In diesem Artikel untersuchen wir, wie man KI-Agenten für die Websuche, Finanzanalyse, Argumentation und abrufgestützte Generierung mithilfe von Phidata und dem lokalen LLM von Ollama erstellt. Der Code verwendet das Modell llama3.2. Wenn Sie ein anderes Modell verwenden möchten, müssen Sie das Modell herunterladen, das Sie verwenden möchten, und die Variable model_id im Code ersetzen.
Eine Open-Source-Plattform zum Erstellen, Versenden und Überwachen von Agentensystemen.
https://www.phidata.com/
Ollama ist eine Plattform und ein Toolset, die die Bereitstellung und Verwendung lokaler großer Sprachmodelle (LLMs) vereinfachen sollen.
https://ollama.ai/
In diesem Artikel verwenden wir das Modell llama3.2.
ollama pull llama3.2
Ein extrem schneller Python-Paket- und Projektmanager, geschrieben in Rust.
https://github.com/astral-sh/uv
Wenn Sie kein UV verwenden möchten, können Sie Pip anstelle von UV verwenden. Dann müssen Sie pip install anstelle von uv add verwenden.
https://docs.astral.sh/uv/getting-started/installation/
Wenn Sie sich für die Verwendung von pip entschieden haben, müssen Sie einen Projektordner erstellen.
uv init phidata-ollama
uv add phidata ollama duckduckgo-search yfinance pypdf lancedb tantivy sqlalchemy
In diesem Artikel werden wir versuchen, 5 KI-Agenten mit Phidata und Ollama zu erstellen.
Hinweis: Bevor Sie beginnen, stellen Sie sicher, dass Ihr Ollama-Server läuft, indem Sie „Ollama Serve“ ausführen.
Der erste Agent, den wir erstellen werden, ist ein Websuchagent, der die Suchmaschine DuckDuckGo verwendet.
from phi.agent import Agent
from phi.model.ollama import Ollama
from phi.tools.duckduckgo import DuckDuckGo
model_id = "llama3.2"
model = Ollama(id=model_id)
web_agent = Agent(
name="Web Agent",
model=model,
tools=[DuckDuckGo()],
instructions=["Always include sources"],
show_tool_calls=True,
markdown=True,
)
web_agent.print_response("Tell me about OpenAI Sora?", stream=True)
Ausgabe:
┏━ Message ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ ┃ ┃ ┃ Tell me about OpenAI Sora? ┃ ┃ ┃ ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛ ┏━ Response (12.0s) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ ┃ ┃ ┃ ┃ ┃ • Running: duckduckgo_news(query=OpenAI Sora) ┃ ┃ ┃ ┃ OpenAI's Sora is a video-generating model that has been trained on ┃ ┃ copyrighted content, which has raised concerns about its legality. ┃ ┃ According to TechCrunch, it appears that OpenAI trained Sora on game ┃ ┃ content, which could be a problem. Additionally, MSN reported that the ┃ ┃ model doesn't feel like the game-changer it was supposed to be. ┃ ┃ ┃ ┃ In other news, Yahoo reported that when asked to generate gymnastics ┃ ┃ videos, Sora produces horrorshow videos with whirling and morphing ┃ ┃ limbs. A lawyer told ExtremeTech that it's "overwhelmingly likely" that ┃ ┃ copyrighted materials are included in Sora's training dataset. ┃ ┃ ┃ ┃ Geeky Gadgets reviewed OpenAI's Sora, stating that while it is included ┃ ┃ in the 0/month Pro Plan, its standalone value for video generation ┃ ┃ is less clear compared to other options. ┃ ┃ ┃ ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
Der zweite Agent, den wir erstellen werden, ist ein Finanzagent, der Yfinance-Tools verwenden wird.
