diff --git a/comps/dataprep/pinecone/config.py b/comps/dataprep/pinecone/config.py
index e6e62db6c..7a761a09c 100644
--- a/comps/dataprep/pinecone/config.py
+++ b/comps/dataprep/pinecone/config.py
@@ -4,13 +4,13 @@
 import os
 
 # Embedding model
-EMBED_MODEL = os.getenv("EMBED_MODEL", "sentence-transformers/all-MiniLM-L6-v2")
+EMBED_MODEL = os.getenv("EMBED_MODEL", "BAAI/bge-base-en-v1.5")
 
 # Pinecone configuration
 PINECONE_API_KEY = os.getenv("PINECONE_API_KEY", "xxx_xxx")
-PINECONE_INDEX_NAME = int(os.getenv("PINECONE_INDEX_NAME", "langchain-test"))
+PINECONE_INDEX_NAME = os.getenv("PINECONE_INDEX_NAME", "langchain-test")
 
 # LLM/Embedding endpoints
 TGI_LLM_ENDPOINT = os.getenv("TGI_LLM_ENDPOINT", "http://localhost:8080")
 TGI_LLM_ENDPOINT_NO_RAG = os.getenv("TGI_LLM_ENDPOINT_NO_RAG", "http://localhost:8081")
-TEI_EMBEDDING_ENDPOINT = os.getenv("TEI_ENDPOINT")
+TEI_EMBEDDING_ENDPOINT = os.getenv("TEI_EMBEDDING_ENDPOINT")
diff --git a/comps/dataprep/pinecone/docker/Dockerfile b/comps/dataprep/pinecone/docker/Dockerfile
index d19ff6ab4..d61ecf65f 100644
--- a/comps/dataprep/pinecone/docker/Dockerfile
+++ b/comps/dataprep/pinecone/docker/Dockerfile
@@ -6,11 +6,16 @@ FROM python:3.11-slim
 
 ENV LANG=C.UTF-8
 
+ARG ARCH="cpu"
+
 RUN apt-get update -y && apt-get install -y --no-install-recommends --fix-missing \
     build-essential \
     libgl1-mesa-glx \
     libjemalloc-dev \
-    vim
+    default-jre \
+    vim \
+    libcairo2
+
 
 RUN useradd -m -s /bin/bash user && \
     mkdir -p /home/user && \
@@ -23,8 +28,18 @@ COPY comps /home/user/comps
 RUN pip install --no-cache-dir --upgrade pip && \
     pip install --no-cache-dir -r /home/user/comps/dataprep/pinecone/requirements.txt
 
+RUN pip install --no-cache-dir --upgrade pip setuptools && \
+    if [ ${ARCH} = "cpu" ]; then pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu; fi && \
+    pip install --no-cache-dir -r /home/user/comps/dataprep/pinecone/requirements.txt
+
 ENV PYTHONPATH=$PYTHONPATH:/home/user
 
+USER root
+
+RUN mkdir -p /home/user/comps/dataprep/pinecone/uploaded_files && chown -R user /home/user/comps/dataprep/pinecone/uploaded_files
+
+USER user
+
 WORKDIR /home/user/comps/dataprep/pinecone
 
 ENTRYPOINT ["python", "prepare_doc_pinecone.py"]
diff --git a/comps/dataprep/pinecone/docker/docker-compose-dataprep-pinecone.yaml b/comps/dataprep/pinecone/docker/docker-compose-dataprep-pinecone.yaml
index 93636f3d0..0ee20389d 100644
--- a/comps/dataprep/pinecone/docker/docker-compose-dataprep-pinecone.yaml
+++ b/comps/dataprep/pinecone/docker/docker-compose-dataprep-pinecone.yaml
@@ -1,19 +1,40 @@
 # Copyright (C) 2024 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
 
 version: "3"
 services:
+  tei-embedding-service:
+    image: ghcr.io/huggingface/text-embeddings-inference:cpu-1.5
+    container_name: tei-embedding-server
+    ports:
+      - "6006:80"
+    volumes:
+      - "./data:/data"
+    shm_size: 1g
+    environment:
+      no_proxy: ${no_proxy}
+      http_proxy: ${http_proxy}
+      https_proxy: ${https_proxy}
+    command: --model-id ${EMBEDDING_MODEL_ID} --auto-truncate
   dataprep-pinecone:
-    image: opea/gen-ai-comps:dataprep-pinecone-xeon-server
+    image: opea/dataprep-pinecone:latest
     container_name: dataprep-pinecone-server
     ports:
-      - "6000:6000"
+      - "6007:6007"
+      - "6008:6008"
+      - "6009:6009"
     ipc: host
     environment:
+      no_proxy: ${no_proxy}
       http_proxy: ${http_proxy}
       https_proxy: ${https_proxy}
       PINECONE_API_KEY: ${PINECONE_API_KEY}
       PINECONE_INDEX_NAME: ${PINECONE_INDEX_NAME}
+      TEI_EMBEDDING_ENDPOINT: ${TEI_EMBEDDING_ENDPOINT}
+      LANGCHAIN_API_KEY: ${LANGCHAIN_API_KEY}
+      HUGGINGFACEHUB_API_TOKEN: ${HUGGINGFACEHUB_API_TOKEN}
     restart: unless-stopped
 
