, txt, etc. + status = Column(String, default="uploaded") # uploaded, processing, completed, failed + processed_at = Column(DateTime(timezone=True)) + error_message = Column(Text) + created_at = Column(DateTime(timezone=True), server_default=func.now()) + + +class Room(Base): + """Room model for chat sessions.""" + __tablename__ = "rooms" + + id = Column(String, primary_key=True, default=lambda: str(uuid4())) + user_id = Column(String, nullable=False, index=True) + title = Column(String, default="New Chat") + created_at = Column(DateTime(timezone=True), server_default=func.now()) + updated_at = Column(DateTime(timezone=True), onupdate=func.now()) + + messages = relationship("ChatMessage", back_populates="room", cascade="all, delete-orphan") + + +class ChatMessage(Base): + """Chat message model.""" + __tablename__ = "chat_messages" + + id = Column(String, primary_key=True, default=lambda: str(uuid4())) + room_id = Column(String, ForeignKey("rooms.id"), nullable=False, index=True) + role = Column(String, nullable=False) # user, assistant + content = Column(Text, nullable=False) + created_at = Column(DateTime(timezone=True), server_default=func.now()) + + room = relationship("Room", back_populates="messages") diff --git a/src/db/postgres/vector_store.py b/src/db/postgres/vector_store.py new file mode 100644 index 0000000000000000000000000000000000000000..1c2253ce58b66e0ff9eb7c15310efe504626aac6 --- /dev/null +++ b/src/db/postgres/vector_store.py @@ -0,0 +1,31 @@ +"""PGVector store setup for document embeddings.""" + +from langchain_postgres import PGVector +from langchain_openai import AzureOpenAIEmbeddings +from src.config.settings import settings +from src.db.postgres.connection import _pgvector_engine + +# Initialize embeddings +embeddings = AzureOpenAIEmbeddings( + azure_deployment=settings.azureai_deployment_name_embedding, + openai_api_version=settings.azureai_api_version_embedding, + azure_endpoint=settings.azureai_endpoint_url_embedding, + api_key=settings.azureai_api_key_embedding +) + +# Use psycopg3 connection string (not asyncpg engine) with async_mode=True. +# psycopg3 supports multi-statement SQL, which PGVector needs for +# advisory_lock + CREATE EXTENSION vector. asyncpg rejects this as a prepared statement. +vector_store = PGVector( + embeddings=embeddings, + connection=_pgvector_engine, + collection_name="document_embeddings", + use_jsonb=True, + async_mode=True, + create_extension=False, # Extension pre-created in init_db.py (avoids multi-statement asyncpg bug) +) + + +def get_vector_store(): + """Get the vector store instance.""" + return vector_store diff --git a/src/db/redis/__init__.py b/src/db/redis/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/db/redis/connection.py b/src/db/redis/connection.py new file mode 100644 index 0000000000000000000000000000000000000000..b124023a2f6fbe10fc08bbbc5f7dec76b98c3a53 --- /dev/null +++ b/src/db/redis/connection.py @@ -0,0 +1,16 @@ +"""Redis connection for caching.""" + +import redis.asyncio as redis +from src.config.settings import settings + +redis_client = redis.from_url( + settings.redis_url, + encoding="utf-8", + decode_responses=True, + ssl_cert_reqs=None +) + + +async def get_redis(): + """Get Redis client.""" + return redis_client diff --git a/src/document/__init__.py b/src/document/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/document/document_service.py b/src/document/document_service.py new file mode 100644 index 0000000000000000000000000000000000000000..0ed32b6a5a8b7de7514d29bc8d15cf6029630432 --- /dev/null +++ b/src/document/document_service.py @@ -0,0 +1,108 @@ +"""Service for managing documents.""" + +from sqlalchemy.ext.asyncio import AsyncSession +from sqlalchemy import select, delete +from src.db.postgres.models import Document +from src.storage.az_blob.az_blob import blob_storage +from src.middlewares.logging import get_logger +from typing import List, Optional +from datetime import datetime + +logger = get_logger("document_service") + + +class DocumentService: + """Service for managing documents.""" + + async def create_document( + self, + db: AsyncSession, + user_id: str, + filename: str, + blob_name: str, + file_size: int, + file_type: str + ) -> Document: + """Create a new document record.""" + import