Initial commit: breakpilot-core - Shared Infrastructure

Docker Compose with 24+ services:
- PostgreSQL (PostGIS), Valkey, MinIO, Qdrant
- Vault (PKI/TLS), Nginx (Reverse Proxy)
- Backend Core API, Consent Service, Billing Service
- RAG Service, Embedding Service
- Gitea, Woodpecker CI/CD
- Night Scheduler, Health Aggregator
- Jitsi (Web/XMPP/JVB/Jicofo), Mailpit

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

rag-service/embedding_client.py

@@ -0,0 +1,123 @@
+import logging
+from typing import Optional
+
+import httpx
+
+from config import settings
+
+logger = logging.getLogger("rag-service.embedding")
+
+_TIMEOUT = httpx.Timeout(timeout=120.0, connect=10.0)
+
+
+class EmbeddingClient:
+    """HTTP client for the embedding-service (port 8087)."""
+
+    def __init__(self) -> None:
+        self._base_url: str = settings.EMBEDDING_SERVICE_URL.rstrip("/")
+
+    def _url(self, path: str) -> str:
+        return f"{self._base_url}{path}"
+
+    # ------------------------------------------------------------------
+    # Embeddings
+    # ------------------------------------------------------------------
+
+    async def generate_embeddings(self, texts: list[str]) -> list[list[float]]:
+        """
+        Send a batch of texts to the embedding service and return a list of
+        embedding vectors.
+        """
+        async with httpx.AsyncClient(timeout=_TIMEOUT) as client:
+            response = await client.post(
+                self._url("/api/v1/embeddings"),
+                json={"texts": texts},
+            )
+            response.raise_for_status()
+            data = response.json()
+            return data.get("embeddings", [])
+
+    async def generate_single_embedding(self, text: str) -> list[float]:
+        """Convenience wrapper for a single text."""
+        results = await self.generate_embeddings([text])
+        if not results:
+            raise ValueError("Embedding service returned empty result")
+        return results[0]
+
+    # ------------------------------------------------------------------
+    # Reranking
+    # ------------------------------------------------------------------
+
+    async def rerank_documents(
+        self,
+        query: str,
+        documents: list[str],
+        top_k: int = 10,
+    ) -> list[dict]:
+        """
+        Ask the embedding service to re-rank documents for a given query.
+        Returns a list of {index, score, text}.
+        """
+        async with httpx.AsyncClient(timeout=_TIMEOUT) as client:
+            response = await client.post(
+                self._url("/api/v1/rerank"),
+                json={
+                    "query": query,
+                    "documents": documents,
+                    "top_k": top_k,
+                },
+            )
+            response.raise_for_status()
+            data = response.json()
+            return data.get("results", [])
+
+    # ------------------------------------------------------------------
+    # Chunking
+    # ------------------------------------------------------------------
+
+    async def chunk_text(
+        self,
+        text: str,
+        strategy: str = "recursive",
+        chunk_size: int = 512,
+        overlap: int = 50,
+    ) -> list[str]:
+        """
+        Ask the embedding service to chunk a long text.
+        Returns a list of chunk strings.
+        """
+        async with httpx.AsyncClient(timeout=_TIMEOUT) as client:
+            response = await client.post(
+                self._url("/api/v1/chunk"),
+                json={
+                    "text": text,
+                    "strategy": strategy,
+                    "chunk_size": chunk_size,
+                    "overlap": overlap,
+                },
+            )
+            response.raise_for_status()
+            data = response.json()
+            return data.get("chunks", [])
+
+    # ------------------------------------------------------------------
+    # PDF extraction
+    # ------------------------------------------------------------------
+
+    async def extract_pdf(self, pdf_bytes: bytes) -> str:
+        """
+        Send raw PDF bytes to the embedding service for text extraction.
+        Returns the extracted text as a string.
+        """
+        async with httpx.AsyncClient(timeout=_TIMEOUT) as client:
+            response = await client.post(
+                self._url("/api/v1/extract-pdf"),
+                files={"file": ("document.pdf", pdf_bytes, "application/pdf")},
+            )
+            response.raise_for_status()
+            data = response.json()
+            return data.get("text", "")
+
+
+# Singleton
+embedding_client = EmbeddingClient()