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112 lines
3.2 KiB
Python
112 lines
3.2 KiB
Python
#!/usr/bin/env python3
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# 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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import os
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import uuid
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from termcolor import cprint
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# Set environment variables
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os.environ["INFERENCE_MODEL"] = "llama3.2:3b-instruct-fp16"
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os.environ["LLAMA_STACK_CONFIG"] = "ollama"
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# Import libraries after setting environment variables
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from llama_stack_client.lib.agents.agent import Agent
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from llama_stack_client.lib.agents.event_logger import EventLogger
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from llama_stack_client.types import Document
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from llama_stack_client.types.agent_create_params import AgentConfig
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from llama_stack.distribution.library_client import LlamaStackAsLibraryClient
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def main():
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# Initialize the client
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client = LlamaStackAsLibraryClient("ollama")
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vector_db_id = f"test-vector-db-{uuid.uuid4().hex}"
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_ = client.initialize()
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model_id = "llama3.2:3b-instruct-fp16"
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# Define the list of document URLs and create Document objects
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urls = [
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"chat.rst",
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"llama3.rst",
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"memory_optimizations.rst",
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"lora_finetune.rst",
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]
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documents = [
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Document(
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document_id=f"num-{i}",
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content=f"https://raw.githubusercontent.com/pytorch/torchtune/main/docs/source/tutorials/{url}",
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mime_type="text/plain",
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metadata={},
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)
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for i, url in enumerate(urls)
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]
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# (Optional) Use the documents as needed with your client here
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client.vector_dbs.register(
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provider_id="sqlite_vec",
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vector_db_id=vector_db_id,
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embedding_model="all-MiniLM-L6-v2",
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embedding_dimension=384,
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)
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client.tool_runtime.rag_tool.insert(
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documents=documents,
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vector_db_id=vector_db_id,
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chunk_size_in_tokens=512,
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)
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# Create agent configuration
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agent_config = AgentConfig(
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model=model_id,
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instructions="You are a helpful assistant",
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enable_session_persistence=False,
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toolgroups=[
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{
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"name": "builtin::rag",
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"args": {
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"vector_db_ids": [vector_db_id],
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},
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}
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],
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)
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# Instantiate the Agent
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agent = Agent(client, agent_config)
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# List of user prompts
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user_prompts = [
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"What are the top 5 topics that were explained in the documentation? Only list succinct bullet points.",
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"Was anything related to 'Llama3' discussed, if so what?",
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"Tell me how to use LoRA",
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"What about Quantization?",
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]
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# Create a session for the agent
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session_id = agent.create_session("test-session")
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# Process each prompt and display the output
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for prompt in user_prompts:
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cprint(f"User> {prompt}", "green")
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response = agent.create_turn(
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messages=[
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{
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"role": "user",
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"content": prompt,
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}
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],
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session_id=session_id,
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)
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# Log and print events from the response
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for log in EventLogger().log(response):
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log.print()
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if __name__ == "__main__":
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main()
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