docs: migrate embedder→embedding_model and require vectordb across tool docs; add provider examples (en/ko/pt-BR) (#3804)
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* docs(tools): migrate embedder->embedding_model, require vectordb; add Chroma/Qdrant examples across en/ko/pt-BR PDF/TXT/XML/MDX/DOCX/CSV/Directory docs

* docs(observability): apply latest Datadog tweaks in ko and pt-BR
This commit is contained in:
Tony Kipkemboi
2025-10-27 13:29:21 -04:00
committed by GitHub
parent 5d6b4c922b
commit 410db1ff39
23 changed files with 540 additions and 390 deletions

View File

@@ -48,27 +48,25 @@ tool = MDXSearchTool(mdx='path/to/your/document.mdx')
A ferramenta utiliza, por padrão, o OpenAI para embeddings e sumarização. Para personalizar, utilize um dicionário de configuração conforme exemplo abaixo:
```python Code
from chromadb.config import Settings
tool = MDXSearchTool(
config=dict(
llm=dict(
provider="ollama", # As opções incluem google, openai, anthropic, llama2, etc.
config=dict(
model="llama2",
# Parâmetros opcionais podem ser incluídos aqui.
# temperature=0.5,
# top_p=1,
# stream=true,
),
),
embedder=dict(
provider="google", # ou openai, ollama, ...
config=dict(
model="models/embedding-001",
task_type="retrieval_document",
# Um título opcional para os embeddings pode ser adicionado aqui.
# title="Embeddings",
),
),
)
config={
"embedding_model": {
"provider": "openai",
"config": {
"model": "text-embedding-3-small",
# "api_key": "sk-...",
},
},
"vectordb": {
"provider": "chromadb", # ou "qdrant"
"config": {
# "settings": Settings(persist_directory="/content/chroma", allow_reset=True, is_persistent=True),
# from qdrant_client.models import VectorParams, Distance
# "vectors_config": VectorParams(size=384, distance=Distance.COSINE),
}
},
}
)
```