Making file parser flexible to deprecate pdf parser (#3073)

Co-authored-by: Suchintan <suchintan@users.noreply.github.com>

fern/workflows/introduction.mdx

@@ -19,7 +19,7 @@ Building blocks supported today:
 - TextPromptBlock: A text only prompt block.
 - SendEmailBlock: Send an email.
 - FileDownloadBlock: Given a goal, Skyvern downloads a file from the website.
-- FileParserBlock: Given a file url, Skyvern downloads the file from the url, and returns the parsed content as the output of the block. Currently only support CSV file format.
+- FileParserBlock: Given a file url, Skyvern downloads the file from the url, and returns the parsed content as the output of the block. Supports CSV, Excel, and PDF file formats.
 - PDFParserBlock: Given a pdf url, Skyvern downloads the PDF file from the url and returns the parsed content as the output of the block.
 - FileUploadBlock: Upload all the downloaded files to a desired destination. Currently only AWS S3 is supported. Please contact support@skyvern.com if you need more integrations.
 - WaitBlock: Wait for a given amount of time.

fern/workflows/workflow-blocks-details.mdx

@@ -43,7 +43,7 @@ This block sends an email.
 This block downloads a file from the website.
 
 ## FileParserBlock
-This block parses a file from the website.
+This block parses PDFs, CSVs, and Excel files from the website.
 
 ## PDFParserBlock
 This block parses a PDF file from the website.

fern/workflows/workflow-blocks.mdx

@@ -228,16 +228,16 @@ Inputs:
 
 Downloads and parses a file to be used within other workflow blocks.
 
-**Supported types:** CSV
+**Supported types:** CSV, TSV, Excel, PDF
 
 ```
 - block_type: file_url_parser
-  label: csv_parser
+  label: file_parser
-  file_type: csv
+  file_type: csv  # Auto-detected from URL extension
-  file_url: <csv_file_url>
+  file_url: <file_url>
 ```
 
 Inputs: 
 
-1. **File URL *(required):*** This block allows you to use a CSV within your workflow. 
+1. **File URL *(required):*** This block allows you to use CSV, TSV, Excel, and PDF files within your workflow. 
     * Since we’re still in beta, you will need to [contact us](https://meetings.hubspot.com/skyvern/demo?uuid=7c83865f-1a92-4c44-9e52-1ba0dbc04f7a) to load a value into this block

poetry.lock

@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 2.1.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 2.1.3 and should not be changed by hand.
 
 [[package]]
 name = "about-time"
@@ -1587,6 +1587,18 @@ files = [
 [package.extras]
 mypy = ["mypy"]
 
+[[package]]
+name = "et-xmlfile"
+version = "2.0.0"
+description = "An implementation of lxml.xmlfile for the standard library"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "et_xmlfile-2.0.0-py3-none-any.whl", hash = "sha256:7a91720bc756843502c3b7504c77b8fe44217c85c537d85037f0f536151b2caa"},
+    {file = "et_xmlfile-2.0.0.tar.gz", hash = "sha256:dab3f4764309081ce75662649be815c4c9081e88f0837825f90fd28317d4da54"},
+]
+
 [[package]]
 name = "exceptiongroup"
 version = "1.3.0"
@@ -2374,7 +2386,7 @@ description = "Lightweight in-process concurrent programming"
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
-markers = "python_version == \"3.12\" or python_version == \"3.13\""
+markers = "python_version >= \"3.12\""
 files = [
     {file = "greenlet-3.2.3-cp310-cp310-macosx_11_0_universal2.whl", hash = "sha256:1afd685acd5597349ee6d7a88a8bec83ce13c106ac78c196ee9dde7c04fe87be"},
     {file = "greenlet-3.2.3-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:761917cac215c61e9dc7324b2606107b3b292a8349bdebb31503ab4de3f559ac"},
@@ -3183,7 +3195,7 @@ description = "Low-level, pure Python DBus protocol wrapper."
 optional = false
 python-versions = ">=3.7"
 groups = ["dev"]
-markers = "platform_machine != \"ppc64le\" and platform_machine != \"s390x\" and sys_platform == \"linux\""
+markers = "sys_platform == \"linux\" and platform_machine != \"ppc64le\" and platform_machine != \"s390x\""
 files = [
     {file = "jeepney-0.9.0-py3-none-any.whl", hash = "sha256:97e5714520c16fc0a45695e5365a2e11b81ea79bba796e26f9f1d178cb182683"},
     {file = "jeepney-0.9.0.tar.gz", hash = "sha256:cf0e9e845622b81e4a28df94c40345400256ec608d0e55bb8a3feaa9163f5732"},
@@ -4829,7 +4841,7 @@ description = "ONNX Runtime is a runtime accelerator for Machine Learning models
 optional = false
 python-versions = ">=3.10"
 groups = ["main"]
-markers = "python_version == \"3.12\" or python_version == \"3.13\""
+markers = "python_version >= \"3.12\""
 files = [
     {file = "onnxruntime-1.22.0-cp310-cp310-macosx_13_0_universal2.whl", hash = "sha256:85d8826cc8054e4d6bf07f779dc742a363c39094015bdad6a08b3c18cfe0ba8c"},
     {file = "onnxruntime-1.22.0-cp310-cp310-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:468c9502a12f6f49ec335c2febd22fdceecc1e4cc96dfc27e419ba237dff5aff"},
@@ -4937,6 +4949,21 @@ jsonschema-path = ">=0.3.1,<0.4.0"
 lazy-object-proxy = ">=1.7.1,<2.0.0"
 openapi-schema-validator = ">=0.6.0,<0.7.0"
 
