says that “any sufficiently superior expertise is indistinguishable from magic”. That’s precisely how a whole lot of as we speak’s AI frameworks really feel. Instruments like GitHub Copilot, Claude Desktop, OpenAI Operator, and Perplexity Comet are automating on a regular basis duties that may’ve appeared unattainable to automate simply 5 years in the past. What’s much more outstanding is that with just some strains of code, we are able to construct our personal refined AI instruments: ones that search by way of recordsdata, browse the online, click on hyperlinks, and even make purchases. It actually does really feel like magic.
Despite the fact that I genuinely believe in data wizards, I don’t consider in magic. I discover it thrilling (and sometimes useful) to know how issues are literally constructed and what’s occurring below the hood. That’s why I’ve determined to share a sequence of posts on agentic AI design ideas that’ll allow you to perceive how all these magical instruments really work.
To achieve a deep understanding, we’ll construct a multi-AI agent system from scratch. We’ll keep away from utilizing frameworks like CrewAI or smolagents and as a substitute work instantly with the muse mannequin API. Alongside the way in which, we’ll discover the basic agentic design patterns: reflection, device use, planning, and multi-agent setups. Then, we’ll mix all this data to construct a multi-AI agent system that may reply advanced data-related questions.
As Richard Feynman put it, “What I can not create, I don’t perceive.” So let’s begin constructing! On this article, we’ll deal with the reflection design sample. However first, let’s determine what precisely reflection is.
What reflection is
Let’s mirror on how we (people) normally work on duties. Think about I have to share the outcomes of a current characteristic launch with my PM. I’ll doubtless put collectively a fast draft after which learn it a couple of times from starting to finish, guaranteeing that every one components are constant, there’s sufficient data, and there are not any typos.
Or let’s take one other instance: writing a SQL question. I’ll both write it step-by-step, checking the intermediate outcomes alongside the way in which, or (if it’s easy sufficient) I’ll draft it all of sudden, execute it, have a look at the consequence (checking for errors or whether or not the consequence matches my expectations), after which tweak the question based mostly on that suggestions. I would rerun it, verify the consequence, and iterate till it’s proper.
So we hardly ever write lengthy texts from prime to backside in a single go. We normally circle again, overview, and tweak as we go. These suggestions loops are what assist us enhance the standard of our work.
LLMs use a special strategy. In the event you ask an LLM a query, by default, it’ll generate a solution token by token, and the LLM received’t have the ability to overview its consequence and repair any points. However in an agentic AI setup, we are able to create suggestions loops for LLMs too, both by asking the LLM to overview and enhance its personal reply or by sharing exterior suggestions with it (just like the outcomes of a SQL execution). And that’s the entire level of reflection. It sounds fairly simple, however it could possibly yield considerably higher outcomes.
There’s a considerable physique of analysis exhibiting the advantages of reflection:
- In “Reflexion: Language Agents with Verbal Reinforcement Learning” Shinn et al. (2023), the authors achieved a 91% cross@1 accuracy on the HumanEval coding benchmark, surpassing the earlier state-of-the-art GPT-4, which scored simply 80%. Additionally they discovered that Reflexion considerably outperforms all baseline approaches on the HotPotQA benchmark (a Wikipedia-based Q&A dataset that challenges brokers to parse content material and purpose over a number of supporting paperwork).
Reflection is very impactful in agentic methods as a result of it may be used to course-correct at many steps of the method:
- When a consumer asks a query, the LLM can use reflection to guage whether or not the request is possible.
- When the LLM places collectively an preliminary plan, it could possibly use reflection to double-check whether or not the plan is smart and will help obtain the objective.
- After every execution step or device name, the agent can consider whether or not it’s on observe and whether or not it’s price adjusting the plan.
- When the plan is absolutely executed, the agent can mirror to see whether or not it has really completed the objective and solved the duty.
It’s clear that reflection can considerably enhance accuracy. Nonetheless, there are trade-offs price discussing. Reflection would possibly require a number of extra calls to the LLM and probably different methods, which may result in elevated latency and prices. So in enterprise instances, it’s price contemplating whether or not the standard enhancements justify the bills and delays within the consumer circulate.
Reflection in frameworks
Since there’s little question that reflection brings worth to AI brokers, it’s extensively utilized in fashionable frameworks. Let’s have a look at some examples.