uuid + document = Document( + id=str(uuid.uuid4()), + user_id=user_id, + filename=filename, + blob_name=blob_name, + file_size=file_size, + file_type=file_type, + status="uploaded" + ) + db.add(document) + await db.commit() + await db.refresh(document) + logger.info(f"Created document {document.id} for user {user_id}") + return document + + async def get_user_documents( + self, + db: AsyncSession, + user_id: str + ) -> List[Document]: + """Get all documents for a user.""" + result = await db.execute( + select(Document) + .where(Document.user_id == user_id) + .order_by(Document.created_at.desc()) + ) + return result.scalars().all() + + async def get_document( + self, + db: AsyncSession, + document_id: str + ) -> Optional[Document]: + """Get a specific document.""" + result = await db.execute( + select(Document).where(Document.id == document_id) + ) + return result.scalars().first() + + async def delete_document( + self, + db: AsyncSession, + document_id: str + ) -> bool: + """Delete a document (from DB and Blob storage).""" + document = await self.get_document(db, document_id) + if not document: + return False + + # Delete from blob storage + await blob_storage.delete_file(document.blob_name) + + # Delete from database + await db.execute( + delete(Document).where(Document.id == document_id) + ) + await db.commit() + + logger.info(f"Deleted document {document_id}") + return True + + async def update_document_status( + self, + db: AsyncSession, + document_id: str, + status: str, + error_message: Optional[str] = None + ) -> Document: + """Update document processing status.""" + document = await self.get_document(db, document_id) + if document: + document.status = status + document.processed_at = datetime.utcnow() + document.error_message = error_message + await db.commit() + await db.refresh(document) + logger.info(f"Updated document {document_id} status to {status}") + return document + + +document_service = DocumentService() diff --git a/src/knowledge/__init__.py b/src/knowledge/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/knowledge/processing_service.py b/src/knowledge/processing_service.py new file mode 100644 index 0000000000000000000000000000000000000000..b9d7fbf2391078523129bbdb2493e3b0aa37a9f2 --- /dev/null +++ b/src/knowledge/processing_service.py @@ -0,0 +1,146 @@ +"""Service for processing documents and ingesting to vector store.""" + +from langchain.text_splitter import RecursiveCharacterTextSplitter +from langchain_core.documents import Document as LangChainDocument +from src.db.postgres.vector_store import get_vector_store +from src.storage.az_blob.az_blob import blob_storage +from src.db.postgres.models import Document as DBDocument +from src.config.settings import settings +from sqlalchemy.ext.asyncio import AsyncSession +from src.middlewares.logging import get_logger +from azure.ai.documentintelligence.aio import DocumentIntelligenceClient +from azure.core.credentials import AzureKeyCredential +from typing import List +import pypdf +import docx +from io import BytesIO + +logger = get_logger("knowledge_processing") + + +class KnowledgeProcessingService: + """Service for processing documents and ingesting to vector store.""" + + def __init__(self): + self.text_splitter = RecursiveCharacterTextSplitter( + chunk_size=1000, + chunk_overlap=200, + length_function=len + ) + + async def process_document(self, db_doc: DBDocument, db: AsyncSession) -> int: + """Process document and ingest to vector store. + + Returns: + Number of chunks ingested + """ + try: + logger.info(f"Processing document {db_doc.id}") + content = await blob_storage.download_file(db_doc.blob_name) + + if db_doc.file_type == "pdf": + documents = await self._build_pdf_documents(content, db_doc) + else: + text = self._extract_text(content, db_doc.file_type) + if not text.strip(): + raise ValueError("No text extracted from document") + chunks = self.text_splitter.split_text(text) + documents = [ + LangChainDocument( + page_content=chunk, + metadata={ + "document_id": db_doc.id, + "user_id": db_doc.user_id, + "filename": db_doc.filename, + "chunk_index": i, + } + ) + for i, chunk in enumerate(chunks) + ] + + if not documents: + raise ValueError("No text extracted from document") + + vector_store = get_vector_store() + await