diff --git a/lotus/sem_ops/sem_extract.py b/lotus/sem_ops/sem_extract.py
index ed9619e..b0c2d3e 100644
--- a/lotus/sem_ops/sem_extract.py
+++ b/lotus/sem_ops/sem_extract.py
@@ -6,7 +6,11 @@
 from lotus.cache import operator_cache
 from lotus.models import LM
 from lotus.templates import task_instructions
-from lotus.types import LMOutput, SemanticExtractOutput, SemanticExtractPostprocessOutput
+from lotus.types import (
+    LMOutput,
+    SemanticExtractOutput,
+    SemanticExtractPostprocessOutput,
+)
 from lotus.utils import show_safe_mode
 
 from .postprocessors import extract_postprocess
@@ -17,7 +21,9 @@ def sem_extract(
     model: LM,
     output_cols: dict[str, str | None],
     extract_quotes: bool = False,
-    postprocessor: Callable[[list[str]], SemanticExtractPostprocessOutput] = extract_postprocess,
+    postprocessor: Callable[
+        [list[str]], SemanticExtractPostprocessOutput
+    ] = extract_postprocess,
     safe_mode: bool = False,
     progress_bar_desc: str = "Extracting",
 ) -> SemanticExtractOutput:
@@ -39,7 +45,9 @@ def sem_extract(
     for doc in docs:
         prompt = task_instructions.extract_formatter(doc, output_cols, extract_quotes)
         lotus.logger.debug(f"input to model: {prompt}")
-        lotus.logger.debug(f"inputs content to model: {[x.get('content') for x in prompt]}")
+        lotus.logger.debug(
+            f"inputs content to model: {[x.get('content') for x in prompt]}"
+        )
         inputs.append(prompt)
 
     # check if safe_mode is enabled
@@ -49,7 +57,11 @@ def sem_extract(
         show_safe_mode(estimated_cost, estimated_LM_calls)
 
     # call model
-    lm_output: LMOutput = model(inputs, response_format={"type": "json_object"}, progress_bar_desc=progress_bar_desc)
+    lm_output: LMOutput = model(
+        inputs,
+        response_format={"type": "json_object"},
+        progress_bar_desc=progress_bar_desc,
+    )
 
     # post process results
     postprocess_output = postprocessor(lm_output.outputs)
@@ -78,8 +90,11 @@ def __call__(
         input_cols: list[str],
         output_cols: dict[str, str | None],
         extract_quotes: bool = False,
-        postprocessor: Callable[[list[str]], SemanticExtractPostprocessOutput] = extract_postprocess,
+        postprocessor: Callable[
+            [list[str]], SemanticExtractPostprocessOutput
+        ] = extract_postprocess,
         return_raw_outputs: bool = False,
+        infer_pdfs: bool = False,
         safe_mode: bool = False,
         progress_bar_desc: str = "Extracting",
     ) -> pd.DataFrame:
@@ -106,7 +121,9 @@ def __call__(
             if column not in self._obj.columns:
                 raise ValueError(f"Column {column} not found in DataFrame")
 
-        multimodal_data = task_instructions.df2multimodal_info(self._obj, input_cols)
+        multimodal_data = task_instructions.df2multimodal_info(
+            self._obj, input_cols, infer_pdfs
+        )
 
         out = sem_extract(
             docs=multimodal_data,
diff --git a/lotus/templates/task_instructions.py b/lotus/templates/task_instructions.py
index fc30efd..4030f14 100644
--- a/lotus/templates/task_instructions.py
+++ b/lotus/templates/task_instructions.py
@@ -1,6 +1,7 @@
 import re
 from typing import Any
 
+import fitz
 import pandas as pd
 
 import lotus
@@ -77,7 +78,9 @@ def filter_formatter_cot(
         cot = cot_reasoning[idx]
         messages.extend(
             [
-                user_message_formatter(ex_multimodal_data, f"Claim: {user_instruction}"),
+                user_message_formatter(
+                    ex_multimodal_data, f"Claim: {user_instruction}"
+                ),
                 {
                     "role": "assistant",
                     "content": f"Reasoning:\n{cot}\n\nAnswer: {ex_ans}",
@@ -85,7 +88,9 @@ def filter_formatter_cot(
             ]
         )
 
-    messages.append(user_message_formatter(multimodal_data, f"Claim: {user_instruction}"))
+    messages.append(
+        user_message_formatter(multimodal_data, f"Claim: {user_instruction}")
+    )
     return messages
 
 
@@ -102,7 +107,9 @@ def filter_formatter_zs_cot(
         {"role": "system", "content": sys_instruction},
     ]
 
-    messages.append(user_message_formatter(multimodal_data, f"Claim: {user_instruction}"))
+    messages.append(
+        user_message_formatter(multimodal_data, f"Claim: {user_instruction}")
+    )
     return messages
 
