forked from phoenix-oss/llama-stack-mirror
e4a1579e63
# What does this PR do? - Configured ruff linter to automatically fix import sorting issues. - Set --exit-non-zero-on-fix to ensure non-zero exit code when fixes are applied. - Enabled the 'I' selection to focus on import-related linting rules. - Ran the linter, and formatted all codebase imports accordingly. - Removed the black dep from the "dev" group since we use ruff Signed-off-by: Sébastien Han <seb@redhat.com> [//]: # (If resolving an issue, uncomment and update the line below) [//]: # (Closes #[issue-number]) ## Test Plan [Describe the tests you ran to verify your changes with result summaries. *Provide clear instructions so the plan can be easily re-executed.*] [//]: # (## Documentation) [//]: # (- [ ] Added a Changelog entry if the change is significant) Signed-off-by: Sébastien Han <seb@redhat.com>
76 lines
2.2 KiB
Python
76 lines
2.2 KiB
Python
# 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 jinja2 import Template
|
|
|
|
from llama_stack.apis.common.content_types import InterleavedContent
|
|
from llama_stack.apis.inference import UserMessage
|
|
from llama_stack.apis.tools.rag_tool import (
|
|
DefaultRAGQueryGeneratorConfig,
|
|
LLMRAGQueryGeneratorConfig,
|
|
RAGQueryGenerator,
|
|
RAGQueryGeneratorConfig,
|
|
)
|
|
from llama_stack.providers.utils.inference.prompt_adapter import (
|
|
interleaved_content_as_str,
|
|
)
|
|
|
|
|
|
async def generate_rag_query(
|
|
config: RAGQueryGeneratorConfig,
|
|
content: InterleavedContent,
|
|
**kwargs,
|
|
):
|
|
"""
|
|
Generates a query that will be used for
|
|
retrieving relevant information from the memory bank.
|
|
"""
|
|
if config.type == RAGQueryGenerator.default.value:
|
|
query = await default_rag_query_generator(config, content, **kwargs)
|
|
elif config.type == RAGQueryGenerator.llm.value:
|
|
query = await llm_rag_query_generator(config, content, **kwargs)
|
|
else:
|
|
raise NotImplementedError(f"Unsupported memory query generator {config.type}")
|
|
return query
|
|
|
|
|
|
async def default_rag_query_generator(
|
|
config: DefaultRAGQueryGeneratorConfig,
|
|
content: InterleavedContent,
|
|
**kwargs,
|
|
):
|
|
return interleaved_content_as_str(content, sep=config.separator)
|
|
|
|
|
|
async def llm_rag_query_generator(
|
|
config: LLMRAGQueryGeneratorConfig,
|
|
content: InterleavedContent,
|
|
**kwargs,
|
|
):
|
|
assert "inference_api" in kwargs, "LLMRAGQueryGenerator needs inference_api"
|
|
inference_api = kwargs["inference_api"]
|
|
|
|
messages = []
|
|
if isinstance(content, list):
|
|
messages = [interleaved_content_as_str(m) for m in content]
|
|
else:
|
|
messages = [interleaved_content_as_str(content)]
|
|
|
|
template = Template(config.template)
|
|
content = template.render({"messages": messages})
|
|
|
|
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
|