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docs: migrate embedder→embedding_model and require vectordb across tool docs; add provider examples (en/ko/pt-BR) (#3804)
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Browse Source 
* 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
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8
docs/en/observability/datadog.mdx
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@@ -93,11 +93,15 @@ After running the application, you can view the traces in [Datadog LLM Observabi
Clicking on a trace will show you the details of the trace, including total tokens used, number of LLM calls, models used, and estimated cost. Clicking into a specific span will narrow down these details, and show related input, output, and metadata.
![Datadog LLM Observability Trace View](/images/datadog-llm-observability-1.png)
<Frame>
<img src="/images/datadog-llm-observability-1.png" alt="Datadog LLM Observability Trace View" />
</Frame>
Additionally, you can view the execution graph view of the trace, which shows the control and data flow of the trace, which will scale with larger agents to show handoffs and relationships between LLM calls, tool calls, and agent interactions.
![Datadog LLM Observability Agent Execution Flow View](/images/datadog-llm-observability-2.png)
<Frame>
<img src="/images/datadog-llm-observability-2.png" alt="Datadog LLM Observability Agent Execution Flow View" />
</Frame>
## References

38
docs/en/tools/file-document/csvsearchtool.mdx
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@@ -54,25 +54,25 @@ The following parameters can be used to customize the `CSVSearchTool`'s behavior
By default, the tool uses OpenAI for both embeddings and summarization. To customize the model, you can use a config dictionary as follows:
```python Code
from chromadb.config import Settings
tool = CSVSearchTool(
config=dict(
llm=dict(
provider="ollama", # or google, openai, anthropic, llama2, ...
config=dict(
model="llama2",
# temperature=0.5,
# top_p=1,
# stream=true,
),
),
embedder=dict(
provider="google", # or openai, ollama, ...
config=dict(
model="models/embedding-001",
task_type="retrieval_document",
# title="Embeddings",
),
),
)
config={
"embedding_model": {
"provider": "openai",
"config": {
"model": "text-embedding-3-small",
# "api_key": "sk-...",
},
},
"vectordb": {
"provider": "chromadb", # or "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),
}
},
}
)
```

36
docs/en/tools/file-document/directorysearchtool.mdx
View File

@@ -46,23 +46,25 @@ tool = DirectorySearchTool(directory='/path/to/directory')
The DirectorySearchTool uses OpenAI for embeddings and summarization by default. Customization options for these settings include changing the model provider and configuration, enhancing flexibility for advanced users.
```python Code
from chromadb.config import Settings
tool = DirectorySearchTool(
config=dict(
llm=dict(
provider="ollama", # Options include ollama, google, anthropic, llama2, and more
config=dict(
model="llama2",
# Additional configurations here
),
),
embedder=dict(
provider="google", # or openai, ollama, ...
config=dict(
model="models/embedding-001",
task_type="retrieval_document",
# title="Embeddings",
),
),
)
config={
"embedding_model": {
"provider": "openai",
"config": {
"model": "text-embedding-3-small",
# "api_key": "sk-...",
},
},
"vectordb": {
"provider": "chromadb", # or "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),
}
},
}
)
```

38
docs/en/tools/file-document/docxsearchtool.mdx
View File

@@ -56,25 +56,25 @@ The following parameters can be used to customize the `DOCXSearchTool`'s behavio
By default, the tool uses OpenAI for both embeddings and summarization. To customize the model, you can use a config dictionary as follows:
```python Code
from chromadb.config import Settings
tool = DOCXSearchTool(
config=dict(
llm=dict(
provider="ollama", # or google, openai, anthropic, llama2, ...
config=dict(
model="llama2",
# temperature=0.5,
# top_p=1,
# stream=true,
),
),
embedder=dict(
provider="google", # or openai, ollama, ...
config=dict(
model="models/embedding-001",
task_type="retrieval_document",
# title="Embeddings",
),
),
)
config={
"embedding_model": {
"provider": "openai",
"config": {
"model": "text-embedding-3-small",
# "api_key": "sk-...",
},
},
"vectordb": {
"provider": "chromadb", # or "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),
}
},
}
)
```

