Add Finance Agent Example (#1752)
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>
This commit is contained in:
minmin-intel
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FinanceAgent/README.md
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145
FinanceAgent/README.md
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# 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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FinanceAgent/assets/finance_agent_arch.png
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FinanceAgent/assets/finance_agent_arch.png
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FinanceAgent/assets/opea-agent-setting.png
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FinanceAgent/assets/opea-agent-setting.png
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FinanceAgent/assets/opea-agent-test.png
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133
FinanceAgent/docker_compose/intel/hpu/gaudi/compose.yaml
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FinanceAgent/docker_compose/intel/hpu/gaudi/compose.yaml
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# Copyright (C) 2024 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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services:
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worker-finqa-agent:
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image: opea/agent:latest
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container_name: finqa-agent-endpoint
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volumes:
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- ${TOOLSET_PATH}:/home/user/tools/
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- ${PROMPT_PATH}:/home/user/prompts/
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ports:
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- "9095:9095"
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ipc: host
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environment:
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ip_address: ${ip_address}
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strategy: react_llama
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with_memory: false
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recursion_limit: ${recursion_limit_worker}
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llm_engine: vllm
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HUGGINGFACEHUB_API_TOKEN: ${HUGGINGFACEHUB_API_TOKEN}
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llm_endpoint_url: ${LLM_ENDPOINT_URL}
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model: ${LLM_MODEL_ID}
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temperature: ${TEMPERATURE}
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max_new_tokens: ${MAX_TOKENS}
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stream: false
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tools: /home/user/tools/finqa_agent_tools.yaml
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custom_prompt: /home/user/prompts/finqa_prompt.py
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require_human_feedback: false
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no_proxy: ${no_proxy}
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http_proxy: ${http_proxy}
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https_proxy: ${https_proxy}
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REDIS_URL_VECTOR: $REDIS_URL_VECTOR
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REDIS_URL_KV: $REDIS_URL_KV
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TEI_EMBEDDING_ENDPOINT: $TEI_EMBEDDING_ENDPOINT
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port: 9095
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worker-research-agent:
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image: opea/agent:latest
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container_name: research-agent-endpoint
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volumes:
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- ${TOOLSET_PATH}:/home/user/tools/
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- ${PROMPT_PATH}:/home/user/prompts/
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ports:
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- "9096:9096"
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ipc: host
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environment:
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ip_address: ${ip_address}
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strategy: react_llama
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with_memory: false
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recursion_limit: ${recursion_limit_worker}
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llm_engine: vllm
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HUGGINGFACEHUB_API_TOKEN: ${HUGGINGFACEHUB_API_TOKEN}
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llm_endpoint_url: ${LLM_ENDPOINT_URL}
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model: ${LLM_MODEL_ID}
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stream: false
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tools: /home/user/tools/research_agent_tools.yaml
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custom_prompt: /home/user/prompts/research_prompt.py
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require_human_feedback: false
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no_proxy: ${no_proxy}
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http_proxy: ${http_proxy}
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https_proxy: ${https_proxy}
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FINNHUB_API_KEY: ${FINNHUB_API_KEY}
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FINANCIAL_DATASETS_API_KEY: ${FINANCIAL_DATASETS_API_KEY}
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port: 9096
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supervisor-react-agent:
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image: opea/agent:latest
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container_name: supervisor-agent-endpoint
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depends_on:
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- worker-finqa-agent
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# - worker-research-agent
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volumes:
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- ${TOOLSET_PATH}:/home/user/tools/
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- ${PROMPT_PATH}:/home/user/prompts/
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ports:
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- "9090:9090"
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ipc: host
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environment:
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ip_address: ${ip_address}
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strategy: react_llama
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with_memory: true
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recursion_limit: ${recursion_limit_supervisor}
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llm_engine: vllm
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HUGGINGFACEHUB_API_TOKEN: ${HUGGINGFACEHUB_API_TOKEN}
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llm_endpoint_url: ${LLM_ENDPOINT_URL}
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model: ${LLM_MODEL_ID}
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temperature: ${TEMPERATURE}
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max_new_tokens: ${MAX_TOKENS}
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stream: true
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tools: /home/user/tools/supervisor_agent_tools.yaml
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custom_prompt: /home/user/prompts/supervisor_prompt.py
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require_human_feedback: false
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no_proxy: ${no_proxy}
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http_proxy: ${http_proxy}
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https_proxy: ${https_proxy}
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WORKER_FINQA_AGENT_URL: $WORKER_FINQA_AGENT_URL
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WORKER_RESEARCH_AGENT_URL: $WORKER_RESEARCH_AGENT_URL
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DOCSUM_ENDPOINT: $DOCSUM_ENDPOINT
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REDIS_URL_VECTOR: $REDIS_URL_VECTOR
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REDIS_URL_KV: $REDIS_URL_KV
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TEI_EMBEDDING_ENDPOINT: $TEI_EMBEDDING_ENDPOINT
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port: 9090
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docsum-vllm-gaudi:
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image: opea/llm-docsum:latest
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container_name: docsum-vllm-gaudi
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ports:
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- ${DOCSUM_PORT:-9000}:9000
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ipc: host
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environment:
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no_proxy: ${no_proxy}
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http_proxy: ${http_proxy}
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https_proxy: ${https_proxy}
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LLM_ENDPOINT: ${LLM_ENDPOINT}
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LLM_MODEL_ID: ${LLM_MODEL_ID}
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HF_TOKEN: ${HF_TOKEN}
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LOGFLAG: ${LOGFLAG:-False}
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MAX_INPUT_TOKENS: ${MAX_INPUT_TOKENS}
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MAX_TOTAL_TOKENS: ${MAX_TOTAL_TOKENS}
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DocSum_COMPONENT_NAME: ${DocSum_COMPONENT_NAME:-OpeaDocSumvLLM}
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restart: unless-stopped
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agent-ui:
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image: opea/agent-ui:latest
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container_name: agent-ui
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environment:
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host_ip: ${host_ip}
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no_proxy: ${no_proxy}
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http_proxy: ${http_proxy}
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https_proxy: ${https_proxy}
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ports:
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- "5175:8080"
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ipc: host
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# Copyright (C) 2025 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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services:
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tei-embedding-serving:
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image: ghcr.io/huggingface/text-embeddings-inference:cpu-1.5
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container_name: tei-embedding-serving
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entrypoint: /bin/sh -c "apt-get update && apt-get install -y curl && text-embeddings-router --json-output --model-id ${EMBEDDING_MODEL_ID} --auto-truncate"
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ports:
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- "${TEI_EMBEDDER_PORT:-10221}:80"
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volumes:
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- "./data:/data"
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shm_size: 1g
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environment:
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no_proxy: ${no_proxy}
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http_proxy: ${http_proxy}
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https_proxy: ${https_proxy}
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host_ip: ${host_ip}
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HF_TOKEN: ${HF_TOKEN}
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healthcheck:
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test: ["CMD", "curl", "-f", "http://${host_ip}:${TEI_EMBEDDER_PORT}/health"]
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interval: 10s
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timeout: 6s
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retries: 48
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redis-vector-db:
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image: redis/redis-stack:7.2.0-v9
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container_name: redis-vector-db
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ports:
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- "${REDIS_PORT1:-6379}:6379"
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- "${REDIS_PORT2:-8001}:8001"
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environment:
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- no_proxy=${no_proxy}
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- http_proxy=${http_proxy}
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- https_proxy=${https_proxy}
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healthcheck:
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test: ["CMD", "redis-cli", "ping"]
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timeout: 10s
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retries: 3
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start_period: 10s
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redis-kv-store:
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image: redis/redis-stack:7.2.0-v9
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container_name: redis-kv-store
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ports:
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- "${REDIS_PORT3:-6380}:6379"
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- "${REDIS_PORT4:-8002}:8001"
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environment:
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- no_proxy=${no_proxy}
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- http_proxy=${http_proxy}
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- https_proxy=${https_proxy}
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healthcheck:
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test: ["CMD", "redis-cli", "ping"]
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timeout: 10s
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retries: 3
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start_period: 10s
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dataprep-redis-finance:
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image: ${REGISTRY:-opea}/dataprep:${TAG:-latest}
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container_name: dataprep-redis-server-finance
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depends_on:
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redis-vector-db:
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condition: service_healthy
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redis-kv-store:
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condition: service_healthy
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tei-embedding-serving:
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condition: service_healthy
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ports:
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- "${DATAPREP_PORT:-6007}:5000"
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environment:
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no_proxy: ${no_proxy}
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http_proxy: ${http_proxy}
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https_proxy: ${https_proxy}
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DATAPREP_COMPONENT_NAME: ${DATAPREP_COMPONENT_NAME}
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REDIS_URL_VECTOR: ${REDIS_URL_VECTOR}
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REDIS_URL_KV: ${REDIS_URL_KV}
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TEI_EMBEDDING_ENDPOINT: ${TEI_EMBEDDING_ENDPOINT}
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LLM_ENDPOINT: ${LLM_ENDPOINT}
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LLM_MODEL: ${LLM_MODEL}
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HUGGINGFACEHUB_API_TOKEN: ${HF_TOKEN}
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HF_TOKEN: ${HF_TOKEN}
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LOGFLAG: true
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36
FinanceAgent/docker_compose/intel/hpu/gaudi/launch_agents.sh
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FinanceAgent/docker_compose/intel/hpu/gaudi/launch_agents.sh
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# Copyright (C) 2025 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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export ip_address=$(hostname -I | awk '{print $1}')
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export HUGGINGFACEHUB_API_TOKEN=${HF_TOKEN}
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export TOOLSET_PATH=$WORKDIR/GenAIExamples/FinanceAgent/tools/
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echo "TOOLSET_PATH=${TOOLSET_PATH}"
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export PROMPT_PATH=$WORKDIR/GenAIExamples/FinanceAgent/prompts/
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echo "PROMPT_PATH=${PROMPT_PATH}"
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export recursion_limit_worker=12
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export recursion_limit_supervisor=10
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vllm_port=8086
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export LLM_MODEL_ID="meta-llama/Llama-3.3-70B-Instruct"
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export LLM_ENDPOINT_URL="http://${ip_address}:${vllm_port}"
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export TEMPERATURE=0.5
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export MAX_TOKENS=4096
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export WORKER_FINQA_AGENT_URL="http://${ip_address}:9095/v1/chat/completions"
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export WORKER_RESEARCH_AGENT_URL="http://${ip_address}:9096/v1/chat/completions"
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export EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"
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export TEI_EMBEDDING_ENDPOINT="http://${ip_address}:10221"
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export REDIS_URL_VECTOR="redis://${ip_address}:6379"
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export REDIS_URL_KV="redis://${ip_address}:6380"
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export MAX_INPUT_TOKENS=2048
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export MAX_TOTAL_TOKENS=4096
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export DocSum_COMPONENT_NAME="OpeaDocSumvLLM"
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export DOCSUM_ENDPOINT="http://${ip_address}:9000/v1/docsum"
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export FINNHUB_API_KEY=${FINNHUB_API_KEY}
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export FINANCIAL_DATASETS_API_KEY=${FINANCIAL_DATASETS_API_KEY}
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docker compose -f compose.yaml up -d
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@@ -0,0 +1,15 @@
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# Copyright (C) 2025 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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export host_ip=${ip_address}
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export DATAPREP_PORT="6007"
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export TEI_EMBEDDER_PORT="10221"
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export REDIS_URL_VECTOR="redis://${ip_address}:6379"
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export REDIS_URL_KV="redis://${ip_address}:6380"
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export LLM_MODEL=$model
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export LLM_ENDPOINT="http://${ip_address}:${vllm_port}"
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export DATAPREP_COMPONENT_NAME="OPEA_DATAPREP_REDIS_FINANCE"
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export EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"
|
||||
export TEI_EMBEDDING_ENDPOINT="http://${ip_address}:${TEI_EMBEDDER_PORT}"
|
||||
|
||||
docker compose -f dataprep_compose.yaml up -d
|
||||
@@ -0,0 +1,7 @@
|
||||
# Copyright (C) 2025 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
export LLM_MODEL_ID="meta-llama/Llama-3.3-70B-Instruct"
|
||||
export MAX_LEN=16384
|
||||
|
||||
docker compose -f vllm_compose.yaml up -d
|
||||
@@ -0,0 +1,35 @@
|
||||
|
||||
# Copyright (C) 2025 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
services:
|
||||
vllm-service:
|
||||
image: ${REGISTRY:-opea}/vllm-gaudi:${TAG:-latest}
|
||||
container_name: vllm-gaudi-server
|
||||
ports:
|
||||
- "8086:8000"
|
||||
volumes:
|
||||
- ${HF_CACHE_DIR}:/data
|
||||
environment:
|
||||
no_proxy: ${no_proxy}
|
||||
http_proxy: ${http_proxy}
|
||||
https_proxy: ${https_proxy}
|
||||
HF_TOKEN: ${HF_TOKEN}
|
||||
HUGGING_FACE_HUB_TOKEN: ${HF_TOKEN}
|
||||
HF_HOME: /data
|
||||
HABANA_VISIBLE_DEVICES: all
|
||||
OMPI_MCA_btl_vader_single_copy_mechanism: none
|
||||
LLM_MODEL_ID: ${LLM_MODEL_ID}
|
||||
VLLM_TORCH_PROFILER_DIR: "/mnt"
|
||||
VLLM_SKIP_WARMUP: true
|
||||
PT_HPU_ENABLE_LAZY_COLLECTIVES: true
|
||||
healthcheck:
|
||||
test: ["CMD-SHELL", "curl -f http://$host_ip:8086/health || exit 1"]
|
||||
interval: 10s
|
||||
timeout: 10s
|
||||
retries: 100
|
||||
runtime: habana
|
||||
cap_add:
|
||||
- SYS_NICE
|
||||
ipc: host
|
||||
command: --model $LLM_MODEL_ID --tensor-parallel-size 4 --host 0.0.0.0 --port 8000 --max-seq-len-to-capture $MAX_LEN
|
||||
28
FinanceAgent/docker_image_build/build.yaml
Normal file
28
FinanceAgent/docker_image_build/build.yaml
Normal file
@@ -0,0 +1,28 @@
|
||||
# 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}
|
||||
40
FinanceAgent/prompts/finqa_prompt.py
Normal file
40
FinanceAgent/prompts/finqa_prompt.py
Normal file
@@ -0,0 +1,40 @@
|
||||
# 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}
|
||||
"""
|
||||
70
FinanceAgent/prompts/research_prompt.py
Normal file
70
FinanceAgent/prompts/research_prompt.py
Normal file
@@ -0,0 +1,70 @@
|
||||
# Copyright (C) 2025 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
REACT_AGENT_LLAMA_PROMPT = """\
|
||||
|
||||
Role: Expert Investor
|
||||
Department: Finance
|
||||
Primary Responsibility: Generation of Customized Financial Analysis Reports
|
||||
|
||||
Role Description:
|
||||
As an Expert Investor within the finance domain, your expertise is harnessed to develop bespoke Financial Analysis Reports that cater to specific client requirements. This role demands a deep dive into financial statements and market data to unearth insights regarding a company's financial performance and stability. Engaging directly with clients to gather essential information and continuously refining the report with their feedback ensures the final product precisely meets their needs and expectations.
