Add Finance Agent Example

FinanceAgent/README.md

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+# Finance Agent
+
+## 1. Overview
+
+## 2. Getting started
+
+### 2.1 Download repos
+
+```bash
+mkdir /path/to/your/workspace/
+export WORKDIR=/path/to/your/workspace/
+genaicomps
+genaiexamples
+```
+
+### 2.2 Set up env vars
+
+```bash
+export HF_CACHE_DIR=/path/to/your/model/cache/
+export HF_TOKEN=<you-hf-token>
+
+```
+
+### 2.3 Build docker images
+
+Build docker images for dataprep, agent, agent-ui.
+
+```bash
+# use docker image build
+```
+
+If deploy on Gaudi, also need to build vllm image.
+
+```bash
+cd $WORKDIR
+git clone https://github.com/HabanaAI/vllm-fork.git
+# get the latest release tag of vllm gaudi
+cd vllm-fork
+VLLM_VER=$(git describe --tags "$(git rev-list --tags --max-count=1)")
+echo "Check out vLLM tag ${VLLM_VER}"
+git checkout ${VLLM_VER}
+docker build --no-cache -f Dockerfile.hpu -t opea/vllm-gaudi:latest --shm-size=128g . --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy
+```
+
+## 3. Deploy with docker compose
+
+### 3.1 Launch vllm endpoint
+
+Below is the command to launch a vllm endpoint on Gaudi that serves `meta-llama/Llama-3.3-70B-Instruct` model on 4 Gaudi cards.
+
+```bash
+export vllm_port=8086
+export vllm_volume=$HF_CACHE_DIR
+export max_length=16384
+export model="meta-llama/Llama-3.3-70B-Instruct"
+docker run -d --runtime=habana --rm --name "vllm-gaudi-server" -e HABANA_VISIBLE_DEVICES=all -p $vllm_port:8000 -v $vllm_volume:/data -e HF_TOKEN=$HF_TOKEN -e HUGGING_FACE_HUB_TOKEN=$HF_TOKEN -e HF_HOME=/data -e OMPI_MCA_btl_vader_single_copy_mechanism=none -e PT_HPU_ENABLE_LAZY_COLLECTIVES=true -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e no_proxy=$no_proxy -e VLLM_SKIP_WARMUP=true --cap-add=sys_nice --ipc=host opea/vllm-gaudi:comps --model ${model} --max-seq-len-to-capture $max_length --tensor-parallel-size 4
+```
+
+### 3.2 Prepare knowledge base
+
+The commands below will upload some example files into the knowledge base. You can also upload files through UI.
+
+First, launch the redis databases and the dataprep microservice.
+
+```bash
+docker compose -f $WORKDIR/GenAIExamples/FinanceAgent/docker_compose/intel/hpu/gaudi/dataprep_compose.yaml up -d
+```
+
+### 3.3 Launch the multi-agent system
+
+### 3.4 Validate agents