 networks:
diff --git a/comps/dataprep/pinecone/prepare_doc_pinecone.py b/comps/dataprep/pinecone/prepare_doc_pinecone.py
index 1a001a1fd..ec0e200a2 100644
--- a/comps/dataprep/pinecone/prepare_doc_pinecone.py
+++ b/comps/dataprep/pinecone/prepare_doc_pinecone.py
@@ -1,40 +1,105 @@
 # Copyright (C) 2024 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
+import json
 import os
+import shutil
+import uuid
+from pathlib import Path
+from typing import List, Optional, Union
 
 from config import EMBED_MODEL, PINECONE_API_KEY, PINECONE_INDEX_NAME
+from fastapi import Body, File, Form, HTTPException, UploadFile
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain_community.embeddings import HuggingFaceBgeEmbeddings, HuggingFaceEmbeddings, HuggingFaceHubEmbeddings
-from langchain_community.vectorstores import Pinecone
+from langchain_pinecone import PineconeVectorStore
+from langchain_text_splitters import HTMLHeaderTextSplitter
+from langsmith import traceable
+from pinecone import Pinecone, ServerlessSpec
 
 from comps import DocPath, opea_microservices, opea_telemetry, register_microservice
-from comps.dataprep.utils import document_loader, get_separators
+from comps.dataprep.utils import (
+    create_upload_folder,
+    document_loader,
+    encode_filename,
+    get_file_structure,
+    get_separators,
+    get_tables_result,
+    parse_html,
+    remove_folder_with_ignore,
+    save_content_to_local_disk,
+)
 
-tei_embedding_endpoint = os.getenv("TEI_ENDPOINT")
+tei_embedding_endpoint = os.getenv("TEI_EMBEDDING_ENDPOINT")
+upload_folder = "./uploaded_files/"
 
 
-@register_microservice(
-    name="opea_service@prepare_doc_pinecone",
-    endpoint="/v1/dataprep",
-    host="0.0.0.0",
-    port=6000,
-    input_datatype=DocPath,
-    output_datatype=None,
-)
-@opea_telemetry
-def ingest_documents(doc_path: DocPath):
+def check_index_existance():
+    print(f"[ check index existence ] checking {PINECONE_INDEX_NAME}")
+    pc = Pinecone(api_key=PINECONE_API_KEY)
+    existing_indexes = [index_info["name"] for index_info in pc.list_indexes()]
+    if PINECONE_INDEX_NAME not in existing_indexes:
+        print("[ check index existence ] index does not exist")
+        return None
+    else:
+        return True
+
+
+def create_index(client):
+    print(f"[ create index ] creating index {PINECONE_INDEX_NAME}")
+    try:
+        client.create_index(
+            name=PINECONE_INDEX_NAME,
+            dimension=768,
+            metric="cosine",
+            spec=ServerlessSpec(cloud="aws", region="us-east-1"),
+        )
+        print(f"[ create index ] index {PINECONE_INDEX_NAME} successfully created")
+    except Exception as e:
+        print(f"[ create index ] fail to create index {PINECONE_INDEX_NAME}: {e}")
+        return False
+    return True
+
+
+def drop_index(index_name):
+    print(f"[ drop index ] dropping index {index_name}")
+    pc = Pinecone(api_key=PINECONE_API_KEY)
+    try:
+        pc.delete_index(index_name)
+        print(f"[ drop index ] index {index_name} deleted")
+    except Exception as e:
+        print(f"[ drop index ] index {index_name} delete failed: {e}")
+        return False
+    return True
+
+
+def ingest_data_to_pinecone(doc_path: DocPath):
     """Ingest document to Pinecone."""
-    doc_path = doc_path.path
-    print(f"Parsing document {doc_path}.")
+    path = doc_path.path
+    print(f"Parsing document {path}.")
 
