Initial commit: import from sinmb79/Gov-chat-bot

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-03-26 12:49:43 +09:00
commit a16c972dbb
104 changed files with 8063 additions and 0 deletions
@@ -0,0 +1,42 @@
+from app.providers.llm import LLMProvider, NullLLMProvider
+from app.providers.embedding import EmbeddingProvider, NotImplementedEmbeddingProvider
+from app.providers.vectordb import VectorDBProvider
+
+# 워밍업 상태 전역 플래그
+_embedding_warmed_up = False
+
+
+def get_llm_provider(config: dict) -> LLMProvider:
+    provider = config.get("LLM_PROVIDER", "none")
+    if provider == "none":
+        return NullLLMProvider()
+    if provider == "anthropic":
+        from app.providers.llm_anthropic import AnthropicLLMProvider
+        return AnthropicLLMProvider(
+            api_key=config.get("ANTHROPIC_API_KEY", ""),
+            model=config.get("LLM_MODEL", "claude-haiku-4-5-20251001"),
+        )
+    if provider == "openai":
+        from app.providers.llm_anthropic import OpenAILLMProvider
+        return OpenAILLMProvider(
+            api_key=config.get("OPENAI_API_KEY", ""),
+            model=config.get("LLM_MODEL", "gpt-4o-mini"),
+        )
+    raise ValueError(f"Unknown LLM provider: {provider}")
+
+
+def get_embedding_provider(config: dict) -> EmbeddingProvider:
+    provider = config.get("EMBEDDING_PROVIDER", "none")
+    if provider == "local":
+        from app.providers.local_embedding import LocalEmbeddingProvider
+        model = config.get("EMBEDDING_MODEL", "jhgan/ko-sroberta-multitask")
+        return LocalEmbeddingProvider(model_name=model)
+    return NotImplementedEmbeddingProvider()
+
+
+def get_vectordb_provider(config: dict) -> VectorDBProvider:
+    from app.providers.chroma import ChromaVectorDBProvider
+    return ChromaVectorDBProvider(
+        host=config.get("CHROMA_HOST", "chromadb"),
+        port=int(config.get("CHROMA_PORT", 8000)),
+    )
@@ -0,0 +1,10 @@
+from dataclasses import dataclass, field
+from typing import Any
+
+
+@dataclass
+class SearchResult:
+    text: str
+    doc_id: str
+    score: float
+    metadata: dict = field(default_factory=dict)
@@ -0,0 +1,91 @@
+from typing import Optional
+
+from app.providers.base import SearchResult
+from app.providers.vectordb import VectorDBProvider
+
+
+class ChromaVectorDBProvider(VectorDBProvider):
+    """
+    ChromaDB 기반 벡터 검색.
+    컬렉션명 = tenant_{tenant_id} (테넌트 격리)
+    """
+
+    def __init__(self, host: str = "chromadb", port: int = 8000):
+        self.host = host
+        self.port = port
+        self._client = None
+
+    def _get_client(self):
+        if self._client is None:
+            import chromadb
+            self._client = chromadb.HttpClient(host=self.host, port=self.port)
+        return self._client
+
+    def _collection_name(self, tenant_id: str) -> str:
+        return f"tenant_{tenant_id}"
+
+    async def upsert(
+        self,
+        tenant_id: str,
+        doc_id: str,
+        chunks: list[str],
+        embeddings: list[list[float]],
+        metadatas: list[dict],
+    ) -> int:
+        client = self._get_client()
+        collection = client.get_or_create_collection(self._collection_name(tenant_id))
+        ids = [f"{doc_id}_{i}" for i in range(len(chunks))]
+        collection.upsert(ids=ids, documents=chunks, embeddings=embeddings, metadatas=metadatas)
+        return len(chunks)
+
+    async def search(
+        self,
+        tenant_id: str,
+        query_vec: list[float],
+        top_k: int = 3,
