Find out how to Construct an AI Journal with LlamaIndex

will share easy methods to construct an AI journal with the LlamaIndex. We are going to cowl one important operate of this AI journal: asking for recommendation. We are going to begin with essentially the most primary implementation and iterate from there. We will see vital enhancements for this operate after we apply design patterns like Agentic Rag and multi-agent workflow.

You could find the supply code of this AI Journal in my GitHub repo right here. And about who I’m.

Overview of AI Journal

I wish to construct my rules by following Ray Dalio’s observe. An AI journal will assist me to self-reflect, monitor my enchancment, and even give me recommendation. The general operate of such an AI journal seems like this:

Right now, we are going to solely cowl the implementation of the seek-advise circulation, which is represented by a number of purple cycles within the above diagram.

Easiest Type: LLM with Massive Context

In essentially the most easy implementation, we will move all of the related content material into the context and connect the query we wish to ask. We will do this in Llamaindex with just a few traces of code.

import pymupdf
from llama_index.llms.openai import OpenAI

path_to_pdf_book = './path/to/pdf/e book.pdf'
def load_book_content():
    textual content = ""
    with pymupdf.open(path_to_pdf_book) as pdf:
        for web page in pdf:
            textual content += str(web page.get_text().encode("utf8", errors='ignore'))
    return textual content

system_prompt_template = """You might be an AI assistant that gives considerate, sensible, and *deeply personalised* recommendations by combining:
- The consumer's private profile and rules
- Insights retrieved from *Ideas* by Ray Dalio
E book Content material: 
```
{book_content}
```
Person profile:
```
{user_profile}
```
Person's query:
```
{user_question}
```
"""

def get_system_prompt(book_content: str, user_profile: str, user_question: str):
    system_prompt = system_prompt_template.format(
        book_content=book_content,
        user_profile=user_profile,
        user_question=user_question
    )
    return system_prompt

def chat():
    llm = get_openai_llm()
    user_profile = enter(">>Inform me about your self: ")
    user_question = enter(">>What do you wish to ask: ")
    user_profile = user_profile.strip()
    book_content = load_book_summary()
    response = llm.full(immediate=get_system_prompt(book_content, user_profile, user_question))
    return response

This method has downsides:

• Low Precision: Loading all of the e book context may immediate LLM to lose give attention to the consumer’s query.
• Excessive Price: Sending over significant-sized content material in each LLM name means excessive value and poor efficiency.

With this method, should you move the entire content material of Ray Dalio’s Ideas e book, responses to questions like “Find out how to deal with stress?” develop into very common. Such responses with out referring to my query made me really feel that the AI was not listening to me. Regardless that it covers many vital ideas like embracing actuality, the 5-step course of to get what you need, and being radically open-minded. I like the recommendation I received to be extra focused to the query I raised. Let’s see how we will enhance it with RAG.

Enhanced Type: Agentic RAG

So, what’s Agentic RAG? Agentic RAG is combining dynamic decision-making and information retrieval. In our AI journal, the Agentic RAG circulation seems like this:

• Query Analysis: Poorly framed questions result in poor question outcomes. The agent will consider the consumer’s question and make clear the questions if the Agent believes it’s vital.
• Query Re-write: Rewrite the consumer enquiry to undertaking it to the listed content material within the semantic area. I discovered these steps important for enhancing the precision throughout the retrieval. Let’s say in case your information base is Q/A pair and you’re indexing the questions half to seek for solutions. Rewriting the consumer’s question assertion to a correct query will assist you discover essentially the most related content material.
• Question Vector Index: Many parameters could be tuned when constructing such an index, together with chunk measurement, overlap, or a unique index sort. For simplicity, we’re utilizing VectorStoreIndex right here, which has a default chunking technique.
• Filter & Artificial: As a substitute of a fancy re-ranking course of, I explicitly instruct LLM to filter and discover related content material within the immediate. I see LLM selecting up essentially the most related content material, though typically it has a decrease similarity rating than others.

With this Agentic RAG, you may retrieve extremely related content material to the consumer’s questions, producing extra focused recommendation.

Let’s look at the implementation. With the LlamaIndex SDK, creating and persisting an index in your native listing is simple.

from llama_index.core import Doc, VectorStoreIndex, StorageContext, load_index_from_storage

Settings.embed_model = OpenAIEmbedding(api_key="ak-xxxx")
PERSISTED_INDEX_PATH = "/path/to/the/listing/persist/index/domestically"

def create_index(content material: str):
    paperwork = [Document(text=content)]
    vector_index = VectorStoreIndex.from_documents(paperwork)
    vector_index.storage_context.persist(persist_dir=PERSISTED_INDEX_PATH)

def load_index():
    storage_context = StorageContext.from_defaults(persist_dir=PERSISTED_INDEX_PATH)
    index = load_index_from_storage(storage_context)
    return index