-    text_splitter = RecursiveCharacterTextSplitter(
-        chunk_size=1500, chunk_overlap=100, add_start_index=True, separators=get_separators()
-    )
-    content = document_loader(doc_path)
-    chunks = text_splitter.split_text(content)
+    if path.endswith(".html"):
+        headers_to_split_on = [
+            ("h1", "Header 1"),
+            ("h2", "Header 2"),
+            ("h3", "Header 3"),
+        ]
+        text_splitter = HTMLHeaderTextSplitter(headers_to_split_on=headers_to_split_on)
+    else:
+        text_splitter = RecursiveCharacterTextSplitter(
+            chunk_size=doc_path.chunk_size,
+            chunk_overlap=doc_path.chunk_overlap,
+            add_start_index=True,
+            separators=get_separators(),
+        )
 
+    content = document_loader(path)
+    chunks = text_splitter.split_text(content)
+    if doc_path.process_table and path.endswith(".pdf"):
+        table_chunks = get_tables_result(path, doc_path.table_strategy)
+        chunks = chunks + table_chunks
     print("Done preprocessing. Created ", len(chunks), " chunks of the original pdf")
+
     # Create vectorstore
     if tei_embedding_endpoint:
         # create embeddings using TEI endpoint service
@@ -43,20 +108,157 @@ def ingest_documents(doc_path: DocPath):
         # create embeddings using local embedding model
         embedder = HuggingFaceBgeEmbeddings(model_name=EMBED_MODEL)
 
+    pc = Pinecone(api_key=PINECONE_API_KEY)
+
+    # Checking Index existence
+    if not check_index_existance():
+        # Creating the index
+        create_index(pc)
+        print("Successfully created the index", PINECONE_INDEX_NAME)
+
     # Batch size
     batch_size = 32
     num_chunks = len(chunks)
+    file_ids = []
+
     for i in range(0, num_chunks, batch_size):
         batch_chunks = chunks[i : i + batch_size]
         batch_texts = batch_chunks
 
-        _ = Pinecone.from_texts(
+        vectorstore = PineconeVectorStore.from_texts(
             texts=batch_texts,
             embedding=embedder,
             index_name=PINECONE_INDEX_NAME,
         )
         print(f"Processed batch {i//batch_size + 1}/{(num_chunks-1)//batch_size + 1}")
 
+    # store file_ids into index file-keys
+    pc = Pinecone(api_key=PINECONE_API_KEY)
+
+
+async def ingest_link_to_pinecone(link_list: List[str]):
+    # Create embedding obj
+    if tei_embedding_endpoint:
+        # create embeddings using TEI endpoint service
+        embedder = HuggingFaceHubEmbeddings(model=tei_embedding_endpoint)
+    else:
+        # create embeddings using local embedding model
+        embedder = HuggingFaceBgeEmbeddings(model_name=EMBED_MODEL)
+
+    pc = Pinecone(api_key=PINECONE_API_KEY)
+
+    # Checking Index existence
+    if not check_index_existance():
+        # Creating the index
+        create_index(pc)
+        print("Successfully created the index", PINECONE_INDEX_NAME)
+
+    # save link contents and doc_ids one by one
+    for link in link_list:
+        content = parse_html([link])[0][0]
+        print(f"[ ingest link ] link: {link} content: {content}")
+        encoded_link = encode_filename(link)
+        save_path = upload_folder + encoded_link + ".txt"
+        print(f"[ ingest link ] save_path: {save_path}")
+        await save_content_to_local_disk(save_path, content)
+
+        vectorstore = PineconeVectorStore.from_texts(
+            texts=content,
+            embedding=embedder,
+            index_name=PINECONE_INDEX_NAME,
+        )
+
+    return True
+
+
+@register_microservice(name="opea_service@prepare_doc_pinecone", endpoint="/v1/dataprep", host="0.0.0.0", port=6007)
+@traceable(run_type="tool")
+async def ingest_documents(
+    files: Optional[Union[UploadFile, List[UploadFile]]] = File(None),
+    link_list: Optional[str] = Form(None),
+    chunk_size: int = Form(1500),
+    chunk_overlap: int = Form(100),
+    process_table: bool = Form(False),
+    table_strategy: str = Form("fast"),
+):
+    print(f"files:{files}")
+    print(f"link_list:{link_list}")
+
+    if files:
+        if not isinstance(files, list):
+            files = [files]
+        uploaded_files = []
+        for file in files:
+            encode_file = encode_filename(file.filename)
+            save_path = upload_folder + encode_file
+            await save_content_to_local_disk(save_path, file)
+            ingest_data_to_pinecone(
+                DocPath(
+                    path=save_path,
+                    chunk_size=chunk_size,
+                    chunk_overlap=chunk_overlap,
+                    process_table=process_table,
+                    table_strategy=table_strategy,
+                )
+            )
+            uploaded_files.append(save_path)
+            print(f"Successfully saved file {save_path}")
+
+        return {"status": 200, "message": "Data preparation succeeded"}
+
+    if link_list:
+        try:
+            link_list = json.loads(link_list)  # Parse JSON string to list
+            if not isinstance(link_list, list):
+                raise HTTPException(status_code=400, detail="link_list should be a list.")
+            await ingest_link_to_pinecone(link_list)
+            print(f"Successfully saved link list {link_list}")
+            return {"status": 200, "message": "Data preparation succeeded"}
+        except json.JSONDecodeError:
+            raise HTTPException(status_code=400, detail="Invalid JSON format for link_list.")
+
+    raise HTTPException(status_code=400, detail="Must provide either a file or a string list.")
+
+
+@register_microservice(
+    name="opea_service@prepare_doc_pinecone_file", endpoint="/v1/dataprep/get_file", host="0.0.0.0", port=6008
+)
+@traceable(run_type="tool")
+async def rag_get_file_structure():
+    print("[ dataprep - get file ] start to get file structure")
+
+    if not Path(upload_folder).exists():
+        print("No file uploaded, return empty list.")
+        return []
+
+    file_content = get_file_structure(upload_folder)
+    return file_content
+
+
+@register_microservice(
+    name="opea_service@prepare_doc_pinecone_del", endpoint="/v1/dataprep/delete_file", host="0.0.0.0", port=6009
+)
+@traceable(run_type="tool")
+async def delete_all(file_path: str = Body(..., embed=True)):
+    """Delete file according to `file_path`.
+
+    `file_path`:
+        - "all": delete all files uploaded
+    """
+    # delete all uploaded files
+    if file_path == "all":
+        print("[dataprep - del] delete all files")
+        remove_folder_with_ignore(upload_folder)
+        assert drop_index(index_name=PINECONE_INDEX_NAME)
+        print("[dataprep - del] successfully delete all files.")
+        create_upload_folder(upload_folder)
+        return {"status": True}
+    else:
+        raise HTTPException(status_code=404, detail="Single file deletion is not implemented yet")
+
 
