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Pinecone support for dataprep and retrieval microservice (#157)
Signed-off-by: Pallavi Jaini <[email protected]>
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# Dataprep Microservice with Pinecone | ||
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# 🚀Start Microservice with Python | ||
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## Install Requirements | ||
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```bash | ||
pip install -r requirements.txt | ||
``` | ||
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## Start Pinecone Server | ||
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Please refer to this [readme](../../../vectorstores/langchain/pinecone/README.md). | ||
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## Setup Environment Variables | ||
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```bash | ||
export http_proxy=${your_http_proxy} | ||
export https_proxy=${your_http_proxy} | ||
export PINECONE_API_KEY=${PINECONE_API_KEY} | ||
export PINECONE_INDEX_NAME=${PINECONE_INDEX_NAME} | ||
``` | ||
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## Start Document Preparation Microservice for Pinecone with Python Script | ||
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Start document preparation microservice for Pinecone with below command. | ||
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```bash | ||
python prepare_doc_pinecone.py | ||
``` | ||
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# 🚀Start Microservice with Docker | ||
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## Build Docker Image | ||
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```bash | ||
cd ../../../../ | ||
docker build -t opea/dataprep-pinecone:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/dataprep/pinecone/docker/Dockerfile . | ||
``` | ||
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## Run Docker with CLI | ||
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```bash | ||
docker run -d --name="dataprep-pinecone-server" -p 6000:6000 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy opea/dataprep-pinecone:latest | ||
``` | ||
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## Setup Environment Variables | ||
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```bash | ||
export http_proxy=${your_http_proxy} | ||
export https_proxy=${your_http_proxy} | ||
export PINECONE_API_KEY=${PINECONE_API_KEY} | ||
export PINECONE_INDEX_NAME=${PINECONE_INDEX_NAME} | ||
``` | ||
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## Run Docker with Docker Compose | ||
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```bash | ||
cd comps/dataprep/pinecone/docker | ||
docker compose -f docker-compose-dataprep-pinecone.yaml up -d | ||
``` | ||
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# Invoke Microservice | ||
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Once document preparation microservice for Pinecone is started, user can use below command to invoke the microservice to convert the document to embedding and save to the database. | ||
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```bash | ||
curl -X POST -H "Content-Type: application/json" -d '{"path":"/path/to/document"}' http://localhost:6000/v1/dataprep | ||
``` |
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# Copyright (C) 2024 Intel Corporation | ||
# SPDX-License-Identifier: Apache-2.0 |
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# Copyright (C) 2024 Intel Corporation | ||
# SPDX-License-Identifier: Apache-2.0 | ||
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import os | ||
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# Embedding model | ||
EMBED_MODEL = os.getenv("EMBED_MODEL", "sentence-transformers/all-MiniLM-L6-v2") | ||
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# Pinecone configuration | ||
PINECONE_API_KEY = os.getenv("PINECONE_API_KEY", "xxx_xxx") | ||
PINECONE_INDEX_NAME = int(os.getenv("PINECONE_INDEX_NAME", "langchain-test")) | ||
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# 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") |
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# Copyright (C) 2024 Intel Corporation | ||
# SPDX-License-Identifier: Apache-2.0 | ||
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FROM python:3.11-slim | ||
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ENV LANG C.UTF-8 | ||
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RUN apt-get update -y && apt-get install -y --no-install-recommends --fix-missing \ | ||
build-essential \ | ||
libgl1-mesa-glx \ | ||
libjemalloc-dev \ | ||
vim | ||
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RUN useradd -m -s /bin/bash user && \ | ||
mkdir -p /home/user && \ | ||
chown -R user /home/user/ | ||
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USER user | ||
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COPY comps /home/user/comps | ||
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RUN pip install --no-cache-dir --upgrade pip && \ | ||
pip install --no-cache-dir -r /home/user/comps/dataprep/pinecone/requirements.txt | ||
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ENV PYTHONPATH=$PYTHONPATH:/home/user | ||
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WORKDIR /home/user/comps/dataprep/pinecone | ||
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ENTRYPOINT ["python", "prepare_doc_pinecone.py"] | ||
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comps/dataprep/pinecone/docker/docker-compose-dataprep-pinecone.yaml
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# Copyright (C) 2024 Intel Corporation | ||
# SPDX-License-Identifier: Apache-2.0 | ||
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version: "3" | ||
services: | ||
dataprep-pinecone: | ||
