knowai 0.2.0

A conversational RAG agent pipeline using LangGraph

knowai

An agentic AI pipeline for multiple, large PDF reports interrogation

Set up

• Clone this repostiory into a local directory of your choosing
• Build a virtual environment
• Install knowai by running: pip install . from the root directory of your clone (OR) install using pip install knowai from PyPI.
• Configure a .env file with the following:
  • AZURE_OPENAI_API_KEY - Your API key
  • AZURE_OPENAI_ENDPOINT - Your Azure endpoint
  • AZURE_OPENAI_DEPLOYMENT - Your LLM deployment name (e.g., "gpt-4o")
  • AZURE_EMBEDDINGS_DEPLOYMENT - Your embeddings model deployment name (e.g., "text-embedding-3-large")
  • AZURE_OPENAI_API_VERSION - Your Azure LLM deployment version (e.g., "2024-02-01")

Building the vectorstore

From the root directory of this repository, run the following from a the terminal (ensuring that your virtual environment is active) to build the vectorstore:

python scripts/build_vectorstore.py <directory_containing_your_input_pdf_files> --vectorstore_path <directory_name_for_vectorstore>

By default, this will create a vectorstore using FAISS named "test_faiss_store" in the root directory of your repository.

Running the knowai in a simple chatbot example via streamlit

From the root directory, run the following in a terminal after you have your virtual environment active:

streamlit run app_chat_simple.py

This will open the app in your default browser.

Using knowai

Once your vector store is built, you can use knowai either programmatically or through the provided Streamlit interface.

Python quick‑start

The package ships with the KnowAIAgent class for fully programmatic access inside notebooks or scripts:

from knowai.core import KnowAIAgent

# Path that you supplied with --vectorstore_path when building
VSTORE_PATH = "test_faiss_store"

agent = KnowAIAgent(vectorstore_path=VSTORE_PATH)

# A single conversational turn
response = await agent.process_turn(
    user_question="Summarize the key findings in the 2025 maritime report",
    selected_files=["my_report.pdf"],
)

print(response["generation"])

The returned dictionary contains:

Key	Description
generation	Final answer synthesised from the selected documents.
individual_answers	Per‑file answers (when bypass_individual_gen=False).
documents_by_file	Retrieved document chunks keyed by filename.
raw_documents_for_synthesis	Raw text block used when bypassing individual generation.
bypass_individual_generation	Whether the bypass mode was used for this turn.

Streamlit chat app

If you prefer a ready‑made UI, launch the demo:

streamlit run app_chat_simple.py

Upload or select PDF files, ask questions in the sidebar, and inspect per‑file answers or the combined response in the main panel.

---

For advanced configuration options (e.g., conversation history length, retriever k values, or combine thresholds) see the docstrings in knowai/core.py and knowai/agent.py.

Containerization

To build and run both the knowai service and the Svelte UI using Docker Compose:

1. Ensure Docker and Docker Compose are installed on your machine.
2. From the directory containing this README (the repo root), navigate to the Svelte example folder:

   cd example_apps/svelte
   

2a. Compile the Svelte app and package the build as svelte-example:

npm install
npm run build
mv dist svelte-example

3. Start the services and build images:

   docker compose up --build
   

   This will:
   • Build the knowai service (listening on port 8000).
   • Build the ui service (Svelte app, listening on port 5173).
4. Open your browser and visit:
   • FastAPI docs: http://localhost:8000/docs
   • Svelte UI: http://localhost:5173
5. To stop and remove containers, press CTRL+C and then run:

   docker compose down
   

Running the knowai CLI Locally

You can start the FastAPI micro-service locally without Docker and point it to either a local vectorstore or one hosted on S3.

Using a Local Vectorstore

1. Ensure you have a built FAISS vectorstore on disk (e.g., test_faiss_store).
2. Start the service:

   python -m knowai.cli
   

3. In another terminal, initialize the session:

   curl -X POST http://127.0.0.1:8000/initialize \
     -H "Content-Type: application/json" \
     -d '{"vectorstore_s3_uri":"/absolute/path/to/your/vectorstore"}'
   

4. Ask a question:

   curl -X POST http://127.0.0.1:8000/ask \
     -H "Content-Type: application/json" \
     -d '{
       "session_id":"<session_id>",
       "question":"Your question here",
       "selected_files":["file1.pdf","file2.pdf"]
     }'
   

Using an S3-Hosted Vectorstore

1. Start the service:

   python -m knowai.cli
   

2. Initialize the session against your S3 bucket:

   curl -X POST http://127.0.0.1:8000/initialize \
     -H "Content-Type: application/json" \
     -d '{"vectorstore_s3_uri":"s3://your-bucket/path"}'
   

3. Ask a question in a similar way:

   curl -X POST http://127.0.0.1:8000/ask \
     -H "Content-Type: application/json" \
     -d '{
       "session_id":"<session_id>",
       "question":"Another question example",
       "selected_files":[]
     }'