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zeekayandGitHub
5b5cf9e9db
Transforms hanzoai/agents from a marketplace config repo into a full agent orchestration platform by merging the AgentField infrastructure: - Go control plane (K8s-like agent orchestration, service mesh, DAG workflows) - Python SDK (FastAPI-based agent framework, LiteLLM, memory, DID/VC) - TypeScript SDK (@hanzo/agents - Vercel AI SDK, MCP, workflows) - Go SDK (idiomatic agent builder + client) - React 19 embedded dashboard (XyFlow DAGs, Recharts, Radix UI) - Docker/K8s/Helm deployment configs - Examples: hello world, deep research, RAG, simulation, benchmarks - Functional test suite across Python/Go/TypeScript All references rebranded: AgentField → Hanzo Agents - Module: github.com/hanzoai/agents/control-plane - Python: hanzo_agents - TypeScript: @hanzo/agents - Go: github.com/hanzoai/agents/sdk/go - Env vars: HANZO_AGENTS_* - Config: hanzo-agents.yaml Previous marketplace configs moved to config/ directory.
30 lines
786 B
Python
30 lines
786 B
Python
"""Shared embedding helper built on FastEmbed for the doc chatbot."""
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from __future__ import annotations
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import os
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from functools import lru_cache
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from typing import Iterable, List
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from fastembed import TextEmbedding
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@lru_cache(maxsize=1)
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def _load_model() -> TextEmbedding:
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model_name = os.getenv("DOC_EMBED_MODEL", "BAAI/bge-small-en-v1.5")
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return TextEmbedding(model_name=model_name)
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def embed_texts(texts: Iterable[str]) -> List[List[float]]:
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"""Embed an iterable of strings and return Python lists."""
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model = _load_model()
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embeddings = list(model.embed(list(texts)))
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return [vector.tolist() for vector in embeddings]
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def embed_query(text: str) -> List[float]:
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"""Shortcut for single-question embeddings."""
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return embed_texts([text])[0]
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