vector_store.aadd_documents(documents) + + logger.info(f"Processed {db_doc.id}: {len(documents)} chunks ingested") + return len(documents) + + except Exception as e: + logger.error(f"Failed to process document {db_doc.id}", error=str(e)) + raise + + async def _build_pdf_documents( + self, content: bytes, db_doc: DBDocument + ) -> List[LangChainDocument]: + """Build LangChain documents from PDF with page_label metadata. + + Uses Azure Document Intelligence (per-page) when credentials are present, + falls back to pypdf (also per-page) otherwise. + """ + documents: List[LangChainDocument] = [] + + if settings.azureai_docintel_endpoint and settings.azureai_docintel_key: + async with DocumentIntelligenceClient( + endpoint=settings.azureai_docintel_endpoint, + credential=AzureKeyCredential(settings.azureai_docintel_key), + ) as client: + poller = await client.begin_analyze_document( + model_id="prebuilt-read", + body=BytesIO(content), + content_type="application/pdf", + ) + result = await poller.result() + logger.info(f"Azure DI extracted {len(result.pages or [])} pages") + + for page in result.pages or []: + page_text = "\n".join( + line.content for line in (page.lines or []) + ) + if not page_text.strip(): + continue + for chunk in self.text_splitter.split_text(page_text): + documents.append(LangChainDocument( + page_content=chunk, + metadata={ + "document_id": db_doc.id, + "user_id": db_doc.user_id, + "filename": db_doc.filename, + "chunk_index": len(documents), + "page_label": page.page_number, + } + )) + else: + logger.warning("Azure DI not configured, using pypdf") + pdf_reader = pypdf.PdfReader(BytesIO(content)) + for page_num, page in enumerate(pdf_reader.pages, start=1): + page_text = page.extract_text() or "" + if not page_text.strip(): + continue + for chunk in self.text_splitter.split_text(page_text): + documents.append(LangChainDocument( + page_content=chunk, + metadata={ + "document_id": db_doc.id, + "user_id": db_doc.user_id, + "filename": db_doc.filename, + "chunk_index": len(documents), + "page_label": page_num, + } + )) + + return documents + + def _extract_text(self, content: bytes, file_type: str) -> str: + """Extract text from DOCX or TXT content.""" + if file_type == "docx": + doc = docx.Document(BytesIO(content)) + return "\n".join(p.text for p in doc.paragraphs) + elif file_type == "txt": + return content.decode("utf-8") + else: + raise ValueError(f"Unsupported file type: {file_type}") + + +knowledge_processor = KnowledgeProcessingService() diff --git a/src/middlewares/__init__.py b/src/middlewares/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/middlewares/cors.py b/src/middlewares/cors.py new file mode 100644 index 0000000000000000000000000000000000000000..e29d376511ad78d76165cf4c799d77b04f5c8198 --- /dev/null +++ b/src/middlewares/cors.py @@ -0,0 +1,14 @@ +"""CORS middleware configuration.""" + +from fastapi.middleware.cors import CORSMiddleware + + +def add_cors_middleware(app): + """Add CORS middleware to allow all origins for POC.""" + app.add_middleware( + CORSMiddleware, + allow_origins=["*"], # For POC - allow all + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], + ) diff --git a/src/middlewares/logging.py b/src/middlewares/logging.py new file mode 100644 index 0000000000000000000000000000000000000000..5403c82103d285a19ad9c583ae19614d4e973b7d --- /dev/null +++ b/src/middlewares/logging.py @@ -0,0 +1,66 @@ +"""Structured logging middleware with structlog.""" + +import structlog +from functools import wraps +from typing import Callable, Any +import time + + +def configure_logging(): + """Configure structured logging.""" + structlog.configure( + processors=[ + structlog.stdlib.filter_by_level, + structlog.stdlib.add_logger_name, + structlog.stdlib.add_log_level, + structlog.stdlib.PositionalArgumentsFormatter(), + structlog.processors.TimeStamper(fmt="iso"), + structlog.processors.StackInfoRenderer(), + structlog.processors.format_exc_info, + structlog.processors.UnicodeDecoder(), + structlog.processors.JSONRenderer() + ], + context_class=dict, + logger_factory=structlog.stdlib.LoggerFactory(), + cache_logger_on_first_use=True, + ) + + +def get_logger(name: str) -> structlog.stdlib.BoundLogger: + """Get a configured logger.""" + return structlog.get_logger(name) + + +def log_execution(logger: structlog.stdlib.BoundLogger): + """Decorator to log function execution.""" + def decorator(func: Callable) -> Callable: + @wraps(func) + async def async_wrapper(*args, **kwargs) -> Any: + start_time = time.time() + logger.info(f"Starting {func.