fixup

.changeset/nasty-jeans-fall.md

@@ -0,0 +1,6 @@
+---
+"llama-cloud-services": patch
+"llama-cloud-services-py": patch
+---
+
+Update llama-cloud api version, and integrate with agent data deletion

.gitignore

@@ -9,4 +9,3 @@ __pycache__/
 node_modules/
 .turbo/
 dist/
-.npmrc

package.json

@@ -8,7 +8,7 @@
   "scripts": {
     "pre-commit-version": "pnpm changeset",
     "version": "./scripts/changeset-version.py version",
-    "publish": "./scripts/changeset-version.py publish --no-js --tag"
+    "publish": "./scripts/changeset-version.py publish"
   },
   "devDependencies": {
     "prettier": "^3.6.2",

pnpm-lock.yaml

@@ -21,21 +21,7 @@ importers:
         specifier: ^3.6.2
         version: 3.6.2
 
-  py:
-    devDependencies:
-      changesets:
-        specifier: ^1.0.2
-        version: 1.0.2
-
-  py/llama_parse:
-    dependencies:
-      llama-cloud-services-py:
-        specifier: workspace:*
-        version: link:..
-    devDependencies:
-      changesets:
-        specifier: ^1.0.2
-        version: 1.0.2
+  py: {}
 
   ts/e2e-tests:
     devDependencies:

pnpm-workspace.yaml

@@ -1,4 +1,3 @@
 packages:
   - "ts/*"
   - "py"
-  - "py/*"

py/CHANGELOG.md

@@ -1,19 +0,0 @@
-# llama-cloud-services-py
-
-## 0.6.72
-
-### Patch Changes
-
-- ad6734b: Fixup and test versioning
-
-## 0.6.71
-
-### Patch Changes
-
-- 51011b9: Escape dollar signs in jupyter notebooks
-
-## 0.6.70
-
-### Patch Changes
-
-- d028397: Update llama-cloud api version, and integrate with agent data deletion

py/llama_cloud_services/beta/agent_data/client.py

@@ -194,21 +194,6 @@ class AsyncAgentDataClient(Generic[AgentDataT]):
     async def delete_item(self, item_id: str) -> None:
         await self.client.beta.delete_agent_data(item_id=item_id)
 
-    @agent_data_retry
-    async def delete(
-        self, filter: Optional[Dict[str, Dict[ComparisonOperator, Any]]] = None
-    ) -> int:
-        """
-        Delete agent data by query, similar to search.
-        Returns the number of deleted items.
-        """
-        response = await self.client.beta.delete_agent_data_by_query_api_v_1_beta_agent_data_delete_post(
-            deployment_name=self.deployment_name,
-            collection=self.collection,
-            filter=filter,
-        )
-        return response.deleted_count
-
     @agent_data_retry
     async def search(
         self,

py/llama_cloud_services/extract/extract.py

@@ -19,12 +19,14 @@ from llama_cloud import (
     ExtractAgent as CloudExtractAgent,
     ExtractConfig,
     ExtractJob,
+    ExtractJobCreate,
     ExtractRun,
     File,
     FileData,
     ExtractMode,
     StatusEnum,
     ExtractTarget,
+    LlamaExtractSettings,
     PaginatedExtractRunsResponse,
 )
 from llama_cloud.client import AsyncLlamaCloud
@@ -461,6 +463,56 @@ class ExtractionAgent:
             )
         )
 
+    async def _run_extraction_test(
+        self,
+        files: Union[FileInput, List[FileInput]],
+        extract_settings: LlamaExtractSettings,
+    ) -> Union[ExtractJob, List[ExtractJob]]:
+        if not isinstance(files, list):
+            files = [files]
+            single_file = True
+        else:
+            single_file = False
+
+        upload_tasks = [self._upload_file(file) for file in files]
+        with augment_async_errors():
+            uploaded_files = await run_jobs(
+                upload_tasks,
+                workers=self.num_workers,
+                desc="Uploading files",
+                show_progress=self.show_progress,
+            )
+
+        async def run_job(file: File) -> ExtractRun:
+            job_queued = await self._client.llama_extract.run_job_test_user(
+                job_create=ExtractJobCreate(
+                    extraction_agent_id=self.id,
+                    file_id=file.id,
+                    data_schema_override=self.data_schema,
+                    config_override=self.config,
+                ),
+                extract_settings=extract_settings,
+            )
+            return await self._wait_for_job_result(job_queued.id)
+
+        job_tasks = [run_job(file) for file in uploaded_files]
+        with augment_async_errors():
+            extract_results = await run_jobs(
+                job_tasks,
+                workers=self.num_workers,
+                desc="Running extraction jobs",
+                show_progress=self.show_progress,
+            )
+
+        if self._verbose:
+            for file, job in zip(files, extract_results):
+                file_repr = (
+                    str(file) if isinstance(file, (str, Path)) else "<bytes/buffer>"
+                )
+                print(f"Running extraction for file {file_repr} under job_id {job.id}")
+
+        return extract_results[0] if single_file else extract_results
+
     async def queue_extraction(
         self,
         files: Union[FileInput, List[FileInput]],
@@ -492,10 +544,12 @@ class ExtractionAgent:
 
         job_tasks = [
             self._client.llama_extract.run_job(
-                extraction_agent_id=self.id,
-                file_id=file.id,
-                data_schema_override=self.data_schema,
-                config_override=self.config,
+                request=ExtractJobCreate(
+                    extraction_agent_id=self.id,
+                    file_id=file.id,
+                    data_schema_override=self.data_schema,
+                    config_override=self.config,
+                ),
             )
             for file in uploaded_files
         ]

py/llama_cloud_services/parse/types.py

@@ -4,10 +4,7 @@ import re
 from pydantic import BaseModel, Field, SerializeAsAny
 from typing import Dict, Any, List, Optional
 
-from llama_cloud_services.parse.utils import (
-    make_api_request,
-    is_jupyter,
-)
+from llama_cloud_services.parse.utils import make_api_request
 from llama_index.core.async_utils import asyncio_run
 from llama_index.core.schema import Document, ImageDocument, ImageNode, TextNode
 
@@ -261,24 +258,6 @@ class JobResult(BaseModel):
         documents = await self.aget_text_documents(split_by_page)
         return [TextNode(text=doc.text, metadata=doc.metadata) for doc in documents]
 
-    def _format_markdown_for_notebook(self, text: Optional[str]) -> Optional[str]:
-        """Format markdown text for Jupyter notebook display by escaping dollar signs."""
-        if text is None:
-            return None
-
-        def escape_dollar_signs(text: str) -> str:
-            """Escape dollar signs in text to prevent Jupyter from interpreting them as LaTeX.
-
-            Args:
-                text: The text to escape
-
-            Returns:
-                Text with dollar signs escaped
-            """
-            return text.replace("$", r"\$")
-
-        return escape_dollar_signs(text)
-
     def get_markdown_documents(self, split_by_page: bool = False) -> List[Document]:
         """
         Get the markdown documents from the job.
@@ -289,22 +268,17 @@ class JobResult(BaseModel):
         if split_by_page:
             return [
                 Document(
-                    text=self._format_markdown_for_notebook(page.md)
-                    if is_jupyter()
-                    else page.md,
+                    text=page.md,
                     metadata={"page_number": page.page, "file_name": self.file_name},
                 )
                 for page in self.pages
             ]
         else:
-            text = self._page_separator.join(
-                [page.md if page.md is not None else "" for page in self.pages]
-            )
             return [
                 Document(
-                    text=self._format_markdown_for_notebook(text)
-                    if is_jupyter()
-                    else text,
+                    text=self._page_separator.join(
+                        [page.md if page.md is not None else "" for page in self.pages]
+                    ),
                     metadata={"file_name": self.file_name},
                 )
             ]
@@ -354,10 +328,7 @@ class JobResult(BaseModel):
         """
         url = f"{self._base_url}/api/v1/parsing/job/{self.job_id}/result/raw/markdown"
         response = await make_api_request(self._client, "GET", url)
-        markdown = response.content.decode("utf-8")
-        return (
-            self._format_markdown_for_notebook(markdown) if is_jupyter() else markdown
-        )
+        return response.content.decode("utf-8")
 
     def get_text(self) -> str:
         """

py/llama_cloud_services/parse/utils.py

@@ -1,4 +1,3 @@
-import functools
 import httpx
 import itertools
 import logging
@@ -357,17 +356,6 @@ def partition_pages(
             return
 
 
-@functools.lru_cache(maxsize=1)
-def is_jupyter() -> bool:
-    """Check if we're running in a Jupyter environment."""
-    try:
-        from IPython import get_ipython
-
-        return get_ipython().__class__.__name__ == "ZMQInteractiveShell"
-    except (ImportError, AttributeError):
-        return False
-
-
 def extract_tables_from_json_results(
     json_results: List[dict], download_path: str
 ) -> List[str]:

py/llama_parse/CHANGELOG.md

@@ -1,8 +0,0 @@
-# llama_parse
-
-## 0.6.72
-
-### Patch Changes
-
-- Updated dependencies [ad6734b]
-  - llama-cloud-services-py@0.6.72

py/llama_parse/package.json

@@ -1,20 +0,0 @@
-{
-  "name": "llama_parse",
-  "version": "0.6.72",
-  "description": "",
-  "main": "index.js",
-  "private": false,
-  "scripts": {
-    "test": "echo \"Error: no test specified\" && exit 1"
-  },
-  "dependencies": {
-    "llama-cloud-services-py": "workspace:*"
-  },
-  "keywords": [],
-  "author": "",
-  "license": "ISC",
-  "packageManager": "pnpm@10.11.1",
-  "devDependencies": {
-    "changesets": "^1.0.2"
-  }
-}

py/llama_parse/pyproject.toml

@@ -11,13 +11,13 @@ dev = [
 
 [project]
 name = "llama-parse"
-version = "0.6.72"
+version = "0.6.69"
 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>=0.6.72"]
+dependencies = ["llama-cloud-services>=0.6.69"]
 
 [project.scripts]
 llama-parse = "llama_parse.cli.main:parse"

py/package.json

@@ -1,10 +1,7 @@
 {
   "name": "llama-cloud-services-py",
-  "version": "0.6.72",
-  "private": false,
+  "version": "0.6.55",
+  "private": "true",
   "license": "MIT",
-  "scripts": {},
-  "devDependencies": {
-    "changesets": "^1.0.2"
-  }
+  "scripts": {}
 }

py/pyproject.toml

@@ -19,7 +19,7 @@ dev = [
 
 [project]
 name = "llama-cloud-services"
-version = "0.6.72"
+version = "0.6.69"
 description = "Tailored SDK clients for LlamaCloud services."
 authors = [{name = "Logan Markewich", email = "logan@runllama.ai"}]
 requires-python = ">=3.9,<4.0"
@@ -27,7 +27,7 @@ readme = "README.md"
 license = "MIT"
 dependencies = [
   "llama-index-core>=0.12.0",
-  "llama-cloud==0.1.43",
+  "llama-cloud==0.1.42",
   "pydantic>=2.8,!=2.10",
   "click>=8.1.7,<9",
   "python-dotenv>=1.0.1,<2",

py/tests/extract/test_benchmark.py

@@ -1,13 +1,16 @@
 import os
 import pytest
 
-from llama_cloud_services.extract import LlamaExtract
+from llama_cloud_services.extract import LlamaExtract, ExtractionAgent
+from time import perf_counter
 from collections import namedtuple
 import json
 import uuid
 from llama_cloud.types import (
     ExtractConfig,
     ExtractMode,
+    LlamaParseParameters,
+    LlamaExtractSettings,
 )
 from tests.extract.util import load_test_dotenv
 
