gem-hunter
主要言語: Jupyter Notebook
Legal Research Copilot Example Solution built with Generative AI capabilities of PostgreSQL on Azure
This solution accelerator is designed as an end-to-end example of a Legal Research Copilot application. It demonstrates the implementation of three information retrieval techniques: vector search, semantic ranking, and GraphRAG on Azure Database for PostgreSQL, and illustrates how they can be combined to deliver high quality responses to legal research questions. The app uses the U.S. Case Law dataset of 0.5 million legal cases as a source of the factual data. For more details on these concepts, please see the accompanying blog to this solution accelerator here.
As the architecture diagram shows, this solution accelerator brings together vector search, semantic ranking, and GraphRAG. Here are some highlights, including the information retrieval pipeline:
Semantic Ranking Enhances vector search accuracy by re-ranking results with a semantic ranker model, significantly improving top results' relevance (e.g., up to a 10–20% boost in NDCG@10 accuracy). The semantic ranker is available as a standalone solution accelerator, detailed in the blog: Introducing Semantic Ranker Solution Accelerator for Azure Database for PostgreSQL. GraphRAG An advanced RAG technique proposed by Microsoft Research to improve quality of RAG system responses by extracting knowledge graph from the source data and leveraging it to provide better context to the LLM. The GraphRAG technique consists of three high level steps: 1. Graph extraction 2. Entity summarization 3. Graph query generation at query time Information Retrieval Pipeline We leverage the structure of the citation graph at the query time by using specialized graph query. The graph query is designed to use the prominence of the legal cases as a signal to improve the accuracy of the information retrieval pipeline. The graph query is expressed as a mixture of traditional relational query and OpenCypher graph query and executed on Postgres using the Apache AGE extension. The resulting information retrieval pipeline is shown below.
For related solution accelerators and articles please see the following:
The steps below guides you to deploy the Azure services necessary for this solution accelerator into your Azure subscription.
⚠️ NOTE: In order to run the Semantic Ranking part of this accelerator, it requires you have an Azure ML Endpoint running a ranking model such as “bge-reranker-v2-m3”. As a way to get this up and running, you can deploy following related solution accelerator which will setup an Azure ML Endpoint for ranking scoring:
👉 Follow the steps in the repo above first before moving on to the deployment steps below.
👉 Once you deploy this accelerator, notate the "/score" REST endpoint URI and the key. You will need these in the steps below when deploying.
git clone https://github.com/Azure-Samples/graphrag-legalcases-postgres.git
cd graphrag-legalcases-postgrescp .env.sample .env
AZURE_ML_SCORING_ENDPOINT={YOUR-AZURE-ML-ENDPOINT}
AZURE_ML_ENDPOINT_KEY={YOUR-AZURE-ML-ENDPOINT-KEY}
AZURE_ML_DEPLOYMENT={YOUR-AZURE-ML-ENDPOINT-KEY}azd auth login
azd up
We recommend and have targeted the use of the sh shell. You can run the provided devcontainer setup by pressing CTRL + Shift + P in Visual Studio Code and selecting the 'Dev Containers: Rebuild and Reopen in Container' option. This will give you access to an sh shell for your work even if you are using a Windows environment.
However, if you prefer to use your Windows environment directly with pwsh instead, you’ll need to follow these steps:
Install PowerShell (PowerShell-7.4.6-win-x64.msi-> https://github.com/PowerShell/PowerShell/releases/tag/v7.4.6)
Install Rust (rustup-init.exe ->https://rustup.rs/)
Install Python 3.12 (Windows installer (64-bit)-> https://www.python.org/downloads/release/python-3120/)
Install Node.js (https://nodejs.org/en).
After that, you need to open a new terminal and run these two commands manually. Please note that for Linux it is not required to run these manually, only for Windows, you need to run:
pip install -r requirements-dev.txt
pip install -e src/backend
After completing the above steps, you need to follow the steps provided in Deployment Steps For using Posix (sh) above.
Errors During Deployment (azd up)
If you encounter any errors during the deployment process, run azd down to clean up the resources and address the issue. Once resolved, you can run azd up again to retry the deployment.
Missing URLs or Configuration
If you lose track of the deployment URLs or configuration, you can use the azd show command to display the existing URLs and other deployment details.
Checking Region Capacity
Before running azd up, ensure that your target region has the desired available capacity for your deployment. You can refer to the main.parameters.json file to check the capacity needed for Chat, Eval, and Embedding generations. By default, ensure the following minimum capacities or you can try to modify these parameters in the main.parameters.json file accordingly if you do not have enough available capacity in your Azure subscription.
Azure Subscription Permissions Ensure that you have the appropriate permissions in your Azure subscription. You should be the subscription owner or have equivalent permissions to successfully deploy resources and enable required features.