FinanceAgent/docker_compose/intel/hpu/gaudi/compose.yaml

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+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+services:
+  worker-finqa-agent:
+    image: opea/agent:latest
+    container_name: finqa-agent-endpoint
+    volumes:
+      - ${TOOLSET_PATH}:/home/user/tools/
+      - ${PROMPT_PATH}:/home/user/prompts/
+    ports:
+      - "9095:9095"
+    ipc: host
+    environment:
+      ip_address: ${ip_address}
+      strategy: react_llama
+      with_memory: false
+      recursion_limit: ${recursion_limit_worker}
+      llm_engine: vllm
+      HUGGINGFACEHUB_API_TOKEN: ${HUGGINGFACEHUB_API_TOKEN}
+      llm_endpoint_url: ${LLM_ENDPOINT_URL}
+      model: ${LLM_MODEL_ID}
+      temperature: ${TEMPERATURE}
+      max_new_tokens: ${MAX_TOKENS}
+      stream: false
+      tools: /home/user/tools/finqa_agent_tools.yaml
+      custom_prompt: /home/user/prompts/finqa_prompt.py
+      require_human_feedback: false
+      no_proxy: ${no_proxy}
+      http_proxy: ${http_proxy}
+      https_proxy: ${https_proxy}
+      REDIS_URL_VECTOR: $REDIS_URL_VECTOR
+      REDIS_URL_KV: $REDIS_URL_KV
+      TEI_EMBEDDING_ENDPOINT: $TEI_EMBEDDING_ENDPOINT
+      port: 9095
+
+  worker-research-agent:
+    image: opea/agent:latest
+    container_name: research-agent-endpoint
+    volumes:
+      - ${TOOLSET_PATH}:/home/user/tools/
+      - ${PROMPT_PATH}:/home/user/prompts/
+    ports:
+      - "9096:9096"
+    ipc: host
+    environment:
+      ip_address: ${ip_address}
+      strategy: react_llama
+      with_memory: false
+      recursion_limit: ${recursion_limit_worker}
+      llm_engine: vllm
+      HUGGINGFACEHUB_API_TOKEN: ${HUGGINGFACEHUB_API_TOKEN}
+      llm_endpoint_url: ${LLM_ENDPOINT_URL}
+      model: ${LLM_MODEL_ID}
+      stream: false
+      tools: /home/user/tools/research_agent_tools.yaml
+      custom_prompt: /home/user/prompts/research_prompt.py
+      require_human_feedback: false
+      no_proxy: ${no_proxy}
+      http_proxy: ${http_proxy}
+      https_proxy: ${https_proxy}
+      FINNHUB_API_KEY: ${FINNHUB_API_KEY}
+      FINANCIAL_DATASETS_API_KEY: ${FINANCIAL_DATASETS_API_KEY}
+      port: 9096
+
+  supervisor-react-agent:
+    image: opea/agent:latest
+    container_name: supervisor-agent-endpoint
+    depends_on:
+      - worker-finqa-agent
+      # - worker-research-agent
+    volumes:
+      - ${TOOLSET_PATH}:/home/user/tools/
+      - ${PROMPT_PATH}:/home/user/prompts/
+    ports:
+      - "9090:9090"
+    ipc: host
+    environment:
+      ip_address: ${ip_address}
+      strategy: react_llama
+      with_memory: true
+      recursion_limit: ${recursion_limit_supervisor}
+      llm_engine: vllm
+      HUGGINGFACEHUB_API_TOKEN: ${HUGGINGFACEHUB_API_TOKEN}
+      llm_endpoint_url: ${LLM_ENDPOINT_URL}
+      model: ${LLM_MODEL_ID}
+      temperature: ${TEMPERATURE}
+      max_new_tokens: ${MAX_TOKENS}
+      stream: true
+      tools: /home/user/tools/supervisor_agent_tools.yaml
+      custom_prompt: /home/user/prompts/supervisor_prompt.py
+      require_human_feedback: false
+      no_proxy: ${no_proxy}
+      http_proxy: ${http_proxy}
+      https_proxy: ${https_proxy}
+      WORKER_FINQA_AGENT_URL: $WORKER_FINQA_AGENT_URL
+      WORKER_RESEARCH_AGENT_URL: $WORKER_RESEARCH_AGENT_URL
+      DOCSUM_ENDPOINT: $DOCSUM_ENDPOINT
+      REDIS_URL_VECTOR: $REDIS_URL_VECTOR
+      REDIS_URL_KV: $REDIS_URL_KV
+      TEI_EMBEDDING_ENDPOINT: $TEI_EMBEDDING_ENDPOINT
+      port: 9090
+
+  docsum-vllm-gaudi:
+    image: opea/llm-docsum:latest
+    container_name: docsum-vllm-gaudi
+    ports:
+      - ${DOCSUM_PORT:-9000}:9000
+    ipc: host
+    environment:
+      no_proxy: ${no_proxy}
+      http_proxy: ${http_proxy}
+      https_proxy: ${https_proxy}
+      LLM_ENDPOINT: ${LLM_ENDPOINT}
+      LLM_MODEL_ID: ${LLM_MODEL_ID}
+      HF_TOKEN: ${HF_TOKEN}
+      LOGFLAG: ${LOGFLAG:-False}
+      MAX_INPUT_TOKENS: ${MAX_INPUT_TOKENS}
+      MAX_TOTAL_TOKENS: ${MAX_TOTAL_TOKENS}
+      DocSum_COMPONENT_NAME: ${DocSum_COMPONENT_NAME:-OpeaDocSumvLLM}
+    restart: unless-stopped