40
docs/en/tools/file-document/mdxsearchtool.mdx
View File

@@ -48,27 +48,25 @@ tool = MDXSearchTool(mdx='path/to/your/document.mdx')
The tool defaults to using OpenAI for embeddings and summarization. For customization, utilize a configuration dictionary as shown below:
```python Code
from chromadb.config import Settings
tool = MDXSearchTool(
config=dict(
llm=dict(
provider="ollama", # Options include google, openai, anthropic, llama2, etc.
config=dict(
model="llama2",
# Optional parameters can be included here.
# temperature=0.5,
# top_p=1,
# stream=true,
),
),
embedder=dict(
provider="google", # or openai, ollama, ...
config=dict(
model="models/embedding-001",
task_type="retrieval_document",
# Optional title for the embeddings can be added here.
# title="Embeddings",
),
),
)
config={
"embedding_model": {
"provider": "openai",
"config": {
"model": "text-embedding-3-small",
# "api_key": "sk-...",
},
},
"vectordb": {
"provider": "chromadb", # or "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),
}
},
}
)
```

76
docs/en/tools/file-document/pdfsearchtool.mdx
View File

@@ -45,28 +45,64 @@ tool = PDFSearchTool(pdf='path/to/your/document.pdf')
## Custom model and embeddings
By default, the tool uses OpenAI for both embeddings and summarization. To customize the model, you can use a config dictionary as follows:
By default, the tool uses OpenAI for both embeddings and summarization. To customize the model, you can use a config dictionary as follows. Note: a vector database is required because generated embeddings must be stored and queried from a vectordb.
```python Code
from crewai_tools import PDFSearchTool
# - embedding_model (required): choose provider + provider-specific config
# - vectordb (required): choose vector DB and pass its config
tool = PDFSearchTool(
config=dict(
llm=dict(
provider="ollama", # or google, openai, anthropic, llama2, ...
config=dict(
model="llama2",
# temperature=0.5,
# top_p=1,
# stream=true,
),
),
embedder=dict(
provider="google", # or openai, ollama, ...
config=dict(
model="models/embedding-001",
task_type="retrieval_document",
# title="Embeddings",
),
),
)
config={
"embedding_model": {
# Supported providers: "openai", "azure", "google-generativeai", "google-vertex",
# "voyageai", "cohere", "huggingface", "jina", "sentence-transformer",
# "text2vec", "ollama", "openclip", "instructor", "onnx", "roboflow", "watsonx", "custom"
"provider": "openai", # or: "google-generativeai", "cohere", "ollama", ...
"config": {
# Model identifier for the chosen provider. "model" will be auto-mapped to "model_name" internally.
"model": "text-embedding-3-small",
# Optional: API key. If omitted, the tool will use provider-specific env vars when available
# (e.g., OPENAI_API_KEY for provider="openai").
# "api_key": "sk-...",
# Provider-specific examples:
# --- Google Generative AI ---
# (Set provider="google-generativeai" above)
# "model": "models/embedding-001",
# "task_type": "retrieval_document",
# "title": "Embeddings",
# --- Cohere ---
# (Set provider="cohere" above)
# "model": "embed-english-v3.0",
# --- Ollama (local) ---
# (Set provider="ollama" above)
# "model": "nomic-embed-text",
},
},
"vectordb": {
"provider": "chromadb", # or "qdrant"
"config": {
# For ChromaDB: pass "settings" (chromadb.config.Settings) or rely on defaults.
# Example (uncomment and import):
# from chromadb.config import Settings
# "settings": Settings(
# persist_directory="/content/chroma",
# allow_reset=True,
# is_persistent=True,
# ),
# For Qdrant: pass "vectors_config" (qdrant_client.models.VectorParams).
# Example (uncomment and import):
# from qdrant_client.models import VectorParams, Distance
# "vectors_config": VectorParams(size=384, distance=Distance.COSINE),
# Note: collection name is controlled by the tool (default: "rag_tool_collection"), not set here.
}
},
}
)
```

