mirror of
https://github.com/meta-llama/llama-stack.git
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91 lines
2.8 KiB
Python
91 lines
2.8 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from typing import List
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from jinja2 import Template
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from llama_models.llama3.api import * # noqa: F403
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from termcolor import cprint
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from llama_toolchain.agentic_system.api import (
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DefaultMemoryQueryGeneratorConfig,
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LLMMemoryQueryGeneratorConfig,
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MemoryQueryGenerator,
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MemoryQueryGeneratorConfig,
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)
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from llama_toolchain.inference.api import * # noqa: F403
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from llama_toolchain.inference.client import InferenceClient
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async def generate_rag_query(
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generator_config: MemoryQueryGeneratorConfig,
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messages: List[Message],
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):
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if generator_config.type == MemoryQueryGenerator.default.value:
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generator = DefaultRAGQueryGenerator(generator_config)
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elif generator_config.type == MemoryQueryGenerator.llm.value:
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generator = LLMRAGQueryGenerator(generator_config)
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else:
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raise NotImplementedError(
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f"Unsupported memory query generator {generator_config.type}"
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)
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query = await generator.gen(messages)
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cprint(f"Generated query >>>: {query}", color="green")
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return query
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class DefaultRAGQueryGenerator:
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def __init__(self, config: DefaultMemoryQueryGeneratorConfig):
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self.config = config
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async def gen(self, messages: List[Message]) -> InterleavedTextMedia:
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query = self.config.sep.join(
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interleaved_text_media_as_str(m.content) for m in messages
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)
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return query
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class LLMRAGQueryGenerator:
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def __init__(self, config: LLMMemoryQueryGeneratorConfig):
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self.config = config
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async def gen(self, messages: List[Message]) -> InterleavedTextMedia:
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# params will have
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"""
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Generates a query that will be used for
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retrieving relevant information from the memory bank.
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"""
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# get template from user
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# user template will assume data has the format of
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# pydantic object representing List[Message]
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m_dict = {"messages": [m.model_dump() for m in messages]}
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template = Template(self.config.template)
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content = template.render(m_dict)
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cprint(f"Rendered Template >>>: {content}", color="yellow")
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# TODO: How to manage these config params better ?
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host = self.config.host
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port = self.config.port
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client = InferenceClient(f"http://{host}:{port}")
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model = self.config.model
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message = UserMessage(content=content)
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response = client.chat_completion(
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ChatCompletionRequest(
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model=model,
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messages=[message],
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stream=False,
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)
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)
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async for chunk in response:
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query = chunk.completion_message.content
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return query
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