+[[package]]
+name = "openpyxl"
+version = "3.1.5"
+description = "A Python library to read/write Excel 2010 xlsx/xlsm files"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "openpyxl-3.1.5-py2.py3-none-any.whl", hash = "sha256:5282c12b107bffeef825f4617dc029afaf41d0ea60823bbb665ef3079dc79de2"},
+    {file = "openpyxl-3.1.5.tar.gz", hash = "sha256:cf0e3cf56142039133628b5acffe8ef0c12bc902d2aadd3e0fe5878dc08d1050"},
+]
+
+[package.dependencies]
+et-xmlfile = "*"
+
 [[package]]
 name = "opentelemetry-api"
 version = "1.34.1"
@@ -5930,7 +5957,7 @@ description = "A high-level API to automate web browsers"
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
-markers = "python_version == \"3.12\" or python_version == \"3.13\""
+markers = "python_version >= \"3.12\""
 files = [
     {file = "playwright-1.53.0-py3-none-macosx_10_13_x86_64.whl", hash = "sha256:48a1a15ce810f0ffe512b6050de9871ea193b41dd3cc1bbed87b8431012419ba"},
     {file = "playwright-1.53.0-py3-none-macosx_11_0_arm64.whl", hash = "sha256:a701f9498a5b87e3f929ec01cea3109fbde75821b19c7ba4bba54f6127b94f76"},
@@ -6227,7 +6254,7 @@ description = "PostgreSQL database adapter for Python"
 optional = false
 python-versions = ">=3.7"
 groups = ["main"]
-markers = "python_version == \"3.12\" or python_version == \"3.11\""
+markers = "python_version < \"3.13\""
 files = [
     {file = "psycopg-3.1.18-py3-none-any.whl", hash = "sha256:4d5a0a5a8590906daa58ebd5f3cfc34091377354a1acced269dd10faf55da60e"},
     {file = "psycopg-3.1.18.tar.gz", hash = "sha256:31144d3fb4c17d78094d9e579826f047d4af1da6a10427d91dfcfb6ecdf6f12b"},
@@ -6280,7 +6307,7 @@ description = "PostgreSQL database adapter for Python -- C optimisation distribu
 optional = false
 python-versions = ">=3.7"
 groups = ["main"]
-markers = "(python_version == \"3.12\" or python_version == \"3.11\") and implementation_name != \"pypy\""
+markers = "python_version < \"3.13\" and implementation_name != \"pypy\""
 files = [
     {file = "psycopg_binary-3.1.18-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:5c323103dfa663b88204cf5f028e83c77d7a715f9b6f51d2bbc8184b99ddd90a"},
     {file = "psycopg_binary-3.1.18-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:887f8d856c91510148be942c7acd702ccf761a05f59f8abc123c22ab77b5a16c"},
@@ -6731,7 +6758,7 @@ description = "A rough port of Node.js's EventEmitter to Python with a few trick
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
-markers = "python_version == \"3.12\" or python_version == \"3.13\""
+markers = "python_version >= \"3.12\""
 files = [
     {file = "pyee-13.0.0-py3-none-any.whl", hash = "sha256:48195a3cddb3b1515ce0695ed76036b5ccc2ef3a9f963ff9f77aec0139845498"},
     {file = "pyee-13.0.0.tar.gz", hash = "sha256:b391e3c5a434d1f5118a25615001dbc8f669cf410ab67d04c4d4e07c55481c37"},
@@ -7044,7 +7071,7 @@ description = "A (partial) reimplementation of pywin32 using ctypes/cffi"
 optional = false
 python-versions = ">=3.6"
 groups = ["dev"]
-markers = "platform_machine != \"ppc64le\" and platform_machine != \"s390x\" and sys_platform == \"win32\""
+markers = "sys_platform == \"win32\" and platform_machine != \"ppc64le\" and platform_machine != \"s390x\""
 files = [
     {file = "pywin32-ctypes-0.2.3.tar.gz", hash = "sha256:d162dc04946d704503b2edc4d55f3dba5c1d539ead017afa00142c38b9885755"},
     {file = "pywin32_ctypes-0.2.3-py3-none-any.whl", hash = "sha256:8a1513379d709975552d202d942d9837758905c8d01eb82b8bcc30918929e7b8"},
@@ -7784,7 +7811,7 @@ description = "Python bindings to FreeDesktop.org Secret Service API"
 optional = false
 python-versions = ">=3.6"
 groups = ["dev"]
-markers = "platform_machine != \"ppc64le\" and platform_machine != \"s390x\" and sys_platform == \"linux\""
+markers = "sys_platform == \"linux\" and platform_machine != \"ppc64le\" and platform_machine != \"s390x\""
 files = [
     {file = "SecretStorage-3.3.3-py3-none-any.whl", hash = "sha256:f356e6628222568e3af06f2eba8df495efa13b3b63081dafd4f7d9a7b7bc9f99"},
     {file = "SecretStorage-3.3.3.tar.gz", hash = "sha256:2403533ef369eca6d2ba81718576c5e0f564d5cca1b58f73a8b23e7d4eeebd77"},
@@ -9128,7 +9155,7 @@ description = "Fast implementation of asyncio event loop on top of libuv"
 optional = false
 python-versions = ">=3.8.0"
 groups = ["main"]
-markers = "sys_platform != \"win32\" and sys_platform != \"cygwin\" and platform_python_implementation != \"PyPy\""
+markers = "platform_python_implementation != \"PyPy\" and sys_platform != \"win32\" and sys_platform != \"cygwin\""
 files = [
     {file = "uvloop-0.21.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:ec7e6b09a6fdded42403182ab6b832b71f4edaf7f37a9a0e371a01db5f0cb45f"},
     {file = "uvloop-0.21.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:196274f2adb9689a289ad7d65700d37df0c0930fd8e4e743fa4834e850d7719d"},
@@ -9763,4 +9790,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.11,<3.14"
-content-hash = "667e626dd8d08bae4f9b852616a9c2c5df9bdd4a3a37f7ef87030d7bfdf51f3b"
+content-hash = "441c7080f7fbccb87de476e56dd86d1e56e4c0b8eaa8378d13c90d94a0a42123"