54
docs/en/tools/file-document/txtsearchtool.mdx
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@@ -57,25 +57,41 @@ By default, the tool uses OpenAI for both embeddings and summarization.
To customize the model, you can use a config dictionary as follows:
```python Code
from chromadb.config import Settings
tool = TXTSearchTool(
config=dict(
llm=dict(
provider="ollama", # or google, openai, anthropic, llama2, ...
config=dict(
model="llama2",
# temperature=0.5,
# top_p=1,
# stream=true,
),
),
embedder=dict(
provider="google", # or openai, ollama, ...
config=dict(
model="models/embedding-001",
task_type="retrieval_document",
# title="Embeddings",
),
),
)
config={
# Required: embeddings provider + config
"embedding_model": {
"provider": "openai", # or google-generativeai, cohere, ollama, ...
"config": {
"model": "text-embedding-3-small",
# "api_key": "sk-...", # optional if env var is set
# Provider examples:
# Google → model: "models/embedding-001", task_type: "retrieval_document"
# Cohere → model: "embed-english-v3.0"
# Ollama → model: "nomic-embed-text"
},
},
# Required: vector database config
"vectordb": {
"provider": "chromadb", # or "qdrant"
"config": {
# Chroma settings (optional persistence)
# "settings": Settings(
# persist_directory="/content/chroma",
# allow_reset=True,
# is_persistent=True,
# ),
# Qdrant vector params example:
# from qdrant_client.models import VectorParams, Distance
# "vectors_config": VectorParams(size=384, distance=Distance.COSINE),
# Note: collection name is controlled by the tool (default: "rag_tool_collection").
}
},
}
)
```

38
docs/en/tools/file-document/xmlsearchtool.mdx
View File

@@ -54,25 +54,25 @@ It is an optional parameter during the tool's initialization but must be provide
By default, the tool uses OpenAI for both embeddings and summarization. To customize the model, you can use a config dictionary as follows:
```python Code
from chromadb.config import Settings
tool = XMLSearchTool(
config=dict(
llm=dict(
provider="ollama", # or google, openai, anthropic, llama2, ...
config=dict(
model="llama2",
# temperature=0.5,
# top_p=1,
# stream=true,
),
),
embedder=dict(
provider="google", # or openai, ollama, ...
config=dict(
model="models/embedding-001",
task_type="retrieval_document",
# title="Embeddings",
),
),
)
config={
"embedding_model": {
"provider": "openai",
"config": {
"model": "text-embedding-3-small",
# "api_key": "sk-...",
},
},
"vectordb": {
"provider": "chromadb", # or "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),
}
},
}
)
```

8
docs/ko/observability/datadog.mdx
View File

@@ -93,11 +93,15 @@ ddtrace-run python crewai_agent.py
트레이스를 클릭하면 사용된 총 토큰, LLM 호출 수, 사용된 모델, 예상 비용 등 트레이스에 대한 세부 정보가 표시됩니다. 특정 스팬(span)을 클릭하면 이러한 세부 정보의 범위가 좁혀지고 관련 입력, 출력 및 메타데이터가 표시됩니다.
![Datadog LLM 옵저버빌리티 추적 보기](/images/datadog-llm-observability-1.png)
<Frame>
<img src="/images/datadog-llm-observability-1.png" alt="Datadog LLM 옵저버빌리티 추적 보기" />
</Frame>
또한, 트레이스의 제어 및 데이터 흐름을 보여주는 트레이스의 실행 그래프 보기를 볼 수 있으며, 이는 더 큰 에이전트로 확장하여 LLM 호출, 도구 호출 및 에이전트 상호 작용 간의 핸드오프와 관계를 보여줍니다.
![Datadog LLM Observability 에이전트 실행 흐름 보기](/images/datadog-llm-observability-2.png)
<Frame>
<img src="/images/datadog-llm-observability-2.png" alt="Datadog LLM Observability 에이전트 실행 흐름 보기" />
</Frame>
## 참조

38
docs/ko/tools/file-document/csvsearchtool.mdx
View File

@@ -54,25 +54,25 @@ tool = CSVSearchTool()
기본적으로 이 도구는 임베딩과 요약 모두에 OpenAI를 사용합니다. 모델을 사용자 지정하려면 다음과 같이 config 딕셔너리를 사용할 수 있습니다:
```python Code
from chromadb.config import Settings
tool = CSVSearchTool(
config=dict(
llm=dict(
provider="ollama", # or google, openai, anthropic, llama2, ...
config=dict(
model="llama2",
# temperature=0.5,
# top_p=1,
# stream=true,
),
),
embedder=dict(
provider="google", # or openai, ollama, ...
config=dict(
model="models/embedding-001",
task_type="retrieval_document",
# title="Embeddings",
),
),
)
config={
"embedding_model": {
"provider": "openai",
"config": {
"model": "text-embedding-3-small",
# "api_key": "sk-...",
},
},
"vectordb": {
"provider": "chromadb", # 또는 "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),
}
},
}
)
```

