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Code Issues Actions 3 Packages Projects Releases Wiki Activity

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
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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),
}
},
}
)
```