fix(rag): use a stable hash for document IDs so dedup survives restarts (#1098)

add_document() and add_documents_batch() derive the persistent ChromaDB
document id from Python's built-in hash():

    doc_id = f"doc_{hash(text) % 10**16}"

str hashing is randomized per process (PYTHONHASHSEED is on by default), so
the same document text gets a different doc_id on every restart. The dedup
check right after — self._collection.get(ids=[doc_id]) — therefore misses
on restart, and identical documents are re-embedded and re-added as
duplicates each time the app restarts, bloating the vector store and
skewing retrieval.

Derive the id from a stable hashlib.sha256 of the text via a shared
_generate_doc_id() helper, used by both add paths so they agree.

tests/test_rag_vector_id_stability.py runs _generate_doc_id in subprocesses
under PYTHONHASHSEED=0/1/random and asserts the id is identical across all
of them (and differs for different text). Fails before this change.

src/rag_vector.py

@@ -7,6 +7,7 @@ configurable embedding endpoint via EMBEDDING_URL env var.
 """
 
 import os
+import hashlib
 import re
 import logging
 import numpy as np
@@ -26,6 +27,10 @@ KEYWORD_WEIGHT = 0.3
 COLLECTION_NAME = "odysseus_rag"
 
 
+def _generate_doc_id(text: str) -> str:
+    return f"doc_{hashlib.sha256(text.encode('utf-8')).hexdigest()[:16]}"
+
+
 class VectorRAG:
     """RAG system using ChromaDB vector storage with hybrid search."""
 
@@ -99,7 +104,7 @@ class VectorRAG:
             return False
 
         try:
-            doc_id = f"doc_{hash(text) % 10**16}"
+            doc_id = _generate_doc_id(text)
             # Check if already exists
             existing = self._collection.get(ids=[doc_id])
             if existing["ids"]:
@@ -135,7 +140,7 @@ class VectorRAG:
             new_metas = []
             new_ids = []
             for t, m in valid:
-                doc_id = f"doc_{hash(t) % 10**16}"
+                doc_id = _generate_doc_id(t)
                 existing = self._collection.get(ids=[doc_id])
                 if not existing["ids"]:
                     new_texts.append(t)