@@ -119,3 +122,27 @@ def extraction_agent(test_case: BenchmarkTestCase, extractor: LlamaExtract):
     # Create new agent
     agent = extractor.create_agent(agent_name, schema, config=test_case.config)
     yield agent
+
+
+@pytest.mark.skipif(
+    "CI" in os.environ or not LLAMA_CLOUD_API_KEY,
+    reason="LLAMA_CLOUD_API_KEY not set or CI environment not suitable for benchmarking",
+)
+@pytest.mark.parametrize("test_case", get_test_cases(), ids=lambda x: x.name)
+@pytest.mark.asyncio(loop_scope="session")
+async def test_extraction(
+    test_case: BenchmarkTestCase, extraction_agent: ExtractionAgent
+) -> None:
+    start = perf_counter()
+    result = await extraction_agent._run_extraction_test(
+        test_case.input_file,
+        extract_settings=LlamaExtractSettings(
+            llama_parse_params=LlamaParseParameters(
+                invalidate_cache=True,
+                do_not_cache=True,
+            )
+        ),
+    )
+    end = perf_counter()
+    print(f"Time taken: {end - start} seconds")
+    print(result)

py/tests/extract/util.py

@@ -7,7 +7,7 @@ from pathlib import Path
 
 
 def load_test_dotenv():
-    load_dotenv(Path(__file__).parent.parent.parent.parent / ".env.dev", override=True)
+    load_dotenv(Path(__file__).parent.parent.parent / ".env.dev", override=True)
 
 
 def json_subset_match_score(expected: Any, actual: Any) -> float:

py/tests/index/test_index.py

@@ -304,9 +304,6 @@ async def test_page_screenshot_retrieval(index_name: str, local_file: str):
     not base_url or not api_key, reason="No platform base url or api key set"
 )
 @pytest.mark.asyncio
-@pytest.mark.skip(
-    reason="Consistently failing with FAILED tests/index/test_index.py::test_page_figure_retrieval - assert 0 > 0 +  where 0 = len([])"
-)
 async def test_page_figure_retrieval(index_name: str, local_figures_file: str):
     index = await LlamaCloudIndex.acreate_index(
         name=index_name,

py/unit_tests/extract/test_extract.py

@@ -118,8 +118,10 @@ async def test_extraction_agent_aextract_accepts_llama_file(
     dummy_llama_extract_iface = SimpleNamespace()
 
     async def fake_run_job(**kwargs):
-        file_id = kwargs.get("file_id")
-        assert file_id == llama_file.id
+        # 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

py/uv.lock

@@ -1,5 +1,5 @@
 version = 1
-revision = 3
+revision = 2
 requires-python = ">=3.9, <4.0"
 resolution-markers = [
     "python_full_version >= '3.14'",
@@ -1582,21 +1582,21 @@ wheels = [
 
 [[package]]
 name = "llama-cloud"
-version = "0.1.43"
+version = "0.1.42"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "certifi" },
     { name = "httpx" },
     { name = "pydantic" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/9b/33/33a8bd3a617c071caf450ca2627969f8b28272d0692f122997c10a32247e/llama_cloud-0.1.43.tar.gz", hash = "sha256:00429f05aea515449d90cde91ef3ed3687fcd93e46f6246d08cbea02f9b397a9", size = 112992, upload-time = "2025-10-02T21:55:38.355Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/21/04/ae0694b582d6aab4d6e7957febb7bff048897ac231ad80ba1bd71547d944/llama_cloud-0.1.42.tar.gz", hash = "sha256:485aa0e364ea648e3aaa3b2c54af7bcb6f2242c50b4f86ec022e137413fff464", size = 112480, upload-time = "2025-09-16T20:25:42.631Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/2b/54/559a67542396d5660a71115b29e0160e9dd784e570e1f4ef55ad22bf5b39/llama_cloud-0.1.43-py3-none-any.whl", hash = "sha256:540605d4dd13c6536a3b75cd4d04b211f29b16d17faee9381e3793a651f1dec1", size = 311460, upload-time = "2025-10-02T21:55:37.282Z" },
+    { url = "https://files.pythonhosted.org/packages/6a/61/85d115699a59d03f0783e119aaf6d534fca95dbe1a4531a8056e6a4774ed/llama_cloud-0.1.42-py3-none-any.whl", hash = "sha256:4ed3edde4a277ff52eeb831188c8476eb079b5e4605ad3142157a0f054b27d96", size = 311857, upload-time = "2025-09-16T20:25:41.479Z" },
 ]
 
 [[package]]
 name = "llama-cloud-services"
-version = "0.6.70"
+version = "0.6.68"
 source = { editable = "." }
 dependencies = [
     { name = "click", version = "8.1.8", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
@@ -1631,7 +1631,7 @@ dev = [
 requires-dist = [
     { name = "click", specifier = ">=8.1.7,<9" },
     { name = "eval-type-backport", marker = "python_full_version < '3.10'", specifier = ">=0.2.0,<0.3" },
-    { name = "llama-cloud", specifier = "==0.1.43" },
+    { name = "llama-cloud", specifier = "==0.1.42" },
     { name = "llama-index-core", specifier = ">=0.12.0" },
     { name = "packaging", specifier = ">=25.0" },
     { name = "platformdirs", specifier = ">=4.3.7,<5" },

scripts/changeset-version.py

@@ -12,15 +12,14 @@ There's 2 things this does:
 
 """
 
-from dataclasses import dataclass
 import json
 import os
 import subprocess
+import sys
 from pathlib import Path
-from typing import Any, List, cast
+from typing import List
 import urllib.request
 import urllib.error
-import re
 
 import click
 import tomlkit
@@ -28,109 +27,54 @@ from packaging.version import Version
 
 
 def _run_command(
-    cmd: List[str], cwd: Path | None = None, env: dict[str, str] | None = None
-) -> None:
-    """Run a command, streaming output to the console, and raise on failure."""
-    subprocess.run(cmd, check=True, text=True, cwd=cwd or Path.cwd(), env=env)
-
-
-def _run_and_capture(
-    cmd: List[str], cwd: Path | None = None, env: dict[str, str] | None = None
-) -> str:
-    """Run a command and return stdout as text, raising on failure."""
-    result = subprocess.run(
-        cmd,
-        check=True,
-        text=True,
-        cwd=cwd or Path.cwd(),
-        env=env,
-        capture_output=True,
+    cmd: List[str], check: bool = True, capture: bool = True, cwd: Path | None = None
+) -> subprocess.CompletedProcess:
+    """Run a command and return the result."""
+    return subprocess.run(
+        cmd, check=check, capture_output=capture, text=True, cwd=cwd or Path.cwd()
     )
-    return result.stdout
 
 
-@dataclass
-class Package:
-    name: str
-    version: str
-    path: Path
+def update_python_versions(version: str) -> None:
+    """llama-cloud-services and llama-parse share a version. llama-parse is just a silly sidecar that proxies to llama-cloud-services
+    for compatibility.
 
-    def python_package_name(self) -> str | None:
-        if "/py/" in str(self.path) or str(self.path).endswith("/py"):
-            return self.name.removesuffix("-py")
-        return None
-
-
-def _get_pnpm_workspace_packages() -> list[Package]:
-    """Return directories for all workspace packages from pnpm list JSON output."""
-    output = _run_and_capture(["pnpm", "list", "-r", "--depth=-1", "--json"])
-
-    data = cast(list[dict[str, Any]], json.loads(output))
-    packages: list[Package] = [
-        Package(name=data["name"], version=data["version"], path=Path(data["path"]))
-        for data in data
-    ]
-    return packages
-
-
-def _sync_package_version_with_pyproject(
-    package_dir: Path, packages: dict[str, Package], js_package_name: str
-) -> None:
-    """Sync version from package.json to pyproject.toml.
-
-    Returns True if pyproject was changed, else False.
+    This function updates the version in both pyproject.toml files.
     """
-    pyproject_path = package_dir / "pyproject.toml"
-    if not pyproject_path.exists():
-        return
+    # Update main pyproject.toml
+    main_path = Path("py/pyproject.toml")
+    main_content = main_path.read_text()
+    main_doc = tomlkit.parse(main_content)
+    if main_doc["project"]["version"] != version:
+        click.echo(f"Updating llama-cloud-services version to {version}")
+        main_doc["project"]["version"] = version
+    main_path.write_text(tomlkit.dumps(main_doc))
 
-    package_version = packages[js_package_name].version
-    py_doc = tomlkit.parse(pyproject_path.read_text())
+    # Update llama_parse/pyproject.toml
+    parse_path = Path("py/llama_parse/pyproject.toml")
+    parse_content = parse_path.read_text()
+    parse_doc = tomlkit.parse(parse_content)
+    if parse_doc["project"]["version"] != version:
+        click.echo(f"Updating llama-parse version to {version}")
+        parse_doc["project"]["version"] = version
+        parse_path.write_text(tomlkit.dumps(parse_doc))
 
-    by_python_name = {
-        pkg.python_package_name(): pkg
-        for pkg in packages.values()
-        if pkg.python_package_name()
-    }
+    # Update the dependency reference
+    dependencies = parse_doc["project"]["dependencies"]
+    for i, dep in enumerate(dependencies):
+        if isinstance(dep, str) and dep.startswith("llama-cloud-services"):
+            dependencies[i] = f"llama-cloud-services>={version}"
+            break
 
-    current_version = py_doc["project"]["version"]
-    assert isinstance(current_version, str)
+    parse_path.write_text(tomlkit.dumps(parse_doc))
 
