Feat: Adds cognito and memory

This commit is contained in:
2025-10-22 11:24:38 -03:00
parent f71b054dca
commit d37d5132eb
45 changed files with 3983 additions and 0 deletions

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FROM public.ecr.aws/lambda/python:3.13
# Copy requirements.txt
COPY requirements.txt ${LAMBDA_TASK_ROOT}
# Install the specified packages
RUN pip install -r requirements.txt
# Copy function code
COPY ./ ${LAMBDA_TASK_ROOT}
# Set the CMD to your handler (could also be done as a parameter override outside of the Dockerfile)
CMD ["agent.agent_call"]

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assistente/README.md Normal file
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# ChatBot
## Getting started
To make it easy for you to get started with GitLab, here's a list of recommended next steps.
Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!
## Add your files
- [ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files
- [ ] [Add files using the command line](https://docs.gitlab.com/topics/git/add_files/#add-files-to-a-git-repository) or push an existing Git repository with the following command:
```
cd existing_repo
git remote add origin https://gitlab.shared.cloud.dnxbrasil.com.br/dnx-br/clientes/ifsp/chatbot.git
git branch -M main
git push -uf origin main
```
## Integrate with your tools
- [ ] [Set up project integrations](https://gitlab.shared.cloud.dnxbrasil.com.br/dnx-br/clientes/ifsp/chatbot/-/settings/integrations)
## Collaborate with your team
- [ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/)
- [ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html)
- [ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically)
- [ ] [Enable merge request approvals](https://docs.gitlab.com/ee/user/project/merge_requests/approvals/)
- [ ] [Set auto-merge](https://docs.gitlab.com/user/project/merge_requests/auto_merge/)
## Test and Deploy
Use the built-in continuous integration in GitLab.
- [ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/)
- [ ] [Analyze your code for known vulnerabilities with Static Application Security Testing (SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)
- [ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)
- [ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)
- [ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)
***
# Editing this README
When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thanks to [makeareadme.com](https://www.makeareadme.com/) for this template.
## Suggestions for a good README
Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.
## Name
Choose a self-explaining name for your project.
## Description
Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors.
## Badges
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## Visuals
Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method.
## Installation
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## Usage
Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.
## Support
Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc.
## Roadmap
If you have ideas for releases in the future, it is a good idea to list them in the README.
## Contributing
State if you are open to contributions and what your requirements are for accepting them.
For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.
You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.
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## License
For open source projects, say how it is licensed.
## Project status
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import json
import time
from langchain_aws import ChatBedrock
from langchain_aws.retrievers import AmazonKnowledgeBasesRetriever
from langgraph.checkpoint.memory import MemorySaver
from langgraph.prebuilt import create_react_agent
from langfuse import Langfuse
from langfuse.langchain import CallbackHandler
from tools import secrets,dynamo
langfuse = Langfuse(
public_key=json.loads(secrets.get_secret())['api-langfuse-public'],
secret_key=json.loads(secrets.get_secret())['api-langfuse-secret'],
host="http://44.200.69.191:3000/"
)
langfuse_handler = CallbackHandler()
def agent_call(event,context):
llm = ChatBedrock(
model_id="us.anthropic.claude-sonnet-4-20250514-v1:0",
region_name="us-east-1",
#aws_access_key_id=os.environ["AWS_ACCESS_KEY_ID"],
#aws_secret_access_key=os.environ["AWS_SECRET_ACCESS_KEY"],
#aws_session_token=os.environ["AWS_SESSION_TOKEN"],
model_kwargs={"temperature": 0.1, 'max_tokens': 1000,},
provider='anthropic'
)
retriever = AmazonKnowledgeBasesRetriever(
knowledge_base_id="PETAZDUOFZ",
region_name="us-east-1",
retrieval_config={"vectorSearchConfiguration": {"numberOfResults": 4}},
)
username=(event['username'])
if event['chat_history']==[]:
history=dynamo.read_memory('frente')
else:
history=event['chat_history']
memory = MemorySaver()
model = llm
tools = [retriever.as_tool()]
prompt="""<rules>
Act like a human in Portuguese Brasil.
You are a assistant for employees and store owners that wants to know about the COMM.pix product, wich makes it possible to use Pix as a payment method outside of Brasil.
Answer questions based on the documents that you have access using the retriever tool, do not create information.
The chat history will be given, without any documents.
If there are info or context missing ask the user before proceding with the document retrieval.
Also return the title of the source document.
If you don't know the answer or can't find it, say so.
<\rules>
<glossary>
<\glossary>
<chain_of_thought>
<\chain_of_thought>
<general_info>
<\general_info>
Answer the following questions as best you can. You have access to the following tools:
{tools}
Chat History:"""+str(history)
agent_executor = create_react_agent(model, tools, checkpointer=memory, prompt=prompt)
config = {"configurable": {"thread_id": "abc123"},"callbacks": [langfuse_handler]}
input_message = event["message"]
dict=input_message[0]
#input_message=[{"role":"user","content":"aluno superior, nunca recebi auxilio, campus são paulo, Meu pai não é registrado, como faço para ganhar auxilio?"}]
response=""
for step in agent_executor.stream({"messages": input_message}, config, stream_mode="values"):
response={"json":(step["messages"][-1].text())}
response['dynamo_reponse']=dynamo.write_memory(username,int(time.time()),dict['role'],dict['content'])
response['chat_history']=history
return (response)

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langchain_core
langchain
langchain_aws
langgraph
langfuse
boto3

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import boto3
def write_memory(user,timestamp,role,content):
dynamodb = boto3.resource('dynamodb')
table = dynamodb.Table('assistente-produtos-servicos-memoria') # Replace 'YourTableName' with your actual table name
item_data = {
'UserId': user, # Replace with your partition key attribute and value
'Timestamp': timestamp, # Replace with your sort key attribute and value (if applicable)
'role': role,
'content': content
}
try:
response = table.put_item(Item=item_data)
except Exception as e:
print("Error adding item:", e)
def read_memory(userid):
dynamodb = boto3.resource('dynamodb')
table = dynamodb.Table('assistente-produtos-servicos-memoria')
# Query parameters
try:
response = table.query(
KeyConditionExpression=boto3.dynamodb.conditions.Key('UserId').eq(userid),
ScanIndexForward=False, # Descending order
Limit=30
)
items = response.get('Items', [])
if items:
latest_items = items
return latest_items
else:
return []
except Exception as e:
print("Error querying DynamoDB:", str(e))

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import boto3
from botocore.exceptions import ClientError
def get_secret():
secret_name = "assistente-produtos-servicos"
region_name = "us-east-1"
# Create a Secrets Manager client
session = boto3.session.Session()
client = session.client(
service_name='secretsmanager',
region_name=region_name
)
try:
get_secret_value_response = client.get_secret_value(
SecretId=secret_name
)
except ClientError as e:
# For a list of exceptions thrown, see
# https://docs.aws.amazon.com/secretsmanager/latest/apireference/API_GetSecretValue.html
raise e
secret = get_secret_value_response['SecretString']
return secret