dataprep for pgvector

comps/dataprep/README.md

@@ -9,3 +9,7 @@ For details, please refer to this [readme](redis/README.md)
 # Dataprep Microservice with Qdrant
 
 For details, please refer to this [readme](qdrant/README.md)
+
+# Dataprep Microservice with PGVector
+
+For details, please refer to this [readme](pgvector/README.md)

comps/dataprep/pgvector/README.md

@@ -0,0 +1,78 @@
+# Dataprep Microservice with PGVector
+
+# 🚀1. Start Microservice with Python（Option 1）
+
+## 1.1 Install Requirements
+
+```bash
+pip install -r requirements.txt
+```
+
+## 1.2 Start PGVector
+
+Please refer to this [readme](../../../vectorstores/langchain/pgvcetor/README.md).
+
+## 1.3 Setup Environment Variables
+
+```bash
+export PG_CONNECTION_STRING=postgresql+psycopg2://testuser:testpwd@${your_ip}:5432/vectordb
+export INDEX_NAME=${your_index_name}
+export LANGCHAIN_TRACING_V2=true
+export LANGCHAIN_API_KEY=${your_langchain_api_key}
+export LANGCHAIN_PROJECT="opea/gen-ai-comps:dataprep"
+```
+
+## 1.4 Start Document Preparation Microservice for PGVector with Python Script
+
+Start document preparation microservice for PGVector with below command.
+
+```bash
+python prepare_doc_pgvector.py
+```
+
+# 🚀2. Start Microservice with Docker (Option 2)
+
+## 2.1 Start PGVector
+
+Please refer to this [readme](../../../vectorstores/langchain/pgvector/README.md).
+
+## 2.2 Setup Environment Variables
+
+```bash
+export PG_CONNECTION_STRING=postgresql+psycopg2://testuser:testpwd@${your_ip}:5432/vectordb
+export INDEX_NAME=${your_index_name}
+export LANGCHAIN_TRACING_V2=true
+export LANGCHAIN_API_KEY=${your_langchain_api_key}
+export LANGCHAIN_PROJECT="opea/dataprep"
+```
+
+## 2.3 Build Docker Image
+
+```bash
+cd comps/dataprep/langchain/pgvector/docker
+docker build -t opea/dataprep-pgvector:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/dataprep/langchain/pgvector/docker/Dockerfile .
+```
+
+## 2.4 Run Docker with CLI (Option A)
+
+```bash
+docker run -d --name="dataprep-pgvector" -p 6007:6007 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e PG_CONNECTION_STRING=$PG_CONNECTION_STRING  -e INDEX_NAME=$INDEX_NAME -e TEI_ENDPOINT=$TEI_ENDPOINT opea/dataprep-pgvector:latest
+```
+
+## 2.5 Run with Docker Compose (Option B)
+
+```bash
+cd comps/dataprep/langchain/pgvector/docker
+docker compose -f docker-compose-dataprep-pgvector.yaml up -d
+```
+
+# 🚀3. Consume Microservice
+
+Once document preparation microservice for PGVector is started, user can use below command to invoke the microservice to convert the document to embedding and save to the database.
+
+```bash
+curl -X POST \
+    -H "Content-Type: application/json" \
+    -d '{"path":"/path/to/document"}' \
+    http://localhost:6007/v1/dataprep
+```

comps/dataprep/pgvector/langchain/__init__.py

@@ -0,0 +1,2 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0

comps/dataprep/pgvector/langchain/config.py

@@ -0,0 +1,20 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import os
+
+# Embedding model
+
+EMBED_MODEL = os.getenv("EMBED_MODEL", "BAAI/bge-base-en-v1.5")
+
+PG_CONNECTION_STRING = os.getenv("PG_CONNECTION_STRING", "localhost")
+
+# Vector Index Configuration
+INDEX_NAME = os.getenv("INDEX_NAME", "rag-pgvector")
+
+# chunk parameters
+CHUNK_SIZE = os.getenv("CHUNK_SIZE", 1500)
+CHUNK_OVERLAP = os.getenv("CHUNK_OVERLAP", 100)
+
+current_file_path = os.path.abspath(__file__)
+parent_dir = os.path.dirname(current_file_path)