|
||||
|
||||
Key Objectives:
|
||||
|
||||
Analytical Precision: Employ meticulous analytical prowess to interpret financial data, identifying underlying trends and anomalies.
|
||||
Effective Communication: Simplify and effectively convey complex financial narratives, making them accessible and actionable to non-specialist audiences.
|
||||
Client Focus: Dynamically tailor reports in response to client feedback, ensuring the final analysis aligns with their strategic objectives.
|
||||
Adherence to Excellence: Maintain the highest standards of quality and integrity in report generation, following established benchmarks for analytical rigor.
|
||||
Performance Indicators: The efficacy of the Financial Analysis Report is measured by its utility in providing clear, actionable insights. This encompasses aiding corporate decision-making, pinpointing areas for operational enhancement, and offering a lucid evaluation of the company's financial health. Success is ultimately reflected in the report's contribution to informed investment decisions and strategic planning.
|
||||
|
||||
Reply TERMINATE when everything is settled.
|
||||
|
||||
|
||||
You have access to the following tools:
|
||||
{tools}
|
||||
|
||||
For writing a comprehensive analysis financial research report, you can use all the tools provided to retrieve information available for the company.
|
||||
|
||||
**Pay attention to the following:**
|
||||
1. Explicitly explain your working plan before you kick off.
|
||||
2. Read the question carefully. Firstly You need get accurate `start_date` and `end_date` value, because most tools need the 2 values like company news, financials. You can get `end_date` with `get_current_date` tool if user doesn't provide. And you can infer `start_date` with `end_date` using the rule `start_date is one year earlier than end_date` if user doesn't provide.
|
||||
3. Use tools one by one for clarity, especially when asking for instructions.
|
||||
4. Provide stock performance, because the financial report is used for stock investment analysis.
|
||||
5. Read the execution history if any to understand the tools that have been called and the information that has been gathered.
|
||||
6. 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:
|
||||
|
||||
When need tool calls, you should use the following format:
|
||||
TOOL CALL: {{"tool": "tool1", "args": {{"arg1": "value1", "arg2": "value2", ...}}}}
|
||||
TOOL CALL: {{"tool": "tool2", "args": {{"arg1": "value1", "arg2": "value2", ...}}}}
|
||||
|
||||
If you have enough financial data, provide the financial report in the following format:
|
||||
FINAL ANSWER: {{"answer": "compile all the analyzed data and insights into a comprehensive financial report, which contains the following paragraphs: income summarization, market position, business overview, risk assessment, competitors analysis, share performance analysis."}}
|
||||
|
||||
Follow these guidelines when formulating your answer:
|
||||
1. If the question contains a false premise or assumption, answer “invalid question”.
|
||||
2. If you are uncertain or do not know the answer, answer “I don't know”.
|
||||
3. Give concise, factual and relevant answers.
|
||||
|
||||
**IMPORTANT:**
|
||||
* Do not generate history messages repeatedly.
|
||||
* Divide the question into sub-questions and conquer sub-questions one by one.
|
||||
* Questions may be time sensitive. Pay attention to the time when the question was asked.
|
||||
* You may need to combine information from multiple tools to answer the question.
|
||||
* 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.
|
||||
* Do not make up tool outputs.
|
||||
|
||||
======= Conversations with user in previous turns =======
|
||||
{thread_history}
|
||||
======= End of previous conversations =======
|
||||
|
||||
======= Your execution History in this turn =========
|
||||
{history}
|
||||
======= End of execution history ==========
|
||||
|
||||
Now take a deep breath and think step by step to answer user's question in this turn.
|
||||
USER MESSAGE: {input}
|
||||
"""
|
||||
34
FinanceAgent/prompts/supervisor_prompt.py
Normal file
34
FinanceAgent/prompts/supervisor_prompt.py
Normal file
@@ -0,0 +1,34 @@
|
||||
# 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 users.
|
||||
You have the following worker agents working for you. You can call them as calling tools.
|
||||
{tools}
|
||||
|
||||
**Procedure:**
|
||||
1. Read the question carefully. Decide which agent you should call to answer the question.
|
||||
2. The worker agents need detailed inputs. Ask the user to clarify when you lack certain info or are uncertain about something. Do not assume anything. For example, user asks about "recent earnings call of Microsoft", ask the user to specify the quarter and year.
|
||||
3. Read the execution history if any to understand the worker agents that have been called and the information that has been gathered.
|
||||
4. Reason about the information gathered so far and decide if you can answer the question or if you need to gather more info.
|
||||
|
||||
**Output format:**
|
||||
You should output your thought process. Finish thinking first. Output tool calls or your answer at the end.
|
||||
When calling worker agents, you should use the following tool-call format:
|
||||
TOOL CALL: {{"tool": "tool1", "args": {{"arg1": "value1", "arg2": "value2", ...}}}}
|
||||
TOOL CALL: {{"tool": "tool2", "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 ==========
|
||||
|
||||
Now take a deep breath and think step by step to answer user's question in this turn.
|
||||
USER MESSAGE: {input}
|
||||
"""
|
||||
98
FinanceAgent/tests/test.py
Normal file
98
FinanceAgent/tests/test.py
Normal file
@@ -0,0 +1,98 @@
|
||||
# Copyright (C) 2025 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import uuid
|
||||
|
||||
import requests
|
||||
|
||||
|
||||
def process_request(url, query, is_stream=False):
|
||||
proxies = {"http": ""}
|
||||
content = json.dumps(query) if query is not None else None
|
||||
try:
|
||||
resp = requests.post(url=url, data=content, proxies=proxies, stream=is_stream)
|
||||
if not is_stream:
|
||||
ret = resp.json()["text"]
|
||||
else:
|
||||
for line in resp.iter_lines(decode_unicode=True):
|
||||
print(line)
|
||||
ret = None
|
||||
|
||||
resp.raise_for_status() # Raise an exception for unsuccessful HTTP status codes
|
||||
return ret
|
||||
except requests.exceptions.RequestException as e:
|
||||
ret = f"An error occurred:{e}"
|
||||
return None
|
||||
|
||||
|
||||
def test_worker_agent(args):
|
||||
url = f"http://{args.ip_addr}:{args.ext_port}/v1/chat/completions"
|
||||
if args.tool_choice is None:
|
||||
query = {"role": "user", "messages": args.prompt, "stream": "false"}
|
||||
else:
|
||||
query = {"role": "user", "messages": args.prompt, "stream": "false", "tool_choice": args.tool_choice}
|
||||
ret = process_request(url, query)
|
||||
print("Response: ", ret)
|
||||
|
||||
|
||||
def add_message_and_run(url, user_message, thread_id, stream=False):
|
||||
print("User message: ", user_message)
|
||||
query = {"role": "user", "messages": user_message, "thread_id": thread_id, "stream": stream}
|
||||
ret = process_request(url, query, is_stream=stream)
|
||||
print("Response: ", ret)
|
||||
|
||||
|
||||
def test_chat_completion_multi_turn(args):
|
||||
url = f"http://{args.ip_addr}:{args.ext_port}/v1/chat/completions"
|
||||
thread_id = f"{uuid.uuid4()}"
|
||||
|
||||
# first turn
|
||||
print("===============First turn==================")
|
||||
user_message = "Key takeaways of Gap's 2024 Q4 earnings call?"
|
||||
add_message_and_run(url, user_message, thread_id, stream=args.stream)
|
||||
print("===============End of first turn==================")
|
||||
|
||||
# second turn
|
||||
print("===============Second turn==================")
|
||||
user_message = "What was Gap's forecast for 2025?"
|
||||
add_message_and_run(url, user_message, thread_id, stream=args.stream)
|
||||
print("===============End of second turn==================")
|
||||
|
||||
|
||||
def test_supervisor_agent_single_turn(args):
|
||||
url = f"http://{args.ip_addr}:{args.ext_port}/v1/chat/completions"
|
||||
query_list = [
|
||||
"What was Gap's revenue growth in 2024?",
|
||||
"Can you summarize Costco's 2025 Q2 earnings call?",
|
||||
# "Should I increase investment in Costco?",
|
||||
]
|
||||
for query in query_list:
|
||||
thread_id = f"{uuid.uuid4()}"
|
||||
add_message_and_run(url, query, thread_id, stream=args.stream)
|
||||
print("=" * 50)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--ip_addr", type=str, default="127.0.0.1", help="endpoint ip address")
|
||||
parser.add_argument("--ext_port", type=str, default="9090", help="endpoint port")
|
||||
parser.add_argument("--stream", action="store_true", help="streaming mode")
|
||||
parser.add_argument("--prompt", type=str, help="prompt message")
|
||||
parser.add_argument("--agent_role", type=str, default="supervisor", help="supervisor or worker")
|
||||
parser.add_argument("--multi-turn", action="store_true", help="multi-turn conversation")
|
||||
parser.add_argument("--tool_choice", nargs="+", help="limit tools")
|
||||
args, _ = parser.parse_known_args()
|
||||
|
||||
print(args)
|
||||
|
||||
if args.agent_role == "supervisor":
|
||||
if args.multi_turn:
|
||||
test_chat_completion_multi_turn(args)
|
||||
else:
|
||||
test_supervisor_agent_single_turn(args)
|
||||
elif args.agent_role == "worker":
|
||||
test_worker_agent(args)
|
||||
else:
|
||||
raise ValueError("Invalid agent role")
|
||||
270
FinanceAgent/tests/test_compose_on_gaudi.sh
Normal file
270
FinanceAgent/tests/test_compose_on_gaudi.sh
Normal file
@@ -0,0 +1,270 @@
|
||||
#!/bin/bash
|
||||
# Copyright (C) 2024 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
set -xe
|
||||
|
||||
export WORKPATH=$(dirname "$PWD")
|
||||
export WORKDIR=$WORKPATH/../../
|
||||
echo "WORKDIR=${WORKDIR}"
|
||||
export ip_address=$(hostname -I | awk '{print $1}')
|
||||
LOG_PATH=$WORKPATH
|
||||
|
||||
#### env vars for LLM endpoint #############
|
||||
model=meta-llama/Llama-3.3-70B-Instruct
|
||||
vllm_image=opea/vllm-gaudi:latest
|
||||
vllm_port=8086
|
||||
vllm_image=$vllm_image
|
||||
HF_CACHE_DIR=${model_cache:-"/data2/huggingface"}
|
||||
vllm_volume=${HF_CACHE_DIR}
|
||||
#######################################
|
||||
|
||||
#### env vars for dataprep #############
|
||||
export host_ip=${ip_address}
|
||||
export DATAPREP_PORT="6007"
|
||||
export TEI_EMBEDDER_PORT="10221"
|
||||
export REDIS_URL_VECTOR="redis://${ip_address}:6379"
|
||||
export REDIS_URL_KV="redis://${ip_address}:6380"
|
||||
export LLM_MODEL=$model
|
||||
export LLM_ENDPOINT="http://${ip_address}:${vllm_port}"
|
||||
export DATAPREP_COMPONENT_NAME="OPEA_DATAPREP_REDIS_FINANCE"
|
||||
export EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"
|
||||
export TEI_EMBEDDING_ENDPOINT="http://${ip_address}:${TEI_EMBEDDER_PORT}"
|
||||
#######################################
|
||||
|
||||
|
||||
|
||||
function get_genai_comps() {
|
||||
if [ ! -d "GenAIComps" ] ; then
|
||||
git clone --depth 1 --branch ${opea_branch:-"main"} https://github.com/opea-project/GenAIComps.git
|
||||
fi
|
||||
}
|
||||
|
||||
function build_dataprep_agent_images() {
|
||||
cd $WORKDIR/GenAIExamples/FinanceAgent/docker_image_build/
|
||||
get_genai_comps
|
||||
echo "Build agent image with --no-cache..."
