mirror of
https://github.com/agentscope-ai/ReMe.git
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4739d39f3f
- Add BuildQueryOp to construct query for memory retrieval-Implement MergeMemoryOp to combine retrieved memories - Create RecallVectorStoreOp to fetch memories from vector store - Develop memory representation and conversion methods - Establish initial project structure and dependencies
67 lines
1.3 KiB
YAML
67 lines
1.3 KiB
YAML
# default config.yaml
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backend: mcp
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language: ""
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thread_pool_max_workers: 32
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ray_max_workers: 1
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mcp:
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transport: sse
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host: "0.0.0.0"
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port: 8002
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http:
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host: "0.0.0.0"
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port: 8002
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timeout_keep_alive: 600
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limit_concurrency: 64
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flow:
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task_retrieve:
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flow_content: build_query_op->recall_vector_store_op->merge_experience_op
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# task_summarizer: simple_summary_op->update_vector_store_op
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# vector_store: vector_store_action_op
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# agent: react_op
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mock_expression_flow:
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flow_content: mock1_op>>((mock4_op>>mock2_op)|mock5_op)>>(mock3_op|mock6_op)
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description: "mock flow"
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input_schema:
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a:
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type: "str"
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description: "mock attr a"
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required: true
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b:
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type: "str"
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description: "mock attr b"
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required: true
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op:
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mock1_op:
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backend: mock1_op
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llm: default
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vector_store: default
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llm:
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default:
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backend: openai_compatible
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model_name: qwen3-30b-a3b-thinking-2507
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params:
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temperature: 0.6
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qwen3_30b_instruct:
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backend: openai_compatible
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model_name: qwen3-30b-a3b-instruct-2507
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embedding_model:
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default:
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backend: openai_compatible
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model_name: text-embedding-v4
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params:
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dimensions: 1024
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vector_store:
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default:
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backend: elasticsearch
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embedding_model: default
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params:
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hosts: "http://localhost:9200"
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