bf5c391e47
Signed-off-by: JoshuaL3000 <joshua.jian.ern.liew@intel.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
132 lines
6.4 KiB
Markdown
132 lines
6.4 KiB
Markdown
# Workflow Executor Agent
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## Overview
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GenAI Workflow Executor Example showcases the capability to handle data/AI workflow operations via LangChain agents to execute custom-defined workflow-based tools. These workflow tools can be interfaced from any 3rd-party tools in the market (no-code/low-code/IDE) such as Alteryx, RapidMiner, Power BI, Intel Data Insight Automation which allows users to create complex data/AI workflow operations for different use-cases.
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### Workflow Executor
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This example demonstrates a single React-LangGraph with a `Workflow Executor` tool to ingest a user prompt to execute workflows and return an agent reasoning response based on the workflow output data.
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First the LLM extracts the relevant information from the user query based on the schema of the tool in `tools/tools.yaml`. Then the agent sends this `AgentState` to the `Workflow Executor` tool.
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`Workflow Executor` tool uses `EasyDataSDK` class as seen under `tools/sdk.py` to interface with several high-level API's. There are 3 steps to this tool implementation:
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1. Starts the workflow with workflow parameters and workflow id extracted from the user query.
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2. Periodically checks the workflow status for completion or failure. This may be through a database which stores the current status of the workflow
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3. Retrieves the output data from the workflow through a storage service.
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The `AgentState` is sent back to the LLM for reasoning. Based on the output data, the LLM generates a response to answer the user's input prompt.
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Below shows an illustration of this flow:
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### Workflow Serving for Agent
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As an example, here we have a Churn Prediction use-case workflow as the serving workflow for the agent execution. It is created through Intel Data Insight Automation platform. The image below shows a snapshot of the Churn Prediction workflow.
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The workflow contains 2 paths which can be seen in the workflow illustrated, the top path and bottom path.
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1. Top path - The training path which ends at the random forest classifier node is the training path. The data is cleaned through a series of nodes and used to train a random forest model for prediction.
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2. Bottom path - The inference path where trained random forest model is used for inferencing based on input parameter.
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For this agent workflow execution, the inferencing path is executed to yield the final output result of the `Model Predictor` node. The same output is returned to the `Workflow Executor` tool through the `Langchain API Serving` node.
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There are `Serving Parameters` in the workflow, which are the tool input variables used to start a workflow instance obtained from `params` the LLM extracts from the user query. Below shows the parameter configuration option for the Intel Data Insight Automation workflow UI.
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Manually running the workflow yields the tabular data output as shown below:
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In the workflow serving for agent, this output will be returned to the `Workflow Executor` tool. The LLM can then answer the user's original question based on this output.
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To start prompting the agent microservice, we will use the following command for this use case:
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```sh
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curl http://${ip_address}:9090/v1/chat/completions -X POST -H "Content-Type: application/json" -d '{
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"query": "I have a data with gender Female, tenure 55, MonthlyAvgCharges 103.7. Predict if this entry will churn. My workflow id is '${workflow_id}'."
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}'
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```
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The user has to provide a `workflow_id` and workflow `params` in the query. `workflow_id` a unique id used for serving the workflow to the microservice. Notice that the `query` string includes all the workflow `params` which the user has defined in the workflow. The LLM will extract these parameters into a dictionary format for the workflow `Serving Parameters` as shown below:
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```python
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params = {"gender": "Female", "tenure": 55, "MonthlyAvgCharges": 103.7}
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```
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These parameters will be passed into the `Workflow Executor` tool to start the workflow execution of specified `workflow_id`. Thus, everything will be handled via the microservice.
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And finally here are the results from the microservice logs:
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## Microservice Setup
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### Start Agent Microservice
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Workflow Executor will have a single docker image. First, build the agent docker image.
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```sh
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git clone https://github.com/opea-project/GenAIExamples.git
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cd GenAIExamples//WorkflowExecAgent/docker_image_build/
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docker compose -f build.yaml build --no-cache
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```
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Configure `GenAIExamples/WorkflowExecAgent/docker_compose/.env` file with the following. Replace the variables according to your usecase.
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```sh
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export SDK_BASE_URL=${SDK_BASE_URL}
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export SERVING_TOKEN=${SERVING_TOKEN}
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export HUGGINGFACEHUB_API_TOKEN=${HF_TOKEN}
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export llm_engine=${llm_engine}
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export llm_endpoint_url=${llm_endpoint_url}
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export ip_address=$(hostname -I | awk '{print $1}')
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export model="mistralai/Mistral-7B-Instruct-v0.3"
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export recursion_limit=${recursion_limit}
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export temperature=0
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export max_new_tokens=1000
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export WORKDIR=${WORKDIR}
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export TOOLSET_PATH=$WORKDIR/GenAIExamples/WorkflowExecAgent/tools/
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export http_proxy=${http_proxy}
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export https_proxy=${https_proxy}
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```
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Launch service by running the docker compose command.
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```sh
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cd $WORKDIR/GenAIExamples/WorkflowExecAgent/docker_compose
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docker compose -f compose.yaml up -d
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```
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### Validate service
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The microservice logs can be viewed using:
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```sh
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docker logs workflowexec-agent-endpoint
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```
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You should be able to see "HTTP server setup successful" upon successful startup.
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You can validate the service using the following command:
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```sh
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curl http://${ip_address}:9090/v1/chat/completions -X POST -H "Content-Type: application/json" -d '{
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"query": "I have a data with gender Female, tenure 55, MonthlyAvgCharges 103.7. Predict if this entry will churn. My workflow id is '${workflow_id}'."
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}'
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```
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Update the `query` with the workflow parameters, workflow id, etc based on the workflow context.
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## Roadmap
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Phase II: Agent memory integration to enable capability to store tool intermediate results, such as workflow instance key.
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