comps/dataprep/pgvector/langchain/docker/Dockerfile

@@ -0,0 +1,37 @@
+
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+FROM python:3.11-slim
+
+ENV LANG C.UTF-8
+
+RUN apt-get update -y && apt-get install -y --no-install-recommends --fix-missing \
+    build-essential \
+    libgl1-mesa-glx \
+    libjemalloc-dev \
+    vim
+
+RUN useradd -m -s /bin/bash user && \
+    mkdir -p /home/user && \
+    chown -R user /home/user/
+
+USER user
+
+COPY comps /home/user/comps
+
+RUN pip install --no-cache-dir --upgrade pip setuptools && \
+    pip install --no-cache-dir -r /home/user/comps/dataprep/pgvector/langchain/requirements.txt
+
+ENV PYTHONPATH=$PYTHONPATH:/home/user
+
+USER root
+
+RUN mkdir -p /home/user/comps/dataprep/pgvector/langchain/uploaded_files && chown -R user /home/user/comps/dataprep/pgvector/langchain/uploaded_files
+
+USER user
+
+WORKDIR /home/user/comps/dataprep/pgvector/langchain
+
+ENTRYPOINT ["python", "prepare_doc_pgvector.py"]
+

comps/dataprep/pgvector/langchain/docker/docker-compose-dataprep-pgvector.yaml

@@ -0,0 +1,39 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+version: "3"
+services:
+  pgvector-vector-db:
+    hostname: db
+    container_name: pgvector-vector-db
+    image: pgvector/pgvector:0.7.0-pg16
+    ports:
+      - "5432:5432"
+    restart: always
+    ipc: host
+    environment:
+      - POSTGRES_DB=vectordb
+      - POSTGRES_USER=testuser
+      - POSTGRES_PASSWORD=testpwd
+      - POSTGRES_HOST_AUTH_METHOD=trust
+    volumes:
+      - ./init.sql:/docker-entrypoint-initdb.d/init.sql
+
+  dataprep-pgvector:
+    image: opea/dataprep-pgvector:latest
+    container_name: dataprep-pgvector
+    ports:
+      - "6007:6007"
+    ipc: host
+    environment:
+      http_proxy: ${http_proxy}
+      https_proxy: ${https_proxy}
+      PG_CONNECTION_STRING: ${PG_CONNECTION_STRING}
+      INDEX_NAME: ${INDEX_NAME}
+      TEI_ENDPOINT: ${TEI_ENDPOINT}
+      LANGCHAIN_API_KEY: ${LANGCHAIN_API_KEY}
+    restart: unless-stopped
+
+networks:
+  default:
+    driver: bridge