 if __name__ == "__main__":
+    create_upload_folder(upload_folder)
     opea_microservices["opea_service@prepare_doc_pinecone"].start()
+    opea_microservices["opea_service@prepare_doc_pinecone_file"].start()
+    opea_microservices["opea_service@prepare_doc_pinecone_del"].start()
diff --git a/comps/dataprep/pinecone/requirements.txt b/comps/dataprep/pinecone/requirements.txt
index 9e6a21681..a2d5c4298 100644
--- a/comps/dataprep/pinecone/requirements.txt
+++ b/comps/dataprep/pinecone/requirements.txt
@@ -1,12 +1,17 @@
 beautifulsoup4
+cairosvg
 docarray[full]
+docx2txt
 easyocr
 fastapi
 huggingface_hub
 langchain
 langchain-community
+langchain-openai
 langchain-pinecone
+langchain-text-splitters
 langsmith
+markdown
 numpy
 opentelemetry-api
 opentelemetry-exporter-otlp
@@ -14,8 +19,13 @@ opentelemetry-sdk
 pandas
 Pillow
 pinecone-client
+prometheus-fastapi-instrumentator
 pymupdf
+pyspark
+python-bidi==0.4.2
 python-docx
+python-pptx
 sentence_transformers
 shortuuid
+unstructured[all-docs]==0.11.5
 uvicorn
diff --git a/comps/retrievers/langchain/pinecone/config.py b/comps/retrievers/langchain/pinecone/config.py
index e6e62db6c..cd7f9e508 100644
--- a/comps/retrievers/langchain/pinecone/config.py
+++ b/comps/retrievers/langchain/pinecone/config.py
@@ -8,9 +8,9 @@
 
 # Pinecone configuration
 PINECONE_API_KEY = os.getenv("PINECONE_API_KEY", "xxx_xxx")
-PINECONE_INDEX_NAME = int(os.getenv("PINECONE_INDEX_NAME", "langchain-test"))
+PINECONE_INDEX_NAME = os.getenv("PINECONE_INDEX_NAME", "langchain-test")
 
 # LLM/Embedding endpoints
 TGI_LLM_ENDPOINT = os.getenv("TGI_LLM_ENDPOINT", "http://localhost:8080")
 TGI_LLM_ENDPOINT_NO_RAG = os.getenv("TGI_LLM_ENDPOINT_NO_RAG", "http://localhost:8081")
-TEI_EMBEDDING_ENDPOINT = os.getenv("TEI_ENDPOINT")
+TEI_EMBEDDING_ENDPOINT = os.getenv("TEI_EMBEDDING_ENDPOINT")
diff --git a/comps/retrievers/langchain/pinecone/docker/Dockerfile b/comps/retrievers/langchain/pinecone/docker/Dockerfile
index 7eedfab10..dbb6d57c2 100644
--- a/comps/retrievers/langchain/pinecone/docker/Dockerfile
+++ b/comps/retrievers/langchain/pinecone/docker/Dockerfile
@@ -4,6 +4,8 @@
 
 FROM langchain/langchain:latest
 
+ARG ARCH="cpu"
+
 RUN apt-get update -y && apt-get install -y --no-install-recommends --fix-missing \
     libgl1-mesa-glx \
     libjemalloc-dev \
@@ -15,15 +17,15 @@ RUN useradd -m -s /bin/bash user && \
 
