Skip to Content

Getting started with Fetch.ai x Langchain

Fetch.ai creates a dynamic communication layer that allows you to abstract away components into individual Agents. Agents are micro-services that are programmed to communicate with other agents, and or humans. By using Agents to represent different parts of your Langchain program you give your project the option to be used by other parties for economic benefit.

Let’s take a look at a simple Langchain example, then see how we can extend this with agents.

A simple langchain example

Let’s create a simple script that can find any information in a PDF. Using a document loader from Langchain, and FAISS vector store along with OpenAI, we can load the PDF, use FAISS to create a vector store, open_ai to create embeddings on the documents, and then use FAISS to do a similarity search. Quite complicated for a small example, but it is only a handful of lines of code:

from langchain_community.document_loaders import PyPDFLoader
import os
from langchain_community.vectorstores import FAISS
from langchain_openai import OpenAIEmbeddings
 
openai_api_key = os.environ['OPENAI_API_KEY']
 
loader = PyPDFLoader("./your-pdf.pdf")
pages = loader.load_and_split()
 
faiss_index = FAISS.from_documents(pages, OpenAIEmbeddings(openai_api_key=openai_api_key))
 
docs = faiss_index.similarity_search("what problem does fetch solve?", k=2)
for doc in docs:
    print(str(doc.metadata["page"]) + ":", doc.page_content[:600])

However, there is a lot of smaller bits happening there. If we use agents for each step, then other agents can use those pieces of code 💡.

A simple communication with agents

Fetch.ai has the concept of an agent which at a base level an agent cannot do what Langchain does, however an agent is the component that links them together.

You can read more about agents communication in our guides.

Let’s install what we need:

poetry init
poetry add uagents

Check out more detailed instructions for installation of uagents library on your end.

First Agent

Our first agent is simple; it sends a message every two seconds to a static address. When this agent receives a message, it prints that to log:

first_agent.py
 
from uagents import Agent, Context, Model
from uagents.setup import fund_agent_if_low
 
 
class Message(Model):
    message: str
 
 
RECIPIENT_ADDRESS = "agent1qf4au6rzaauxhy2jze6v85rspgvredx9m42p0e0cukz0hv4dh2sqjuhujpp"
 
agent = Agent(
    name="agent",
    port=8000,
    seed="",
    endpoint=["http://127.0.0.1:8000/submit"],
)
 
fund_agent_if_low(agent.wallet.address())
 
 
@agent.on_interval(period=2.0)
async def send_message(ctx: Context):
    await ctx.send(RECIPIENT_ADDRESS, Message(message="hello there"))
 
 
@agent.on_message(model=Message)
async def message_handler(ctx: Context, sender: str, msg: Message):
    ctx.logger.info(f"Received message from {sender}: {msg.message}")
 
 
if __name__ == "__main__":
    agent.run()

This first agent introduces a few core concepts you will need to be aware of when creating any agent.

Agents are defined with the Agent class:

first_agent.py
 
agent = Agent(
    name="agent",
    port=8000,
    seed="",
    endpoint=["http://127.0.0.1:8000/submit"],
)

A seed is a unique phrase which uagents library uses to create a unique private key pair for your agent. If you change your seed you may lose access to previous messages, and also, the agent’s address registered to the Almanac will change subsequently. The port allows us to define a local port for messages to be received. The endpoint defines the path to the in-built Rest API. The name defines the name of the agent.

There are more options for the Agent class; see Agent Class for further reference.

We then need to define our communication model:

first_agent.py
 
class Message(Model):
    message: str

The Model defines the object sent from agent to agent and represents the type of messages the agent is able to handle. For explicit communication, both agents must handle the same Model class. Model is the base class that inherits from Pydantic BaseModel.

With the fund_agent_if_low(agent.wallet.address()) function, agents will ultimately pay for discoverability as the economy of agents matures. There is a placeholder for registration here.

Finally, agents have two decorated functions.

