Update README for concept-linking

Change structure of requirements for entire project

Dockerfile

@@ -2,9 +2,8 @@ FROM python:3.11-slim
 
 WORKDIR /code
 
-COPY requirements.txt ./
-RUN pip install --no-cache-dir -r requirements.txt
-
 COPY . .
+RUN pip install --no-cache-dir -r requirements_docker.txt
+
 
 CMD ["python", "-u", "-m", "server.server", "--host", "0.0.0.0", "--port", "4444", "--reload"]

concept_linking/README.md

@@ -1,61 +1,126 @@
-# D: Preproscessing Layer - Concept Linking
+# Concept Linking
 
-Repository of group D in KNOX pipeline.
+---
+## Background
+In group D, four different solutions have been implemented. These will be mentioned further in a later section.
+At default the solution that will be running is PromptEngineering.
 
-## Description
+To change which solution to run in the pipeline perform the following changes...
 
-This repository is for type classification of already provided sententes with given entity mentions. Several different solutions were created, in order to find the best one.
+First change directory to the 'server' folder in the root directory.
 
-### Dependencies
+Next, open the server.py file.
+On line 24, under the text "#Begin ConceptLinking"
+Change the code, to run the desired solution.
 
-- Python
-- PIP
-- Git
 
-### Installing
+## Requirements
+For any of the four solutions, it is necessary to install the requirements found
+in the requirements.txt file inside /{Solution_Name}/requirements.txt
+
+However since this is a joint codebase for both relation-extraction and concept-linking,
+there is a global requirements.txt file in the root directory.
+It follows a nested structure, meaning that installing only to one if the root folder, 
+will install all the rest.
+
+It will install both the necessary requirements for both groups' solutions.
+However since it is possible to change which of the four concept-linking solutions to run, 
+it is also necessary to the requirements to be installed accordingly.
+This is done by navigation to
 
 ```
-git clone https://github.com/Knox-AAU/PreprocessingLayer_Concept-Linking
+./concept_linking/requirements.txt
 ```
+In this file, there is listed a reference to the four different requirements.txt files.
+Remove the #(comment) from the one referencing the solution you want to run.
+
+### Example
+Install the requirements for the PromptEngineering solution
 
-### Initial Setup
+Navigate to the following directory
 
-- Navigate to root folder
-- Run the following command for installing all requirements:
+```
+../concept_linking/solutions/PromptEngineering/
+```
 
+And run the following command
 ```
 pip install -r requirements.txt
 ```
 
-### Adding modules
+## Solutions
 
-- Navigate to root folder.
-- Run the following command to add all installed modules:
+Following below is brief description of each of the four solutions and how to get started.
+
+---
 
-```
-pip freeze > requirements.txt
-```
 
-### Executing program
+### Machine Learning
+description WIP
 
-- Navigate to main.py in Program directory
-- Run main.py with Python
+### Prompt Engineering
+Uses the LLM Llama2. A prompt is given to the model. 
 
 ```
-python .\program\main.py
+ prompt_template = {
+                    "system_message": ("The input sentence is all your knowledge. \n"
+                                       "Do not answer if it can't be found in the sentence. \n"
+                                       "Do not use bullet points. \n"
+                                       "Do not identify entity mentions yourself, use the provided ones \n"
+                                       "Given the input in the form of the content from a file: \n"
+                                       "[Sentence]: {content_sentence} \n"
+                                       "[EntityMention]: {content_entity} \n"),
+
+                    "user_message": ("Classify the [EntityMention] in regards to ontology classes: {ontology_classes} \n"
+                                     "The output answer must be in JSON in the following format: \n"
+                                     "{{ \n"
+                                     "'Entity': 'Eiffel Tower', \n"
+                                     "'Class': 'ArchitecturalStructure' \n"
+                                     "}} \n"),
+
+                    "max_tokens": 4092
+                }
 ```
 
-or:
+The variables {content_sentence} and {content_entity} is found in a previous part of the KNOX pipeline.
+The variable {ontology_classes} fetched by the Ontology endpoint provided by group E(Database Layer)
 