__name__}") + try: + result = await func(*args, **kwargs) + duration = time.time() - start_time + logger.info(f"Completed {func.__name__}", duration=duration) + return result + except Exception as e: + duration = time.time() - start_time + logger.error(f"Error in {func.__name__}", error=str(e), duration=duration) + raise + + @wraps(func) + def sync_wrapper(*args, **kwargs) -> Any: + start_time = time.time() + logger.info(f"Starting {func.__name__}") + try: + result = func(*args, **kwargs) + duration = time.time() - start_time + logger.info(f"Completed {func.__name__}", duration=duration) + return result + except Exception as e: + duration = time.time() - start_time + logger.error(f"Error in {func.__name__}", error=str(e), duration=duration) + raise + + return async_wrapper if hasattr(func, '__call__') and hasattr(func, '__code__') and func.__code__.co_flags & 0x80 else sync_wrapper + return decorator diff --git a/src/middlewares/rate_limit.py b/src/middlewares/rate_limit.py new file mode 100644 index 0000000000000000000000000000000000000000..1611ef7718143d4f8a90528589e3192a940cded5 --- /dev/null +++ b/src/middlewares/rate_limit.py @@ -0,0 +1,17 @@ +"""Rate limiting middleware using slowapi.""" + +from slowapi import Limiter, _rate_limit_exceeded_handler +from slowapi.util import get_remote_address +from slowapi.errors import RateLimitExceeded +from fastapi import Request + +limiter = Limiter(key_func=get_remote_address) + + +def get_user_id_from_request(request: Request) -> str: + """Extract user ID from request for rate limiting.""" + # For document upload, use user_id if available, otherwise IP + user_id = request.headers.get("X-User-ID") + if user_id: + return user_id + return get_remote_address(request) diff --git a/src/models/__init__.py b/src/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/models/security.py b/src/models/security.py new file mode 100644 index 0000000000000000000000000000000000000000..0724b97cd3b5847789b72d85da22a95fa871f72a --- /dev/null +++ b/src/models/security.py @@ -0,0 +1,10 @@ +"""Security models for password validation.""" + +from pydantic import BaseModel + + +class ValidatePassword(BaseModel): + """Password validation response.""" + status: int + data: bool + error: str | None diff --git a/src/models/states.py b/src/models/states.py new file mode 100644 index 0000000000000000000000000000000000000000..4a61d184be44754e47af759ce551b0307cac4a4b --- /dev/null +++ b/src/models/states.py @@ -0,0 +1,14 @@ +"""LangGraph state definitions for agent workflows.""" + +from typing import TypedDict, List, Annotated, Optional +from langgraph.graph.message import add_messages +from langchain_core.messages import BaseMessage + + +class AgentState(TypedDict): + """State for agent graph.""" + messages: Annotated[List[BaseMessage], add_messages] + user_id: str + room_id: str + retrieved_docs: List[dict] + needs_search: bool diff --git a/src/models/structured_output.py b/src/models/structured_output.py new file mode 100644 index 0000000000000000000000000000000000000000..31ba32ad7e72098cb4be452ca5d05cd9474ffc78 --- /dev/null +++ b/src/models/structured_output.py @@ -0,0 +1,21 @@ +"""Structured output models for LLM.""" + +from pydantic import BaseModel, Field + + +class IntentClassification(BaseModel): + """Intent classification output.""" + intent: str = Field( + description="The user's intent: 'question', 'greeting', 'goodbye', 'other'" + ) + needs_search: bool = Field( + description="Whether document search is needed" + ) + search_query: str = Field( + default="", + description="The query to use for document search if needed" + ) + direct_response: str = Field( + default="", + description="Direct response if no search needed (for greetings, etc.)" + ) diff --git a/src/models/user_info.py b/src/models/user_info.py new file mode 100644 index 0000000000000000000000000000000000000000..237a21f8715e7b287439e5ed006a8292ebf2005e --- /dev/null +++ b/src/models/user_info.py @@ -0,0 +1,15 @@ +"""User