diff --git a/core/quivr_core/processor/implementations/spaCy_processor.py b/core/quivr_core/processor/implementations/spaCy_processor.py
new file mode 100644
index 000000000000..344493c1018a
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+++ b/core/quivr_core/processor/implementations/spaCy_processor.py
@@ -0,0 +1,143 @@
+import logging
+import os
+import spacy
+import aiofiles
+import pandas as pd
+import fitz  # PyMuPDF for PDF processing
+import docx  # python-docx for DOCX processing
+from langchain_core.documents import Document
+from langchain_text_splitters import RecursiveCharacterTextSplitter, TextSplitter
+
+from quivr_core.files.file import QuivrFile
+from quivr_core.processor.processor_base import ProcessorBase
+from quivr_core.processor.registry import FileExtension
+from quivr_core.processor.splitter import SplitterConfig
+
+logger = logging.getLogger("quivr_core")
+
+
+class SpaCyProcessor(ProcessorBase):
+    """
+    SpaCyProcessor for handling various text file types with spaCy NLP.
+
+    It extracts and processes text content using spaCy's NLP pipeline.
+
+    ## Installation
+    ```bash
+    pip install spacy pandas pymupdf python-docx
+    python -m spacy download en_core_web_sm
+    ```
+    """
+
+    supported_extensions = [
+        FileExtension.pdf,
+        FileExtension.docx,
+        FileExtension.txt,
+        FileExtension.csv,
+    ]
+
+    def __init__(
+        self,
+        splitter: TextSplitter | None = None,
+        splitter_config: SplitterConfig = SplitterConfig(),
+        spacy_model: str = "en_core_web_sm"
+    ) -> None:
+        # Load spaCy model
+        try:
+            self.nlp = spacy.load(spacy_model)
+        except Exception as e:
+            logger.error(f"Failed to load spaCy model '{spacy_model}': {e}")
+            raise
+
+        self.splitter_config = splitter_config
+
+        if splitter:
+            self.text_splitter = splitter
+        else:
+            self.text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
+                chunk_size=splitter_config.chunk_size,
+                chunk_overlap=splitter_config.chunk_overlap,
+            )
+
+    @property
+    def processor_metadata(self):
+        return {
+            "processor_cls": "SpaCyProcessor",
+            "chunk_overlap": self.splitter_config.chunk_overlap,
+        }
+
+    async def process_file_inner(self, file: QuivrFile) -> list[Document]:
+        # Extract text based on file type
+        try:
+            if file.extension == FileExtension.pdf:
+                text = await self.extract_text_from_pdf(file)
+            elif file.extension == FileExtension.docx:
+                text = await self.extract_text_from_docx(file)
+            elif file.extension == FileExtension.txt:
+                text = await self.extract_text_from_txt(file)
+            elif file.extension == FileExtension.csv:
+                text = await self.extract_text_from_csv(file)
+            else:
+                raise ValueError(f"Unsupported file type: {file.extension}")
+            
+            # Check for empty text
+            if not text:
+                logger.warning(f"No content extracted from file: {file.path}")
+                return []
+
+            # Apply spaCy NLP processing
+            doc = Document(page_content=text)
+            processed_docs = self.text_splitter.split_documents([doc])
+
+            for doc in processed_docs:
+                spacy_doc = self.nlp(doc.page_content)
+                doc.metadata.update({
+                    "chunk_size": len(spacy_doc),
+                    "entities": [(ent.text, ent.label_) for ent in spacy_doc.ents],
+                    "sentences": [sent.text for sent in spacy_doc.sents]
+                })
+                doc.page_content = spacy_doc.text
+
+            return processed_docs
+
+        except Exception as e:
+            logger.error(f"Error processing file '{file.path}': {e}")
+            return []
+
+    async def extract_text_from_pdf(self, file: QuivrFile) -> str:
+        try:
+            async with file.open():
+                doc = fitz.open(file.path)
+                text = ""
+                for page in doc:
+                    text += page.get_text()
+                return text
+        except Exception as e:
+            logger.error(f"Error extracting text from PDF '{file.path}': {e}")
+            return ""
+
+    async def extract_text_from_docx(self, file: QuivrFile) -> str:
+        try:
+            doc = docx.Document(file.path)
+            text = "\n".join([para.text for para in doc.paragraphs])
+            return text
+        except Exception as e:
+            logger.error(f"Error extracting text from DOCX '{file.path}': {e}")
+            return ""
+
+    async def extract_text_from_txt(self, file: QuivrFile) -> str:
+        try:
+            async with aiofiles.open(file.path, mode="r") as f:
+                content = await f.read()
+            return content
+        except Exception as e:
+            logger.error(f"Error extracting text from TXT '{file.path}': {e}")
+            return ""
+
+    async def extract_text_from_csv(self, file: QuivrFile) -> str:
+        try:
+            df = pd.read_csv(file.path)
+            return ' '.join(df.astype(str).values.flatten())
+        except Exception as e:
+            logger.error(f"Error extracting text from CSV '{file.path}': {e}")
+            return ""