v1.0.6

cookbook/examples/agents/deep_knowledge.py

@@ -4,132 +4,206 @@
 queries into sub-questions, and synthesizing comprehensive answers. It's designed to explore
 topics deeply and thoroughly by following chains of reasoning.
 
-In this example, the agent uses the Agno documentation as a knowledge base and
-DuckDuckGo to answer questions from the internet.
+In this example, the agent uses the Agno documentation as a knowledge base
 
 Key Features:
-- Uses both internal knowledge base and web search
-- Iterative search refinement
+- Iteratively searches a knowledge base
 - Source attribution and citations
 
-Run `pip install openai lancedb tantivy duckduckgo-search inquirer agno` to install dependencies.
+Run `pip install openai lancedb tantivy inquirer agno` to install dependencies.
 """
 
 from textwrap import dedent
+from typing import List, Optional
 
+import inquirer
+import typer
 from agno.agent import Agent
 from agno.embedder.openai import OpenAIEmbedder
 from agno.knowledge.url import UrlKnowledge
 from agno.models.openai import OpenAIChat
-from agno.tools.duckduckgo import DuckDuckGoTools
-from agno.utils.pprint import pprint_run_response
+from agno.storage.agent.sqlite import SqliteAgentStorage
 from agno.vectordb.lancedb import LanceDb, SearchType
-
-# Initialize the knowledge base with your preferred documentation or knowledge source
-# Here we use Agno docs as an example, but you can replace with any relevant URLs
-knowledge = UrlKnowledge(
-    urls=["https://docs.agno.com/llms-full.txt"],
-    vector_db=LanceDb(
-        uri="tmp/lancedb",
-        table_name="knowledge_base",
-        search_type=SearchType.hybrid,
-        embedder=OpenAIEmbedder(id="text-embedding-3-small"),
-    ),
-)
-# Load the knowledge base (comment out after first run)
-# knowledge.load()
-
-# Create the DeepKnowledge agent
-agent = Agent(
-    model=OpenAIChat(id="gpt-4o"),
-    description=dedent("""\
-    You are DeepKnowledge, an advanced reasoning agent designed to provide thorough,
-    well-researched answers to any query by searching your knowledge base and the web.
-
-    Your strengths include:
-    - Breaking down complex topics into manageable components
-    - Connecting information across multiple domains
-    - Providing nuanced, well-researched answers
-    - Maintaining intellectual honesty and citing sources
-    - Explaining complex concepts in clear, accessible terms"""),
-    instructions=dedent("""\
-    Your mission is to leave no stone unturned in your pursuit of the correct answer.
-
-    To achieve this, follow these steps:
-    1. **Analyze the question and break it down into key components**. You MUST identify at least 3-5 key components to search for.
-    2. **Initial Search:** Searching your knowledge base for relevant information. You must make multiple search queries to get all relevant information.
-    3. **Evaluation:** If the answer from the knowledge base is incomplete, ambiguous, or insufficient,
-        do not settle. Instead, proceed to conduct a web search to gather additional context and data.
-    4. **Iterative Process:**
-        - Continue alternating between your knowledge base and web searches.
-        - Reevaluate the completeness of your answer after each search iteration.
-        - Repeat the search process until you are confident that every aspect of the question is addressed.
-    4. **Reasoning Documentation:** Clearly document your reasoning process:
-        - Note when additional searches were triggered.
-        - Indicate which pieces of information came from the knowledge base versus web sources.
-        - Explain how you reconciled any conflicting or ambiguous information.
-    5. **Final Synthesis:** Only finalize and present your answer once you have verified it through multiple search passes.
-        Include all pertinent details and provide proper references for both internal and web-sourced data.
-    6. **Continuous Improvement:** If new, relevant information emerges even after presenting your answer,
-        be prepared to update or expand upon your response.
-
-    **Communication Style:**
-    - Use clear and concise language.
-    - Organize your response with numbered steps, bullet points, or short paragraphs as needed.
-    - Be transparent about your search process and cite your sources.
-    - Ensure that your final answer is comprehensive and leaves no part of the query unaddressed.
-
-    Remember: **Do not finalize your answer until every angle of the question has been explored.**"""),
-    knowledge=knowledge,
-    tools=[DuckDuckGoTools()],
-    show_tool_calls=True,
-    markdown=True,
-)
-
-# Run the agent if the script is executed directly
-if __name__ == "__main__":
-    import inquirer
-
-    # Example Agno-related questions
-    example_topics = [
+from rich import print
+
+
+def initialize_knowledge_base():
