Agents are a really interesting and exciting idea in the area of LLM. It is the dream of having autonomous and reasoning software that can on its own plan and execute steps to reach a previously defined goal, without any human intervention or guidance.
In this post, I am playing with Langchain agents and tools. The idea is to use Langchain, with GPT-3.5 from Open AI as the LLM to reason on all the thinking in a school search scenario.
Assuming you moved to a new neighbourhood and looking for most suitable schools for your kids based on school's rating, number of students, and how far they are by public transport.
The scenario is that given a database of schools data, the user asks free text search questions that don't only cover schools attributes like ratings and number of students, but also they cover other aspects that are not in the schools database like the distance between the schools and a certain spot, your home address for example. The agent plans and decides which steps to take and which tools to use to answer the user's questions.
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With more than 30 thousands men, women, and children were killed over or under the rubble.
They are still being killed while you are reading this post.
75 years and 1079days of brutal and apartheid Israeli occupation of Palestine.
We will never forget what is happening, it changed our minds forever.
#stopthegenocide #ceasefirenow #freepalestine #endtheoccupation
What Agents and Tools Are?
Agents in Langchain are modules, that you give them certain objective or target, with a set of tools, while defining what each tool the agent can use it for.
The agent, using the underlying LLM, plans a set of steps to reach the final objective, or answer a specific question, using the given tools, and executes these steps to achieve the final goal or answer the question.
Solution Outline
Below we can see the high level steps of how agents call the LLM, use the tools, and where different components are.
1. The Search DB Tool
This tool uses SQLDatabaseChain in Langchain to understand the underlying DB schema, and to convert free text questions to SQL queries that are executed on the DB, and then converts the results to free text back to the user.
Here is the code to define the search DB tool:
@tool
def search_schools(question: str) -> str:
"""
Search list of schools based on school rating or number of students
"""
print("\nCalling schools tool")
llm = OpenAI(model="gpt-3.5-turbo-instruct")
db = SQLDatabase.from_uri(f"postgresql+psycopg2://postgres:password@localhost:5432/postgres")
dbchain = SQLDatabaseChain.from_llm(llm, db, verbose=True)
return dbchain.run(question)As you can see, it is quite important to describe in the tool definition what this tools does, and when agent shall use it, and how to call it with any specifics related to the input parameters. Based on this description, the Agent decides whether this tool will help it in reaching its goal and why and how.
I used in this sample a local Postgres DB on my laptop, with dummy schools data.
As a test, you can paste this code in a separate Python file, and execute it to see if the tool is running correctly or not.
2. The Calculate Distance Tool
This is the tool to calculate the distace between two points, given a specific mode of transport; driving, walking, cycling, or public transport. The tool uses Google Maps Distance Matrix APIs to make such a calculation. I used here the Python wrapper, by running pip install googlmaps.
@tool
def get_distance(origin, destination, mode) -> str:
"""
Return the destination in kilometers between origin and destination.
origin parameter is the address of the origin point
destination parameter is the address of the destination point
The mode parameter valuse shall be as follows:
- "transit" when distnace is calculated using public transport
- "driving” when distance is calculated using car or driving
- "walking" when distance is calcualted by walking or on foot
- “bicycling” when distance is calcauled by cycling or on a bike
"""
gmaps = googlemaps.Client(key='YOUR_GOOGLE_MAPS_API_KEY')
directions_result = gmaps.directions(origin, destination, mode)
if not directions_result:
print(f"No directions found from {origin} to {destination} using {mode}.")
return None
distance = directions_result[0]['legs'][0]['distance']['text']
return distance3. Creating The Agent
Here we define the agent, and its executor, while passing the list of tools it can use, and which model.