36
docs/ko/tools/file-document/directorysearchtool.mdx
View File

@@ -46,23 +46,25 @@ tool = DirectorySearchTool(directory='/path/to/directory')
DirectorySearchTool은 기본적으로 OpenAI를 사용하여 임베딩 및 요약을 수행합니다. 이 설정의 커스터마이즈 옵션에는 모델 공급자 및 구성을 변경하는 것이 포함되어 있어, 고급 사용자를 위한 유연성을 향상시킵니다.
```python Code
from chromadb.config import Settings
tool = DirectorySearchTool(
config=dict(
llm=dict(
provider="ollama", # Options include ollama, google, anthropic, llama2, and more
config=dict(
model="llama2",
# Additional configurations here
),
),
embedder=dict(
provider="google", # or openai, ollama, ...
config=dict(
model="models/embedding-001",
task_type="retrieval_document",
# title="Embeddings",
),
),
)
config={
"embedding_model": {
"provider": "openai",
"config": {
"model": "text-embedding-3-small",
# "api_key": "sk-...",
},
},
"vectordb": {
"provider": "chromadb", # 또는 "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),
}
},
}
)
```

38
docs/ko/tools/file-document/docxsearchtool.mdx
View File

@@ -56,25 +56,25 @@ tool = DOCXSearchTool(docx='path/to/your/document.docx')
기본적으로 이 도구는 임베딩과 요약 모두에 OpenAI를 사용합니다. 모델을 커스터마이즈하려면 다음과 같이 config 딕셔너리를 사용할 수 있습니다:
```python Code
from chromadb.config import Settings
tool = DOCXSearchTool(
config=dict(
llm=dict(
provider="ollama", # or google, openai, anthropic, llama2, ...
config=dict(
model="llama2",
# temperature=0.5,
# top_p=1,
# stream=true,
),
),
embedder=dict(
provider="google", # or openai, ollama, ...
config=dict(
model="models/embedding-001",
task_type="retrieval_document",
# title="Embeddings",
),
),
)
config={
"embedding_model": {
"provider": "openai",
"config": {
"model": "text-embedding-3-small",
# "api_key": "sk-...",
},
},
"vectordb": {
"provider": "chromadb", # 또는 "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),
}
},
}
)
```

40
docs/ko/tools/file-document/mdxsearchtool.mdx
View File

@@ -48,27 +48,25 @@ tool = MDXSearchTool(mdx='path/to/your/document.mdx')
이 도구는 기본적으로 임베딩과 요약을 위해 OpenAI를 사용합니다. 커스터마이징을 위해 아래와 같이 설정 딕셔너리를 사용할 수 있습니다.
```python Code
from chromadb.config import Settings
tool = MDXSearchTool(
config=dict(
llm=dict(
provider="ollama", # 옵션에는 google, openai, anthropic, llama2 등이 있습니다.
config=dict(
model="llama2",
# 선택적 파라미터를 여기에 포함할 수 있습니다.
# temperature=0.5,
# top_p=1,
# stream=true,
),
),
embedder=dict(
provider="google", # 또는 openai, ollama, ...
config=dict(
model="models/embedding-001",
task_type="retrieval_document",
# 임베딩에 대한 선택적 제목을 여기에 추가할 수 있습니다.
# title="Embeddings",
),
),
)
config={
"embedding_model": {
"provider": "openai",
"config": {
"model": "text-embedding-3-small",
# "api_key": "sk-...",
},
},
"vectordb": {
"provider": "chromadb", # 또는 "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),
}
},
}
)
```