The concept of reflection was first proposed within the paper “ReAct: Synergizing Reasoning and Acting in Language Models” by Yao et al. (2022). ReAct is a framework that mixes interleaving levels of Reasoning (reflection by way of specific thought traces) and Appearing (task-relevant actions in an surroundings). On this framework, reasoning guides the selection of actions, and actions produce new observations that inform additional reasoning. The reasoning stage itself is a mixture of reflection and planning.
This framework turned fairly fashionable, so there at the moment are a number of off-the-shelf implementations, similar to:
- The DSPy framework by Databricks has a
ReActclass, - In LangGraph, you should use the
create_react_agentperform, - Code brokers within the smolagents library by HuggingFace are additionally based mostly on the ReAct structure.
Reflection from scratch
Now that we’ve realized the speculation and explored present implementations, it’s time to get our palms soiled and construct one thing ourselves. Within the ReAct strategy, brokers use reflection at every step, combining planning with reflection. Nonetheless, to know the affect of reflection extra clearly, we’ll have a look at it in isolation.
For instance, we’ll use text-to-SQL: we’ll give an LLM a query and anticipate it to return a sound SQL question. We’ll be working with a flight delay dataset and the ClickHouse SQL dialect.
We’ll begin through the use of direct era with none reflection as our baseline. Then, we’ll strive utilizing reflection by asking the mannequin to critique and enhance the SQL, or by offering it with extra suggestions. After that, we’ll measure the standard of our solutions to see whether or not reflection really results in higher outcomes.
Direct era
We’ll start with essentially the most simple strategy, direct era, the place we ask the LLM to generate SQL that solutions a consumer question.
pip set up anthropicWe have to specify the API Key for the Anthropic API.
import os
os.environ['ANTHROPIC_API_KEY'] = config['ANTHROPIC_API_KEY']The subsequent step is to initialise the consumer, and we’re all set.
import anthropic
consumer = anthropic.Anthropic()Now we are able to use this consumer to ship messages to the LLM. Let’s put collectively a perform to generate SQL based mostly on a consumer question. I’ve specified the system immediate with primary directions and detailed details about the information schema. I’ve additionally created a perform to ship the system immediate and consumer question to the LLM.
base_sql_system_prompt = '''
You're a senior SQL developer and your job is to assist generate a SQL question based mostly on consumer necessities.
You might be working with ClickHouse database. Specify the format (Tab Separated With Names) within the SQL question output to make sure that column names are included within the output.
Don't use depend(*) in your queries since it is a dangerous observe with columnar databases, choose utilizing depend().
Be certain that the question is syntactically right and optimized for efficiency, making an allowance for ClickHouse particular options (i.e. that ClickHouse is a columnar database and helps features like ARRAY JOIN, SAMPLE, and so forth.).
Return solely the SQL question with none extra explanations or feedback.
You'll be working with flight_data desk which has the next schema:
Column Title | Knowledge Sort | Null % | Instance Worth | Description
--- | --- | --- | --- | ---
yr | Int64 | 0.0 | 2024 | Yr of flight
month | Int64 | 0.0 | 1 | Month of flight (1–12)
day_of_month | Int64 | 0.0 | 1 | Day of the month
day_of_week | Int64 | 0.0 | 1 | Day of week (1=Monday … 7=Sunday)
fl_date | datetime64[ns] | 0.0 | 2024-01-01 00:00:00 | Flight date (YYYY-MM-DD)
op_unique_carrier | object | 0.0 | 9E | Distinctive provider code
op_carrier_fl_num | float64 | 0.0 | 4814.0 | Flight quantity for reporting airline
origin | object | 0.0 | JFK | Origin airport code
origin_city_name | object | 0.0 | "New York, NY" | Origin metropolis identify
origin_state_nm | object | 0.0 | New York | Origin state identify
dest | object | 0.0 | DTW | Vacation spot airport code