|
||||
docker compose -f build.yaml build --no-cache
|
||||
}
|
||||
|
||||
function build_agent_image_local(){
|
||||
cd $WORKDIR/GenAIComps/
|
||||
docker build -t opea/agent:latest -f comps/agent/src/Dockerfile . --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy
|
||||
}
|
||||
|
||||
function build_vllm_docker_image() {
|
||||
echo "Building the vllm docker image"
|
||||
cd $WORKPATH
|
||||
echo $WORKPATH
|
||||
if [ ! -d "./vllm-fork" ]; then
|
||||
git clone https://github.com/HabanaAI/vllm-fork.git
|
||||
fi
|
||||
cd ./vllm-fork
|
||||
# VLLM_VER=$(git describe --tags "$(git rev-list --tags --max-count=1)")
|
||||
VLLM_VER=v0.6.6.post1+Gaudi-1.20.0
|
||||
git checkout ${VLLM_VER} &> /dev/null
|
||||
docker build --no-cache -f Dockerfile.hpu -t $vllm_image --shm-size=128g . --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy
|
||||
if [ $? -ne 0 ]; then
|
||||
echo "$vllm_image failed"
|
||||
exit 1
|
||||
else
|
||||
echo "$vllm_image successful"
|
||||
fi
|
||||
}
|
||||
|
||||
|
||||
function start_vllm_service_70B() {
|
||||
echo "token is ${HF_TOKEN}"
|
||||
echo "start vllm gaudi service"
|
||||
echo "**************model is $model**************"
|
||||
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 $vllm_image --model ${model} --max-seq-len-to-capture 16384 --tensor-parallel-size 4
|
||||
sleep 10s
|
||||
echo "Waiting vllm gaudi ready"
|
||||
n=0
|
||||
until [[ "$n" -ge 200 ]] || [[ $ready == true ]]; do
|
||||
docker logs vllm-gaudi-server &> ${LOG_PATH}/vllm-gaudi-service.log
|
||||
n=$((n+1))
|
||||
if grep -q "Uvicorn running on" ${LOG_PATH}/vllm-gaudi-service.log; then
|
||||
break
|
||||
fi
|
||||
if grep -q "No such container" ${LOG_PATH}/vllm-gaudi-service.log; then
|
||||
echo "container vllm-gaudi-server not found"
|
||||
exit 1
|
||||
fi
|
||||
sleep 10s
|
||||
done
|
||||
sleep 10s
|
||||
echo "Service started successfully"
|
||||
}
|
||||
|
||||
|
||||
function stop_llm(){
|
||||
cid=$(docker ps -aq --filter "name=vllm-gaudi-server")
|
||||
echo "Stopping container $cid"
|
||||
if [[ ! -z "$cid" ]]; then docker rm $cid -f && sleep 1s; fi
|
||||
|
||||
}
|
||||
|
||||
function start_dataprep(){
|
||||
docker compose -f $WORKPATH/docker_compose/intel/hpu/gaudi/dataprep_compose.yaml up -d
|
||||
sleep 1m
|
||||
}
|
||||
|
||||
function validate() {
|
||||
local CONTENT="$1"
|
||||
local EXPECTED_RESULT="$2"
|
||||
local SERVICE_NAME="$3"
|
||||
echo "EXPECTED_RESULT: $EXPECTED_RESULT"
|
||||
echo "Content: $CONTENT"
|
||||
if echo "$CONTENT" | grep -q "$EXPECTED_RESULT"; then
|
||||
echo "[ $SERVICE_NAME ] Content is as expected: $CONTENT"
|
||||
echo 0
|
||||
else
|
||||
echo "[ $SERVICE_NAME ] Content does not match the expected result: $CONTENT"
|
||||
echo 1
|
||||
fi
|
||||
}
|
||||
|
||||
function ingest_validate_dataprep() {
|
||||
# test /v1/dataprep/ingest
|
||||
echo "=========== Test ingest ==========="
|
||||
local CONTENT=$(python $WORKPATH/tests/test_redis_finance.py --port $DATAPREP_PORT --test_option ingest)
|
||||
local EXIT_CODE=$(validate "$CONTENT" "200" "dataprep-redis-finance")
|
||||
echo "$EXIT_CODE"
|
||||
local EXIT_CODE="${EXIT_CODE:0-1}"
|
||||
if [ "$EXIT_CODE" == "1" ]; then
|
||||
docker logs dataprep-redis-server-finance
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# test /v1/dataprep/get
|
||||
echo "=========== Test get ==========="
|
||||
local CONTENT=$(python $WORKPATH/tests/test_redis_finance.py --port $DATAPREP_PORT --test_option get)
|
||||
local EXIT_CODE=$(validate "$CONTENT" "Request successful" "dataprep-redis-finance")
|
||||
echo "$EXIT_CODE"
|
||||
local EXIT_CODE="${EXIT_CODE:0-1}"
|
||||
if [ "$EXIT_CODE" == "1" ]; then
|
||||
docker logs dataprep-redis-server-finance
|
||||
exit 1
|
||||
fi
|
||||
}
|
||||
|
||||
function stop_dataprep() {
|
||||
echo "Stopping databases"
|
||||
cid=$(docker ps -aq --filter "name=dataprep-redis-server*" --filter "name=redis-*" --filter "name=tei-embedding-*")
|
||||
if [[ ! -z "$cid" ]]; then docker stop $cid && docker rm $cid && sleep 1s; fi
|
||||
|
||||
}
|
||||
|
||||
function start_agents() {
|
||||
echo "Starting Agent services"
|
||||
cd $WORKDIR/GenAIExamples/FinanceAgent/docker_compose/intel/hpu/gaudi/
|
||||
bash launch_agents.sh
|
||||
sleep 2m
|
||||
}
|
||||
|
||||
|
||||
function validate_agent_service() {
|
||||
# # test worker finqa agent
|
||||
echo "======================Testing worker finqa agent======================"
|
||||
export agent_port="9095"
|
||||
prompt="What is Gap's revenue in 2024?"
|
||||
local CONTENT=$(python3 $WORKDIR/GenAIExamples/FinanceAgent/tests/test.py --prompt "$prompt" --agent_role "worker" --ext_port $agent_port)
|
||||
echo $CONTENT
|
||||
local EXIT_CODE=$(validate "$CONTENT" "15" "finqa-agent-endpoint")
|
||||
echo $EXIT_CODE
|
||||
local EXIT_CODE="${EXIT_CODE:0-1}"
|
||||
if [ "$EXIT_CODE" == "1" ]; then
|
||||
docker logs finqa-agent-endpoint
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# # test worker research agent
|
||||
echo "======================Testing worker research agent======================"
|
||||
export agent_port="9096"
|
||||
prompt="generate NVDA financial research report"
|
||||
local CONTENT=$(python3 $WORKDIR/GenAIExamples/AgentQnA/tests/test.py --prompt "$prompt" --agent_role "worker" --ext_port $agent_port --tool_choice "get_current_date" --tool_choice "get_share_performance")
|
||||
local EXIT_CODE=$(validate "$CONTENT" "NVDA" "research-agent-endpoint")
|
||||
echo $CONTENT
|
||||
echo $EXIT_CODE
|
||||
local EXIT_CODE="${EXIT_CODE:0-1}"
|
||||
if [ "$EXIT_CODE" == "1" ]; then
|
||||
docker logs research-agent-endpoint
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# test supervisor react agent
|
||||
echo "======================Testing supervisor agent: single turns ======================"
|
||||
export agent_port="9090"
|
||||
local CONTENT=$(python3 $WORKDIR/GenAIExamples/FinanceAgent/tests/test.py --agent_role "supervisor" --ext_port $agent_port --stream)
|
||||
echo $CONTENT
|
||||
# local EXIT_CODE=$(validate "$CONTENT" "" "react-agent-endpoint")
|
||||
# echo $EXIT_CODE
|
||||
# local EXIT_CODE="${EXIT_CODE:0-1}"
|
||||
# if [ "$EXIT_CODE" == "1" ]; then
|
||||
# docker logs react-agent-endpoint
|
||||
# exit 1
|
||||
# fi
|
||||
|
||||
echo "======================Testing supervisor agent: multi turns ======================"
|
||||
local CONTENT=$(python3 $WORKDIR/GenAIExamples/FinanceAgent/tests/test.py --agent_role "supervisor" --ext_port $agent_port --multi-turn --stream)
|
||||
echo $CONTENT
|
||||
# local EXIT_CODE=$(validate "$CONTENT" "" "react-agent-endpoint")
|
||||
# echo $EXIT_CODE
|
||||
# local EXIT_CODE="${EXIT_CODE:0-1}"
|
||||
# if [ "$EXIT_CODE" == "1" ]; then
|
||||
# docker logs react-agent-endpoint
|
||||
# exit 1
|
||||
# fi
|
||||
|
||||
}
|
||||
|
||||
function stop_agent_docker() {
|
||||
cd $WORKPATH/docker_compose/intel/hpu/gaudi/
|
||||
container_list=$(cat compose.yaml | grep container_name | cut -d':' -f2)
|
||||
for container_name in $container_list; do
|
||||
cid=$(docker ps -aq --filter "name=$container_name")
|
||||
echo "Stopping container $container_name"
|
||||
if [[ ! -z "$cid" ]]; then docker rm $cid -f && sleep 1s; fi
|
||||
done
|
||||
}
|
||||
|
||||
|
||||
echo "workpath: $WORKPATH"
|
||||
echo "=================== Stop containers ===================="
|
||||
stop_llm
|
||||
stop_agent_docker
|
||||
stop_dataprep
|
||||
|
||||
cd $WORKPATH/tests
|
||||
|
||||
# echo "=================== #1 Building docker images===================="
|
||||
build_vllm_docker_image
|
||||
build_dataprep_agent_images
|
||||
|
||||
#### for local test
|
||||
# build_agent_image_local
|
||||
# echo "=================== #1 Building docker images completed===================="
|
||||
|
||||
# echo "=================== #2 Start vllm endpoint===================="
|
||||
start_vllm_service_70B
|
||||
# echo "=================== #2 vllm endpoint started===================="
|
||||
|
||||
# echo "=================== #3 Start dataprep and ingest data ===================="
|
||||
start_dataprep
|
||||
ingest_validate_dataprep
|
||||
# echo "=================== #3 Data ingestion and validation completed===================="
|
||||
|
||||
echo "=================== #4 Start agents ===================="
|
||||
start_agents
|
||||
validate_agent_service
|
||||
echo "=================== #4 Agent test passed ===================="
|
||||
|
||||
echo "=================== #5 Stop microservices ===================="
|
||||
stop_agent_docker
|
||||
stop_dataprep
|
||||
stop_llm
|
||||
echo "=================== #5 Microservices stopped===================="
|
||||
|
||||
echo y | docker system prune
|
||||
|
||||
echo "ALL DONE!!"
|
||||
70
FinanceAgent/tests/test_redis_finance.py
Normal file
70
FinanceAgent/tests/test_redis_finance.py
Normal file
@@ -0,0 +1,70 @@
|
||||
# Copyright (C) 2025 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
import json
|
||||
import os
|
||||
|
||||
import requests
|
||||
|
||||
|
||||
def test_html(url, link_list):
|
||||
proxies = {"http": ""}
|
||||
payload = {"link_list": json.dumps(link_list)}
|
||||
try:
|
||||
resp = requests.post(url=url, data=payload, proxies=proxies)
|
||||
print(resp.text)
|
||||
resp.raise_for_status() # Raise an exception for unsuccessful HTTP status codes
|
||||
print("Request successful!")
|
||||
except requests.exceptions.RequestException as e:
|
||||
print("An error occurred:", e)
|
||||
|
||||
|
||||
def test_delete(url, filename):
|
||||
proxies = {"http": ""}
|
||||
payload = {"file_path": filename}
|
||||
try:
|
||||
resp = requests.post(url=url, json=payload, proxies=proxies)
|
||||
print(resp.text)
|
||||
resp.raise_for_status() # Raise an exception for unsuccessful HTTP status codes
|
||||
print("Request successful!")
|
||||
except requests.exceptions.RequestException as e:
|
||||
print("An error occurred:", e)
|
||||
|
||||
|
||||
def test_get(url):
|
||||
proxies = {"http": ""}
|
||||
try:
|
||||
resp = requests.post(url=url, proxies=proxies)
|
||||
print(resp.text)
|
||||
resp.raise_for_status() # Raise an exception for unsuccessful HTTP status codes
|
||||
print("Request successful!")
|
||||
except requests.exceptions.RequestException as e:
|
||||
print("An error occurred:", e)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import argparse
|
||||
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--test_option", type=str, default="ingest", help="ingest, get, delete")
|
||||
parser.add_argument("--port", type=str, default="6007", help="port number")
|
||||
args = parser.parse_args()
|
||||
|
||||
port = args.port
|
||||
|
||||
if args.test_option == "ingest":
|
||||
url = f"http://localhost:{port}/v1/dataprep/ingest"
|
||||
link_list = [
|
||||
"https://www.fool.com/earnings/call-transcripts/2025/03/06/costco-wholesale-cost-q2-2025-earnings-call-transc/",
|
||||
"https://www.fool.com/earnings/call-transcripts/2025/03/07/gap-gap-q4-2024-earnings-call-transcript/",
|
||||
]
|
||||
test_html(url, link_list)
|
||||
elif args.test_option == "delete":
|
||||
url = f"http://localhost:{port}/v1/dataprep/delete"
|
||||
filename = "Costco Wholesale"
|
||||
test_delete(url, filename)
|
||||
elif args.test_option == "get":
|
||||
url = f"http://localhost:{port}/v1/dataprep/get"
|
||||
test_get(url)
|
||||
else:
|
||||
raise ValueError("Invalid test_option value. Please choose from ingest, get, delete.")
|
||||
20
FinanceAgent/tools/finqa_agent_tools.yaml
Normal file
20
FinanceAgent/tools/finqa_agent_tools.yaml
Normal file
@@ -0,0 +1,20 @@
|
||||
# Copyright (C) 2025 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
search_knowledge_base:
|
||||
description: Search knowledge base of SEC filings.
|
||||
callable_api: finqa_tools.py:get_context_bm25_llm
|
||||
args_schema:
|
||||
query:
|
||||
type: str
|
||||
description: the query to search for. Should be detailed. Do not include the company name.
|
||||
company:
|
||||
type: str
|
||||
description: the company of interest.
|
||||
year:
|
||||
type: str
|
||||
description: the year of interest, can only specify one year. can be an empty string.
|
||||
quarter:
|
||||
type: str
|
||||
description: the quarter of interest, can only specify one quarter. can be 'Q1', 'Q2', 'Q3', 'Q4'. can be an empty string.