You can run the default integration with MSR GraphRAG data by selecting the corresponding option from the frontend. However, if you want to run entire Microsoft's GraphRAG integration alongside our RRF graph solution, follow the steps below to initialize, auto-tune, and index the graphrag folder for your data. The output of these GraphRAG steps is already available in the data folder. However, if you prefer to run the library yourself, follow the steps below. If you’d rather use the provided CSV files directly, you can skip ahead to step 8.
Run the following command to create the required folder structure:
mkdir -p ./graphrag/input
Before running the GraphRAG library, you need to prepare and place the input CSV file into the ./graphrag/input folder. You will need to create the input CSV file in the following steps.
The original case table contains multiple columns, including:
id – Unique identifier for each case.data – A large JSON object containing detailed case information.description_vector – A 1536-dimensional vector used for vector search.However, not all json fields from data are required for GraphRAG processing. To simplify the dataset, we will create a filtered table (cases_filtered) that retains only the necessary columns while excluding the description_vector and create a csv file from it in the next step.
CREATE TABLE cases_filtered ( id TEXT, name TEXT, court_id TEXT, court_name TEXT, description TEXT ); INSERT INTO cases_filtered (id, name, court_id, court_name, description) SELECT id, data->>'name' AS name, (data->'court'->>'id') AS court_id, data->'court'->>'name' AS court_name, data->'casebody'->'opinions'->0->>'text' AS description FROM cases_updated;
Please note that original case table is already provided in data folder as cases_final.csv after azd up command, you will obtain it in a table format named cases_updated.
To generate the required CSV file, run the following SQL command inside your PostgreSQL database:
COPY (
SELECT id, name, court_id, court_name, description
FROM demo_cases_filtered
) TO '/home/postgres/cases_filtered_final.csv'
WITH CSV HEADER;This command extracts the required fields from demo_cases_filtered and saves them as cases_filtered_final.csv inside the PostgreSQL container.
Next, copy the generated CSV file from the PostgreSQL container to your local input directory:
docker cp <container-name>:/home/postgres/cases_filtered_final.csv /<your-path>/graphrag/input/cases_filtered_final.csv
Replace <container-name> with the actual name of your running PostgreSQL container and <your-path> with the local directory path where the GraphRAG input folder is located.
Ensure that the file cases_filtered_final.csv is correctly placed inside ./graphrag/input/ before running the GraphRAG library.
Activate the Poetry environment by running:
poetry shell
poetry install
This will install the necessary dependencies to use the graphrag command.
Initialize the folder with the following command:
graphrag init --root ./graphrag
This will create the required files for the process.
GRAPHRAG_API_KEY in the .env file, depending on the OpenAI model type you are using.settings.yaml file with the following configuration with your data columns instead:input:
type: file # or 'blob'
file_type: csv # or 'text'
base_dir: "input"
file_encoding: utf-8
file_pattern: ".*\\.csv$"
source_column: id
text_column: description
title_column: name
document_attribute_columns:
- court_id
- court_name
settings.yaml file with the following model configuration for your Azure OpenAI.llm:
api_key: ${GRAPHRAG_API_KEY}
type: azure_openai_chat
model: gpt-4o-mini
model_supports_json: true
api_base: https://<your-azure-openai>.openai.azure.com
api_version: <your-azure-openai-4o-mini-version>
deployment_name: gpt-4o-mini
embeddings:
async_mode: threaded # or asyncio
vector_store:
type: lancedb
db_uri: 'output/lancedb'
container_name: default
overwrite: true
llm:
api_key: ${GRAPHRAG_API_KEY}
type: azure_openai_embedding
model: text-embedding-3-small
api_base: https://<your-azure-openai>.openai.azure.com
api_version: <your-azure-openai-text-embedding-3-small-version>
deployment_name: text-embedding-3-small
Auto-tune your prompts according to your data by running:
python -m graphrag prompt-tune --root ./graphrag/ --config ./graphrag/settings.yaml --no-discover-entity-types --output ./graphrag/prompts/
Begin the indexing process for your knowledge graph with:
graphrag index --root ./graphrag
This process may take about an hour to complete, depending on your rate limits.
After indexing, use the notebook.ipynb file to convert the following Parquet files to CSV and save them in the data folder:
final_documentsfinal_text_unitsfinal_communitiesfinal_community_reports
Once the CSV files are in the data folder, proceed to the next step to create additional vector fields and generate embeddings.Embeddings need to be generated during post-provisioning to modify the CSV files for final_text_units and final_community_reports.
Before running the initial deployment, set the RUN_POST_EMBEDDING parameter to true in the scripts/setup_postgres_seeddata.sh file. This ensures that new fields are created and embeddings are generated. Then, execute:
azd up
Once the tables with embeddings are generated, convert them into CSV files and place them in the data folder. This will speed up future deployments.
Finally, after generating the embeddings and saving them in CSV format, update the RUN_POST_EMBEDDING parameter to false in the scripts/setup_postgres_seeddata.sh file to prevent redundant processing.
Run the following command to complete the deployment:
azd up
If the server is already running, please execute the following command first to re-provision the servers:
azd down
.env and settings.yaml are accurate.This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.
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