+    """Initialize the knowledge base with your preferred documentation or knowledge source
+    Here we use Agno docs as an example, but you can replace with any relevant URLs
+    """
+    agent_knowledge = UrlKnowledge(
+        urls=["https://docs.agno.com/llms-full.txt"],
+        vector_db=LanceDb(
+            uri="tmp/lancedb",
+            table_name="deep_knowledge_knowledge",
+            search_type=SearchType.hybrid,
+            embedder=OpenAIEmbedder(id="text-embedding-3-small"),
+        ),
+    )
+    # Load the knowledge base (comment out after first run)
+    agent_knowledge.load()
+    return agent_knowledge
+
+
+def get_agent_storage():
+    """Return agent storage"""
+    return SqliteAgentStorage(
+        table_name="deep_knowledge_sessions", db_file="tmp/agents.db"
+    )
+
+
+def create_agent(session_id: Optional[str] = None) -> Agent:
+    """Create and return a configured DeepKnowledge agent."""
+    agent_knowledge = initialize_knowledge_base()
+    agent_storage = get_agent_storage()
+    return Agent(
+        name="DeepKnowledge",
+        session_id=session_id,
+        model=OpenAIChat(id="gpt-4o"),
+        description=dedent("""\
+        You are DeepKnowledge, an advanced reasoning agent designed to provide thorough,
+        well-researched answers to any query by searching your knowledge base.
+
+        Your strengths include:
+        - Breaking down complex topics into manageable components
+        - Connecting information across multiple domains
+        - Providing nuanced, well-researched answers
+        - Maintaining intellectual honesty and citing sources
+        - Explaining complex concepts in clear, accessible terms"""),
+        instructions=dedent("""\
+        Your mission is to leave no stone unturned in your pursuit of the correct answer.
+
+        To achieve this, follow these steps:
+        1. **Analyze the input and break it down into key components**.
+        2. **Search terms**: You must identify at least 3-5 key search terms to search for.
+        3. **Initial Search:** Searching your knowledge base for relevant information. You must make atleast 3 searches to get all relevant information.
+        4. **Evaluation:** If the answer from the knowledge base is incomplete, ambiguous, or insufficient - Ask the user for clarification. Do not make informed guesses.
+        5. **Iterative Process:**
+            - Continue searching your knowledge base till you have a comprehensive answer.
+            - Reevaluate the completeness of your answer after each search iteration.
+            - Repeat the search process until you are confident that every aspect of the question is addressed.
+        4. **Reasoning Documentation:** Clearly document your reasoning process:
+            - Note when additional searches were triggered.
+            - Indicate which pieces of information came from the knowledge base and where it was sourced from.
+            - Explain how you reconciled any conflicting or ambiguous information.
+        5. **Final Synthesis:** Only finalize and present your answer once you have verified it through multiple search passes.
+            Include all pertinent details and provide proper references.
+        6. **Continuous Improvement:** If new, relevant information emerges even after presenting your answer,
+            be prepared to update or expand upon your response.
+
+        **Communication Style:**
+        - Use clear and concise language.
+        - Organize your response with numbered steps, bullet points, or short paragraphs as needed.
+        - Be transparent about your search process and cite your sources.
+        - Ensure that your final answer is comprehensive and leaves no part of the query unaddressed.
+
+        Remember: **Do not finalize your answer until every angle of the question has been explored.**"""),
+        additional_context=dedent("""\
+        You should only respond with the final answer and the reasoning process.
+        No need to include irrelevant information.
+
+        - User ID: {user_id}
+        - Memory: You have access to your previous search results and reasoning process.
+        """),
+        knowledge=agent_knowledge,
+        storage=agent_storage,
+        add_history_to_messages=True,
+        num_history_responses=3,
+        show_tool_calls=True,
+        read_chat_history=True,
+        markdown=True,
+    )
+
+
+def get_example_topics() -> List[str]:
+    """Return a list of example topics for the agent."""
+    return [
         "What are AI agents and how do they work in Agno?",
         "What chunking strategies does Agno support for text processing?",
-        "Which vector databases can I use with Agno?",
         "How does knowledge retrieval work in Agno?",
         "What types of embeddings does Agno support?",
     ]
 
