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| from src.adapters.cache import CacheAdapter | |
| from src.adapters.data import DataAdapter | |
| from src.adapters.embeddings import EmbeddingsAdapter, VectorStoreAdapter | |
| from src.adapters.graph import GraphAdapter | |
| from src.adapters.llm import LLMAdapter | |
| from src.config import config | |
| from src.lib.redis.client import _redis_client | |
| def _build_small_llm(provider: str): | |
| if provider == "ollama": | |
| from src.lib.llm.client_ollama import _llm_small_client | |
| return _llm_small_client | |
| if provider == "azure": | |
| from src.lib.llm.client_azure import _llm_small_client_azure | |
| return _llm_small_client_azure | |
| if provider == "openai": | |
| from src.lib.llm.client_openai import _llm_small_client_openai | |
| return _llm_small_client_openai | |
| if provider == "anthropic": | |
| from src.lib.llm.client_anthropic import _llm_small_client_anthropic | |
| return _llm_small_client_anthropic | |
| if provider == "deepseek": | |
| from src.lib.llm.client_deepseek import _llm_small_client_deepseek | |
| return _llm_small_client_deepseek | |
| if provider == "gcp_vertex": | |
| from src.lib.llm.client_small import _llm_small_client | |
| return _llm_small_client | |
| if provider == "amazon_bedrock": | |
| from src.lib.llm.client_bedrock import _llm_small_client_bedrock | |
| return _llm_small_client_bedrock | |
| raise ValueError(f"Unsupported small LLM provider: {provider}") | |
| def _build_large_llm(provider: str): | |
| if provider == "ollama": | |
| from src.lib.llm.client_ollama import _llm_large_client | |
| return _llm_large_client | |
| if provider == "azure": | |
| from src.lib.llm.client_large import _llm_large_client | |
| return _llm_large_client | |
| if provider == "openai": | |
| from src.lib.llm.client_openai import _llm_large_client_openai | |
| return _llm_large_client_openai | |
| if provider == "anthropic": | |
| from src.lib.llm.client_anthropic import _llm_large_client_anthropic | |
| return _llm_large_client_anthropic | |
| if provider == "deepseek": | |
| from src.lib.llm.client_deepseek import _llm_large_client_deepseek | |
| return _llm_large_client_deepseek | |
| if provider == "gcp_vertex": | |
| from src.lib.llm.client_vertex import _llm_large_client_vertex | |
| return _llm_large_client_vertex | |
| if provider == "amazon_bedrock": | |
| from src.lib.llm.client_bedrock import _llm_large_client_bedrock | |
| return _llm_large_client_bedrock | |
| raise ValueError(f"Unsupported large LLM provider: {provider}") | |
| def _build_embeddings(provider: str): | |
| if provider == "mock": | |
| from src.lib.embeddings.client_mock import _mock_embeddings_client | |
| return _mock_embeddings_client | |
| if provider == "fastembed": | |
| from src.lib.embeddings.client_fastembed import _fastembed_client | |
| return _fastembed_client | |
| if provider == "ollama": | |
| from src.lib.embeddings.client_ollama import _embeddings_ollama_client | |
| return _embeddings_ollama_client | |
| if provider == "azure": | |
| from src.lib.embeddings.client import _embeddings_client | |
| return _embeddings_client | |
| if provider == "openai": | |
| from src.lib.embeddings.client_openai import _embeddings_openai_client | |
| return _embeddings_openai_client | |
| if provider == "gcp_vertex": | |
| from src.lib.embeddings.client_vertex import _embeddings_vertex_client | |
| return _embeddings_vertex_client | |
| if provider == "amazon_bedrock": | |
| from src.lib.embeddings.client_bedrock import _embeddings_bedrock_client | |
| return _embeddings_bedrock_client | |
| raise ValueError(f"Unsupported embeddings provider: {provider}") | |
| _llm_small = LLMAdapter() | |
| _llm_small.add_client(_build_small_llm(config.llm_small_provider)) | |
| _llm_large = LLMAdapter() | |
| _llm_large.add_client(_build_large_llm(config.llm_large_provider)) | |
| llm_small_adapter = _llm_small | |
| llm_large_adapter = _llm_large | |
| cache_adapter = CacheAdapter() | |
| cache_adapter.add_client(_redis_client) | |
| graph_adapter = GraphAdapter() | |
| if config.graph_db == "neo4j": | |
| from src.lib.neo4j.client import _neo4j_client | |
| graph_adapter.add_client(_neo4j_client) | |
| elif config.graph_db == "networkx": | |
| from src.lib.postgresql.networkx import get_networkx_graph_client | |
| graph_adapter.add_client(get_networkx_graph_client()) | |
| if config.data_db == "mongo": | |
| from src.lib.mongo.client import _mongo_client | |
| data_adapter = DataAdapter() | |
| data_adapter.add_client(_mongo_client) | |
| elif config.data_db == "postgresql": | |
| from src.lib.postgresql.data import get_postgresql_data_client | |
| data_adapter = DataAdapter() | |
| data_adapter.add_client(get_postgresql_data_client()) | |
| _embeddings_primary = EmbeddingsAdapter() | |
| _embeddings_primary.add_client(_build_embeddings(config.embeddings_provider)) | |
| _embeddings_small = EmbeddingsAdapter() | |
| if config.models_mode == "remote" or config.embeddings_provider == "ollama": | |
| # Keep remote mode free of local torch/sentence-transformers. | |
| # Architect similarity checks still work as long as both sides share a space. | |
| _embeddings_small.add_client(_build_embeddings(config.embeddings_provider)) | |
| else: | |
| from src.lib.embeddings.client_small import _embeddings_small_client | |
| _embeddings_small.add_client(_embeddings_small_client) | |
| embeddings_adapter = _embeddings_primary | |
| embeddings_small_adapter = _embeddings_small | |
| if config.vector_db == "milvus": | |
| from src.lib.milvus.client import _milvus_client | |
| vector_store_adapter = VectorStoreAdapter() | |
| vector_store_adapter.add_client(_milvus_client) | |
| elif config.vector_db == "postgresql": | |
| from src.lib.postgresql.vectors import get_postgresql_vector_store_client | |
| vector_store_adapter = VectorStoreAdapter() | |
| vector_store_adapter.add_client(get_postgresql_vector_store_client()) | |