diff --git a/python/app/rag/embeddings/reranker.py b/python/app/rag/embeddings/reranker.py
index 552b65d..5d8c668 100755
--- a/python/app/rag/embeddings/reranker.py
+++ b/python/app/rag/embeddings/reranker.py
@@ -13,20 +13,24 @@ def normalize_text(text: str) -> str:
     return _WHITESPACE_RE.sub(" ", text).strip().lower()
 
 
-def document_signature(document: Document) -> str:
-    """Generate a signature for a document based on its content."""
+def document_signature(document: Document) -> bytes:
+    """Generate a compact signature for a document's normalized content."""
     normalized_content = normalize_text(document.page_content)
-    return sha1(normalized_content.encode("utf-8")).hexdigest()
+    return sha1(normalized_content.encode("utf-8")).digest()
 
 
-def deduplicate_documents(documents: list[Document]) -> list[Document]:
+def deduplicate_documents(
+    documents: list[Document],
+    seen_signatures: set[bytes] | None = None,
+    max_seen_signatures: int | None = None,
+) -> list[Document]:
     """
-    Remove duplicate documents based on content similarity.
+    Remove duplicate documents by normalized content.
 
-    Uses SHA1 hashing of normalized content to detect duplicates.
-    Safe for low-memory environments (no ML models).
+    A shared signature set deduplicates across batches. Its optional cap bounds
+    extra memory; duplicates remain deduplicated within each individual batch.
     """
-    seen_signatures: set[str] = set()
+    batch_signatures: set[bytes] = set()
     deduplicated: list[Document] = []
 
     for document in documents:
@@ -34,10 +38,20 @@ def deduplicate_documents(documents: list[Document]) -> list[Document]:
             continue
 
         signature = document_signature(document)
-        if signature in seen_signatures:
+        if signature in batch_signatures:
             continue
+        batch_signatures.add(signature)
 
-        seen_signatures.add(signature)
+        if seen_signatures is not None and signature in seen_signatures:
+            continue
+        if (
+            seen_signatures is not None
+            and (
+                max_seen_signatures is None
+                or len(seen_signatures) < max_seen_signatures
+            )
+        ):
+            seen_signatures.add(signature)
         deduplicated.append(document)
 
     return deduplicated
diff --git a/python/app/rag/embeddings/vector_db.py b/python/app/rag/embeddings/vector_db.py
index 5589baa..2c3330e 100755
--- a/python/app/rag/embeddings/vector_db.py
+++ b/python/app/rag/embeddings/vector_db.py
@@ -20,8 +20,16 @@ def __init__(self, collection_name):
         )
 
     def add_documents(self, documents, batch_size=100):
+        # Keep one content-signature set across batches without copying the
+        # complete document corpus into a second list.
+        max_seen_signatures = int(os.getenv("RAG_DEDUP_MAX_SIGNATURES", "50000"))
+        seen_signatures: set[bytes] = set()
         for i in range(0, len(documents), batch_size):
-            batch = deduplicate_documents(documents[i : i + batch_size])
+            batch = deduplicate_documents(
+                documents[i : i + batch_size],
+                seen_signatures=seen_signatures,
+                max_seen_signatures=max_seen_signatures,
+            )
 
             if not batch:
                 continue
diff --git a/python/tests/test_vector_db.py b/python/tests/test_vector_db.py
new file mode 100644
index 0000000..c562346
--- /dev/null
+++ b/python/tests/test_vector_db.py
@@ -0,0 +1,59 @@
+import os
+import unittest
+from unittest.mock import MagicMock, patch
+
+from langchain_core.documents import Document
+
+from app.rag.embeddings.vector_db import vector_db
+
+
+class VectorDatabaseIngestionTests(unittest.TestCase):
+    def test_duplicate_chunks_across_batches_are_submitted_once(self):
+        unique = [
+            Document(
+                page_content=f"Source passage {index}",
+                metadata={"job_id": "job-1", "chunk_index": index},
+            )
+            for index in range(100)
+        ]
+        duplicated = [
+            Document(
+                page_content=f"  source   passage {index}  ",
+                metadata={"job_id": "job-1", "chunk_index": index + 100},
+            )
+            for index in range(100)
+        ]
+        collection = MagicMock()
+        database = vector_db.__new__(vector_db)
+        database.collection = collection
+
+        database.add_documents(unique + duplicated, batch_size=100)
+
+        submitted = sum(
+            len(call.args[0])
+            for call in collection.add_documents.call_args_list
+        )
+        self.assertEqual(submitted, 100)
+        self.assertEqual(collection.add_documents.call_count, 1)
+
+
+    def test_signature_memory_cap_is_respected_across_batches(self):
+        documents = [
+            Document(page_content=content, metadata={"job_id": "job-1"})
+            for content in ("alpha", "beta", "gamma", "alpha", "beta", "gamma")
+        ]
+        collection = MagicMock()
+        database = vector_db.__new__(vector_db)
+        database.collection = collection
+
+        with patch.dict(os.environ, {"RAG_DEDUP_MAX_SIGNATURES": "2"}):
+            database.add_documents(documents, batch_size=3)
+
+        submitted_batches = [
+            len(call.args[0])
+            for call in collection.add_documents.call_args_list
+        ]
+        self.assertEqual(submitted_batches, [3, 1])
+
+if __name__ == "__main__":
+    unittest.main()