ДОбавлены эмбеддинги на базе гигачата

pyproject.toml

@@ -8,6 +8,8 @@ dependencies = [
   "psycopg[binary]>=3.1.18",
   "pgvector>=0.2.5",
   "pydantic>=2.7.0",
+  "python-dotenv>=1.0.0",
+  "gigachat>=0.2.0",
 ]
 
 [project.scripts]

src/rag_agent/config.py

@@ -2,8 +2,15 @@ from __future__ import annotations
 
 import os
 from dataclasses import dataclass
+from pathlib import Path
 from typing import Iterable, Sequence
 
+from dotenv import load_dotenv
+
+# Load .env from repo root when config is used (e.g. for local runs)
+_repo_root = Path(__file__).resolve().parent.parent.parent
+load_dotenv(_repo_root / ".env")
+
 
 @dataclass(frozen=True)
 class AppConfig:

src/rag_agent/index/embeddings.py

@@ -1,9 +1,17 @@
 from __future__ import annotations
 
 import hashlib
+import os
 from dataclasses import dataclass
+from pathlib import Path
 from typing import Iterable, Protocol
 
+from dotenv import load_dotenv
+
+# Ensure .env is loaded when resolving embedding client (e.g. GIGACHAT_CREDENTIALS)
+_repo_root = Path(__file__).resolve().parent.parent.parent.parent
+load_dotenv(_repo_root / ".env")
+
 
 class EmbeddingClient(Protocol):
     def embed_texts(self, texts: Iterable[str]) -> list[list[float]]:
@@ -25,5 +33,50 @@ class StubEmbeddingClient:
         return vectors
 
 
+_GIGACHAT_BATCH_SIZE = 50
+
+
+class GigaChatEmbeddingClient:
+    """Embeddings via GigaChat API. Credentials from env GIGACHAT_CREDENTIALS."""
+
+    def __init__(
+        self,
+        credentials: str,
+        model: str = "Embeddings",
+        verify_ssl_certs: bool = False,
+    ) -> None:
+        self._credentials = credentials.strip()
+        self._model = model
+        self._verify_ssl_certs = verify_ssl_certs
+
+    def embed_texts(self, texts: Iterable[str]) -> list[list[float]]:
+        from gigachat import GigaChat
+
+        texts_list = list(texts)
+        if not texts_list:
+            return []
+
+        result: list[list[float]] = []
+        for i in range(0, len(texts_list), _GIGACHAT_BATCH_SIZE):
+            batch = texts_list[i : i + _GIGACHAT_BATCH_SIZE]
+            with GigaChat(
+                credentials=self._credentials,
+                verify_ssl_certs=self._verify_ssl_certs,
+            ) as giga:
+                response = giga.embeddings(model=self._model, input=batch)
+            # Preserve order by index
+            by_index = {item.index: item.embedding for item in response.data}
+            result.extend(by_index[j] for j in range(len(batch)))
+        return result
+
+
 def get_embedding_client(dim: int) -> EmbeddingClient:
+    credentials = os.getenv("GIGACHAT_CREDENTIALS", "").strip()
+    if credentials:
+        return GigaChatEmbeddingClient(
+            credentials=credentials,
+            model=os.getenv("GIGACHAT_EMBEDDINGS_MODEL", "Embeddings"),
+            verify_ssl_certs=os.getenv("GIGACHAT_VERIFY_SSL", "false").lower()
+            in ("1", "true", "yes"),
+        )
     return StubEmbeddingClient(dim=dim)