Handle token budget when recommend test context

unit_tests/assistants/recommend_test_context.py

@@ -19,7 +19,7 @@ ENCODING = (
     if USE_USER_MODEL
     else get_encoding("cl100k_base")
 )
-# TODO: handle token budget
+
 TOKEN_BUDGET = int(CONTEXT_SIZE.get(MODEL, DEFAULT_CONTEXT_SIZE) * 0.9)
 
 
@@ -58,29 +58,49 @@ JSON Format Example:
 """
 
 
+def _mk_user_msg(func_to_test: FuncToTest, contexts: List) -> str:
+    """
+    Create a user message to be sent to the model within the token budget.
+    """
+    msg = None
+    while msg is None:
+        context_content = "\n\n".join([str(c) for c in contexts])
+
+        msg = recommend_symbol_context_prompt.format(
+            function_content=func_to_test.func_content,
+            context_content=context_content,
+            function_name=func_to_test.func_name,
+            file_path=func_to_test.file_path,
+        )
+
+        token_count = len(ENCODING.encode(msg, disallowed_special=()))
+        if contexts and token_count > TOKEN_BUDGET:
+            # Remove the last context and try again
+            contexts.pop()
+            msg = None
+
+    return msg
+
+
 def get_recommended_symbols(
     func_to_test: FuncToTest, known_context: Optional[List] = None
 ) -> List[str]:
     known_context = known_context or []
-    context_content = "\n\n".join([str(c) for c in known_context])
-
-    msg = recommend_symbol_context_prompt.format(
-        function_content=func_to_test.func_content,
-        context_content=context_content,
-        function_name=func_to_test.func_name,
-        file_path=func_to_test.file_path,
-    )
+    msg = _mk_user_msg(func_to_test, known_context)
 
     json_res = {}
     if USE_USER_MODEL:
         # Use the wrapped api parameters
-        json_res = chat_completion_no_stream_return_json(
-            messages=[{"role": "user", "content": msg}],
-            llm_config={
-                "model": MODEL,
-                "temperature": 0.1,
-            },
-        ) or {}
+        json_res = (
+            chat_completion_no_stream_return_json(
+                messages=[{"role": "user", "content": msg}],
+                llm_config={
+                    "model": MODEL,
+                    "temperature": 0.1,
+                },
+            )
+            or {}
+        )
 
     else:
         response = create_chat_completion_content(