Two bugs, either one fatal:
- litellm-pgvector's embeddings endpoints take a precomputed vector per
chunk (they don't call the embedding model themselves) — the script
posted {content, metadata} with no embedding field, guaranteed 422.
- The batch endpoint expects {"embeddings": [...]}; the script posted a
bare JSON array as the body.
Now embeds each file's chunks via LiteLLM's /v1/embeddings first (using
LITELLM_PGVECTOR_EMBEDDING_KEY, already minted for exactly this) before
batch-inserting. Also made both source files optional — a missing file is
skipped, not a hard failure, since neither exists in this checkout yet.
litellm.aembedding can't infer a provider from a bare model name plus a
custom api_base — litellm-pgvector's embedding_service.py was hitting
'litellm.BadRequestError: LLM Provider NOT provided' on every query-time
embedding (i.e. every search). Same openai/ prefix already used for
qwen3.8-27b-local and local-embedding in litellm-config.yaml.
POST /v1/vector_stores always minted a random UUID for the new store's id,
ignoring the request entirely. litellm-config.yaml's vector_store_registry
addresses this store by a fixed id (memory-and-notes), which could never
match a real row as a result — every search/write 404'd.
Added an optional id field to VectorStoreCreateRequest; create_vector_store
uses it when given, falls back to a random UUID otherwise (unchanged
behavior for callers that don't care).
schema.prisma hardcoded vector(1536), OpenAI ada-002's size. This stack's
actual embedding model is nomic-embed-text-v1.5, confirmed 768-dim live
against /v1/embeddings. Left as-is, the first insert after a schema push
would fail on a pgvector dimension mismatch.
Without it, litellm's pg_vector provider sent Authorization: Bearer None,
which round-tripped to the real api.openai.com and came back with
'Incorrect API key provided: None' — masking the actual problem and making
every vector store call fail. Points at litellm-pgvector's own
LITELLM_PGVECTOR_API_KEY (its SERVER_API_KEY), already present in .env.
Smoke-tested end-to-end against the live deploy for issue #24, alongside
the litellm-pgvector fixes in the following commits.
Today's embedding-server crash-loop (missing nomic-embed-text GGUF) was a
manual step nobody ran. update.sh now runs both downloader profiles
itself, every time, before bringing services up -- no separate command to
remember.
- docker-compose.yml: downloader/downloader-embedding commands gain a
`test -f ... && skip || curl ...` guard, so re-running update.sh never
re-downloads an existing model file.
- scripts/update.sh: runs both profiles after image pull/build, before
service recreation.
- scripts/download-model.sh removed -- folded in, redundant standalone
script.
- README.md / docs/memory-knowledgebase.md updated accordingly.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_018WHfjWrSEcGhCoeu6dQfDa
Confirmed in llama-server logs: a real request hit exactly n_gen=4096
(the old floor) and returned no answer -- reasoning_content alone ate
the whole budget before any content was written, exactly the failure
mode this config's own comment predicted. 16384 is the user-chosen
middle ground between 8192 and 32768: ~10 min worst-case at ~26.7 t/s,
well under the 65536-token context window.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_018WHfjWrSEcGhCoeu6dQfDa
lazytainer panics on every start with 'Could not determine container ID
of lazytainer': its self-detection (vmorganp/Lazytainer,
configureFromLabels()) matches os.Hostname() against the Docker
container-ID list, but network_mode: host makes the container inherit
the host machine's hostname instead of its own container ID, so the
match never succeeds. Upstream's own example compose file doesn't use
host networking (default bridge + published ports) — nothing about
lazytainer requires it.
It also couldn't have seen the traffic it's meant to watch: llama-server
has no published host port (issue #15) and only exists on the ai-stack
bridge network, which host networking has no visibility into. Joining
ai-stack instead fixes both problems at once.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_018WHfjWrSEcGhCoeu6dQfDa
README.md and docs/proxy-key-onboarding.md were edited by later commits
(b996b1f, 24d749b) that the litellm-pgvector revert didn't touch, so they
still named the now-removed MEMORY_RETRIEVAL_EMBEDDING_KEY var. Left
docs/research/langchain-pgvector-vs-litellm-pgvector.md as-is — it's a
dated record of the decision being reversed, not live config.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_018WHfjWrSEcGhCoeu6dQfDa
This reverts commit abeadc49c8.
Restores vendor/litellm-pgvector/ and the vector_store_registry wiring
(in-band file_search tool-call support) at the user's request, after
re-confirming against docs.litellm.ai/docs/completion/knowledgebase and
litellm-pgvector's own README that pg_vector is still not an in-process
vector_store_registry backend -- it requires this same standalone
connector service either way, so there is no simpler 'native' path that
was missed. Trading back in: 793 lines of vendored code, the untested
Prisma migration, and the git-context build risk noted in VENDORED.md
(all flagged as unverified against real hardware in issue #24), in
exchange for the file_search in-band tool call memory-retrieval did not
support.
Conflicts resolved on top of later commits (Redis, update.sh key-minting
fold-in):
- .env.example / docs/memory-knowledgebase.md: kept the auto-mint-via-
update.sh language, renamed MEMORY_RETRIEVAL_* back to
LITELLM_PGVECTOR_*.
- scripts/generate-secrets.sh: left deleted -- its job was folded into
update.sh in 24d749b, unrelated to this revert.