-    # Create choices including numbered topics and custom option
-    choices = [f"{i + 1}. {topic}" for i, topic in enumerate(example_topics)]
-    choices.append("Enter custom question...")
-
-    # Create and show the selection prompt
-    questions = [
-        inquirer.List(
-            "topic",
-            message="Select a topic or ask a different question:",
-            choices=choices,
-        )
-    ]
-    answer = inquirer.prompt(questions)
 
-    # Handle custom input or selection
-    if answer["topic"] == "Enter question...":
+def handle_session_selection() -> Optional[str]:
+    """Handle session selection and return the selected session ID."""
+    agent_storage = get_agent_storage()
+
+    new = typer.confirm("Do you want to start a new session?", default=True)
+    if new:
+        return None
+
+    existing_sessions: List[str] = agent_storage.get_all_session_ids()
+    if not existing_sessions:
+        print("No existing sessions found. Starting a new session.")
+        return None
+
+    print("\nExisting sessions:")
+    for i, session in enumerate(existing_sessions, 1):
+        print(f"{i}. {session}")
+
+    session_idx = typer.prompt(
+        "Choose a session number to continue (or press Enter for most recent)",
+        default=1,
+    )
+
+    try:
+        return existing_sessions[int(session_idx) - 1]
+    except (ValueError, IndexError):
+        return existing_sessions[0]
+
+
+def run_interactive_loop(agent: Agent):
+    """Run the interactive question-answering loop."""
+    example_topics = get_example_topics()
+
+    while True:
+        choices = [f"{i + 1}. {topic}" for i, topic in enumerate(example_topics)]
+        choices.extend(["Enter custom question...", "Exit"])
+
         questions = [
-            inquirer.Text(
-                "custom",
-                message="Enter your question:",
-                default="What are AI agents and how do they work in Agno?",
+            inquirer.List(
+                "topic",
+                message="Select a topic or ask a different question:",
+                choices=choices,
             )
         ]
-        custom_answer = inquirer.prompt(questions)
-        topic = custom_answer["custom"]
+        answer = inquirer.prompt(questions)
+
+        if answer["topic"] == "Exit":
+            break
+
+        if answer["topic"] == "Enter custom question...":
+            questions = [inquirer.Text("custom", message="Enter your question:")]
+            custom_answer = inquirer.prompt(questions)
+            topic = custom_answer["custom"]
+        else:
+            topic = example_topics[int(answer["topic"].split(".")[0]) - 1]
+
+        agent.print_response(topic, stream=True)
+
+
+def deep_knowledge_agent():
+    """Main function to run the DeepKnowledge agent."""
+
+    session_id = handle_session_selection()
+    agent = create_agent(session_id)
+
+    print("\n🤔 Welcome to DeepKnowledge - Your Advanced Research Assistant! 📚")
+    if session_id is None:
+        session_id = agent.session_id
+        if session_id is not None:
+            print(f"[bold green]Started New Session: {session_id}[/bold green]\n")
+        else:
+            print("[bold green]Started New Session[/bold green]\n")
     else:
-        # Extract the actual topic from the numbered choice
-        topic = example_topics[int(answer["topic"].split(".")[0]) - 1]
+        print(f"[bold blue]Continuing Previous Session: {session_id}[/bold blue]\n")
+
+    run_interactive_loop(agent)
 
-    # Print the response
-    pprint_run_response(agent.run(topic, stream=True), markdown=True)
+
+if __name__ == "__main__":
+    typer.run(deep_knowledge_agent)
 
 # Example prompts to try:
 """

libs/agno/pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "agno"
-version = "1.0.5"
+version = "1.0.6"
 description = "Agno: a lightweight framework for building multi-modal Agents"
 requires-python = ">=3.7,<4"
 readme = "README.md"