 
@@ -117,7 +124,11 @@ def filter_formatter(
     if cot_reasoning:
         assert examples_multimodal_data is not None and examples_answer is not None
         return filter_formatter_cot(
-            multimodal_data, user_instruction, examples_multimodal_data, examples_answer, cot_reasoning
+            multimodal_data,
+            user_instruction,
+            examples_multimodal_data,
+            examples_answer,
+            cot_reasoning,
         )
     elif strategy == "zs-cot":
         return filter_formatter_zs_cot(multimodal_data, user_instruction)
@@ -133,18 +144,24 @@ def filter_formatter(
 
     if examples_multimodal_data:
         assert examples_answer is not None
-        assert isinstance(examples_multimodal_data, list) and isinstance(examples_answer, list)
+        assert isinstance(examples_multimodal_data, list) and isinstance(
+            examples_answer, list
+        )
         for i in range(len(examples_multimodal_data)):
             ex_multimodal_data = examples_multimodal_data[i]
             ex_ans = examples_answer[i]
             messages.extend(
                 [
-                    user_message_formatter(ex_multimodal_data, f"Claim: {user_instruction}"),
+                    user_message_formatter(
+                        ex_multimodal_data, f"Claim: {user_instruction}"
+                    ),
                     {"role": "assistant", "content": str(ex_ans)},
                 ]
             )
 
-    messages.append(user_message_formatter(multimodal_data, f"Claim: {user_instruction}"))
+    messages.append(
+        user_message_formatter(multimodal_data, f"Claim: {user_instruction}")
+    )
     return messages
 
 
@@ -178,7 +195,9 @@ def map_formatter_cot(
             ]
         )
 
-    messages.append(user_message_formatter(multimodal_data, f"Instruction: {user_instruction}"))
+    messages.append(
+        user_message_formatter(multimodal_data, f"Instruction: {user_instruction}")
+    )
     return messages
 
 
@@ -195,7 +214,9 @@ def map_formatter_zs_cot(
         {"role": "system", "content": sys_instruction},
     ]
 
-    messages.append(user_message_formatter(multimodal_data, f"Instruction: {user_instruction}"))
+    messages.append(
+        user_message_formatter(multimodal_data, f"Instruction: {user_instruction}")
+    )
     return messages
 
 
@@ -210,7 +231,11 @@ def map_formatter(
     if cot_reasoning:
         assert examples_multimodal_data is not None and examples_answer is not None
         return map_formatter_cot(
-            multimodal_data, user_instruction, examples_multimodal_data, examples_answer, cot_reasoning
+            multimodal_data,
+            user_instruction,
+            examples_multimodal_data,
+            examples_answer,
+            cot_reasoning,
         )
     elif strategy == "zs-cot":
         return map_formatter_zs_cot(multimodal_data, user_instruction)
@@ -228,21 +253,29 @@ def map_formatter(
         for ex_df_txt, ex_ans in zip(examples_multimodal_data, examples_answer):
             messages.extend(
                 [
-                    user_message_formatter(ex_df_txt, f"Instruction: {user_instruction}"),
+                    user_message_formatter(
+                        ex_df_txt, f"Instruction: {user_instruction}"
+                    ),
                     {"role": "assistant", "content": str(ex_ans)},
                 ]
             )
 
-    messages.append(user_message_formatter(multimodal_data, f"Instruction: {user_instruction}"))
+    messages.append(
+        user_message_formatter(multimodal_data, f"Instruction: {user_instruction}")
+    )
     return messages
 
 
 def extract_formatter(
-    multimodal_data: dict[str, Any], output_cols: dict[str, str | None], extract_quotes: bool = True
+    multimodal_data: dict[str, Any],
+    output_cols: dict[str, str | None],
+    extract_quotes: bool = True,
 ) -> list[dict[str, str]]:
     output_col_names = list(output_cols.keys())
     # Set the description to be the key if no value is provided
-    output_cols_with_desc: dict[str, str] = {col: col if desc is None else desc for col, desc in output_cols.items()}
+    output_cols_with_desc: dict[str, str] = {
+        col: col if desc is None else desc for col, desc in output_cols.items()
+    }
 
     all_fields = output_col_names
     if extract_quotes:
@@ -271,10 +304,14 @@ def df2text(df: pd.DataFrame, cols: list[str]) -> list[str]:
     """Formats the given DataFrame into a string containing info from cols."""
 
     def custom_format_row(x: pd.Series, cols: list[str]) -> str:
-        return "".join([f"[{cols[i].capitalize()}]: «{x[cols[i]]}»\n" for i in range(len(cols))])
+        return "".join(
+            [f"[{cols[i].capitalize()}]: «{x[cols[i]]}»\n" for i in range(len(cols))]
+        )
 
     def clean_and_escape_column_name(column_name: str) -> str:
-        clean_name = re.sub(r"[^\w]", "", column_name)  # Remove spaces and special characters
+        clean_name = re.sub(
+            r"[^\w]", "", column_name
+        )  # Remove spaces and special characters
         return clean_name
 