72
docs/ko/tools/file-document/pdfsearchtool.mdx
View File

@@ -45,28 +45,60 @@ tool = PDFSearchTool(pdf='path/to/your/document.pdf')
## 커스텀 모델 및 임베딩
기본적으로 이 도구는 임베딩과 요약 모두에 OpenAI를 사용합니다. 모델을 커스터마이즈하려면 다음과 같이 config 딕셔너리를 사용할 수 있습니다:
기본적으로 이 도구는 임베딩과 요약 모두에 OpenAI를 사용합니다. 모델을 커스터마이즈하려면 다음과 같이 config 딕셔너리를 사용할 수 있습니다. 참고: 임베딩은 벡터DB에 저장되어야 하므로 vectordb 설정이 필요합니다.
```python Code
from crewai_tools import PDFSearchTool
from chromadb.config import Settings # Chroma 영속성 설정
tool = PDFSearchTool(
config=dict(
llm=dict(
provider="ollama", # or google, openai, anthropic, llama2, ...
config=dict(
model="llama2",
# temperature=0.5,
# top_p=1,
# stream=true,
),
),
embedder=dict(
provider="google", # or openai, ollama, ...
config=dict(
model="models/embedding-001",
task_type="retrieval_document",
# title="Embeddings",
),
),
)
config={
# 필수: 임베딩 제공자와 설정
"embedding_model": {
# 사용 가능 공급자: "openai", "azure", "google-generativeai", "google-vertex",
# "voyageai", "cohere", "huggingface", "jina", "sentence-transformer",
# "text2vec", "ollama", "openclip", "instructor", "onnx", "roboflow", "watsonx", "custom"
"provider": "openai",
"config": {
# "model" 키는 내부적으로 "model_name"으로 매핑됩니다.
"model": "text-embedding-3-small",
# 선택: API 키 (미설정 시 환경변수 사용)
# "api_key": "sk-...",
# 공급자별 예시
# --- Google ---
# (provider를 "google-generativeai"로 설정)
# "model": "models/embedding-001",
# "task_type": "retrieval_document",
# --- Cohere ---
# (provider를 "cohere"로 설정)
# "model": "embed-english-v3.0",
# --- Ollama(로컬) ---
# (provider를 "ollama"로 설정)
# "model": "nomic-embed-text",
},
},
# 필수: 벡터DB 설정
"vectordb": {
"provider": "chromadb", # 또는 "qdrant"
"config": {
# Chroma 설정 예시
# "settings": Settings(
# persist_directory="/content/chroma",
# allow_reset=True,
# is_persistent=True,
# ),
# Qdrant 설정 예시
# from qdrant_client.models import VectorParams, Distance
# "vectors_config": VectorParams(size=384, distance=Distance.COSINE),
# 참고: 컬렉션 이름은 도구에서 관리합니다(기본값: "rag_tool_collection").
}
},
}
)
```

47
docs/ko/tools/file-document/txtsearchtool.mdx
View File

@@ -57,25 +57,34 @@ tool = TXTSearchTool(txt='path/to/text/file.txt')
모델을 커스터마이징하려면 다음과 같이 config 딕셔너리를 사용할 수 있습니다:
```python Code
from chromadb.config import Settings
tool = TXTSearchTool(
config=dict(
llm=dict(
provider="ollama", # or google, openai, anthropic, llama2, ...
config=dict(
model="llama2",
# temperature=0.5,
# top_p=1,
# stream=true,
),
),
embedder=dict(
provider="google", # or openai, ollama, ...
config=dict(
model="models/embedding-001",
task_type="retrieval_document",
# title="Embeddings",
),
),
)
config={
# 필수: 임베딩 제공자 + 설정
"embedding_model": {
"provider": "openai", # 또는 google-generativeai, cohere, ollama 등
"config": {
"model": "text-embedding-3-small",
# "api_key": "sk-...", # 환경변수 사용 시 생략 가능
# 공급자별 예시: Google → model: "models/embedding-001", task_type: "retrieval_document"
},
},
# 필수: 벡터DB 설정
"vectordb": {
"provider": "chromadb", # 또는 "qdrant"
"config": {
# Chroma 설정(영속성 예시)
# "settings": Settings(persist_directory="/content/chroma", allow_reset=True, is_persistent=True),
# Qdrant 벡터 파라미터 예시:
# from qdrant_client.models import VectorParams, Distance
# "vectors_config": VectorParams(size=384, distance=Distance.COSINE),
# 참고: 컬렉션 이름은 도구에서 관리합니다(기본값: "rag_tool_collection").
}
},
}
)
```

