|
| 1 | +import os |
| 2 | +import streamlit as st |
| 3 | + |
| 4 | +from interface.core.config import ( |
| 5 | + update_llm_settings, |
| 6 | + update_embedding_settings, |
| 7 | + Config, |
| 8 | + load_config, |
| 9 | +) |
| 10 | + |
| 11 | + |
| 12 | +LLM_PROVIDERS = [ |
| 13 | + "openai", |
| 14 | + "azure", |
| 15 | + "bedrock", |
| 16 | + "gemini", |
| 17 | + "ollama", |
| 18 | + "huggingface", |
| 19 | +] |
| 20 | + |
| 21 | + |
| 22 | +def _llm_fields(provider: str) -> list[tuple[str, str, bool]]: |
| 23 | + """Return list of (label, env_key, is_secret) for LLM provider.""" |
| 24 | + p = provider.lower() |
| 25 | + if p == "openai": |
| 26 | + return [ |
| 27 | + ("Model", "OPEN_AI_LLM_MODEL", False), |
| 28 | + ("API Key", "OPEN_AI_KEY", True), |
| 29 | + ] |
| 30 | + if p == "azure": |
| 31 | + return [ |
| 32 | + ("Endpoint", "AZURE_OPENAI_LLM_ENDPOINT", False), |
| 33 | + ("Deployment(Model)", "AZURE_OPENAI_LLM_MODEL", False), |
| 34 | + ("API Version", "AZURE_OPENAI_LLM_API_VERSION", False), |
| 35 | + ("API Key", "AZURE_OPENAI_LLM_KEY", True), |
| 36 | + ] |
| 37 | + if p == "bedrock": |
| 38 | + return [ |
| 39 | + ("Model", "AWS_BEDROCK_LLM_MODEL", False), |
| 40 | + ("Access Key ID", "AWS_BEDROCK_LLM_ACCESS_KEY_ID", True), |
| 41 | + ("Secret Access Key", "AWS_BEDROCK_LLM_SECRET_ACCESS_KEY", True), |
| 42 | + ("Region", "AWS_BEDROCK_LLM_REGION", False), |
| 43 | + ] |
| 44 | + if p == "gemini": |
| 45 | + return [ |
| 46 | + ("Model", "GEMINI_LLM_MODEL", False), |
| 47 | + # ChatGoogleGenerativeAI uses GOOGLE_API_KEY at process level, but factory currently reads only model |
| 48 | + ] |
| 49 | + if p == "ollama": |
| 50 | + return [ |
| 51 | + ("Model", "OLLAMA_LLM_MODEL", False), |
| 52 | + ("Base URL", "OLLAMA_LLM_BASE_URL", False), |
| 53 | + ] |
| 54 | + if p == "huggingface": |
| 55 | + return [ |
| 56 | + ("Endpoint URL", "HUGGING_FACE_LLM_ENDPOINT", False), |
| 57 | + ("Repo ID", "HUGGING_FACE_LLM_REPO_ID", False), |
| 58 | + ("Model", "HUGGING_FACE_LLM_MODEL", False), |
| 59 | + ("API Token", "HUGGING_FACE_LLM_API_TOKEN", True), |
| 60 | + ] |
| 61 | + return [] |
| 62 | + |
| 63 | + |
| 64 | +def _embedding_fields(provider: str) -> list[tuple[str, str, bool]]: |
| 65 | + p = provider.lower() |
| 66 | + if p == "openai": |
| 67 | + return [ |
| 68 | + ("Model", "OPEN_AI_EMBEDDING_MODEL", False), |
| 69 | + ("API Key", "OPEN_AI_KEY", True), |
| 70 | + ] |
| 71 | + if p == "azure": |
| 72 | + return [ |
| 73 | + ("Endpoint", "AZURE_OPENAI_EMBEDDING_ENDPOINT", False), |
| 74 | + ("Deployment(Model)", "AZURE_OPENAI_EMBEDDING_MODEL", False), |
| 75 | + ("API Version", "AZURE_OPENAI_EMBEDDING_API_VERSION", False), |
| 76 | + ("API Key", "AZURE_OPENAI_EMBEDDING_KEY", True), |
| 77 | + ] |
| 78 | + if p == "bedrock": |
| 79 | + return [ |
| 80 | + ("Model", "AWS_BEDROCK_EMBEDDING_MODEL", False), |
| 81 | + ("Access Key ID", "AWS_BEDROCK_EMBEDDING_ACCESS_KEY_ID", True), |
| 82 | + ("Secret Access Key", "AWS_BEDROCK_EMBEDDING_SECRET_ACCESS_KEY", True), |
| 83 | + ("Region", "AWS_BEDROCK_EMBEDDING_REGION", False), |
| 84 | + ] |
| 85 | + if p == "gemini": |
| 86 | + return [ |
| 87 | + ("Model", "GEMINI_EMBEDDING_MODEL", False), |
| 88 | + ("API Key", "GEMINI_EMBEDDING_KEY", True), |
| 89 | + ] |
| 90 | + if p == "ollama": |
| 91 | + return [ |
| 92 | + ("Model", "OLLAMA_EMBEDDING_MODEL", False), |
| 93 | + ("Base URL", "OLLAMA_EMBEDDING_BASE_URL", False), |
| 94 | + ] |
| 95 | + if p == "huggingface": |
| 96 | + return [ |
| 97 | + ("Model", "HUGGING_FACE_EMBEDDING_MODEL", False), |
| 98 | + ("Repo ID", "HUGGING_FACE_EMBEDDING_REPO_ID", False), |
| 99 | + ("API Token", "HUGGING_FACE_EMBEDDING_API_TOKEN", True), |
| 100 | + ] |
| 101 | + return [] |
| 102 | + |
| 103 | + |
| 104 | +def render_llm_section(config: Config | None = None) -> None: |
| 105 | + st.subheader("LLM 설정") |
| 106 | + |
| 107 | + if config is None: |
| 108 | + try: |
| 109 | + config = load_config() |
| 110 | + except Exception: |