FinanceAgent/docker_compose/intel/hpu/gaudi/dataprep_compose.yaml

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+# Copyright (C) 2025 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+services:
+  tei-embedding-serving:
+    image: ghcr.io/huggingface/text-embeddings-inference:cpu-1.5
+    container_name: tei-embedding-serving
+    entrypoint: /bin/sh -c "apt-get update && apt-get install -y curl && text-embeddings-router --json-output --model-id ${EMBEDDING_MODEL_ID} --auto-truncate"
+    ports:
+      - "${TEI_EMBEDDER_PORT:-10221}:80"
+    volumes:
+      - "./data:/data"
+    shm_size: 1g
+    environment:
+      no_proxy: ${no_proxy}
+      http_proxy: ${http_proxy}
+      https_proxy: ${https_proxy}
+      host_ip: ${host_ip}
+      HF_TOKEN: ${HF_TOKEN}
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://${host_ip}:${TEI_EMBEDDER_PORT}/health"]
+      interval: 10s
+      timeout: 6s
+      retries: 48
+
+  redis-vector-db:
+    image: redis/redis-stack:7.2.0-v9
+    container_name: redis-vector-db
+    ports:
+      - "${REDIS_PORT1:-6379}:6379"
+      - "${REDIS_PORT2:-8001}:8001"
+    environment:
+      - no_proxy=${no_proxy}
+      - http_proxy=${http_proxy}
+      - https_proxy=${https_proxy}
+    healthcheck:
+      test: ["CMD", "redis-cli", "ping"]
+      timeout: 10s
+      retries: 3
+      start_period: 10s
+
+  redis-kv-store:
+    image: redis/redis-stack:7.2.0-v9
+    container_name: redis-kv-store
+    ports:
+      - "${REDIS_PORT3:-6380}:6379"
+      - "${REDIS_PORT4:-8002}:8001"
+    environment:
+      - no_proxy=${no_proxy}
+      - http_proxy=${http_proxy}
+      - https_proxy=${https_proxy}
+    healthcheck:
+      test: ["CMD", "redis-cli", "ping"]
+      timeout: 10s
+      retries: 3
+      start_period: 10s
+
+  dataprep-redis-finance:
+    image: ${REGISTRY:-opea}/dataprep:${TAG:-latest}
+    container_name: dataprep-redis-server-finance
+    depends_on:
+      redis-vector-db:
+        condition: service_healthy
+      redis-kv-store:
+        condition: service_healthy
+      tei-embedding-serving:
+        condition: service_healthy
+    ports:
+      - "${DATAPREP_PORT:-6007}:5000"
+    environment:
+      no_proxy: ${no_proxy}
+      http_proxy: ${http_proxy}
+      https_proxy: ${https_proxy}
+      DATAPREP_COMPONENT_NAME: ${DATAPREP_COMPONENT_NAME}
+      REDIS_URL_VECTOR: ${REDIS_URL_VECTOR}
+      REDIS_URL_KV: ${REDIS_URL_KV}
+      TEI_EMBEDDING_ENDPOINT: ${TEI_EMBEDDING_ENDPOINT}
+      LLM_ENDPOINT: ${LLM_ENDPOINT}
+      LLM_MODEL: ${LLM_MODEL}
+      HUGGINGFACEHUB_API_TOKEN: ${HF_TOKEN}
+      HF_TOKEN: ${HF_TOKEN}
+      LOGFLAG: true