dest_city_name | object | 0.0 | "Detroit, MI" | Vacation spot metropolis identify
dest_state_nm | object | 0.0 | Michigan | Vacation spot state identify
crs_dep_time | Int64 | 0.0 | 1252 | Scheduled departure time (native, hhmm)
dep_time | float64 | 1.31 | 1247.0 | Precise departure time (native, hhmm)
dep_delay | float64 | 1.31 | -5.0 | Departure delay in minutes (unfavorable if early)
taxi_out | float64 | 1.35 | 31.0 | Taxi out time in minutes
wheels_off | float64 | 1.35 | 1318.0 | Wheels-off time (native, hhmm)
wheels_on | float64 | 1.38 | 1442.0 | Wheels-on time (native, hhmm)
taxi_in | float64 | 1.38 | 7.0 | Taxi in time in minutes
crs_arr_time | Int64 | 0.0 | 1508 | Scheduled arrival time (native, hhmm)
arr_time | float64 | 1.38 | 1449.0 | Precise arrival time (native, hhmm)
arr_delay | float64 | 1.61 | -19.0 | Arrival delay in minutes (unfavorable if early)
cancelled | int64 | 0.0 | 0 | Cancelled flight indicator (0=No, 1=Sure)
cancellation_code | object | 98.64 | B | Cause for cancellation (if cancelled)
diverted | int64 | 0.0 | 0 | Diverted flight indicator (0=No, 1=Sure)
crs_elapsed_time | float64 | 0.0 | 136.0 | Scheduled elapsed time in minutes
actual_elapsed_time | float64 | 1.61 | 122.0 | Precise elapsed time in minutes
air_time | float64 | 1.61 | 84.0 | Flight time in minutes
distance | float64 | 0.0 | 509.0 | Distance between origin and vacation spot (miles)
carrier_delay | int64 | 0.0 | 0 | Provider-related delay in minutes
weather_delay | int64 | 0.0 | 0 | Climate-related delay in minutes
nas_delay | int64 | 0.0 | 0 | Nationwide Air System delay in minutes
security_delay | int64 | 0.0 | 0 | Safety delay in minutes
late_aircraft_delay | int64 | 0.0 | 0 | Late plane delay in minutes
'''
def generate_direct_sql(rec):
# making an LLM name
message = consumer.messages.create(
mannequin = "claude-3-5-haiku-latest",
# I selected smaller mannequin in order that it is simpler for us to see the affect
max_tokens = 8192,
system=base_sql_system_prompt,
messages = [
{'role': 'user', 'content': rec['question']}
]
)
sql = message.content material[0].textual content
# cleansing the output
if sql.endswith('```'):
sql = sql[:-3]
if sql.startswith('```sql'):
sql = sql[6:]
return sqlThat’s it. Now let’s take a look at our text-to-SQL resolution. I’ve created a small evaluation set of 20 question-and-answer pairs that we are able to use to verify whether or not our system is working effectively. Right here’s one instance:
{
'query': 'What was the very best pace in mph?',
'reply': '''
choose max(distance / (air_time / 60)) as max_speed
from flight_data
the place air_time > 0
format TabSeparatedWithNames'''
}Let’s use our text-to-SQL perform to generate SQL for all consumer queries within the take a look at set.
# load analysis set
with open('./knowledge/flight_data_qa_pairs.json', 'r') as f:
qa_pairs = json.load(f)
qa_pairs_df = pd.DataFrame(qa_pairs)
tmp = []
# executing LLM for every query in our eval set
for rec in tqdm.tqdm(qa_pairs_df.to_dict('data')):
llm_sql = generate_direct_sql(rec)
tmp.append(
{
'id': rec['id'],
'llm_direct_sql': llm_sql
}
)
llm_direct_df = pd.DataFrame(tmp)
direct_result_df = qa_pairs_df.merge(llm_direct_df, on = 'id')Now we now have our solutions, and the subsequent step is to measure the standard.
Measuring high quality
Sadly, there’s no single right reply on this state of affairs, so we are able to’t simply examine the SQL generated by the LLM to a reference reply. We have to provide you with a technique to measure high quality.
There are some features of high quality that we are able to verify with goal standards, however to verify whether or not the LLM returned the correct reply, we’ll want to make use of an LLM. So I’ll use a mixture of approaches:
- First, we’ll use goal standards to verify whether or not the right format was specified within the SQL (we instructed the LLM to make use of
TabSeparatedWithNames). - Second, we are able to execute the generated question and see whether or not ClickHouse returns an execution error.
- Lastly, we are able to create an LLM choose that compares the output from the generated question to our reference reply and checks whether or not they differ.
