README
¶
Agent with retriever (agent_with_retriever)
Examples that wire a vector retriever into agent-sdk-go. Pick one backend per run.
| Backend | Package | Example entrypoint |
|---|---|---|
| Weaviate | pkg/retriever/weaviate |
go run ./agent_with_retriever/weaviate |
| PostgreSQL + pgvector | pkg/retriever/pgvector |
go run ./agent_with_retriever/pgvector |
Sample KB JSON (edit per backend, then re-seed): docker/weaviate/sample-documents.json, docker/pgvector/sample-documents.json. Infra: ../docker/ (compose + seed scripts).
Prerequisites
- Runtime —
AGENT_RUNTIME=local(default): in-process, no Temporal. OptionalAGENT_RUNTIME=temporal: fromexamples/, runtask infra:temporal:up(andtask infra:temporal:waitif the example fails to connect). That starts the compose dev server onlocalhost:7233. For Temporal CLI, Cloud, or other hosts, seetemporal-setup.md. examples/.env—LLM_APIKEY,LLM_MODEL, andEMBEDDING_OPENAI_APIKEY(see.env.defaults)- Task (
go-task) and Docker for the vector store you use (task infra:weaviate:uportask infra:pgvector:up)
From examples/:
task infra:status # see what is up
Retriever modes
Set RETRIEVER_MODE in .env (default agentic):
| Mode | Behavior |
|---|---|
agentic |
Retriever exposed as a tool; LLM decides when to search |
prefetch |
Search runs once before the first LLM call; context injected into system prompt |
hybrid |
Prefetch and retriever tools |
Prefetch/hybrid embed your exact user message — use concrete questions aligned with the sample KB (returns, shipping, warranty, etc.).
Weaviate
Weaviate embeds queries via nearText (text2vec-openai in Docker). Chat LLM can differ from the embedding provider.
Setup
cd examples
task infra:weaviate:up
task infra:weaviate:down # when finished
Compose: docker/docker-compose.yml. Seed: docker/weaviate/seed.sh.
EMBEDDING_OPENAI_APIKEY must be set in examples/.env before up (baked into the container). After a key change: task infra:weaviate:down && task infra:weaviate:up.
Environment
WEAVIATE_HOST=localhost:8080
WEAVIATE_SCHEME=http
WEAVIATE_CLASS=Document
WEAVIATE_RETRIEVER_NAME=weaviate-kb
RETRIEVER_MODE=agentic
# WEAVIATE_MIN_SCORE=0.5 # optional; SDK default 0.75
Run
go run ./agent_with_retriever/weaviate "What is the return policy?"
RETRIEVER_MODE=prefetch go run ./agent_with_retriever/weaviate "What is the return policy?"
Weaviate troubleshooting
| Symptom | What to do |
|---|---|
| Compose / API key errors | Set EMBEDDING_OPENAI_APIKEY, then task infra:weaviate:down && task infra:weaviate:up |
Connection refused :8080 |
task infra:status, curl -s http://localhost:8080/v1/.well-known/ready, docker logs weaviate |
| Empty search / no relevant docs | Re-seed with task infra:weaviate:up; check WEAVIATE_CLASS=Document; list objects: curl -s "http://localhost:8080/v1/objects?class=Document&limit=5"; try RETRIEVER_MODE=prefetch |
| Port 8080 / 50051 in use | task infra:weaviate:down; set WEAVIATE_HTTP_PORT / WEAVIATE_GRPC_PORT before up |
| LLM ignores KB (agentic) | Confirm objects exist; use prefetch mode |
LOG_LEVEL=debug go run ./agent_with_retriever/weaviate "What is the return policy?"
pgvector
Client-side OpenAI-compatible embeddings, then cosine search in Postgres (pgvector).
Setup
cd examples
task infra:pgvector:up
task infra:pgvector:down # when finished
Schema: docker/pgvector/setup.sql. Seed: docker/pgvector/seed.sh.
Default DSN (in .env.defaults): postgres://postgres:secret@localhost:5432/vectordb?sslmode=disable
Environment
PGVECTOR_DSN=postgres://postgres:secret@localhost:5432/vectordb?sslmode=disable
PGVECTOR_TABLE=documents
PGVECTOR_RETRIEVER_NAME=pgvector-kb
EMBEDDING_OPENAI_MODEL=text-embedding-3-small
EMBEDDING_OPENAI_APIKEY=sk-...
PGVECTOR_MIN_SCORE=0.35
RETRIEVER_MODE=agentic
With Anthropic/Gemini chat, EMBEDDING_OPENAI_APIKEY is still required for search (not LLM_APIKEY).
Run
go run ./agent_with_retriever/pgvector "What is the return policy?"
RETRIEVER_MODE=prefetch go run ./agent_with_retriever/pgvector "What is the return policy?"
pgvector troubleshooting
| Symptom | What to do |
|---|---|
no relevant documents found |
task infra:status; row count: docker exec pgvector psql -U postgres -d vectordb -t -c "SELECT COUNT(*) FROM documents;"; re-seed or lower PGVECTOR_MIN_SCORE |
embedding config / Anthropic chat |
Set EMBEDDING_OPENAI_APIKEY; re-seed: task infra:pgvector:down && task infra:pgvector:up |
PGVECTOR_DSN is required |
Use default DSN or match compose PGVECTOR_* vars |
| Dimension / SQL errors | Model must match vector(1536) in setup.sql; re-seed after model change |
| Port 5432 in use | task infra:pgvector:down; set PGVECTOR_PORT and update PGVECTOR_DSN |
LOG_LEVEL=debug go run ./agent_with_retriever/pgvector "What is the return policy?"
Look for pgvector search done with docs=0 vs embedding errors.
Directories
¶
| Path | Synopsis |
|---|---|
|
Package common holds shared configuration and agent options for the agent_with_retriever examples.
|
Package common holds shared configuration and agent options for the agent_with_retriever examples. |
|
Example agent using a PostgreSQL pgvector retriever.
|
Example agent using a PostgreSQL pgvector retriever. |
|
Example agent using a Weaviate vector retriever.
|
Example agent using a Weaviate vector retriever. |