README.md

metadata

title: Light PDF web QA chatbot
emoji: 🌍
colorFrom: blue
colorTo: yellow
sdk: gradio
sdk_version: 6.10.0
app_file: app.py
pinned: true
license: mit
short_description: Chat with websites and documents using Gemma 3 1B

Chat with a pdf file or web page using a small language model (Gemma 3 1B) through a Gradio interface. Quick responses even just using CPU. Default dataset is the user guide for my Redaction app, which you can try out here for basic usage, or here for GPU-enabled features

AWS Bedrock Knowledge Base mode

Set USE_BEDROCK_KB=1 to bypass local FAISS embedding/retrieval and local/Gemini/Bedrock Converse generation. The app calls Bedrock Agent Runtime retrieve_and_generate against an existing Knowledge Base (same flow as the council Lambda chat solution). Local “Change data source” ingest is unused in this mode.

Variable	Default	Purpose
USE_BEDROCK_KB	0	Enable Bedrock KB backend
KNOWLEDGE_BASE_ID	(empty)	Bedrock Knowledge Base ID (required when enabled)
BEDROCK_MODEL_ID	amazon.nova-pro-v1:0	Foundation model ID for retrieve_and_generate
GUARDRAIL_ID	(empty)	Optional Bedrock Guardrail ID
GUARDRAIL_VERSION	(empty)	Optional Guardrail version
AWS_REGION / AWS_DEFAULT_REGION	(empty; falls back to eu-west-2)	Region for the agent-runtime client and model ARN

Credentials follow the existing AWS pattern (instance/task role, or AWS_ACCESS_KEY / AWS_SECRET_KEY via AWS_CONFIG_PATH). IAM needs bedrock:RetrieveAndGenerate and bedrock:Retrieve (scored passage shortlist for the sources panel), plus permission to invoke the foundation model — broader than Converse-only access used when RUN_AWS_FUNCTIONS=1.

The sources accordion uses Bedrock Retrieve results (ranked by score). RetrieveAndGenerate citations alone often omit unused chunks and do not include retrieval scores.

Arize Phoenix tracing (local)

Send retrieve/generate spans to a locally hosted Arize Phoenix collector (same pattern as custom Pi spans in the doc-redaction agent — not LangChain auto-instrumentation).

1. Run Phoenix locally (UI typically at http://localhost:6006).
2. Set env vars (e.g. in config/app_config.env):

ARIZE_TRACING_ENABLED=1
ARIZE_BACKEND=phoenix
PHOENIX_COLLECTOR_ENDPOINT=http://localhost:6006
PHOENIX_PROJECT_NAME=light-pdf-qa-chatbot

3. Install tracing deps (arize-phoenix-otel, OpenInference semconv, OpenTelemetry) from requirements.txt.
4. Ask a question in the Gradio UI; confirm chat.retrieve and chat.generate spans in Phoenix. Multi-turn chats share session.id = Gradio session hash.

Variable	Default	Purpose
ARIZE_TRACING_ENABLED	0	Enable OTEL export
ARIZE_BACKEND	phoenix	phoenix (local) or ax (Arize cloud)
PHOENIX_COLLECTOR_ENDPOINT	http://localhost:6006	Phoenix collector base URL
PHOENIX_PROJECT_NAME	light-pdf-qa-chatbot	Phoenix project name
PHOENIX_API_KEY	(empty)	Optional if Phoenix requires auth

For Arize AX (ARIZE_BACKEND=ax), set ARIZE_SPACE_ID / ARIZE_API_KEY and install arize-otel.