ollamatools-phi-1991

cookbook/providers/ollama_tools/README.md

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+# OllamaTools Cookbook
+
+> Note: Fork and clone this repository if needed
+
+### 1. [Install](https://github.com/ollama/ollama?tab=readme-ov-file#macos) ollama and run models
+
+Run your chat model
+
+```shell
+ollama run llama3.2
+```
+
+Message `/bye` to exit the chat model
+
+### 2. Create and activate a virtual environment
+
+```shell
+python3 -m venv ~/.venvs/aienv
+source ~/.venvs/aienv/bin/activate
+```
+
+### 3. Install libraries
+
+```shell
+pip install -U ollama duckduckgo-search duckdb yfinance phidata
+```
+
+### 4. Run Agent without Tools
+
+- Streaming on
+
+```shell
+python cookbook/providers/ollama_tools/basic_stream.py
+```
+
+- Streaming off
+
+```shell
+python cookbook/providers/ollama_tools/basic.py
+```
+
+### 5. Run Agent with Tools
+
+- Yahoo Finance with streaming on
+
+```shell
+python cookbook/providers/ollama_tools/agent_stream.py
+```
+
+- Yahoo Finance without streaming
+
+```shell
+python cookbook/providers/ollama_tools/agent.py
+```
+
+- Finance Agent
+
+```shell
+python cookbook/providers/ollama_tools/finance_agent.py
+```
+
+- Data Analyst
+
+```shell
+python cookbook/providers/ollama_tools/data_analyst.py
+```
+
+- Web Search
+
+```shell
+python cookbook/providers/ollama_tools/web_search.py
+```
+
+### 6. Run Agent that returns structured output
+
+```shell
+python cookbook/providers/ollama_tools/structured_output.py
+```
+
+### 7. Run Agent that uses storage
+
+```shell
+python cookbook/providers/ollama_tools/storage.py
+```
+
+### 8. Run Agent that uses knowledge
+
+```shell
+python cookbook/providers/ollama_tools/knowledge.py
+```

cookbook/providers/ollama_tools/agent.py

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+"""Run `pip install yfinance` to install dependencies."""
+
+from phi.agent import Agent, RunResponse  # noqa
+from phi.model.ollama import OllamaTools
+from phi.tools.yfinance import YFinanceTools
+
+agent = Agent(
+    model=OllamaTools(id="llama3.2"),
+    tools=[YFinanceTools(stock_price=True)],
+    show_tool_calls=True,
+    markdown=True,
+)
+
+# Get the response in a variable
+# run: RunResponse = agent.run("What is the stock price of NVDA and TSLA")
+# print(run.content)
+
+# Print the response in the terminal
+agent.print_response("What is the stock price of NVDA and TSLA")

cookbook/providers/ollama_tools/agent_stream.py

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+"""Run `pip install yfinance` to install dependencies."""
+
+from typing import Iterator  # noqa
+from phi.agent import Agent, RunResponse  # noqa
+from phi.model.ollama import OllamaTools
+from phi.tools.yfinance import YFinanceTools
+
+agent = Agent(
+    model=OllamaTools(id="llama3.2"),
+    tools=[YFinanceTools(stock_price=True)],
+    instructions=["Use tables where possible."],
+    markdown=True,
+    show_tool_calls=True,
+)
+
+# Get the response in a variable
+# run_response: Iterator[RunResponse] = agent.run("What is the stock price of NVDA and TSLA", stream=True)
+# for chunk in run_response:
+#     print(chunk.content)
+
+# Print the response in the terminal
+agent.print_response("What is the stock price of NVDA and TSLA", stream=True)

cookbook/providers/ollama_tools/agent_team.py

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+from phi.agent import Agent
+from phi.model.ollama import OllamaTools
+from phi.tools.duckduckgo import DuckDuckGo
+from phi.tools.yfinance import YFinanceTools
+
+web_agent = Agent(
+    name="Web Agent",
+    role="Search the web for information",
+    model=OllamaTools(id="llama3.2"),
+    tools=[DuckDuckGo()],
+    instructions=["Always include sources"],
+    show_tool_calls=True,
+    markdown=True,
+)
+
+finance_agent = Agent(
+    name="Finance Agent",
+    role="Get financial data",
+    model=OllamaTools(id="llama3.2"),
+    tools=[YFinanceTools(stock_price=True, analyst_recommendations=True, company_info=True)],
+    instructions=["Use tables to display data"],
+    show_tool_calls=True,
+    markdown=True,
+)
+
+agent_team = Agent(
+    team=[web_agent, finance_agent],
+    instructions=["Always include sources", "Use tables to display data"],
+    show_tool_calls=True,
+    markdown=True,
+)
+
+agent_team.print_response("Summarize analyst recommendations and share the latest news for NVDA", stream=True)

cookbook/providers/ollama_tools/basic.py

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+from phi.agent import Agent, RunResponse  # noqa
+from phi.model.ollama import OllamaTools
+
+agent = Agent(model=OllamaTools(id="llama3.2"), markdown=True)
+
+# Get the response in a variable
+# run: RunResponse = agent.run("Share a 2 sentence horror story")
+# print(run.content)
+
+# Print the response in the terminal
+agent.print_response("Share a 2 sentence horror story")