|
||||
return_output: retrieved_data
|
||||
100
FinanceAgent/tools/finqa_tools.py
Normal file
100
FinanceAgent/tools/finqa_tools.py
Normal file
@@ -0,0 +1,100 @@
|
||||
# Copyright (C) 2025 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
from tools.utils import *
|
||||
|
||||
|
||||
def get_context_bm25_llm(query, company, year, quarter=""):
|
||||
k = 5
|
||||
|
||||
company_list = get_company_list()
|
||||
company = get_company_name_in_kb(company, company_list)
|
||||
if "Cannot find" in company or "Database is empty" in company:
|
||||
return company
|
||||
|
||||
print(f"Company: {company}")
|
||||
# chunks
|
||||
index_name = f"chunks_{company}"
|
||||
vector_store = get_vectorstore(index_name)
|
||||
chunks_bm25 = bm25_search_broad(query, company, year, quarter, k=k, doc_type="chunks")
|
||||
chunks_sim = similarity_search(vector_store, k, query, company, year, quarter)
|
||||
chunks = chunks_bm25 + chunks_sim
|
||||
|
||||
# tables
|
||||
try:
|
||||
index_name = f"tables_{company}"
|
||||
vector_store_table = get_vectorstore(index_name)
|
||||
# get tables matching metadata
|
||||
tables_bm25 = bm25_search_broad(query, company, year, quarter, k=k, doc_type="tables")
|
||||
tables_sim = similarity_search(vector_store_table, k, query, company, year, quarter)
|
||||
tables = tables_bm25 + tables_sim
|
||||
except:
|
||||
tables = []
|
||||
|
||||
# get unique results
|
||||
context = get_unique_docs(chunks + tables)
|
||||
print("Context:\n", context[:500])
|
||||
|
||||
if context:
|
||||
query = f"{query} for {company} in {year} {quarter}"
|
||||
prompt = ANSWER_PROMPT.format(query=query, documents=context)
|
||||
response = generate_answer(prompt)
|
||||
response = parse_response(response)
|
||||
else:
|
||||
response = f"No relevant information found for {company} in {year} {quarter}."
|
||||
print("Search result:\n", response)
|
||||
return response
|
||||
|
||||
|
||||
def search_full_doc(query, company):
|
||||
company = company.upper()
|
||||
|
||||
# decide if company is in company list
|
||||
company_list = get_company_list()
|
||||
company = get_company_name_in_kb(company, company_list)
|
||||
if "Cannot find" in company or "Database is empty" in company:
|
||||
return company
|
||||
|
||||
# search most similar doc title
|
||||
index_name = f"titles_{company}"
|
||||
vector_store = get_vectorstore_titles(index_name)
|
||||
k = 1
|
||||
docs = vector_store.similarity_search(query, k=k)
|
||||
if docs:
|
||||
doc = docs[0]
|
||||
doc_title = doc.page_content
|
||||
print(f"Most similar doc title: {doc_title}")
|
||||
|
||||
kvstore = RedisKVStore(redis_uri=REDIS_URL_KV)
|
||||
doc = kvstore.get(doc_title, f"full_doc_{company}")
|
||||
content = doc["full_doc"]
|
||||
doc_length = doc["doc_length"]
|
||||
print(f"Doc length: {doc_length}")
|
||||
print(f"Full doc content: {content[:100]}...")
|
||||
# once summary is done, can save to kvstore
|
||||
# first delete the old record
|
||||
# kvstore.delete(doc_title, f"full_doc_{company}")
|
||||
# then save the new record with summary
|
||||
# kvstore.put(doc_title, {"full_doc": content, "summary":summary,"doc_length":doc_length, **metadata}, f"full_doc_{company}")
|
||||
return content
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# company="Gap"
|
||||
# year="2024"
|
||||
# quarter="Q4"
|
||||
|
||||
company = "Costco"
|
||||
year = "2025"
|
||||
quarter = "Q2"
|
||||
|
||||
collection_name = f"chunks_{company}"
|
||||
search_metadata = ("company", company)
|
||||
|
||||
resp = get_context_bm25_llm("revenue", company, year, quarter)
|
||||
print("***Response:\n", resp)
|
||||
print("=" * 50)
|
||||
|
||||
print("testing retrieve full doc")
|
||||
query = f"{company} {year} {quarter} earning call"
|
||||
search_full_doc(query, company)
|
||||
146
FinanceAgent/tools/redis_kv.py
Normal file
146
FinanceAgent/tools/redis_kv.py
Normal file
@@ -0,0 +1,146 @@
|
||||
# Copyright (C) 2025 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
import json
|
||||
from collections import OrderedDict
|
||||
from typing import Any, Dict, List, Optional, Tuple
|
||||
|
||||
from redis import Redis
|
||||
from redis.asyncio import Redis as AsyncRedis
|
||||
|
||||
DEFAULT_COLLECTION = "data"
|
||||
DEFAULT_BATCH_SIZE = 1
|
||||
|
||||
|
||||
class RedisKVStore:
|
||||
def __init__(
|
||||
self,
|
||||
redis_uri: Optional[str] = "redis://127.0.0.1:6379",
|
||||
**kwargs: Any,
|
||||
):
|
||||
try:
|
||||
# connect to redis from url
|
||||
self._redis_client = Redis.from_url(redis_uri, **kwargs)
|
||||
self._async_redis_client = AsyncRedis.from_url(redis_uri, **kwargs)
|
||||
except ValueError as e:
|
||||
raise ValueError(f"Redis failed to connect: {e}")
|
||||
|
||||
def put(self, key: str, val: dict, collection: str = DEFAULT_COLLECTION) -> None:
|
||||
"""Put a key-value pair into the store.
|
||||
|
||||
Args:
|
||||
key (str): key
|
||||
val (dict): value
|
||||
collection (str): collection name
|
||||
"""
|
||||
self._redis_client.hset(name=collection, key=key, value=json.dumps(val))
|
||||
|
||||
async def aput(self, key: str, val: dict, collection: str = DEFAULT_COLLECTION) -> None:
|
||||
"""Put a key-value pair into the store.
|
||||
|
||||
Args:
|
||||
key (str): key
|
||||
val (dict): value
|
||||
collection (str): collection name
|
||||
"""
|
||||
await self._async_redis_client.hset(name=collection, key=key, value=json.dumps(val))
|
||||
|
||||
def put_all(
|
||||
self,
|
||||
kv_pairs: List[Tuple[str, dict]],
|
||||
collection: str = DEFAULT_COLLECTION,
|
||||
batch_size: int = DEFAULT_BATCH_SIZE,
|
||||
) -> None:
|
||||
"""Put a dictionary of key-value pairs into the store.
|
||||
|
||||
Args:
|
||||
kv_pairs (List[Tuple[str, dict]]): key-value pairs
|
||||
collection (str): collection name
|
||||
"""
|
||||
with self._redis_client.pipeline() as pipe:
|
||||
cur_batch = 0
|
||||
for key, val in kv_pairs:
|
||||
pipe.hset(name=collection, key=key, value=json.dumps(val))
|
||||
cur_batch += 1
|
||||
|
||||
if cur_batch >= batch_size:
|
||||
cur_batch = 0
|
||||
pipe.execute()
|
||||
|
||||
if cur_batch > 0:
|
||||
pipe.execute()
|
||||
|
||||
def get(self, key: str, collection: str = DEFAULT_COLLECTION) -> Optional[dict]:
|
||||
"""Get a value from the store.
|
||||
|
||||
Args:
|
||||
key (str): key
|
||||
collection (str): collection name
|
||||
"""
|
||||
val_str = self._redis_client.hget(name=collection, key=key)
|
||||
if val_str is None:
|
||||
return None
|
||||
return json.loads(val_str)
|
||||
|
||||
async def aget(self, key: str, collection: str = DEFAULT_COLLECTION) -> Optional[dict]:
|
||||
"""Get a value from the store.
|
||||
|
||||
Args:
|
||||
key (str): key
|
||||
collection (str): collection name
|
||||
"""
|
||||
val_str = await self._async_redis_client.hget(name=collection, key=key)
|
||||
if val_str is None:
|
||||
return None
|
||||
return json.loads(val_str)
|
||||
|
||||
def get_all(self, collection: str = DEFAULT_COLLECTION) -> Dict[str, dict]:
|
||||
"""Get all values from the store."""
|
||||
collection_kv_dict = OrderedDict()
|
||||
for key, val_str in self._redis_client.hscan_iter(name=collection):
|
||||
value = json.loads(val_str)
|
||||
collection_kv_dict[key.decode()] = value
|
||||
return collection_kv_dict
|
||||
|
||||
async def aget_all(self, collection: str = DEFAULT_COLLECTION) -> Dict[str, dict]:
|
||||
"""Get all values from the store."""
|
||||
collection_kv_dict = OrderedDict()
|
||||
async for key, val_str in self._async_redis_client.hscan_iter(name=collection):
|
||||
value = json.loads(val_str)
|
||||
collection_kv_dict[key.decode()] = value
|
||||
return collection_kv_dict
|
||||
|
||||
def delete(self, key: str, collection: str = DEFAULT_COLLECTION) -> bool:
|
||||
"""Delete a value from the store.
|
||||
|
||||
Args:
|
||||
key (str): key
|
||||
collection (str): collection name
|
||||
"""
|
||||
deleted_num = self._redis_client.hdel(collection, key)
|
||||
return bool(deleted_num > 0)
|
||||
|
||||
async def adelete(self, key: str, collection: str = DEFAULT_COLLECTION) -> bool:
|
||||
"""Delete a value from the store.
|
||||
|
||||
Args:
|
||||
key (str): key
|
||||
collection (str): collection name
|
||||
"""
|
||||
deleted_num = await self._async_redis_client.hdel(collection, key)
|
||||
return bool(deleted_num > 0)
|
||||
|
||||
@classmethod
|
||||
def from_host_and_port(
|
||||
cls,
|
||||
host: str,
|
||||
port: int,
|
||||
):
|
||||
"""Load a RedisPersistence from a Redis host and port.
|
||||
|
||||
Args:
|
||||
host (str): Redis host
|
||||
port (int): Redis port
|
||||
"""
|
||||
url = f"redis://{host}:{port}".format(host=host, port=port)
|
||||
return cls(redis_uri=url)
|
||||
124
FinanceAgent/tools/research_agent_tools.yaml
Normal file
124
FinanceAgent/tools/research_agent_tools.yaml
Normal file
@@ -0,0 +1,124 @@
|
||||
# Copyright (C) 2025 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
get_company_profile:
|
||||
description: get a company's profile information.
|
||||
callable_api: research_tools.py:get_company_profile
|
||||
args_schema:
|
||||
symbol:
|
||||
type: str
|
||||
description: the company name or ticker symbol.
|
||||
return_output: profile
|
||||
|
||||
get_company_news:
|
||||
description: retrieve market news related to designated company.
|
||||
callable_api: research_tools.py:get_company_news
|
||||
args_schema:
|
||||
symbol:
|
||||
type: str
|
||||
description: the company name or ticker symbol.
|
||||
start_date:
|
||||
type: str
|
||||
description: start date of the search period for the company's basic financials, yyyy-mm-dd.
|
||||
end_date:
|
||||
type: str
|
||||
description: end date of the search period for the company's basic financials, yyyy-mm-dd.
|
||||
max_news_num:
|
||||
type: int
|
||||
description: maximum number of news to return, default to 10.
|
||||
return_output: news
|
||||
|
||||
get_basic_financials_history:
|
||||
description: get historical basic financials for a designated company.
|
||||
callable_api: research_tools.py:get_basic_financials_history
|
||||
args_schema:
|
||||
symbol:
|
||||
type: str
|
||||
description: the company name or ticker symbol.
|
||||
freq:
|
||||
type: str
|
||||
description: reporting frequency of the company's basic financials, such as annual, quarterly.
|
||||
start_date:
|
||||
type: str
|
||||
description: start date of the search period for the company's basic financials, yyyy-mm-dd.
|
||||
end_date:
|
||||
type: str
|
||||
description: end date of the search period for the company's basic financials, yyyy-mm-dd.
|
||||
selected_columns:
|
||||
type: list
|
||||
description: List of column names of news to return, should be chosen from 'assetTurnoverTTM', 'bookValue', 'cashRatio', 'currentRatio', 'ebitPerShare', 'eps', 'ev', 'fcfMargin', 'fcfPerShareTTM', 'grossMargin', 'inventoryTurnoverTTM', 'longtermDebtTotalAsset', 'longtermDebtTotalCapital', 'longtermDebtTotalEquity', 'netDebtToTotalCapital', 'netDebtToTotalEquity', 'netMargin', 'operatingMargin', 'payoutRatioTTM', 'pb', 'peTTM', 'pfcfTTM', 'pretaxMargin', 'psTTM', 'ptbv', 'quickRatio', 'receivablesTurnoverTTM', 'roaTTM', 'roeTTM', 'roicTTM', 'rotcTTM', 'salesPerShare', 'sgaToSale', 'tangibleBookValue', 'totalDebtToEquity', 'totalDebtToTotalAsset', 'totalDebtToTotalCapital', 'totalRatio','10DayAverageTradingVolume', '13WeekPriceReturnDaily', '26WeekPriceReturnDaily', '3MonthADReturnStd', '3MonthAverageTradingVolume', '52WeekHigh', '52WeekHighDate', '52WeekLow', '52WeekLowDate', '52WeekPriceReturnDaily', '5DayPriceReturnDaily', 'assetTurnoverAnnual', 'assetTurnoverTTM', 'beta', 'bookValuePerShareAnnual', 'bookValuePerShareQuarterly', 'bookValueShareGrowth5Y', 'capexCagr5Y', 'cashFlowPerShareAnnual', 'cashFlowPerShareQuarterly', 'cashFlowPerShareTTM', 'cashPerSharePerShareAnnual', 'cashPerSharePerShareQuarterly', 'currentDividendYieldTTM', 'currentEv/freeCashFlowAnnual', 'currentEv/freeCashFlowTTM', 'currentRatioAnnual', 'currentRatioQuarterly', 'dividendGrowthRate5Y', 'dividendPerShareAnnual', 'dividendPerShareTTM', 'dividendYieldIndicatedAnnual', 'ebitdPerShareAnnual', 'ebitdPerShareTTM', 'ebitdaCagr5Y', 'ebitdaInterimCagr5Y', 'enterpriseValue', 'epsAnnual', 'epsBasicExclExtraItemsAnnual', 'epsBasicExclExtraItemsTTM', 'epsExclExtraItemsAnnual', 'epsExclExtraItemsTTM', 'epsGrowth3Y', 'epsGrowth5Y', 'epsGrowthQuarterlyYoy', 'epsGrowthTTMYoy', 'epsInclExtraItemsAnnual', 'epsInclExtraItemsTTM', 'epsNormalizedAnnual', 'epsTTM', 'focfCagr5Y', 'grossMargin5Y', 'grossMarginAnnual', 'grossMarginTTM', 'inventoryTurnoverAnnual', 'inventoryTurnoverTTM', 'longTermDebt/equityAnnual', 'longTermDebt/equityQuarterly', 'marketCapitalization', 'monthToDatePriceReturnDaily', 'netIncomeEmployeeAnnual', 'netIncomeEmployeeTTM', 'netInterestCoverageAnnual', 'netInterestCoverageTTM', 'netMarginGrowth5Y', 'netProfitMargin5Y', 'netProfitMarginAnnual', 'netProfitMarginTTM', 'operatingMargin5Y'.
