Merge pull request #6 from topoteretes/feature/improve_actions

Feature/improve actions

.github/actions/image_builder/action.yaml

@@ -0,0 +1,36 @@
+name: 'Build Docker images for PromethAI'
+description: 'Build PromethAI-related Docker images and push to the Docker registry (AWS ECR)'
+inputs:
+  stage:
+    description: 'The stage of the pipeline, such as "dev" or "prd", for the PromethAI app'
+    required: true
+  aws_account_id:
+    description: 'The AWS account ID for the PromethAI app'
+    required: true
+  should_publish:
+    description: 'Whether to publish the PromethAI Docker image to AWS ECR; should be either "true" or "false"'
+    required: true
+  ecr_image_repo_name:
+    description: 'The Docker image ECR repository name for the PromethAI app, such as "workflows"'
+    required: true
+  dockerfile_location:
+    description: 'The  directory location of the Dockerfile for the PromethAI app'
+    required: true
+
+runs:
+  using: "composite"
+  steps:
+    - name: Build PromethAI App Docker image
+      shell: bash
+      env:
+        STAGE: ${{ inputs.stage }}
+      run: |
+        export SHA_SHORT="$(git rev-parse --short HEAD)"
+        export CUR_DATE="$(date +%Y%m%d%H%M%S)"
+        export VERSION="${{ inputs.stage }}-$CUR_DATE-$SHA_SHORT"
+        export STAGE="${{ inputs.stage }}"
+        export APP_DIR="$PWD/${{ inputs.dockerfile_location }}"
+        image_name="${{ inputs.ecr_image_repo_name }}" docker_login="true" version="$VERSION" account="${{ inputs.aws_account_id }}" app_dir="$APP_DIR" publish="${{ inputs.should_publish }}" ./bin/dockerize
+        echo "Docker tag is: $VERSION"
+        echo $VERSION > /tmp/.DOCKER_IMAGE_VERSION
+  

.github/workflows/cd.yaml

@@ -0,0 +1,76 @@
+name: Publishing promethai-backend Docker image
+
+on:
+  push:
+    branches:
+      - dev
+      - feature/*
+    paths-ignore:
+      - '**.md'
+
+env:
+  AWS_ROLE_DEV_CICD: "arn:aws:iam::463722570299:role/promethai-dev-base-role-github-ci-cd"
+  AWS_ACCOUNT_ID_DEV: "463722570299"
+
+jobs:
+
+  publish_docker_to_ecr:
+    name: Publish Docker PromethAI image
+    runs-on: ubuntu-latest
+    permissions:
+      id-token: write
+      contents: read
+    steps:
+      - name: Take code from repo
+        uses: actions/checkout@v3
+      - name: Set environment variable for stage
+        id: set-env
+        run: |
+          if [[ "${{ github.ref }}" == "refs/heads/main" ]]; then
+            echo "STAGE=prd" >> $GITHUB_ENV
+            echo "::set-output name=stage::prd"
+          else
+            echo "STAGE=dev" >> $GITHUB_ENV
+            echo "::set-output name=stage::dev"
+          fi
+      - name: Use output
+        run: echo "The stage is ${{ steps.set-env.outputs.stage }}"
+      - name: Configure AWS credentials
+        uses: aws-actions/configure-aws-credentials@v1
+        with:
+          role-to-assume: ${{ env.AWS_ROLE_DEV_CICD }}
+          aws-region: eu-west-1
+      - name: Create Docker image and push to ECR
+        uses: ./.github/actions/image_builder
+        id: generate-promethai-docker
+        with:
+          stage: dev
+          aws_account_id: ${{ env.AWS_ACCOUNT_ID_DEV }}
+          should_publish: true
+          ecr_image_repo_name: promethai-dev-backend-promethai-backend-memory
+          dockerfile_location: ./level_2
+      - name: Export Docker image tag
+        id: export-promethai-docker-tag
+        run: |
+          export DOCKER_TAG=$(cat /tmp/.DOCKER_IMAGE_VERSION)
+          echo "Docker tag is: $DOCKER_TAG"
+          echo "promethai_docker_tag_backend=$DOCKER_TAG" >> $GITHUB_OUTPUT
+    outputs:
+      promethai_docker_tag_backend: ${{ steps.export-promethai-docker-tag.outputs.promethai_docker_tag_backend }}
+
+#  apply_tf:
+#    name: Trigger terraform apply workflow
+#    runs-on: ubuntu-latest
+#    needs: publish_docker_to_ecr
+#    steps:
+#      - name: TF apply workflow triggers step
+#        uses: actions/github-script@v6
+#        with:
+#          github-token: ${{ secrets.PAT_FOR_CROSS_REPOS_CICD_TRIGGERING }}
+#          script: |
+#            await github.rest.actions.createWorkflowDispatch({
+#              owner: 'topoteretes',
+#              repo: 'PromethAI-Infra',
+#              workflow_id: 'terraform.apply.yml',
+#              ref: 'main'
+#            })

