Add application file

assistant.py

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+import json
+from pathlib import Path
+from typing import Optional
+from textwrap import dedent
+from typing import List
+
+from phi.assistant import Assistant
+from phi.tools import Toolkit
+from phi.tools.exa import ExaTools
+from phi.tools.shell import ShellTools
+from phi.tools.calculator import Calculator
+from phi.tools.duckduckgo import DuckDuckGo
+from phi.tools.yfinance import YFinanceTools
+from phi.tools.file import FileTools
+from phi.llm.openai import OpenAIChat
+from phi.knowledge import AssistantKnowledge
+from phi.embedder.openai import OpenAIEmbedder
+from phi.assistant.duckdb import DuckDbAssistant
+from phi.assistant.python import PythonAssistant
+from phi.storage.assistant.postgres import PgAssistantStorage
+from phi.utils.log import logger
+from phi.vectordb.pgvector import PgVector2
+
+db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
+cwd = Path(__file__).parent.resolve()
+scratch_dir = cwd.joinpath("scratch")
+if not scratch_dir.exists():
+    scratch_dir.mkdir(exist_ok=True, parents=True)
+
+
+def get_llm_os(
+    llm_id: str = "gpt-4o",
+    calculator: bool = False,
+    ddg_search: bool = False,
+    file_tools: bool = False,
+    shell_tools: bool = False,
+    data_analyst: bool = False,
+    python_assistant: bool = False,
+    research_assistant: bool = False,
+    investment_assistant: bool = False,
+    user_id: Optional[str] = None,
+    run_id: Optional[str] = None,
+    debug_mode: bool = True,
+) -> Assistant:
+    logger.info(f"-*- Creating {llm_id} LLM OS -*-")
+
+    # Add tools available to the LLM OS
+    tools: List[Toolkit] = []
+    extra_instructions: List[str] = []
+    if calculator:
+        tools.append(
+            Calculator(
+                add=True,
+                subtract=True,
+                multiply=True,
+                divide=True,
+                exponentiate=True,
+                factorial=True,
+                is_prime=True,
+                square_root=True,
+            )
+        )
+    if ddg_search:
+        tools.append(DuckDuckGo(fixed_max_results=3))
+    if shell_tools:
+        tools.append(ShellTools())
+        extra_instructions.append(
+            "You can use the `run_shell_command` tool to run shell commands. For example, `run_shell_command(args='ls')`."
+        )
+    if file_tools:
+        tools.append(FileTools(base_dir=cwd))
+        extra_instructions.append(
+            "You can use the `read_file` tool to read a file, `save_file` to save a file, and `list_files` to list files in the working directory."
+        )
+
+    # Add team members available to the LLM OS
+    team: List[Assistant] = []
+    if data_analyst:
+        _data_analyst = DuckDbAssistant(
+            name="Data Analyst",
+            role="Analyze movie data and provide insights",
+            semantic_model=json.dumps(
+                {
+                    "tables": [
+                        {
+                            "name": "movies",
+                            "description": "CSV of my favorite movies.",
+                            "path": "https://phidata-public.s3.amazonaws.com/demo_data/IMDB-Movie-Data.csv",
+                        }
+                    ]
+                }
+            ),
+            base_dir=scratch_dir,
+        )
+        team.append(_data_analyst)
+        extra_instructions.append(
+            "To answer questions about my favorite movies, delegate the task to the `Data Analyst`."
+        )
+    if python_assistant:
+        _python_assistant = PythonAssistant(
+            name="Python Assistant",
+            role="Write and run python code",
+            pip_install=True,
+            charting_libraries=["streamlit"],
+            base_dir=scratch_dir,
+        )
+        team.append(_python_assistant)
+        extra_instructions.append("To write and run python code, delegate the task to the `Python Assistant`.")
+    if research_assistant:
+        _research_assistant = Assistant(
+            name="Research Assistant",
+            role="Write a research report on a given topic",
+            llm=OpenAIChat(model=llm_id),
+            description="You are a Senior New York Times researcher tasked with writing a cover story research report.",
+            instructions=[
+                "For a given topic, use the `search_exa` to get the top 10 search results.",
+                "Carefully read the results and generate a final - NYT cover story worthy report in the <report_format> provided below.",
+                "Make your report engaging, informative, and well-structured.",
+                "Remember: you are writing for the New York Times, so the quality of the report is important.",
+            ],
+            expected_output=dedent(
+                """\
+            An engaging, informative, and well-structured report in the following format:
+            <report_format>
+            ## Title
+
+            - **Overview** Brief introduction of the topic.
+            - **Importance** Why is this topic significant now?
+
+            ### Section 1
+            - **Detail 1**
+            - **Detail 2**
+
+            ### Section 2
+            - **Detail 1**
+            - **Detail 2**
+
+            ## Conclusion
+            - **Summary of report:** Recap of the key findings from the report.
+            - **Implications:** What these findings mean for the future.
+
+            ## References
+            - [Reference 1](Link to Source)
+            - [Reference 2](Link to Source)
+            </report_format>
+            """
+            ),
+            tools=[ExaTools(num_results=5, text_length_limit=1000)],
+            # This setting tells the LLM to format messages in markdown
+            markdown=True,
+            add_datetime_to_instructions=True,
+            debug_mode=debug_mode,
+        )
+        team.append(_research_assistant)
+        extra_instructions.append(
+            "To write a research report, delegate the task to the `Research Assistant`. "
+            "Return the report in the <report_format> to the user as is, without any additional text like 'here is the report'."