-    # update workspace dependency strings by replacing the first version after == or >=
-    deps = py_doc["project"]["dependencies"] or []
-    changed = False
-    for i, dep in enumerate(deps):
-        if not isinstance(dep, str):
-            continue
-        pkg = (cast(str, dep).split("==")[0]).split(">=")[0]
-        if pkg not in by_python_name:
-            continue
-        target_version = by_python_name[pkg].version
-        new_dep = re.sub(
-            r"(==|>=)\s*([0-9A-Za-z_.+-]+)",
-            lambda m: m.group(1) + target_version,
-            dep,
-            count=1,
-        )
-        if new_dep != dep:
-            deps[i] = new_dep
-            changed = True
-
-    if current_version != package_version:
-        py_doc["project"]["version"] = package_version
-        changed = True
-
-    if changed:
-        pyproject_path.write_text(tomlkit.dumps(py_doc))
-        click.echo(
-            f"Updated {pyproject_path} version to {package_version} and synced dependency specs"
-        )
+    click.echo(f"Updated Python packages to version {version}")
 
 
 def lock_python_dependencies() -> None:
     """Lock Python dependencies."""
     try:
-        _run_command(["uv", "lock"])
+        _run_command(["uv", "lock"], capture=False)
         click.echo("Locked Python dependencies")
     except subprocess.CalledProcessError as e:
         click.echo(f"Warning: Failed to lock Python dependencies: {e}", err=True)
@@ -144,64 +88,60 @@ def cli() -> None:
 
 @cli.command()
 def version() -> None:
-    """Apply changeset versions, then sync versions for co-located JS/Py packages.
+    """Apply changeset versions, and propagate them to Python packages."""
+    # First, run changeset version to update all package.json files (including py/package.json)
+    _run_command(["npx", "@changesets/cli", "version"], capture=False, check=True)
 
-    - Runs changesets to bump package.json versions.
-    - Discovers all workspace packages via pnpm.
-    - For any directory containing both package.json and pyproject.toml, and with
-      package.json private: false, set pyproject [project].version to match the JS version.
-    - If a pyproject is updated, run `uv sync` in that directory to update its lock file.
-    """
-    # Ensure we're at the repo root
-    os.chdir(Path(__file__).parent.parent)
+    # Get the updated Python package version from py/package.json (updated by changesets)
+    py_package_path = Path("py/package.json")
+    if not py_package_path.exists():
+        click.echo("Python package.json not found", err=True)
+        sys.exit(1)
 
-    # First, run changeset version to update all package.json files
-    _run_command(["npx", "@changesets/cli", "version"])
+    with open(py_package_path) as f:
+        py_package = json.load(f)
 
-    # Enumerate workspace packages and perform syncs
-    packages = _get_pnpm_workspace_packages()
-    version_map = {pkg.name: pkg for pkg in packages}
-    for pkg in packages:
-        _sync_package_version_with_pyproject(pkg.path, version_map, pkg.name)
+    new_version = py_package["version"]
+    # Update Python pyproject.toml files based on the package.json version
+    update_python_versions(new_version)
+
+    click.echo(f"Successfully propagated version {new_version} to all Python packages")
 
 
 @cli.command()
 @click.option("--tag", is_flag=True, help="Tag the packages after publishing")
 @click.option("--dry-run", is_flag=True, help="Dry run the publish")
-@click.option("--js/--no-js", default=True, help="Publish the js package")
-@click.option("--py/--no-py", default=True, help="Publish the py package")
-def publish(tag: bool, dry_run: bool, js: bool, py: bool) -> None:
+def publish(tag: bool, dry_run: bool) -> None:
     """Publish all packages."""
     # move to the root
     os.chdir(Path(__file__).parent.parent)
 
-    if js:
-        if not os.getenv("NPM_TOKEN"):
-            click.echo("NPM_TOKEN is not set, skipping publish", err=True)
-            raise click.Abort("No token set")
-    if py:
-        if not os.getenv("LLAMA_PARSE_PYPI_TOKEN"):
-            click.echo("LLAMA_PARSE_PYPI_TOKEN is not set, skipping publish", err=True)
-            raise click.Abort("No token set")
+    if not os.getenv("NPM_TOKEN"):
+        click.echo("NPM_TOKEN is not set, skipping publish", err=True)
+        raise click.Abort("No token set")
+    if not os.getenv("UV_PUBLISH_TOKEN"):
+        click.echo("UV_PUBLISH_TOKEN is not set, skipping publish", err=True)
+        raise click.Abort("No token set")
+    if not os.getenv("LLAMA_PARSE_PYPI_TOKEN"):
+        click.echo("LLAMA_PARSE_PYPI_TOKEN is not set, skipping publish", err=True)
+        raise click.Abort("No token set")
 
     # not general script. Just checks each of the 2 packages to see if they need to be published.
-    if js:
-        maybe_publish_npm(dry_run)
-    if py:
-        maybe_publish_pypi(dry_run)
+    maybe_publish_ts_package(dry_run)
+    maybe_publish_py_packages(dry_run)
 
     if tag:
         if dry_run:
             click.echo("Dry run, skipping tag. Would run:")
             click.echo("  npx @changesets/cli tag")
             click.echo("  git push --tags")
+            return
         else:
-            # Let changesets create JS-related tags as usual
-            _run_command(["npx", "@changesets/cli", "tag"])
-            _run_command(["git", "push", "--tags"])
+            _run_command(["npx", "@changesets/cli", "tag"], check=True, capture=True)
+            _run_command(["git", "push", "--tags"], check=True, capture=True)
 
 
-def maybe_publish_npm(dry_run: bool) -> None:
+def maybe_publish_ts_package(dry_run: bool) -> None:
     """Publish the ts package if it needs to be published."""
     target_dir = Path("ts/llama_cloud_services")
     ts_path_package = target_dir / "package.json"
@@ -209,11 +149,10 @@ def maybe_publish_npm(dry_run: bool) -> None:
     version = package_json["version"]
 
     # Check if this version is already published on npm
-    result = subprocess.run(
+    result = _run_command(
         ["npm", "view", "llama-cloud-services", "versions", "--json"],
         check=True,
-        capture_output=True,
-        text=True,
+        capture=True,
         cwd=target_dir,
     )
 
@@ -223,18 +162,20 @@ def maybe_publish_npm(dry_run: bool) -> None:
             f"npm  package llama-cloud-services@{version} already published, skipping"
         )
         return
-    click.echo(f"Publishing npm package llama-cloud-services@{version}")
+    click.echo(f"Publishing llama-cloud-services@{version}")
     # defer to the package.json publish script
     if dry_run:
         click.echo("Dry run, skipping publish. Would run:")
         click.echo("  pnpm run publish")
         return
     else:
-        _run_command(["pnpm", "run", "build"], cwd=target_dir)
-        _run_command(["pnpm", "publish"], cwd=target_dir)
+        output = _run_command(
+            ["pnpm", "runpublish"], check=True, capture=True, cwd=target_dir
+        )
+        click.echo(output.stdout)
 
 
-def maybe_publish_pypi(dry_run: bool) -> None:
+def maybe_publish_py_packages(dry_run: bool) -> None:
     """Publish the py packages if they need to be published."""
     for pyproject in list(Path("py").glob("*/pyproject.toml")) + [
         Path("py/pyproject.toml")
@@ -243,22 +184,35 @@ def maybe_publish_pypi(dry_run: bool) -> None:
         if is_published(name, version):
             click.echo(f"PyPI package {name}@{version} already published, skipping")
             continue
-        click.echo(f"Publishing PyPI package {name}@{version}")
+        click.echo(f"Publishing {name}@{version}")
 
         # Use different tokens for different packages
         env = os.environ.copy()
-        token = os.environ["LLAMA_PARSE_PYPI_TOKEN"]
-        env["UV_PUBLISH_TOKEN"] = token
+        if name == "llama-parse":
+            # llama-parse uses its own token
+            env["UV_PUBLISH_TOKEN"] = os.environ["LLAMA_PARSE_PYPI_TOKEN"]
+        else:
+            # llama-cloud-services uses the main PyPI token
+            env["UV_PUBLISH_TOKEN"] = os.environ["UV_PUBLISH_TOKEN"]
+
         if dry_run:
+            token = env["UV_PUBLISH_TOKEN"]
             summary = (token[:3] + "***") if len(token) <= 6 else token[:6] + "****"
             click.echo(
                 f"Dry run, skipping publish. Would run with publish token {summary}:"
             )
-            click.echo("  uv build")
-            click.echo("  uv publish")
+            click.echo("  uv publish --dry-run")
+            return
         else:
-            _run_command(["uv", "build"], cwd=pyproject.parent)
-            _run_command(["uv", "publish"], cwd=pyproject.parent, env=env)
+            result = subprocess.run(
+                ["uv", "publish"],
+                check=True,
+                capture_output=True,
+                text=True,
+                cwd=pyproject.parent,
+                env=env,
+            )
+            click.echo(result.stdout)
 
 
 def current_version(pyproject: Path) -> tuple[str, str]:

ts/llama_cloud_services/CHANGELOG.md

@@ -1,11 +1,5 @@
 # llama-cloud-services
 
-## 0.3.7
-
-### Patch Changes
-
-- d028397: Update llama-cloud api version, and integrate with agent data deletion
-
 ## v0.1.0
 
 First release for `llama-cloud-services`.

ts/llama_cloud_services/package.json

@@ -1,10 +1,9 @@
 {
   "name": "llama-cloud-services",
-  "version": "0.3.7",
+  "version": "0.3.6",
   "type": "module",
   "license": "MIT",
   "scripts": {
-    "get-openapi": "node ./scripts/get-openapi.js",
     "generate": "./node_modules/.bin/openapi-ts",
     "build": "pnpm run generate && bunchee",
     "dev": "bunchee --watch",

ts/llama_cloud_services/scripts/download.mjs

@@ -1,21 +0,0 @@
-import fs from 'fs';
-
-async function downloadOpenApiSpec() {
-    try {
-        const response = await fetch('https://api.cloud.llamaindex.ai/api/openapi.json');
-
-        if (!response.ok) {
-            throw new Error(`HTTP error! status: ${response.status}`);
-        }
-
-        const data = await response.json();
-
-        fs.writeFileSync('openapi.json', JSON.stringify(data, null, 2));
-        console.log('Successfully downloaded openapi.json');
-    } catch (error) {
-        console.error('Error downloading OpenAPI spec:', error);
-        process.exit(1);
-    }
-}
-
-downloadOpenApiSpec();

ts/llama_cloud_services/src/beta/agent/client.ts

@@ -4,7 +4,6 @@ import {
   aggregateAgentDataApiV1BetaAgentDataAggregatePost,
   createAgentDataApiV1BetaAgentDataPost,
   deleteAgentDataApiV1BetaAgentDataItemIdDelete,
-  deleteAgentDataByQueryApiV1BetaAgentDataDeletePost,
   getAgentDataApiV1BetaAgentDataItemIdGet,
   searchAgentDataApiV1BetaAgentDataSearchPost,
   updateAgentDataApiV1BetaAgentDataItemIdPut,
@@ -13,7 +12,6 @@ import {
 } from "../../client";
 import type {
   AggregateAgentDataOptions,
-  DeleteAgentDataOptions,
   SearchAgentDataOptions,
   TypedAgentData,
   TypedAgentDataItems,
@@ -114,24 +112,6 @@ export class AgentClient<T = unknown> {
     });
   }
 