 COPY comps /home/user/comps
 
-RUN chmod +x /home/user/comps/retrievers/langchain/pinecone/run.sh
-
 USER user
 
 RUN pip install --no-cache-dir --upgrade pip && \
+    if [ ${ARCH} = "cpu" ]; then pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu; fi && \
     pip install --no-cache-dir -r /home/user/comps/retrievers/langchain/pinecone/requirements.txt
 
+
 ENV PYTHONPATH=$PYTHONPATH:/home/user
 
 WORKDIR /home/user/comps/retrievers/langchain/pinecone
 
-ENTRYPOINT ["/home/user/comps/retrievers/langchain/pinecone/run.sh"]
+ENTRYPOINT ["python", "retriever_pinecone.py"]
\ No newline at end of file
diff --git a/comps/retrievers/langchain/pinecone/docker/docker_compose_retriever.yaml b/comps/retrievers/langchain/pinecone/docker/docker_compose_retriever.yaml
index f9aac5b0b..3c0f7cef2 100644
--- a/comps/retrievers/langchain/pinecone/docker/docker_compose_retriever.yaml
+++ b/comps/retrievers/langchain/pinecone/docker/docker_compose_retriever.yaml
@@ -24,7 +24,9 @@ services:
       https_proxy: ${https_proxy}
       PINECONE_API_KEY: ${PINECONE_API_KEY}
       INDEX_NAME: ${PINECONE_INDEX_NAME}
+      PINECONE_INDEX_NAME: ${PINECONE_INDEX_NAME}
       LANGCHAIN_API_KEY: ${LANGCHAIN_API_KEY}
+      TEI_EMBEDDING_ENDPOINT: ${TEI_EMBEDDING_ENDPOINT}
     restart: unless-stopped
 