.github/workflows/cd_prd.yaml

@@ -0,0 +1,99 @@
+on:
+  push:
+    branches:
+        - main
+    paths-ignore:
+      - '**.md'
+      - 'examples/**'
+name: Publishing promethai-backend Docker image to prd ECR
+
+env:
+  AWS_ROLE_DEV_CICD: "arn:aws:iam::463722570299:role/promethai-dev-base-role-github-ci-cd"
+  AWS_ACCOUNT_ID_DEV: "463722570299"
+  ENVIRONMENT: prd
+
+jobs:
+
+  publish_docker_to_ecr:
+    name: Publish Docker PromethAI image
+    runs-on: ubuntu-latest
+    permissions:
+      id-token: write
+      contents: read
+    steps:
+      - name: Take code from repo
+        uses: actions/checkout@v3
+      - name: Set environment variable for stage
+        id: set-env
+        run: |
+          if [[ "${{ github.ref }}" == "refs/heads/main" ]]; then
+            echo "STAGE=prd" >> $GITHUB_ENV
+            echo "::set-output name=stage::prd"
+          else
+            echo "STAGE=dev" >> $GITHUB_ENV
+            echo "::set-output name=stage::dev"
+          fi
+      - name: Use output
+        run: echo "The stage is ${{ steps.set-env.outputs.stage }}"
+      - name: Configure AWS credentials
+        uses: aws-actions/configure-aws-credentials@v1
+        with:
+          role-to-assume: ${{ env.AWS_ROLE_DEV_CICD }}
+          aws-region: eu-west-1
+      - name: Create Docker image and push to ECR
+        uses: ./.github/actions/image_builder
+        id: generate-promethai-docker
+        with:
+          stage: prd
+          aws_account_id: ${{ env.AWS_ACCOUNT_ID_DEV }}
+          should_publish: true
+          ecr_image_repo_name: promethai-prd-backend-promethai-backend-memory
+          dockerfile_location: ./level_2
+      - name: Export Docker image tag
+        id: export-promethai-docker-tag
+        run: |
+          export DOCKER_TAG=$(cat /tmp/.DOCKER_IMAGE_VERSION)
+          echo "Docker tag is: $DOCKER_TAG"
+          echo "promethai_docker_tag_backend=$DOCKER_TAG" >> $GITHUB_OUTPUT
+
+#      - name: Create Tag and Release
+#        runs-on: ubuntu-latest
+#        uses: actions/checkout@v3
+#        needs: publish_docker_to_ecr  # ensure this job runs after Docker image is pushed
+#        steps:
+#          - name: Check out code
+#            uses: actions/checkout@v3
+#          - name: Bump version and push tag
+#            id: bump_version_and_push_tag
+#            uses: anothrNick/github-tag-action@1.34.0
+#            env:
+#              GITHUB_TOKEN: ${{ secrets.PAT_FOR_CROSS_REPOS_CICD_TRIGGERING }}
+#              WITH_V: true
+#              DEFAULT_BUMP: 'minor'  # or 'minor' or 'major'
+#          - name: Create Release
+#            id: create_release
+#            uses: actions/create-release@v1
+#            env:
+#              GITHUB_TOKEN: ${{ secrets.PAT_FOR_CROSS_REPOS_CICD_TRIGGERING }}
+#            with:
+#              tag_name: ${{ steps.bump_version_and_push_tag.outputs.tag }}
+#              release_name: Release ${{ steps.bump_version_and_push_tag.outputs.tag }}
+    outputs:
+      promethai_docker_tag_backend: ${{ steps.export-promethai-docker-tag.outputs.promethai_docker_tag_backend }}
+
+#  apply_tf:
+#    name: Trigger terraform apply workflow
+#    runs-on: ubuntu-latest
+#    needs: publish_docker_to_ecr
+#    steps:
+#      - name: TF apply workflow triggers step
+#        uses: actions/github-script@v6
+#        with:
+#          github-token: ${{ secrets.PAT_FOR_CROSS_REPOS_CICD_TRIGGERING }}
+#          script: |
+#            await github.rest.actions.createWorkflowDispatch({
+#              owner: 'topoteretes',
+#              repo: 'PromethAI-Infra',
+#              workflow_id: 'terraform.apply.yml',
+#              ref: 'main'
+#            })

