Pinecone update to OPEA

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")

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"]

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:

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()