 networks:
diff --git a/comps/retrievers/langchain/pinecone/ingest.py b/comps/retrievers/langchain/pinecone/ingest.py
deleted file mode 100644
index e17b5ebf5..000000000
--- a/comps/retrievers/langchain/pinecone/ingest.py
+++ /dev/null
@@ -1,100 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-# Copyright (C) 2024 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-
-#
-
-import io
-import os
-
-import numpy as np
-from config import EMBED_MODEL, INDEX_NAME, PINECONE_API_KEY
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_community.embeddings import HuggingFaceBgeEmbeddings, HuggingFaceEmbeddings, HuggingFaceHubEmbeddings
-from langchain_community.vectorstores import Pinecone
-from PIL import Image
-
-tei_embedding_endpoint = os.getenv("TEI_EMBEDDING_ENDPOINT")
-
-if os.getenv("PINECONE_API_KEY", None) is None:
-    raise Exception("Missing `PINECONE_API_KEY` environment variable.")
-
-
-def pdf_loader(file_path):
-    try:
-        import easyocr
-        import fitz
-    except ImportError:
-        raise ImportError(
-            "`PyMuPDF` or 'easyocr' package is not found, please install it with "
-            "`pip install pymupdf or pip install easyocr.`"
-        )
-
-    doc = fitz.open(file_path)
-    reader = easyocr.Reader(["en"])
-    result = ""
-    for i in range(doc.page_count):
-        page = doc.load_page(i)
-        pagetext = page.get_text().strip()
-        if pagetext:
-            result = result + pagetext
-        if len(doc.get_page_images(i)) > 0:
-            for img in doc.get_page_images(i):
-                if img:
-                    pageimg = ""
-                    xref = img[0]
-                    img_data = doc.extract_image(xref)
-                    img_bytes = img_data["image"]
-                    pil_image = Image.open(io.BytesIO(img_bytes))
-                    img = np.array(pil_image)
-                    img_result = reader.readtext(img, paragraph=True, detail=0)
-                    pageimg = pageimg + ", ".join(img_result).strip()
-                    if pageimg.endswith("!") or pageimg.endswith("?") or pageimg.endswith("."):
-                        pass
-                    else:
-                        pageimg = pageimg + "."
-                result = result + pageimg
-    return result
-
-
-def ingest_documents():
-    """Ingest PDF to Pinecone from the data/ directory that
-    contains Edgar 10k filings data for Nike."""
-    # Load list of pdfs
-    company_name = "Nike"
-    data_path = "../data/"
-    doc_path = [os.path.join(data_path, file) for file in os.listdir(data_path)][0]
-
-    print("Parsing 10k filing doc for NIKE", doc_path)
-
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1500, chunk_overlap=100, add_start_index=True)
-    content = pdf_loader(doc_path)
-    chunks = text_splitter.split_text(content)
-
-    print("Done preprocessing. Created ", len(chunks), " chunks of the original pdf")
-    # Create vectorstore
-    if tei_embedding_endpoint:
-        # create embeddings using TEI endpoint service
-        embedder = HuggingFaceHubEmbeddings(model=tei_embedding_endpoint)
-    else:
-        # create embeddings using local embedding model
-        embedder = HuggingFaceBgeEmbeddings(model_name=EMBED_MODEL)
-
-    # Batch size
-    batch_size = 32
-    num_chunks = len(chunks)
-    for i in range(0, num_chunks, batch_size):
-        batch_chunks = chunks[i : i + batch_size]
-        batch_texts = [f"Company: {company_name}. " + chunk for chunk in batch_chunks]
-
-        _ = Pinecone.from_texts(
-            texts=batch_texts,
-            embedding=embedder,
-            index_name=INDEX_NAME,
-        )
-        print(f"Processed batch {i//batch_size + 1}/{(num_chunks-1)//batch_size + 1}")
-
-
-if __name__ == "__main__":
-    ingest_documents()
diff --git a/comps/retrievers/langchain/pinecone/requirements.txt b/comps/retrievers/langchain/pinecone/requirements.txt
new file mode 100644
index 000000000..32df7f40f
--- /dev/null
+++ b/comps/retrievers/langchain/pinecone/requirements.txt
@@ -0,0 +1,22 @@
+beautifulsoup4
+docarray[full]
+easyocr
+fastapi
+huggingface_hub
+langchain
+langchain-community
+langchain-pinecone
+langsmith
+numpy
+opentelemetry-api
+opentelemetry-exporter-otlp
+opentelemetry-sdk
+pandas
+Pillow
+pinecone-client
+prometheus_fastapi_instrumentator
+pymupdf
+python-docx
+sentence_transformers
+shortuuid
+uvicorn
diff --git a/comps/retrievers/langchain/pinecone/retriever_pinecone.py b/comps/retrievers/langchain/pinecone/retriever_pinecone.py
new file mode 100644
index 000000000..ba8e6526f
--- /dev/null
+++ b/comps/retrievers/langchain/pinecone/retriever_pinecone.py
@@ -0,0 +1,84 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import os
+import time
+
+from config import EMBED_MODEL, PINECONE_API_KEY, PINECONE_INDEX_NAME
+from langchain_community.embeddings import HuggingFaceBgeEmbeddings, HuggingFaceHubEmbeddings
+from langchain_pinecone import PineconeVectorStore
+from langsmith import traceable
+from pinecone import Pinecone, ServerlessSpec
+
+from comps import (
+    EmbedDoc,
+    SearchedDoc,
+    ServiceType,
+    TextDoc,
+    opea_microservices,
+    register_microservice,
+    register_statistics,
+    statistics_dict,
+)
+