+
+#### Using LocalLlama API server
+It is possible to use a local LlamaServer. It can be found in ../concept_linking/tools/LlamaServer.
+A README for setting up an instance of this server can be found in the directory given above.
+
+#### Using the Llama API server hosted in the KNOX pipeline
+WIP
+Go to the directory /concept_linking/PromptEngineering/main
+set the api_url accordingly
+``` 
+api_url={domain or ip+port of llama server hosted in the knox pipeline}
 ```
-cd .\program\
-python .\main.py
-```
+Refer to the <a href="https://docs.google.com/spreadsheets/d/1dvVQSEvw15ulNER8qvl1P8Ufq-p3vLU0PswUeahhThg/edit#gid=0" target="_blank">Server Distribution document</a>
+ for specific dns and ip+port information.
 
-## Report
+### String Comparison
+description WIP
 
-Description of the project can be found in the report on [Overleaf](https://www.overleaf.com/project/65000513b10b4521e8907099) (requires permission)
 
-## Authors
+### Untrained Spacy
+description WIP
+
+
+
+---
+
+## Tools
+
+### LlamaServer
 
-Lucas, Gamma, Vi, Mikkel, Caspar & Rune
+### OntologyGraphBuilder
+
+---
+
+## Report
+Description of the project can be found in the report on Overleaf (requires permission)
+
+## Authors
+Lucas, Gamma, Vi, Mikkel, Caspar & Rune

concept_linking/requirements.txt

@@ -0,0 +1,13 @@
+# Current solution
+#Requirements for PromptEngineering
+-r solutions/PromptEngineering/requirements.txt
+
+# Other solutions
+#Requirements for MachineLearning
+#-r solutions/MachineLearning/requirements.txt
+
+#Requirements for StringComparison
+#-r solutions/StringComparison/requirements.txt
+
+#Requirements for UntrainedSpacy
+#-r solutions/UntrainedSpacy/requirements.txt

concept_linking/tools/LlamaServer/README.md

@@ -0,0 +1,53 @@
+# Tools: LlamaServer
+
+## Description
+Local API server based on Llama2.
+
+## Getting started
+It is possible to use the LlamaServer found in ../concept_linking/tools/LlamaServer
+as a local llama api server.
+
+The server uses a quantized version of the Llama2-7B model.
+It needs to be present in the directory given above.
+However, if it is not present just run the server a single time, and it will be downloaded.
+This is necessary before trying to create a docker image for the server.
+
+[//]: # (This instance has been optimized for usage with Nvidia CUDA supported GPUs'.)
+
+[//]: # (The software will automatically detect a valid CUDA gpu if one is present.)
+
+[//]: # (If not, the solution will fall back to using the CPU.)
+
+## Requirements
+Install the requirements for this solution
+
+Navigate to the following directory
+
+```
+../concept_linking/tools/LlamaServer/
+```
+
+And run the following command
+```
+pip install -r requirements.txt
+```
+
+Since this is meant as a tool for running Llama locally on Windows. It is required to have a C++ installation.
+C++ can be installed via the Visual Studio Installer.
+Select "Desktop development with C++"
+
+## Docker
+To build the server as a docker image, change the directory in a terminal to ../concept_linking/tools/LlamaServer.
+Run the following command
+
+```
+docker build -t llama-cpu-server . 
+```
+
+* Open Docker desktop
+* Select Images, under 'Actions' select 'run'
+* A prompt will now appear, expand the 'Optional settings'
+* Under 'Ports' set 'Host Port' to 5000
+* Press run.
+
+The server should now be up and running

relation_extraction/requirements.txt

@@ -0,0 +1,5 @@
+requests==2.31.0
+strsimpy==0.2.1
+mock==5.1.0
+python-dotenv==0.21.0
+Flask==3.0.0

requirements.txt

@@ -1,5 +1,7 @@
-requests==2.31.0
-strsimpy==0.2.1
-mock==5.1.0
-python-dotenv==0.21.0
-Flask==3.0.0
+#For python
+#----------
+# Concept linking
+-r /concept_linking/requirements.txt
+
+# Relation extraction
+-r /relation_extraction/requirements.txt

requirements_docker.txt

@@ -0,0 +1,7 @@
+#For docker
+#----------
+# Concept linking
+-r /code/concept_linking/requirements.txt
+
+# Relation extraction
+-r /code/relation_extraction/requirements.txt

server/server.py

@@ -2,9 +2,11 @@
 import json
 import os
 from relation_extraction.relation_extractor import RelationExtractor
+from concept_linking.solutions.PromptEngineering.main import perform_entity_type_classification
 
 app = Flask(__name__)
 
+
 @app.route('/tripleconstruction', methods=["POST"])
 def do_triple_construction():
     print("Received POST request...")
@@ -20,18 +22,20 @@ def do_triple_construction():
         post_json = json.loads(post_data)
 
         RelationExtractor.begin_extraction(post_json)
-        #Begin ConceptLinking
+        perform_entity_type_classification(post_json)
 
         message = "Post request was successfully processed. Relation extraction and concept linking completed."
         return jsonify(message=message), 200
 
     except Exception as e:
         return jsonify(error=f"Error occured: {str(e)}"), 422
 
+
 @app.errorhandler(404)
 def page_not_found(error):
     message = "Invalid endpoint"
     return jsonify(error=message), 404
 
+
 if __name__ == '__main__':
-    app.run(host='0.0.0.0', port=4444)
+    app.run(host='0.0.0.0', port=4444)