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1
docs/docs/concepts/apis/index.mdx
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@ -23,5 +23,4 @@ A Llama Stack API is described as a collection of REST endpoints. We currently s
We are working on adding a few more APIs to complete the application lifecycle. These will include:
- **Batch Inference**: run inference on a dataset of inputs
- **Batch Agents**: run agents on a dataset of inputs
- **Synthetic Data Generation**: generate synthetic data for model development
- **Batches**: OpenAI-compatible batch management for inference

2
docs/docs/distributions/k8s/ui-k8s.yaml.template
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@ -44,7 +44,7 @@ spec:
# Navigate to the UI directory
echo "Navigating to UI directory..."
cd /app/llama_stack/ui
cd /app/llama_stack_ui
# Check if package.json exists
if [ ! -f "package.json" ]; then

31
docs/docs/getting_started/detailed_tutorial.mdx
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@ -239,8 +239,13 @@ client = LlamaStackClient(base_url="http://localhost:8321")
models = client.models.list()
# Select the first LLM
llm = next(m for m in models if m.model_type == "llm" and m.provider_id == "ollama")
model_id = llm.identifier
llm = next(
m for m in models
if m.custom_metadata
and m.custom_metadata.get("model_type") == "llm"
and m.custom_metadata.get("provider_id") == "ollama"
)
model_id = llm.id
print("Model:", model_id)
@ -279,8 +284,13 @@ import uuid
client = LlamaStackClient(base_url=f"http://localhost:8321")
models = client.models.list()
llm = next(m for m in models if m.model_type == "llm" and m.provider_id == "ollama")
model_id = llm.identifier
llm = next(
m for m in models
if m.custom_metadata
and m.custom_metadata.get("model_type") == "llm"
and m.custom_metadata.get("provider_id") == "ollama"
)
model_id = llm.id
agent = Agent(client, model=model_id, instructions="You are a helpful assistant.")
@ -450,8 +460,11 @@ import uuid
client = LlamaStackClient(base_url="http://localhost:8321")
# Create a vector database instance
embed_lm = next(m for m in client.models.list() if m.model_type == "embedding")
embedding_model = embed_lm.identifier
embed_lm = next(
m for m in client.models.list()
if m.custom_metadata and m.custom_metadata.get("model_type") == "embedding"
)
embedding_model = embed_lm.id
vector_db_id = f"v{uuid.uuid4().hex}"
# The VectorDB API is deprecated; the server now returns its own authoritative ID.
# We capture the correct ID from the response's .identifier attribute.
@ -489,9 +502,11 @@ client.tool_runtime.rag_tool.insert(
llm = next(
m
for m in client.models.list()
if m.model_type == "llm" and m.provider_id == "ollama"
if m.custom_metadata
and m.custom_metadata.get("model_type") == "llm"
and m.custom_metadata.get("provider_id") == "ollama"
)
model = llm.identifier
model = llm.id
# Create the RAG agent
rag_agent = Agent(

8
docs/openapi_generator/pyopenapi/operations.py
View file

@ -170,7 +170,7 @@ def _get_endpoint_functions(
for webmethod in webmethods:
print(f"Processing {colored(func_name, 'white')}...")
operation_name = func_name
if webmethod.method == "GET":
prefix = "get"
elif webmethod.method == "DELETE":
@ -196,16 +196,10 @@ def _get_endpoint_functions(
def _get_defining_class(member_fn: str, derived_cls: type) -> type:
"Find the class in which a member function is first defined in a class inheritance hierarchy."