As soon as we now have an index, we will create a question engine on high of that. The question engine is a robust abstraction that means that you can modify the parameters throughout the question(e.g., TOP Okay) and the synthesis behaviour after the content material retrieval. In my implementation, I overwrite the response_mode NO_TEXT as a result of the agent will course of the e book content material returned by the operate name and synthesize the ultimate consequence. Having the question engine to synthesize the consequence earlier than passing it to the agent can be redundant.

from llama_index.core.indices.vector_store import VectorIndexRetriever
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.core.response_synthesizers import ResponseMode
from llama_index.core import  VectorStoreIndex, get_response_synthesizer

def _create_query_engine_from_index(index: VectorStoreIndex):
    # configure retriever
    retriever = VectorIndexRetriever(
        index=index,
        similarity_top_k=TOP_K,
    )
    # return the unique content material with out utilizing LLM to synthesizer. For later analysis.
    response_synthesizer = get_response_synthesizer(response_mode=ResponseMode.NO_TEXT)
    # assemble question engine
    query_engine = RetrieverQueryEngine(
        retriever=retriever,
        response_synthesizer=response_synthesizer
    )
    return query_engine

The immediate seems like the next:

You might be an assistant that helps reframe consumer questions into clear, concept-driven statements that match 
the model and subjects of Ideas by Ray Dalio, and carry out search for precept e book for related content material. 

Background:
Ideas teaches structured occupied with life and work choices.
The important thing concepts are:
* Radical fact and radical transparency
* Choice-making frameworks
* Embracing errors as studying

Process:
- Process 1: Make clear the consumer's query if wanted. Ask follow-up questions to make sure you perceive the consumer's intent.
- Process 2: Rewrite a consumer’s query into a press release that will match how Ray Dalio frames concepts in Ideas. Use formal, logical, impartial tone.
- Process 3: Lookup precept e book with given re-wrote statements. You must present at the least {REWRITE_FACTOR} rewrote variations.
- Process 4: Discover essentially the most related from the e book content material as your fina solutions.

Lastly, we will construct the agent with these capabilities outlined.

def get_principle_rag_agent():
    index = load_persisted_index()
    query_engine = _create_query_engine_from_index(index)

    def look_up_principle_book(original_question: str, rewrote_statement: Record[str]) -> Record[str]:
        consequence = []
        for q in rewrote_statement:
            response = query_engine.question(q)
            content material = [n.get_content() for n in response.source_nodes]
            consequence.prolong(content material)
        return consequence

    def clarify_question(original_question: str, your_questions_to_user: Record[str]) -> str:
        """
        Make clear the consumer's query if wanted. Ask follow-up questions to make sure you perceive the consumer's intent.
        """
        response = ""
        for q in your_questions_to_user:
            print(f"Query: {q}")
            r = enter("Response:")
            response += f"Query: {q}nResponse: {r}n"
        return response

    instruments = [
        FunctionTool.from_defaults(
            fn=look_up_principle_book,
            name="look_up_principle_book",
            description="Look up principle book with re-wrote queries. Getting the suggestions from the Principle book by Ray Dalio"),
        FunctionTool.from_defaults(
            fn=clarify_question,
            name="clarify_question",
            description="Clarify the user's question if needed. Ask follow-up questions to ensure you understand the user's intent.",
        )
    ]

    agent = FunctionAgent(
        title="principle_reference_loader",
        description="You're a useful agent will primarily based on consumer's query and search for essentially the most related content material in precept e book.n",
        system_prompt=QUESTION_REWRITE_PROMPT,
        instruments=instruments,
    )
    return agent

rag_agent = get_principle_rag_agent()
response = await agent.run(chat_history=chat_history)

There are just a few observations I had throughout the implementations:

• One attention-grabbing truth I discovered is that offering a non-used parameter, original_question , within the operate signature helps. I discovered that once I don’t have such a parameter, LLM typically doesn’t observe the rewrite instruction and passes the unique query in rewrote_statement the parameter. Having original_question parameters one way or the other emphasizes the rewriting mission to LLM.
• Completely different LLMs behave fairly in another way given the identical immediate. I discovered DeepSeek V3 rather more reluctant to set off operate calls than different mannequin suppliers. This doesn’t essentially imply it’s not usable. If a purposeful name ought to be initiated 90% of the time, it ought to be a part of the workflow as a substitute of being registered as a operate name. Additionally, in comparison with OpenAI’s fashions, I discovered Gemini good at citing the supply of the e book when it synthesizes the outcomes.
• The extra content material you load into the context window, the extra inference functionality the mannequin wants. A smaller mannequin with much less inference energy is extra more likely to get misplaced within the massive context supplied.

Nevertheless, to finish the seek-advice operate, you’ll want a number of Brokers working collectively as a substitute of a single Agent. Let’s speak about easy methods to chain your Brokers collectively into workflows.

Closing Type: Agent Workflow

Earlier than we begin, I like to recommend this text by Anthropic, Constructing Efficient Brokers. The one-liner abstract of the articles is that you must at all times prioritise constructing a workflow as a substitute of a dynamic agent when doable. In LlamaIndex, you are able to do each. It means that you can create an agent workflow with extra automated routing or a personalized workflow with extra express management of the transition of steps. I’ll present an instance of each implementations.