from phi.agent import Agent
from phi.model.ollama import Ollama
from phi.tools.yfinance import YFinanceTools
model_id = "llama3.2"
model = Ollama(id=model_id)
finance_agent = Agent(
name="Finance Agent",
model=model,
tools=[YFinanceTools(stock_price=True, analyst_recommendations=True, company_info=True, company_news=True)],
instructions=["Use tables to display data"],
show_tool_calls=True,
markdown=True,
)
finance_agent.print_response("Summarize analyst recommendations for NVDA", stream=True)
Ausgabe:
┏━ Message ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ ┃ ┃ ┃ Summarize analyst recommendations for NVDA ┃ ┃ ┃ ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛ ┏━ Response (3.9s) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ ┃ ┃ ┃ ┃ ┃ • Running: get_analyst_recommendations(symbol=NVDA) ┃ ┃ ┃ ┃ Based on the analyst recommendations, here is a summary: ┃ ┃ ┃ ┃ • The overall sentiment is bullish, with 12 strong buy and buy ┃ ┃ recommendations. ┃ ┃ • There are no strong sell or sell recommendations. ┃ ┃ • The average price target for NVDA is around 0-0. ┃ ┃ • Analysts expect NVDA to continue its growth trajectory, driven by ┃ ┃ its strong products and services in the tech industry. ┃ ┃ ┃ ┃ Please note that these recommendations are subject to change and may ┃ ┃ not reflect the current market situation. It's always a good idea to do ┃ ┃ your own research and consult with a financial advisor before making ┃ ┃ any investment decisions. ┃ ┃ ┃ ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
Der dritte Agent, den wir erstellen werden, ist ein Agententeam, das die Suchmaschine DuckDuckGo und die YFinance-Tools verwendet.
from phi.agent import Agent
from phi.model.ollama import Ollama
from phi.tools.duckduckgo import DuckDuckGo
from phi.tools.yfinance import YFinanceTools
web_instructions = 'Always include sources'
finance_instructions = 'Use tables to display data'
model_id = "llama3.2"
model = Ollama(id=model_id)
web_agent = Agent(
name="Web Agent",
role="Search the web for information",
model=model,
tools=[DuckDuckGo()],
instructions=[web_instructions],
show_tool_calls=True,
markdown=True,
)
finance_agent = Agent(
name="Finance Agent",
role="Get financial data",
model=model,
tools=[YFinanceTools(stock_price=True, analyst_recommendations=True, company_info=True)],
instructions=[finance_instructions],
show_tool_calls=True,
markdown=True,
)
agent_team = Agent(
model=model,
team=[web_agent, finance_agent],
instructions=[web_instructions, finance_instructions],
show_tool_calls=True,
markdown=True,
)
agent_team.print_response("Summarize analyst recommendations and share the latest news for NVDA", stream=True)
Der vierte Agent, den wir erstellen werden, ist ein Inferenzagent, der eine Aufgabe verwendet.
from phi.agent import Agent from phi.model.ollama import Ollama model_id = "llama3.2" model = Ollama(id=model_id) task = ( "Three missionaries and three cannibals want to cross a river." "There is a boat that can carry up to two people, but if the number of cannibals exceeds the number of missionaries, the missionaries will be eaten." ) reasoning_agent = Agent(model=model, reasoning=True, markdown=True, structured_outputs=True) reasoning_agent.print_response(task, stream=True, show_full_reasoning=True)
Ausgabe:
┏━ Message ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ ┃ ┃ ┃ Three missionaries and three cannibals want to cross a river.There is a ┃ ┃ boat that can carry up to two people, but if the number of cannibals ┃ ┃ exceeds the number of missionaries, the missionaries will be eaten. ┃ ┃ ┃ ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛ [Reasoning steps and output as in the original document]
Der fünfte Agent, den wir erstellen werden, ist ein RAG-Agent, der eine PDF-Wissensdatenbank und eine LanceDB-Vektordatenbank verwendet.
from phi.agent import Agent
from phi.model.openai import OpenAIChat
from phi.embedder.openai import OpenAIEmbedder
from phi.embedder.ollama import OllamaEmbedder
from phi.model.ollama import Ollama
from phi.knowledge.pdf import PDFUrlKnowledgeBase
from phi.vectordb.lancedb import LanceDb, SearchType
model_id = "llama3.2"
model = Ollama(id=model_id)
embeddings = OllamaEmbedder().get_embedding("The quick brown fox jumps over the lazy dog.")
