Changes to checkin text2graph microservice

.github/workflows/docker/compose/text2graph-compose.yaml

@@ -0,0 +1,9 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+# this file should be run in the root of the repo
+services:
+  text2graph:
+    build:
+      dockerfile: comps/text2graph/src/Dockerfile
+    image: ${REGISTRY:-opea}/text2graph:${TAG:-latest}

comps/cores/mega/constants.py

@@ -34,6 +34,7 @@ class ServiceType(Enum):
     ANIMATION = 17
     IMAGE2IMAGE = 18
     TEXT2SQL = 19
+    TEXT2GRAPH = 20
 
 
 class MegaServiceEndpoint(Enum):

comps/text2graph/deployment/docker_compose/compose.yaml

@@ -0,0 +1,29 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+services:
+  text2graph:
+     image: opea/text2graph:latest
+     container_name: text2graph
+     ports:
+         - ${TEXT2GRAPH_PORT:-8090}:8090
+     environment:
+         - no_proxy=${no_proxy}
+         - https_proxy=${https_proxy}
+         - http_proxy=${http_proxy}
+         - LLM_MODEL_ID=${LLM_MODEL_ID:-"Babelscape/rebel-large"}
+         - HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN}
+     ipc: host
+     restart: always
+
+  text2graph-gaudi:
+    image: opea/text2graph:${TAG:-latest}
+    container_name: text2graph-gaudi-server
+    ports:
+      - ${TEXT2GRAPH_PORT:-9090}:8080
+    environment:
+      - TGI_LLM_ENDPOINT=${TGI_LLM_ENDPOINT:-8080}:8080
+
+networks:
+  default:
+    driver: bridge

comps/text2graph/src/Dockerfile

@@ -0,0 +1,47 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+FROM ubuntu:22.04
+
+WORKDIR /home/graph_extract
+
+FROM python:3.11-slim
+ENV LANG=C.UTF-8
+ARG ARCH=cpu
+
+RUN apt-get update -y && apt-get install vim -y && apt-get install -y --no-install-recommends --fix-missing \
+    build-essential 
+
+RUN useradd -m -s /bin/bash user && \
+    mkdir -p /home/user && \
+    chown -R user /home/user/
+
+COPY comps /home/user/comps
+
+RUN pip install --no-cache-dir --upgrade pip setuptools && \
+    if [ ${ARCH} = "cpu" ]; then \
+        pip install --no-cache-dir --extra-index-url https://download.pytorch.org/whl/cpu -r /home/user/comps/text2graph/src/requirements.txt; \
+    else \
+        pip install --no-cache-dir -r /home/user/comps/text2graph/src/requirements.txt; \
+    fi
+
+ENV https_proxy=${https_proxy}
+ENV http_proxy=${http_proxy}
+ENV no_proxy=${no_proxy}
+ENV LLM_ID=${LLM_ID:-"Babelscape/rebel-large"}
+ENV SPAN_LENGTH=${SPAN_LENGTH:-"1024"}
+ENV OVERLAP=${OVERLAP:-"100"}
+ENV MAX_LENGTH=${MAX_NEW_TOKENS:-"256"}
+ENV HUGGINGFACEHUB_API_TOKEN=${HF_TOKEN}
+ENV HF_TOKEN=${HF_TOKEN}
+ENV LLM_MODEL_ID=${LLM_ID}
+ENV TGI_PORT=8008
+ENV PYTHONPATH="/home/user/":$PYTHONPATH
+
+USER user
+
+WORKDIR /home/user/comps/text2graph/src/
+
+RUN bash -c 'source /home/user/comps/text2graph/src/setup_service_env.sh'
+
+ENTRYPOINT ["python", "opea_text2graph_microservice.py"]