-  /**
-   * Delete all matching agent data, returns the total number of deleted items
-   */
-  async delete(options: DeleteAgentDataOptions): Promise<number> {
-    const response = await deleteAgentDataByQueryApiV1BetaAgentDataDeletePost({
-      throwOnError: true,
-      body: {
-        deployment_name: this.deploymentName,
-        ...(this.collection !== undefined && {
-          collection: this.collection,
-        }),
-        ...(options.filter !== undefined && { filter: options.filter }),
-      },
-      client: this.client,
-    });
-    return response.data.deleted_count;
-  }
-
   /**
    * Search agent data
    */

ts/llama_cloud_services/src/beta/agent/types.ts

@@ -127,14 +127,6 @@ export interface SearchAgentDataOptions {
   includeTotal?: boolean;
 }
 
-/**
- * Options for deleting agent data
- */
-export interface DeleteAgentDataOptions {
-  /** Filter options for the deletion. */
-  filter?: Record<string, FilterOperation>;
-}
-
 /**
  * Options for aggregating agent data
  */

ts/llama_cloud_services/src/client/schemas.gen.ts

@@ -1530,6 +1530,27 @@ export const Body_run_job_on_file_api_v1_extraction_jobs_file_postSchema = {
   title: "Body_run_job_on_file_api_v1_extraction_jobs_file_post",
 } as const;
 
+export const Body_run_job_test_user_api_v1_extraction_jobs_test_postSchema = {
+  properties: {
+    job_create: {
+      $ref: "#/components/schemas/ExtractJobCreate",
+    },
+    extract_settings: {
+      anyOf: [
+        {
+          $ref: "#/components/schemas/LlamaExtractSettings",
+        },
+        {
+          type: "null",
+        },
+      ],
+    },
+  },
+  type: "object",
+  required: ["job_create"],
+  title: "Body_run_job_test_user_api_v1_extraction_jobs_test_post",
+} as const;
+
 export const Body_screenshot_api_parsing_screenshot_postSchema = {
   properties: {
     file: {
@@ -2775,6 +2796,30 @@ export const Body_upload_file_api_v1_parsing_upload_postSchema = {
   title: "Body_upload_file_api_v1_parsing_upload_post",
 } as const;
 
+export const Body_upload_file_v2_api_v2alpha1_parse_upload_postSchema = {
+  properties: {
+    configuration: {
+      type: "string",
+      title: "Configuration",
+    },
+    file: {
+      anyOf: [
+        {
+          type: "string",
+          format: "binary",
+        },
+        {
+          type: "null",
+        },
+      ],
+      title: "File",
+    },
+  },
+  type: "object",
+  required: ["configuration"],
+  title: "Body_upload_file_v2_api_v2alpha1_parse_upload_post",
+} as const;
+
 export const BoxAuthMechanismSchema = {
   type: "string",
   enum: ["developer_token", "ccg"],
@@ -3135,6 +3180,12 @@ export const ChatMessageSchema = {
   title: "ChatMessage",
 } as const;
 
+export const ChunkModeSchema = {
+  type: "string",
+  enum: ["PAGE", "DOCUMENT", "SECTION", "GROUPED_PAGES"],
+  title: "ChunkMode",
+} as const;
+
 export const ClassificationResultSchema = {
   properties: {
     reasoning: {
@@ -5435,13 +5486,6 @@ export const CustomClaimsSchema = {
       description: "Whether the user is allowed to delete organizations.",
       default: false,
     },
-    allowed_spreadsheet: {
-      type: "boolean",
-      title: "Allowed Spreadsheet",
-      description:
-        "Whether the user is allowed to access the spreadsheet feature.",
-      default: false,
-    },
   },
   type: "object",
   title: "CustomClaims",
@@ -6169,54 +6213,6 @@ export const DeleteParamsSchema = {
   description: "Schema for the parameters of a delete job.",
 } as const;
 
-export const DeleteRequestSchema = {
-  properties: {
-    deployment_name: {
-      type: "string",
-      title: "Deployment Name",
-      description: "The agent deployment's name to delete data for",
-    },
-    collection: {
-      type: "string",
-      title: "Collection",
-      description: "The logical agent data collection to delete from",
-      default: "default",
-    },
-    filter: {
-      anyOf: [
-        {
-          additionalProperties: {
-            $ref: "#/components/schemas/FilterOperation",
-          },
-          type: "object",
-        },
-        {
-          type: "null",
-        },
-      ],
-      title: "Filter",
-      description: "Optional filters to select which items to delete",
-    },
-  },
-  type: "object",
-  required: ["deployment_name"],
-  title: "DeleteRequest",
-  description: "API request body for bulk deleting agent data by query",
-} as const;
-
-export const DeleteResponseSchema = {
-  properties: {
-    deleted_count: {
-      type: "integer",
-      title: "Deleted Count",
-    },
-  },
-  type: "object",
-  required: ["deleted_count"],
-  title: "DeleteResponse",
-  description: "API response for bulk delete operation",
-} as const;
-
 export const DirectRetrievalParamsSchema = {
   properties: {
     mode: {
@@ -6950,20 +6946,6 @@ export const ExtractConfigSchema = {
       description: "Whether to invalidate the cache for the extraction.",
       default: false,
     },
-    num_pages_context: {
-      anyOf: [
-        {
-          type: "integer",
-          minimum: 1,
-        },
-        {
-          type: "null",
-        },
-      ],
-      title: "Num Pages Context",
-      description:
-        "Number of pages to pass as context on long document extraction.",
-    },
     page_range: {
       anyOf: [
         {
@@ -7220,7 +7202,6 @@ export const ExtractModelsSchema = {
     "openai-gpt-5-mini",
     "gemini-2.0-flash",
     "gemini-2.5-flash",
-    "gemini-2.5-flash-lite",
     "gemini-2.5-pro",
     "openai-gpt-4o",
     "openai-gpt-4o-mini",
@@ -7868,52 +7849,6 @@ export const ExtractTargetSchema = {
   title: "ExtractTarget",
 } as const;
 
-export const ExtractedTableSchema = {
-  properties: {
-    table_id: {
-      type: "integer",
-      title: "Table Id",
-      description: "Unique identifier for this table within the file",
-    },
-    sheet_name: {
-      type: "string",
-      title: "Sheet Name",
-      description: "Worksheet name where table was found",
-    },
-    row_span: {
-      type: "integer",
-      title: "Row Span",
-      description: "Number of rows in the table",
-    },
-    col_span: {
-      type: "integer",
-      title: "Col Span",
-      description: "Number of columns in the table",
-    },
-    has_headers: {
-      type: "boolean",
-      title: "Has Headers",
-      description: "Whether the table has header rows",
-    },
-    metadata_json: {
-      anyOf: [
-        {
-          type: "string",
-        },
-        {
-          type: "null",
-        },
-      ],
-      title: "Metadata Json",
-      description: "JSON metadata with detailed table information",
-    },
-  },
-  type: "object",
-  required: ["table_id", "sheet_name", "row_span", "col_span", "has_headers"],
-  title: "ExtractedTable",
-  description: "A single extracted table from a spreadsheet",
-} as const;
-
 export const FailPageModeSchema = {
   type: "string",
   enum: ["raw_text", "blank_page", "error_message"],
@@ -10893,6 +10828,140 @@ export const LegacyParseJobConfigSchema = {
   description: "Configuration for llamaparse job",
 } as const;
 
+export const LlamaExtractSettingsSchema = {
+  properties: {
+    max_file_size: {
+      type: "integer",
+      title: "Max File Size",
+      description: "The maximum file size (in bytes) allowed for the document.",
+      default: 104857600,
+    },
+    max_file_size_ui: {
+      type: "integer",
+      title: "Max File Size Ui",
+      description: "The maximum file size (in bytes) allowed for the document.",
+      default: 31457280,
+    },
+    max_pages: {
+      type: "integer",
+      title: "Max Pages",
+      description: "The maximum number of pages allowed for the document.",
+      default: 500,
+    },
+    chunk_mode: {
+      $ref: "#/components/schemas/ChunkMode",
+      description: "The mode to use for chunking the document.",
+      default: "SECTION",
+    },
+    max_chunk_size: {
+      type: "integer",
+      title: "Max Chunk Size",
+      description:
+        "The maximum size of the chunks (in tokens) to use for chunking the document.",
+      default: 10000,
+    },
+    extraction_agent_config: {
+      additionalProperties: {
+        $ref: "#/components/schemas/StructParseConf",
+      },
+      type: "object",
+      title: "Extraction Agent Config",
+      description: "The configuration for the extraction agent.",
+    },
+    use_multimodal_parsing: {
+      type: "boolean",
+      title: "Use Multimodal Parsing",
+      description: "Whether to use experimental multimodal parsing.",
+      default: false,
+    },
+    use_pixel_extraction: {
+      type: "boolean",
+      title: "Use Pixel Extraction",
+      description:
+        "DEPRECATED: Whether to use extraction over pixels for multimodal mode.",
+      default: false,
+    },
+    llama_parse_params: {
+      $ref: "#/components/schemas/LlamaParseParameters",
+      description: "LlamaParse related settings.",
+      default: {
+        languages: ["en"],
+        parsing_instruction: "",
+        disable_ocr: false,
+        annotate_links: true,
+        adaptive_long_table: true,
+        compact_markdown_table: false,
+        disable_reconstruction: false,
+        disable_image_extraction: false,
+        invalidate_cache: false,
+        outlined_table_extraction: true,
+        merge_tables_across_pages_in_markdown: false,
+        output_pdf_of_document: false,
+        do_not_cache: false,
+        fast_mode: false,
+        skip_diagonal_text: false,
+        preserve_layout_alignment_across_pages: false,
+        preserve_very_small_text: false,
+        gpt4o_mode: false,
+        do_not_unroll_columns: false,
+        extract_layout: false,
+        high_res_ocr: false,
+        html_make_all_elements_visible: false,
+        layout_aware: false,
+        specialized_chart_parsing_agentic: false,
+        specialized_chart_parsing_plus: false,
+        specialized_chart_parsing_efficient: false,
+        specialized_image_parsing: false,
+        precise_bounding_box: false,
+        html_remove_navigation_elements: false,
+        html_remove_fixed_elements: false,
+        guess_xlsx_sheet_name: false,
+        use_vendor_multimodal_model: false,
+        page_prefix: `<<<PAGE:{pageNumber}>>>
+
+`,
+        page_suffix: `
+
+<<<END_PAGE>>>`,
+        take_screenshot: false,
+        is_formatting_instruction: true,
+        premium_mode: false,
+        continuous_mode: false,
+        auto_mode: false,
+        auto_mode_trigger_on_table_in_page: false,
+        auto_mode_trigger_on_image_in_page: false,
+        structured_output: false,
+        extract_charts: false,
+        spreadsheet_extract_sub_tables: false,
+        spreadsheet_force_formula_computation: false,
+        inline_images_in_markdown: false,
+        strict_mode_image_extraction: false,
+        strict_mode_image_ocr: false,
+        strict_mode_reconstruction: false,
+        strict_mode_buggy_font: false,
+        save_images: true,
+        hide_headers: false,
+        hide_footers: false,
+        ignore_document_elements_for_layout_detection: false,
+        output_tables_as_HTML: false,
+        internal_is_screenshot_job: false,
+        parse_mode: "parse_page_with_llm",
+        page_error_tolerance: 0.05,
+        replace_failed_page_mode: "raw_text",
+      },
+    },
+    multimodal_parse_resolution: {
+      $ref: "#/components/schemas/MultimodalParseResolution",
+      description: "The resolution to use for multimodal parsing.",
+      default: "medium",
+    },
+  },
+  type: "object",
+  title: "LlamaExtractSettings",
+  description: `All settings for the extraction agent. Only the settings in ExtractConfig
+are exposed to the user.`,
+} as const;
+
 export const LlamaParseParametersSchema = {
   properties: {
     webhook_configurations: {
@@ -12533,6 +12602,12 @@ export const MetronomeDashboardTypeSchema = {
   title: "MetronomeDashboardType",
 } as const;
 
