haylanandClaude-Bot
abeadc49c8
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>
65 lines
3.0 KiB
YAML
65 lines
3.0 KiB
YAML
model_list:
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- model_name: qwen3.8-27b-local
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litellm_params:
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# Static name — llama.cpp serves whatever model it loaded regardless of
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# what's requested here; this string isn't shell-expanded (this file
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# isn't docker-compose.yml, .env vars don't reach it).
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model: openai/qwen3.8-27b-local
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api_base: http://llama-server:8080/v1
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api_key: local
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# Qwen3 is a reasoning model — it spends output tokens on
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# reasoning_content before ever writing content. Callers that don't
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# set their own max_tokens (Open WebUI's default request didn't) hit
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# llama.cpp's low default, so the model runs out mid-thought and
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# content comes back empty. This is a floor, not a cap — any caller
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# that passes its own max_tokens still overrides it.
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max_tokens: 4096
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model_info:
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# Shadow cloud-cost estimate — priced against Claude Sonnet 5's published
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# rate, not real spend (this proxy only ever routes to the local model).
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# Source: https://platform.claude.com/docs/en/about-claude/pricing,
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# checked 2026-08-25. Update these two numbers if that page changes.
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input_cost_per_token: 0.000002 # $2 / MTok
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output_cost_per_token: 0.00001 # $10 / MTok
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- model_name: local-embedding
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litellm_params:
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# Served by the dedicated embedding-server (nomic-embed-text-v1.5), not
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# the chat model — see docker-compose.yml. Called by memory-retrieval
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# to embed knowledgebase content, and available directly at
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# /v1/embeddings for anything else that wants it.
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model: openai/local-embedding
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api_base: http://embedding-server:8080/v1
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api_key: local
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model_info:
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mode: embedding
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# SearXNG-backed web search — a standalone REST endpoint (/v1/search/searxng-search),
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# NOT a model-callable tool and not auto-injected into chat completions. See
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# docs/research/litellm-searxng-search.md. Requires the litellm container to
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# resolve search.home — see the `extra_hosts` entry in docker-compose.yml.
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search_tools:
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- search_tool_name: searxng-search
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litellm_params:
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search_provider: searxng
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api_base: http://search.home/
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# Knowledgebase / RAG lives outside LiteLLM's own registry now — see the
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# memory-retrieval service (docker-compose.yml) and
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# docs/research/langchain-pgvector-vs-litellm-pgvector.md. LiteLLM's native
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# vector_store_registry has no Qdrant provider and no langchain_postgres
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# provider either, so registering a store here isn't an option; callers
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# query memory-retrieval's /query endpoint directly instead of an in-band
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# file_search tool call.
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router_settings:
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# ponytail: LiteLLM's request-prioritization scheduler is beta (see
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# docs/proxy-request-priority.md) — exact settings key/shape must be
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# confirmed against LiteLLM's current docs and smoke-tested against
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# llama.cpp before workloads depend on it. Single-instance deployment,
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# no Redis configured — add one only if the scheduler turns out to need it.
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enable_priority_scheduling: true
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general_settings:
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master_key: os.environ/LITELLM_MASTER_KEY
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