+tei_embedding_endpoint = os.getenv("TEI_EMBEDDING_ENDPOINT")
+
+
+@register_microservice(
+    name="opea_service@retriever_pinecone",
+    service_type=ServiceType.RETRIEVER,
+    endpoint="/v1/retrieval",
+    host="0.0.0.0",
+    port=7000,
+)
+@traceable(run_type="retriever")
+@register_statistics(names=["opea_service@retriever_pinecone"])
+def retrieve(input: EmbedDoc) -> SearchedDoc:
+    start = time.time()
+
+    pc = Pinecone(api_key=PINECONE_API_KEY)
+
+    index = pc.Index(PINECONE_INDEX_NAME)
+    print(index.describe_index_stats()["total_vector_count"])
+    # check if the Pinecone index has data
+    if index.describe_index_stats()["total_vector_count"] == 0:
+        result = SearchedDoc(retrieved_docs=[], initial_query=input.text)
+        statistics_dict["opea_service@retriever_pinecone"].append_latency(time.time() - start, None)
+        return result
+
+    search_res = vector_db.max_marginal_relevance_search(query=input.text, k=input.k, fetch_k=input.fetch_k)
+    # if the Pinecone index has data, perform the search
+    if input.search_type == "similarity":
+        docs_and_similarities = vector_db.similarity_search_by_vector_with_score(embedding=input.embedding, k=input.k)
+        search_res = [doc for doc, _ in docs_and_similarities]
+    elif input.search_type == "similarity_distance_threshold":
+        if input.distance_threshold is None:
+            raise ValueError("distance_threshold must be provided for " + "similarity_distance_threshold retriever")
+        docs_and_similarities = vector_db.similarity_search_by_vector_with_score(embedding=input.embedding, k=input.k)
+        search_res = [doc for doc, similarity in docs_and_similarities if similarity > input.distance_threshold]
+    elif input.search_type == "similarity_score_threshold":
+        docs_and_similarities = vector_db.similarity_search_by_vector_with_score(query=input.text, k=input.k)
+        search_res = [doc for doc, similarity in docs_and_similarities if similarity > input.score_threshold]
+    elif input.search_type == "mmr":
+        search_res = vector_db.max_marginal_relevance_search(
+            query=input.text, k=input.k, fetch_k=input.fetch_k, lambda_mult=input.lambda_mult
+        )
+    searched_docs = []
+    for r in search_res:
+        searched_docs.append(TextDoc(text=r.page_content))
+    result = SearchedDoc(retrieved_docs=searched_docs, initial_query=input.text)
+    statistics_dict["opea_service@retriever_pinecone"].append_latency(time.time() - start, None)
+    return result
+
+
+if __name__ == "__main__":
+    # Create vectorstore
+    if tei_embedding_endpoint:
+        # create embeddings using TEI endpoint service
+        embeddings = HuggingFaceHubEmbeddings(model=tei_embedding_endpoint)
+    else:
+        # create embeddings using local embedding model
+        embeddings = HuggingFaceBgeEmbeddings(model_name=EMBED_MODEL)
+
+    vector_db = PineconeVectorStore(embedding=embeddings, index_name=PINECONE_INDEX_NAME)
+    opea_microservices["opea_service@retriever_pinecone"].start()
diff --git a/comps/retrievers/langchain/pinecone/run.sh b/comps/retrievers/langchain/pinecone/run.sh
deleted file mode 100644
index ba658360b..000000000
--- a/comps/retrievers/langchain/pinecone/run.sh
+++ /dev/null
@@ -1,9 +0,0 @@
-#!/bin/sh
-
-# Copyright (C) 2024 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-
-cd /home/user/comps/retrievers/langchain/pinecone
-python ingest.py
-
-python retriever_pinecone.py
diff --git a/tests/test_dataprep_pinecone.sh b/tests/test_dataprep_pinecone.sh
new file mode 100755
index 000000000..a92a86c64
--- /dev/null
+++ b/tests/test_dataprep_pinecone.sh
@@ -0,0 +1,59 @@
+#!/bin/bash
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+set -xe
+
+WORKPATH=$(dirname "$PWD")
+ip_address=$(hostname -I | awk '{print $1}')
+function build_docker_images() {
+    cd $WORKPATH
+
+    # build dataprep image for pinecone
+    docker build -t opea/dataprep-pinecone:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f $WORKPATH/comps/dataprep/pinecone/docker/Dockerfile .
+}
+
+function start_service() {
+    export PINECONE_API_KEY=$PINECONE_KEY
+    export PINECONE_INDEX_NAME="test-index"
+    export HUGGINGFACEHUB_API_TOKEN=$HF_TOKEN
+
+    docker run -d --name="dataprep-pinecone" -p 6007:6007 -p 6008:6008 -p 6009:6009 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e no_proxy=$no_proxy -e PINECONE_API_KEY=$PINECONE_API_KEY -e PINECONE_INDEX_NAME=$PINECONE_INDEX_NAME opea/dataprep-pinecone:latest
+
+    sleep 1m
+}
+
+function validate_microservice() {
+    URL="http://$ip_address:6007/v1/dataprep"
+    echo 'The OPEA platform includes: Detailed framework of composable building blocks for state-of-the-art generative AI systems including LLMs, data stores, and prompt engines' > ./dataprep_file.txt
+    curl --noproxy $ip_address --location --request POST \
+      --form 'files=@./dataprep_file.txt' $URL
+
+    DELETE_URL="http://$ip_address:6009/v1/dataprep/delete_file"
+    curl --noproxy $ip_address --location --request POST \
+      -d '{"file_path": "all"}' -H 'Content-Type: application/json' $DELETE_URL
+}
+
+function stop_docker() {
+    cid=$(docker ps -aq --filter "name=vectorstore-pinecone*")