The first one is the agent.on_interval() function. This one sends a message every 2 seconds. ctx.send() has the args of destination_address and Message which we defined earlier.

first_agent.py
 
@agent.on_interval(period=2.0)
async def send_message(ctx: Context):
    await ctx.send(RECIPIENT_ADDRESS, Message(message="hello there"))

The second one is agent.on_message() which is a little different; when the agent receives a message at the endpoint we defined earlier, the uagent library unpacks the message and triggers any function which handles that message; in our case, the agent.on_message() function:

first_agent.py
 
@agent.on_message(model=Message)
async def message_handler(ctx: Context, sender: str, msg: Message):
    ctx.logger.info(f"Received message from {sender}: {msg.message}")

Second Agent

Agent two doesn’t do anything different to agent one; it has different args for the Agent instantiation, and instead of sending a message on_event("startup"), agent two just logs its address to screen. Whenever agent two receives a message matching Message data model, it will send a response to the sender.

second_agent.py
 
from uagents.setup import fund_agent_if_low
from uagents import Agent, Context, Model
 
 
class Message(Model):
    message: str
 
 
agent = Agent(
    name="agent 2",
    port=8001,
    seed="",
    endpoint=["http://127.0.0.1:8001/submit"],
)
 
fund_agent_if_low(agent.wallet.address())
 
 
@agent.on_event("startup")
async def start(ctx: Context):
    ctx.logger.info(f"agent address is {agent.address}")
 
 
@agent.on_message(model=Message)
async def message_handler(ctx: Context, sender: str, msg: Message):
    ctx.logger.info(f"Received message from {sender}: {msg.message}")
 
    await ctx.send(sender, Message(message="hello there"))
 
 
if __name__ == "__main__":
    agent.run()

Okay, let’s now run these agents.

Running the agents

Let’s run the second agent’s script first using this command: poetry run python second_agent.py

We must run the second agent first to get its unique address. This is shown in output in the log. Let’s update first_agent.py script by filling the RECIPIENT_ADDRESS field with the address of the second agent from of the output we previously got by running second_agent.py script.

Updated first_agent.py script sample:

first_agent.py
from uagents import Agent, Context, Model
from uagents.setup import fund_agent_if_low
 
class Message(Model):
    message: bool
 
RECIPIENT_ADDRESS="agent...."
 
agent = Agent(
        ...

Then, let’s run the script for the first agent using this command: poetry run python first_agent.py

Great! You should now be seeing some log out output with our messages being displayed.

Output

  • First Agent:

    INFO:     [agent]: Registering on almanac contract...
INFO:     [agent]: Registering on almanac contract...complete
INFO:     [agent]: Starting server on http://0.0.0.0:8000 (Press CTRL+C to quit)
INFO:     [agent]: Received message from agent1qf4au6rzaauxhy2jze6v85rspgvredx9m42p0e0cukz0hv4dh2sqjuhujpp: hello there
INFO:     [agent]: Received message from agent1qf4au6rzaauxhy2jze6v85rspgvredx9m42p0e0cukz0hv4dh2sqjuhujpp: hello there
INFO:     [agent]: Received message from agent1qf4au6rzaauxhy2jze6v85rspgvredx9m42p0e0cukz0hv4dh2sqjuhujpp: hello there

  • Second Agent:

    INFO:     [agent 2]: Registering on almanac contract...
INFO:     [agent 2]: Registering on almanac contract...complete
INFO:     [agent 2]: agent address is agent1qf4au6rzaauxhy2jze6v85rspgvredx9m42p0e0cukz0hv4dh2sqjuhujpp
INFO:     [agent 2]: Starting server on http://0.0.0.0:8001 (Press CTRL+C to quit)

Wrapping them together - Building a Function

Let’s go further now and change our agents scripts by splitting the logic of the Langchain example above. Let’s have one agent that sends a PDF path and questions it wants to be answered about that PDF by the other agent. Conversely, the other agent returns information on the PDF based on the questions asked by using Langchain tools.

Agent one: providing PDF and requesting information

This agent sends DocumentUnderstanding model which contains a local path to a PDF, and a question that the other agent must answer about the PDF. It’s a small update on our first agent script.

However now, .on_message(model=DocumentsResponse) expects a DocumentsResponse object instead of a string.

To learn more about communication with other agents check out the following Guide.

langchain_agent_one.py
 
from uagents import Agent, Context, Protocol, Model
from ai_engine import UAgentResponse, UAgentResponseType
from typing import List
 
 
class DocumentUnderstanding(Model):
    pdf_path: str
    question: str
 
 
class DocumentsResponse(Model):
    learnings: List
 
 
agent = Agent(
    name="find_in_pdf",
    seed="",
    port=8001,
    endpoint=["http://127.0.0.1:8001/submit"]
)
 
print("uAgent address: ", agent.address)
summary_protocol = Protocol("Text Summarizer")
 
RECIPIENT_PDF_AGENT = ""
 
 
@agent.on_event("startup")
async def on_startup(ctx: Context):
    await ctx.send(RECIPIENT_PDF_AGENT,
                   DocumentUnderstanding(pdf_path="../a-little-story.pdf", question="What's the synopsis?"))
 
