first commit

api/clients/__init__.py

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+"""API clients."""

api/clients/chroma_store.py

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+from __future__ import annotations
+
+import logging
+from typing import Any
+
+import chromadb
+from chromadb.config import Settings as ChromaSettings
+
+from api.config import settings
+
+
+logger = logging.getLogger(__name__)
+
+
+class ChromaVectorStore:
+    def __init__(self) -> None:
+        self._client = chromadb.HttpClient(
+            host=settings.chroma_host,
+            port=settings.chroma_port,
+            ssl=settings.chroma_ssl,
+            settings=ChromaSettings(anonymized_telemetry=False),
+        )
+        self._collection_name = settings.chroma_collection
+        logger.info(
+            "Chroma client configured: host=%s port=%s collection=%s ssl=%s",
+            settings.chroma_host,
+            settings.chroma_port,
+            self._collection_name,
+            settings.chroma_ssl,
+        )
+
+    @property
+    def collection_name(self) -> str:
+        return self._collection_name
+
+    def get_collection(self):
+        return self._client.get_or_create_collection(
+            name=self._collection_name,
+            metadata={"hnsw:space": "cosine"},
+        )
+
+    def count(self) -> int:
+        return int(self.get_collection().count())
+
+    def reset_collection(self) -> None:
+        logger.warning("Resetting Chroma collection: %s", self._collection_name)
+        try:
+            self._client.delete_collection(self._collection_name)
+        except Exception:
+            logger.exception(
+                "Could not delete Chroma collection before reset, continuing with create_collection"
+            )
+        self.get_collection()
+
+    def upsert(
+        self,
+        ids: list[str],
+        documents: list[str],
+        embeddings: list[list[float]],
+        metadatas: list[dict[str, Any]],
+    ) -> None:
+        logger.info(
+            "Upserting %s embeddings into Chroma collection=%s",
+            len(ids),
+            self._collection_name,
+        )
+        self.get_collection().upsert(
+            ids=ids,
+            documents=documents,
+            embeddings=embeddings,
+            metadatas=metadatas,
+        )
+
+    def query(
+        self,
+        query_embeddings: list[list[float]],
+        n_results: int,
+    ) -> dict[str, Any]:
+        logger.info(
+            "Running vector query against collection=%s n_results=%s",
+            self._collection_name,
+            n_results,
+        )
+        return self.get_collection().query(
+            query_embeddings=query_embeddings,
+            n_results=n_results,
+            include=["distances", "metadatas", "documents"],
+        )

api/clients/openrouter_client.py

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+from __future__ import annotations
+
+from openai import AsyncOpenAI
+
+from api.config import settings
+
+
+def build_openai_client() -> AsyncOpenAI:
+    return AsyncOpenAI(
+        api_key=settings.openai_api_key,
+        base_url=settings.openai_base_url,
+        timeout=settings.llm_timeout_seconds,
+    )