+        )
+    if investment_assistant:
+        _investment_assistant = Assistant(
+            name="Investment Assistant",
+            role="Write a investment report on a given company (stock) symbol",
+            llm=OpenAIChat(model=llm_id),
+            description="You are a Senior Investment Analyst for Goldman Sachs tasked with writing an investment report for a very important client.",
+            instructions=[
+                "For a given stock symbol, get the stock price, company information, analyst recommendations, and company news",
+                "Carefully read the research and generate a final - Goldman Sachs worthy investment report in the <report_format> provided below.",
+                "Provide thoughtful insights and recommendations based on the research.",
+                "When you share numbers, make sure to include the units (e.g., millions/billions) and currency.",
+                "REMEMBER: This report is for a very important client, so the quality of the report is important.",
+            ],
+            expected_output=dedent(
+                """\
+            <report_format>
+            ## [Company Name]: Investment Report
+
+            ### **Overview**
+            {give a brief introduction of the company and why the user should read this report}
+            {make this section engaging and create a hook for the reader}
+
+            ### Core Metrics
+            {provide a summary of core metrics and show the latest data}
+            - Current price: {current price}
+            - 52-week high: {52-week high}
+            - 52-week low: {52-week low}
+            - Market Cap: {Market Cap} in billions
+            - P/E Ratio: {P/E Ratio}
+            - Earnings per Share: {EPS}
+            - 50-day average: {50-day average}
+            - 200-day average: {200-day average}
+            - Analyst Recommendations: {buy, hold, sell} (number of analysts)
+
+            ### Financial Performance
+            {analyze the company's financial performance}
+
+            ### Growth Prospects
+            {analyze the company's growth prospects and future potential}
+
+            ### News and Updates
+            {summarize relevant news that can impact the stock price}
+
+            ### [Summary]
+            {give a summary of the report and what are the key takeaways}
+
+            ### [Recommendation]
+            {provide a recommendation on the stock along with a thorough reasoning}
+
+            </report_format>
+            """
+            ),
+            tools=[YFinanceTools(stock_price=True, company_info=True, analyst_recommendations=True, company_news=True)],
+            # This setting tells the LLM to format messages in markdown
+            markdown=True,
+            add_datetime_to_instructions=True,
+            debug_mode=debug_mode,
+        )
+        team.append(_investment_assistant)
+        extra_instructions.extend(
+            [
+                "To get an investment report on a stock, delegate the task to the `Investment Assistant`. "
+                "Return the report in the <report_format> to the user without any additional text like 'here is the report'.",
+                "Answer any questions they may have using the information in the report.",
+                "Never provide investment advise without the investment report.",
+            ]
+        )
+
+    # Create the LLM OS Assistant
+    llm_os = Assistant(
+        name="llm_os",
+        run_id=run_id,
+        user_id=user_id,
+        llm=OpenAIChat(model=llm_id),
+        description=dedent(
+            """\
+        You are the most advanced AI system in the world called `LLM-OS`.
+        You have access to a set of tools and a team of AI Assistants at your disposal.
+        Your goal is to assist the user in the best way possible.\
+        """
+        ),
+        instructions=[
+            "When the user sends a message, first **think** and determine if:\n"
+            " - You can answer by using a tool available to you\n"
+            " - You need to search the knowledge base\n"
+            " - You need to search the internet\n"
+            " - You need to delegate the task to a team member\n"
+            " - You need to ask a clarifying question",
+            "If the user asks about a topic, first ALWAYS search your knowledge base using the `search_knowledge_base` tool.",
+            "If you dont find relevant information in your knowledge base, use the `duckduckgo_search` tool to search the internet.",
+            "If the user asks to summarize the conversation or if you need to reference your chat history with the user, use the `get_chat_history` tool.",
+            "If the users message is unclear, ask clarifying questions to get more information.",
+            "Carefully read the information you have gathered and provide a clear and concise answer to the user.",
+            "Do not use phrases like 'based on my knowledge' or 'depending on the information'.",
+            "You can delegate tasks to an AI Assistant in your team depending of their role and the tools available to them.",
+        ],
+        extra_instructions=extra_instructions,
+        # Add long-term memory to the LLM OS backed by a PostgreSQL database
+        storage=PgAssistantStorage(table_name="llm_os_runs", db_url=db_url),
+        # Add a knowledge base to the LLM OS
+        knowledge_base=AssistantKnowledge(
+            vector_db=PgVector2(
+                db_url=db_url,
+                collection="llm_os_documents",
+                embedder=OpenAIEmbedder(model="text-embedding-3-small", dimensions=1536),
+            ),
+            # 3 references are added to the prompt when searching the knowledge base
+            num_documents=3,
+        ),
+        # Add selected tools to the LLM OS
+        tools=tools,
+        # Add selected team members to the LLM OS
+        team=team,
+        # Show tool calls in the chat
+        show_tool_calls=True,
+        # This setting gives the LLM a tool to search the knowledge base for information
+        search_knowledge=True,
+        # This setting gives the LLM a tool to get chat history
+        read_chat_history=True,
+        # This setting adds chat history to the messages
+        add_chat_history_to_messages=True,
+        # This setting adds 6 previous messages from chat history to the messages sent to the LLM
+        num_history_messages=6,
+        # This setting tells the LLM to format messages in markdown
+        markdown=True,
+        # This setting adds the current datetime to the instructions
+        add_datetime_to_instructions=True,
+        # Add an introductory Assistant message
+        introduction=dedent(
+            """\
+        Hi, I'm your LLM OS.
+        I have access to a set of tools and AI Assistants to assist you.
+        Let's solve some problems together!\
+        """
+        ),
+        debug_mode=debug_mode,
+    )
+    return llm_os