pyproject.toml

@@ -78,6 +78,7 @@ lark = "^1.2.2"
 libcst = "^1.8.2"
 curlparser = "^0.1.0"
 lmnr = {extras = ["all"], version = "^0.7.0"}
+openpyxl = "^3.1.5"
 
 [tool.poetry.group.dev.dependencies]
 isort = "^5.13.2"

skyvern-frontend/src/routes/workflows/editor/nodes/FileParserNode/FileParserNode.tsx

@@ -10,6 +10,8 @@ import { useDebugStore } from "@/store/useDebugStore";
 import { cn } from "@/util/utils";
 import { NodeHeader } from "../components/NodeHeader";
 import { useParams } from "react-router-dom";
+import { WorkflowDataSchemaInputGroup } from "@/components/DataSchemaInputGroup/WorkflowDataSchemaInputGroup";
+import { dataSchemaExampleForFileExtraction } from "../types";
 
 function FileParserNode({ id, data }: NodeProps<FileParserNode>) {
   const { updateNodeData } = useReactFlow();
@@ -21,8 +23,17 @@ function FileParserNode({ id, data }: NodeProps<FileParserNode>) {
     urlBlockLabel !== undefined && urlBlockLabel === label;
   const [inputs, setInputs] = useState({
     fileUrl: data.fileUrl,
+    jsonSchema: data.jsonSchema,
   });
 
+  function handleChange(key: string, value: unknown) {
+    if (!data.editable) {
+      return;
+    }
+    setInputs({ ...inputs, [key]: value });
+    updateNodeData(id, { [key]: value });
+  }
+
   const isFirstWorkflowBlock = useIsFirstBlockInWorkflow({ id });
 
   return (
@@ -75,15 +86,19 @@ function FileParserNode({ id, data }: NodeProps<FileParserNode>) {
               nodeId={id}
               value={inputs.fileUrl}
               onChange={(value) => {
-                if (!data.editable) {
+                handleChange("fileUrl", value);
-                  return;
-                }
-                setInputs({ ...inputs, fileUrl: value });
-                updateNodeData(id, { fileUrl: value });
               }}
               className="nopan text-xs"
             />
           </div>
+          <WorkflowDataSchemaInputGroup
+            exampleValue={dataSchemaExampleForFileExtraction}
+            value={inputs.jsonSchema}
+            onChange={(value) => {
+              handleChange("jsonSchema", value);
+            }}
+            suggestionContext={{}}
+          />
         </div>
       </div>
     </div>

skyvern-frontend/src/routes/workflows/editor/nodes/FileParserNode/types.ts

@@ -1,9 +1,11 @@
 import type { Node } from "@xyflow/react";
 import { NodeBaseData } from "../types";
+import { AppNode } from "..";
 import { debuggableWorkflowBlockTypes } from "@/routes/workflows/types/workflowTypes";
 
 export type FileParserNodeData = NodeBaseData & {
   fileUrl: string;
+  jsonSchema: string;
 };
 
 export type FileParserNode = Node<FileParserNodeData, "fileParser">;
@@ -14,5 +16,10 @@ export const fileParserNodeDefaultData: FileParserNodeData = {
   label: "",
   fileUrl: "",
   continueOnFailure: false,
+  jsonSchema: "null",
   model: null,
 } as const;
+
+export function isFileParserNode(node: AppNode): node is FileParserNode {
+  return node.type === "fileParser";
+}