- scripts/update.sh: renamed the MEMORY_RETRIEVAL_* secret/mint calls to
LITELLM_PGVECTOR_* to match.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_018WHfjWrSEcGhCoeu6dQfDa
New redis service (redis:7-alpine, password-protected, no persistence
volume — everything it holds is cache/coordination state). litellm gets
REDIS_HOST/REDIS_PORT/REDIS_PASSWORD, which is all LiteLLM needs to use it
for router state, rate limits/budgets, and cache invalidation — no
litellm-config.yaml block required (docs.litellm.ai/docs/proxy/caching).
REDIS_PASSWORD added to .env.example and update.sh's auto-generated
secrets.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
set_if_blank/mint_key_if_blank used sed -i "s|^KEY=.*|...|", which only
replaces an existing KEY= line — a no-op if the line isn't there at all.
Any .env created before a var was added to .env.example (SEARXNG_LAN_IP,
PGVECTOR_DB_PASSWORD, MEMORY_RETRIEVAL_*, etc.) silently never got that var
filled in. Both helpers now check for "line missing" as a third case and
append it.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
update.sh now creates .env from .env.example if missing, idempotently fills
in every random secret (same logic generate-secrets.sh had, now removed),
resolves SEARXNG_LAN_IP from search.home via the host's own DNS, and mints
OPENWEBUI_LITELLM_KEY / MEMORY_RETRIEVAL_EMBEDDING_KEY through LiteLLM's own
/key/generate API once litellm is up — no more manual Admin UI step for the
stack's own two workload keys. Docs updated to point at update.sh as the
one command; docs/proxy-key-onboarding.md keeps the manual/API steps as the
fallback and for onboarding other workloads.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Per docs/research/langchain-pgvector-vs-litellm-pgvector.md (issue #25):
the vendored litellm-pgvector connector (793 lines, Prisma migrations, a
fragile git-context build) is replaced by a ~90-line FastAPI service
(services/memory-retrieval/) wrapping langchain_postgres.PGVector directly
against pgvector-db. Same gateway boundary — it still calls litellm for
embeddings, nothing talks to Postgres or the model directly except this
service.
- New services/memory-retrieval/ (main.py, Dockerfile, requirements.txt):
POST /ingest, POST /query, GET /health.
- docker-compose.yml: litellm-pgvector service replaced by memory-retrieval;
pgvector-db and embedding-server untouched.
- litellm-config.yaml: vector_store_registry block removed (no
langchain_postgres provider exists to register against; callers query
memory-retrieval directly instead of an in-band file_search tool call —
that mechanism was never confirmed working per issue #24 anyway).
- scripts/ingest-memory.sh rewritten for the new /ingest endpoint (same
per-line chunking, no dedup).
- .env vars renamed: LITELLM_PGVECTOR_API_KEY/LITELLM_PGVECTOR_EMBEDDING_KEY
-> MEMORY_RETRIEVAL_API_KEY/MEMORY_RETRIEVAL_EMBEDDING_KEY.
- vendor/litellm-pgvector/ removed entirely.
- docs/memory-knowledgebase.md updated for the new setup/query flow.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Verdict: switch. See docs/research/langchain-pgvector-vs-litellm-pgvector.md
for full reasoning, LOC comparison, and a concrete replacement service spec.
Research/doc only — no compose/config/vendor changes.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
The server's Docker/BuildKit couldn't do a git-context build of a public
github.com repo (fails with "could not read Username ... terminal prompts
disabled" — an auth-shaped error for what should be an anonymous clone).
Rather than debug that, vendor litellm-pgvector's small source tree
directly (vendor/litellm-pgvector/, see VENDORED.md for provenance/update
steps) and build from the local path.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
One script to bring the running stack in sync after any change: git pull,
validate compose config, rebuild the litellm-pgvector local build, re-pull
images, recreate what changed. Documented in README's quickstart, and
CLAUDE.md now tells agents to run it after touching compose/config/scripts
rather than just describing a change as done.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
- search_tools block in litellm-config.yaml (SearXNG as a first-class
search_provider, standalone /v1/search endpoint, not a model tool) plus
extra_hosts on the litellm service so it can resolve search.home.
- New embedding-server (nomic-embed-text-v1.5 on a second llama.cpp
instance), pgvector-db, and litellm-pgvector services — LiteLLM's native
knowledgebase feature has no Qdrant backend, so this is the only
self-hosted path (docs/research/litellm-knowledgebase.md).
- vector_store_registry + local-embedding model entry in
litellm-config.yaml, wiring it together.
- scripts/ingest-memory.sh to load data/memory.md and
data/claude-legacy-memory.md into the knowledgebase.
- docs/memory-knowledgebase.md documenting the whole setup; data/
gitignored (personal memory content, not meant to be committed).
- New .env vars (SEARXNG_LAN_IP, PGVECTOR_DB_PASSWORD,
LITELLM_PGVECTOR_API_KEY, LITELLM_PGVECTOR_EMBEDDING_KEY,
EMBEDDING_MODEL_FILE) and generate-secrets.sh support for the
auto-generatable ones.
Resolves#22 and #23 (wayfinder map #21). Not yet verified on real
hardware — see #24.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Answers whether SearXNG is natively supported by LiteLLM's /v1/search
feature, whether it's a model-tool or automatic retrieval, what
docker-compose.yml networking change is needed for the litellm container
to reach the LAN's search.home host, and how it interacts with this
project's known-flaky Qwen3.8-27B tool-calling.
Research only — litellm-config.yaml and docker-compose.yml are unchanged.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
haylan