cookbook/providers/ollama_tools/basic_stream.py

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+from typing import Iterator  # noqa
+from phi.agent import Agent, RunResponse  # noqa
+from phi.model.ollama import OllamaTools
+
+agent = Agent(model=OllamaTools(id="llama3.2"), markdown=True)
+
+# Get the response in a variable
+# run_response: Iterator[RunResponse] = agent.run("Share a 2 sentence horror story", stream=True)
+# for chunk in run_response:
+#     print(chunk.content)
+
+# Print the response in the terminal
+agent.print_response("Share a 2 sentence horror story", stream=True)

cookbook/providers/ollama_tools/data_analyst.py

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+"""Run `pip install duckdb` to install dependencies."""
+
+from textwrap import dedent
+from phi.agent import Agent
+from phi.model.ollama import OllamaTools
+from phi.tools.duckdb import DuckDbTools
+
+duckdb_tools = DuckDbTools(create_tables=False, export_tables=False, summarize_tables=False)
+duckdb_tools.create_table_from_path(
+    path="https://phidata-public.s3.amazonaws.com/demo_data/IMDB-Movie-Data.csv", table="movies"
+)
+
+agent = Agent(
+    model=OllamaTools(id="llama3.2"),
+    tools=[duckdb_tools],
+    markdown=True,
+    show_tool_calls=True,
+    additional_context=dedent("""\
+    You have access to the following tables:
+    - movies: contains information about movies from IMDB.
+    """),
+)
+agent.print_response("What is the average rating of movies?", stream=False)

cookbook/providers/ollama_tools/finance_agent.py

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+"""Run `pip install yfinance` to install dependencies."""
+
+from phi.agent import Agent
+from phi.model.ollama import OllamaTools
+from phi.tools.yfinance import YFinanceTools
+
+agent = Agent(
+    model=OllamaTools(id="llama3.2"),
+    tools=[YFinanceTools(stock_price=True, analyst_recommendations=True, stock_fundamentals=True)],
+    show_tool_calls=True,
+    description="You are an investment analyst that researches stocks and helps users make informed decisions.",
+    instructions=["Use tables to display data where possible."],
+    markdown=True,
+)
+
+# agent.print_response("Share the NVDA stock price and analyst recommendations", stream=True)
+agent.print_response("Summarize fundamentals for TSLA", stream=True)

cookbook/providers/ollama_tools/knowledge.py

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+"""Run `pip install duckduckgo-search sqlalchemy pgvector pypdf openai ollama` to install dependencies."""
+
+from phi.agent import Agent
+from phi.model.ollama import OllamaTools
+from phi.knowledge.pdf import PDFUrlKnowledgeBase
+from phi.vectordb.pgvector import PgVector
+
+db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
+
+knowledge_base = PDFUrlKnowledgeBase(
+    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+    vector_db=PgVector(table_name="recipes", db_url=db_url),
+)
+knowledge_base.load(recreate=False)  # Comment out after first run
+
+agent = Agent(
+    model=OllamaTools(id="llama3.2"),
+    knowledge_base=knowledge_base,
+    use_tools=True,
+    show_tool_calls=True,
+)
+agent.print_response("How to make Thai curry?", markdown=True)

cookbook/providers/ollama_tools/storage.py

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+"""Run `pip install duckduckgo-search sqlalchemy ollama` to install dependencies."""
+
+from phi.agent import Agent
+from phi.model.ollama import OllamaTools
+from phi.tools.duckduckgo import DuckDuckGo
+from phi.storage.agent.postgres import PgAgentStorage
+
+db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
+
+agent = Agent(
+    model=OllamaTools(id="llama3.2"),
+    storage=PgAgentStorage(table_name="agent_sessions", db_url=db_url),
+    tools=[DuckDuckGo()],
+    add_history_to_messages=True,
+)
+agent.print_response("How many people live in Canada?")
+agent.print_response("What is their national anthem called?")

cookbook/providers/ollama_tools/structured_output.py

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+from typing import List
+from rich.pretty import pprint  # noqa
+from pydantic import BaseModel, Field
+from phi.agent import Agent, RunResponse  # noqa
+from phi.model.ollama import OllamaTools
+
+
+class MovieScript(BaseModel):
+    setting: str = Field(..., description="Provide a nice setting for a blockbuster movie.")
+    ending: str = Field(..., description="Ending of the movie. If not available, provide a happy ending.")
+    genre: str = Field(
+        ..., description="Genre of the movie. If not available, select action, thriller or romantic comedy."
+    )
+    name: str = Field(..., description="Give a name to this movie")
+    characters: List[str] = Field(..., description="Name of characters for this movie.")
+    storyline: str = Field(..., description="3 sentence storyline for the movie. Make it exciting!")
+
+
+# Agent that uses JSON mode
+movie_agent = Agent(
+    model=OllamaTools(id="llama3.2"),
+    description="You write movie scripts.",
+    response_model=MovieScript,
+)
+
+# Get the response in a variable
+# run: RunResponse = movie_agent.run("New York")
+# pprint(run.content)
+
+movie_agent.print_response("New York")

cookbook/providers/ollama_tools/web_search.py

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+"""Run `pip install duckduckgo-search` to install dependencies."""
+
+from phi.agent import Agent
+from phi.model.ollama import OllamaTools
+from phi.tools.duckduckgo import DuckDuckGo
+
+agent = Agent(model=OllamaTools(id="llama3.2"), tools=[DuckDuckGo()], show_tool_calls=True, markdown=True)
+agent.print_response("Whats happening in France?", stream=True)

phi/model/ollama/__init__.py

@@ -1,2 +1,3 @@
 from phi.model.ollama.chat import Ollama
 from phi.model.ollama.hermes import Hermes
+from phi.model.ollama.tools import OllamaTools