bump version

py/llama_cloud_services/extract/extract.py

@@ -227,7 +227,7 @@ class SourceText:
             raise ValueError(f"Unsupported file type: {type(self.file)}")
 
 
-FileInput = Union[str, Path, BufferedIOBase, SourceText]
+FileInput = Union[str, Path, BufferedIOBase, SourceText, File]
 
 
 def run_in_thread(
@@ -406,6 +406,8 @@ class ExtractionAgent:
 
     async def _upload_file(self, file_input: FileInput) -> File:
         source_text = None
+        if isinstance(file_input, File):
+            return file_input
         if isinstance(file_input, SourceText):
             source_text = file_input
         elif isinstance(file_input, (str, Path)):
@@ -533,7 +535,7 @@ class ExtractionAgent:
 
         upload_tasks = [self._upload_file(file) for file in files]
         with augment_async_errors():
-            uploaded_files = await run_jobs(
+            uploaded_files: List[File] = await run_jobs(
                 upload_tasks,
                 workers=self.num_workers,
                 desc="Uploading files",
@@ -987,8 +989,13 @@ class LlamaExtract(BaseComponent):
                 f"Could not determine file type. Please provide a filename with one of these supported extensions: {supported_list}"
             )
 
-    def _convert_file_to_file_data(self, file_input: FileInput) -> Union[FileData, str]:
+    def _convert_file_to_file_data(
+        self, file_input: FileInput
+    ) -> Union[FileData, str, File]:
         """Convert FileInput to FileData or text string for stateless extraction."""
+        if isinstance(file_input, File):
+            return file_input
+
         if isinstance(file_input, SourceText):
             if file_input.text_content is not None:
                 return file_input.text_content
@@ -1084,24 +1091,23 @@ class LlamaExtract(BaseComponent):
         for file_input in files:
             file_data_or_text = self._convert_file_to_file_data(file_input)
 
-            if isinstance(file_data_or_text, str):
+            if isinstance(file_data_or_text, File):
+                file_args = {"file_id": file_data_or_text.id}
+
+            elif isinstance(file_data_or_text, str):
                 # It's text content
-                job = await self._async_client.llama_extract.extract_stateless(
-                    project_id=self._project_id,
-                    organization_id=self._organization_id,
-                    data_schema=processed_schema,
-                    config=config,
-                    text=file_data_or_text,
-                )
+                file_args = {"text": file_data_or_text}
             else:
                 # It's FileData
-                job = await self._async_client.llama_extract.extract_stateless(
-                    project_id=self._project_id,
-                    organization_id=self._organization_id,
-                    data_schema=processed_schema,
-                    config=config,
-                    file=file_data_or_text,
-                )
+                file_args = {"file": file_data_or_text}
+
+            job = await self._async_client.llama_extract.extract_stateless(
+                project_id=self._project_id,
+                organization_id=self._organization_id,
+                data_schema=processed_schema,
+                config=config,
+                **file_args,
+            )
             jobs.append(job)
 
         return jobs[0] if len(jobs) == 1 else jobs

py/llama_parse/pyproject.toml

@@ -11,13 +11,13 @@ dev = [
 
 [project]
 name = "llama-parse"
-version = "0.6.59"
+version = "0.6.60"
 description = "Parse files into RAG-Optimized formats."
 authors = [{name = "Logan Markewich", email = "logan@llamaindex.ai"}]
 requires-python = ">=3.9,<4.0"
 readme = "README.md"
 license = "MIT"
-dependencies = ["llama-cloud-services>=0.6.59"]
+dependencies = ["llama-cloud-services>=0.6.60"]
 
 [project.scripts]
 llama-parse = "llama_parse.cli.main:parse"

py/pyproject.toml

@@ -19,7 +19,7 @@ dev = [
 
 [project]
 name = "llama-cloud-services"
-version = "0.6.59"
+version = "0.6.60"
 description = "Tailored SDK clients for LlamaCloud services."
 authors = [{name = "Logan Markewich", email = "logan@runllama.ai"}]
 requires-python = ">=3.9,<4.0"