.github/workflows/ci.yaml

@@ -0,0 +1,25 @@
+name: Test build docker image for PromethAI backend app
+
+on: pull_request
+
+env:
+  AWS_ACCOUNT_ID_DEV: "463722570299"
+
+jobs:
+
+  build_docker:
+    name: Build PromethAI Backend Docker App Image
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check out PromethAI code
+        uses: actions/checkout@v3
+
+      - name: Build PromethAI backend Docker image tag
+        id: backend-docker-tag
+        run: |
+          export SHA_SHORT="$(git rev-parse --short HEAD)"
+          export CUR_DATE="$(date +%Y%m%d%H%M%S)"
+          export VERSION="dev-$CUR_DATE-$SHA_SHORT"
+          image_name="backend-memory" docker_login="false" version="$VERSION" account="${{ env.AWS_ACCOUNT_ID_DEV }}" app_dir="level_2" publish="false" ./bin/dockerize
+          export DOCKER_TAG=$(cat level_2/tmp/.DOCKER_IMAGE_VERSION)
+          echo "Successfully built PromethAI backend Docker tag is: $DOCKER_TAG"

bin/dockerize

@@ -0,0 +1,30 @@
+STAGE=${stage:-"dev"}
+SHA_SHORT="$(git rev-parse --short HEAD)"
+CUR_DATE="$(date +%Y%m%d%H%M%S)"
+VERSION="$STAGE-$CUR_DATE-$SHA_SHORT"
+IMAGE_NAME=${image_name:-promethai-${STAGE}-promethai-backend-memory}
+
+REPO_NAME="${AWS_REPOSITORY}/${IMAGE_NAME}"
+FULL_IMAGE_NAME="${REPO_NAME}:${VERSION}"
+APP_DIR=${app_dir:-"./level_2"}   # Updated this line
+
+PUBLISH=${publish:-false}
+
+echo "Building docker image ${FULL_IMAGE_NAME} located in dir ${app_dir}"
+
+pushd "${APP_DIR}" &&
+  docker buildx build --platform linux/amd64 \
+    --build-arg STAGE=${STAGE} \
+    -t "${FULL_IMAGE_NAME}" . &&
+  echo "${VERSION}" >/tmp/.DOCKER_IMAGE_VERSION &&
+  echo "Successfully built docker image ${FULL_IMAGE_NAME}"
+
+if [ "${PUBLISH}" = true ]; then
+  echo "Pushing docker image ${FULL_IMAGE_NAME} to ECR repository to AWS account ${AWS_DEPLOYMENT_ACCOUNT}"
+  if [ "${PUBLISH}" = true ]; then
+    echo "logging in"
+    aws ecr get-login-password --region "${AWS_REGION}" | docker login --username AWS --password-stdin "${AWS_REPOSITORY}"
+  fi
+  docker push "${FULL_IMAGE_NAME}" &&
+    echo "Successfully pushed docker image ${FULL_IMAGE_NAME} to ECR repository"
+fi