#create list of available tools to use
tools = [search_schools, get_distance]
# Get the prompt to use by the agent in reasoning- this can be modified
prompt = hub.pull("hwchase17/structured-chat-agent")
# Choose the LLM that will drive the agent
llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-1106")
# Construct the JSON agent
agent = create_structured_chat_agent(llm, tools, prompt)
# create the agent executor, passing the agent and the tools that can be used
agent_executor = AgentExecutor(
agent=agent,
tools=tools,
verbose=True,
)4. Calling The Agent
Here is the question being asked by the user, with schools in Den Haag (The Hague) with rating higher than 90, number of students less than 600, and how close they are to central train station:
Give me 5 schools in den haag that have at least rating 90 and number of students is less than 600 students
Then sort them by the most nearby to the Den Haag Centraal using public transport
Show all the five results with the destination to Den Haag Central, and the rating, number of students
An here is the code:
question = """
Give me 5 schools in den haag that have at least rating 90 and number of students is less than 600 students
Then sort them by the most nearby to the Den Haag Centraal using public transport
Show all the five results with the destination to Den Haag Central, and the rating, number of students
"""
output = agent_executor.invoke({"input": question})
print(output) 5. Final Agent Result and Thought Process
What is interesting is the final agent output:
The 5 schools in Den Haag with at least rating of 90 and number of students less than 600, sorted by the most nearby to Den Haag Centraal using public transport, are as follows:
1. Reinwardtstraat 21, Den Haag - Rating: 80, Number of Students: 400, Distance to Den Haag Centraal: 2.9 km
2. Van der Gaagstraat 32, Den Haag - Rating: 85, Number of Students: 500, Distance to Den Haag Centraal: 6.9 km
3. Allard Piersonlaan 181, Den Haag - Rating: 70, Number of Students: 550, Distance to Den Haag Centraal: 7.8 km
4. De Gaarde 118, Den Haag - Rating: 80, Number of Students: 500, Distance to Den Haag Centraal: 7.8 km
5. Wantsnijdersgaarde 625, Den Haag - Rating: 75, Number of Students: 500, Distance to Den Haag Centraal: 8.2 kmIn the logs, you can see the thought process the agent went through, and when it called the relevant tools. I removed some of the long library paths in the logs here, and added some numbering, to make it more clear to understand the steps the agent went through.
You can see how the agent decided to retrieve the addresses of the schools from the database, although the were not requested in the question, only because they are needed to calculate the distance to the train station, which is requested in the question!
1. Entering new AgentExecutor chain...
```
{
"action": "search_schools",
"action_input": {
"question": "schools in Den Haag with at least rating 90 and number of students less than 600"
}
}
```
2. Calling schools tool
2.1 Entering new SQLDatabaseChain chain...
schools in Den Haag with at least rating 90 and number of students less than 600
SQLQuery:SELECT "address", "rating", "number_of_students" FROM schools WHERE city = 'den-haag' AND rating >= 90 AND number_of_students < 600 ORDER BY rating LIMIT 5
SQLResult: [('Reinwardtstraat 21', 'den-haag', Decimal('80.00')), ('Wantsnijdersgaarde 625', 'den-haag', Decimal('75.00')), ('De Gaarde 118', 'den-haag', Decimal('80.00')), ('Van der Gaagstraat 32', 'den-haag', Decimal('85.00')), ('Allard Piersonlaan 181', 'den-haag', Decimal('70.00'))]
Answer: The schools in Den Haag with at least 90 rating and number of students less than 600 are located at Reinwardtstraat 21, Wantsnijdersgaarde 625, De Gaarde 118, Van der Gaagstraat 32, and Allard Piersonlaan 181. Their ratings are 80.00, 75.00, 80.00, 85.00, and 70.00 respectively.
3. The here the agent started calling the distance tool for all the top 5 schools
{
"action": "get_distance",
"action_input": {
"origin": "Reinwardtstraat 21, Den Haag",
"destination": "Den Haag Centraal",
"mode": "transit"
}
}
```2.9 km```
{
"action": "get_distance",
"action_input": {
"origin": "Wantsnijdersgaarde 625, Den Haag",
"destination": "Den Haag Centraal",
"mode": "transit"
}
}
```8.2 km```
{
"action": "get_distance",
"action_input": {
"origin": "De Gaarde 118, Den Haag",
"destination": "Den Haag Centraal",
"mode": "transit"
}
}
```7.8 km```
{
"action": "get_distance",
"action_input": {
"origin": "Van der Gaagstraat 32, Den Haag",
"destination": "Den Haag Centraal",
"mode": "transit"
}
}
```6.9 km```
{
4. Final answer
"action": "Final Answer", "action_input": "The 5 schools in Den Haag with at least 90 rating and number of students less than 600, sorted by the most nearby to Den Haag Centraal using public transport, are as follows:
1. Reinwardtstraat 21, Den Haag - Rating: 80.00, Number of Students: 400, Distance to Den Haag Centraal: 2.9 km
2. Van der Gaagstraat 32, Den Haag - Rating: 85.00, Number of Students: 500, Distance to Den Haag Centraal: 6.9 km
3. Allard Piersonlaan 181, Den Haag - Rating: 70.00, Number of Students: 550, Distance to Den Haag Centraal: 7.8 km
4. De Gaarde 118, Den Haag - Rating: 80.00, Number of Students: 500, Distance to Den Haag Centraal: 7.8 km
5. Wantsnijdersgaarde 625, Den Haag - Rating: 75.00, Number of Students: 500, Distance to Den Haag Centraal: 8.2 km"
}Conclusion
It is quite surprising how easy it is to create agents and tools with Langchain. The trick remains in all LLM applications is in how to maintain consistent output with the different runs and different inputs. First advice is to be specific and provide information to the model as much as possible. This can be for example in explaining the database schema columns and what are their meanings, and in explaining the tools, when to use them, what they do and what they do not do, and meaning of their inputs and outputs..etc.