image: opea/gen-ai-comps:dataprep-pinecone-xeon-server | ||
container_name: dataprep-pinecone-server | ||
ports: | ||
- "6000:6000" | ||
ipc: host | ||
environment: | ||
http_proxy: ${http_proxy} | ||
https_proxy: ${https_proxy} | ||
PINECONE_API_KEY: ${PINECONE_API_KEY} | ||
PINECONE_INDEX_NAME: ${PINECONE_INDEX_NAME} | ||
restart: unless-stopped | ||
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networks: | ||
default: | ||
driver: bridge |
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# Copyright (C) 2024 Intel Corporation | ||
# SPDX-License-Identifier: Apache-2.0 | ||
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import os | ||
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from config import EMBED_MODEL, PINECONE_API_KEY, PINECONE_INDEX_NAME | ||
from langchain.text_splitter import RecursiveCharacterTextSplitter | ||
from langchain_community.embeddings import HuggingFaceBgeEmbeddings, HuggingFaceEmbeddings, HuggingFaceHubEmbeddings | ||
from langchain_community.vectorstores import Pinecone | ||
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from comps import DocPath, opea_microservices, opea_telemetry, register_microservice | ||
from comps.dataprep.utils import document_loader | ||
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tei_embedding_endpoint = os.getenv("TEI_ENDPOINT") | ||
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@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): | ||
"""Ingest document to Pinecone.""" | ||
doc_path = doc_path.path | ||
print(f"Parsing document {doc_path}.") | ||
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text_splitter = RecursiveCharacterTextSplitter(chunk_size=1500, chunk_overlap=100, add_start_index=True) | ||
content = document_loader(doc_path) | ||
chunks = text_splitter.split_text(content) | ||
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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) | ||
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# 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 | ||
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_ = Pinecone.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}") | ||
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if __name__ == "__main__": | ||
opea_microservices["opea_service@prepare_doc_pinecone"].start() |
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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 | ||
pymupdf | ||
python-docx | ||
sentence_transformers | ||
shortuuid |
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# Copyright (C) 2024 Intel Corporation | ||
# SPDX-License-Identifier: Apache-2.0 |
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# Copyright (C) 2024 Intel Corporation | ||
# SPDX-License-Identifier: Apache-2.0 | ||
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import os | ||
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# Embedding model | ||
EMBED_MODEL = os.getenv("EMBED_MODEL", "sentence-transformers/all-MiniLM-L6-v2") | ||
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# Pinecone configuration | ||
PINECONE_API_KEY = os.getenv("PINECONE_API_KEY", "xxx_xxx") | ||
PINECONE_INDEX_NAME = int(os.getenv("PINECONE_INDEX_NAME", "langchain-test")) | ||
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# 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") |
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# Copyright (C) 2024 Intel Corporation | ||
# SPDX-License-Identifier: Apache-2.0 | ||
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FROM langchain/langchain:latest | ||
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RUN apt-get update -y && apt-get install -y --no-install-recommends --fix-missing \ | ||
libgl1-mesa-glx \ | ||
libjemalloc-dev \ | ||
vim | ||
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RUN useradd -m -s /bin/bash user && \ | ||
mkdir -p /home/user && \ | ||
chown -R user /home/user/ | ||
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COPY comps /home/user/comps | ||
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RUN chmod +x /home/user/comps/retrievers/langchain/pinecone/run.sh | ||
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USER user | ||
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RUN pip install --no-cache-dir --upgrade pip && \ | ||
pip install --no-cache-dir -r /home/user/comps/retrievers/requirements.txt | ||
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ENV PYTHONPATH=$PYTHONPATH:/home/user | ||
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WORKDIR /home/user/comps/retrievers/langchain/pinecone | ||
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ENTRYPOINT ["/home/user/comps/retrievers/langchain/pinecone/run.sh"] |
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comps/retrievers/langchain/pinecone/docker/docker_compose_retriever.yaml
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# Copyright (C) 2024 Intel Corporation | ||
# SPDX-License-Identifier: Apache-2.0 | ||
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version: "3.8" | ||
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services: | ||
tei_xeon_service: | ||
image: ghcr.io/huggingface/text-embeddings-inference:cpu-1.2 | ||
container_name: tei-xeon-server | ||
ports: | ||
- "6060:80" | ||
volumes: | ||
- "./data:/data" | ||
shm_size: 1g | ||
command: --model-id ${RETRIEVE_MODEL_ID} | ||
retriever: | ||
image: opea/retriever-pinecone:latest | ||
container_name: retriever-pinecone-server | ||
ports: | ||
- "7000:7000" | ||
ipc: host | ||
environment: | ||
http_proxy: ${http_proxy} | ||
https_proxy: ${https_proxy} | ||
PINECONE_API_KEY: ${PINECONE_API_KEY} | ||
INDEX_NAME: ${PINECONE_INDEX_NAME} | ||
LANGCHAIN_API_KEY: ${LANGCHAIN_API_KEY} | ||
restart: unless-stopped | ||
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networks: | ||
default: | ||
driver: bridge |
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