| 111 | + config = None # UI 일관성을 위한 옵셔널 처리 |
| 112 | + |
| 113 | + llm_col, emb_col = st.columns(2) |
| 114 | + |
| 115 | + with llm_col: |
| 116 | + st.markdown("**Chat LLM**") |
| 117 | + default_llm_provider = ( |
| 118 | + ( |
| 119 | + st.session_state.get("LLM_PROVIDER") |
| 120 | + or os.getenv("LLM_PROVIDER") |
| 121 | + or "openai" |
| 122 | + ) |
| 123 | + ).lower() |
| 124 | + try: |
| 125 | + default_llm_index = LLM_PROVIDERS.index(default_llm_provider) |
| 126 | + except ValueError: |
| 127 | + default_llm_index = 0 |
| 128 | + provider = st.selectbox( |
| 129 | + "공급자", |
| 130 | + options=LLM_PROVIDERS, |
| 131 | + index=default_llm_index, |
| 132 | + key="llm_provider", |
| 133 | + ) |
| 134 | + fields = _llm_fields(provider) |
| 135 | + values: dict[str, str | None] = {} |
| 136 | + for label, env_key, is_secret in fields: |
| 137 | + prefill = st.session_state.get(env_key) or os.getenv(env_key) or "" |
| 138 | + if is_secret: |
| 139 | + values[env_key] = st.text_input( |
| 140 | + label, value=prefill, type="password", key=f"llm_{env_key}" |
| 141 | + ) |
| 142 | + else: |
| 143 | + values[env_key] = st.text_input( |
| 144 | + label, value=prefill, key=f"llm_{env_key}" |
| 145 | + ) |
| 146 | + |
| 147 | + # 메시지 영역: 버튼 컬럼 밖(섹션 폭)으로 배치하여 좁은 폭에 눌려 깨지는 문제 방지 |
| 148 | + llm_msg = st.empty() |
| 149 | + |
| 150 | + save_cols = st.columns([1, 1, 2]) |
| 151 | + with save_cols[0]: |
| 152 | + if st.button("저장", key="llm_save"): |
| 153 | + try: |
| 154 | + update_llm_settings(provider=provider, values=values) |
| 155 | + llm_msg.success("LLM 설정이 저장되었습니다.") |
| 156 | + except Exception as e: |
| 157 | + llm_msg.error(f"저장 실패: {e}") |
| 158 | + with save_cols[1]: |
| 159 | + if st.button("검증", key="llm_validate"): |
| 160 | + # 가벼운 검증: 필수 키 존재 여부만 확인 |
| 161 | + try: |
| 162 | + update_llm_settings(provider=provider, values=values) |
| 163 | + llm_msg.success( |
| 164 | + "형식 검증 완료. 실제 호출은 실행 경로에서 재검증됩니다." |
| 165 | + ) |
| 166 | + except Exception as e: |
| 167 | + llm_msg.error(f"검증 실패: {e}") |
| 168 | + |
| 169 | + with emb_col: |
| 170 | + st.markdown("**Embeddings**") |
| 171 | + default_emb_provider = ( |
| 172 | + ( |
| 173 | + st.session_state.get("EMBEDDING_PROVIDER") |
| 174 | + or os.getenv("EMBEDDING_PROVIDER") |
| 175 | + or "openai" |
| 176 | + ) |
| 177 | + ).lower() |
| 178 | + try: |
| 179 | + default_emb_index = LLM_PROVIDERS.index(default_emb_provider) |
| 180 | + except ValueError: |
| 181 | + default_emb_index = 0 |
| 182 | + e_provider = st.selectbox( |
| 183 | + "공급자", |
| 184 | + options=LLM_PROVIDERS, |
| 185 | + index=default_emb_index, |
| 186 | + key="embedding_provider", |
| 187 | + ) |
| 188 | + e_fields = _embedding_fields(e_provider) |
| 189 | + e_values: dict[str, str | None] = {} |
| 190 | + for label, env_key, is_secret in e_fields: |
| 191 | + prefill = st.session_state.get(env_key) or os.getenv(env_key) or "" |
| 192 | + if is_secret: |
| 193 | + e_values[env_key] = st.text_input( |
| 194 | + label, value=prefill, type="password", key=f"emb_{env_key}" |
| 195 | + ) |
| 196 | + else: |
| 197 | + e_values[env_key] = st.text_input( |
| 198 | + label, value=prefill, key=f"emb_{env_key}" |
| 199 | + ) |
| 200 | + |
| 201 | + # 메시지 영역: 버튼 컬럼 밖(섹션 폭) |
| 202 | + emb_msg = st.empty() |
| 203 | + |
| 204 | + e_cols = st.columns([1, 1, 2]) |
| 205 | + with e_cols[0]: |
| 206 | + if st.button("저장", key="emb_save"): |
| 207 | + try: |
| 208 | + update_embedding_settings(provider=e_provider, values=e_values) |
| 209 | + emb_msg.success("Embeddings 설정이 저장되었습니다.") |
| 210 | + except Exception as e: |
| 211 | + emb_msg.error(f"저장 실패: {e}") |
| 212 | + with e_cols[1]: |
| 213 | + if st.button("검증", key="emb_validate"): |
| 214 | + try: |
| 215 | + update_embedding_settings(provider=e_provider, values=e_values) |
| 216 | + emb_msg.success("형식 검증 완료.") |
| 217 | + except Exception as e: |
| 218 | + emb_msg.error(f"검증 실패: {e}") |
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