Let’s begin by executing the SQL. It’s price noting that our get_clickhouse_data perform doesn’t throw an exception. As an alternative, it returns textual content explaining the error, which could be dealt with by the LLM later.
CH_HOST = 'http://localhost:8123' # default tackle
import requests
import pandas as pd
import tqdm
# perform to execute SQL question
def get_clickhouse_data(question, host = CH_HOST, connection_timeout = 1500):
r = requests.publish(host, params = {'question': question},
timeout = connection_timeout)
if r.status_code == 200:
return r.textual content
else:
return 'Database returned the next error:n' + r.textual content
# getting the outcomes of SQL execution
direct_result_df['llm_direct_output'] = direct_result_df['llm_direct_sql'].apply(get_clickhouse_data)
direct_result_df['answer_output'] = direct_result_df['answer'].apply(get_clickhouse_data)The subsequent step is to create an LLM choose. For this, I’m utilizing a sequence‑of‑thought strategy that prompts the LLM to offer its reasoning earlier than giving the ultimate reply. This provides the mannequin time to assume by way of the issue, which improves response high quality.
llm_judge_system_prompt = '''
You're a senior analyst and your job is to check two SQL question outcomes and decide if they're equal.
Focus solely on the information returned by the queries, ignoring any formatting variations.
Keep in mind the preliminary consumer question and data wanted to reply it. For instance, if consumer requested for the common distance, and each queries return the identical common worth however in one in all them there's additionally a depend of data, it is best to take into account them equal, since each present the identical requested data.
Reply with a JSON of the next construction:
false>
Be certain that ONLY JSON is within the output.
You'll be working with flight_data desk which has the next schema:
Column Title | Knowledge Sort | Null % | Instance Worth | Description
--- | --- | --- | --- | ---
yr | Int64 | 0.0 | 2024 | Yr of flight
month | Int64 | 0.0 | 1 | Month of flight (1–12)
day_of_month | Int64 | 0.0 | 1 | Day of the month
day_of_week | Int64 | 0.0 | 1 | Day of week (1=Monday … 7=Sunday)
fl_date | datetime64[ns] | 0.0 | 2024-01-01 00:00:00 | Flight date (YYYY-MM-DD)
op_unique_carrier | object | 0.0 | 9E | Distinctive provider code
op_carrier_fl_num | float64 | 0.0 | 4814.0 | Flight quantity for reporting airline
origin | object | 0.0 | JFK | Origin airport code
origin_city_name | object | 0.0 | "New York, NY" | Origin metropolis identify
origin_state_nm | object | 0.0 | New York | Origin state identify
dest | object | 0.0 | DTW | Vacation spot airport code
dest_city_name | object | 0.0 | "Detroit, MI" | Vacation spot metropolis identify
dest_state_nm | object | 0.0 | Michigan | Vacation spot state identify
crs_dep_time | Int64 | 0.0 | 1252 | Scheduled departure time (native, hhmm)
dep_time | float64 | 1.31 | 1247.0 | Precise departure time (native, hhmm)
dep_delay | float64 | 1.31 | -5.0 | Departure delay in minutes (unfavorable if early)
taxi_out | float64 | 1.35 | 31.0 | Taxi out time in minutes
wheels_off | float64 | 1.35 | 1318.0 | Wheels-off time (native, hhmm)
wheels_on | float64 | 1.38 | 1442.0 | Wheels-on time (native, hhmm)
taxi_in | float64 | 1.38 | 7.0 | Taxi in time in minutes
crs_arr_time | Int64 | 0.0 | 1508 | Scheduled arrival time (native, hhmm)
arr_time | float64 | 1.38 | 1449.0 | Precise arrival time (native, hhmm)
arr_delay | float64 | 1.61 | -19.0 | Arrival delay in minutes (unfavorable if early)
cancelled | int64 | 0.0 | 0 | Cancelled flight indicator (0=No, 1=Sure)
cancellation_code | object | 98.64 | B | Cause for cancellation (if cancelled)
diverted | int64 | 0.0 | 0 | Diverted flight indicator (0=No, 1=Sure)
crs_elapsed_time | float64 | 0.0 | 136.0 | Scheduled elapsed time in minutes
actual_elapsed_time | float64 | 1.61 | 122.0 | Precise elapsed time in minutes
air_time | float64 | 1.61 | 84.0 | Flight time in minutes
distance | float64 | 0.0 | 509.0 | Distance between origin and vacation spot (miles)
carrier_delay | int64 | 0.0 | 0 | Provider-related delay in minutes