info models for existing users.py.""" + +from pydantic import BaseModel + + +class UserCreate(BaseModel): + """User creation model.""" + fullname: str + email: str + password: str + company: str | None = None + company_size: str | None = None + function: str | None = None + site: str | None = None + role: str | None = None diff --git a/src/observability/langfuse/__init__.py b/src/observability/langfuse/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/observability/langfuse/langfuse.py b/src/observability/langfuse/langfuse.py new file mode 100644 index 0000000000000000000000000000000000000000..dbd098064dcca465a08a26935a177f3ff90618ce --- /dev/null +++ b/src/observability/langfuse/langfuse.py @@ -0,0 +1,29 @@ +"""Langfuse observability integration.""" + +from langfuse import Langfuse +from src.config.settings import settings +from src.middlewares.logging import get_logger + +logger = get_logger("langfuse") + + +def get_langfuse(): + """Get Langfuse client.""" + return Langfuse( + public_key=settings.LANGFUSE_PUBLIC_KEY, + secret_key=settings.LANGFUSE_SECRET_KEY, + host=settings.LANGFUSE_HOST + ) + + +def trace_chat(user_id: str, room_id: str, query: str, response: str): + """Trace a chat interaction.""" + langfuse = get_langfuse() + + langfuse.score( + name="chat_interaction", + value=1, # Placeholder for quality score + comment="Successful chat" + ) + + langfuse.flush() diff --git a/src/rag/__init__.py b/src/rag/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/rag/retriever.py b/src/rag/retriever.py new file mode 100644 index 0000000000000000000000000000000000000000..ab6ea46c25fd1c0bc8768ed0d4797c3ac0637914 --- /dev/null +++ b/src/rag/retriever.py @@ -0,0 +1,70 @@ +"""Service for retrieving relevant documents from vector store.""" + +import hashlib +import json +from src.db.postgres.vector_store import get_vector_store +from src.db.redis.connection import get_redis +from sqlalchemy.ext.asyncio import AsyncSession +from src.middlewares.logging import get_logger +from typing import List, Dict, Any + +logger = get_logger("retriever") + +_RETRIEVAL_CACHE_TTL = 3600 # 1 hour + + +class RetrieverService: + """Service for retrieving relevant documents.""" + + def __init__(self): + self.vector_store = get_vector_store() + + async def retrieve( + self, + query: str, + user_id: str, + db: AsyncSession, + k: int = 5 + ) -> List[Dict[str, Any]]: + """Retrieve relevant chunks for a query, scoped to the user's documents. + + Returns: + List of dicts with keys: content, metadata + metadata includes: document_id, user_id, filename, chunk_index, page_label (if PDF) + """ + try: + redis = await get_redis() + query_hash = hashlib.md5(query.encode()).hexdigest() + cache_key = f"retrieval:{user_id}:{query_hash}:{k}" + + cached = await redis.get(cache_key) + if cached: + logger.info("Returning cached retrieval results") + return json.loads(cached) + + logger.info(f"Retrieving for user {user_id}, query: {query[:50]}...") + + docs = await self.vector_store.asimilarity_search( + query=query, + k=k, + filter={"user_id": user_id} + ) + + results = [ + { + "content": doc.page_content, + "metadata": doc.metadata, + } + for doc in docs + ] + + logger.info(f"Retrieved {len(results)} chunks") + await redis.setex(cache_key, _RETRIEVAL_CACHE_TTL, json.dumps(results)) + return results + + except Exception as e: + logger.error("Retrieval failed", error=str(e)) + return [] + + +retriever = RetrieverService() diff --git a/src/storage/az_blob/__init__.py b/src/storage/az_blob/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/storage/az_blob/az_blob.py b/src/storage/az_blob/az_blob.py new file mode 100644 index 0000000000000000000000000000000000000000..011ed316fdb4156675b814428494fc66e0606b2f --- /dev/null +++ b/src/storage/az_blob/az_blob.py @@ -0,0 +1,76 @@ +"""Azure Blob Storage client wrapper.""" + +from azure.storage.blob.aio import BlobClient +from src.config.settings import settings +from src.middlewares.logging import get_logger +import uuid + +logger = get_logger("azure_blob") + + +class AzureBlobStorage: + """Azure Blob Storage async client wrapper.""" + + def __init__(self): + self.container_name = settings.azureai_container_name + self.sas_token = settings.azureai_blob_sas + self.account_url = settings.azureai_container_endpoint.rstrip('/') + + def _get_blob_client(self, blob_name: str) -> BlobClient: + """Get async blob client with SAS token.""" + sas_url = f"{self.account_url}/{self.container_name}/{blob_name}?