Let’s check out how one can construct a dynamic workflow. Here’s a code instance.

interviewer = FunctionAgent(
        title="interviewer",
        description="Helpful agent to make clear consumer's questions",
        system_prompt=_intervierw_prompt,
        can_handoff_to = ["retriver"]
        instruments=instruments
)
interviewer = FunctionAgent(
        title="retriever",
        description="Helpful agent to retrive precept e book's content material.",
        system_prompt=_retriver_prompt,
        can_handoff_to = ["advisor"]
        instruments=instruments
)
advisor = FunctionAgent(
        title="advisor",
        description="Helpful agent to advise consumer.",
        system_prompt=_advisor_prompt,
        can_handoff_to = []
        instruments=instruments
)
workflow = AgentWorkflow(
        brokers=[interviewer, advisor, retriever],
        root_agent="interviewer",
    )
handler = await workflow.run(user_msg="Find out how to deal with stress?")

It’s dynamic as a result of the Agent transition relies on the operate name of the LLM mannequin. Underlying, LlamaIndex workflow offers agent descriptions as capabilities for LLM fashions. When the LLM mannequin triggers such “Agent Operate Name”, LlamaIndex will path to your subsequent corresponding agent for the next step processing. Your earlier agent’s output has been added to the workflow inside state, and your following agent will choose up the state as a part of the context of their name to the LLM mannequin. You additionally leverage state and reminiscence elements to handle the workflow’s inside state or load exterior information(reference the doc right here).

Nevertheless, as I’ve recommended, you may explicitly management the steps in your workflow to realize extra management. With LlamaIndex, it may be finished by extending the workflow object. For instance:

class ReferenceRetrivalEvent(Occasion):
    query: str

class Recommendation(Occasion):
    rules: Record[str]
    profile: dict
    query: str
    book_content: str

class AdviceWorkFlow(Workflow):
    def __init__(self, verbose: bool = False, session_id: str = None):
        state = get_workflow_state(session_id)
        self.rules = state.load_principle_from_cases()
        self.profile = state.load_profile()
        self.verbose = verbose
        tremendous().__init__(timeout=None, verbose=verbose)

    @step
    async def interview(self, ctx: Context,
                        ev: StartEvent) -> ReferenceRetrivalEvent:
        # Step 1: Interviewer agent asks inquiries to the consumer
        interviewer = get_interviewer_agent()
        query = await _run_agent(interviewer, query=ev.user_msg, verbose=self.verbose)

        return ReferenceRetrivalEvent(query=query)

    @step
    async def retrieve(self, ctx: Context, ev: ReferenceRetrivalEvent) -> Recommendation:
        # Step 2: RAG agent retrieves related content material from the e book
        rag_agent = get_principle_rag_agent()
        book_content = await _run_agent(rag_agent, query=ev.query, verbose=self.verbose)
        return Recommendation(rules=self.rules, profile=self.profile,
                      query=ev.query, book_content=book_content)

    @step
    async def recommendation(self, ctx: Context, ev: Recommendation) -> StopEvent:
        # Step 3: Adviser agent offers recommendation primarily based on the consumer's profile, rules, and e book content material
        advisor = get_adviser_agent(ev.profile, ev.rules, ev.book_content)
        advise = await _run_agent(advisor, query=ev.query, verbose=self.verbose)
        return StopEvent(consequence=advise)

The precise occasion sort’s return controls the workflow’s step transition. As an illustration, retrieve step returns an Recommendation occasion that may set off the execution of the recommendation step. It’s also possible to leverage the Recommendation occasion to move the mandatory data you want.

Through the implementation, in case you are aggravated by having to start out over the workflow to debug some steps within the center, the context object is crucial once you wish to failover the workflow execution. You possibly can retailer your state in a serialised format and get better your workflow by unserialising it to a context object. Your workflow will proceed executing primarily based on the state as a substitute of beginning over.

workflow = AgentWorkflow(
    brokers=[interviewer, advisor, retriever],
    root_agent="interviewer",
)
strive:
    handler = w.run()
    consequence = await handler
besides Exception as e:
    print(f"Error throughout preliminary run: {e}")
    await fail_over()
    # Optionally available, serialised and save the contexct for debugging 
    ctx_dict = ctx.to_dict(serializer=JsonSerializer())
    json_dump_and_save(ctx_dict)
    # Resume from the identical context
    ctx_dict = load_failed_dict()
    restored_ctx = Context.from_dict(workflow, ctx_dict,serializer=JsonSerializer())
    handler = w.run(ctx=handler.ctx)
    consequence = await handler

Abstract

On this put up, we now have mentioned easy methods to use LlamaIndex to implement an AI journal’s core operate. The important thing studying contains:

• Utilizing Agentic RAG to leverage LLM functionality to dynamically rewrite the unique question and synthesis consequence.
• Use a Personalized Workflow to realize extra express management over step transitions. Construct dynamic brokers when vital.

The bitterce code of this AI journal is in my GitHub repo right here. I hope you get pleasure from this text and this small app I constructed. Cheers!