     # take cols that are in df
@@ -286,7 +323,9 @@ def clean_and_escape_column_name(column_name: str) -> str:
     formatted_rows: list[str] = []
 
     if lotus.settings.serialization_format == SerializationFormat.DEFAULT:
-        formatted_rows = projected_df.apply(lambda x: custom_format_row(x, cols), axis=1).tolist()
+        formatted_rows = projected_df.apply(
+            lambda x: custom_format_row(x, cols), axis=1
+        ).tolist()
     elif lotus.settings.serialization_format == SerializationFormat.JSON:
         formatted_rows = projected_df.to_json(orient="records", lines=True).splitlines()
     elif lotus.settings.serialization_format == SerializationFormat.XML:
@@ -298,33 +337,71 @@ def clean_and_escape_column_name(column_name: str) -> str:
                 "You can install it with the following command:\n\n"
                 "    pip install 'lotus-ai[xml]'"
             )
-        projected_df = projected_df.rename(columns=lambda x: clean_and_escape_column_name(x))
-        full_xml = projected_df.to_xml(root_name="data", row_name="row", pretty_print=False, index=False)
+        projected_df = projected_df.rename(
+            columns=lambda x: clean_and_escape_column_name(x)
+        )
+        full_xml = projected_df.to_xml(
+            root_name="data", row_name="row", pretty_print=False, index=False
+        )
         root = ET.fromstring(full_xml)
-        formatted_rows = [ET.tostring(row, encoding="unicode", method="xml") for row in root.findall("row")]
+        formatted_rows = [
+            ET.tostring(row, encoding="unicode", method="xml")
+            for row in root.findall("row")
+        ]
 
     return formatted_rows
 
 
-def df2multimodal_info(df: pd.DataFrame, cols: list[str]) -> list[dict[str, Any]]:
+def pdf2text(file_path: str) -> str:
+    try:
+        with fitz.open(file_path) as doc:
+            return " ".join(page.get_text() for page in doc)
+    except Exception as e:
+        lotus.logger.debug(f"Error while processing pdf at file path {file_path}: {e}")
+        return ""
+
+
+def df2multimodal_info(
+    df: pd.DataFrame, cols: list[str], infer_pdfs: bool = False
+) -> list[dict[str, Any]]:
     """
     Formats the given DataFrame into a string containing info from cols.
     Return a list of dictionaries, each containing text and image data.
     """
+
+    # We want to modify `df` to replace PDF paths with their text, but without modifying the source
+    df_copy = df.copy()
+
     image_cols = [col for col in cols if isinstance(df[col].dtype, ImageDtype)]
+    if infer_pdfs:
+        pdf_cols = [
+            col
+            for col in cols
+            if col not in image_cols
+            and df[col].apply(lambda x: isinstance(x, str) and x.endswith(".pdf")).all()
+        ]
+        for col in pdf_cols:
+            df_copy[col] = df_copy[col].apply(pdf2text)
+
     text_cols = [col for col in cols if col not in image_cols]
-    text_rows = df2text(df, text_cols)
+
+    text_rows = df2text(df_copy, text_cols)
     multimodal_data = [
         {
             "text": text_rows[i],
-            "image": {col.capitalize(): df[col].array.get_image(i, "base64") for col in image_cols},
+            "image": {
+                col.capitalize(): df[col].array.get_image(i, "base64")
+                for col in image_cols
+            },
         }
         for i in range(len(df))
     ]
     return multimodal_data
 
 
-def merge_multimodal_info(first: list[dict[str, Any]], second: list[dict[str, Any]]) -> list[dict[str, Any]]:
+def merge_multimodal_info(
+    first: list[dict[str, Any]], second: list[dict[str, Any]]
+) -> list[dict[str, Any]]:
     """
     Merges two multimodal info lists into one. Each row of first is merged with each row of second.
 
@@ -337,9 +414,11 @@ def merge_multimodal_info(first: list[dict[str, Any]], second: list[dict[str, An
     """
     return [
         {
-            "text": f"{first[i]['text']}\n{second[j]['text']}"
-            if first[i]["text"] != "" and second[j]["text"] != ""
-            else first[i]["text"] + second[j]["text"],
+            "text": (
+                f"{first[i]['text']}\n{second[j]['text']}"
+                if first[i]["text"] != "" and second[j]["text"] != ""
+                else first[i]["text"] + second[j]["text"]
+            ),
             "image": {**first[i]["image"], **second[j]["image"]},
         }
         for i in range(len(first))
diff --git a/requirements.txt b/requirements.txt
index 226370b..901350a 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -5,4 +5,5 @@ numpy==1.26.4
 pandas==2.2.2
 sentence-transformers==3.0.1
 tiktoken==0.7.0
-tqdm==4.66.4
\ No newline at end of file
+tqdm==4.66.4
+PyMuPDF==1.25.1