|
||||
return_output: history_financials
|
||||
|
||||
get_basic_financials:
|
||||
description: get latest basic financials for a designated company.
|
||||
callable_api: research_tools.py:get_basic_financials
|
||||
args_schema:
|
||||
symbol:
|
||||
type: str
|
||||
description: the company name or ticker symbol.
|
||||
selected_columns:
|
||||
type: list
|
||||
description: List of column names of news to return, should be chosen from 'assetTurnoverTTM', 'bookValue', 'cashRatio', 'currentRatio', 'ebitPerShare', 'eps', 'ev', 'fcfMargin', 'fcfPerShareTTM', 'grossMargin', 'inventoryTurnoverTTM', 'longtermDebtTotalAsset', 'longtermDebtTotalCapital', 'longtermDebtTotalEquity', 'netDebtToTotalCapital', 'netDebtToTotalEquity', 'netMargin', 'operatingMargin', 'payoutRatioTTM', 'pb', 'peTTM', 'pfcfTTM', 'pretaxMargin', 'psTTM', 'ptbv', 'quickRatio', 'receivablesTurnoverTTM', 'roaTTM', 'roeTTM', 'roicTTM', 'rotcTTM', 'salesPerShare', 'sgaToSale', 'tangibleBookValue', 'totalDebtToEquity', 'totalDebtToTotalAsset', 'totalDebtToTotalCapital', 'totalRatio','10DayAverageTradingVolume', '13WeekPriceReturnDaily', '26WeekPriceReturnDaily', '3MonthADReturnStd', '3MonthAverageTradingVolume', '52WeekHigh', '52WeekHighDate', '52WeekLow', '52WeekLowDate', '52WeekPriceReturnDaily', '5DayPriceReturnDaily', 'assetTurnoverAnnual', 'assetTurnoverTTM', 'beta', 'bookValuePerShareAnnual', 'bookValuePerShareQuarterly', 'bookValueShareGrowth5Y', 'capexCagr5Y', 'cashFlowPerShareAnnual', 'cashFlowPerShareQuarterly', 'cashFlowPerShareTTM', 'cashPerSharePerShareAnnual', 'cashPerSharePerShareQuarterly', 'currentDividendYieldTTM', 'currentEv/freeCashFlowAnnual', 'currentEv/freeCashFlowTTM', 'currentRatioAnnual', 'currentRatioQuarterly', 'dividendGrowthRate5Y', 'dividendPerShareAnnual', 'dividendPerShareTTM', 'dividendYieldIndicatedAnnual', 'ebitdPerShareAnnual', 'ebitdPerShareTTM', 'ebitdaCagr5Y', 'ebitdaInterimCagr5Y', 'enterpriseValue', 'epsAnnual', 'epsBasicExclExtraItemsAnnual', 'epsBasicExclExtraItemsTTM', 'epsExclExtraItemsAnnual', 'epsExclExtraItemsTTM', 'epsGrowth3Y', 'epsGrowth5Y', 'epsGrowthQuarterlyYoy', 'epsGrowthTTMYoy', 'epsInclExtraItemsAnnual', 'epsInclExtraItemsTTM', 'epsNormalizedAnnual', 'epsTTM', 'focfCagr5Y', 'grossMargin5Y', 'grossMarginAnnual', 'grossMarginTTM', 'inventoryTurnoverAnnual', 'inventoryTurnoverTTM', 'longTermDebt/equityAnnual', 'longTermDebt/equityQuarterly', 'marketCapitalization', 'monthToDatePriceReturnDaily', 'netIncomeEmployeeAnnual', 'netIncomeEmployeeTTM', 'netInterestCoverageAnnual', 'netInterestCoverageTTM', 'netMarginGrowth5Y', 'netProfitMargin5Y', 'netProfitMarginAnnual', 'netProfitMarginTTM', 'operatingMargin5Y'.
|
||||
return_output: basic_financials
|
||||
|
||||
get_current_date:
|
||||
description: get current date.
|
||||
callable_api: research_tools.py:get_current_date
|
||||
return_output: current_date
|
||||
|
||||
analyze_balance_sheet:
|
||||
description: gets balance sheets for a given ticker over a given period.
|
||||
callable_api: research_tools.py:analyze_balance_sheet
|
||||
args_schema:
|
||||
symbol:
|
||||
type: str
|
||||
description: the company name or ticker symbol.
|
||||
period:
|
||||
type: str
|
||||
description: The period of the balance sheets, possible values such as annual, quarterly, ttm. Default is 'annual'.
|
||||
limit:
|
||||
type: int
|
||||
description: The number of balance sheets to return. Default is 10.
|
||||
return_output: balance_sheet
|
||||
|
||||
analyze_income_stmt:
|
||||
description: gets income statements for a given ticker over a given period.
|
||||
callable_api: research_tools.py:analyze_income_stmt
|
||||
args_schema:
|
||||
symbol:
|
||||
type: str
|
||||
description: the company name or ticker symbol.
|
||||
period:
|
||||
type: str
|
||||
description: The period of the balance sheets, possible values, such as annual, quarterly, ttm. Default is 'annual'.
|
||||
limit:
|
||||
type: int
|
||||
description: The number of balance sheets to return. Default is 10.
|
||||
return_output: income_stmt
|
||||
|
||||
analyze_cash_flow:
|
||||
description: gets cash flow statements for a given ticker over a given period.
|
||||
callable_api: research_tools.py:analyze_cash_flow
|
||||
args_schema:
|
||||
symbol:
|
||||
type: str
|
||||
description: the company name or ticker symbol.
|
||||
period:
|
||||
type: str
|
||||
description: The period of the balance sheets, possible values, such as annual, quarterly, ttm. Default is 'annual'.
|
||||
limit:
|
||||
type: int
|
||||
description: The number of balance sheets to return. Default is 10.
|
||||
return_output: cash_flow
|
||||
|
||||
get_share_performance:
|
||||
description: gets stock prices for a given ticker over 60 days.
|
||||
callable_api: research_tools.py:get_share_performance
|
||||
args_schema:
|
||||
symbol:
|
||||
type: str
|
||||
description: the company name or ticker symbol.
|
||||
end_date:
|
||||
type: str
|
||||
description: end date of the search period for the company's basic financials, yyyy-mm-dd.
|
||||
return_output: stock_price
|
||||
468
FinanceAgent/tools/research_tools.py
Normal file
468
FinanceAgent/tools/research_tools.py
Normal file
@@ -0,0 +1,468 @@
|
||||
# Copyright (C) 2024 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
import json
|
||||
import os
|
||||
import random
|
||||
from collections import defaultdict
|
||||
from datetime import date, datetime, timedelta
|
||||
from textwrap import dedent
|
||||
from typing import Annotated, Any, List, Optional
|
||||
|
||||
import pandas as pd
|
||||
|
||||
finnhub_client = None
|
||||
|
||||
try:
|
||||
if os.environ.get("FINNHUB_API_KEY") is None:
|
||||
print("Please set the environment variable FINNHUB_API_KEY to use the Finnhub API.")
|
||||
else:
|
||||
import finnhub
|
||||
|
||||
finnhub_client = finnhub.Client(api_key=os.environ["FINNHUB_API_KEY"])
|
||||
print("Finnhub client initialized")
|
||||
|
||||
except:
|
||||
pass
|
||||
|
||||
|
||||
# https://github.com/langchain-ai/langchain/blob/master/libs/community/langchain_community/utilities/financial_datasets.py
|
||||
"""
|
||||
Util that calls several of financial datasets stock market REST APIs.
|
||||
Docs: https://docs.financialdatasets.ai/
|
||||
"""
|
||||
|
||||
import requests
|
||||
from pydantic import BaseModel
|
||||
|
||||
FINANCIAL_DATASETS_BASE_URL = "https://api.financialdatasets.ai/"
|
||||
|
||||
|
||||
class FinancialDatasetsAPIWrapper(BaseModel):
|
||||
"""Wrapper for financial datasets API."""
|
||||
|
||||
financial_datasets_api_key: Optional[str] = None
|
||||
|
||||
def __init__(self, **data: Any):
|
||||
super().__init__(**data)
|
||||
self.financial_datasets_api_key = data["api_key"]
|
||||
|
||||
@property
|
||||
def _api_key(self) -> str:
|
||||
if self.financial_datasets_api_key is None:
|
||||
raise ValueError(
|
||||
"API key is required for the FinancialDatasetsAPIWrapper. "
|
||||
"Please provide the API key by either:\n"
|
||||
"1. Manually specifying it when initializing the wrapper: "
|
||||
"FinancialDatasetsAPIWrapper(financial_datasets_api_key='your_api_key')\n"
|
||||
"2. Setting it as an environment variable: FINANCIAL_DATASETS_API_KEY"
|
||||
)
|
||||
return self.financial_datasets_api_key
|
||||
|
||||
def get_income_statements(
|
||||
self,
|
||||
ticker: str,
|
||||
period: str,
|
||||
limit: Optional[int],
|
||||
) -> Optional[dict]:
|
||||
"""Get the income statements for a stock `ticker` over a `period` of time.
|
||||
|
||||
:param ticker: the stock ticker
|
||||
:param period: the period of time to get the balance sheets for.
|
||||
Possible values are: annual, quarterly, ttm.
|
||||
:param limit: the number of results to return, default is 10
|
||||
:return: a list of income statements
|
||||
"""
|
||||
url = (
|
||||
f"{FINANCIAL_DATASETS_BASE_URL}financials/income-statements/"
|
||||
f"?ticker={ticker}"
|
||||
f"&period={period}"
|
||||
f"&limit={limit if limit else 10}"
|
||||
)
|
||||
|
||||
# Add the api key to the headers
|
||||
headers = {"X-API-KEY": self._api_key}
|
||||
|
||||
# Execute the request
|
||||
response = requests.get(url, headers=headers)
|
||||
data = response.json()
|
||||
|
||||
return data.get("income_statements", None)
|
||||
|
||||
def get_balance_sheets(
|
||||
self,
|
||||
ticker: str,
|
||||
period: str,
|
||||
limit: Optional[int],
|
||||
) -> List[dict]:
|
||||
"""Get the balance sheets for a stock `ticker` over a `period` of time.
|
||||
|
||||
:param ticker: the stock ticker
|
||||
:param period: the period of time to get the balance sheets for.
|
||||
Possible values are: annual, quarterly, ttm.
|
||||
:param limit: the number of results to return, default is 10
|
||||
:return: a list of balance sheets
|
||||
"""
|
||||
url = (
|
||||
f"{FINANCIAL_DATASETS_BASE_URL}financials/balance-sheets/"
|
||||
f"?ticker={ticker}"
|
||||
f"&period={period}"
|
||||
f"&limit={limit if limit else 10}"
|
||||
)
|
||||
|
||||
# Add the api key to the headers
|
||||
headers = {"X-API-KEY": self._api_key}
|
||||
|
||||
# Execute the request
|
||||
response = requests.get(url, headers=headers)
|
||||
data = response.json()
|
||||
|
||||
return data.get("balance_sheets", None)
|
||||
|
||||
def get_cash_flow_statements(
|
||||
self,
|
||||
ticker: str,
|
||||
period: str,
|
||||
limit: Optional[int],
|
||||
) -> List[dict]:
|
||||
"""Get the cash flow statements for a stock `ticker` over a `period` of time.
|
||||
|
||||
:param ticker: the stock ticker
|
||||
:param period: the period of time to get the balance sheets for.
|
||||
Possible values are: annual, quarterly, ttm.
|
||||
:param limit: the number of results to return, default is 10
|
||||
:return: a list of cash flow statements
|
||||
"""
|
||||
|
||||
url = (
|
||||
f"{FINANCIAL_DATASETS_BASE_URL}financials/cash-flow-statements/"
|
||||
f"?ticker={ticker}"
|
||||
f"&period={period}"
|
||||
f"&limit={limit if limit else 10}"
|
||||
)
|
||||
|
||||
# Add the api key to the headers
|
||||
headers = {"X-API-KEY": self._api_key}
|
||||
|
||||
# Execute the request
|
||||
response = requests.get(url, headers=headers)
|
||||
data = response.json()
|
||||
|
||||
return data.get("cash_flow_statements", None)
|
||||
|
||||
def run(self, mode: str, ticker: str, **kwargs: Any) -> str:
|
||||
if mode == "get_income_statements":
|
||||
period = kwargs.get("period", "annual")
|
||||
limit = kwargs.get("limit", 10)
|
||||
return json.dumps(self.get_income_statements(ticker, period, limit))
|
||||
elif mode == "get_balance_sheets":
|
||||
period = kwargs.get("period", "annual")
|
||||
limit = kwargs.get("limit", 10)
|
||||
return json.dumps(self.get_balance_sheets(ticker, period, limit))
|
||||
elif mode == "get_cash_flow_statements":
|
||||
period = kwargs.get("period", "annual")
|
||||
limit = kwargs.get("limit", 10)
|
||||
return json.dumps(self.get_cash_flow_statements(ticker, period, limit))
|
||||
else:
|
||||
raise ValueError(f"Invalid mode {mode} for financial datasets API.")
|
||||
|
||||
|
||||
financial_datasets_client = None
|
||||
|
||||
try:
|
||||
if os.environ.get("FINANCIAL_DATASETS_API_KEY") is None:
|
||||
print("Please set the environment variable FINANCIAL_DATASETS_API_KEY to use the financialdatasets.ai data.")