+        threshold: float = 0.70,
+    ) -> list[SearchResult]:
+        client = self._get_client()
+        collection_name = self._collection_name(tenant_id)
+        try:
+            collection = client.get_collection(collection_name)
+        except Exception:
+            return []
+
+        results = collection.query(
+            query_embeddings=[query_vec],
+            n_results=min(top_k, collection.count()),
+            include=["documents", "metadatas", "distances"],
+        )
+
+        search_results = []
+        if not results["ids"] or not results["ids"][0]:
+            return []
+
+        for i, doc_id in enumerate(results["ids"][0]):
+            # Chroma distances: 1 - cosine_similarity (낮을수록 유사)
+            distance = results["distances"][0][i]
+            score = 1.0 - distance  # cosine similarity로 변환
+            if score >= threshold:
+                search_results.append(
+                    SearchResult(
+                        text=results["documents"][0][i],
+                        doc_id=doc_id,
+                        score=score,
+                        metadata=results["metadatas"][0][i] or {},
+                    )
+                )
+        return search_results
+
+    async def delete(self, tenant_id: str, doc_id: str) -> None:
+        client = self._get_client()
+        collection_name = self._collection_name(tenant_id)
+        try:
+            collection = client.get_collection(collection_name)
+            # doc_id로 시작하는 모든 청크 삭제
+            all_ids = collection.get()["ids"]
+            ids_to_delete = [id_ for id_ in all_ids if id_.startswith(f"{doc_id}_")]
+            if ids_to_delete:
+                collection.delete(ids=ids_to_delete)
+        except Exception:
+            pass
@@ -0,0 +1,30 @@
+from abc import ABC, abstractmethod
+
+
+class EmbeddingProvider(ABC):
+    @abstractmethod
+    async def embed(self, texts: list[str]) -> list[list[float]]:
+        ...
+
+    @abstractmethod
+    async def warmup(self) -> None:
+        ...
+
+    @property
+    @abstractmethod
+    def dimension(self) -> int:
+        ...
+
+
+class NotImplementedEmbeddingProvider(EmbeddingProvider):
+    """Phase 1에서 LocalEmbeddingProvider로 교체 예정"""
+
+    async def embed(self, texts: list[str]) -> list:
+        raise NotImplementedError("Embedding provider not configured. Set EMBEDDING_PROVIDER.")
+
+    async def warmup(self) -> None:
+        pass  # 예외 없이 통과
+
+    @property
+    def dimension(self) -> int:
+        return 768
@@ -0,0 +1,28 @@
+from abc import ABC, abstractmethod
+from typing import Optional
+
+
+class LLMProvider(ABC):
+    @abstractmethod
+    async def generate(
+        self,
+        system_prompt: str,
+        user_message: str,
+        context_chunks: list,
+        max_tokens: int = 512,
+    ) -> Optional[str]:
+        """실패 시 None 반환. 예외 raise 금지."""
+        ...
+
+
+class NullLLMProvider(LLMProvider):
+    """LLM_PROVIDER=none 기본값"""
+
+    async def generate(
+        self,
+        system_prompt: str = "",
+        user_message: str = "",
+        context_chunks: list = None,
+        max_tokens: int = 512,
+    ) -> None:
+        return None
@@ -0,0 +1,82 @@
+"""
+Anthropic Claude LLM Provider.
+근거(context_chunks)가 있을 때만 호출.
+할루시네이션 방지: 근거 없으면 None 반환.
+"""
+from typing import Optional
+
+from app.providers.llm import LLMProvider
+
+SYSTEM_PROMPT_TEMPLATE = """당신은 {tenant_name}AI 안내 도우미입니다.
+반드시 아래 근거 문서에 있는 내용만을 바탕으로 답변하세요.
+근거 없는 내용은 절대 추측하거나 생성하지 마세요.