38
docs/ko/tools/file-document/xmlsearchtool.mdx
View File

@@ -54,25 +54,25 @@ tool = XMLSearchTool(xml='path/to/your/xmlfile.xml')
기본적으로 이 도구는 임베딩과 요약 모두에 OpenAI를 사용합니다. 모델을 커스터마이징하려면 다음과 같이 config 딕셔너리를 사용할 수 있습니다.
```python Code
from chromadb.config import Settings
tool = XMLSearchTool(
config=dict(
llm=dict(
provider="ollama", # or google, openai, anthropic, llama2, ...
config=dict(
model="llama2",
# temperature=0.5,
# top_p=1,
# stream=true,
),
),
embedder=dict(
provider="google", # or openai, ollama, ...
config=dict(
model="models/embedding-001",
task_type="retrieval_document",
# title="Embeddings",
),
),
)
config={
"embedding_model": {
"provider": "openai",
"config": {
"model": "text-embedding-3-small",
# "api_key": "sk-...",
},
},
"vectordb": {
"provider": "chromadb", # 또는 "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),
}
},
}
)
```

9
docs/pt-BR/observability/datadog.mdx
View File

@@ -93,11 +93,14 @@ Depois de executar o aplicativo, você pode visualizar os traços na [Datadog LL
Ao clicar em um rastreamento, você verá os detalhes do rastreamento, incluindo o total de tokens usados, o número de chamadas LLM, os modelos usados e o custo estimado. Clicar em um intervalo específico reduzirá esses detalhes e mostrará a entrada, a saída e os metadados relacionados.
![Visualização do rastreamento de observabilidade do Datadog LLM](/images/datadog-llm-observability-1.png)
<Frame>
<img src="/images/datadog-llm-observability-1.png" alt="Visualização do rastreamento de observabilidade do Datadog LLM" />
</Frame>
Além disso, você pode visualizar a visualização do gráfico de execução do rastreamento, que mostra o controle e o fluxo de dados do rastreamento, que será dimensionado com agentes maiores para mostrar transferências e relacionamentos entre chamadas LLM, chamadas de ferramentas e interações de agentes.
![Visualização do fluxo de execução do agente de observabilidade do Datadog LLM](/images/datadog-llm-observability-2.png)
<Frame>
<img src="/images/datadog-llm-observability-2.png" alt="Visualização do fluxo de execução do agente de observabilidade do Datadog LLM" />
</Frame>
## Referências

36
docs/pt-BR/tools/file-document/directorysearchtool.mdx
View File

@@ -46,23 +46,25 @@ tool = DirectorySearchTool(directory='/path/to/directory')
O DirectorySearchTool utiliza OpenAI para embeddings e sumarização por padrão. As opções de personalização dessas configurações incluem a alteração do provedor de modelo e configurações, ampliando a flexibilidade para usuários avançados.
```python Code
from chromadb.config import Settings
tool = DirectorySearchTool(
config=dict(
llm=dict(
provider="ollama", # As opções incluem ollama, google, anthropic, llama2 e mais
config=dict(
model="llama2",
# Configurações adicionais aqui
),
),
embedder=dict(
provider="google", # ou openai, ollama, ...
config=dict(
model="models/embedding-001",
task_type="retrieval_document",
# 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),
}
},
}
)
```

38
docs/pt-BR/tools/file-document/docxsearchtool.mdx
View File

@@ -56,25 +56,25 @@ Os seguintes parâmetros podem ser usados para customizar o comportamento da `DO
Por padrão, a ferramenta utiliza o OpenAI tanto para embeddings quanto para sumarização. Para customizar o modelo, você pode usar um dicionário de configuração como no exemplo:
```python Code
from chromadb.config import Settings
tool = DOCXSearchTool(
config=dict(
llm=dict(
provider="ollama", # ou google, openai, anthropic, llama2, ...
config=dict(
model="llama2",
# 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",
# 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),
}
},
}
)
```

