chore: Create the context cache based on the token count of previous request

before this change, we estimate the token count of the contents to cache and use it to compare with the threshold user set. but that's not precise , so we use the actual prompt token count of previous llm request.

We won't create cache for the very initial request

PiperOrigin-RevId: 814484840

tests/unittests/flows/llm_flows/test_context_cache_processor.py

@@ -452,3 +452,195 @@ class TestContextCacheRequestProcessor:
     assert llm_request.cache_config == self.cache_config
     assert llm_request.cache_metadata is not None
     assert llm_request.cache_metadata.invocations_used == 11  # 10 + 1
+
+  async def test_cacheable_contents_token_count_extraction(self):
+    """Test that previous prompt token count is extracted and set."""
+    agent = LlmAgent(name="test_agent")
+
+    # Create event with usage metadata
+    event_with_tokens = Event(
+        author="test_agent",
+        usage_metadata=types.UsageMetadata(
+            prompt_token_count=1024,
+            response_token_count=256,
+            total_token_count=1280,
+        ),
+    )
+
+    events = [event_with_tokens]
+
+    invocation_context = self.create_invocation_context(
+        agent,
+        context_cache_config=self.cache_config,
+        session_events=events,
+    )
+
+    llm_request = LlmRequest(
+        model="gemini-2.0-flash",
+        contents=[
+            types.Content(
+                role="user",
+                parts=[types.Part(text="Hello")],
+            )
+        ],
+    )
+
+    async for event in self.processor.run_async(
+        invocation_context, llm_request
+    ):
+      pass
+
+    # Should extract token count from the event
+    assert llm_request.cacheable_contents_token_count == 1024
+
+  async def test_cacheable_contents_token_count_no_usage_metadata(self):
+    """Test when no usage metadata is available."""
+    agent = LlmAgent(name="test_agent")
+
+    events = [
+        Event(author="test_agent", usage_metadata=None),
+        Event(author="other_agent"),
+    ]
+
+    invocation_context = self.create_invocation_context(
+        agent,
+        context_cache_config=self.cache_config,
+        session_events=events,
+    )
+
+    llm_request = LlmRequest(
+        model="gemini-2.0-flash",
+        contents=[
+            types.Content(
+                role="user",
+                parts=[types.Part(text="Hello")],
+            )
+        ],
+    )
+
+    async for event in self.processor.run_async(
+        invocation_context, llm_request
+    ):
+      pass
+
+    # Should not set token count when no usage metadata
+    assert llm_request.cacheable_contents_token_count is None
+
+  async def test_cacheable_contents_token_count_agent_filtering(self):
+    """Test that token count is filtered by agent name."""
+    agent = LlmAgent(name="target_agent")
+
+    events = [
+        Event(
+            author="other_agent",
+            usage_metadata=types.UsageMetadata(prompt_token_count=2048),
+        ),
+        Event(
+            author="target_agent",
+            usage_metadata=types.UsageMetadata(prompt_token_count=1024),
+        ),
+    ]
+
+    invocation_context = self.create_invocation_context(
+        agent,
+        context_cache_config=self.cache_config,
+        session_events=events,
+    )
+
+    llm_request = LlmRequest(
+        model="gemini-2.0-flash",
+        contents=[
+            types.Content(
+                role="user",
+                parts=[types.Part(text="Hello")],
+            )
+        ],
+    )
+
+    async for event in self.processor.run_async(
+        invocation_context, llm_request
+    ):
+      pass
+
+    # Should use target_agent's token count, not other_agent's
+    assert llm_request.cacheable_contents_token_count == 1024
+
+  async def test_cacheable_contents_token_count_latest_selected(self):
+    """Test that the most recent token count is selected."""
+    agent = LlmAgent(name="test_agent")
+
+    events = [
+        Event(
+            author="test_agent",
+            usage_metadata=types.UsageMetadata(prompt_token_count=512),
+        ),
+        Event(
+            author="test_agent",
+            usage_metadata=types.UsageMetadata(prompt_token_count=1024),
+        ),
+    ]
+
+    invocation_context = self.create_invocation_context(
+        agent,
+        context_cache_config=self.cache_config,
+        session_events=events,
+    )
+
+    llm_request = LlmRequest(
+        model="gemini-2.0-flash",
+        contents=[
+            types.Content(
+                role="user",
+                parts=[types.Part(text="Hello")],
+            )
+        ],
+    )
+
+    async for event in self.processor.run_async(
+        invocation_context, llm_request
+    ):
+      pass
+
+    # Should use the latest (most recent) token count
+    assert llm_request.cacheable_contents_token_count == 1024
+
+  async def test_cache_metadata_and_token_count_both_found(self):
+    """Test that both cache metadata and token count are found in single pass."""
+    agent = LlmAgent(name="test_agent")
+    cache_metadata = self.create_cache_metadata(invocations_used=5)
+
+    events = [
+        Event(
+            author="test_agent",
+            cache_metadata=cache_metadata,
+            usage_metadata=types.UsageMetadata(prompt_token_count=1024),
+            invocation_id="previous_invocation",
+        ),
+    ]
+
+    invocation_context = self.create_invocation_context(
+        agent,
+        context_cache_config=self.cache_config,
+        session_events=events,
+        invocation_id="current_invocation",
+    )
+
+    llm_request = LlmRequest(
+        model="gemini-2.0-flash",
+        contents=[
+            types.Content(
+                role="user",
+                parts=[types.Part(text="Hello")],
+            )
+        ],
+    )
+
+    async for event in self.processor.run_async(
+        invocation_context, llm_request
+    ):
+      pass
+
+    # Should find both cache metadata and token count
+    assert llm_request.cache_metadata is not None
+    assert llm_request.cache_metadata.invocations_used == 6  # 5 + 1
+    assert llm_request.cacheable_contents_token_count == 1024