comps/text2graph/src/README.md

@@ -0,0 +1,118 @@
+# Text to graph triplet extractor
+
+Creating graphs from text is about converting unstructured text into structured data is challenging.
+It's gained significant traction with the advent of Large Language Models (LLMs), bringing it more into the mainstream. There are two main approaches to extract graph triplets depending on the types of LLM architectures like decode and encoder-decoder models.
+
+## Decoder Models
+
+Decoder-only models are faster during inference as they skip the encoding. This is ideal for tasks where the
+input-output mapping is simpler or where multitasking is required. It is suitable for generating outputs based on
+prompts or when computational efficiency is a priority. In certain cases, the decoder only models struggle with
+tasks requiring deep contextual understanding or when input-output structures are highly heterogeneous.
+
+## Encoder-decoder models
+
+This microservice provides an encoder decoder architecture approach to graph triplet extraction. Models like REBEL, is based on the BART family/like model and fine-tuned for relation extraction and classification tasks. The approach works better when handling complex relations applications and data source. Encoder decoder models often achieve high performance on benchmarks due to their ability to encode contextual information effectively. It is suitable for tasks requiring detailed parsing of text into structured formats, such as knowledge graph construction from unstructured data.
+
+# Features
+
+Input text from a document or string(s) in text format and the graph triplets and nodes are identified.  
+Subsequent processing needs to be done such as performing entity disambiguation to merge duplicate entities
+before generating cypher code
+
+## Implementation
+
+The text-to-graph microservice able to extract from unstructured text in document, textfile, or string formats
+The service is hosted in a docker. The text2graph extraction requires logic and LLMs to be hosted.
+LLM hosting is done with TGI for Gaudi's and natively running on CPUs for CPU.
+
+# 🚀1. Start Microservice with Docker
+
+Option 1 running on CPUs
+
+## Install Requirements
+
+```bash
+ pip install -r requirements.txt
+```
+
+## Environment variables : Configure LLM Parameters based on the model selected.
+
+```
+export LLM_ID=${LLM_ID:-"Babelscape/rebel-large"}
+export SPAN_LENGTH=${SPAN_LENGTH:-"1024"}
+export OVERLAP=${OVERLAP:-"100"}
+export MAX_LENGTH=${MAX_NEW_TOKENS:-"256"}
+export HUGGINGFACEHUB_API_TOKEN=""
+export LLM_MODEL_ID=${LLM_ID}
+export TGI_PORT=8008
+```
+
+##Echo env variables
+
+```
+echo "Extractor details"
+echo LLM_ID=${LLM_ID}
+echo SPAN_LENGTH=${SPAN_LENGTH}
+echo OVERLAP=${OVERLAP}
+echo MAX_LENGTH=${MAX_LENGTH}
+```
+
+### Start TGI Service
+
+```bash
+export HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN}
+export LLM_MODEL_ID="mistralai/Mistral-7B-Instruct-v0.3"
+export TGI_PORT=8008
+
+docker run -d --name="text2graph-tgi-endpoint" --ipc=host -p $TGI_PORT:80 -v ./data:/data --shm-size 1g -e HF_TOKEN=${HUGGINGFACEHUB_API_TOKEN} -e model=${LLM_MODEL_ID} ghcr.io/huggingface/text-generation-inference:2.1.0 --model-id $LLM_MODEL_ID
+```
+
+### Verify the TGI Service
+
+```bash
+export your_ip=$(hostname -I | awk '{print $1}')
+curl http://${your_ip}:${TGI_PORT}/generate \
+  -X POST \
+  -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}' \
+  -H 'Content-Type: application/json'
+```
+
+### Setup Environment Variables to host TGI
+
+```bash
+export TGI_LLM_ENDPOINT="http://${your_ip}:${TGI_PORT}"
+```
+
+### Start Text2Graph Microservice with Docker
+
+Command to build text2graph microservice
+
+```bash
+docker build -f Dockerfile -t user_name:graph_extractor ../../../
+```
+
+Command to launch text2graph microservice
+
+```bash
+docker run -i -t --net=host --ipc=host -p 8090 user_name:graph_extractor
+```
+
+The docker launches the text2graph microservice. To run it interactive.
+
+# Validation and testing
+
+## Text to triplets
+
+Test directory is under GenAIComps/tests/text2graph/
+There are two files in this directory.
+
+- example_from_file.py : Example python script that downloads a text file and extracts triplets
+
+- test_text2graph_opea.sh : The main script that checks for health and builds docker, extracts and generates triplets.
+
+## Check if services are up
+
+### Setup validation process
+
+For set up use http://localhost:8090/docs for swagger documentation, list of commands, interactive GUI.