skyvern-frontend/src/routes/workflows/editor/panels/WorkflowNodeLibraryPanel.tsx

@@ -162,20 +162,19 @@ const nodeLibraryItems: Array<{
       />
     ),
     title: "File Parser Block",
-    description: "Parse data from files",
+    description: "Parse PDFs, CSVs, and Excel files",
-  },
-  {
-    nodeType: "pdfParser",
-    icon: (
-      <WorkflowBlockIcon
-        workflowBlockType={WorkflowBlockTypes.PDFParser}
-        className="size-6"
-      />
-    ),
-    title: "PDF Parser Block",
-    description: "Extract data from PDF files",
   },
   // {
+  //   nodeType: "pdfParser",
+  //   icon: (
+  //     <WorkflowBlockIcon
+  //       workflowBlockType={WorkflowBlockTypes.PDFParser}
+  //       className="size-6"
+  //     />
+  //   ),
+  //   title: "PDF Parser Block",
+  //   description: "Extract data from PDF files",
+  // },
   //   nodeType: "upload",
   //   icon: (
   //     <WorkflowBlockIcon

skyvern-frontend/src/routes/workflows/editor/workflowEditorUtils.ts

@@ -56,7 +56,10 @@ import { ParametersState } from "./types";
 import { AppNode, isWorkflowBlockNode, WorkflowBlockNode } from "./nodes";
 import { codeBlockNodeDefaultData } from "./nodes/CodeBlockNode/types";
 import { downloadNodeDefaultData } from "./nodes/DownloadNode/types";
-import { fileParserNodeDefaultData } from "./nodes/FileParserNode/types";
+import {
+  isFileParserNode,
+  fileParserNodeDefaultData,
+} from "./nodes/FileParserNode/types";
 import {
   isLoopNode,
   LoopNode,
@@ -468,6 +471,7 @@ function convertToNode(
         data: {
           ...commonData,
           fileUrl: block.file_url,
+          jsonSchema: JSON.stringify(block.json_schema, null, 2),
         },
       };
     }
@@ -1254,7 +1258,8 @@ function getWorkflowBlock(node: WorkflowBlockNode): BlockYAML {
         ...base,
         block_type: "file_url_parser",
         file_url: node.data.fileUrl,
-        file_type: "csv",
+        file_type: "csv", // Backend will auto-detect based on file extension
+        json_schema: JSONParseSafe(node.data.jsonSchema),
       };
     }
     case "textPrompt": {
@@ -2187,6 +2192,15 @@ function getWorkflowErrors(nodes: Array<AppNode>): Array<string> {
     }
   });
 
+  const fileParserNodes = nodes.filter(isFileParserNode);
+  fileParserNodes.forEach((node) => {
+    try {
+      JSON.parse(node.data.jsonSchema);
+    } catch {
+      errors.push(`${node.data.label}: Data schema is not valid JSON.`);
+    }
+  });
+
   const waitNodes = nodes.filter(isWaitNode);
   waitNodes.forEach((node) => {
     const waitTimeString = node.data.waitInSeconds.trim();

skyvern-frontend/src/routes/workflows/types/workflowTypes.ts

@@ -354,7 +354,8 @@ export type SendEmailBlock = WorkflowBlockBase & {
 export type FileURLParserBlock = WorkflowBlockBase & {
   block_type: "file_url_parser";
   file_url: string;
-  file_type: "csv";
+  file_type: "csv" | "excel" | "pdf";
+  json_schema: Record<string, unknown> | null;
 };
 
 export type ValidationBlock = WorkflowBlockBase & {

skyvern-frontend/src/routes/workflows/types/workflowYamlTypes.ts

@@ -308,7 +308,8 @@ export type SendEmailBlockYAML = BlockYAMLBase & {
 export type FileUrlParserBlockYAML = BlockYAMLBase & {
   block_type: "file_url_parser";
   file_url: string;
-  file_type: "csv";
+  file_type: "csv" | "excel" | "pdf";
+  json_schema?: Record<string, unknown> | null;
 };
 
 export type ForLoopBlockYAML = BlockYAMLBase & {

skyvern/forge/sdk/workflow/models/block.py

@@ -21,6 +21,7 @@ from typing import Annotated, Any, Awaitable, Callable, Literal, Union
 from urllib.parse import quote, urlparse
 
 import filetype
+import pandas as pd
 import structlog
 from email_validator import EmailNotValidError, validate_email
 from jinja2.sandbox import SandboxedEnvironment
@@ -2342,6 +2343,8 @@ class SendEmailBlock(Block):
 
 class FileType(StrEnum):
     CSV = "csv"
+    EXCEL = "excel"
+    PDF = "pdf"
 
 
 class FileParserBlock(Block):
@@ -2349,6 +2352,7 @@ class FileParserBlock(Block):
 
     file_url: str
     file_type: FileType
+    json_schema: dict[str, Any] | None = None
 
     def get_all_parameters(
         self,
@@ -2364,6 +2368,18 @@ class FileParserBlock(Block):
             self.file_url, workflow_run_context
         )
 