knowledge_base = PDFUrlKnowledgeBase(
urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
vector_db=LanceDb(
table_name="recipes",
uri="tmp/lancedb",
search_type=SearchType.vector,
embedder=OllamaEmbedder(),
),
)
knowledge_base.load()
agent = Agent(
model=model,
knowledge=knowledge_base,
show_tool_calls=True,
markdown=True,
)
agent.print_response("Please tell me how to make green curry.", stream=True)
Ausgabe:
uv run rag_agent.py
WARNING model "openhermes" not found, try pulling it first
WARNING model "openhermes" not found, try pulling it first
INFO Creating collection
INFO Loading knowledge base
INFO Reading:
https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf
WARNING model "openhermes" not found, try pulling it first
WARNING model "openhermes" not found, try pulling it first
WARNING model "openhermes" not found, try pulling it first
WARNING model "openhermes" not found, try pulling it first
WARNING model "openhermes" not found, try pulling it first
WARNING model "openhermes" not found, try pulling it first
WARNING model "openhermes" not found, try pulling it first
WARNING model "openhermes" not found, try pulling it first
WARNING model "openhermes" not found, try pulling it first
WARNING model "openhermes" not found, try pulling it first
WARNING model "openhermes" not found, try pulling it first
WARNING model "openhermes" not found, try pulling it first
WARNING model "openhermes" not found, try pulling it first
WARNING model "openhermes" not found, try pulling it first
INFO Added 14 documents to knowledge base
WARNING model "openhermes" not found, try pulling it first
ERROR Error searching for documents: list index out of range
┏━ Message ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ ┃
┃ Please tell me how to make green curry. ┃
┃ ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
┏━ Response (5.4s) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ ┃
┃ ┃
┃ • Running: search_knowledge_base(query=green curry recipe) ┃
┃ ┃
┃ ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ ┃
┃ ┃ Green Curry Recipe ┃ ┃
┃ ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛ ┃
┃ ┃
┃ ** Servings: 4-6 people** ┃
┃ ┃
┃ Ingredients: ┃
┃ ┃
┃ • 2 tablespoons vegetable oil ┃
┃ • 2 cloves garlic, minced ┃
┃ • 1 tablespoon grated fresh ginger ┃
┃ • 2 tablespoons Thai red curry paste ┃
┃ • 2 cups coconut milk ┃
┃ • 1 cup mixed vegetables (such as bell peppers, bamboo shoots, and ┃
┃ Thai eggplant) ┃
┃ • 1 pound boneless, skinless chicken breasts or thighs, cut into ┃
┃ bite-sized pieces ┃
┃ • 2 tablespoons fish sauce ┃
┃ • 1 tablespoon palm sugar ┃
┃ • 1/4 teaspoon ground white pepper ┃
┃ • Salt to taste ┃
┃ • Fresh basil leaves for garnish ┃
┃ ┃
┃ Instructions: ┃
┃ ┃
┃ 1 Prepare the curry paste: In a blender or food processor, combine the ┃
┃ curry paste, garlic, ginger, fish sauce, palm sugar, and white ┃
┃ pepper. Blend until smooth. ┃
┃ 2 Heat oil in a pan: Heat the oil in a large skillet or Dutch oven ┃
┃ over medium-high heat. ┃
┃ 3 Add the curry paste: Pour the blended curry paste into the hot oil ┃
┃ and stir constantly for 1-2 minutes, until fragrant. ┃
┃ 4 Add coconut milk: Pour in the coconut milk and bring the mixture to ┃
┃ a simmer. ┃
┃ 5 Add vegetables and chicken: Add the mixed vegetables and chicken ┃
┃ pieces to the pan. Stir gently to combine. ┃
┃ 6 Reduce heat and cook: Reduce the heat to medium-low and let the ┃
┃ curry simmer, uncovered, for 20-25 minutes or until the chicken is ┃
┃ cooked through and the sauce has thickened. ┃
┃ 7 Season with salt and taste: Season the curry with salt to taste. ┃
┃ Serve hot garnished with fresh basil leaves. ┃
┃ ┃
┃ Tips and Variations: ┃
┃ ┃
┃ • Adjust the level of spiciness by using more or less Thai red curry ┃
┃ paste. ┃
┃ • Add other protein sources like shrimp, tofu, or tempeh for a ┃
┃ vegetarian or vegan option. ┃
┃ • Experiment with different vegetables, such as zucchini or carrots, ┃
┃ to add variety. ┃
┃ ┃
┃ Tools Used: Python ┃
┃ ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
In diesem Artikel haben wir untersucht, wie man mithilfe von Phidata und dem lokalen LLM von Ollama KI-Agenten für die Websuche, Finanzanalyse, Argumentation und abrufgestützte Generierung erstellt.
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