GenAIExamples/AgentQnA

Agents for Question Answering

Table of contents

1. Overview
2. Deploy with Docker
3. How to interact with the agent system with UI
4. Validate Services
5. Register Tools
6. Monitoring and Tracing

Overview

This example showcases a hierarchical multi-agent system for question-answering applications. The architecture diagram below shows a supervisor agent that interfaces with the user and dispatches tasks to two worker agents to gather information and come up with answers. The worker RAG agent uses the retrieval tool to retrieve relevant documents from a knowledge base - a vector database. The worker SQL agent retrieves relevant data from a SQL database. Although not included in this example by default, other tools such as a web search tool or a knowledge graph query tool can be used by the supervisor agent to gather information from additional sources.

The AgentQnA example is implemented using the component-level microservices defined in GenAIComps. The flow chart below shows the information flow between different microservices for this example.

---
config:
  flowchart:
    nodeSpacing: 400
    rankSpacing: 100
    curve: linear
  themeVariables:
    fontSize: 50px
---
flowchart LR
    %% Colors %%
    classDef blue fill:#ADD8E6,stroke:#ADD8E6,stroke-width:2px,fill-opacity:0.5
    classDef orange fill:#FBAA60,stroke:#ADD8E6,stroke-width:2px,fill-opacity:0.5
    classDef orchid fill:#C26DBC,stroke:#ADD8E6,stroke-width:2px,fill-opacity:0.5
    classDef invisible fill:transparent,stroke:transparent;

    %% Subgraphs %%
    subgraph DocIndexRetriever-MegaService["DocIndexRetriever MegaService "]
        direction LR
        EM([Embedding MicroService]):::blue
        RET([Retrieval MicroService]):::blue
        RER([Rerank MicroService]):::blue
    end
    subgraph UserInput[" User Input "]
        direction LR
        a([User Input Query]):::orchid
        Ingest([Ingest data]):::orchid
    end
    AG_REACT([Agent MicroService - react]):::blue
    AG_RAG([Agent MicroService - rag]):::blue
    AG_SQL([Agent MicroService - sql]):::blue
    LLM_gen{{LLM Service <br>}}
    DP([Data Preparation MicroService]):::blue
    TEI_RER{{Reranking service<br>}}
    TEI_EM{{Embedding service <br>}}
    VDB{{Vector DB<br><br>}}
    R_RET{{Retriever service <br>}}



    %% Questions interaction
    direction LR
    a[User Input Query] --> AG_REACT
    AG_REACT --> AG_RAG
    AG_REACT --> AG_SQL
    AG_RAG --> DocIndexRetriever-MegaService
    EM ==> RET
    RET ==> RER
    Ingest[Ingest data] --> DP

    %% Embedding service flow
    direction LR
    AG_RAG <-.-> LLM_gen
    AG_SQL <-.-> LLM_gen
    AG_REACT <-.-> LLM_gen
    EM <-.-> TEI_EM
    RET <-.-> R_RET
    RER <-.-> TEI_RER

    direction TB
    %% Vector DB interaction
    R_RET <-.-> VDB
    DP <-.-> VDB

Why should AI Agents be used for question-answering?

1. Improve relevancy of retrieved context. RAG agents can rephrase user queries, decompose user queries, and iterate to get the most relevant context for answering a user's question. Compared to conventional RAG, RAG agents significantly improve the correctness and relevancy of the answer because of the iterations it goes through.
2. Expand scope of skills. The supervisor agent interacts with multiple worker agents that specialize in different skills (e.g., retrieve documents, write SQL queries, etc.). Thus, it can answer questions with different methods.
3. Hierarchical multi-agents improve performance. Expert worker agents, such as RAG agents and SQL agents, can provide high-quality output for different aspects of a complex query, and the supervisor agent can aggregate the information to provide a comprehensive answer. If only one agent is used and all tools are provided to this single agent, it can lead to large overhead or not use the best tool to provide accurate answers.

Deploy with docker

1. Set up environment

First, clone the GenAIExamples repo.

export WORKDIR=<your-work-directory>
cd $WORKDIR
git clone https://github.com/opea-project/GenAIExamples.git

Second, set up environment variables.

For proxy environments only

export http_proxy="Your_HTTP_Proxy"
export https_proxy="Your_HTTPs_Proxy"
# Example: no_proxy="localhost, 127.0.0.1, 192.168.1.1"
export no_proxy="Your_No_Proxy"

For using open-source llms

export HUGGINGFACEHUB_API_TOKEN=<your-HF-token>
export HF_CACHE_DIR=<directory-where-llms-are-downloaded> #so that no need to redownload every time

[Optional] OPANAI_API_KEY to use OpenAI models

export OPENAI_API_KEY=<your-openai-key>

Third, set up environment variables for the selected hardware using the corresponding set_env.sh

Gaudi

source $WORKDIR/GenAIExamples/AgentQnA/docker_compose/intel/hpu/gaudi/set_env.sh

Xeon

source $WORKDIR/GenAIExamples/AgentQnA/docker_compose/intel/cpu/xeon/set_env.sh

2. Launch the multi-agent system.

We make it convenient to launch the whole system with docker compose, which includes microservices for LLM, agents, UI, retrieval tool, vector database, dataprep, and telemetry. There are 3 docker compose files, which make it easy for users to pick and choose. Users can choose a different retrieval tool other than the DocIndexRetriever example provided in our GenAIExamples repo. Users can choose not to launch the telemetry containers.