+    if [[ ! -z "$cid" ]]; then docker stop $cid && docker rm $cid && sleep 1s; fi
+
+    cid=$(docker ps -aq --filter "name=dataprep-pinecone*")
+    if [[ ! -z "$cid" ]]; then docker stop $cid && docker rm $cid && sleep 1s; fi
+}
+
+function main() {
+
+    stop_docker
+
+    build_docker_images
+    start_service
+
+    validate_microservice
+
+    stop_docker
+    echo y | docker system prune
+
+}
+
+main
diff --git a/tests/test_retrievers_langchain_pinecone.sh b/tests/test_retrievers_langchain_pinecone.sh
new file mode 100755
index 000000000..3e5215ba7
--- /dev/null
+++ b/tests/test_retrievers_langchain_pinecone.sh
@@ -0,0 +1,71 @@
+#!/bin/bash
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+set -xe
+
+WORKPATH=$(dirname "$PWD")
+ip_address=$(hostname -I | awk '{print $1}')
+function build_docker_images() {
+    cd $WORKPATH
+    docker build --no-cache -t opea/retriever-pinecone:comps --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/retrievers/langchain/pinecone/docker/Dockerfile .
+}
+
+function start_service() {
+
+    # tei endpoint
+    tei_endpoint=5008
+    model="BAAI/bge-base-en-v1.5"
+    docker run -d --name="test-comps-retriever-tei-endpoint" -e HTTPS_PROXY=$https_proxy -e HTTP_PROXY=$https_proxy -p $tei_endpoint:80 -v ./data:/data --pull always ghcr.io/huggingface/text-embeddings-inference:cpu-1.2 --model-id $model
+    sleep 30s
+    export TEI_EMBEDDING_ENDPOINT="http://${ip_address}:${tei_endpoint}"
+
+    # pinecone retriever
+    export PINECONE_API_KEY=$PINECONE_KEY
+    export PINECONE_INDEX_NAME="langchain-test"
+    export HUGGINGFACEHUB_API_TOKEN=$HF_TOKEN
+    retriever_port=5009
+    unset http_proxy
+    docker run -d --name="test-comps-retriever-pinecone-server" -p ${retriever_port}:7000 --ipc=host -e TEI_EMBEDDING_ENDPOINT=$TEI_EMBEDDING_ENDPOINT -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e PINECONE_API_KEY=$PINECONE_API_KEY -e PINECONE_INDEX_NAME=$PINECONE_INDEX_NAME -e INDEX_NAME=$PINECONE_INDEX_NAME opea/retriever-pinecone:comps
+
+    sleep 2m
+}
+
+function validate_microservice() {
+    retriever_port=5009
+    test_embedding="[0.3212316218862614, 0.05284697028105079, 0.792736615029739, -0.01450667589035648, -0.7358454555705813, -0.5159104761926909, 0.3535153166047822, -0.6465310827905328, -0.3260418169245214, 0.5427377177268364, 0.839674125021304, 0.27459120894125255, -0.9833857616143291, 0.4763752586395751, 0.7048355150785723, 0.4935209825796325, -0.09655411499027178, -0.5739389241976944, 0.34450497876796815, -0.03401327136919208, -0.8247080270670755, -0.9430721851019634, 0.4702688485035773, 0.3872526674852217, -0.13436894777006136, 0.27166203983338266, 0.7724679346611174, 0.49524109590526666, 0.9810730976435518, 0.2143402533230332, 0.35235793217357947, -0.3199320624935764, -0.3535996110405917, 0.1982603781951089, -0.37547349902996063, -0.6148649695355071, 0.388521078627599, 0.7073360849235228, 0.1768845283243352, -0.38289339223361885, 0.36390326284734775, -0.4790146416310761, -0.5412301982310956, 0.33793186533237507, -0.7028178009236765, -0.6850965350085609, -0.519584428926227, 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-0.744174955251268, 0.7691350055313244, -0.20065548721684312, 0.8869269473893813, -0.02043469943990095, 0.6747773545635596, -0.08840723444251264, 0.29835753335664084, -0.06410433319206965, 0.6915278973312651, 0.35470936730145075, -0.8143883316077478, 0.3700125242841532, 0.21752822647915626, -0.8620510146349405, -0.9872766671960136, -0.4418160577207253, -0.22054594310628928, -0.12301077500821433, -0.32532691454130314, -0.13151154223491113, -0.11476973253362455, -0.6347877217496254, -0.7764229239974911, 0.8494414471799672, -0.8096141861298036, -0.126108099532108, -0.3910538453811505, 0.7416491690145808, -0.9147820237179922, -0.09053536925720418, 0.6536341825563443, 0.655602583013402, 0.1757558598054938, -0.2501459855449637, 0.23414048418314914, -0.2944157385030681, 0.9386472406881659, -0.18806566910431344, -0.29109490690006345, -0.06582041104197667, -0.24458043176038613, 0.22893907834264082, -0.6322528508563678, -0.7885667746432836, 0.10383516801892911, 0.25661930212021256, 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-0.2199845691372495, 0.7554317108927318, 0.9809450136647395, -0.9430090133566618, 0.23635288316941683]"
+    http_proxy='' curl  --noproxy $ip_address http://${ip_address}:$retriever_port/v1/retrieval \
+        -X POST \
+        -d "{\"text\":\"test\",\"embedding\":${test_embedding}}" \
+        -H 'Content-Type: application/json'
+    docker logs test-comps-retriever-pinecone-server
+    docker logs test-comps-retriever-tei-endpoint
+}
+
+function stop_docker() {
+    cid_retrievers=$(docker ps -aq --filter "name=test-comps-retrievers*")
+    if [[ ! -z "$cid_retrievers" ]]; then
+        docker stop $cid_retrievers && docker rm $cid_retrievers && sleep 1s
+    fi
+
+    cid_pinecone=$(docker ps -aq --filter "name=test-pinecone-vector-db")
+    if [[ ! -z "$cid_pinecone" ]]; then
+        docker stop $cid_pinecone && docker rm $cid_pinecone && sleep 1s
+    fi
+}
+
+function main() {
+
+    stop_docker
+
+    build_docker_images
+    start_service
+
+    validate_microservice
+
+    stop_docker
+    echo y | docker system prune
+
+}
+
+main