haylanandClaude-Bot
f508f5670a
- 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>
51 lines
2.1 KiB
Bash
51 lines
2.1 KiB
Bash
#!/usr/bin/env bash
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# Loads data/memory.md and data/claude-legacy-memory.md into the LiteLLM
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# knowledgebase (the "memory-and-notes" vector store, see litellm-config.yaml)
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# via litellm-pgvector's batch-embeddings endpoint.
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#
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# ponytail: one chunk per non-empty, non-heading line — both source files are
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# already one fact/paragraph per line (no hard-wrapping), so this needs no
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# real chunking logic. Re-run after editing either file; there's no dedup, so
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# this appends duplicates on a second run against unchanged content — clear
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# the store first (DELETE the vector_store_id's rows) if you need a clean
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# reload.
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set -euo pipefail
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cd "$(dirname "$0")/.."
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[ -f .env ] && set -a && . ./.env && set +a
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: "${LITELLM_PGVECTOR_API_KEY:?Set LITELLM_PGVECTOR_API_KEY in .env first}"
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LITELLM_PGVECTOR_URL="${LITELLM_PGVECTOR_URL:-http://localhost:8000}"
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VECTOR_STORE_ID="memory-and-notes"
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# Must match litellm-config.yaml's vector_store_registry entry — the
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# registry just points at a store the backend must already know about.
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# Ignores failure if it already exists (no documented idempotency check).
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curl -sf -X POST "${LITELLM_PGVECTOR_URL}/v1/vector_stores" \
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-H "Authorization: Bearer ${LITELLM_PGVECTOR_API_KEY}" \
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-H "Content-Type: application/json" \
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-d "{\"name\": \"${VECTOR_STORE_ID}\"}" > /dev/null 2>&1 || true
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ingest_file() {
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local file="$1" section=""
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local batch="[]"
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while IFS= read -r line; do
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case "$line" in
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"#"*) section="${line#\# }"; section="${section#\#\# }"; continue ;;
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""|"---") continue ;;
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esac
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batch=$(jq --arg content "$line" --arg source "$file" --arg section "$section" \
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'. += [{"content": $content, "metadata": {"source": $source, "section": $section}}]' <<<"$batch")
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done < "$file"
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echo "Ingesting $(jq 'length' <<<"$batch") chunks from $file..."
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curl -sf -X POST "${LITELLM_PGVECTOR_URL}/v1/vector_stores/${VECTOR_STORE_ID}/embeddings/batch" \
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-H "Authorization: Bearer ${LITELLM_PGVECTOR_API_KEY}" \
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-H "Content-Type: application/json" \
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-d "$batch" > /dev/null
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}
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ingest_file data/memory.md
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ingest_file data/claude-legacy-memory.md
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echo "Done."
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