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Basic Rag application

A simple app shows the basic capability of RAG system.

You should follow the below steps to run the application:

Step 1: Load the data

  • Navigate to the basic-rag directory.

  • Run the below command to load the data in a vector store.

    python load_data.py

Step 2: Query the LLM model with context

python main.py

Visual Explaination

flowchart TD

%% Data Preparation (load_data.py)
subgraph A[Data Preparation load_data.py]
    A1["📂 Read .txt Files<br/>from ./weather_data"]:::input
    A2["✂️ Split Text into Chunks"]:::process
    A3["🧠 Generate Embeddings<br/>(Azure AI Inference)"]:::embedding
    A4["💾 Store Embeddings in<br/>Chroma Vector DB"]:::vector
    A1 --> A2 --> A3 --> A4
end

%% Query & Retrieval (main.py)
subgraph B[Query & Retrieval main.py]
    B1["\❓ User Question Input"]:::input
    B2["🧠 Embed Query<br/>(Azure AI Inference)"]:::embedding
    B3["🔍 Search Chroma Vector DB<br/>(Top-k Similarity Search)"]:::vector
    B4["📄 Retrieve Relevant Chunks"]:::retrieval
    B5["🧾 Format Context with<br/>PromptTemplate"]:::process
    B6["🤖 Query GPT-4.1<br/>(GitHub Inference)"]:::llm
    B7["✅ Return Final Answer<br/>+ Source Docs"]:::output
    B1 --> B2 --> B3 --> B4 --> B5 --> B6 --> B7
end

%% Data flow from vector store to retrieval
A4 --> B3

%% Style Definitions
classDef input fill:#FFF3E0,stroke:#FB8C00,stroke-width:2px;
classDef process fill:#E0F7FA,stroke:#00ACC1,stroke-width:2px;
classDef embedding fill:#FBE9E7,stroke:#FF7043,stroke-width:2px;
classDef vector fill:#F1F8E9,stroke:#7CB342,stroke-width:2px;
classDef retrieval fill:#FFFDE7,stroke:#FDD835,stroke-width:2px;
classDef llm fill:#EDE7F6,stroke:#7E57C2,stroke-width:2px;
classDef output fill:#E8F5E9,stroke:#43A047,stroke-width:2px;

Output

🔍 Question: What was the maximum temperature in June 2022?
💡 Answer: {'query': 'What was the maximum temperature in June 2022?', 'result': 'The maximum temperature in June 2022 was 32.5°C, recorded on June 6, 2022.', 'source_documents': [Document(id='ebb04be4-b5d5-4783-9a20-63eb09cec0cb', metadata={'source': 'bangalore.txt'}, 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