weather_delay | int64 | 0.0 | 0 | Climate-related delay in minutes
nas_delay | int64 | 0.0 | 0 | Nationwide Air System delay in minutes
security_delay | int64 | 0.0 | 0 | Safety delay in minutes
late_aircraft_delay | int64 | 0.0 | 0 | Late plane delay in minutes
'''
llm_judge_user_prompt_template = '''
Right here is the preliminary consumer question:
{user_query}
Right here is the SQL question generated by the primary analyst:
SQL:
{sql1}
Database output:
{result1}
Right here is the SQL question generated by the second analyst:
SQL:
{sql2}
Database output:
{result2}
'''
def llm_judge(rec, field_to_check):
# assemble the consumer immediate
user_prompt = llm_judge_user_prompt_template.format(
user_query = rec['question'],
sql1 = rec['answer'],
result1 = rec['answer_output'],
sql2 = rec[field_to_check + '_sql'],
result2 = rec[field_to_check + '_output']
)
# make an LLM name
message = consumer.messages.create(
mannequin = "claude-sonnet-4-5",
max_tokens = 8192,
temperature = 0.1,
system = llm_judge_system_prompt,
messages=[
{'role': 'user', 'content': user_prompt}
]
)
knowledge = message.content material[0].textual content
# Strip markdown code blocks
knowledge = knowledge.strip()
if knowledge.startswith('```json'):
knowledge = knowledge[7:]
elif knowledge.startswith('```'):
knowledge = knowledge[3:]
if knowledge.endswith('```'):
knowledge = knowledge[:-3]
knowledge = knowledge.strip()
return json.masses(knowledge)Now, let’s run the LLM choose to get the outcomes.
tmp = []
for rec in tqdm.tqdm(direct_result_df.to_dict('data')):
strive:
judgment = llm_judge(rec, 'llm_direct')
besides Exception as e:
print(f"Error processing document {rec['id']}: {e}")
proceed
tmp.append(
{
'id': rec['id'],
'llm_judge_reasoning': judgment['reasoning'],
'llm_judge_equivalence': judgment['equivalence']
}
)
judge_df = pd.DataFrame(tmp)
direct_result_df = direct_result_df.merge(judge_df, on = 'id')Let’s have a look at one instance to see how the LLM choose works.
# consumer question
In 2024, what share of time all airplanes spent within the air?
# right reply
choose (sum(air_time) / sum(actual_elapsed_time)) * 100 as percentage_in_air
the place yr = 2024
from flight_data
format TabSeparatedWithNames
percentage_in_air
81.43582596894757
# generated by LLM reply
SELECT
spherical(sum(air_time) / (sum(air_time) + sum(taxi_out) + sum(taxi_in)) * 100, 2) as air_time_percentage
FROM flight_data
WHERE yr = 2024
FORMAT TabSeparatedWithNames
air_time_percentage
81.39
# LLM choose response
{
'reasoning': 'Each queries calculate the share of time airplanes
spent within the air, however use totally different denominators. The primary question
makes use of actual_elapsed_time (which incorporates air_time + taxi_out + taxi_in
+ any floor delays), whereas the second makes use of solely (air_time + taxi_out
+ taxi_in). The second question is strategy is extra correct for answering
"time airplanes spent within the air" because it excludes floor delays.
Nonetheless, the outcomes are very shut (81.44% vs 81.39%), suggesting minimal
affect. These are materially totally different approaches that occur to yield
related outcomes',
'equivalence': FALSE
}The reasoning is smart, so we are able to belief our choose. Now, let’s verify all LLM-generated queries.
def get_llm_accuracy(sql, output, equivalence):
issues = []
if 'format tabseparatedwithnames' not in sql.decrease():
issues.append('No format laid out in SQL')
if 'Database returned the next error' in output:
issues.append('SQL execution error')
if not equivalence and ('SQL execution error' not in issues):
issues.append('Incorrect reply offered')
if len(issues) == 0:
return 'No issues detected'
else:
return ' + '.be a part of(issues)
direct_result_df['llm_direct_sql_quality_heuristics'] = direct_result_df.apply(
lambda row: get_llm_accuracy(row['llm_direct_sql'], row['llm_direct_output'], row['llm_judge_equivalence']), axis=1)The LLM returned the right reply in 70% of instances, which isn’t dangerous. However there’s positively room for enchancment, because it usually both offers the improper reply or fails to specify the format appropriately (typically inflicting SQL execution errors).