+export const MultimodalParseResolutionSchema = {
+  type: "string",
+  enum: ["medium", "high"],
+  title: "MultimodalParseResolution",
+} as const;
+
 export const NodeRelationshipSchema = {
   type: "string",
   enum: ["1", "2", "3", "4", "5"],
@@ -13355,48 +13430,6 @@ export const PaginatedResponse_QuotaConfiguration_Schema = {
   title: "PaginatedResponse[QuotaConfiguration]",
 } as const;
 
-export const PaginatedResponse_SpreadsheetJob_Schema = {
-  properties: {
-    items: {
-      items: {
-        $ref: "#/components/schemas/SpreadsheetJob",
-      },
-      type: "array",
-      title: "Items",
-      description: "The list of items.",
-    },
-    next_page_token: {
-      anyOf: [
-        {
-          type: "string",
-        },
-        {
-          type: "null",
-        },
-      ],
-      title: "Next Page Token",
-      description:
-        "A token, which can be sent as page_token to retrieve the next page. If this field is omitted, there are no subsequent pages.",
-    },
-    total_size: {
-      anyOf: [
-        {
-          type: "integer",
-        },
-        {
-          type: "null",
-        },
-      ],
-      title: "Total Size",
-      description:
-        "The total number of items available. This is only populated when specifically requested. The value may be an estimate and can be used for display purposes only.",
-    },
-  },
-  type: "object",
-  required: ["items"],
-  title: "PaginatedResponse[SpreadsheetJob]",
-} as const;
-
 export const ParseConfigurationSchema = {
   properties: {
     id: {
@@ -17808,6 +17841,69 @@ export const ProjectUpdateSchema = {
   description: "Schema for updating a project.",
 } as const;
 
+export const PromptConfSchema = {
+  properties: {
+    system_prompt: {
+      type: "string",
+      title: "System Prompt",
+      description: "The system prompt to use for the extraction.",
+      default:
+        "Given a JSON schema, extract the data from the provided SOURCE TEXT according to the schema. Only output information that is explicitly stated or can be inferred from the SOURCE TEXT.",
+    },
+    extraction_prompt: {
+      type: "string",
+      title: "Extraction Prompt",
+      description: "The prompt to use for the extraction.",
+      default: "The extracted data using the given JSON schema.",
+    },
+    error_handling_prompt: {
+      type: "string",
+      title: "Error Handling Prompt",
+      description: "The prompt to use for error handling.",
+      default:
+        "If the source text does not contain enough information to extract the value, explain the reason very briefly. Else, output null and fill out the value__ field.",
+    },
+    reasoning_prompt: {
+      type: "string",
+      title: "Reasoning Prompt",
+      description: "The prompt to use for reasoning.",
+      default: `
+Provide a brief explanation for how you arrived at the extracted value based on the source text provided.
+- For inferred values, explain the reasoning behind the extraction briefly.
+- For simple verbatim extraction, output 'VERBATIM EXTRACTION'.
+- When supporting data is not present in the source text, output 'INSUFFICIENT DATA' and emit blank or null values for the value__ field.
+`,
+    },
+    cite_sources_prompt: {
+      additionalProperties: {
+        type: "string",
+      },
+      type: "object",
+      title: "Cite Sources Prompt",
+      description: "The prompt to use for citing sources.",
+      default: {
+        description: `
+### Citation Rules (read carefully):
+- You must ANNOTATE every value with the MOST RELEVANT short EXACT substring from the source text that supports it.
+- For inferred values, cite the text used to infer it in the matching_text field or output 'INFERRED FROM TEXT'
+- If no support exists, output 'INSUFFICIENT DATA' and leave value__ null or '', 0.0, False etc depending on the type of the field.
+`,
+        page: "Cite the page number of the source text that the extracted value is from. The page number is the integer that appears right after <<<PAGE:. If no page number is present in this format, use the default value of 1.",
+        matching_text:
+          'Cite the **MOST RELEVANT EXACT TEXT from the SOURCE TEXT** that supports the extracted value within 80 characters. If the exact substring is >80 chars, truncate with ellipsis "...". Provide only the single most relevant citation.',
+      },
+    },
+    scratchpad_prompt: {
+      type: "string",
+      title: "Scratchpad Prompt",
+      description: "The prompt to use for scratchpad.",
+      default: "Use for intermediate step-by-step reasoning. Be concise.",
+    },
+  },
+  type: "object",
+  title: "PromptConf",
+} as const;
+
 export const PublicModelNameSchema = {
   type: "string",
   enum: [
@@ -17830,7 +17926,6 @@ export const PublicModelNameSchema = {
     "gemini-2.5-pro",
     "gemini-2.0-flash",
     "gemini-2.0-flash-lite",
-    "gemini-2.5-flash-lite",
     "gemini-1.5-flash",
     "gemini-1.5-pro",
   ],
@@ -18657,6 +18752,12 @@ export const RoleSchema = {
   description: "Schema for a role.",
 } as const;
 
+export const SchemaRelaxModeSchema = {
+  type: "string",
+  enum: ["FULL", "TOP_LEVEL", "LEAF"],
+  title: "SchemaRelaxMode",
+} as const;
+
 export const SearchRequestSchema = {
   properties: {
     page_size: {
@@ -18849,135 +18950,6 @@ BM25: Uses Qdrant's FastEmbed BM25 model for sparse embeddings
 AUTO: Automatically selects based on deployment mode (BYOC uses term frequency, Cloud uses Splade)`,
 } as const;
 
-export const SpreadsheetJobSchema = {
-  properties: {
-    id: {
-      type: "string",
-      title: "Id",
-      description: "The ID of the job",
-    },
-    user_id: {
-      type: "string",
-      title: "User Id",
-      description: "The ID of the user",
-    },
-    project_id: {
-      type: "string",
-      format: "uuid",
-      title: "Project Id",
-      description: "The ID of the project",
-    },
-    file_id: {
-      type: "string",
-      format: "uuid",
-      title: "File Id",
-      description: "The ID of the file to parse",
-    },
-    config: {
-      $ref: "#/components/schemas/SpreadsheetParsingConfig",
-      description: "Configuration for the parsing job",
-    },
-    status: {
-      $ref: "#/components/schemas/StatusEnum",
-      description: "The status of the parsing job",
-    },
-    created_at: {
-      type: "string",
-      title: "Created At",
-      description: "When the job was created",
-    },
-    updated_at: {
-      type: "string",
-      title: "Updated At",
-      description: "When the job was last updated",
-    },
-    success: {
-      anyOf: [
-        {
-          type: "boolean",
-        },
-        {
-          type: "null",
-        },
-      ],
-      title: "Success",
-      description: "Whether the job completed successfully",
-    },
-    tables: {
-      items: {
-        $ref: "#/components/schemas/ExtractedTable",
-      },
-      type: "array",
-      title: "Tables",
-      description: "All extracted tables (populated when job is complete)",
-    },
-    errors: {
-      items: {
-        type: "string",
-      },
-      type: "array",
-      title: "Errors",
-      description: "Any errors encountered",
-    },
-  },
-  type: "object",
-  required: [
-    "id",
-    "user_id",
-    "project_id",
-    "file_id",
-    "config",
-    "status",
-    "created_at",
-    "updated_at",
-  ],
-  title: "SpreadsheetJob",
-  description: "A spreadsheet parsing job",
-} as const;
-
-export const SpreadsheetJobCreateSchema = {
-  properties: {
-    file_id: {
-      type: "string",
-      format: "uuid",
-      title: "File Id",
-      description: "The ID of the file to parse",
-    },
-    config: {
-      $ref: "#/components/schemas/SpreadsheetParsingConfig",
-      description: "Configuration for the parsing job",
-    },
-  },
-  type: "object",
-  required: ["file_id"],
-  title: "SpreadsheetJobCreate",
-  description: "Request to create a spreadsheet parsing job",
-} as const;
-
-export const SpreadsheetParsingConfigSchema = {
-  properties: {
-    sheet_names: {
-      anyOf: [
-        {
-          items: {
-            type: "string",
-          },
-          type: "array",
-        },
-        {
-          type: "null",
-        },
-      ],
-      title: "Sheet Names",
-      description:
-        "The names of the sheets to parse. If empty, all sheets will be parsed.",
-    },
-  },
-  type: "object",
-  title: "SpreadsheetParsingConfig",
-  description: "Configuration for spreadsheet parsing",
-} as const;
-
 export const StatusEnumSchema = {
   type: "string",
   enum: ["PENDING", "SUCCESS", "ERROR", "PARTIAL_SUCCESS", "CANCELLED"],
@@ -18985,6 +18957,101 @@ export const StatusEnumSchema = {
   description: "Enum for representing the status of a job",
 } as const;
 
