Deep Knowledge

1from textwrap import dedent
2from typing import List, Optional
3
4import inquirer
5import typer
6from kern.agent import Agent
7from kern.db.base import SessionType
8from kern.db.sqlite import SqliteDb
9from kern.knowledge.embedder.openai import OpenAIEmbedder
10from kern.knowledge.knowledge import Knowledge
11from kern.models.openai import OpenAIResponses
12from kern.vectordb.lancedb import LanceDb, SearchType
13from rich import print
14
15
16def initialize_knowledge_base():
17    """Initialize the knowledge base with your preferred documentation or knowledge source
18    Here we use Kern docs as an example, but you can replace with any relevant URLs
19    """
20    agent_knowledge = Knowledge(
21        vector_db=LanceDb(
22            uri="tmp/lancedb",
23            table_name="deep_knowledge_knowledge",
24            search_type=SearchType.hybrid,
25            embedder=OpenAIEmbedder(id="text-embedding-3-small"),
26        ),
27    )
28    agent_knowledge.insert(
29        url="https://kern.ndx.rocks/llms-full.txt",
30    )
31    return agent_knowledge
32
33
34def get_agent_db():
35    """Return agent storage"""
36    return SqliteDb(session_table="deep_knowledge_sessions", db_file="tmp/agents.db")
37
38
39def create_agent(session_id: Optional[str] = None) -> Agent:
40    """Create and return a configured DeepKnowledge agent."""
41    agent_knowledge = initialize_knowledge_base()
42    agent_db = get_agent_db()
43    return Agent(
44        name="DeepKnowledge",
45        session_id=session_id,
46        model=OpenAIResponses(id="gpt-5.2"),
47        description=dedent("""\
48        You are DeepKnowledge, an advanced reasoning agent designed to provide thorough,
49        well-researched answers to any query by searching your knowledge base.
50
51        Your strengths include:
52        - Breaking down complex topics into manageable components
53        - Connecting information across multiple domains
54        - Providing nuanced, well-researched answers
55        - Maintaining intellectual honesty and citing sources
56        - Explaining complex concepts in clear, accessible terms"""),
57        instructions=dedent("""\
58        Your mission is to leave no stone unturned in your pursuit of the correct answer.
59
60        To achieve this, follow these steps:
61        1. **Analyze the input and break it down into key components**.
62        2. **Search terms**: You must identify at least 3-5 key search terms to search for.
63        3. **Initial Search:** Searching your knowledge base for relevant information. You must make atleast 3 searches to get all relevant information.
64        4. **Evaluation:** If the answer from the knowledge base is incomplete, ambiguous, or insufficient - Ask the user for clarification. Do not make informed guesses.
65        5. **Iterative Process:**
66            - Continue searching your knowledge base till you have a comprehensive answer.
67            - Reevaluate the completeness of your answer after each search iteration.
68            - Repeat the search process until you are confident that every aspect of the question is addressed.
69        4. **Reasoning Documentation:** Clearly document your reasoning process:
70            - Note when additional searches were triggered.
71            - Indicate which pieces of information came from the knowledge base and where it was sourced from.
72            - Explain how you reconciled any conflicting or ambiguous information.
73        5. **Final Synthesis:** Only finalize and present your answer once you have verified it through multiple search passes.
74            Include all pertinent details and provide proper references.
75        6. **Continuous Improvement:** If new, relevant information emerges even after presenting your answer,
76            be prepared to update or expand upon your response.
77
78        **Communication Style:**
79        - Use clear and concise language.
80        - Organize your response with numbered steps, bullet points, or short paragraphs as needed.
81        - Be transparent about your search process and cite your sources.
82        - Ensure that your final answer is comprehensive and leaves no part of the query unaddressed.
83
84        Remember: **Do not finalize your answer until every angle of the question has been explored.**"""),
85        additional_context=dedent("""\
86        You should only respond with the final answer and the reasoning process.
87        No need to include irrelevant information.
88
89        - User ID: {user_id}
90        - Memory: You have access to your previous search results and reasoning process.
91        """),
92        knowledge=agent_knowledge,
93        db=agent_db,
94        add_history_to_context=True,
95        num_history_runs=3,
96        read_chat_history=True,
97        markdown=True,
98    )
99
100
101def get_example_topics() -> List[str]:
102    """Return a list of example topics for the agent."""
103    return [
104        "What are AI agents and how do they work in Kern?",
105        "What chunking strategies does Kern support for text processing?",
106        "How can I implement custom tools in Kern?",
107        "How does knowledge retrieval work in Kern?",
108        "What types of embeddings does Kern support?",
109    ]
110
111
112def handle_session_selection() -> Optional[str]:
113    """Handle session selection and return the selected session ID."""
114    agent_db = get_agent_db()
115
116    new = typer.confirm("Do you want to start a new session?", default=True)
117    if new:
118        return None
119
120    existing_sessions: List[str] = agent_db.get_sessions(session_type=SessionType.AGENT)
121    if not existing_sessions:
122        print("No existing sessions found. Starting a new session.")
123        return None
124
125    print("\nExisting sessions:")
126    for i, session in enumerate(existing_sessions, 1):
127        print(f"{i}. {session}")
128
129    session_idx = typer.prompt(
130        "Choose a session number to continue (or press Enter for most recent)",
131        default=1,
132    )
133
134    try:
135        return existing_sessions[int(session_idx) - 1]
136    except (ValueError, IndexError):
137        return existing_sessions[0]
138
139
140def run_interactive_loop(agent: Agent):
141    """Run the interactive question-answering loop."""
142    example_topics = get_example_topics()
143
144    while True:
145        choices = [f"{i + 1}. {topic}" for i, topic in enumerate(example_topics)]
146        choices.extend(["Enter custom question...", "Exit"])
147
148        questions = [
149            inquirer.List(
150                "topic",
151                message="Select a topic or ask a different question:",
152                choices=choices,
153            )
154        ]
155        answer = inquirer.prompt(questions)
156
157        if answer["topic"] == "Exit":
158            break
159
160        if answer["topic"] == "Enter custom question...":
161            questions = [inquirer.Text("custom", message="Enter your question:")]
162            custom_answer = inquirer.prompt(questions)
163            topic = custom_answer["custom"]
164        else:
165            topic = example_topics[int(answer["topic"].split(".")[0]) - 1]
166
167        agent.print_response(topic, stream=True)
168
169
170def deep_knowledge_agent():
171    """Main function to run the DeepKnowledge agent."""
172
173    session_id = handle_session_selection()
174    agent = create_agent(session_id)
175
176    print("\n🤔 Welcome to DeepKnowledge - Your Advanced Research Assistant! 📚")
177    if session_id is None:
178        session_id = agent.session_id
179        if session_id is not None:
180            print(f"[bold green]Started New Session: {session_id}[/bold green]\n")
181        else:
182            print("[bold green]Started New Session[/bold green]\n")
183    else:
184        print(f"[bold blue]Continuing Previous Session: {session_id}[/bold blue]\n")
185
186    run_interactive_loop(agent)
187
188
189if __name__ == "__main__":
190    typer.run(deep_knowledge_agent)
191
192# Example prompts to try:
193"""
194Explore Kern's capabilities with these queries:
1951. "What are the different types of agents in Kern?"
1962. "How does Kern handle knowledge base management?"
1973. "What embedding models does Kern support?"
1984. "How can I implement custom tools in Kern?"
1995. "What storage options are available for workflow caching?"
2006. "How does Kern handle streaming responses?"
2017. "What types of LLM providers does Kern support?"
2028. "How can I implement custom knowledge sources?"
203"""