level_2/Readme.md

@@ -9,7 +9,8 @@ Initial code lets you do three operations:
 
 1. Add to memory
 2. Retrieve from memory
-3. Structure the data to schema and load to duckdb
+3. Structure the data to schema 
+4. Load to a database
 
 #How to use
 
@@ -38,16 +39,17 @@ The Memory API provides the following endpoints:
 - /run-buffer (POST)
 - /buffer/create-context (POST)
 
-Here is a payload example:
 
+
+## How To Get Started
+
+1. We do a post request to add-memory endpoint with the following payload:
+It will upload Jack London "Call of the Wild" to SEMANTIC memory
 ```
-{
+curl -X POST http://localhost:8000/semantic/add-memory -H "Content-Type: application/json" -d '{
   "payload": {
     "user_id": "681",
-    "session_id": "471",
-    "model_speed": "slow",
-    "prompt": "I want ",
-    "pdf_url": "https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf",
+    "prompt": "I am adding docs",
     "params": {
         "version": "1.0",
         "agreement_id": "AG123456",
@@ -60,7 +62,75 @@ Here is a payload example:
         "license": "MIT",
         "validity_start": "2023-08-01",
         "validity_end": "2024-07-31"
+    },
+    "loader_settings": {
+        "format": "PDF",
+        "source": "url",
+        "path": "https://www.ibiblio.org/ebooks/London/Call%20of%20Wild.pdf"
     }
   }
-}
-```
+}'
+```
+
+2. We run the buffer with the prompt "I want to know how does Buck adapt to life in the wild and then have that info translated to German "
+
+```
+curl -X POST http://localhost:8000/run-buffer -H "Content-Type: application/json" -d '{
+  "payload": {
+    "user_id": "681",
+    "prompt": "I want to know how does Buck adapt to life in the wild and then have that info translated to German ",
+    "params": {
+        "version": "1.0",
+        "agreement_id": "AG123456",
+        "privacy_policy": "https://example.com/privacy",
+        "terms_of_service": "https://example.com/terms",
+        "format": "json",
+        "schema_version": "1.1",
+        "checksum": "a1b2c3d4e5f6",
+        "owner": "John Doe",
+        "license": "MIT",
+        "validity_start": "2023-08-01",
+        "validity_end": "2024-07-31"
+    },
+    "attention_modulators": {
+        "relevance": 0.0,
+        "saliency": 0.1
+    }
+  }
+}'
+```
+
+
+Other attention modulators that could be implemented: 
+
+        "frequency": 0.5, 
+        "repetition": 0.5,
+        "length": 0.5,
+        "position": 0.5,
+        "context": 0.5,
+        "emotion": 0.5,
+        "sentiment": 0.5,
+        "perspective": 0.5,
+        "style": 0.5,
+        "grammar": 0.5,
+        "spelling": 0.5,
+        "logic": 0.5,
+        "coherence": 0.5,
+        "cohesion": 0.5,
+        "plausibility": 0.5,
+        "consistency": 0.5,
+        "informativeness": 0.5,
+        "specificity": 0.5,
+        "detail": 0.5,
+        "accuracy": 0.5,
+        "topicality": 0.5,
+        "focus": 0.5,
+        "clarity": 0.5,
+        "simplicity": 0.5,
+        "naturalness": 0.5,
+        "fluency": 0.5,
+        "variety": 0.5,
+        "vividness": 0.5,
+        "originality": 0.5,
+        "creativity": 0.5,
+        "humor": 0.5,

level_2/api.py

@@ -1,23 +1,14 @@
-from io import BytesIO
+import logging
+import os
+from typing import Dict, Any
 
-from langchain.document_loaders import PyPDFLoader
-
-from level_2_pdf_vectorstore__dlt_contracts import Memory
+import uvicorn
 from fastapi import FastAPI
 from fastapi.responses import JSONResponse
 from pydantic import BaseModel
-from typing import Dict, Any
-import re
-import json
-import logging
-import os
-import uvicorn
-from fastapi import Request
-import yaml
-from fastapi import HTTPException
-from fastapi import FastAPI, UploadFile, File