+    def _detect_file_type_from_url(self, file_url: str) -> FileType:
+        """Detect file type based on file extension in the URL."""
+        url_lower = file_url.lower()
+        if url_lower.endswith((".xlsx", ".xls", ".xlsm")):
+            return FileType.EXCEL
+        elif url_lower.endswith(".pdf"):
+            return FileType.PDF
+        elif url_lower.endswith(".tsv"):
+            return FileType.CSV  # TSV files are handled by the CSV parser
+        else:
+            return FileType.CSV  # Default to CSV for .csv and any other extensions
+
     def validate_file_type(self, file_url_used: str, file_path: str) -> None:
         if self.file_type == FileType.CSV:
             try:
@@ -2371,6 +2387,121 @@ class FileParserBlock(Block):
                     csv.Sniffer().sniff(file.read(1024))
             except csv.Error as e:
                 raise InvalidFileType(file_url=file_url_used, file_type=self.file_type, error=str(e))
+        elif self.file_type == FileType.EXCEL:
+            try:
+                # Try to read the file with pandas to validate it's a valid Excel file
+                pd.read_excel(file_path, nrows=1, engine="openpyxl")
+            except Exception as e:
+                raise InvalidFileType(
+                    file_url=file_url_used, file_type=self.file_type, error=f"Invalid Excel file format: {str(e)}"
+                )
+        elif self.file_type == FileType.PDF:
+            try:
+                # Try to read the file with PyPDF to validate it's a valid PDF file
+                reader = PdfReader(file_path)
+                # Just check if we can access pages, don't read content yet
+                _ = len(reader.pages)
+            except Exception as e:
+                raise InvalidFileType(file_url=file_url_used, file_type=self.file_type, error=str(e))
+
+    async def _parse_csv_file(self, file_path: str) -> list[dict[str, Any]]:
+        """Parse CSV/TSV file and return list of dictionaries."""
+        parsed_data = []
+        with open(file_path) as file:
+            # Try to detect the delimiter (comma for CSV, tab for TSV)
+            sample = file.read(1024)
+            file.seek(0)  # Reset file pointer
+
+            # Use csv.Sniffer to detect the delimiter
+            try:
+                dialect = csv.Sniffer().sniff(sample)
+                delimiter = dialect.delimiter
+            except csv.Error:
+                # Default to comma if detection fails
+                delimiter = ","
+
+            reader = csv.DictReader(file, delimiter=delimiter)
+            for row in reader:
+                parsed_data.append(row)
+        return parsed_data
+
+    def _clean_dataframe_for_json(self, df: pd.DataFrame) -> list[dict[str, Any]]:
+        """Clean DataFrame to ensure it can be serialized to JSON."""
+        # Replace NaN and NaT values with "nan" string
+        df_cleaned = df.replace({pd.NA: "nan", pd.NaT: "nan"})
+        df_cleaned = df_cleaned.where(pd.notna(df_cleaned), "nan")
+
+        # Convert to list of dictionaries
+        records = df_cleaned.to_dict("records")
+
+        # Additional cleaning for any remaining problematic values
+        for record in records:
+            for key, value in record.items():
+                if pd.isna(value) or value == "NaN" or value == "NaT":
+                    record[key] = "nan"
+                elif isinstance(value, (pd.Timestamp, pd.DatetimeTZDtype)):
+                    # Convert pandas timestamps to ISO format strings
+                    record[key] = value.isoformat() if pd.notna(value) else "nan"
+
+        return records
+
+    async def _parse_excel_file(self, file_path: str) -> list[dict[str, Any]]:
+        """Parse Excel file and return list of dictionaries."""
+        try:
+            # Read Excel file with pandas, specifying engine explicitly
+            df = pd.read_excel(file_path, engine="openpyxl")
+            # Clean and convert DataFrame to list of dictionaries
+            return self._clean_dataframe_for_json(df)
+        except ImportError as e:
+            raise InvalidFileType(
+                file_url=self.file_url,
+                file_type=self.file_type,
+                error=f"Missing required dependency for Excel parsing: {str(e)}. Please install openpyxl: pip install openpyxl",
+            )
+        except Exception as e:
+            raise InvalidFileType(
+                file_url=self.file_url, file_type=self.file_type, error=f"Failed to parse Excel file: {str(e)}"
+            )
+
+    async def _parse_pdf_file(self, file_path: str) -> str:
+        """Parse PDF file and return extracted text."""
+        try:
+            reader = PdfReader(file_path)
+            extracted_text = ""
+            page_count = len(reader.pages)
+            for i in range(page_count):
+                extracted_text += reader.pages[i].extract_text() + "\n"
+            return extracted_text
+        except PdfReadError as e:
+            raise InvalidFileType(file_url=self.file_url, file_type=self.file_type, error=str(e))
+
+    async def _extract_with_ai(
+        self, content: str | list[dict[str, Any]], workflow_run_context: WorkflowRunContext
+    ) -> dict[str, Any]:
+        """Extract structured data using AI based on json_schema."""
+        # Use local variable to avoid mutating the instance
+        schema_to_use = self.json_schema or {
+            "type": "object",
+            "properties": {
+                "output": {
+                    "type": "object",
+                    "description": "Information extracted from the file",
+                }
+            },
+        }
+
+        # Convert content to string for AI processing
+        if isinstance(content, list):
+            # For CSV/Excel data, convert to a readable format
+            content_str = json.dumps(content, indent=2)
+        else:
+            content_str = content
+
+        llm_prompt = prompt_engine.load_prompt(
+            "extract-information-from-file-text", extracted_text_content=content_str, json_schema=schema_to_use
+        )
+        llm_response = await app.LLM_API_HANDLER(prompt=llm_prompt, prompt_name="extract-information-from-file-text")
+        return llm_response
 