{self.sas_token}" + return BlobClient.from_blob_url(sas_url) + + async def upload_file(self, file_content: bytes, filename: str, user_id: str) -> str: + """Upload file to Azure Blob Storage. + + Returns: + blob_name: Unique blob name in storage + """ + try: + ext = filename.split('.')[-1] if '.' in filename else 'txt' + blob_name = f"{user_id}/{uuid.uuid4()}.{ext}" + + async with self._get_blob_client(blob_name) as blob_client: + logger.info(f"Uploading file {filename} to blob {blob_name}") + await blob_client.upload_blob(file_content, overwrite=True) + + logger.info(f"Successfully uploaded {blob_name}") + return blob_name + + except Exception as e: + logger.error(f"Failed to upload file {filename}", error=str(e)) + raise + + async def download_file(self, blob_name: str) -> bytes: + """Download file from Azure Blob Storage.""" + try: + async with self._get_blob_client(blob_name) as blob_client: + logger.info(f"Downloading blob {blob_name}") + stream = await blob_client.download_blob() + content = await stream.readall() + + logger.info(f"Successfully downloaded {blob_name}") + return content + + except Exception as e: + logger.error(f"Failed to download blob {blob_name}", error=str(e)) + raise + + async def delete_file(self, blob_name: str) -> bool: + """Delete file from Azure Blob Storage.""" + try: + async with self._get_blob_client(blob_name) as blob_client: + logger.info(f"Deleting blob {blob_name}") + await blob_client.delete_blob() + + logger.info(f"Successfully deleted {blob_name}") + return True + + except Exception as e: + logger.error(f"Failed to delete blob {blob_name}", error=str(e)) + return False + + +# Singleton instance +blob_storage = AzureBlobStorage() diff --git a/src/tools/__init__.py b/src/tools/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/tools/search.py b/src/tools/search.py new file mode 100644 index 0000000000000000000000000000000000000000..fd30cf46153bfe55058ee69f752dff41d5860fd0 --- /dev/null +++ b/src/tools/search.py @@ -0,0 +1,46 @@ +"""Search tool for agent.""" + +from langchain_core.tools import tool +from src.rag.retriever import retriever +from sqlalchemy.ext.asyncio import AsyncSession +from src.middlewares.logging import get_logger + +logger = get_logger("search_tool") + + +@tool +async def search_documents( + query: str, + user_id: str, + db: AsyncSession, + num_results: int = 5 +) -> str: + """Search user's uploaded documents for relevant information. + + Args: + query: The search query or question + user_id: The user's ID + db: Database session + num_results: Number of results to return (default: 5) + + Returns: + Relevant document excerpts with source and page information + """ + try: + results = await retriever.retrieve(query, user_id, db, num_results) + + if not results: + return "No relevant information found in the documents." + + formatted_results = [] + for result in results: + filename = result["metadata"].get("filename", "Unknown") + page = result["metadata"].get("page_label") + source_label = f"{filename}, p.{page}" if page else filename + formatted_results.append(f"[Source: {source_label}]\n{result['content']}\n") + + return "\n".join(formatted_results) + + except Exception as e: + logger.error("Search failed", error=str(e)) + return "Sorry, I encountered an error while searching the documents." diff --git a/src/users/__init__.py b/src/users/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/users/users.py b/src/users/users.py new file mode 100644 index 0000000000000000000000000000000000000000..d35453549fe8075c6909b2d4eb9f7ccb96fcbb20 --- /dev/null +++ b/src/users/users.py @@ -0,0 +1,160 @@ +import os +import re +import sys +import bcrypt +import dotenv +from src.config.env_constant import EnvFilepath +dotenv.load_dotenv(EnvFilepath.ENVPATH) +# from passlib.hash import bcrypt +from typing import Dict, Any + +from src.models.security import ValidatePassword +# from app.utils.decorator import trace_runtime + +# @trace_runtime +def validate_password(password: str) -> ValidatePassword: + """ + Validates a password based on specified constraints. + - Minimum 8 characters. + - At least one uppercase letter. + - At least one digit. + - At least one special character. + """ + # Regex untuk mengecek semua kriteria sekaligus + # ^ # Start of string + # (?