+export const StructModeSchema = {
+  type: "string",
+  enum: [
+    "STRUCT_PARSE",
+    "JSON_MODE",
+    "FUNC_CALL",
+    "STRUCT_RELAXED",
+    "UNSTRUCTURED",
+  ],
+  title: "StructMode",
+} as const;
+
+export const StructParseConfSchema = {
+  properties: {
+    model: {
+      $ref: "#/components/schemas/ExtractModels",
+      description: "The model to use for the structured parsing.",
+      default: "openai-gpt-4-1",
+    },
+    temperature: {
+      type: "number",
+      title: "Temperature",
+      description: "The temperature to use for the structured parsing.",
+      default: 0,
+    },
+    relaxation_mode: {
+      $ref: "#/components/schemas/SchemaRelaxMode",
+      description: "The relaxation mode to use for the structured parsing.",
+      default: "LEAF",
+    },
+    struct_mode: {
+      $ref: "#/components/schemas/StructMode",
+      description: "The struct mode to use for the structured parsing.",
+      default: "STRUCT_PARSE",
+    },
+    fetch_logprobs: {
+      type: "boolean",
+      title: "Fetch Logprobs",
+      description: "Whether to fetch logprobs for the structured parsing.",
+      default: false,
+    },
+    handle_missing: {
+      type: "boolean",
+      title: "Handle Missing",
+      description: "Whether to handle missing fields in the schema.",
+      default: false,
+    },
+    use_reasoning: {
+      type: "boolean",
+      title: "Use Reasoning",
+      description: "Whether to use reasoning for the structured extraction.",
+      default: false,
+    },
+    cite_sources: {
+      type: "boolean",
+      title: "Cite Sources",
+      description: "Whether to cite sources for the structured extraction.",
+      default: false,
+    },
+    prompt_conf: {
+      $ref: "#/components/schemas/PromptConf",
+      description: "The prompt configuration for the structured parsing.",
+      default: {
+        system_prompt:
+          "Given a JSON schema, extract the data from the provided SOURCE TEXT according to the schema. Only output information that is explicitly stated or can be inferred from the SOURCE TEXT.",
+        extraction_prompt: "The extracted data using the given JSON schema.",
+        error_handling_prompt:
+          "If the source text does not contain enough information to extract the value, explain the reason very briefly. Else, output null and fill out the value__ field.",
+        reasoning_prompt: `
+Provide a brief explanation for how you arrived at the extracted value based on the source text provided.
+- For inferred values, explain the reasoning behind the extraction briefly.
+- For simple verbatim extraction, output 'VERBATIM EXTRACTION'.
+- When supporting data is not present in the source text, output 'INSUFFICIENT DATA' and emit blank or null values for the value__ field.
+`,
+        cite_sources_prompt: {
+          description: `
+### Citation Rules (read carefully):
+- You must ANNOTATE every value with the MOST RELEVANT short EXACT substring from the source text that supports it.
+- For inferred values, cite the text used to infer it in the matching_text field or output 'INFERRED FROM TEXT'
+- If no support exists, output 'INSUFFICIENT DATA' and leave value__ null or '', 0.0, False etc depending on the type of the field.
+`,
+          matching_text:
+            'Cite the **MOST RELEVANT EXACT TEXT from the SOURCE TEXT** that supports the extracted value within 80 characters. If the exact substring is >80 chars, truncate with ellipsis "...". Provide only the single most relevant citation.',
+          page: "Cite the page number of the source text that the extracted value is from. The page number is the integer that appears right after <<<PAGE:. If no page number is present in this format, use the default value of 1.",
+        },
+        scratchpad_prompt:
+          "Use for intermediate step-by-step reasoning. Be concise.",
+      },
+    },
+  },
+  type: "object",
+  title: "StructParseConf",
+  description: "Configuration for the structured parsing agent.",
+} as const;
+
 export const SupportedLLMModelSchema = {
   properties: {
     name: {

ts/llama_cloud_services/src/client/zod.gen.ts

@@ -691,6 +691,115 @@ export const zBodyRunJobOnFileApiV1ExtractionJobsFilePost = z.object({
   config_override: z.union([z.string(), z.null()]).optional(),
 });
 
+export const zExtractTarget = z.enum(["PER_DOC", "PER_PAGE"]);
+
+export const zExtractMode = z.enum([
+  "FAST",
+  "BALANCED",
+  "PREMIUM",
+  "MULTIMODAL",
+]);
+
+export const zPublicModelName = z.enum([
+  "openai-gpt-4o",
+  "openai-gpt-4o-mini",
+  "openai-gpt-4-1",
+  "openai-gpt-4-1-mini",
+  "openai-gpt-4-1-nano",
+  "openai-gpt-5",
+  "openai-gpt-5-mini",
+  "openai-gpt-5-nano",
+  "openai-text-embedding-3-small",
+  "openai-text-embedding-3-large",
+  "openai-whisper-1",
+  "anthropic-sonnet-3.5",
+  "anthropic-sonnet-3.5-v2",
+  "anthropic-sonnet-3.7",
+  "anthropic-sonnet-4.0",
+  "gemini-2.5-flash",
+  "gemini-2.5-pro",
+  "gemini-2.0-flash",
+  "gemini-2.0-flash-lite",
+  "gemini-1.5-flash",
+  "gemini-1.5-pro",
+]);
+
+export const zExtractModels = z.enum([
+  "openai-gpt-4-1",
+  "openai-gpt-4-1-mini",
+  "openai-gpt-4-1-nano",
+  "openai-gpt-5",
+  "openai-gpt-5-mini",
+  "gemini-2.0-flash",
+  "gemini-2.5-flash",
+  "gemini-2.5-pro",
+  "openai-gpt-4o",
+  "openai-gpt-4o-mini",
+]);
+
+export const zDocumentChunkMode = z.enum(["PAGE", "SECTION"]);
+
+export const zExtractConfig = z.object({
+  priority: z
+    .union([z.enum(["low", "medium", "high", "critical"]), z.null()])
+    .optional(),
+  extraction_target: zExtractTarget.optional(),
+  extraction_mode: zExtractMode.optional(),
+  parse_model: z.union([zPublicModelName, z.null()]).optional(),
+  extract_model: z.union([zExtractModels, z.null()]).optional(),
+  multimodal_fast_mode: z.boolean().optional().default(false),
+  system_prompt: z.union([z.string(), z.null()]).optional(),
+  use_reasoning: z.boolean().optional().default(false),
+  cite_sources: z.boolean().optional().default(false),
+  confidence_scores: z.boolean().optional().default(false),
+  chunk_mode: zDocumentChunkMode.optional(),
+  high_resolution_mode: z.boolean().optional().default(false),
+  invalidate_cache: z.boolean().optional().default(false),
+  page_range: z.union([z.string(), z.null()]).optional(),
+});
+
+export const zExtractJobCreate = z.object({
+  priority: z
+    .union([z.enum(["low", "medium", "high", "critical"]), z.null()])
+    .optional(),
+  webhook_configurations: z
+    .union([z.array(zWebhookConfiguration), z.null()])
+    .optional(),
+  extraction_agent_id: z.string().uuid(),
+  file_id: z.string().uuid(),
+  data_schema_override: z
+    .union([z.object({}), z.string(), z.null()])
+    .optional(),
+  config_override: z.union([zExtractConfig, z.null()]).optional(),
+});
+
+export const zChunkMode = z.enum([
+  "PAGE",
+  "DOCUMENT",
+  "SECTION",
+  "GROUPED_PAGES",
+]);
+
+export const zMultimodalParseResolution = z.enum(["medium", "high"]);
+
+export const zLlamaExtractSettings = z.object({
+  max_file_size: z.number().int().optional().default(104857600),
+  max_file_size_ui: z.number().int().optional().default(31457280),
+  max_pages: z.number().int().optional().default(500),
+  chunk_mode: zChunkMode.optional(),
+  max_chunk_size: z.number().int().optional().default(10000),
+  extraction_agent_config: z.object({}).optional(),
+  use_multimodal_parsing: z.boolean().optional().default(false),
+  use_pixel_extraction: z.boolean().optional().default(false),
+  llama_parse_params: zLlamaParseParameters.optional(),
+  multimodal_parse_resolution: zMultimodalParseResolution.optional(),
+});
+
+export const zBodyRunJobTestUserApiV1ExtractionJobsTestPost = z.object({
+  job_create: zExtractJobCreate,
+  extract_settings: z.union([zLlamaExtractSettings, z.null()]).optional(),
+});
+
 export const zBodyScreenshotApiParsingScreenshotPost = z.object({
   file: z.union([z.string(), z.null()]).optional(),
   do_not_cache: z.boolean().optional().default(false),
@@ -963,6 +1072,11 @@ export const zBodyUploadFileApiV1ParsingUploadPost = z.object({
   page_footer_suffix: z.string().optional(),
 });
 
+export const zBodyUploadFileV2ApiV2Alpha1ParseUploadPost = z.object({
+  configuration: z.string(),
+  file: z.union([z.string(), z.null()]).optional(),
+});
+
 export const zBoxAuthMechanism = z.enum(["developer_token", "ccg"]);
 
 export const zSupportedLlmModelNames = z.enum([
@@ -1586,7 +1700,6 @@ export const zCustomClaims = z.object({
   allowed_classify: z.boolean().optional().default(true),
   api_datasource_access: z.boolean().optional().default(false),
   allow_org_deletion: z.boolean().optional().default(false),
-  allowed_spreadsheet: z.boolean().optional().default(false),
 });
 
 export const zCustomerPortalSessionCreatePayload = z.object({
@@ -1742,16 +1855,6 @@ export const zDefaultOrganizationUpdate = z.object({
   organization_id: z.string().uuid(),
 });
 
-export const zDeleteRequest = z.object({
-  deployment_name: z.string(),
-  collection: z.string().optional().default("default"),
-  filter: z.union([z.object({}), z.null()]).optional(),
-});
-
-export const zDeleteResponse = z.object({
-  deleted_count: z.number().int(),
-});
-
 export const zRetrieverPipeline = z.object({
   name: z.union([z.string().min(1).max(3000), z.null()]),
   description: z.union([z.string().max(15000), z.null()]),
@@ -1767,8 +1870,6 @@ export const zDirectRetrievalParams = z.object({
   pipelines: z.array(zRetrieverPipeline).optional(),
 });
 
-export const zDocumentChunkMode = z.enum(["PAGE", "SECTION"]);
-
 export const zDocumentIngestionJobParams = z.object({
   custom_metadata: z.union([z.object({}), z.null()]).optional(),
   resource_info: z.union([z.object({}), z.null()]).optional(),
@@ -2021,74 +2122,6 @@ Query: {query_str}
 Answer: `),
 });
 