     async def execute(
         self,
@@ -2381,6 +2512,7 @@ class FileParserBlock(Block):
         **kwargs: dict,
     ) -> BlockResult:
         workflow_run_context = self.get_workflow_run_context(workflow_run_id)
+
         if (
             self.file_url
             and workflow_run_context.has_parameter(self.file_url)
@@ -2412,21 +2544,71 @@ class FileParserBlock(Block):
             file_path = await download_from_s3(self.get_async_aws_client(), self.file_url)
         else:
             file_path = await download_file(self.file_url)
+
+        # Auto-detect file type based on file extension
+        detected_file_type = self._detect_file_type_from_url(self.file_url)
+        self.file_type = detected_file_type
+
         # Validate the file type
         self.validate_file_type(self.file_url, file_path)
-        # Parse the file into a list of dictionaries where each dictionary represents a row in the file
+
-        parsed_data = []
+        LOG.debug(
-        with open(file_path) as file:
+            "FileParserBlock: After file type validation",
-            if self.file_type == FileType.CSV:
+            file_type=self.file_type,
-                reader = csv.DictReader(file)
+            json_schema_present=self.json_schema is not None,
-                for row in reader:
+            json_schema_type=type(self.json_schema),
-                    parsed_data.append(row)
+        )
+
+        # Parse the file based on type
+        parsed_data: str | list[dict[str, Any]]
+        if self.file_type == FileType.CSV:
+            parsed_data = await self._parse_csv_file(file_path)
+        elif self.file_type == FileType.EXCEL:
+            parsed_data = await self._parse_excel_file(file_path)
+        elif self.file_type == FileType.PDF:
+            parsed_data = await self._parse_pdf_file(file_path)
+        else:
+            return await self.build_block_result(
+                success=False,
+                failure_reason=f"Unsupported file type: {self.file_type}",
+                output_parameter_value=None,
+                status=BlockStatus.failed,
+                workflow_run_block_id=workflow_run_block_id,
+                organization_id=organization_id,
+            )
+
+        # If json_schema is provided, use AI to extract structured data
+        final_data: str | list[dict[str, Any]] | dict[str, Any]
+        LOG.debug(
+            "FileParserBlock: JSON schema check",
+            has_json_schema=self.json_schema is not None,
+            json_schema_type=type(self.json_schema),
+            json_schema=self.json_schema,
+        )
+
+        if self.json_schema:
+            try:
+                ai_extracted_data = await self._extract_with_ai(parsed_data, workflow_run_context)
+                final_data = ai_extracted_data
+            except Exception as e:
+                return await self.build_block_result(
+                    success=False,
+                    failure_reason=f"Failed to extract data with AI: {str(e)}",
+                    output_parameter_value=None,
+                    status=BlockStatus.failed,
+                    workflow_run_block_id=workflow_run_block_id,
+                    organization_id=organization_id,
+                )
+        else:
+            # Return raw parsed data
+            final_data = parsed_data
+
         # Record the parsed data
-        await self.record_output_parameter_value(workflow_run_context, workflow_run_id, parsed_data)
+        await self.record_output_parameter_value(workflow_run_context, workflow_run_id, final_data)
         return await self.build_block_result(
             success=True,
             failure_reason=None,
-            output_parameter_value=parsed_data,
+            output_parameter_value=final_data,
             status=BlockStatus.completed,
             workflow_run_block_id=workflow_run_block_id,
             organization_id=organization_id,
@@ -2434,6 +2616,11 @@ class FileParserBlock(Block):
 
 
 class PDFParserBlock(Block):
+    """
+    DEPRECATED: Use FileParserBlock with file_type=FileType.PDF instead.
+    This block will be removed in a future version.
+    """
+
     block_type: Literal[BlockType.PDF_PARSER] = BlockType.PDF_PARSER
 
     file_url: str

skyvern/forge/sdk/workflow/models/yaml.py

@@ -244,6 +244,7 @@ class FileParserBlockYAML(BlockYAML):
 
     file_url: str
     file_type: FileType
+    json_schema: dict[str, Any] | None = None
 
 
 class PDFParserBlockYAML(BlockYAML):

skyvern/forge/sdk/workflow/service.py

@@ -1926,6 +1926,7 @@ class WorkflowService:
                 output_parameter=output_parameter,
                 file_url=block_yaml.file_url,
                 file_type=block_yaml.file_type,
+                json_schema=block_yaml.json_schema,
                 continue_on_failure=block_yaml.continue_on_failure,
             )
         elif block_yaml.block_type == BlockType.PDF_PARSER:

tests/unit_tests/test_file_parser_block.py

@@ -0,0 +1,252 @@
+import os
+import tempfile
+from datetime import datetime
+from unittest.mock import MagicMock, patch
+
+import pandas as pd
+import pytest
+
+from skyvern.forge.sdk.workflow.models.block import FileParserBlock, FileType
+from skyvern.forge.sdk.workflow.models.parameter import OutputParameter
+
+
+class TestFileParserBlock:
+    @pytest.fixture
+    def file_parser_block(self):
+        """Create a basic FileParserBlock instance for testing."""
+        # Create a mock OutputParameter with all required fields
+        mock_output_parameter = MagicMock(spec=OutputParameter)
+        mock_output_parameter.parameter_type = "output"
+        mock_output_parameter.key = "test_output"
+        mock_output_parameter.output_parameter_id = "test_id"
+        mock_output_parameter.workflow_id = "test_workflow_id"
+        mock_output_parameter.created_at = datetime.now()
+        mock_output_parameter.modified_at = datetime.now()
+        mock_output_parameter.deleted_at = None
+
+        return FileParserBlock(
+            label="test_parser", output_parameter=mock_output_parameter, file_url="test.csv", file_type=FileType.CSV
+        )
+
+    @pytest.fixture
+    def csv_file(self):
+        """Create a temporary CSV file for testing."""
+        with tempfile.NamedTemporaryFile(mode="w", suffix=".csv", delete=False) as f:
+            f.write("name,age,city\nJohn,30,New York\nJane,25,Boston")
+            temp_file = f.name
+
+        yield temp_file
+        os.unlink(temp_file)
+
+    @pytest.fixture
+    def excel_file(self):
+        """Create a temporary Excel file for testing."""
+        df = pd.DataFrame({"name": ["John", "Jane"], "age": [30, 25], "city": ["New York", "Boston"]})
+
+        with tempfile.NamedTemporaryFile(suffix=".xlsx", delete=False) as f:
+            df.to_excel(f.name, index=False)
+            temp_file = f.name
+
+        yield temp_file
+        os.unlink(temp_file)
+
+    @pytest.fixture
+    def tsv_file(self):
+        """Create a temporary TSV file for testing."""
+        with tempfile.NamedTemporaryFile(mode="w", suffix=".tsv", delete=False) as f:
+            f.write("name\tage\tcity\nJohn\t30\tNew York\nJane\t25\tBoston")
+            temp_file = f.name
+
+        yield temp_file
+        os.unlink(temp_file)
+
+    def test_file_type_enum_values(self):
+        """Test that FileType enum has the expected values."""
+        assert FileType.CSV == "csv"
+        assert FileType.EXCEL == "excel"
+        assert FileType.PDF == "pdf"
+
+    def test_file_parser_block_initialization(self, file_parser_block):
+        """Test that FileParserBlock initializes correctly."""
+        assert file_parser_block.label == "test_parser"
+        assert file_parser_block.file_url == "test.csv"
+        assert file_parser_block.file_type == FileType.CSV
+        assert file_parser_block.json_schema is None
+
+    def test_file_parser_block_with_schema(self):
+        """Test that FileParserBlock can be initialized with a schema."""
+        schema = {"type": "object", "properties": {"name": {"type": "string"}, "age": {"type": "integer"}}}
+
+        # Create a mock OutputParameter
+        mock_output_parameter = MagicMock(spec=OutputParameter)
+        mock_output_parameter.parameter_type = "output"
+        mock_output_parameter.key = "test_output"
+        mock_output_parameter.output_parameter_id = "test_id"
+        mock_output_parameter.workflow_id = "test_workflow_id"
+        mock_output_parameter.created_at = datetime.now()
+        mock_output_parameter.modified_at = datetime.now()
+        mock_output_parameter.deleted_at = None
+
+        block = FileParserBlock(
+            label="test_parser",
+            output_parameter=mock_output_parameter,
+            file_url="test.csv",
+            file_type=FileType.CSV,
+            json_schema=schema,
+        )
+
+        assert block.json_schema == schema
+
+    @pytest.mark.asyncio
+    async def test_parse_csv_file(self, file_parser_block, csv_file):
+        """Test CSV file parsing."""
+        result = await file_parser_block._parse_csv_file(csv_file)
+
+        expected = [{"name": "John", "age": "30", "city": "New York"}, {"name": "Jane", "age": "25", "city": "Boston"}]
+
+        assert result == expected
+
+    @pytest.mark.asyncio
+    async def test_parse_excel_file(self, file_parser_block, excel_file):
+        """Test Excel file parsing."""
+        result = await file_parser_block._parse_excel_file(excel_file)
+
+        expected = [{"name": "John", "age": 30, "city": "New York"}, {"name": "Jane", "age": 25, "city": "Boston"}]
+
+        assert result == expected
+
+    @pytest.mark.asyncio
+    async def test_parse_tsv_file(self, file_parser_block, tsv_file):
+        """Test TSV file parsing."""
+        result = await file_parser_block._parse_csv_file(tsv_file)
+
+        expected = [{"name": "John", "age": "30", "city": "New York"}, {"name": "Jane", "age": "25", "city": "Boston"}]
+
+        assert result == expected
+
+    @pytest.mark.asyncio
+    async def test_validate_csv_file_type(self, file_parser_block, csv_file):
+        """Test CSV file type validation."""
+        # Should not raise an exception
+        file_parser_block.validate_file_type("test.csv", csv_file)
+
+    @pytest.mark.asyncio
+    async def test_validate_excel_file_type(self, file_parser_block, excel_file):
+        """Test Excel file type validation."""