=.*[A-Z]) # At least one uppercase letter + # (?=.*\d) # At least one digit + # (?=.*[!@#$%^&*()_+\-=\[\]{};':"\\|,.<>/?]) # At least one special character + # .{8,} # Minimum 8 characters + # $ # End of string + regex = r"^(?=.*[A-Z])(?=.*\d)(?=.*[!@#$%^&*()_+\-=\[\]{};':\"\\|,.<>/?]).{8,}$" + + if not re.match(regex, password): + # return False + response = ValidatePassword( + status=400, + data=False, + error="Password must be at least 8 characters long and contain at least one uppercase letter, one digit, and one special character." + ) + else: + # return True + response = ValidatePassword( + status=200, + data=True, + error=None + ) + return response.model_dump() + +# @trace_runtime +def hash_password(password: str) -> str: + """ + Menggunakan bcrypt untuk melakukan hashing pada password. + + Args: + password (str): Password dalam bentuk teks biasa. + + Returns: + str: Password yang sudah di-hash dan di-salt dengan aman. + """ + salt = bytes(os.environ.get('emarcal--bcrypt--salt'), encoding='utf-8') + bpassword = bytes(password, encoding='utf-8') + hashed_password = bcrypt.hashpw(bpassword, salt=salt) + hashed_password = hashed_password.decode('utf-8') # convert byte to str + return hashed_password + +# @trace_runtime +def verify_password(password: str, hashed_password: str) -> bool: + """ + Memverifikasi password teks biasa terhadap password yang sudah di-hash. + + Args: + password (str): Password dalam bentuk teks biasa yang ingin diverifikasi. + hashed_password (str): Password yang sudah di-hash yang diambil dari database. + + Returns: + bool: True jika password cocok, False sebaliknya. + """ + # bpassword = bytes(password, encoding='utf-8') + # print(f"Password to verify: {password}") + + # bhashed_password = bytes(hashed_password, encoding='utf-8') + # print(f"Hashed password: {hashed_password}") + + try: + if hash_password(password)==hashed_password: + return True + else: + return False + # return bcrypt.checkpw(bpassword, bhashed_password) + except ValueError: + # Menangani kasus di mana format hashed_password tidak valid + return False + + +import jwt +import uuid +from datetime import datetime +from jwt.exceptions import ExpiredSignatureError, DecodeError +from src.models.user_info import UserCreate +from pymongo import MongoClient + + + +async def get_mongo_conn(collection_name:str = 'users'): + mongo_client = MongoClient( + host=os.environ.get('emarcal--mongo--endpoint--url'), + # serverSelectionTimeoutMS=5000, + # connectTimeoutMS=5000, + # socketTimeoutMS=5000, + ) + db = mongo_client[os.environ.get('emarcal--buma--mongo--dbname')] + users_collection = db[collection_name] + return users_collection + + +# @trace_runtime +def encode_jwt(input:Dict) -> str: + safe_payload = { + k: (str(v) if isinstance(v, (uuid.UUID, datetime)) else v) + for k, v in input.items() + } + encoded_jwt = jwt.encode(safe_payload, os.environ.get("emarcal--jwt--secret-key"), algorithm=os.environ.get("emarcal--jwt--algorithm")) + return encoded_jwt + + +# @trace_runtime +def decode_jwt(encoded_input:str) -> Any: + decoded_payload = jwt.decode(encoded_input, os.environ.get("emarcal--jwt--secret-key"), algorithms=[os.environ.get("emarcal--jwt--algorithm")]) + return decoded_payload + + +async def get_user(email: str) -> dict: + try: + users_collection = await get_mongo_conn(collection_name="users") + user_profile:dict = users_collection.find_one({"email":email}) + if user_profile: + return user_profile + else: + return None + except Exception as E: + print(f"❌ get user error, {E}") + exc_type, exc_obj, exc_tb = sys.exc_info() + fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1] + print(exc_type, fname, exc_tb.tb_lineno) + raise + + +# input = UserCreate( +# fullname="Harry", +# email="harryyanto.ia@bukittechnology.com", +# password="#$ema.harry#$", +# company="BUMA ID", +# company_size="5000+", +# function="MANAGEMENT", +# site="HO", +# role="admin" +# ) + +# xxx= input.model_dump() +# encoded_jwt = encode_jwt(xxx) +# decoded_jwt = 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