# This import must be dynamic here
from llama_stack.apis.tools import RAGToolRuntime, ToolRuntime
# iterate in reverse member resolution order to find most specific class first
for cls in reversed(inspect.getmro(derived_cls)):
for name, _ in inspect.getmembers(cls, inspect.isfunction):
if name == member_fn:
# HACK ALERT
if cls == RAGToolRuntime:
return ToolRuntime
return cls
raise ValidationError(

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@ -974,11 +974,11 @@ paths:
get:
responses:
'200':
description: A ListModelsResponse.
description: A OpenAIListModelsResponse.
content:
application/json:
schema:
$ref: '#/components/schemas/ListModelsResponse'
$ref: '#/components/schemas/OpenAIListModelsResponse'
'400':
$ref: '#/components/responses/BadRequest400'
'429':
@ -991,8 +991,8 @@ paths:
$ref: '#/components/responses/DefaultError'
tags:
- Models
summary: List all models.
description: List all models.
summary: List models using the OpenAI API.
description: List models using the OpenAI API.
parameters: []
deprecated: false
post:
@ -1982,40 +1982,6 @@ paths:
schema:
type: string
deprecated: false
/v1/synthetic-data-generation/generate:
post:
responses:
'200':
description: >-
Response containing filtered synthetic data samples and optional statistics
content:
application/json:
schema:
$ref: '#/components/schemas/SyntheticDataGenerationResponse'
'400':
$ref: '#/components/responses/BadRequest400'
'429':
$ref: >-
#/components/responses/TooManyRequests429
'500':
$ref: >-
#/components/responses/InternalServerError500
default:
$ref: '#/components/responses/DefaultError'
tags:
- SyntheticDataGeneration (Coming Soon)
summary: >-
Generate synthetic data based on input dialogs and apply filtering.
description: >-
Generate synthetic data based on input dialogs and apply filtering.
parameters: []
requestBody:
content:
application/json:
schema:
$ref: '#/components/schemas/SyntheticDataGenerateRequest'
required: true
deprecated: false
/v1/tool-runtime/invoke:
post:
responses:
@ -2086,69 +2052,6 @@ paths:
schema:
$ref: '#/components/schemas/URL'
deprecated: false
/v1/tool-runtime/rag-tool/insert:
post:
responses:
'200':
description: OK
'400':
$ref: '#/components/responses/BadRequest400'
'429':
$ref: >-
#/components/responses/TooManyRequests429
'500':
$ref: >-
#/components/responses/InternalServerError500
default:
$ref: '#/components/responses/DefaultError'
tags:
- ToolRuntime
summary: >-
Index documents so they can be used by the RAG system.
description: >-
Index documents so they can be used by the RAG system.
parameters: []
requestBody:
content:
application/json:
schema:
$ref: '#/components/schemas/InsertRequest'
required: true
deprecated: false
/v1/tool-runtime/rag-tool/query:
post:
responses:
'200':
description: >-
RAGQueryResult containing the retrieved content and metadata
content:
application/json:
schema:
$ref: '#/components/schemas/RAGQueryResult'
'400':
$ref: '#/components/responses/BadRequest400'
'429':
$ref: >-
#/components/responses/TooManyRequests429
'500':
$ref: >-
#/components/responses/InternalServerError500
default:
$ref: '#/components/responses/DefaultError'
tags:
- ToolRuntime
summary: >-
Query the RAG system for context; typically invoked by the agent.
description: >-
Query the RAG system for context; typically invoked by the agent.
parameters: []
requestBody:
content:
application/json:
schema:
$ref: '#/components/schemas/QueryRequest'
required: true
deprecated: false
/v1/toolgroups:
get:
responses:
@ -5619,6 +5522,88 @@ components:
title: ListRoutesResponse
description: >-
Response containing a list of all available API routes.
OpenAIModel:
type: object
properties:
id:
type: string
object:
type: string
const: model
default: model
created:
type: integer
owned_by:
type: string
custom_metadata:
type: object
additionalProperties:
oneOf:
- type: 'null'
- type: boolean
- type: number
- type: string
- type: array
- type: object
additionalProperties: false
required:
- id
- object
- created
- owned_by
title: OpenAIModel
description: A model from OpenAI.
OpenAIListModelsResponse:
type: object
properties:
data:
type: array
items:
$ref: '#/components/schemas/OpenAIModel'
additionalProperties: false
required:
- data
title: OpenAIListModelsResponse
ModelType:
type: string
enum:
- llm
- embedding
- rerank
title: ModelType
description: >-
Enumeration of supported model types in Llama Stack.