+
+근거 문서:
+{context}
+
+규칙:
+1. 근거 문서에 없는 내용은 "담당자에게 문의해 주세요"로 안내
+2. 답변은 간결하고 명확하게 (3문장 이내)
+3. 전문 용어는 쉬운 말로 바꿔 설명
+"""
+
+
+class AnthropicLLMProvider(LLMProvider):
+    def __init__(self, api_key: str, model: str = "claude-haiku-4-5-20251001"):
+        self.api_key = api_key
+        self.model = model
+
+    async def generate(
+        self,
+        system_prompt: str,
+        user_message: str,
+        context_chunks: list,
+        max_tokens: int = 512,
+    ) -> Optional[str]:
+        """근거 없으면 None 반환. 예외 발생 시 None 반환."""
+        if not context_chunks:
+            return None  # 할루시네이션 방지 — 근거 없으면 LLM 미호출
+
+        try:
+            import anthropic
+            client = anthropic.AsyncAnthropic(api_key=self.api_key)
+            message = await client.messages.create(
+                model=self.model,
+                max_tokens=max_tokens,
+                system=system_prompt,
+                messages=[{"role": "user", "content": user_message}],
+            )
+            return message.content[0].text if message.content else None
+        except Exception:
+            return None  # 실패 시 None — 호출자가 Tier D로 폴백
+
+
+class OpenAILLMProvider(LLMProvider):
+    def __init__(self, api_key: str, model: str = "gpt-4o-mini"):
+        self.api_key = api_key
+        self.model = model
+
+    async def generate(
+        self,
+        system_prompt: str,
+        user_message: str,
+        context_chunks: list,
+        max_tokens: int = 512,
+    ) -> Optional[str]:
+        if not context_chunks:
+            return None
+
+        try:
+            import openai
+            client = openai.AsyncOpenAI(api_key=self.api_key)
+            response = await client.chat.completions.create(
+                model=self.model,
+                max_tokens=max_tokens,
+                messages=[
+                    {"role": "system", "content": system_prompt},
+                    {"role": "user", "content": user_message},
+                ],
+            )
+            return response.choices[0].message.content
+        except Exception:
+            return None
@@ -0,0 +1,32 @@
+from __future__ import annotations
+
+from app.providers.embedding import EmbeddingProvider
+
+import app.providers as providers_module
+
+
+class LocalEmbeddingProvider(EmbeddingProvider):
+    """
+    jhgan/ko-sroberta-multitask 기반 로컬 임베딩.
+    sentence-transformers 패키지 필요.
+    """
+
+    def __init__(self, model_name: str = "jhgan/ko-sroberta-multitask"):
+        self.model_name = model_name
+        self._model = None
+
+    async def warmup(self) -> None:
+        """모델 로드. 최초 1회 실행."""
+        from sentence_transformers import SentenceTransformer
+        self._model = SentenceTransformer(self.model_name)
+        providers_module._embedding_warmed_up = True
+
+    async def embed(self, texts: list[str]) -> list[list[float]]:
+        if self._model is None:
+            await self.warmup()
+        embeddings = self._model.encode(texts, convert_to_numpy=True)
+        return embeddings.tolist()
+
+    @property
+    def dimension(self) -> int:
+        return 768
@@ -0,0 +1,30 @@
+from abc import ABC, abstractmethod
+
+from app.providers.base import SearchResult
+
+
+class VectorDBProvider(ABC):
+    @abstractmethod
+    async def upsert(
+        self,
+        tenant_id: str,
+        doc_id: str,
+        chunks: list[str],
+        embeddings: list[list[float]],
+        metadatas: list[dict],
+    ) -> int:
+        ...
+
+    @abstractmethod
+    async def search(
+        self,
+        tenant_id: str,
+        query_vec: list[float],
+        top_k: int = 3,
+        threshold: float = 0.70,
+    ) -> list[SearchResult]:
+        ...
+
+    @abstractmethod
+    async def delete(self, tenant_id: str, doc_id: str) -> None:
+        ...