5e0addcc55
Why: - The chunks collection needs four named vectors (dense_nomic, dense_openai, sparse, late_interaction) and payload indexes defined at creation time per ADR-0001; sparse/multivector fields cannot be added to an existing collection without recreating it, so schema drift here is expensive. - Creating it at FastAPI startup would mirror the DDL-at-boot anti-pattern ADR-0009 already rejects for Postgres and ADR-0012 rejects for LangGraph's setup(), so it is a deployment step instead. Changes: - src/infrastructure/qdrant/collection.py: ensure_chunks_collection(), idempotent and schema-verifying (raises on dimension/modifier mismatch rather than silently accepting a misconfigured collection). - src/cli/qdrant_bootstrap.py: the operator entry point (python -m src.cli.qdrant_bootstrap). - QdrantSettings gains collection/upsert_batch_size/upsert_concurrency. Impact: - Deployments must run the new bootstrap command before the first upload; see ADR-0001's new "Collection provisioning" section.
Talie chatbot service
Architecture decisions live in docs/adr. The first implementation
milestone is documented in the ingestion vertical-slice plan.
Local Langfuse
This repo includes a root-level development Compose file for Langfuse:
Start Langfuse locally:
cp .env.langfuse.example .env.langfuse
# edit .env.langfuse and replace CHANGE_ME values
docker compose --env-file .env.langfuse -f docker-compose.langfuse.yml up -d
Open:
http://localhost:3000
If the chatbot app runs on your host machine, configure it with:
LANGFUSE_HOST=http://localhost:3000
If the chatbot app later runs inside the same Compose project/network as Langfuse, configure it with:
LANGFUSE_HOST=http://langfuse-web:3000
A future app stack can be launched together with Langfuse using multiple Compose files:
docker compose \
-f docker-compose.yml \
-f docker-compose.langfuse.yml \
--env-file .env \
--env-file .env.langfuse \
up -d