|
||||
else:
|
||||
financial_datasets_client = FinancialDatasetsAPIWrapper(api_key=os.environ["FINANCIAL_DATASETS_API_KEY"])
|
||||
print("FINANCIAL DATASETS client initialized")
|
||||
|
||||
except Exception as e:
|
||||
print(str(e))
|
||||
|
||||
|
||||
def get_company_profile(symbol: Annotated[str, "ticker symbol"]) -> str:
|
||||
"""Get a company's profile information."""
|
||||
profile = finnhub_client.company_profile2(symbol=symbol)
|
||||
if not profile:
|
||||
return f"Failed to find company profile for symbol {symbol} from finnhub!"
|
||||
|
||||
formatted_str = (
|
||||
"[Company Introduction]:\n\n{name} is a leading entity in the {finnhubIndustry} sector. "
|
||||
"Incorporated and publicly traded since {ipo}, the company has established its reputation as "
|
||||
"one of the key players in the market. As of today, {name} has a market capitalization "
|
||||
"of {marketCapitalization:.2f} in {currency}, with {shareOutstanding:.2f} shares outstanding."
|
||||
"\n\n{name} operates primarily in the {country}, trading under the ticker {ticker} on the {exchange}. "
|
||||
"As a dominant force in the {finnhubIndustry} space, the company continues to innovate and drive "
|
||||
"progress within the industry."
|
||||
).format(**profile)
|
||||
|
||||
return formatted_str
|
||||
|
||||
|
||||
def get_company_news(
|
||||
symbol: Annotated[str, "ticker symbol"],
|
||||
start_date: Annotated[
|
||||
str,
|
||||
"start date of the search period for the company's basic financials, yyyy-mm-dd",
|
||||
],
|
||||
end_date: Annotated[
|
||||
str,
|
||||
"end date of the search period for the company's basic financials, yyyy-mm-dd",
|
||||
],
|
||||
max_news_num: Annotated[int, "maximum number of news to return, default to 10"] = 10,
|
||||
):
|
||||
"""Retrieve market news related to designated company."""
|
||||
news = finnhub_client.company_news(symbol, _from=start_date, to=end_date)
|
||||
if len(news) == 0:
|
||||
print(f"No company news found for symbol {symbol} from finnhub!")
|
||||
news = [
|
||||
{
|
||||
"date": datetime.fromtimestamp(n["datetime"]).strftime("%Y%m%d%H%M%S"),
|
||||
"headline": n["headline"],
|
||||
"summary": n["summary"],
|
||||
}
|
||||
for n in news
|
||||
]
|
||||
# Randomly select a subset of news if the number of news exceeds the maximum
|
||||
if len(news) > max_news_num:
|
||||
news = random.choices(news, k=max_news_num)
|
||||
news.sort(key=lambda x: x["date"])
|
||||
output = pd.DataFrame(news)
|
||||
|
||||
return output.to_json(orient="split")
|
||||
|
||||
|
||||
def get_basic_financials_history(
|
||||
symbol: Annotated[str, "ticker symbol"],
|
||||
freq: Annotated[
|
||||
str,
|
||||
"reporting frequency of the company's basic financials: annual / quarterly",
|
||||
],
|
||||
start_date: Annotated[
|
||||
str,
|
||||
"start date of the search period for the company's basic financials, yyyy-mm-dd",
|
||||
],
|
||||
end_date: Annotated[
|
||||
str,
|
||||
"end date of the search period for the company's basic financials, yyyy-mm-dd",
|
||||
],
|
||||
selected_columns: Annotated[
|
||||
list[str] | None,
|
||||
"List of column names of news to return, should be chosen from 'assetTurnoverTTM', 'bookValue', 'cashRatio', 'currentRatio', 'ebitPerShare', 'eps', 'ev', 'fcfMargin', 'fcfPerShareTTM', 'grossMargin', 'inventoryTurnoverTTM', 'longtermDebtTotalAsset', 'longtermDebtTotalCapital', 'longtermDebtTotalEquity', 'netDebtToTotalCapital', 'netDebtToTotalEquity', 'netMargin', 'operatingMargin', 'payoutRatioTTM', 'pb', 'peTTM', 'pfcfTTM', 'pretaxMargin', 'psTTM', 'ptbv', 'quickRatio', 'receivablesTurnoverTTM', 'roaTTM', 'roeTTM', 'roicTTM', 'rotcTTM', 'salesPerShare', 'sgaToSale', 'tangibleBookValue', 'totalDebtToEquity', 'totalDebtToTotalAsset', 'totalDebtToTotalCapital', 'totalRatio'",
|
||||
] = None,
|
||||
) -> str:
|
||||
|
||||
if freq not in ["annual", "quarterly"]:
|
||||
return f"Invalid reporting frequency {freq}. Please specify either 'annual' or 'quarterly'."
|
||||
|
||||
basic_financials = finnhub_client.company_basic_financials(symbol, "all")
|
||||
if not basic_financials["series"]:
|
||||
return f"Failed to find basic financials for symbol {symbol} from finnhub! Try a different symbol."
|
||||
|
||||
output_dict = defaultdict(dict)
|
||||
for metric, value_list in basic_financials["series"][freq].items():
|
||||
if selected_columns and metric not in selected_columns:
|
||||
continue
|
||||
for value in value_list:
|
||||
if value["period"] >= start_date and value["period"] <= end_date:
|
||||
output_dict[metric].update({value["period"]: value["v"]})
|
||||
|
||||
financials_output = pd.DataFrame(output_dict)
|
||||
financials_output = financials_output.rename_axis(index="date")
|
||||
|
||||
return financials_output.to_json(orient="split")
|
||||
|
||||
|
||||
def get_basic_financials(
|
||||
symbol: Annotated[str, "ticker symbol"],
|
||||
selected_columns: Annotated[
|
||||
list[str] | None,
|
||||
"List of column names of news to return, should be chosen from 'assetTurnoverTTM', 'bookValue', 'cashRatio', 'currentRatio', 'ebitPerShare', 'eps', 'ev', 'fcfMargin', 'fcfPerShareTTM', 'grossMargin', 'inventoryTurnoverTTM', 'longtermDebtTotalAsset', 'longtermDebtTotalCapital', 'longtermDebtTotalEquity', 'netDebtToTotalCapital', 'netDebtToTotalEquity', 'netMargin', 'operatingMargin', 'payoutRatioTTM', 'pb', 'peTTM', 'pfcfTTM', 'pretaxMargin', 'psTTM', 'ptbv', 'quickRatio', 'receivablesTurnoverTTM', 'roaTTM', 'roeTTM', 'roicTTM', 'rotcTTM', 'salesPerShare', 'sgaToSale', 'tangibleBookValue', 'totalDebtToEquity', 'totalDebtToTotalAsset', 'totalDebtToTotalCapital', 'totalRatio','10DayAverageTradingVolume', '13WeekPriceReturnDaily', '26WeekPriceReturnDaily', '3MonthADReturnStd', '3MonthAverageTradingVolume', '52WeekHigh', '52WeekHighDate', '52WeekLow', '52WeekLowDate', '52WeekPriceReturnDaily', '5DayPriceReturnDaily', 'assetTurnoverAnnual', 'assetTurnoverTTM', 'beta', 'bookValuePerShareAnnual', 'bookValuePerShareQuarterly', 'bookValueShareGrowth5Y', 'capexCagr5Y', 'cashFlowPerShareAnnual', 'cashFlowPerShareQuarterly', 'cashFlowPerShareTTM', 'cashPerSharePerShareAnnual', 'cashPerSharePerShareQuarterly', 'currentDividendYieldTTM', 'currentEv/freeCashFlowAnnual', 'currentEv/freeCashFlowTTM', 'currentRatioAnnual', 'currentRatioQuarterly', 'dividendGrowthRate5Y', 'dividendPerShareAnnual', 'dividendPerShareTTM', 'dividendYieldIndicatedAnnual', 'ebitdPerShareAnnual', 'ebitdPerShareTTM', 'ebitdaCagr5Y', 'ebitdaInterimCagr5Y', 'enterpriseValue', 'epsAnnual', 'epsBasicExclExtraItemsAnnual', 'epsBasicExclExtraItemsTTM', 'epsExclExtraItemsAnnual', 'epsExclExtraItemsTTM', 'epsGrowth3Y', 'epsGrowth5Y', 'epsGrowthQuarterlyYoy', 'epsGrowthTTMYoy', 'epsInclExtraItemsAnnual', 'epsInclExtraItemsTTM', 'epsNormalizedAnnual', 'epsTTM', 'focfCagr5Y', 'grossMargin5Y', 'grossMarginAnnual', 'grossMarginTTM', 'inventoryTurnoverAnnual', 'inventoryTurnoverTTM', 'longTermDebt/equityAnnual', 'longTermDebt/equityQuarterly', 'marketCapitalization', 'monthToDatePriceReturnDaily', 'netIncomeEmployeeAnnual', 'netIncomeEmployeeTTM', 'netInterestCoverageAnnual', 'netInterestCoverageTTM', 'netMarginGrowth5Y', 'netProfitMargin5Y', 'netProfitMarginAnnual', 'netProfitMarginTTM', 'operatingMargin5Y', 'operatingMarginAnnual', 'operatingMarginTTM', 'payoutRatioAnnual', 'payoutRatioTTM', 'pbAnnual', 'pbQuarterly', 'pcfShareAnnual', 'pcfShareTTM', 'peAnnual', 'peBasicExclExtraTTM', 'peExclExtraAnnual', 'peExclExtraTTM', 'peInclExtraTTM', 'peNormalizedAnnual', 'peTTM', 'pfcfShareAnnual', 'pfcfShareTTM', 'pretaxMargin5Y', 'pretaxMarginAnnual', 'pretaxMarginTTM', 'priceRelativeToS&P50013Week', 'priceRelativeToS&P50026Week', 'priceRelativeToS&P5004Week', 'priceRelativeToS&P50052Week', 'priceRelativeToS&P500Ytd', 'psAnnual', 'psTTM', 'ptbvAnnual', 'ptbvQuarterly', 'quickRatioAnnual', 'quickRatioQuarterly', 'receivablesTurnoverAnnual', 'receivablesTurnoverTTM', 'revenueEmployeeAnnual', 'revenueEmployeeTTM', 'revenueGrowth3Y', 'revenueGrowth5Y', 'revenueGrowthQuarterlyYoy', 'revenueGrowthTTMYoy', 'revenuePerShareAnnual', 'revenuePerShareTTM', 'revenueShareGrowth5Y', 'roa5Y', 'roaRfy', 'roaTTM', 'roe5Y', 'roeRfy', 'roeTTM', 'roi5Y', 'roiAnnual', 'roiTTM', 'tangibleBookValuePerShareAnnual', 'tangibleBookValuePerShareQuarterly', 'tbvCagr5Y', 'totalDebt/totalEquityAnnual', 'totalDebt/totalEquityQuarterly', 'yearToDatePriceReturnDaily'",
|
||||
] = None,
|
||||
) -> str:
|
||||
"""Get latest basic financials for a designated company."""
|
||||
basic_financials = finnhub_client.company_basic_financials(symbol, "all")
|
||||
if not basic_financials["series"]:
|
||||
return f"Failed to find basic financials for symbol {symbol} from finnhub! Try a different symbol."
|
||||
|
||||
output_dict = basic_financials["metric"]
|
||||
for metric, value_list in basic_financials["series"]["quarterly"].items():
|
||||
value = value_list[0]
|
||||
output_dict.update({metric: value["v"]})
|
||||
|
||||
results = {}
|
||||
for k in selected_columns:
|
||||
if k in output_dict:
|
||||
results[k] = output_dict[k]
|
||||
|
||||
return json.dumps(results, indent=2)
|
||||
|
||||
|
||||
def get_current_date():
|
||||
return date.today().strftime("%Y-%m-%d")
|
||||
|
||||
|
||||
def combine_prompt(instruction, resource):
|
||||
prompt = f"Resource: {resource}\n\nInstruction: {instruction}"
|
||||
return prompt
|
||||
|
||||
|
||||
def analyze_balance_sheet(
|
||||
symbol: Annotated[str, "ticker symbol"],
|
||||
period: Annotated[
|
||||
str, "the period of time to get the balance sheets for. Possible values are: annual, quarterly, ttm."
|
||||
],
|
||||
limit: int = 10,
|
||||
) -> str:
|
||||
"""Retrieve the balance sheet for the given ticker symbol with the related section of its 10-K report.
|
||||
|
||||
Then return with an instruction on how to analyze the balance sheet.
|
||||
"""
|
||||
|
||||
balance_sheet = financial_datasets_client.run(
|
||||
mode="get_balance_sheets",
|
||||
ticker=symbol,
|
||||
period=period,
|
||||
limit=limit,
|
||||
)
|
||||
|
||||
df_string = "Balance sheet:\n" + balance_sheet
|
||||
|
||||
instruction = dedent(
|
||||
"""
|
||||
Delve into a detailed scrutiny of the company's balance sheet for the most recent fiscal year, pinpointing
|
||||
the structure of assets, liabilities, and shareholders' equity to decode the firm's financial stability and
|
||||
operational efficiency. Focus on evaluating the liquidity through current assets versus current liabilities,
|
||||
the solvency via long-term debt ratios, and the equity position to gauge long-term investment potential.
|
||||
Contrast these metrics with previous years' data to highlight financial trends, improvements, or deteriorations.
|
||||
Finalize with a strategic assessment of the company's financial leverage, asset management, and capital structure,
|
||||
providing insights into its fiscal health and future prospects in a single paragraph. Less than 130 words.
|
||||
"""
|
||||
)
|
||||
|
||||
prompt = combine_prompt(instruction, df_string)
|
||||
return prompt
|
||||
|
||||
|
||||
def analyze_income_stmt(
|
||||
symbol: Annotated[str, "ticker symbol"],
|
||||
period: Annotated[
|
||||
str, "the period of time to get the balance sheets for. Possible values are: annual, quarterly, ttm."