-export const zExtractTarget = z.enum(["PER_DOC", "PER_PAGE"]);
-
-export const zExtractMode = z.enum([
-  "FAST",
-  "BALANCED",
-  "PREMIUM",
-  "MULTIMODAL",
-]);
-
-export const zPublicModelName = z.enum([
-  "openai-gpt-4o",
-  "openai-gpt-4o-mini",
-  "openai-gpt-4-1",
-  "openai-gpt-4-1-mini",
-  "openai-gpt-4-1-nano",
-  "openai-gpt-5",
-  "openai-gpt-5-mini",
-  "openai-gpt-5-nano",
-  "openai-text-embedding-3-small",
-  "openai-text-embedding-3-large",
-  "openai-whisper-1",
-  "anthropic-sonnet-3.5",
-  "anthropic-sonnet-3.5-v2",
-  "anthropic-sonnet-3.7",
-  "anthropic-sonnet-4.0",
-  "gemini-2.5-flash",
-  "gemini-2.5-pro",
-  "gemini-2.0-flash",
-  "gemini-2.0-flash-lite",
-  "gemini-2.5-flash-lite",
-  "gemini-1.5-flash",
-  "gemini-1.5-pro",
-]);
-
-export const zExtractModels = z.enum([
-  "openai-gpt-4-1",
-  "openai-gpt-4-1-mini",
-  "openai-gpt-4-1-nano",
-  "openai-gpt-5",
-  "openai-gpt-5-mini",
-  "gemini-2.0-flash",
-  "gemini-2.5-flash",
-  "gemini-2.5-flash-lite",
-  "gemini-2.5-pro",
-  "openai-gpt-4o",
-  "openai-gpt-4o-mini",
-]);
-
-export const zExtractConfig = z.object({
-  priority: z
-    .union([z.enum(["low", "medium", "high", "critical"]), z.null()])
-    .optional(),
-  extraction_target: zExtractTarget.optional(),
-  extraction_mode: zExtractMode.optional(),
-  parse_model: z.union([zPublicModelName, z.null()]).optional(),
-  extract_model: z.union([zExtractModels, z.null()]).optional(),
-  multimodal_fast_mode: z.boolean().optional().default(false),
-  system_prompt: z.union([z.string(), z.null()]).optional(),
-  use_reasoning: z.boolean().optional().default(false),
-  cite_sources: z.boolean().optional().default(false),
-  confidence_scores: z.boolean().optional().default(false),
-  chunk_mode: zDocumentChunkMode.optional(),
-  high_resolution_mode: z.boolean().optional().default(false),
-  invalidate_cache: z.boolean().optional().default(false),
-  num_pages_context: z.union([z.number().int().gte(1), z.null()]).optional(),
-  page_range: z.union([z.string(), z.null()]).optional(),
-});
-
 export const zExtractAgent = z.object({
   id: z.string().uuid(),
   name: z.string(),
@@ -2134,21 +2167,6 @@ export const zExtractJob = z.object({
   file: zFile,
 });
 
-export const zExtractJobCreate = z.object({
-  priority: z
-    .union([z.enum(["low", "medium", "high", "critical"]), z.null()])
-    .optional(),
-  webhook_configurations: z
-    .union([z.array(zWebhookConfiguration), z.null()])
-    .optional(),
-  extraction_agent_id: z.string().uuid(),
-  file_id: z.string().uuid(),
-  data_schema_override: z
-    .union([z.object({}), z.string(), z.null()])
-    .optional(),
-  config_override: z.union([zExtractConfig, z.null()]).optional(),
-});
-
 export const zExtractJobCreateBatch = z.object({
   extraction_agent_id: z.string().uuid(),
   file_ids: z.array(z.string().uuid()).min(1),
@@ -2216,15 +2234,6 @@ export const zExtractStatelessRequest = z.object({
   file: z.union([zFileData, z.null()]).optional(),
 });
 
-export const zExtractedTable = z.object({
-  table_id: z.number().int(),
-  sheet_name: z.string(),
-  row_span: z.number().int(),
-  col_span: z.number().int(),
-  has_headers: z.boolean(),
-  metadata_json: z.union([z.string(), z.null()]).optional(),
-});
-
 export const zFileCountByStatusResponse = z.object({
   counts: z.object({}),
   total_count: z.number().int(),
@@ -2978,30 +2987,6 @@ export const zPaginatedResponseQuotaConfiguration = z.object({
   items: z.array(zQuotaConfiguration),
 });
 
-export const zSpreadsheetParsingConfig = z.object({
-  sheet_names: z.union([z.array(z.string()), z.null()]).optional(),
-});
-
-export const zSpreadsheetJob = z.object({
-  id: z.string(),
-  user_id: z.string(),
-  project_id: z.string().uuid(),
-  file_id: z.string().uuid(),
-  config: zSpreadsheetParsingConfig,
-  status: zStatusEnum,
-  created_at: z.string(),
-  updated_at: z.string(),
-  success: z.union([z.boolean(), z.null()]).optional(),
-  tables: z.array(zExtractedTable).optional(),
-  errors: z.array(z.string()).optional(),
-});
-
-export const zPaginatedResponseSpreadsheetJob = z.object({
-  items: z.array(zSpreadsheetJob),
-  next_page_token: z.union([z.string(), z.null()]).optional(),
-  total_size: z.union([z.number().int(), z.null()]).optional(),
-});
-
 export const zParseConfiguration = z.object({
   id: z.string(),
   name: z.string(),
@@ -3415,6 +3400,49 @@ export const zProjectUpdate = z.object({
   name: z.string().min(1).max(3000),
 });
 
+export const zPromptConf = z.object({
+  system_prompt: z
+    .string()
+    .optional()
+    .default(
+      "Given a JSON schema, extract the data from the provided SOURCE TEXT according to the schema. Only output information that is explicitly stated or can be inferred from the SOURCE TEXT.",
+    ),
+  extraction_prompt: z
+    .string()
+    .optional()
+    .default("The extracted data using the given JSON schema."),
+  error_handling_prompt: z
+    .string()
+    .optional()
+    .default(
+      "If the source text does not contain enough information to extract the value, explain the reason very briefly. Else, output null and fill out the value__ field.",
+    ),
+  reasoning_prompt: z.string().optional().default(`
+Provide a brief explanation for how you arrived at the extracted value based on the source text provided.
+- For inferred values, explain the reasoning behind the extraction briefly.
+- For simple verbatim extraction, output 'VERBATIM EXTRACTION'.
+- When supporting data is not present in the source text, output 'INSUFFICIENT DATA' and emit blank or null values for the value__ field.
+`),
+  cite_sources_prompt: z
+    .object({})
+    .optional()
+    .default({
+      description: `
+### Citation Rules (read carefully):
+- You must ANNOTATE every value with the MOST RELEVANT short EXACT substring from the source text that supports it.
+- For inferred values, cite the text used to infer it in the matching_text field or output 'INFERRED FROM TEXT'
+- If no support exists, output 'INSUFFICIENT DATA' and leave value__ null or '', 0.0, False etc depending on the type of the field.
+`,
+      page: "Cite the page number of the source text that the extracted value is from. The page number is the integer that appears right after <<<PAGE:. If no page number is present in this format, use the default value of 1.",
+      matching_text:
+        'Cite the **MOST RELEVANT EXACT TEXT from the SOURCE TEXT** that supports the extracted value within 80 characters. If the exact substring is >80 chars, truncate with ellipsis "...". Provide only the single most relevant citation.',
+    }),
+  scratchpad_prompt: z
+    .string()
+    .optional()
+    .default("Use for intermediate step-by-step reasoning. Be concise."),
+});
+
 export const zRelatedNodeInfo = z.object({
   node_id: z.string(),
   node_type: z.union([zObjectType, z.string(), z.null()]).optional(),
@@ -3517,6 +3545,8 @@ export const zRole = z.object({
   permissions: z.array(zPermission),
 });
 
+export const zSchemaRelaxMode = z.enum(["FULL", "TOP_LEVEL", "LEAF"]);
+
 export const zSearchRequest = z.object({
   page_size: z.union([z.number().int(), z.null()]).optional(),
   page_token: z.union([z.string(), z.null()]).optional(),
@@ -3528,9 +3558,24 @@ export const zSearchRequest = z.object({
   offset: z.union([z.number().int().gte(0).lte(1000), z.null()]).optional(),
 });
 
-export const zSpreadsheetJobCreate = z.object({
-  file_id: z.string().uuid(),
-  config: zSpreadsheetParsingConfig.optional(),
+export const zStructMode = z.enum([
+  "STRUCT_PARSE",
+  "JSON_MODE",
+  "FUNC_CALL",
+  "STRUCT_RELAXED",
+  "UNSTRUCTURED",
+]);
+
+export const zStructParseConf = z.object({
+  model: zExtractModels.optional(),
+  temperature: z.number().optional().default(0),
+  relaxation_mode: zSchemaRelaxMode.optional(),
+  struct_mode: zStructMode.optional(),
+  fetch_logprobs: z.boolean().optional().default(false),
+  handle_missing: z.boolean().optional().default(false),
+  use_reasoning: z.boolean().optional().default(false),
+  cite_sources: z.boolean().optional().default(false),
+  prompt_conf: zPromptConf.optional(),
 });
 
 export const zSupportedLlmModel = z.object({
@@ -3972,33 +4017,6 @@ export const zCreateIntentAndCustomerSessionApiV1BillingCreateIntentAndCustomerS
 export const zGetMetronomeDashboardApiV1BillingMetronomeDashboardGetResponse =
   zMetronomeDashboardResponse;
 
-export const zListJobsApiV1ExtractionJobsGetResponse = z.array(zExtractJob);
-
-export const zRunJobApiV1ExtractionJobsPostResponse = zExtractJob;
-
-export const zGetJobApiV1ExtractionJobsJobIdGetResponse = zExtractJob;
-
-export const zRunJobOnFileApiV1ExtractionJobsFilePostResponse = zExtractJob;
-
-export const zRunBatchJobsApiV1ExtractionJobsBatchPostResponse =
-  z.array(zExtractJob);
-
-export const zGetJobResultApiV1ExtractionJobsJobIdResultGetResponse =
-  zExtractResultset;
-
-export const zListExtractRunsApiV1ExtractionRunsGetResponse =
-  zPaginatedExtractRunsResponse;
-
-export const zGetLatestRunFromUiApiV1ExtractionRunsLatestFromUiGetResponse =
-  z.union([zExtractRun, z.null()]);
-
-export const zGetRunByJobIdApiV1ExtractionRunsByJobJobIdGetResponse =
-  zExtractRun;
-
-export const zGetRunApiV1ExtractionRunsRunIdGetResponse = zExtractRun;
-
-export const zExtractStatelessApiV1ExtractionRunPostResponse = zExtractJob;
-
 export const zListExtractionAgentsApiV1ExtractionExtractionAgentsGetResponse =
   z.array(zExtractAgent);
 
@@ -4023,6 +4041,35 @@ export const zGetExtractionAgentApiV1ExtractionExtractionAgentsExtractionAgentId
 export const zUpdateExtractionAgentApiV1ExtractionExtractionAgentsExtractionAgentIdPutResponse =
   zExtractAgent;
 
+export const zListJobsApiV1ExtractionJobsGetResponse = z.array(zExtractJob);
+
+export const zRunJobApiV1ExtractionJobsPostResponse = zExtractJob;
+
+export const zGetJobApiV1ExtractionJobsJobIdGetResponse = zExtractJob;
+
+export const zRunJobTestUserApiV1ExtractionJobsTestPostResponse = zExtractJob;
+
+export const zRunJobOnFileApiV1ExtractionJobsFilePostResponse = zExtractJob;
+
+export const zRunBatchJobsApiV1ExtractionJobsBatchPostResponse =
+  z.array(zExtractJob);
+
+export const zGetJobResultApiV1ExtractionJobsJobIdResultGetResponse =
+  zExtractResultset;
+
+export const zListExtractRunsApiV1ExtractionRunsGetResponse =
+  zPaginatedExtractRunsResponse;
+
+export const zGetLatestRunFromUiApiV1ExtractionRunsLatestFromUiGetResponse =
+  z.union([zExtractRun, z.null()]);
+
+export const zGetRunByJobIdApiV1ExtractionRunsByJobJobIdGetResponse =
+  zExtractRun;
+
+export const zGetRunApiV1ExtractionRunsRunIdGetResponse = zExtractRun;
+
+export const zExtractStatelessApiV1ExtractionRunPostResponse = zExtractJob;
+
 export const zListApiKeysApiV1BetaApiKeysGetResponse = zApiKeyQueryResponse;
 
 export const zCreateApiKeyApiV1BetaApiKeysPostResponse = zApiKey;
@@ -4053,9 +4100,6 @@ export const zSearchAgentDataApiV1BetaAgentDataSearchPostResponse =
 export const zAggregateAgentDataApiV1BetaAgentDataAggregatePostResponse =
   zPaginatedResponseAggregateGroup;
 
