ai-intake-bot 0.1.0

Persona- and RAG-based intake SDK (prototype)

ai_intake_bot

Minimal SDK for persona-based and RAG-based conversational intake.

What this repo is: an SDK, not an app. No data persistence, no auth system, no background jobs.

Quickstart (persona mode with FakeLLM)

• Create a venv and install deps:

  python -m venv .venv source .venv/bin/activate python -m pip install -U pip pip install -e ".[dev]"

• Example (persona, expert_eval) using the FakeLLM for deterministic results:

from ai_intake_bot.core.engine import IntakeBot
from ai_intake_bot.core.llm import FakeLLM

bot = IntakeBot(
    mode="persona",
    template="expert_eval",
    persona="expert_reviewer",
    problem={"description": "Cannot login", "emotional_state": "frustrated", "goals": ["restore access"]},
    api_key="sk-test",
    qdrant_url=None,
    qdrant_api_key=None,
    files=None,
    selection_probability=0.5,
    enable_alerts=True,
    extra_system_prompt=None,
)
bot.set_llm(FakeLLM())
out = bot.handle("Please evaluate this scenario")
print(out)

Using a real LLM (OpenAI)

• This SDK supports optional, explicit use of a real LLM. It will only call an LLM when you inject one via IntakeBot.set_llm() (so the default is safe for local development and tests).

• To use OpenAI's API, set OPENAI_API_KEY in your environment and then:

from ai_intake_bot.core.engine import IntakeBot
from ai_intake_bot.core.llm import OpenAIChatLLM

llm = OpenAIChatLLM(model="gpt-4o")
bot = IntakeBot(...)
bot.set_llm(llm)
response = bot.handle("Ask something")

Security model: the SDK never persists secrets to disk. OpenAIChatLLM expects OPENAI_API_KEY to be set and will not log its value.

RAG with Qdrant (dev-friendly)

• The RAG engine uses a LocalVectorStore by default; if you provide qdrant_url when constructing IntakeBot, the RAG engine will upload chunks to the configured Qdrant instance (ephemeral collection by default) and ground responses on the retrieved documents.

• For a full integration with LangChain and Qdrant follow the example in scripts/run_qdrant_integration.sh (this script starts a Qdrant Docker container and runs the Qdrant integration test). The integration requires langchain_qdrant, langchain_openai, and an OpenAI key for embeddings.

Demos

• scripts/demo_persona_openai.py — Runs an expert_eval persona using OpenAI (requires OPENAI_API_KEY).

• scripts/demo_rag_qdrant.py — Ingests a PDF using LangChain's PyPDFLoader, splits with RecursiveCharacterTextSplitter, embeds with OpenAIEmbeddings, writes to Qdrant (requires QDRANT_URL and OPENAI_API_KEY), and performs a retrieval + chat.

• scripts/demo_tts.py — Demonstrates the TTS adapters. Prefers macOS say, falls back to pyttsx3, and ultimately to a no-op adapter.

Run demos (examples):

• Persona demo (OpenAI):

  export OPENAI_API_KEY=<your_key> python scripts/demo_persona_openai.py

• RAG demo (Qdrant + LangChain):

  export OPENAI_API_KEY=<your_key> export QDRANT_URL=http://localhost:6333 python scripts/demo_rag_qdrant.py

• TTS demo (local):

  python scripts/demo_tts.py

Security reminder: never commit real API keys. Use .env files for local testing and set CI secrets for integration runs.

AI_INTake