|
||||
],
|
||||
limit: int = 10,
|
||||
) -> str:
|
||||
"""Retrieve the income statement for the given ticker symbol with the related section of its 10-K report.
|
||||
|
||||
Then return with an instruction on how to analyze the income statement.
|
||||
"""
|
||||
# Retrieve the income statement
|
||||
income_stmt = financial_datasets_client.run(
|
||||
mode="get_income_statements",
|
||||
ticker=symbol,
|
||||
period=period,
|
||||
limit=limit,
|
||||
)
|
||||
df_string = "Income statement:\n" + income_stmt
|
||||
|
||||
# Analysis instruction
|
||||
instruction = dedent(
|
||||
"""
|
||||
Conduct a comprehensive analysis of the company's income statement for the current fiscal year.
|
||||
Start with an overall revenue record, including Year-over-Year or Quarter-over-Quarter comparisons,
|
||||
and break down revenue sources to identify primary contributors and trends. Examine the Cost of
|
||||
Goods Sold for potential cost control issues. Review profit margins such as gross, operating,
|
||||
and net profit margins to evaluate cost efficiency, operational effectiveness, and overall profitability.
|
||||
Analyze Earnings Per Share to understand investor perspectives. Compare these metrics with historical
|
||||
data and industry or competitor benchmarks to identify growth patterns, profitability trends, and
|
||||
operational challenges. The output should be a strategic overview of the company’s financial health
|
||||
in a single paragraph, less than 130 words, summarizing the previous analysis into 4-5 key points under
|
||||
respective subheadings with specific discussion and strong data support.
|
||||
"""
|
||||
)
|
||||
|
||||
# Combine the instruction, section text, and income statement
|
||||
prompt = combine_prompt(instruction, df_string)
|
||||
|
||||
return prompt
|
||||
|
||||
|
||||
def analyze_cash_flow(
|
||||
symbol: Annotated[str, "ticker symbol"],
|
||||
period: Annotated[
|
||||
str, "the period of time to get the balance sheets for. Possible values are: annual, quarterly, ttm."
|
||||
],
|
||||
limit: int = 10,
|
||||
) -> str:
|
||||
"""Retrieve the cash flow statement for the given ticker symbol with the related section of its 10-K report.
|
||||
|
||||
Then return with an instruction on how to analyze the cash flow statement.
|
||||
"""
|
||||
|
||||
cash_flow = financial_datasets_client.run(
|
||||
mode="get_cash_flow_statements",
|
||||
ticker=symbol,
|
||||
period=period,
|
||||
limit=limit,
|
||||
)
|
||||
|
||||
df_string = "Cash flow statement:\n" + cash_flow
|
||||
|
||||
instruction = dedent(
|
||||
"""
|
||||
Dive into a comprehensive evaluation of the company's cash flow for the latest fiscal year, focusing on cash inflows
|
||||
and outflows across operating, investing, and financing activities. Examine the operational cash flow to assess the
|
||||
core business profitability, scrutinize investing activities for insights into capital expenditures and investments,
|
||||
and review financing activities to understand debt, equity movements, and dividend policies. Compare these cash movements
|
||||
to prior periods to discern trends, sustainability, and liquidity risks. Conclude with an informed analysis of the company's
|
||||
cash management effectiveness, liquidity position, and potential for future growth or financial challenges in a single paragraph.
|
||||
Less than 130 words.
|
||||
"""
|
||||
)
|
||||
|
||||
prompt = combine_prompt(instruction, df_string)
|
||||
return prompt
|
||||
|
||||
|
||||
def get_share_performance(
|
||||
symbol: Annotated[str, "Ticker symbol of the stock (e.g., 'AAPL' for Apple)"],
|
||||
end_date: Annotated[
|
||||
str,
|
||||
"end date of the search period for the company's basic financials, yyyy-mm-dd",
|
||||
],
|
||||
) -> str:
|
||||
"""Plot the stock performance of a company compared to the S&P 500 over the past year."""
|
||||
filing_date = datetime.strptime(end_date, "%Y-%m-%d")
|
||||
|
||||
start = (filing_date - timedelta(days=60)).strftime("%Y-%m-%d")
|
||||
end = filing_date.strftime("%Y-%m-%d")
|
||||
interval = "day" # possible values are {'second', 'minute', 'day', 'week', 'month', 'year'}
|
||||
interval_multiplier = 1 # every 1 day
|
||||
|
||||
# create the URL
|
||||
url = (
|
||||
f"https://api.financialdatasets.ai/prices/"
|
||||
f"?ticker={symbol}"
|
||||
f"&interval={interval}"
|
||||
f"&interval_multiplier={interval_multiplier}"
|
||||
f"&start_date={start}"
|
||||
f"&end_date={end}"
|
||||
)
|
||||
|
||||
headers = {"X-API-KEY": "your_api_key_here"}
|
||||
|
||||
response = requests.get(url, headers=headers)
|
||||
# parse prices from the response
|
||||
|
||||
prices = response.json().get("prices")
|
||||
|
||||
df_string = "Past 60 days Stock prices:\n" + json.dumps(prices)
|
||||
|
||||
instruction = dedent(
|
||||
"""
|
||||
Dive into a comprehensive evaluation of the company's stock price for the latest 60 days.
|
||||
Less than 130 words.
|
||||
"""
|
||||
)
|
||||
|
||||
prompt = combine_prompt(instruction, df_string)
|
||||
return prompt
|
||||
114
FinanceAgent/tools/sum_agent_tools.py
Normal file
114
FinanceAgent/tools/sum_agent_tools.py
Normal file
@@ -0,0 +1,114 @@
|
||||
# Copyright (C) 2025 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
import json
|
||||
|
||||
import requests
|
||||
|
||||
try:
|
||||
from tools.redis_kv import RedisKVStore
|
||||
from tools.utils import *
|
||||
except ImportError:
|
||||
from redis_kv import RedisKVStore
|
||||
from utils import *
|
||||
|
||||
|
||||
def get_summary_else_doc(query, company):
|
||||
# search most similar doc title
|
||||
index_name = f"titles_{company}"
|
||||
vector_store = get_vectorstore_titles(index_name)
|
||||
k = 1
|
||||
docs = vector_store.similarity_search(query, k=k)
|
||||
if docs:
|
||||
doc = docs[0]
|
||||
doc_title = doc.page_content
|
||||
print(f"Most similar doc title: {doc_title}")
|
||||
|
||||
kvstore = RedisKVStore(redis_uri=REDIS_URL_KV)
|
||||
try:
|
||||
# Check if summary already exists in the KV store
|
||||
content = kvstore.get(f"{doc_title}_summary", f"full_doc_{company}")["summary"]
|
||||
is_summary = True
|
||||
print("Summary already exists in KV store.")
|
||||
except Exception as e:
|
||||
doc = kvstore.get(doc_title, f"full_doc_{company}")
|
||||
content = doc["full_doc"]
|
||||
is_summary = False
|
||||
print("No summary found in KV store, returning full document content.")
|
||||
else:
|
||||
print(f"No similar document found for query: {query}")
|
||||
doc_title = None
|
||||
content = None
|
||||
is_summary = False
|
||||
return doc_title, content, is_summary
|
||||
|
||||
|
||||
def save_doc_summary(summary, doc_title, company):
|
||||
"""Adds a summary to the existing document in the key-value store.
|
||||
|
||||
Args:
|
||||
kvstore: The key-value store instance.
|
||||
summary: The summary to be added.
|
||||
doc_title: The title of the document.
|
||||
company: The company associated with the document.
|
||||
"""
|
||||
kvstore = RedisKVStore(redis_uri=REDIS_URL_KV)
|
||||
# doc_dict = kvstore.get(doc_title, f"full_doc_{company}")
|
||||
|
||||
# # Add the summary to the dictionary
|
||||
# doc_dict["summary"] = summary
|
||||
|
||||
# Save the updated value back to the store
|
||||
kvstore.put(f"{doc_title}_summary", {"summary": summary}, collection=f"full_doc_{company}")
|
||||
|
||||
|
||||
def summarize(doc_name, company):
|
||||
docsum_url = os.environ.get("DOCSUM_ENDPOINT")
|
||||
print(f"Docsum Endpoint URL: {docsum_url}")
|
||||
|
||||
company = format_company_name(company)
|
||||
|
||||
doc_title, sum, is_summary = get_summary_else_doc(doc_name, company)
|
||||
if not doc_title:
|
||||
return f"Cannot find documents related to {doc_title} in KV store."
|
||||
|
||||
if not is_summary:
|
||||
data = {
|
||||
"messages": sum,
|
||||
"max_tokens": 512,
|
||||
"language": "en",
|
||||
"stream": False,
|
||||
"summary_type": "auto",
|
||||
"chunk_size": 2000,
|
||||
}
|
||||
|
||||
headers = {"Content-Type": "application/json"}
|
||||
try:
|
||||
print("Computing Summary with OPEA DocSum...")
|
||||
resp = requests.post(url=docsum_url, data=json.dumps(data), headers=headers)
|
||||
ret = resp.json()["text"]
|
||||
resp.raise_for_status() # Raise an exception for unsuccessful HTTP status codes
|
||||
except requests.exceptions.RequestException as e:
|
||||
ret = f"An error occurred:{e}"
|
||||
# save summary into db
|
||||
print("Saving Summary into KV Store...")
|
||||
save_doc_summary(ret, doc_title, company)
|
||||
return ret
|
||||
else:
|
||||
return sum
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# company = "Gap"
|
||||
# year = "2024"
|
||||
# quarter = "Q4"
|
||||
|
||||
company = "Costco"
|
||||
year = "2025"
|
||||
quarter = "Q2"
|
||||
|
||||
print("testing summarize")
|
||||
doc_name = f"{company} {year} {quarter} earning call"
|
||||
ret = summarize(doc_name, company)
|
||||
print("Summary: ", ret)
|
||||
print("=" * 50)
|
||||
32
FinanceAgent/tools/supervisor_agent_tools.yaml
Normal file
32
FinanceAgent/tools/supervisor_agent_tools.yaml
Normal file
@@ -0,0 +1,32 @@
|
||||
# Copyright (C) 2025 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
finqa_agent:
|
||||
description: answer financial questions about a company.
|
||||
callable_api: supervisor_tools.py:finqa_agent
|
||||
args_schema:
|
||||
query:
|
||||
type: str
|
||||
description: should include company name and time, for example, which business unit had the highest growth for Microsoft in 2024.
|
||||
return_output: retrieved_data
|
||||
|
||||
summarization_tool:
|
||||
description: Searches KV store for summary, if it doesn't exist pulls full document and summarize it
|
||||
callable_api: sum_agent_tools.py:summarize
|
||||
args_schema:
|
||||
doc_name:
|
||||
type: str
|
||||
description: Descriptive name of the document
|
||||
company:
|
||||
type: str
|
||||
description: Name of the company document belongs to
|
||||
return_output: summary
|
||||
|
||||
# research_agent:
|
||||
# description: generate research report on a specified company with fundamentals analysis, sentiment analysis and risk analysis.
|
||||
# callable_api: supervisor_tools.py:research_agent
|
||||
# args_schema:
|
||||
# company:
|
||||
# type: str
|
||||
# description: the company name
|
||||
# return_output: report
|
||||
28
FinanceAgent/tools/supervisor_tools.py
Normal file
28
FinanceAgent/tools/supervisor_tools.py
Normal file
@@ -0,0 +1,28 @@
|
||||
# Copyright (C) 2025 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
import os
|
||||
|
||||
import requests
|
||||
|
||||
|
||||
def finqa_agent(query: str):
|
||||
url = os.environ.get("WORKER_FINQA_AGENT_URL")
|
||||
print(url)
|
||||
proxies = {"http": ""}
|
||||
payload = {
|
||||
"messages": query,
|
||||
}
|
||||
response = requests.post(url, json=payload, proxies=proxies)
|
||||
return response.json()["text"]
|
||||
|
||||
|
||||
def research_agent(company: str):
|
||||
url = os.environ.get("WORKER_RESEARCH_AGENT_URL")
|
||||
print(url)
|
||||
proxies = {"http": ""}
|
||||
payload = {
|
||||
"messages": company,
|
||||
}
|
||||
response = requests.post(url, json=payload, proxies=proxies)
|
||||
return response.json()["text"]
|
||||
359
FinanceAgent/tools/utils.py
Normal file
359
FinanceAgent/tools/utils.py
Normal file
@@ -0,0 +1,359 @@
|
||||
# Copyright (C) 2025 Intel Corporation
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
import os
|
||||
|
||||
from langchain_community.embeddings import HuggingFaceBgeEmbeddings
|
||||
from langchain_community.retrievers import BM25Retriever
|
||||
from langchain_core.documents import Document
|
||||
from langchain_huggingface import HuggingFaceEndpointEmbeddings
|
||||
from langchain_redis import RedisConfig, RedisVectorStore
|
||||
from openai import OpenAI
|
||||
|
||||
try:
|
||||
from tools.redis_kv import RedisKVStore
|
||||
except ImportError:
|
||||
from redis_kv import RedisKVStore
|
||||
|
||||
# Embedding model
|
||||
EMBED_MODEL = os.getenv("EMBED_MODEL", "BAAI/bge-base-en-v1.5")
|
||||
TEI_EMBEDDING_ENDPOINT = os.getenv("TEI_EMBEDDING_ENDPOINT", "")
|
||||
|
||||
# Redis URL
|
||||
REDIS_URL_VECTOR = os.getenv("REDIS_URL_VECTOR", "redis://localhost:6379/")
|
||||
REDIS_URL_KV = os.getenv("REDIS_URL_KV", "redis://localhost:6380/")
|
||||
|
||||
# LLM config
|
||||
LLM_MODEL = os.getenv("model", "meta-llama/Llama-3.3-70B-Instruct")
|
||||
LLM_ENDPOINT = os.getenv("llm_endpoint_url", "http://localhost:8086")
|
||||
print(f"LLM endpoint: {LLM_ENDPOINT}")
|
||||
MAX_TOKENS = 1024
|
||||
TEMPERATURE = 0.2
|
||||
|
||||
COMPANY_NAME_PROMPT = """\
|
||||
Here is the list of company names in the knowledge base:
|
||||
{company_list}
|
||||
|
||||
This is the company of interest: {company}
|
||||
|
||||
Determine if the company of interest is the same as any of the companies in the knowledge base.