40
docs/pt-BR/tools/file-document/mdxsearchtool.mdx
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),
}
},
}
)
```

72
docs/pt-BR/tools/file-document/pdfsearchtool.mdx
View File

@@ -45,28 +45,60 @@ tool = PDFSearchTool(pdf='path/to/your/document.pdf')
## Modelo e embeddings personalizados
Por padrão, a ferramenta utiliza OpenAI tanto para embeddings quanto para sumarização. Para personalizar o modelo, você pode usar um dicionário de configuração como no exemplo abaixo:
Por padrão, a ferramenta utiliza OpenAI para embeddings e sumarização. Para personalizar, use um dicionário de configuração conforme abaixo. Observação: um banco vetorial (vectordb) é necessário, pois os embeddings gerados precisam ser armazenados e consultados.
```python Code
from crewai_tools import PDFSearchTool
from chromadb.config import Settings # Persistência no Chroma
tool = PDFSearchTool(
config=dict(
llm=dict(
provider="ollama", # ou google, openai, anthropic, llama2, ...
config=dict(
model="llama2",
# 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",
# title="Embeddings",
),
),
)
config={
# Obrigatório: provedor de embeddings + configuração
"embedding_model": {
# Provedores suportados: "openai", "azure", "google-generativeai", "google-vertex",
# "voyageai", "cohere", "huggingface", "jina", "sentence-transformer",
# "text2vec", "ollama", "openclip", "instructor", "onnx", "roboflow", "watsonx", "custom"
"provider": "openai",
"config": {
# "model" é mapeado internamente para "model_name".
"model": "text-embedding-3-small",
# Opcional: chave da API (se ausente, usa variáveis de ambiente do provedor)
# "api_key": "sk-...",
# Exemplos específicos por provedor
# --- Google ---
# (defina provider="google-generativeai")
# "model": "models/embedding-001",
# "task_type": "retrieval_document",
# --- Cohere ---
# (defina provider="cohere")
# "model": "embed-english-v3.0",
# --- Ollama (local) ---
# (defina provider="ollama")
# "model": "nomic-embed-text",
},
},
# Obrigatório: configuração do banco vetorial
"vectordb": {
"provider": "chromadb", # ou "qdrant"
"config": {
# Exemplo Chroma:
# "settings": Settings(
# persist_directory="/content/chroma",
# allow_reset=True,
# is_persistent=True,
# ),
# Exemplo Qdrant:
# from qdrant_client.models import VectorParams, Distance
# "vectors_config": VectorParams(size=384, distance=Distance.COSINE),
# Observação: o nome da coleção é controlado pela ferramenta (padrão: "rag_tool_collection").
}
},
}
)
```

52
docs/pt-BR/tools/file-document/txtsearchtool.mdx
View File

@@ -57,25 +57,39 @@ Por padrão, a ferramenta utiliza o OpenAI tanto para embeddings quanto para sum
Para personalizar o modelo, você pode usar um dicionário de configuração como o exemplo a seguir:
```python Code
from chromadb.config import Settings
tool = TXTSearchTool(
config=dict(
llm=dict(
provider="ollama", # ou google, openai, anthropic, llama2, ...
config=dict(
model="llama2",
# 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",
# title="Embeddings",
),
),
)
config={
# Obrigatório: provedor de embeddings + configuração
"embedding_model": {
"provider": "openai", # ou google-generativeai, cohere, ollama, ...
"config": {
"model": "text-embedding-3-small",
# "api_key": "sk-...", # opcional se variável de ambiente estiver definida
# Exemplos por provedor:
# Google → model: "models/embedding-001", task_type: "retrieval_document"
},
},
# Obrigatório: configuração do banco vetorial
"vectordb": {
"provider": "chromadb", # ou "qdrant"
"config": {
# Configurações do Chroma (persistência opcional)
# "settings": Settings(
# persist_directory="/content/chroma",
# allow_reset=True,
# is_persistent=True,
# ),
# Exemplo de parâmetros de vetor do Qdrant:
# from qdrant_client.models import VectorParams, Distance
# "vectors_config": VectorParams(size=384, distance=Distance.COSINE),
# Observação: o nome da coleção é controlado pela ferramenta (padrão: "rag_tool_collection").
}
},
}
)
```

38
docs/pt-BR/tools/file-document/xmlsearchtool.mdx
View File

@@ -54,25 +54,25 @@ Este parâmetro é opcional durante a inicialização da ferramenta, mas deve se
Por padrão, a ferramenta utiliza a OpenAI tanto para embeddings quanto para sumarização. Para personalizar o modelo, você pode usar um dicionário de configuração conforme o exemplo a seguir:
```python Code
from chromadb.config import Settings
tool = XMLSearchTool(
config=dict(
llm=dict(
provider="ollama", # ou google, openai, anthropic, llama2, ...
config=dict(
model="llama2",
# 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",
# 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),
}
},
}
)
```