Initial commit: MCP server + web upload interface

Ezer Mishpati - AI legal decision drafting system with:
- MCP server (FastMCP) with document processing pipeline
- Web upload interface (FastAPI) for file upload and classification
- pgvector-based semantic search
- Hebrew legal document chunking and embedding

mcp-server/src/legal_mcp/services/embeddings.py

@@ -0,0 +1,55 @@
+"""Embedding service using Voyage AI API."""
+
+from __future__ import annotations
+
+import logging
+
+import voyageai
+
+from legal_mcp import config
+
+logger = logging.getLogger(__name__)
+
+_client: voyageai.Client | None = None
+
+
+def _get_client() -> voyageai.Client:
+    global _client
+    if _client is None:
+        _client = voyageai.Client(api_key=config.VOYAGE_API_KEY)
+    return _client
+
+
+async def embed_texts(texts: list[str], input_type: str = "document") -> list[list[float]]:
+    """Embed a batch of texts using Voyage AI.
+
+    Args:
+        texts: List of texts to embed (max 128 per call).
+        input_type: "document" for indexing, "query" for search queries.
+
+    Returns:
+        List of embedding vectors (1024 dimensions each).
+    """
+    if not texts:
+        return []
+
+    client = _get_client()
+    all_embeddings = []
+
+    # Voyage AI supports up to 128 texts per batch
+    for i in range(0, len(texts), 128):
+        batch = texts[i : i + 128]
+        result = client.embed(
+            batch,
+            model=config.VOYAGE_MODEL,
+            input_type=input_type,
+        )
+        all_embeddings.extend(result.embeddings)
+
+    return all_embeddings
+
+
+async def embed_query(query: str) -> list[float]:
+    """Embed a single search query."""
+    results = await embed_texts([query], input_type="query")
+    return results[0]