-from typing import List
-import requests
+
+from level_2_pdf_vectorstore__dlt_contracts import Memory
+from dotenv import load_dotenv
 # Set up logging
 logging.basicConfig(
     level=logging.INFO,  # Set the logging level (e.g., DEBUG, INFO, WARNING, ERROR, CRITICAL)
@@ -25,26 +16,21 @@ logging.basicConfig(
 )
 
 logger = logging.getLogger(__name__)
-from dotenv import load_dotenv
+
 
 
 load_dotenv()
 OPENAI_API_KEY = os.getenv("OPENAI_API_KEY", "")
-
 app = FastAPI(debug=True)
 
 
-from fastapi import Depends
-
-
 class ImageResponse(BaseModel):
     success: bool
     message: str
 
-
-
-
-@app.get("/", )
+@app.get(
+    "/",
+)
 async def root():
     """
     Root endpoint that returns a welcome message.
@@ -57,187 +43,99 @@ def health_check():
     Health check endpoint that returns the server status.
     """
     return {"status": "OK"}
-
-
-
-
-
-
-#curl -X POST -H "Content-Type: application/json" -d '{"data": "YourPayload"}' -F "files=@/path/to/your/pdf/file.pdf" http://127.0.0.1:8000/upload/
-
-
 class Payload(BaseModel):
     payload: Dict[str, Any]
 
-# @app.post("/upload/", response_model=dict)
-# async def upload_pdf_and_payload(
-#         payload: Payload,
-#         # files: List[UploadFile] = File(...),
-# ):
-#     try:
-#         # Process the payload
-#         decoded_payload = payload.payload
-#     # except:
-#     #     pass
-#     #
-#     # return JSONResponse(content={"response": decoded_payload}, status_code=200)
-#
-#         # Download the remote PDF if URL is provided
-#         if 'pdf_url' in decoded_payload:
-#             pdf_response = requests.get(decoded_payload['pdf_url'])
-#             pdf_content = pdf_response.content
-#
-#             logging.info("Downloaded PDF from URL")
-#
-#             # Create an in-memory file-like object for the PDF content
-#             pdf_stream = BytesIO(pdf_content)
-#
-#             contents = pdf_stream.read()
-#
-#             tmp_location = os.path.join('/tmp', "tmp.pdf")
-#             with open(tmp_location, 'wb') as tmp_file:
-#                 tmp_file.write(contents)
-#
-#             logging.info("Wrote PDF from URL")
-#
-#             # Process the PDF using PyPDFLoader
-#             loader = PyPDFLoader(tmp_location)
-#             pages = loader.load_and_split()
-#             logging.info(" PDF split into pages")
-#             Memory_ = Memory(index_name="my-agent", user_id='555' )
-#             await Memory_.async_init()
-#             Memory_._add_episodic_memory(user_input="I want to get a schema for my data", content =pages)
-#
-#
-#             # Run the buffer
-#             response = Memory_._run_buffer(user_input="I want to get a schema for my data")
-#             return JSONResponse(content={"response": response}, status_code=200)
-#
-#             #to do: add the user id to the payload
-#             #to do add the raw pdf to payload
-#             # bb = await Memory_._run_buffer(user_input=decoded_payload['prompt'])
-#             # print(bb)
-#
-#
-#     except Exception as e:
-#
-#         return {"error": str(e)}
-#             # Here you can perform your processing on the PDF contents
-#             # results.append({"filename": file.filename, "size": len(contents)})
-#
-#             # Append the in-memory file to the files list
-#             # files.append(UploadFile(pdf_stream, filename="downloaded.pdf"))
-#
-
-
 def memory_factory(memory_type):
     load_dotenv()
+
     class Payload(BaseModel):
         payload: Dict[str, Any]
+
     @app.post("/{memory_type}/add-memory", response_model=dict)
     async def add_memory(
-            payload: Payload,
-            # files: List[UploadFile] = File(...),
+        payload: Payload,