Launch on Gaudi

On Gaudi, meta-llama/Meta-Llama-3.3-70B-Instruct will be served using vllm. The command below will launch the multi-agent system with the DocIndexRetriever as the retrieval tool for the Worker RAG agent.

cd $WORKDIR/GenAIExamples/AgentQnA/docker_compose/intel/hpu/gaudi/
docker compose -f $WORKDIR/GenAIExamples/DocIndexRetriever/docker_compose/intel/cpu/xeon/compose.yaml -f compose.yaml up -d

Note

: To enable the web search tool, skip this step and proceed to the "[Optional] Web Search Tool Support" section.

To enable Open Telemetry Tracing, compose.telemetry.yaml file need to be merged along with default compose.yaml file. Gaudi example with Open Telemetry feature:

cd $WORKDIR/GenAIExamples/AgentQnA/docker_compose/intel/hpu/gaudi/
docker compose -f $WORKDIR/GenAIExamples/DocIndexRetriever/docker_compose/intel/cpu/xeon/compose.yaml -f compose.yaml -f compose.telemetry.yaml up -d

[Optional] Web Search Tool Support

Instructions

A web search tool is supported in this example and can be enabled by running docker compose with the `compose.webtool.yaml` file. The Google Search API is used. Follow the [instructions](https://python.langchain.com/docs/integrations/tools/google_search) to create an API key and enable the Custom Search API on a Google account. The environment variables `GOOGLE_CSE_ID` and `GOOGLE_API_KEY` need to be set.

cd $WORKDIR/GenAIExamples/AgentQnA/docker_compose/intel/hpu/gaudi/
export GOOGLE_CSE_ID="YOUR_ID"
export GOOGLE_API_KEY="YOUR_API_KEY"
docker compose -f $WORKDIR/GenAIExamples/DocIndexRetriever/docker_compose/intel/cpu/xeon/compose.yaml -f compose.yaml -f compose.webtool.yaml up -d

Launch on Xeon

On Xeon, only OpenAI models are supported. The command below will launch the multi-agent system with the DocIndexRetriever as the retrieval tool for the Worker RAG agent.

export OPENAI_API_KEY=<your-openai-key>
cd $WORKDIR/GenAIExamples/AgentQnA/docker_compose/intel/cpu/xeon
docker compose -f $WORKDIR/GenAIExamples/DocIndexRetriever/docker_compose/intel/cpu/xeon/compose.yaml -f compose_openai.yaml up -d

3. Ingest Data into the vector database

The run_ingest_data.sh script will use an example jsonl file to ingest example documents into a vector database. Other ways to ingest data and other types of documents supported can be found in the OPEA dataprep microservice located in the opea-project/GenAIComps repo.

cd  $WORKDIR/GenAIExamples/AgentQnA/retrieval_tool/
bash run_ingest_data.sh

Note

: This is a one-time operation.

How to interact with the agent system with UI

The UI microservice is launched in the previous step with the other microservices. To see the UI, open a web browser to http://${ip_address}:5173 to access the UI. Note the ip_address here is the host IP of the UI microservice.

1. create Admin Account with a random value
2. add opea agent endpoint http://$ip_address:9090/v1 which is a openai compatible api

3. test opea agent with ui

[Optional] Deploy using Helm Charts

Refer to the AgentQnA helm chart for instructions on deploying AgentQnA on Kubernetes.

Validate Services

1. First look at logs for each of the agent docker containers:

# worker RAG agent
docker logs rag-agent-endpoint

# worker SQL agent
docker logs sql-agent-endpoint

# supervisor agent
docker logs react-agent-endpoint

Look for the message "HTTP server setup successful" to confirm the agent docker container has started successfully.

2. Use python to validate each agent is working properly:

# RAG worker agent
python $WORKDIR/GenAIExamples/AgentQnA/tests/test.py --prompt "Tell me about Michael Jackson song Thriller" --agent_role "worker" --ext_port 9095

# SQL agent
python $WORKDIR/GenAIExamples/AgentQnA/tests/test.py --prompt "How many employees in company" --agent_role "worker" --ext_port 9096

# supervisor agent: this will test a two-turn conversation
python $WORKDIR/GenAIExamples/AgentQnA/tests/test.py --agent_role "supervisor" --ext_port 9090

How to register other tools with the AI agent

The tools folder contains YAML and Python files for additional tools for the supervisor and worker agents. Refer to the "Provide your own tools" section in the instructions here to add tools and customize the AI agents.

Monitor and Tracing

Follow OpenTelemetry OPEA Guide to understand how to use OpenTelemetry tracing and metrics in OPEA.
For AgentQnA specific tracing and metrics monitoring, follow OpenTelemetry on AgentQnA section.