py/unit_tests/extract/test_extract.py

@@ -0,0 +1,166 @@
+import os
+from types import SimpleNamespace
+
+import pytest
+
+from llama_cloud.types import File as CloudFile
+from llama_cloud_services.extract import LlamaExtract, ExtractionAgent
+
+
+@pytest.fixture(autouse=True)
+def _set_dummy_env(monkeypatch):
+    monkeypatch.setenv("LLAMA_CLOUD_API_KEY", "test-api-key")
+    monkeypatch.setenv("LLAMA_CLOUD_BASE_URL", "https://example.test")
+
+
+@pytest.fixture
+def llama_file() -> CloudFile:
+    return CloudFile(
+        id="file_123",
+        name="sample.pdf",
+        external_file_id="ext_123",
+        project_id="proj_123",
+    )
+
+
+@pytest.fixture
+def extractor() -> LlamaExtract:
+    return LlamaExtract(
+        api_key=os.environ["LLAMA_CLOUD_API_KEY"],
+        base_url=os.environ["LLAMA_CLOUD_BASE_URL"],
+        verify=False,
+    )
+
+
+@pytest.fixture
+def no_external_validation(monkeypatch):
+    import llama_cloud_services.extract.extract as extract_mod
+
+    async def _noop_validate_schema(client, data_schema):
+        return data_schema
+
+    # Disable config warnings and external schema validation
+    monkeypatch.setattr(
+        extract_mod, "_extraction_config_warning", lambda *_args, **_kwargs: None
+    )
+    monkeypatch.setattr(extract_mod, "_validate_schema", _noop_validate_schema)
+
+
+def test_convert_fileinput_accepts_llama_file_directly(
+    extractor: LlamaExtract, llama_file: CloudFile
+):
+    result = extractor._convert_file_to_file_data(llama_file)
+    assert result is llama_file
+
+
+@pytest.mark.asyncio
+async def test_queue_extraction_with_llama_file_uses_file_id(
+    extractor: LlamaExtract, llama_file: CloudFile, no_external_validation, monkeypatch
+):
+    calls = []
+
+    async def fake_extract_stateless(**kwargs):
+        calls.append(kwargs)
+        return SimpleNamespace(id="job_1")
+
+    # Patch the client's method that would normally hit the network
+    monkeypatch.setattr(
+        extractor._async_client.llama_extract,
+        "extract_stateless",
+        fake_extract_stateless,
+    )
+
+    # Minimal schema and dummy config (warnings disabled by fixture)
+    schema = {"type": "object", "properties": {}}
+    dummy_config = SimpleNamespace()
+
+    job = await extractor.queue_extraction(schema, dummy_config, llama_file)
+
+    assert getattr(job, "id") == "job_1"
+    assert len(calls) == 1
+    kwargs = calls[0]
+    assert "file_id" in kwargs and kwargs["file_id"] == llama_file.id
+    assert "file" not in kwargs
+    assert "text" not in kwargs
+
+
+@pytest.mark.asyncio
+async def test_extraction_agent_upload_file_accepts_llama_file_directly(
+    llama_file: CloudFile,
+):
+    # Build a minimal agent without hitting external services
+    dummy_async_client = SimpleNamespace()
+    dummy_agent = SimpleNamespace(id="agent_1", name="dummy", data_schema={}, config={})
+
+    agent = ExtractionAgent(
+        client=dummy_async_client,
+        agent=dummy_agent,
+        project_id=None,
+        organization_id=None,
+        check_interval=0,
+        max_timeout=0,
+        num_workers=1,
+        show_progress=False,
+        verbose=False,
+        verify=False,
+        httpx_timeout=1,
+    )
+
+    result = await agent._upload_file(llama_file)
+    assert result is llama_file
+
+
+@pytest.mark.asyncio
+async def test_extraction_agent_aextract_accepts_llama_file(
+    monkeypatch, llama_file: CloudFile
+):
+    # Build a minimal agent without network
+    dummy_llama_extract_iface = SimpleNamespace()
+
+    async def fake_run_job(**kwargs):
+        # Ensure we are receiving a request with the right file_id
+        request = kwargs.get("request")
+        assert hasattr(request, "file_id")
+        assert request.file_id == llama_file.id
+        return SimpleNamespace(id="job_42")
+
+    dummy_llama_extract_iface.run_job = fake_run_job
+    dummy_async_client = SimpleNamespace(llama_extract=dummy_llama_extract_iface)
+    dummy_agent = SimpleNamespace(id="agent_1", name="dummy", data_schema={}, config={})
+
+    agent = ExtractionAgent(
+        client=dummy_async_client,
+        agent=dummy_agent,
+        project_id=None,
+        organization_id=None,
+        check_interval=0,
+        max_timeout=0,
+        num_workers=1,
+        show_progress=False,
+        verbose=False,
+        verify=False,
+        httpx_timeout=1,
+    )
+
+    # Ensure _upload_file returns the File directly and is called with our File
+    calls = {}
+
+    async def fake_upload_file(file_input):
+        calls["upload_called_with"] = file_input
+        assert file_input is llama_file
+        return file_input
+
+    monkeypatch.setattr(agent, "_upload_file", fake_upload_file)
+
+    # Avoid polling logic by short-circuiting result wait
+    async def fake_wait(job_id: str):
+        assert job_id == "job_42"
+        return SimpleNamespace(id="run_42", status="SUCCESS", data={})
+
+    monkeypatch.setattr(agent, "_wait_for_job_result", fake_wait)
+
+    result = await agent.aextract(llama_file)
+
+    assert calls.get("upload_called_with") is llama_file
+    assert getattr(result, "status") == "SUCCESS"
+    assert getattr(result, "id") == "run_42"