RegisterModelRequest:
type: object
properties:
model_id:
type: string
description: The identifier of the model to register.
provider_model_id:
type: string
description: >-
The identifier of the model in the provider.
provider_id:
type: string
description: The identifier of the provider.
metadata:
type: object
additionalProperties:
oneOf:
- type: 'null'
- type: boolean
- type: number
- type: string
- type: array
- type: object
description: Any additional metadata for this model.
model_type:
$ref: '#/components/schemas/ModelType'
description: The type of model to register.
additionalProperties: false
required:
- model_id
title: RegisterModelRequest
Model:
type: object
properties:
@ -5676,57 +5661,6 @@ components:
title: Model
description: >-
A model resource representing an AI model registered in Llama Stack.
ModelType:
type: string
enum:
- llm
- embedding
- rerank
title: ModelType
description: >-
Enumeration of supported model types in Llama Stack.
ListModelsResponse:
type: object
properties:
data:
type: array
items:
$ref: '#/components/schemas/Model'
additionalProperties: false
required:
- data
title: ListModelsResponse
RegisterModelRequest:
type: object
properties:
model_id:
type: string
description: The identifier of the model to register.
provider_model_id:
type: string
description: >-
The identifier of the model in the provider.
provider_id:
type: string
description: The identifier of the provider.
metadata:
type: object
additionalProperties:
oneOf:
- type: 'null'
- type: boolean
- type: number
- type: string
- type: array
- type: object
description: Any additional metadata for this model.
model_type:
$ref: '#/components/schemas/ModelType'
description: The type of model to register.
additionalProperties: false
required:
- model_id
title: RegisterModelRequest
RunModerationRequest:
type: object
properties:
@ -8144,20 +8078,6 @@ components:
- error
title: ViolationLevel
description: Severity level of a safety violation.
AgentTurnInputType:
type: object
properties:
type:
type: string
const: agent_turn_input
default: agent_turn_input
description: >-
Discriminator type. Always "agent_turn_input"
additionalProperties: false
required:
- type
title: AgentTurnInputType
description: Parameter type for agent turn input.
AggregationFunctionType:
type: string
enum:
@ -8400,7 +8320,6 @@ components:
- $ref: '#/components/schemas/UnionType'
- $ref: '#/components/schemas/ChatCompletionInputType'
- $ref: '#/components/schemas/CompletionInputType'
- $ref: '#/components/schemas/AgentTurnInputType'
discriminator:
propertyName: type
mapping:
@ -8413,7 +8332,6 @@ components:
union: '#/components/schemas/UnionType'
chat_completion_input: '#/components/schemas/ChatCompletionInputType'
completion_input: '#/components/schemas/CompletionInputType'
agent_turn_input: '#/components/schemas/AgentTurnInputType'
params:
$ref: '#/components/schemas/ScoringFnParams'
additionalProperties: false
@ -8494,7 +8412,6 @@ components:
- $ref: '#/components/schemas/UnionType'
- $ref: '#/components/schemas/ChatCompletionInputType'
- $ref: '#/components/schemas/CompletionInputType'
- $ref: '#/components/schemas/AgentTurnInputType'
discriminator:
propertyName: type
mapping:
@ -8507,7 +8424,6 @@ components:
union: '#/components/schemas/UnionType'
chat_completion_input: '#/components/schemas/ChatCompletionInputType'
completion_input: '#/components/schemas/CompletionInputType'
agent_turn_input: '#/components/schemas/AgentTurnInputType'
RegisterScoringFunctionRequest:
type: object
properties:
@ -8744,45 +8660,29 @@ components:
required:
- shield_id
title: RegisterShieldRequest
CompletionMessage:
InvokeToolRequest:
type: object
properties:
role:
tool_name:
type: string
const: assistant
default: assistant
description: The name of the tool to invoke.
kwargs:
type: object
additionalProperties:
oneOf:
- type: 'null'
- type: boolean
- type: number
- type: string
- type: array
- type: object
description: >-
Must be "assistant" to identify this as the model's response
content:
$ref: '#/components/schemas/InterleavedContent'
description: The content of the model's response
stop_reason:
type: string
enum:
- end_of_turn
- end_of_message
- out_of_tokens
description: >-
Reason why the model stopped generating. Options are: - `StopReason.end_of_turn`:
The model finished generating the entire response. - `StopReason.end_of_message`:
The model finished generating but generated a partial response -- usually,
a tool call. The user may call the tool and continue the conversation
with the tool's response. - `StopReason.out_of_tokens`: The model ran
out of token budget.
tool_calls:
type: array
items:
$ref: '#/components/schemas/ToolCall'
description: >-
List of tool calls. Each tool call is a ToolCall object.