+        # files: List[UploadFile] = File(...),
     ):
         try:
-
             logging.info(" Init PDF processing")
 
-
             decoded_payload = payload.payload
 
-            if 'pdf_url' in decoded_payload:
-                pdf_response = requests.get(decoded_payload['pdf_url'])
-                pdf_content = pdf_response.content
+            Memory_ = Memory(user_id=decoded_payload["user_id"])
 
-                logging.info("Downloaded PDF from URL")
+            await Memory_.async_init()
 
-                # Create an in-memory file-like object for the PDF content
-                pdf_stream = BytesIO(pdf_content)
-
-                contents = pdf_stream.read()
-
-                tmp_location = os.path.join('/tmp', "tmp.pdf")
-                with open(tmp_location, 'wb') as tmp_file:
-                    tmp_file.write(contents)
-
-                logging.info("Wrote PDF from URL")
-
-                # Process the PDF using PyPDFLoader
-                loader = PyPDFLoader(tmp_location)
-                # pages = loader.load_and_split()
-                logging.info(" PDF split into pages")
-
-                Memory_ = Memory(user_id=decoded_payload['user_id'])
-
-                await Memory_.async_init()
-
-                memory_class = getattr(Memory_, f"_add_{memory_type}_memory", None)
-                output= await memory_class(observation=str(loader), params =decoded_payload['params'])
-                return JSONResponse(content={"response": output}, status_code=200)
+            memory_class = getattr(Memory_, f"_add_{memory_type}_memory", None)
+            output = await memory_class(
+                observation=decoded_payload["prompt"],
+                loader_settings=decoded_payload["loader_settings"],
+                params=decoded_payload["params"],
+            )
+            return JSONResponse(content={"response": output}, status_code=200)
 
         except Exception as e:
-
-            return JSONResponse(content={"response": {"error": str(e)}}, status_code=503)
+            return JSONResponse(
+                content={"response": {"error": str(e)}}, status_code=503
+            )
 
     @app.post("/{memory_type}/fetch-memory", response_model=dict)
     async def fetch_memory(
-            payload: Payload,
-            # files: List[UploadFile] = File(...),
+        payload: Payload,
+        # files: List[UploadFile] = File(...),
     ):
         try:
-
             decoded_payload = payload.payload
 
-            Memory_ = Memory(user_id=decoded_payload['user_id'])
+            Memory_ = Memory(user_id=decoded_payload["user_id"])
 
             await Memory_.async_init()
 
             memory_class = getattr(Memory_, f"_fetch_{memory_type}_memory", None)
-            output = memory_class(observation=decoded_payload['prompt'])
+            output = memory_class(observation=decoded_payload["prompt"])
             return JSONResponse(content={"response": output}, status_code=200)
 
         except Exception as e:
-
-            return JSONResponse(content={"response": {"error": str(e)}}, status_code=503)
+            return JSONResponse(
+                content={"response": {"error": str(e)}}, status_code=503
+            )
 
     @app.post("/{memory_type}/delete-memory", response_model=dict)
     async def delete_memory(
-            payload: Payload,
-            # files: List[UploadFile] = File(...),
+        payload: Payload,
+        # files: List[UploadFile] = File(...),
     ):
         try:
-
             decoded_payload = payload.payload
 
-            Memory_ = Memory(user_id=decoded_payload['user_id'])
+            Memory_ = Memory(user_id=decoded_payload["user_id"])
 
             await Memory_.async_init()
 