comps/dataprep/pgvector/langchain/prepare_doc_pgvector.py

@@ -0,0 +1,140 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import json
+import os
+import uuid
+from pathlib import Path
+from typing import List, Optional, Union
+
+from config import CHUNK_OVERLAP, CHUNK_SIZE, EMBED_MODEL, INDEX_NAME, PG_CONNECTION_STRING
+from fastapi import File, Form, HTTPException, UploadFile
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.embeddings import HuggingFaceBgeEmbeddings, HuggingFaceHubEmbeddings
+from langchain_community.vectorstores import PGVector
+from langsmith import traceable
+
+from comps import DocPath, ServiceType, opea_microservices, register_microservice, register_statistics
+from comps.dataprep.utils import document_loader, parse_html
+
+tei_embedding_endpoint = os.getenv("TEI_ENDPOINT")
+
+
+async def save_file_to_local_disk(save_path: str, file):
+    save_path = Path(save_path)
+    with save_path.open("wb") as fout:
+        try:
+            content = await file.read()
+            fout.write(content)
+        except Exception as e:
+            print(f"Write file failed. Exception: {e}")
+            raise HTTPException(status_code=500, detail=f"Write file {save_path} failed. Exception: {e}")
+
+
+def ingest_doc_to_pgvector(doc_path: DocPath):
+    """Ingest document to PGVector."""
+    doc_path = doc_path.path
+    print(f"Parsing document {doc_path}.")
+
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1500, chunk_overlap=100, add_start_index=True)
+    content = document_loader(doc_path)
+    chunks = text_splitter.split_text(content)
+    print("Done preprocessing. Created ", len(chunks), " chunks of the original pdf")
+    print("PG Connection", PG_CONNECTION_STRING)
+
+    # 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 = batch_chunks
+
+        _ = PGVector.from_texts(
+            texts=batch_texts, embedding=embedder, collection_name=INDEX_NAME, connection_string=PG_CONNECTION_STRING
+        )
+        print(f"Processed batch {i//batch_size + 1}/{(num_chunks-1)//batch_size + 1}")
+    return True
+
+
+def ingest_link_to_pgvector(link_list: List[str]):
+    data_collection = parse_html(link_list)
+
+    texts = []
+    metadatas = []
+    for data, meta in data_collection:
+        doc_id = str(uuid.uuid4())
+        metadata = {"source": meta, "identify_id": doc_id}
+        texts.append(data)
+        metadatas.append(metadata)
+
+    # 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)
+
+    _ = PGVector.from_texts(
+        texts=texts,
+        embedding=embedder,
+        metadatas=metadatas,
+        collection_name=INDEX_NAME,
+        connection_string=PG_CONNECTION_STRING,
+    )
+
+
+@register_microservice(
+    name="opea_service@prepare_doc_pgvector",
+    service_type=ServiceType.DATAPREP,
+    endpoint="/v1/dataprep",
+    host="0.0.0.0",
+    port=6007,
+)
+@traceable(run_type="tool")
+@register_statistics(names=["opea_service@dataprep_pgvector"])
+async def ingest_documents(
+    files: Optional[Union[UploadFile, List[UploadFile]]] = File(None), link_list: Optional[str] = Form(None)
+):
+    print(f"files:{files}")
+    print(f"link_list:{link_list}")
+    if files and link_list:
+        raise HTTPException(status_code=400, detail="Provide either a file or a string list, not both.")
+
+    if files:
+        if not isinstance(files, list):
+            files = [files]
+        upload_folder = "./uploaded_files/"
+        if not os.path.exists(upload_folder):
+            Path(upload_folder).mkdir(parents=True, exist_ok=True)
+        for file in files:
+            save_path = upload_folder + file.filename
+            await save_file_to_local_disk(save_path, file)
+            ingest_doc_to_pgvector(DocPath(path=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.")
+            ingest_link_to_pgvector(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.")
+
+
+if __name__ == "__main__":
+    opea_microservices["opea_service@prepare_doc_pgvector"].start()

comps/dataprep/pgvector/langchain/requirements.txt

@@ -0,0 +1,21 @@
+beautifulsoup4
+docarray[full]
+easyocr
+fastapi
+huggingface_hub
+langchain
+langchain-community
+langsmith
+numpy
+opentelemetry-api
+opentelemetry-exporter-otlp
+opentelemetry-sdk
+pandas
+pgvector==0.2.5
+Pillow
+prometheus-fastapi-instrumentator==7.0.0
+psycopg2-binary
+pymupdf
+python-docx
+sentence_transformers
+shortuuid

comps/vectorstores/langchain/pgvector/README.md

@@ -17,5 +17,5 @@ export POSTGRES_DB=vectordb
 ## 3. Run Pgvector service
 
 ```bash
-docker run --name vectorstore-postgres -e POSTGRES_USER=${POSTGRES_USER} -e POSTGRES_HOST_AUTH_METHOD=trust -e POSTGRES_DB=${POSTGRES_DB} -e POSTGRES_PASSWORD=${POSTGRES_PASSWORD} -d -v ./init.sql:/docker-entrypoint-initdb.d/init.sql pgvector/pgvector:0.7.0-pg16
+docker run --name vectorstore-postgres -e POSTGRES_USER=${POSTGRES_USER} -e POSTGRES_HOST_AUTH_METHOD=trust -e POSTGRES_DB=${POSTGRES_DB} -e POSTGRES_PASSWORD=${POSTGRES_PASSWORD} -d -v ./init.sql:/docker-entrypoint-initdb.d/init.sql -p 5432:5432 pgvector/pgvector:0.7.0-pg16
 ```

comps/vectorstores/langchain/pgvector/__init__.py

@@ -1,13 +1,2 @@
-# Copyright (c) 2024 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0