A dictionary of arguments to pass to the tool.
additionalProperties: false
required:
- role
- content
- stop_reason
title: CompletionMessage
description: >-
A message containing the model's (assistant) response in a chat conversation.
- tool_name
- kwargs
title: InvokeToolRequest
ImageContentItem:
type: object
properties:
@ -8829,41 +8729,6 @@ components:
mapping:
image: '#/components/schemas/ImageContentItem'
text: '#/components/schemas/TextContentItem'
Message:
oneOf:
- $ref: '#/components/schemas/UserMessage'
- $ref: '#/components/schemas/SystemMessage'
- $ref: '#/components/schemas/ToolResponseMessage'
- $ref: '#/components/schemas/CompletionMessage'
discriminator:
propertyName: role
mapping:
user: '#/components/schemas/UserMessage'
system: '#/components/schemas/SystemMessage'
tool: '#/components/schemas/ToolResponseMessage'
assistant: '#/components/schemas/CompletionMessage'
SystemMessage:
type: object
properties:
role:
type: string
const: system
default: system
description: >-
Must be "system" to identify this as a system message
content:
$ref: '#/components/schemas/InterleavedContent'
description: >-
The content of the "system prompt". If multiple system messages are provided,
they are concatenated. The underlying Llama Stack code may also add other
system messages (for example, for formatting tool definitions).
additionalProperties: false
required:
- role
- content
title: SystemMessage
description: >-
A system message providing instructions or context to the model.
TextContentItem:
type: object
properties:
@ -8882,179 +8747,6 @@ components:
- text
title: TextContentItem
description: A text content item
ToolCall:
type: object
properties:
call_id:
type: string
tool_name:
oneOf:
- type: string
enum:
- brave_search
- wolfram_alpha
- photogen
- code_interpreter
title: BuiltinTool
- type: string
arguments:
type: string
additionalProperties: false
required:
- call_id
- tool_name
- arguments
title: ToolCall
ToolResponseMessage:
type: object
properties:
role:
type: string
const: tool
default: tool
description: >-
Must be "tool" to identify this as a tool response
call_id:
type: string
description: >-
Unique identifier for the tool call this response is for
content:
$ref: '#/components/schemas/InterleavedContent'
description: The response content from the tool
additionalProperties: false
required:
- role
- call_id
- content
title: ToolResponseMessage
description: >-
A message representing the result of a tool invocation.
URL:
type: object
properties:
uri:
type: string
description: The URL string pointing to the resource
additionalProperties: false
required:
- uri
title: URL
description: A URL reference to external content.
UserMessage:
type: object
properties:
role:
type: string
const: user
default: user
description: >-
Must be "user" to identify this as a user message
content:
$ref: '#/components/schemas/InterleavedContent'
description: >-
The content of the message, which can include text and other media
context:
$ref: '#/components/schemas/InterleavedContent'
description: >-
(Optional) This field is used internally by Llama Stack to pass RAG context.
This field may be removed in the API in the future.
additionalProperties: false
required:
- role
- content
title: UserMessage
description: >-
A message from the user in a chat conversation.