+        file_parser_block.file_type = FileType.EXCEL
+        # Should not raise an exception
+        file_parser_block.validate_file_type("test.xlsx", excel_file)
+
+    @pytest.mark.asyncio
+    async def test_validate_invalid_csv_file(self, file_parser_block):
+        """Test validation of invalid CSV file."""
+        # Create a binary file that's definitely not CSV
+        with tempfile.NamedTemporaryFile(mode="wb", suffix=".csv", delete=False) as f:
+            f.write(b"\x00\x01\x02\x03\x04\x05\x06\x07\x08\x09\x0a\x0b\x0c\x0d\x0e\x0f")
+            temp_file = f.name
+
+        try:
+            with pytest.raises(Exception):
+                file_parser_block.validate_file_type("test.csv", temp_file)
+        finally:
+            os.unlink(temp_file)
+
+    @pytest.mark.asyncio
+    async def test_extract_with_ai_with_schema(self, file_parser_block):
+        """Test AI extraction with a provided schema."""
+        schema = {
+            "type": "object",
+            "properties": {
+                "extracted_data": {
+                    "type": "object",
+                    "properties": {
+                        "names": {"type": "array", "items": {"type": "string"}},
+                        "total_count": {"type": "integer"},
+                    },
+                }
+            },
+        }
+
+        file_parser_block.json_schema = schema
+
+        # Mock the LLM response
+        mock_response = {"extracted_data": {"names": ["John", "Jane"], "total_count": 2}}
+
+        with patch("skyvern.forge.sdk.workflow.models.block.app.LLM_API_HANDLER") as mock_llm:
+            mock_llm.return_value = mock_response
+
+            with patch("skyvern.forge.sdk.workflow.models.block.prompt_engine.load_prompt") as mock_prompt:
+                mock_prompt.return_value = "mocked prompt"
+
+                result = await file_parser_block._extract_with_ai([{"name": "John"}, {"name": "Jane"}], MagicMock())
+
+                assert result == mock_response
+                mock_llm.assert_called_once()
+                mock_prompt.assert_called_once()
+
+    @pytest.mark.asyncio
+    async def test_extract_with_ai_without_schema(self, file_parser_block):
+        """Test AI extraction without a provided schema (should use default)."""
+        # Mock the LLM response
+        mock_response = {"output": {"summary": "Extracted data from file"}}
+
+        with patch("skyvern.forge.sdk.workflow.models.block.app.LLM_API_HANDLER") as mock_llm:
+            mock_llm.return_value = mock_response
+
+            with patch("skyvern.forge.sdk.workflow.models.block.prompt_engine.load_prompt") as mock_prompt:
+                mock_prompt.return_value = "mocked prompt"
+
+                result = await file_parser_block._extract_with_ai("Some text content", MagicMock())
+
+                assert result == mock_response
+                # Should NOT mutate the instance - json_schema should remain None
+                assert file_parser_block.json_schema is None
+                mock_llm.assert_called_once()
+                mock_prompt.assert_called_once()
+
+    def test_detect_file_type_from_url(self, file_parser_block):
+        """Test file type detection based on URL extension."""
+        # Test Excel files
+        assert file_parser_block._detect_file_type_from_url("https://example.com/data.xlsx") == FileType.EXCEL
+        assert file_parser_block._detect_file_type_from_url("https://example.com/data.xls") == FileType.EXCEL
+        assert file_parser_block._detect_file_type_from_url("https://example.com/data.xlsm") == FileType.EXCEL
+
+        # Test PDF files
+        assert file_parser_block._detect_file_type_from_url("https://example.com/document.pdf") == FileType.PDF
+
+        # Test CSV files (default)
+        assert file_parser_block._detect_file_type_from_url("https://example.com/data.csv") == FileType.CSV
+        assert file_parser_block._detect_file_type_from_url("https://example.com/data.tsv") == FileType.CSV
+        assert file_parser_block._detect_file_type_from_url("https://example.com/data.txt") == FileType.CSV
+        assert file_parser_block._detect_file_type_from_url("https://example.com/data") == FileType.CSV
+
+    def test_clean_dataframe_for_json(self, file_parser_block):
+        """Test DataFrame cleaning for JSON serialization."""
+        # Create a DataFrame with NaN, NaT, and timestamp values
+        df = pd.DataFrame(
+            {
+                "OrderDate": ["2018-01-01", pd.NaT, "2018-01-03"],
+                "Region": ["North", "South", pd.NA],
+                "Sales": [1000.0, pd.NA, 3000.0],
+                "Timestamp": [pd.Timestamp("2018-01-01"), pd.NaT, pd.Timestamp("2018-01-03")],
+            }
+        )
+
+        # Clean the DataFrame
+        result = file_parser_block._clean_dataframe_for_json(df)
+
+        # Check that NaN and NaT values are converted to "nan" string
+        assert result[0]["OrderDate"] == "2018-01-01"
+        assert result[0]["Region"] == "North"
+        assert result[0]["Sales"] == 1000.0
+        assert result[0]["Timestamp"] == "2018-01-01T00:00:00"
+
+        assert result[1]["OrderDate"] == "nan"
+        assert result[1]["Region"] == "South"
+        assert result[1]["Sales"] == "nan"
+        assert result[1]["Timestamp"] == "nan"
+
+        assert result[2]["OrderDate"] == "2018-01-03"
+        assert result[2]["Region"] == "nan"
+        assert result[2]["Sales"] == 3000.0
+        assert result[2]["Timestamp"] == "2018-01-03T00:00:00"