1852e6bcc3
Signed-off-by: minmin-intel <minmin.hou@intel.com> Signed-off-by: Rita Brugarolas <rita.brugarolas.brufau@intel.com> Signed-off-by: rbrugaro <rita.brugarolas.brufau@intel.com> Co-authored-by: rbrugaro <rita.brugarolas.brufau@intel.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: lkk <33276950+lkk12014402@users.noreply.github.com> Co-authored-by: lkk12014402 <kaokao.lv@intel.com>
146 lines
4.3 KiB
Markdown
146 lines
4.3 KiB
Markdown
# Finance Agent
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## 1. Overview
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The architecture of this Finance Agent example is shown in the figure below. The agent has 3 main functions:
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1. Summarize long financial documents and provide key points.
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2. Answer questions over financial documents, such as SEC filings.
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3. Conduct research of a public company and provide an investment report of the company.
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The `dataprep` microservice can ingest financial documents in two formats:
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1. PDF documents stored locally, such as SEC filings saved in local directory.
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2. URLs, such as earnings call transcripts ([example](https://www.fool.com/earnings/call-transcripts/2025/03/06/costco-wholesale-cost-q2-2025-earnings-call-transc/)) and online SEC filings ([example](https://investors.3m.com/financials/sec-filings/content/0000066740-25-000006/0000066740-25-000006.pdf)).
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Please note:
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1. Each financial document should be about one company.
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2. URLs ending in `.htm` are not supported.
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## 2. Getting started
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### 2.1 Download repos
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```bash
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mkdir /path/to/your/workspace/
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export WORKDIR=/path/to/your/workspace/
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genaicomps
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genaiexamples
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```
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### 2.2 Set up env vars
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```bash
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export HF_CACHE_DIR=/path/to/your/model/cache/
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export HF_TOKEN=<you-hf-token>
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```
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### 2.3 Build docker images
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Build docker images for dataprep, agent, agent-ui.
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```bash
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cd GenAIExamples/FinanceAgent/docker_image_build
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docker compose -f build.yaml build --no-cache
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```
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If deploy on Gaudi, also need to build vllm image.
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```bash
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cd $WORKDIR
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git clone https://github.com/HabanaAI/vllm-fork.git
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# get the latest release tag of vllm gaudi
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cd vllm-fork
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VLLM_VER=$(git describe --tags "$(git rev-list --tags --max-count=1)")
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echo "Check out vLLM tag ${VLLM_VER}"
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git checkout ${VLLM_VER}
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docker build --no-cache -f Dockerfile.hpu -t opea/vllm-gaudi:latest --shm-size=128g . --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy
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```
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## 3. Deploy with docker compose
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### 3.1 Launch vllm endpoint
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Below is the command to launch a vllm endpoint on Gaudi that serves `meta-llama/Llama-3.3-70B-Instruct` model on 4 Gaudi cards.
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```bash
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cd $WORKDIR/GenAIExamples/FinanceAgent/docker_compose/intel/hpu/gaudi
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bash launch_vllm.sh
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```
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### 3.2 Prepare knowledge base
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The commands below will upload some example files into the knowledge base. You can also upload files through UI.
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First, launch the redis databases and the dataprep microservice.
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```bash
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# inside $WORKDIR/GenAIExamples/FinanceAgent/docker_compose/intel/hpu/gaudi/
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bash launch_dataprep.sh
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```
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Validate datat ingest data and retrieval from database:
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```bash
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python $WORKPATH/tests/test_redis_finance.py --port 6007 --test_option ingest
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python $WORKPATH/tests/test_redis_finance.py --port 6007 --test_option get
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```
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### 3.3 Launch the multi-agent system
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```bash
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# inside $WORKDIR/GenAIExamples/FinanceAgent/docker_compose/intel/hpu/gaudi/
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bash launch_agents.sh
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```
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### 3.4 Validate agents
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FinQA Agent:
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```bash
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export agent_port="9095"
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prompt="What is Gap's revenue in 2024?"
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python3 $WORKDIR/GenAIExamples/FinanceAgent/tests/test.py --prompt "$prompt" --agent_role "worker" --ext_port $agent_port
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```
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Research Agent:
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```bash
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export agent_port="9096"
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prompt="generate NVDA financial research report"
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python3 $WORKDIR/GenAIExamples/FinanceAgent/tests/test.py --prompt "$prompt" --agent_role "worker" --ext_port $agent_port --tool_choice "get_current_date" --tool_choice "get_share_performance"
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```
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Supervisor ReAct Agent:
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```bash
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export agent_port="9090"
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python3 $WORKDIR/GenAIExamples/FinanceAgent/tests/test.py --agent_role "supervisor" --ext_port $agent_port --stream
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```
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Supervisor ReAct Agent Multi turn:
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```bash
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python3 $WORKDIR/GenAIExamples/FinanceAgent/tests/test.py --agent_role "supervisor" --ext_port $agent_port --multi-turn --stream
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```
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## How to interact with the agent system with UI
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The UI microservice is launched in the previous step with the other microservices.
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To see the UI, open a web browser to `http://${ip_address}:5175` to access the UI. Note the `ip_address` here is the host IP of the UI microservice.
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1. `create Admin Account` with a random value
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2. use an opea agent endpoint, for example, the `Research Agent` endpoint `http://$ip_address:9096/v1`, which is a openai compatible api
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3. test opea agent with ui
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