Including a mirrored image step
To enhance the standard of our resolution, let’s strive including a mirrored image step the place we ask the mannequin to overview and refine its reply.
For a mirrored image name, I’ll hold the identical system immediate because it accommodates all the mandatory details about SQL and the information schema. However I’ll tweak the consumer message to share the preliminary consumer question and the generated SQL, asking the LLM to critique and enhance it.
simple_reflection_user_prompt_template = '''
Your job is to evaluate the SQL question generated by one other analyst and suggest enhancements if mandatory.
Test whether or not the question is syntactically right and optimized for efficiency.
Take note of nuances in knowledge (particularly time stamps varieties, whether or not to make use of complete elapsed time or time within the air, and so forth).
Be certain that the question solutions the preliminary consumer query precisely.
Because the consequence return the next JSON:
{{
'reasoning': '<your reasoning right here, 2-4 sentences on why you made adjustments or not>',
'refined_sql': '<the improved SQL question right here>'
}}
Be certain that ONLY JSON is within the output and nothing else. Be certain that the output JSON is legitimate.
Right here is the preliminary consumer question:
{user_query}
Right here is the SQL question generated by one other analyst:
{sql}
'''
def simple_reflection(rec) -> str:
# developing a consumer immediate
user_prompt = simple_reflection_user_prompt_template.format(
user_query=rec['question'],
sql=rec['llm_direct_sql']
)
# making an LLM name
message = consumer.messages.create(
mannequin="claude-3-5-haiku-latest",
max_tokens = 8192,
system=base_sql_system_prompt,
messages=[
{'role': 'user', 'content': user_prompt}
]
)
knowledge = message.content material[0].textual content
# strip markdown code blocks
knowledge = knowledge.strip()
if knowledge.startswith('```json'):
knowledge = knowledge[7:]
elif knowledge.startswith('```'):
knowledge = knowledge[3:]
if knowledge.endswith('```'):
knowledge = knowledge[:-3]
knowledge = knowledge.strip()
return json.masses(knowledge.substitute('n', ' '))Let’s refine the queries with reflection and measure the accuracy. We don’t see a lot enchancment within the ultimate high quality. We’re nonetheless at 70% right solutions.
Let’s have a look at particular examples to know what occurred. First, there are a few instances the place the LLM managed to repair the issue, both by correcting the format or by including lacking logic to deal with zero values.
Nonetheless, there are additionally instances the place the LLM overcomplicated the reply. The preliminary SQL was right (matching the golden set reply), however then the LLM determined to ‘enhance’ it. A few of these enhancements are affordable (e.g., accounting for nulls or excluding cancelled flights). Nonetheless, for some purpose, it determined to make use of ClickHouse sampling, despite the fact that we don’t have a lot knowledge and our desk doesn’t assist sampling. Because of this, the refined question returned an execution error: Database returned the next error: Code: 141. DB::Exception: Storage default.flight_data would not assist sampling. (SAMPLING_NOT_SUPPORTED).
Reflection with exterior suggestions
Reflection didn’t enhance accuracy a lot. That is doubtless as a result of we didn’t present any extra data that may assist the mannequin generate a greater consequence. Let’s strive sharing exterior suggestions with the mannequin:
The results of our verify on whether or not the format is specified appropriately
The output from the database (both knowledge or an error message)
Let’s put collectively a immediate for this and generate a brand new model of the SQL.
feedback_reflection_user_prompt_template = '''
Your job is to evaluate the SQL question generated by one other analyst and suggest enhancements if mandatory.
Test whether or not the question is syntactically right and optimized for efficiency.
Take note of nuances in knowledge (particularly time stamps varieties, whether or not to make use of complete elapsed time or time within the air, and so forth).
Be certain that the question solutions the preliminary consumer query precisely.
Because the consequence return the next JSON:
{{
'reasoning': '<your reasoning right here, 2-4 sentences on why you made adjustments or not>',
'refined_sql': '<the improved SQL question right here>'
}}
Be certain that ONLY JSON is within the output and nothing else. Be certain that the output JSON is legitimate.