|
||||
If yes, map the company of interest to the company name in the knowledge base. Output the company name in {{}}. Example: {{3M}}.
|
||||
If none of the companies in the knowledge base match the company of interest, output "NONE".
|
||||
"""
|
||||
|
||||
ANSWER_PROMPT = """\
|
||||
You are a financial analyst. Read the documents below and answer the question.
|
||||
Documents:
|
||||
{documents}
|
||||
|
||||
Question: {query}
|
||||
Now take a deep breath and think step by step to answer the question. Wrap your final answer in {{}}. Example: {{The company has a revenue of $100 million.}}
|
||||
"""
|
||||
|
||||
|
||||
def format_company_name(company):
|
||||
company = company.upper()
|
||||
|
||||
# decide if company is in company list
|
||||
company_list = get_company_list()
|
||||
print(f"company_list {company_list}")
|
||||
company = get_company_name_in_kb(company, company_list)
|
||||
if "Cannot find" in company or "Database is empty" in company:
|
||||
raise ValueError(f"Company not found in knowledge base: {company}")
|
||||
print(f"Company: {company}")
|
||||
return company
|
||||
|
||||
|
||||
def get_embedder():
|
||||
if TEI_EMBEDDING_ENDPOINT:
|
||||
# create embeddings using TEI endpoint service
|
||||
# Huggingface API token for TEI embedding endpoint
|
||||
HUGGINGFACEHUB_API_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN", "")
|
||||
assert HUGGINGFACEHUB_API_TOKEN, "HuggingFace API token is required for TEI embedding endpoint."
|
||||
embedder = HuggingFaceEndpointEmbeddings(model=TEI_EMBEDDING_ENDPOINT)
|
||||
else:
|
||||
# create embeddings using local embedding model
|
||||
embedder = HuggingFaceBgeEmbeddings(model_name=EMBED_MODEL)
|
||||
return embedder
|
||||
|
||||
|
||||
def generate_answer(prompt):
|
||||
"""Use vllm endpoint to generate the answer."""
|
||||
# send request to vllm endpoint
|
||||
client = OpenAI(
|
||||
base_url=f"{LLM_ENDPOINT}/v1",
|
||||
api_key="token-abc123",
|
||||
)
|
||||
|
||||
params = {
|
||||
"max_tokens": MAX_TOKENS,
|
||||
"temperature": TEMPERATURE,
|
||||
}
|
||||
|
||||
completion = client.chat.completions.create(
|
||||
model=LLM_MODEL, messages=[{"role": "user", "content": prompt}], **params
|
||||
)
|
||||
|
||||
# get response
|
||||
response = completion.choices[0].message.content
|
||||
print(f"LLM Response: {response}")
|
||||
return response
|
||||
|
||||
|
||||
def parse_response(response):
|
||||
if "{" in response:
|
||||
ret = response.split("{")[1].split("}")[0]
|
||||
else:
|
||||
ret = ""
|
||||
return ret
|
||||
|
||||
|
||||
def get_company_list():
|
||||
kvstore = RedisKVStore(redis_uri=REDIS_URL_KV)
|
||||
company_list_dict = kvstore.get("company", "company_list")
|
||||
if company_list_dict:
|
||||
company_list = company_list_dict["company"]
|
||||
return company_list
|
||||
else:
|
||||
return []
|
||||
|
||||
|
||||
def get_company_name_in_kb(company, company_list):
|
||||
if not company_list:
|
||||
return "Database is empty."
|
||||
|
||||
company = company.upper()
|
||||
if company in company_list:
|
||||
return company
|
||||
|
||||
prompt = COMPANY_NAME_PROMPT.format(company_list=company_list, company=company)
|
||||
response = generate_answer(prompt)
|
||||
if "NONE" in response.upper():
|
||||
return f"Cannot find {company} in knowledge base."
|
||||
else:
|
||||
ret = parse_response(response)
|
||||
if ret:
|
||||
return ret
|
||||
else:
|
||||
return "Failed to parse LLM response."
|
||||
|
||||
|
||||
def get_docs_matching_metadata(metadata, collection_name):
|
||||
"""
|
||||
metadata: ("company_year", "3M_2023")
|
||||
docs: list of documents
|
||||
"""
|
||||
key = metadata[0]
|
||||
value = metadata[1]
|
||||
kvstore = RedisKVStore(redis_uri=REDIS_URL_KV)
|
||||
collection = kvstore.get_all(collection_name) # collection is a dict
|
||||
|
||||
matching_docs = []
|
||||
for idx in collection:
|
||||
doc = collection[idx]
|
||||
if doc["metadata"][key] == value:
|
||||
print(f"Found doc with matching metadata {metadata}")
|
||||
print(doc["metadata"]["doc_title"])
|
||||
matching_docs.append(doc)
|
||||
print(f"Number of docs found with search_metadata {metadata}: {len(matching_docs)}")
|
||||
return matching_docs
|
||||
|
||||
|
||||
def convert_docs(docs):
|
||||
# docs: list of dicts
|
||||
converted_docs_content = []
|
||||
converted_docs_summary = []
|
||||
for doc in docs:
|
||||
content = doc["content"]
|
||||
# convert content to Document object
|
||||
metadata = {"type": "content", **doc["metadata"]}
|
||||
converted_content = Document(id=doc["metadata"]["doc_id"], page_content=content, metadata=metadata)
|
||||
|
||||
# convert summary to Document object
|
||||
metadata = {"type": "summary", "content": content, **doc["metadata"]}
|
||||
converted_summary = Document(id=doc["metadata"]["doc_id"], page_content=doc["summary"], metadata=metadata)
|
||||
converted_docs_content.append(converted_content)
|
||||
converted_docs_summary.append(converted_summary)
|
||||
return converted_docs_content, converted_docs_summary
|
||||
|
||||
|
||||
def bm25_search(query, metadata, company, doc_type="chunks", k=10):
|
||||
collection_name = f"{doc_type}_{company}"
|
||||
print(f"Collection name: {collection_name}")
|
||||
|
||||
docs = get_docs_matching_metadata(metadata, collection_name)
|
||||
|
||||
if docs:
|
||||
docs_text, docs_summary = convert_docs(docs)
|
||||
# BM25 search over content
|
||||
retriever = BM25Retriever.from_documents(docs_text, k=k)
|
||||
docs_bm25 = retriever.invoke(query)
|
||||
print(f"BM25: Found {len(docs_bm25)} docs over content with search metadata: {metadata}")
|
||||
|
||||
# BM25 search over summary/title
|
||||
retriever = BM25Retriever.from_documents(docs_summary, k=k)
|
||||
docs_bm25_summary = retriever.invoke(query)
|
||||
print(f"BM25: Found {len(docs_bm25_summary)} docs over summary with search metadata: {metadata}")
|
||||
results = docs_bm25 + docs_bm25_summary
|
||||
else:
|
||||
results = []
|
||||
return results
|
||||
|
||||
|
||||
def bm25_search_broad(query, company, year, quarter, k=10, doc_type="chunks"):
|
||||
# search with company filter, but query is query_company_quarter
|
||||
metadata = ("company", f"{company}")
|
||||
query1 = f"{query} {year} {quarter}"
|
||||
docs1 = bm25_search(query1, metadata, company, k=k, doc_type=doc_type)
|
||||
|
||||
# search with metadata filters
|
||||
metadata = ("company_year_quarter", f"{company}_{year}_{quarter}")
|
||||
print(f"BM25: Searching for docs with metadata: {metadata}")
|
||||
docs = bm25_search(query, metadata, company, k=k, doc_type=doc_type)
|
||||
if not docs:
|
||||
print("BM25: No docs found with company, year and quarter filter, only search with company and year filter")
|
||||
metadata = ("company_year", f"{company}_{year}")
|
||||
docs = bm25_search(query, metadata, company, k=k, doc_type=doc_type)
|
||||
if not docs:
|
||||
print("BM25: No docs found with company and year filter, only search with company filter")
|
||||
metadata = ("company", f"{company}")
|
||||
docs = bm25_search(query, metadata, company, k=k, doc_type=doc_type)
|
||||
|
||||
docs = docs + docs1
|
||||
if docs:
|
||||
return docs
|
||||
else:
|
||||
return []
|
||||
|
||||
|
||||
def set_filter(metadata_filter):
|
||||
# metadata_filter: tuple of (key, value)
|
||||
from redisvl.query.filter import Text
|
||||
|
||||
key = metadata_filter[0]
|
||||
value = metadata_filter[1]
|
||||
filter_condition = Text(key) == value
|
||||
return filter_condition
|
||||
|
||||
|
||||
def similarity_search(vector_store, k, query, company, year, quarter=None):
|
||||
query1 = f"{query} {year} {quarter}"
|
||||
filter_condition = set_filter(("company", company))
|
||||
docs1 = vector_store.similarity_search(query1, k=k, filter=filter_condition)
|
||||
print(f"Similarity search: Found {len(docs1)} docs with company filter and query: {query1}")
|
||||
|
||||
filter_condition = set_filter(("company_year_quarter", f"{company}_{year}_{quarter}"))
|
||||
docs = vector_store.similarity_search(query, k=k, filter=filter_condition)
|
||||
|
||||
if not docs: # if no relevant document found, relax the filter
|
||||
print("No relevant document found with company, year and quarter filter, only search with company and year")
|
||||
filter_condition = set_filter(("company_year", f"{company}_{year}"))
|
||||
docs = vector_store.similarity_search(query, k=k, filter=filter_condition)
|
||||
|
||||
if not docs: # if no relevant document found, relax the filter
|
||||
print("No relevant document found with company_year filter, only search with company.....")
|
||||
filter_condition = set_filter(("company", company))
|
||||
docs = vector_store.similarity_search(query, k=k, filter=filter_condition)
|
||||
|
||||
print(f"Similarity search: Found {len(docs)} docs with filter and query: {query}")
|
||||
|
||||
docs = docs + docs1
|
||||
if not docs:
|
||||
return []
|
||||
else:
|
||||
return docs
|
||||
|
||||
|
||||
def get_index_name(doc_type: str, metadata: dict):
|
||||
company = metadata["company"]
|
||||
if doc_type == "chunks":
|
||||
index_name = f"chunks_{company}"
|
||||
elif doc_type == "tables":
|
||||
index_name = f"tables_{company}"
|
||||
elif doc_type == "titles":
|
||||
index_name = f"titles_{company}"
|
||||
elif doc_type == "full_doc":
|
||||
index_name = f"full_doc_{company}"
|
||||
else:
|
||||
raise ValueError("doc_type should be either chunks, tables, titles, or full_doc.")
|
||||
return index_name
|
||||
|
||||
|
||||
def get_content(doc):
|
||||
# doc can be converted doc
|
||||
# of saved doc in vector store
|
||||
if "type" in doc.metadata and doc.metadata["type"] == "summary":
|
||||
print("BM25 retrieved doc...")
|
||||
content = doc.metadata["content"]
|
||||
elif "type" in doc.metadata and doc.metadata["type"] == "content":
|
||||
print("BM25 retrieved doc...")
|
||||
content = doc.page_content
|
||||
else:
|
||||
print("Dense retriever doc...")
|
||||
|
||||
doc_id = doc.metadata["doc_id"]
|
||||
# doc_summary=doc.page_content
|
||||
kvstore = RedisKVStore(redis_uri=REDIS_URL_KV)
|
||||
collection_name = get_index_name(doc.metadata["doc_type"], doc.metadata)
|
||||
result = kvstore.get(doc_id, collection_name)
|
||||
content = result["content"]
|
||||
|
||||
# print(f"***Doc Metadata:\n{doc.metadata}")
|
||||
# print(f"***Content: {content[:100]}...")
|
||||
|
||||
return content
|
||||
|
||||
|
||||
def get_unique_docs(docs):
|
||||
results = []
|
||||
context = ""
|
||||
i = 1
|
||||
for doc in docs:
|
||||
content = get_content(doc)
|
||||
if content not in results:
|
||||
results.append(content)
|
||||
doc_title = doc.metadata["doc_title"]
|
||||
ret_doc = f"Doc [{i}] from {doc_title}:\n{content}\n"
|
||||
context += ret_doc
|
||||
i += 1
|
||||
print(f"Number of unique docs found: {len(results)}")
|
||||
return context
|
||||
|
||||
|
||||
def get_vectorstore(index_name):
|
||||
config = RedisConfig(
|
||||
index_name=index_name,
|
||||
redis_url=REDIS_URL_VECTOR,
|
||||
metadata_schema=[
|
||||
{"name": "company", "type": "text"},
|
||||
{"name": "year", "type": "text"},
|
||||
{"name": "quarter", "type": "text"},
|
||||
{"name": "doc_type", "type": "text"},
|
||||
{"name": "doc_title", "type": "text"},
|
||||
{"name": "doc_id", "type": "text"},
|
||||
{"name": "company_year", "type": "text"},
|
||||
{"name": "company_year_quarter", "type": "text"},
|
||||
],
|
||||
)
|
||||
embedder = get_embedder()
|
||||
vector_store = RedisVectorStore(embedder, config=config)
|
||||
return vector_store
|
||||
|
||||
|
||||
def get_vectorstore_titles(index_name):
|
||||
config = RedisConfig(
|
||||
index_name=index_name,
|
||||
redis_url=REDIS_URL_VECTOR,
|
||||
metadata_schema=[
|
||||
{"name": "company", "type": "text"},
|
||||
{"name": "year", "type": "text"},
|
||||
{"name": "quarter", "type": "text"},
|
||||
{"name": "doc_type", "type": "text"},
|
||||
{"name": "doc_title", "type": "text"},
|
||||
{"name": "company_year", "type": "text"},
|
||||
{"name": "company_year_quarter", "type": "text"},
|
||||
],
|
||||
)
|
||||
embedder = get_embedder()
|
||||
vector_store = RedisVectorStore(embedder, config=config)
|
||||
return vector_store
|
||||
Reference in New Issue
Block a user