             memory_class = getattr(Memory_, f"_delete_{memory_type}_memory", None)
-            output = memory_class(observation=decoded_payload['prompt'])
+            output = memory_class(observation=decoded_payload["prompt"])
             return JSONResponse(content={"response": output}, status_code=200)
 
         except Exception as e:
+            return JSONResponse(
+                content={"response": {"error": str(e)}}, status_code=503
+            )
 
-            return JSONResponse(content={"response": {"error": str(e)}}, status_code=503)
 
 memory_list = ["episodic", "buffer", "semantic"]
 for memory_type in memory_list:
     memory_factory(memory_type)
 
 
-
 @app.get("/available-buffer-actions", response_model=dict)
 async def available_buffer_actions(
-        payload: Payload,
-        # files: List[UploadFile] = File(...),
+    payload: Payload,
+    # files: List[UploadFile] = File(...),
 ):
     try:
-
         decoded_payload = payload.payload
 
-        Memory_ = Memory(user_id=decoded_payload['user_id'])
+        Memory_ = Memory(user_id=decoded_payload["user_id"])
 
         await Memory_.async_init()
 
@@ -246,126 +144,73 @@ async def available_buffer_actions(
         return JSONResponse(content={"response": output}, status_code=200)
 
     except Exception as e:
-
         return JSONResponse(content={"response": {"error": str(e)}}, status_code=503)
 
+
 @app.post("/run-buffer", response_model=dict)
-async def available_buffer_actions(
-        payload: Payload,
-        # files: List[UploadFile] = File(...),
+async def run_buffer(
+    payload: Payload,
+    # files: List[UploadFile] = File(...),
 ):
     try:
-
         decoded_payload = payload.payload
 
-        Memory_ = Memory(user_id=decoded_payload['user_id'])
+        Memory_ = Memory(user_id=decoded_payload["user_id"])
 
         await Memory_.async_init()
 
         # memory_class = getattr(Memory_, f"_delete_{memory_type}_memory", None)
-        output = await Memory_._run_buffer(user_input=decoded_payload['prompt'], params=decoded_payload['params'])
+        output = await Memory_._run_main_buffer(
+            user_input=decoded_payload["prompt"], params=decoded_payload["params"], attention_modulators=decoded_payload["attention_modulators"]
+        )
         return JSONResponse(content={"response": output}, status_code=200)
 
     except Exception as e:
-
         return JSONResponse(content={"response": {"error": str(e)}}, status_code=503)
 
+
 @app.post("/buffer/create-context", response_model=dict)
-async def available_buffer_actions(
-        payload: Payload,
-        # files: List[UploadFile] = File(...),
+async def create_context(
+    payload: Payload,
+    # files: List[UploadFile] = File(...),
 ):
     try:
-
         decoded_payload = payload.payload
 
-        Memory_ = Memory(user_id=decoded_payload['user_id'])
+        Memory_ = Memory(user_id=decoded_payload["user_id"])
 
         await Memory_.async_init()
 
         # memory_class = getattr(Memory_, f"_delete_{memory_type}_memory", None)
-        output = await Memory_._create_buffer_context(user_input=decoded_payload['prompt'], params=decoded_payload['params'])
+        output = await Memory_._create_buffer_context(
+            user_input=decoded_payload["prompt"], params=decoded_payload["params"], attention_modulators=decoded_payload["attention_modulators"]
+        )
         return JSONResponse(content={"response": output}, status_code=200)
 
     except Exception as e:
-
         return JSONResponse(content={"response": {"error": str(e)}}, status_code=503)
 
 
-#
-    #     # Process each uploaded PDF file
-    #     results = []
-    #     for file in files:
-    #         contents = await file.read()