FinanceAgent/docker_compose/intel/hpu/gaudi/launch_agents.sh

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+
+# Copyright (C) 2025 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+export ip_address=$(hostname -I | awk '{print $1}')
+export HUGGINGFACEHUB_API_TOKEN=${HF_TOKEN}
+export TOOLSET_PATH=$WORKDIR/GenAIExamples/FinanceAgent/tools/
+echo "TOOLSET_PATH=${TOOLSET_PATH}"
+export PROMPT_PATH=$WORKDIR/GenAIExamples/FinanceAgent/prompts/
+echo "PROMPT_PATH=${PROMPT_PATH}"
+export recursion_limit_worker=12
+export recursion_limit_supervisor=10
+
+vllm_port=8086
+export LLM_MODEL_ID="meta-llama/Llama-3.3-70B-Instruct"
+export LLM_ENDPOINT_URL="http://${ip_address}:${vllm_port}"
+export TEMPERATURE=0.5
+export MAX_TOKENS=4096
+
+export WORKER_FINQA_AGENT_URL="http://${ip_address}:9095/v1/chat/completions"
+export WORKER_RESEARCH_AGENT_URL="http://${ip_address}:9096/v1/chat/completions"
+
+export EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"
+export TEI_EMBEDDING_ENDPOINT="http://${ip_address}:10221"
+export REDIS_URL_VECTOR="redis://${ip_address}:6379"
+export REDIS_URL_KV="redis://${ip_address}:6380"
+
+export MAX_INPUT_TOKENS=2048
+export MAX_TOTAL_TOKENS=4096
+export DocSum_COMPONENT_NAME="OpeaDocSumvLLM"
+export DOCSUM_ENDPOINT="http://${ip_address}:9000/v1/docsum"
+
+export FINNHUB_API_KEY=${FINNHUB_API_KEY}
+export FINANCIAL_DATASETS_API_KEY=${FINANCIAL_DATASETS_API_KEY}
+
+docker compose -f compose.yaml up -d

FinanceAgent/docker_image_build/build.yaml

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+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+services:
+  dataprep:
+    build:
+      context: GenAIComps
+      dockerfile: comps/dataprep/src/Dockerfile
+      args:
+        http_proxy: ${http_proxy}
+        https_proxy: ${https_proxy}
+        no_proxy: ${no_proxy}
+    image: ${REGISTRY:-opea}/dataprep:${TAG:-latest}
+  agent:
+    build:
+      context: GenAIComps
+      dockerfile: comps/agent/src/Dockerfile
+      args:
+        http_proxy: ${http_proxy}
+        https_proxy: ${https_proxy}
+        no_proxy: ${no_proxy}
+    image: ${REGISTRY:-opea}/agent:${TAG:-latest}
+  # agent-ui:
+  #   build:
+  #     context: ../ui
+  #     dockerfile: ./docker/Dockerfile
+  #   extends: agent
+  #   image: ${REGISTRY:-opea}/agent-ui:${TAG:-latest}

FinanceAgent/prompts/finqa_prompt.py

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+# Copyright (C) 2025 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+REACT_AGENT_LLAMA_PROMPT = """\
+You are a helpful assistant engaged in multi-turn conversations with Financial analysts.
+You have access to the following two tools:
+{tools}
+
+**Procedure:**
+1. Read the question carefully. Divide the question into sub-questions and conquer sub-questions one by one.
+3. If there is execution history, read it carefully and reason about the information gathered so far and decide if you can answer the question or if you need to call more tools.
+
+**Output format:**
+You should output your thought process. Finish thinking first. Output tool calls or your answer at the end.
+When making tool calls, you should use the following format:
+TOOL CALL: {{"tool": "tool1", "args": {{"arg1": "value1", "arg2": "value2", ...}}}}
+
+If you can answer the question, provide the answer in the following format:
+FINAL ANSWER: {{"answer": "your answer here"}}
+
+
+======= Conversations with user in previous turns =======
+{thread_history}
+======= End of previous conversations =======
+
+======= Your execution History in this turn =========
+{history}
+======= End of execution history ==========
+
+**Tips:**
+* You may need to do multi-hop calculations and call tools multiple times to get an answer.
+* Do not assume any financial figures. Always rely on the tools to get the factual information.
+* If you need a certain financial figure, search for the figure instead of the financial statement name.
+* If you did not get the answer at first, do not give up. Reflect on the steps that you have taken and try a different way. Think out of the box. You hard work will be rewarded.
+* Give concise, factual and relevant answers.
+* If the user question is too ambiguous, ask for clarification.
+
+Now take a deep breath and think step by step to answer user's question in this turn.
+USER MESSAGE: {input}
+"""