Right here is the preliminary consumer question:
{user_query}
Right here is the SQL question generated by one other analyst:
{sql}
Right here is the database output of this question:
{output}
We run an automated verify on the SQL question to verify whether or not it has fomatting points. Here is the output:
{formatting}
'''
def feedback_reflection(rec) -> str:
# outline message for formatting
if 'No format laid out in SQL' in rec['llm_direct_sql_quality_heuristics']:
formatting = 'SQL lacking formatting. Specify "format TabSeparatedWithNames" to make sure that column names are additionally returned'
else:
formatting = 'Formatting is right'
# developing a consumer immediate
user_prompt = feedback_reflection_user_prompt_template.format(
user_query = rec['question'],
sql = rec['llm_direct_sql'],
output = rec['llm_direct_output'],
formatting = formatting
)
# making an LLM name
message = consumer.messages.create(
mannequin = "claude-3-5-haiku-latest",
max_tokens = 8192,
system = base_sql_system_prompt,
messages = [
{'role': 'user', 'content': user_prompt}
]
)
knowledge = message.content material[0].textual content
# strip markdown code blocks
knowledge = knowledge.strip()
if knowledge.startswith('```json'):
knowledge = knowledge[7:]
elif knowledge.startswith('```'):
knowledge = knowledge[3:]
if knowledge.endswith('```'):
knowledge = knowledge[:-3]
knowledge = knowledge.strip()
return json.masses(knowledge.substitute('n', ' '))After operating our accuracy measurements, we are able to see that accuracy has improved considerably: 17 right solutions (85% accuracy) in comparison with 14 (70% accuracy).
If we verify the instances the place the LLM fastened the problems, we are able to see that it was capable of right the format, tackle SQL execution errors, and even revise the enterprise logic (e.g., utilizing air time for calculating pace).
Let’s additionally do some error evaluation to look at the instances the place the LLM made errors. Within the desk under, we are able to see that the LLM struggled with defining sure timestamps, incorrectly calculating complete time, or utilizing complete time as a substitute of air time for pace calculations. Nonetheless, a number of the discrepancies are a bit tough:
- Within the final question, the time interval wasn’t explicitly outlined, so it’s affordable for the LLM to make use of 2010–2023. I wouldn’t take into account this an error, and I’d modify the analysis as a substitute.
- One other instance is easy methods to outline airline pace:
avg(distance/time)orsum(distance)/sum(time). Each choices are legitimate since nothing was specified within the consumer question or system immediate (assuming we don’t have a predefined calculation technique).
Total, I believe we achieved a fairly good consequence. Our ultimate 85% accuracy represents a big 15% level enchancment. You possibly can probably transcend one iteration and run 2–3 rounds of reflection, but it surely’s price assessing while you hit diminishing returns in your particular case, since every iteration goes with elevated price and latency.
Yow will discover the total code on GitHub.
Abstract
It’s time to wrap issues up. On this article, we began our journey into understanding how the magic of agentic AI methods works. To determine it out, we’ll implement a multi-agent text-to-data device utilizing solely API calls to basis fashions. Alongside the way in which, we’ll stroll by way of the important thing design patterns step-by-step: beginning as we speak with reflection, and shifting on to device use, planning, and multi-agent coordination.
On this article, we began with essentially the most elementary sample — reflection. Reflection is on the core of any agentic circulate, because the LLM must mirror on its progress towards reaching the top objective.
Reflection is a comparatively simple sample. We merely ask the identical or a special mannequin to analyse the consequence and try to enhance it. As we realized in observe, sharing exterior suggestions with the mannequin (like outcomes from static checks or database output) considerably improves accuracy. A number of analysis research and our personal expertise with the text-to-SQL agent show the advantages of reflection. Nonetheless, these accuracy positive factors come at a value: extra tokens spent and better latency attributable to a number of API calls.
Thanks for studying. I hope this text was insightful. Keep in mind Einstein’s recommendation: “The necessary factor is to not cease questioning. Curiosity has its personal purpose for present.” Could your curiosity lead you to your subsequent nice perception.
Reference
This text is impressed by the “Agentic AI” course by Andrew Ng from DeepLearning.AI.
