agents to use tools api

llama_stack/providers/inline/agents/meta_reference/rag/context_retriever.py

@@ -1,72 +0,0 @@
-# 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 typing import List
-
-from jinja2 import Template
-
-from llama_stack.apis.agents import (
-    DefaultMemoryQueryGeneratorConfig,
-    LLMMemoryQueryGeneratorConfig,
-    MemoryQueryGenerator,
-    MemoryQueryGeneratorConfig,
-)
-from llama_stack.apis.inference import Message, UserMessage
-from llama_stack.providers.utils.inference.prompt_adapter import (
-    interleaved_content_as_str,
-)
-
-
-async def generate_rag_query(
-    config: MemoryQueryGeneratorConfig,
-    messages: List[Message],
-    **kwargs,
-):
-    """
-    Generates a query that will be used for
-    retrieving relevant information from the memory bank.
-    """
-    if config.type == MemoryQueryGenerator.default.value:
-        query = await default_rag_query_generator(config, messages, **kwargs)
-    elif config.type == MemoryQueryGenerator.llm.value:
-        query = await llm_rag_query_generator(config, messages, **kwargs)
-    else:
-        raise NotImplementedError(f"Unsupported memory query generator {config.type}")
-    return query
-
-
-async def default_rag_query_generator(
-    config: DefaultMemoryQueryGeneratorConfig,
-    messages: List[Message],
-    **kwargs,
-):
-    return config.sep.join(interleaved_content_as_str(m.content) for m in messages)
-
-
-async def llm_rag_query_generator(
-    config: LLMMemoryQueryGeneratorConfig,
-    messages: List[Message],
-    **kwargs,
-):
-    assert "inference_api" in kwargs, "LLMRAGQueryGenerator needs inference_api"
-    inference_api = kwargs["inference_api"]
-
-    m_dict = {"messages": [m.model_dump() for m in messages]}
-
-    template = Template(config.template)
-    content = template.render(m_dict)
-
-    model = config.model
-    message = UserMessage(content=content)
-    response = await inference_api.chat_completion(
-        model_id=model,
-        messages=[message],
-        stream=False,
-    )
-
-    query = response.completion_message.content
-
-    return query