-export const zDeleteAgentDataByQueryApiV1BetaAgentDataDeletePostResponse =
-  zDeleteResponse;
-
 export const zListQuotaConfigurationsApiV1BetaQuotaManagementGetResponse =
   zPaginatedResponseQuotaConfiguration;
 
@@ -4091,18 +4135,6 @@ export const zQueryParseConfigurationsApiV1BetaParseConfigurationsQueryPostRespo
 export const zGetLatestParseConfigurationApiV1BetaParseConfigurationsLatestGetResponse =
   z.union([zParseConfiguration, z.null()]);
 
-export const zListSpreadsheetJobsApiV1BetaSpreadsheetJobsGetResponse =
-  zPaginatedResponseSpreadsheetJob;
-
-export const zCreateSpreadsheetJobApiV1BetaSpreadsheetJobsPostResponse =
-  zSpreadsheetJob;
-
-export const zGetSpreadsheetJobApiV1BetaSpreadsheetJobsSpreadsheetJobIdGetResponse =
-  zSpreadsheetJob;
-
-export const zGetTableDownloadPresignedUrlApiV1BetaSpreadsheetJobsSpreadsheetJobIdTablesTableIdResultGetResponse =
-  zPresignedUrl;
-
 export const zUploadFileV2ApiV2Alpha1ParseUploadPostResponse = zParsingJob;
 
 export const zGetSupportedFileExtensionsApiParsingSupportedFileExtensionsGetResponse =

ts/llama_cloud_services/tests/agent_client.test.ts

@@ -1,246 +0,0 @@
-import { describe, it, expect, beforeEach, vi } from "vitest";
-import { AgentClient, createAgentDataClient } from "../src/beta/agent/index.js";
-import * as sdk from "../src/client/index.js";
-
-describe("AgentClient", () => {
-  beforeEach(() => {
-    vi.restoreAllMocks();
-  });
-
-  it("createItem sends correct payload and returns typed data", async () => {
-    const spy = vi
-      .spyOn(sdk, "createAgentDataApiV1BetaAgentDataPost")
-      .mockResolvedValue({
-        data: {
-          id: "1",
-          deployment_name: "dep",
-          collection: "col",
-          data: { foo: "bar" },
-          created_at: "2024-01-01T00:00:00Z",
-          updated_at: "2024-01-01T00:00:00Z",
-        },
-      } as any);
-
-    const client = new AgentClient<{ foo: string }>({
-      deploymentName: "dep",
-      collection: "col",
-    });
-    const result = await client.createItem({ foo: "bar" });
-
-    expect(spy).toHaveBeenCalledOnce();
-    const call = spy.mock.calls[0][0];
-    expect(call.body.deployment_name).toBe("dep");
-    expect(call.body.collection).toBe("col");
-    expect(call.body.data).toEqual({ foo: "bar" });
-
-    expect(result.id).toBe("1");
-    expect(result.deploymentName).toBe("dep");
-    expect(result.collection).toBe("col");
-    expect(result.data).toEqual({ foo: "bar" });
-    expect(result.createdAt).toEqual(new Date("2024-01-01T00:00:00Z"));
-    expect(result.updatedAt).toEqual(new Date("2024-01-01T00:00:00Z"));
-  });
-
-  it("getItem returns null for 404 errors", async () => {
-    const spy = vi
-      .spyOn(sdk, "getAgentDataApiV1BetaAgentDataItemIdGet")
-      .mockImplementation(async () => {
-        const err: any = new Error("Not found");
-        err.response = { status: 404 };
-        throw err;
-      });
-
-    const client = new AgentClient({ deploymentName: "dep" });
-    const res = await client.getItem("missing-id");
-
-    expect(spy).toHaveBeenCalledOnce();
-    expect(res).toBeNull();
-  });
-
-  it("updateItem updates and returns typed data", async () => {
-    const spy = vi
-      .spyOn(sdk, "updateAgentDataApiV1BetaAgentDataItemIdPut")
-      .mockResolvedValue({
-        data: {
-          id: "123",
-          deployment_name: "dep",
-          collection: "col",
-          data: { foo: "baz" },
-          created_at: "2024-01-01T00:00:00Z",
-          updated_at: "2024-01-02T00:00:00Z",
-        },
-      } as any);
-
-    const client = new AgentClient<{ foo: string }>({
-      deploymentName: "dep",
-      collection: "col",
-    });
-    const res = await client.updateItem("123", { foo: "baz" });
-
-    expect(spy).toHaveBeenCalledOnce();
-    const call = spy.mock.calls[0][0];
-    expect(call.path.item_id).toBe("123");
-    expect(call.body.data).toEqual({ foo: "baz" });
-
-    expect(res.id).toBe("123");
-    expect(res.updatedAt).toEqual(new Date("2024-01-02T00:00:00Z"));
-  });
-
-  it("deleteItem calls delete endpoint with correct path", async () => {
-    const spy = vi
-      .spyOn(sdk, "deleteAgentDataApiV1BetaAgentDataItemIdDelete")
-      .mockResolvedValue({} as any);
-
-    const client = new AgentClient({ deploymentName: "dep" });
-    await client.deleteItem("abc");
-
-    expect(spy).toHaveBeenCalledOnce();
-    expect(spy.mock.calls[0][0].path.item_id).toBe("abc");
-  });
-
-  it("delete by query returns deleted count", async () => {
-    const spy = vi
-      .spyOn(sdk, "deleteAgentDataByQueryApiV1BetaAgentDataDeletePost")
-      .mockResolvedValue({ data: { deleted_count: 7 } } as any);
-
-    const client = new AgentClient({
-      deploymentName: "dep",
-      collection: "col",
-    });
-    const count = await client.delete({
-      filter: { status: { op: "eq", value: "accepted" } as any },
-    });
-
-    expect(spy).toHaveBeenCalledOnce();
-    const body = spy.mock.calls[0][0].body;
-    expect(body.deployment_name).toBe("dep");
-    expect(body.collection).toBe("col");
-    expect(count).toBe(7);
-  });
-
-  it("search maps items and optional fields correctly", async () => {
-    const now = "2024-01-01T00:00:00Z";
-    const spy = vi
-      .spyOn(sdk, "searchAgentDataApiV1BetaAgentDataSearchPost")
-      .mockResolvedValue({
-        data: {
-          items: [
-            {
-              id: "1",
-              deployment_name: "dep",
-              collection: "col",
-              data: { foo: "bar" },
-              created_at: now,
-              updated_at: now,
-            },
-          ],
-          total_size: 1,
-          next_page_token: "next",
-        },
-      } as any);
-
-    const client = new AgentClient<{ foo: string }>({
-      deploymentName: "dep",
-      collection: "col",
-    });
-    const result = await client.search({
-      includeTotal: true,
-      orderBy: "created_at desc",
-      pageSize: 1,
-      offset: 0,
-    });
-
-    expect(spy).toHaveBeenCalledOnce();
-    const body = spy.mock.calls[0][0].body;
-    expect(body.deployment_name).toBe("dep");
-    expect(body.collection).toBe("col");
-    expect(body.include_total).toBe(true);
-    expect(body.order_by).toBe("created_at desc");
-    expect(body.page_size).toBe(1);
-    expect(body.offset).toBe(0);
-
-    expect(result.items).toHaveLength(1);
-    expect(result.totalSize).toBe(1);
-    expect(result.nextPageToken).toBe("next");
-    expect(result.items[0].createdAt).toEqual(new Date(now));
-  });
-
-  it("aggregate maps groups and optional fields correctly", async () => {
-    const spy = vi
-      .spyOn(sdk, "aggregateAgentDataApiV1BetaAgentDataAggregatePost")
-      .mockResolvedValue({
-        data: {
-          items: [
-            {
-              group_key: { status: "accepted" },
-              count: 3,
-              first_item: { foo: "bar" },
-            },
-          ],
-          total_size: 1,
-          next_page_token: "tok",
-        },
-      } as any);
-
-    const client = new AgentClient<{ foo: string }>({
-      deploymentName: "dep",
-      collection: "col",
-    });
-    const result = await client.aggregate({
-      groupBy: ["status"],
-      count: true,
-      first: true,
-      pageSize: 1,
-      offset: 0,
-    });
-
-    expect(spy).toHaveBeenCalledOnce();
-    const body = spy.mock.calls[0][0].body;
-    expect(body.deployment_name).toBe("dep");
-    expect(body.collection).toBe("col");
-    expect(body.group_by).toEqual(["status"]);
-    expect(body.count).toBe(true);
-    expect(body.first).toBe(true);
-    expect(body.page_size).toBe(1);
-    expect(body.offset).toBe(0);
-
-    expect(result.items).toHaveLength(1);
-    expect(result.totalSize).toBe(1);
-    expect(result.nextPageToken).toBe("tok");
-    expect(result.items[0].groupKey).toEqual({ status: "accepted" });
-    expect(result.items[0].count).toBe(3);
-    expect(result.items[0].firstItem).toEqual({ foo: "bar" });
-  });
-
-  it("createAgentDataClient infers deployment name from env", async () => {
-    const spy = vi
-      .spyOn(sdk, "searchAgentDataApiV1BetaAgentDataSearchPost")
-      .mockResolvedValue({
-        data: { items: [], total_size: 0 },
-      } as any);
-
-    const client = createAgentDataClient({
-      env: { LLAMA_DEPLOY_DEPLOYMENT_NAME: "env-dep" },
-    });
-    await client.search({});
-
-    const body = spy.mock.calls[0][0].body;
-    expect(body.deployment_name).toBe("env-dep");
-  });
-
-  it("createAgentDataClient infers deployment name from windowUrl (non-local)", async () => {
-    const spy = vi
-      .spyOn(sdk, "deleteAgentDataByQueryApiV1BetaAgentDataDeletePost")
-      .mockResolvedValue({
-        data: { deleted_count: 0 },
-      } as any);
-
-    const client = createAgentDataClient({
-      windowUrl: "https://app.llamaindex.ai/deployments/abc/ui/",
-    });
-    await client.delete({});
-
-    const body = spy.mock.calls[0][0].body;
-    expect(body.deployment_name).toBe("abc");
-  });
-});