SyntheticDataGenerateRequest:
type: object
properties:
dialogs:
type: array
items:
$ref: '#/components/schemas/Message'
description: >-
List of conversation messages to use as input for synthetic data generation
filtering_function:
type: string
enum:
- none
- random
- top_k
- top_p
- top_k_top_p
- sigmoid
description: >-
Type of filtering to apply to generated synthetic data samples
model:
type: string
description: >-
(Optional) The identifier of the model to use. The model must be registered
with Llama Stack and available via the /models endpoint
additionalProperties: false
required:
- dialogs
- filtering_function
title: SyntheticDataGenerateRequest
SyntheticDataGenerationResponse:
type: object
properties:
synthetic_data:
type: array
items:
type: object
additionalProperties:
oneOf:
- type: 'null'
- type: boolean
- type: number
- type: string
- type: array
- type: object
description: >-
List of generated synthetic data samples that passed the filtering criteria
statistics:
type: object
additionalProperties:
oneOf:
- type: 'null'
- type: boolean
- type: number
- type: string
- type: array
- type: object
description: >-
(Optional) Statistical information about the generation process and filtering
results
additionalProperties: false
required:
- synthetic_data
title: SyntheticDataGenerationResponse
description: >-
Response from the synthetic data generation. Batch of (prompt, response, score)
tuples that pass the threshold.
InvokeToolRequest:
type: object
properties:
tool_name:
type: string
description: The name of the tool to invoke.
kwargs:
type: object
additionalProperties:
oneOf:
- type: 'null'
- type: boolean
- type: number
- type: string
- type: array
- type: object
description: >-
A dictionary of arguments to pass to the tool.
additionalProperties: false
required:
- tool_name
- kwargs
title: InvokeToolRequest
ToolInvocationResult:
type: object
properties:
@ -9085,6 +8777,17 @@ components:
additionalProperties: false
title: ToolInvocationResult
description: Result of a tool invocation.
URL:
type: object
properties:
uri:
type: string
description: The URL string pointing to the resource
additionalProperties: false
required:
- uri
title: URL
description: A URL reference to external content.
ToolDef:
type: object
properties:
@ -9155,274 +8858,6 @@ components:
title: ListToolDefsResponse
description: >-
Response containing a list of tool definitions.
RAGDocument:
type: object
properties:
document_id:
type: string
description: The unique identifier for the document.
content:
oneOf:
- type: string
- $ref: '#/components/schemas/InterleavedContentItem'
- type: array
items:
$ref: '#/components/schemas/InterleavedContentItem'
- $ref: '#/components/schemas/URL'
description: The content of the document.
mime_type:
type: string
description: The MIME type of the document.
metadata:
type: object
additionalProperties:
oneOf:
- type: 'null'
- type: boolean
- type: number
- type: string
- type: array
- type: object
description: Additional metadata for the document.
additionalProperties: false
required:
- document_id
- content
- metadata
title: RAGDocument
description: >-
A document to be used for document ingestion in the RAG Tool.
InsertRequest:
type: object
properties:
documents:
type: array
items:
$ref: '#/components/schemas/RAGDocument'
description: >-
List of documents to index in the RAG system
vector_store_id:
type: string
description: >-
ID of the vector database to store the document embeddings
chunk_size_in_tokens:
type: integer
description: >-
(Optional) Size in tokens for document chunking during indexing
additionalProperties: false
required:
- documents
- vector_store_id
- chunk_size_in_tokens
title: InsertRequest
DefaultRAGQueryGeneratorConfig:
type: object
properties:
type:
type: string
const: default
default: default
description: >-
Type of query generator, always 'default'
separator:
type: string
default: ' '
description: >-
String separator used to join query terms
additionalProperties: false
required:
- type
- separator
title: DefaultRAGQueryGeneratorConfig
description: >-
Configuration for the default RAG query generator.
LLMRAGQueryGeneratorConfig:
type: object
properties:
type:
type: string
const: llm
default: llm
description: Type of query generator, always 'llm'
model:
type: string
description: >-
Name of the language model to use for query generation
template:
type: string
description: >-
Template string for formatting the query generation prompt
additionalProperties: false
required:
- type
- model
- template
title: LLMRAGQueryGeneratorConfig
description: >-
Configuration for the LLM-based RAG query generator.