-    #         tmp_location = os.path.join('/tmp', "tmp.pdf")
-    #         with open(tmp_location, 'wb') as tmp_file:
-    #             tmp_file.write(contents)
-    #         loader = PyPDFLoader(tmp_location)
-    #         pages = loader.load_and_split()
-    #
-    #         stm = ShortTermMemory(user_id=decoded_payload['user_id'])
-    #         stm.episodic_buffer.main_buffer(prompt=decoded_payload['prompt'], pages=pages)
-    #         # Here you can perform your processing on the PDF contents
-    #         results.append({"filename": file.filename, "size": len(contents)})
-    #
-    #     return {"message": "Upload successful", "results": results}
-    #
-    # except Exception as e:
-    #     return {"error": str(e)}
+@app.post("/buffer/get-tasks", response_model=dict)
+async def create_context(
+    payload: Payload,
+    # files: List[UploadFile] = File(...),
+):
+    try:
+        decoded_payload = payload.payload
 
+        Memory_ = Memory(user_id=decoded_payload["user_id"])
 
-# @app.post("/clear-cache", response_model=dict)
-# async def clear_cache(request_data: Payload) -> dict:
-#     """
-#     Endpoint to clear the cache.
-#
-#     Parameters:
-#     request_data (Payload): The request data containing the user and session IDs.
-#
-#     Returns:
-#     dict: A dictionary with a message indicating the cache was cleared.
-#     """
-#     json_payload = request_data.payload
-#     agent = Agent()
-#     agent.set_user_session(json_payload["user_id"], json_payload["session_id"])
-#     try:
-#         agent.clear_cache()
-#         return JSONResponse(content={"response": "Cache cleared"}, status_code=200)
-#     except Exception as e:
-#         raise HTTPException(status_code=500, detail=str(e))
-#
-# @app.post("/correct-prompt-grammar", response_model=dict)
-# async def prompt_to_correct_grammar(request_data: Payload) -> dict:
-#     json_payload = request_data.payload
-#     agent = Agent()
-#     agent.set_user_session(json_payload["user_id"], json_payload["session_id"])
-#     logging.info("Correcting grammar %s", json_payload["prompt_source"])
-#
-#     output = agent.prompt_correction(json_payload["prompt_source"], model_speed= json_payload["model_speed"])
-#     return JSONResponse(content={"response": {"result": json.loads(output)}})
-
-
-# @app.post("/action-add-zapier-calendar-action", response_model=dict,dependencies=[Depends(auth)])
-# async def action_add_zapier_calendar_action(
-#     request: Request, request_data: Payload
-# ) -> dict:
-#     json_payload = request_data.payload
-#     agent = Agent()
-#     agent.set_user_session(json_payload["user_id"], json_payload["session_id"])
-#     # Extract the bearer token from the header
-#     auth_header = request.headers.get("Authorization")
-#     if auth_header:
-#         bearer_token = auth_header.replace("Bearer ", "")
-#     else:
-#         bearer_token = None
-#     outcome = agent.add_zapier_calendar_action(
-#         prompt_base=json_payload["prompt_base"],
-#         token=bearer_token,
-#         model_speed=json_payload["model_speed"],
-#     )
-#     return JSONResponse(content={"response": outcome})
+        await Memory_.async_init()
 
+        # memory_class = getattr(Memory_, f"_delete_{memory_type}_memory", None)
+        output = await Memory_._get_task_list(
+            user_input=decoded_payload["prompt"], params=decoded_payload["params"], attention_modulators=decoded_payload["attention_modulators"]
+        )
+        return JSONResponse(content={"response": output}, status_code=200)
 
+    except Exception as e:
+        return JSONResponse(content={"response": {"error": str(e)}}, status_code=503)
 
 def start_api_server(host: str = "0.0.0.0", port: int = 8000):
     """