fix: rename llama_stack_api dir (#4155)

# What does this PR do?

the directory structure was src/llama-stack-api/llama_stack_api

instead it should just be src/llama_stack_api to match the other
packages.

update the structure and pyproject/linting config

---------

Signed-off-by: Charlie Doern <cdoern@redhat.com>
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>

src/llama_stack_api/rag_tool.py

@@ -0,0 +1,168 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from enum import Enum, StrEnum
+from typing import Annotated, Any, Literal
+
+from pydantic import BaseModel, Field, field_validator
+
+from llama_stack_api.common.content_types import URL, InterleavedContent
+
+
+class RRFRanker(BaseModel):
+    """
+    Reciprocal Rank Fusion (RRF) ranker configuration.
+
+    :param type: The type of ranker, always "rrf"
+    :param impact_factor: The impact factor for RRF scoring. Higher values give more weight to higher-ranked results.
+                         Must be greater than 0
+    """
+
+    type: Literal["rrf"] = "rrf"
+    impact_factor: float = Field(default=60.0, gt=0.0)  # default of 60 for optimal performance
+
+
+class WeightedRanker(BaseModel):
+    """
+    Weighted ranker configuration that combines vector and keyword scores.
+
+    :param type: The type of ranker, always "weighted"
+    :param alpha: Weight factor between 0 and 1.
+                 0 means only use keyword scores,
+                 1 means only use vector scores,
+                 values in between blend both scores.
+    """
+
+    type: Literal["weighted"] = "weighted"
+    alpha: float = Field(
+        default=0.5,
+        ge=0.0,
+        le=1.0,
+        description="Weight factor between 0 and 1. 0 means only keyword scores, 1 means only vector scores.",
+    )
+
+
+Ranker = Annotated[
+    RRFRanker | WeightedRanker,
+    Field(discriminator="type"),
+]
+
+
+class RAGDocument(BaseModel):
+    """
+    A document to be used for document ingestion in the RAG Tool.
+
+    :param document_id: The unique identifier for the document.
+    :param content: The content of the document.
+    :param mime_type: The MIME type of the document.
+    :param metadata: Additional metadata for the document.
+    """
+
+    document_id: str
+    content: InterleavedContent | URL
+    mime_type: str | None = None
+    metadata: dict[str, Any] = Field(default_factory=dict)
+
+
+class RAGQueryResult(BaseModel):
+    """Result of a RAG query containing retrieved content and metadata.
+
+    :param content: (Optional) The retrieved content from the query
+    :param metadata: Additional metadata about the query result
+    """
+
+    content: InterleavedContent | None = None
+    metadata: dict[str, Any] = Field(default_factory=dict)
+
+
+class RAGQueryGenerator(Enum):
+    """Types of query generators for RAG systems.
+
+    :cvar default: Default query generator using simple text processing
+    :cvar llm: LLM-based query generator for enhanced query understanding
+    :cvar custom: Custom query generator implementation
+    """
+
+    default = "default"
+    llm = "llm"
+    custom = "custom"
+
+
+class RAGSearchMode(StrEnum):
+    """
+    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
+    """
+
+    VECTOR = "vector"
+    KEYWORD = "keyword"
+    HYBRID = "hybrid"
+
+
+class DefaultRAGQueryGeneratorConfig(BaseModel):
+    """Configuration for the default RAG query generator.
+
+    :param type: Type of query generator, always 'default'
+    :param separator: String separator used to join query terms
+    """
+
+    type: Literal["default"] = "default"
+    separator: str = " "
+
+
+class LLMRAGQueryGeneratorConfig(BaseModel):
+    """Configuration for the LLM-based RAG query generator.
+
+    :param type: Type of query generator, always 'llm'
+    :param model: Name of the language model to use for query generation
+    :param template: Template string for formatting the query generation prompt
+    """
+
+    type: Literal["llm"] = "llm"
+    model: str
+    template: str
+
+
+RAGQueryGeneratorConfig = Annotated[
+    DefaultRAGQueryGeneratorConfig | LLMRAGQueryGeneratorConfig,
+    Field(discriminator="type"),
+]
+
+
+class RAGQueryConfig(BaseModel):
+    """
+    Configuration for the RAG query generation.
+
+    :param query_generator_config: Configuration for the query generator.
+    :param max_tokens_in_context: Maximum number of tokens in the context.
+    :param max_chunks: Maximum number of chunks to retrieve.
+    :param chunk_template: 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"
+    :param mode: Search mode for retrieval—either "vector", "keyword", or "hybrid". Default "vector".
+    :param ranker: Configuration for the ranker to use in hybrid search. Defaults to RRF ranker.
+    """
+
+    # This config defines how a query is generated using the messages
+    # for memory bank retrieval.
+    query_generator_config: RAGQueryGeneratorConfig = Field(default=DefaultRAGQueryGeneratorConfig())
+    max_tokens_in_context: int = 4096
+    max_chunks: int = 5
+    chunk_template: str = "Result {index}\nContent: {chunk.content}\nMetadata: {metadata}\n"
+    mode: RAGSearchMode | None = RAGSearchMode.VECTOR
+    ranker: Ranker | None = Field(default=None)  # Only used for hybrid mode
+
+    @field_validator("chunk_template")
+    def validate_chunk_template(cls, v: str) -> str:
+        if "{chunk.content}" not in v:
+            raise ValueError("chunk_template must contain {chunk.content}")
+        if "{index}" not in v:
+            raise ValueError("chunk_template must contain {index}")
+        if len(v) == 0:
+            raise ValueError("chunk_template must not be empty")
+        return v