RAGQueryConfig:
type: object
properties:
query_generator_config:
oneOf:
- $ref: '#/components/schemas/DefaultRAGQueryGeneratorConfig'
- $ref: '#/components/schemas/LLMRAGQueryGeneratorConfig'
discriminator:
propertyName: type
mapping:
default: '#/components/schemas/DefaultRAGQueryGeneratorConfig'
llm: '#/components/schemas/LLMRAGQueryGeneratorConfig'
description: Configuration for the query generator.
max_tokens_in_context:
type: integer
default: 4096
description: Maximum number of tokens in the context.
max_chunks:
type: integer
default: 5
description: Maximum number of chunks to retrieve.
chunk_template:
type: string
default: >
Result {index}
Content: {chunk.content}
Metadata: {metadata}
description: >-
Template for formatting each retrieved chunk in the context. Available
placeholders: {index} (1-based chunk ordinal), {chunk.content} (chunk
content string), {metadata} (chunk metadata dict). Default: "Result {index}\nContent:
{chunk.content}\nMetadata: {metadata}\n"
mode:
$ref: '#/components/schemas/RAGSearchMode'
default: vector
description: >-
Search mode for retrieval—either "vector", "keyword", or "hybrid". Default
"vector".
ranker:
$ref: '#/components/schemas/Ranker'
description: >-
Configuration for the ranker to use in hybrid search. Defaults to RRF
ranker.
additionalProperties: false
required:
- query_generator_config
- max_tokens_in_context
- max_chunks
- chunk_template
title: RAGQueryConfig
description: >-
Configuration for the RAG query generation.
RAGSearchMode:
type: string
enum:
- vector
- keyword
- hybrid
title: RAGSearchMode
description: >-
Search modes for RAG query retrieval: - VECTOR: Uses vector similarity search
for semantic matching - KEYWORD: Uses keyword-based search for exact matching
- HYBRID: Combines both vector and keyword search for better results
RRFRanker:
type: object
properties:
type:
type: string
const: rrf
default: rrf
description: The type of ranker, always "rrf"
impact_factor:
type: number
default: 60.0
description: >-
The impact factor for RRF scoring. Higher values give more weight to higher-ranked
results. Must be greater than 0
additionalProperties: false
required:
- type
- impact_factor
title: RRFRanker
description: >-
Reciprocal Rank Fusion (RRF) ranker configuration.
Ranker:
oneOf:
- $ref: '#/components/schemas/RRFRanker'
- $ref: '#/components/schemas/WeightedRanker'
discriminator:
propertyName: type
mapping:
rrf: '#/components/schemas/RRFRanker'
weighted: '#/components/schemas/WeightedRanker'
WeightedRanker:
type: object
properties:
type:
type: string
const: weighted
default: weighted
description: The type of ranker, always "weighted"
alpha:
type: number
default: 0.5
description: >-
Weight factor between 0 and 1. 0 means only use keyword scores, 1 means
only use vector scores, values in between blend both scores.
additionalProperties: false
required:
- type
- alpha
title: WeightedRanker
description: >-
Weighted ranker configuration that combines vector and keyword scores.
QueryRequest:
type: object
properties:
content:
$ref: '#/components/schemas/InterleavedContent'
description: >-
The query content to search for in the indexed documents
vector_store_ids:
type: array
items:
type: string
description: >-
List of vector database IDs to search within
query_config:
$ref: '#/components/schemas/RAGQueryConfig'
description: >-
(Optional) Configuration parameters for the query operation
additionalProperties: false
required:
- content
- vector_store_ids
title: QueryRequest
RAGQueryResult:
type: object
properties:
content:
$ref: '#/components/schemas/InterleavedContent'
description: >-
(Optional) The retrieved content from the query
metadata:
type: object
additionalProperties:
oneOf:
- type: 'null'
- type: boolean
- type: number
- type: string
- type: array
- type: object
description: >-
Additional metadata about the query result
additionalProperties: false
required:
- metadata
title: RAGQueryResult
description: >-
Result of a RAG query containing retrieved content and metadata.
ToolGroup:
type: object
properties:
@ -10686,8 +10121,6 @@ tags:
description: ''
- name: Shields
description: ''
- name: SyntheticDataGeneration (Coming Soon)
description: ''
- name: ToolGroups
description: ''
- name: ToolRuntime
@ -10710,7 +10143,6 @@ x-tagGroups:
- Scoring
- ScoringFunctions
- Shields
- SyntheticDataGeneration (Coming Soon)
- ToolGroups
- ToolRuntime
- VectorIO

2579
docs/static/stainless-llama-stack-spec.yaml vendored
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