RAG智能代理

Required Corrective Actions

[1] Replace or properly secure exposed wiring to meet electrical safety standards. [2] Install additional fire extinguishers in compliance with fire code requirements. [3] Reinforce or replace temporary support beams to ensure structural stability.

Example Usage (Python, scikit‑learn)

import mlflow import mlflow.sklearn from sklearn.ensemble import RandomForestRegressor from sklearn.metrics import mean_squared_error from sklearn.datasets import load_diabetes from sklearn.model_selection import train_test_split

mlflow.set_experiment("Diabetes-Regression") with mlflow.start_run(): X, y = load_diabetes(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) model = RandomForestRegressor(n_estimators=100, random_state=42) model.fit(X_train, y_train) mlflow.log_param("n_estimators", 100) preds = model.predict(X_test) mse = mean_squared_error(y_test, preds) mlflow.log_metric("mse", mse) mlflow.sklearn.log_model(model, artifact_path="model") model_uri = f"runs:/{mlflow.active_run().info.run_id}/model" mlflow.register_model(model_uri, "DiabetesRF")

• The run appears in the MLflow UI where you can compare metrics across experiments.
• The model is stored under artifact_path="model" and can be registered for lifecycle management【2】.

Coverage-Guided Fuzz Testing

• To run fuzz-testing using Google's atheris and coverage:

$ uv run coverage run -m src.EmailRAG.fuzzer -atheris_runs=100

$ uv run coverage run -m src.rag_agent.fuzzer -atheris_runs=100

• Generate HTML report and view it:

$ uv run python -m coverage html
$ (cd htmlcov && uv run python -m http.server 8000)

Sample Cypher Queries:

• Visit with id:56:

MATCH (v:Visit) WHERE v.id = 56 RETURN v;

• Which patient was involved in Visit id:56

MATCH (p:Patient)-[h:HAS]->(v:Visit) where v.id=56 return v,h,p

• Which physician treated the patient i Visit id:56

MATCH (p:Patient)-[h:HAS]->(v:Visit)<-[t:TREATS]-(ph:Physician) where v.id=56 return v,h,p,t,ph

• All relationships going in and out of Visit id:56

MATCH (v:Visit)-[r]-(n) where v.id=56 return v,r,n

Sample Cypher Accumulation:

• Total visits and bill paid by payer Aetna in Texas:

MATCH (p:Payer)<-[c:COVERED_BY]-(v:Visit)-[:AT]->(h:Hospital)
WHERE p.name = "Aetna"
AND h.state_name = "TX"
RETURN COUNT(*) as num_visits,
SUM(c.billing_amount) as total_billing_amount;

Environment

Add a .env with the following environment variables:

ENVIRONMENT=development
DB_USERNAME=
DB_PASSWORD=
NEO4J_AUTH=username/password
LANGSMITH_TRACING="true"
LANGSMITH_API_KEY=
LANGSMITH_ENDPOINT="https://api.smith.langchain.com"
LANGSMITH_PROJECT=
GOOGLE_CLOUD_PROJECT=
GOOGLE_CLOUD_LOCATION="asia-southeast1"
GOOGLE_API_KEY=
GEMINI_API_KEY=
OLLAMA_API_KEY=
USER_AGENT="USER_AGENT"

• Install tkinter:

$ sudo apt install -y python3.13-tk

Home controller endpoints:

$ c3 -v https://localhost:4433/invoke -m 300 -X POST -d '{"message": "What is task decomposition?"}'

Hospital controller endpoints:

$ c3 -v https://localhost:4433/healthcare/invoke -m 300 -X POST -d '{"message": "Which hospital has the shortest wait time?"}'

Infrastructure components:

• All of the following components run on k8s cluster:

1. PostgreSQL for checkpoints and vector DB
2. Neo4J for graph query
3. Ollama as LLM model server

Core Components

Typical Workflow

1. Define an MLflow Project → write an MLproject file that lists entry points and required environments.
2. Run Experiments → execute the project; each run is recorded by Tracking (params, metrics, artifacts).
3. Log a Model → after training, call mlflow.<flavor>.log_model() to store the model artifact.
4. Register the Model → use the Model Registry to create a new version and assign a stage (e.g., “Staging”).
5. Deploy / Serve → retrieve the model from the registry for batch inference, REST serving, or integration into production pipelines.

Question & Answer RAG Deep Agent

RAG Deep Agent answers question from Vector and Graph Database

Answering questions from mocked-up API call:

• output/user_questions.md:

Which hospital has the shortest wait time?

• output/final_answer.md:

```

Answering questions from Postgres Vector Store:

• output/user_request_{timestamp}.md:

``` What is task decomposition?

---

Answering question from Neo4J graph DB

• output/user_questions.md:

Which physician has treated the most patients covered by Cigna?

• output/final_answer.md:

```

The physician who has treated the most patients covered by Cigna is Kayla Lawson, who has seen 10 patients.

Answering question from Neo4J graph AND vector DB

• output/user_questions.md:

Query the graph database to show me the reviews written by patient 7674

• output/final_answer.md:

```