 
@agent.on_message(model=DocumentsResponse)
async def document_load(ctx: Context, sender: str, msg: DocumentsResponse):
    ctx.logger.info(msg.learnings)
 
 
agent.include(summary_protocol, publish_manifest=True)
agent.run()

Agent two: wrapping the Langchain bits

Agent two defines the same models as agent one, but this time, it wraps the logic for the Langchain PDF question in the document_load() function, which is decorated with .on_message(model=DocumentUnderstanding, replies=DocumentsResponse) . You can specify a replies argument in your on_message decorators; this is useful for being more explicit with communication.

langchain_agent_two.py
 
from langchain_community.document_loaders import PyPDFLoader
import os
from langchain_community.vectorstores import FAISS
from langchain_openai import OpenAIEmbeddings
from uagents import Agent, Context, Protocol, Model
from typing import List
 
 
class DocumentUnderstanding(Model):
    pdf_path: str
    question: str
 
 
class DocumentsResponse(Model):
    learnings: List
 
 
pdf_questioning_agent = Agent(
    name="pdf_questioning_agent",
    seed="",
    port=8003,
    endpoint=["http://127.0.0.1:8003/submit"],
)
 
print("uAgent address: ", pdf_questioning_agent.address)
pdf_loader_protocol = Protocol("Text Summariser")
 
 
@pdf_questioning_agent.on_message(model=DocumentUnderstanding, replies=DocumentsResponse)
async def document_load(ctx: Context, sender: str, msg: DocumentUnderstanding):
    loader = PyPDFLoader(msg.pdf_path)
    pages = loader.load_and_split()
    openai_api_key = os.environ['OPENAI_API_KEY']
    learnings = []
 
    faiss_index = FAISS.from_documents(pages, OpenAIEmbeddings(openai_api_key=openai_api_key))
 
    docs = faiss_index.similarity_search(msg.question, k=2)
 
    for doc in docs:
        learnings.append(str(doc.metadata["page"]) + ":" + doc.page_content[:600])
 
    await ctx.send(sender, DocumentsResponse(learnings=learnings))
 
 
pdf_questioning_agent.include(pdf_loader_protocol, publish_manifest=True)
pdf_questioning_agent.run()

With these agents now being defined, it is time to run them. Let’s run Agent two first to get its address and then update Agent one to send a message to it by filling the RECIPIENT_PDF_AGENT field in-line.

Expected Output

Run poetry run python langchain_agent_two.py first and then poetry run python langchain_agent_one.py.

You should get something similar to the following for each agent:

  • Langchain Agent 1:

    uAgent address agent:  agent1qv9qmj3ug83vcrg774g2quz0urmlyqlmzh6a5t3r88q3neejlrffz405p7x
INFO:     [find_in_pdf]: Manifest published successfully: Text Summarizer
INFO:     [find_in_pdf]: Registration on Almanac API successful
INFO:     [find_in_pdf]: Almanac contract registration is up to date!
INFO:     [find_in_pdf]: Starting server on http://0.0.0.0:8001 (Press CTRL+C to quit)
INFO:     [find_in_pdf]: ['0: This is a simple story about two ... ]

  • Langchain Agent 2:

    uAgent address:  agent1qfwfpz6dpyzvz0f0tgxax58fpppaknnqm99fpggmm2wffjcxgqe8sn4cwx3
INFO:     [pdf_questioning_agent]: Manifest published successfully: Text Summarizer
INFO:     [pdf_questioning_agent]: Registration on Almanac API successful
INFO:     [pdf_questioning_agent]: Almanac contract registration is up to date!
INFO:     [pdf_questioning_agent]: Starting server on http://0.0.0.0:8003 (Press CTRL+C to quit)
INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
INFO:faiss.loader:Loading faiss with AVX2 support.
INFO:faiss.loader:Successfully loaded faiss with AVX2 support.
INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"

Next steps

In the next part of this introduction, we will create a multi-agent workflow where we split the logic of the PDF agent into two more agents: the first one which verifies a PDF, loads and then splits the PDF and the second one which uses FAISS to do the similarity search.

Last updated on
Introduction