From 8ff6f815edb07457fc19ae51e37dec28b3b77b35 Mon Sep 17 00:00:00 2001 From: minmin-intel Date: Wed, 12 Mar 2025 00:06:23 +0000 Subject: [PATCH 01/20] set up structure for FinanceAgent example Signed-off-by: minmin-intel --- FinanceAgent/README.md | 0 .../intel/hpu/gaudi/compose.yaml | 95 +++++++++++++++++++ FinanceAgent/docker_image_build/build.yaml | 19 ++++ FinanceAgent/prompts/finqa_prompt.py | 37 ++++++++ FinanceAgent/prompts/supervisor_prompt.py | 31 ++++++ FinanceAgent/tools/finqa_agent_tools.yaml | 0 FinanceAgent/tools/research_agent_tools.yaml | 0 .../tools/supervisor_agent_tools.yaml | 0 8 files changed, 182 insertions(+) create mode 100644 FinanceAgent/README.md create mode 100644 FinanceAgent/docker_compose/intel/hpu/gaudi/compose.yaml create mode 100644 FinanceAgent/docker_image_build/build.yaml create mode 100644 FinanceAgent/prompts/finqa_prompt.py create mode 100644 FinanceAgent/prompts/supervisor_prompt.py create mode 100644 FinanceAgent/tools/finqa_agent_tools.yaml create mode 100644 FinanceAgent/tools/research_agent_tools.yaml create mode 100644 FinanceAgent/tools/supervisor_agent_tools.yaml diff --git a/FinanceAgent/README.md b/FinanceAgent/README.md new file mode 100644 index 0000000000..e69de29bb2 diff --git a/FinanceAgent/docker_compose/intel/hpu/gaudi/compose.yaml b/FinanceAgent/docker_compose/intel/hpu/gaudi/compose.yaml new file mode 100644 index 0000000000..2f3a4fafce --- /dev/null +++ b/FinanceAgent/docker_compose/intel/hpu/gaudi/compose.yaml @@ -0,0 +1,95 @@ +# Copyright (C) 2024 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + +services: + worker-finqa-agent: + image: opea/agent:latest + container_name: finqa-agent-endpoint + volumes: + - ${TOOLSET_PATH}:/home/user/tools/ + - ${PROMPT_PATH}:/home/user/prompts/ + ports: + - "9095:9095" + ipc: host + environment: + ip_address: ${ip_address} + strategy: react_llama + with_memory: false + recursion_limit: ${recursion_limit_worker} + llm_engine: vllm + HUGGINGFACEHUB_API_TOKEN: ${HUGGINGFACEHUB_API_TOKEN} + llm_endpoint_url: ${LLM_ENDPOINT_URL} + model: ${LLM_MODEL_ID} + temperature: ${temperature} + max_new_tokens: ${max_new_tokens} + stream: false + tools: /home/user/tools/finqa_agent_tools.yaml + custom_prompt: /home/user/prompts/finqa_prompt.py + require_human_feedback: false + no_proxy: ${no_proxy} + http_proxy: ${http_proxy} + https_proxy: ${https_proxy} + port: 9095 + + worker-research-agent: + image: opea/agent:latest + container_name: research-agent-endpoint + volumes: + - ${TOOLSET_PATH}:/home/user/tools/ + - ${PROMPT_PATH}:/home/user/prompts/ + ports: + - "9096:9096" + ipc: host + environment: + ip_address: ${ip_address} + strategy: react_llama + with_memory: false + recursion_limit: ${recursion_limit_worker} + llm_engine: vllm + HUGGINGFACEHUB_API_TOKEN: ${HUGGINGFACEHUB_API_TOKEN} + llm_endpoint_url: ${LLM_ENDPOINT_URL} + model: ${LLM_MODEL_ID} + temperature: ${temperature} + max_new_tokens: ${max_new_tokens} + stream: false + tools: /home/user/tools/research_agent_tools.yaml + custom_prompt: /home/user/prompts/research_prompt.py + require_human_feedback: false + no_proxy: ${no_proxy} + http_proxy: ${http_proxy} + https_proxy: ${https_proxy} + port: 9096 + + supervisor-react-agent: + image: opea/agent:latest + container_name: supervisor-agent-endpoint + depends_on: + - worker-finqa-agent + - worker-research-agent + volumes: + - ${TOOLSET_PATH}:/home/user/tools/ + - ${PROMPT_PATH}:/home/user/prompts/ + ports: + - "9090:9090" + ipc: host + environment: + ip_address: ${ip_address} + strategy: react_llama + with_memory: true + recursion_limit: ${recursion_limit_supervisor} + llm_engine: vllm + HUGGINGFACEHUB_API_TOKEN: ${HUGGINGFACEHUB_API_TOKEN} + llm_endpoint_url: ${LLM_ENDPOINT_URL} + model: ${LLM_MODEL_ID} + temperature: ${temperature} + max_new_tokens: ${max_new_tokens} + stream: true + tools: /home/user/tools/supervisor_agent_tools.yaml + custom_prompt: /home/user/prompts/supervisor_prompt.py + require_human_feedback: false + no_proxy: ${no_proxy} + http_proxy: ${http_proxy} + https_proxy: ${https_proxy} + WORKER_FINQA_AGENT_URL: $WORKER_FINQA_AGENT_URL + WORKER_RESEARCH_AGENT_URL: $WORKER_RESEARCH_AGENT_URL + port: 9090 diff --git a/FinanceAgent/docker_image_build/build.yaml b/FinanceAgent/docker_image_build/build.yaml new file mode 100644 index 0000000000..723a61c873 --- /dev/null +++ b/FinanceAgent/docker_image_build/build.yaml @@ -0,0 +1,19 @@ +# Copyright (C) 2024 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + +services: + 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} diff --git a/FinanceAgent/prompts/finqa_prompt.py b/FinanceAgent/prompts/finqa_prompt.py new file mode 100644 index 0000000000..877aa342ed --- /dev/null +++ b/FinanceAgent/prompts/finqa_prompt.py @@ -0,0 +1,37 @@ +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} +""" diff --git a/FinanceAgent/prompts/supervisor_prompt.py b/FinanceAgent/prompts/supervisor_prompt.py new file mode 100644 index 0000000000..cf5883da80 --- /dev/null +++ b/FinanceAgent/prompts/supervisor_prompt.py @@ -0,0 +1,31 @@ +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} +""" diff --git a/FinanceAgent/tools/finqa_agent_tools.yaml b/FinanceAgent/tools/finqa_agent_tools.yaml new file mode 100644 index 0000000000..e69de29bb2 diff --git a/FinanceAgent/tools/research_agent_tools.yaml b/FinanceAgent/tools/research_agent_tools.yaml new file mode 100644 index 0000000000..e69de29bb2 diff --git a/FinanceAgent/tools/supervisor_agent_tools.yaml b/FinanceAgent/tools/supervisor_agent_tools.yaml new file mode 100644 index 0000000000..e69de29bb2 From edfafe7fd9b753ad5fb5ea5bd4c2d943a62c1121 Mon Sep 17 00:00:00 2001 From: minmin-intel Date: Wed, 12 Mar 2025 19:07:58 +0000 Subject: [PATCH 02/20] add tools and readme Signed-off-by: minmin-intel --- FinanceAgent/README.md | 57 +++ FinanceAgent/docker_image_build/build.yaml | 9 + FinanceAgent/tests/test.py | 91 +++++ FinanceAgent/tests/test_compose_on_gaudi.sh | 0 FinanceAgent/tools/finqa_agent_tools.yaml | 17 + FinanceAgent/tools/finqa_tools.py | 90 +++++ FinanceAgent/tools/redis_kv.py | 146 ++++++++ .../tools/supervisor_agent_tools.yaml | 26 ++ FinanceAgent/tools/supervisor_tools.py | 25 ++ FinanceAgent/tools/utils.py | 343 ++++++++++++++++++ 10 files changed, 804 insertions(+) create mode 100644 FinanceAgent/tests/test.py create mode 100644 FinanceAgent/tests/test_compose_on_gaudi.sh create mode 100644 FinanceAgent/tools/finqa_tools.py create mode 100644 FinanceAgent/tools/redis_kv.py create mode 100644 FinanceAgent/tools/supervisor_tools.py create mode 100644 FinanceAgent/tools/utils.py diff --git a/FinanceAgent/README.md b/FinanceAgent/README.md index e69de29bb2..d6d1007997 100644 --- a/FinanceAgent/README.md +++ b/FinanceAgent/README.md @@ -0,0 +1,57 @@ +# Finance Agent +## 1. Overview + + +## 2. Getting started +### 2.1 Dowload repos +```bash +mkdir /path/to/your/workspace/ +export WORKDIR=/path/to/your/workspace/ +genaicomps +genaiexamples +``` +### 2.2 Set up env vars +```bash +export HF_CACHE_DIR=/path/to/your/model/cache/ +export HF_TOKEN= + +``` + +### 2.3 Build docker images +Build docker images for dataprep, agent, agent-ui. +```bash +# use docker image build +``` + +If deploy on Gaudi, also need to build vllm image. +```bash +cd $WORKDIR +git clone https://github.com/HabanaAI/vllm-fork.git +# get the latest release tag of vllm gaudi +VLLM_VER=$(git describe --tags "$(git rev-list --tags --max-count=1)") +echo "Check out vLLM tag ${VLLM_VER}" +git checkout ${VLLM_VER} +docker build --no-cache -f Dockerfile.hpu -t opea/vllm-gaudi:latest --shm-size=128g . --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy +``` + +## 3. Deploy with docker compose +### 3.1 Launch vllm endpoint +Below is the command to launch a vllm endpoint on Gaudi that serves `meta-llama/Llama-3.3-70B-Instruct` model on 4 Gaudi cards. +```bash +export vllm_port=8086 +export vllm_volume=$HF_CACHE_DIR +export max_length=16384 +export model="meta-llama/Llama-3.3-70B-Instruct" +docker run -d --runtime=habana --rm --name "vllm-gaudi-server" -e HABANA_VISIBLE_DEVICES=all -p $vllm_port:8000 -v $vllm_volume:/data -e HF_TOKEN=$HF_TOKEN -e HUGGING_FACE_HUB_TOKEN=$HF_TOKEN -e HF_HOME=/data -e OMPI_MCA_btl_vader_single_copy_mechanism=none -e PT_HPU_ENABLE_LAZY_COLLECTIVES=true -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e no_proxy=$no_proxy -e VLLM_SKIP_WARMUP=true --cap-add=sys_nice --ipc=host opea/vllm-gaudi:comps --model ${model} --max-seq-len-to-capture $max_length --tensor-parallel-size 4 +``` +### 3.2 Prepare knowledge base +The commands below will upload some example files into the knowledge base. You can also upload files through UI. + +First, launch the redis databases and the dataprep microservice. +```bash + +``` + +### 3.3 Launch the multi-agent system +### 3.4 Validate agents + diff --git a/FinanceAgent/docker_image_build/build.yaml b/FinanceAgent/docker_image_build/build.yaml index 723a61c873..ac0d56dc4d 100644 --- a/FinanceAgent/docker_image_build/build.yaml +++ b/FinanceAgent/docker_image_build/build.yaml @@ -2,6 +2,15 @@ # 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 diff --git a/FinanceAgent/tests/test.py b/FinanceAgent/tests/test.py new file mode 100644 index 0000000000..2f0bbd557f --- /dev/null +++ b/FinanceAgent/tests/test.py @@ -0,0 +1,91 @@ +# 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" + query = {"role": "user", "messages": args.prompt, "stream": "false"} + 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") + args, _ = parser.parse_known_args() + + print(args) + + if args.agent_role == "supervisor": + test_chat_completion_multi_turn(args) + elif args.agent_role == "worker": + test_worker_agent(args) + else: + raise ValueError("Invalid agent role") diff --git a/FinanceAgent/tests/test_compose_on_gaudi.sh b/FinanceAgent/tests/test_compose_on_gaudi.sh new file mode 100644 index 0000000000..e69de29bb2 diff --git a/FinanceAgent/tools/finqa_agent_tools.yaml b/FinanceAgent/tools/finqa_agent_tools.yaml index e69de29bb2..bcc5cfd1c0 100644 --- a/FinanceAgent/tools/finqa_agent_tools.yaml +++ b/FinanceAgent/tools/finqa_agent_tools.yaml @@ -0,0 +1,17 @@ +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 \ No newline at end of file diff --git a/FinanceAgent/tools/finqa_tools.py b/FinanceAgent/tools/finqa_tools.py new file mode 100644 index 0000000000..aff31a82ab --- /dev/null +++ b/FinanceAgent/tools/finqa_tools.py @@ -0,0 +1,90 @@ +from 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) + + 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}." + 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" + 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) \ No newline at end of file diff --git a/FinanceAgent/tools/redis_kv.py b/FinanceAgent/tools/redis_kv.py new file mode 100644 index 0000000000..9f88d02a6b --- /dev/null +++ b/FinanceAgent/tools/redis_kv.py @@ -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) \ No newline at end of file diff --git a/FinanceAgent/tools/supervisor_agent_tools.yaml b/FinanceAgent/tools/supervisor_agent_tools.yaml index e69de29bb2..5b8817c5a1 100644 --- a/FinanceAgent/tools/supervisor_agent_tools.yaml +++ b/FinanceAgent/tools/supervisor_agent_tools.yaml @@ -0,0 +1,26 @@ +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_agent: + description: generate high-lelvel summary of a financial document. + callable_api: supervisor_tools.py:summarize_doc + args_schema: + doc_title: + type: str + description: the description of the document, should include company name and time, for example, Apple 2023 Q4 earnings call. + 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 diff --git a/FinanceAgent/tools/supervisor_tools.py b/FinanceAgent/tools/supervisor_tools.py new file mode 100644 index 0000000000..68b9215142 --- /dev/null +++ b/FinanceAgent/tools/supervisor_tools.py @@ -0,0 +1,25 @@ +import requests +import os + +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 summarize_doc(doc_title): + pass + +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"] \ No newline at end of file diff --git a/FinanceAgent/tools/utils.py b/FinanceAgent/tools/utils.py new file mode 100644 index 0000000000..9e7d953813 --- /dev/null +++ b/FinanceAgent/tools/utils.py @@ -0,0 +1,343 @@ +from langchain_community.retrievers import BM25Retriever +from langchain_redis import RedisConfig, RedisVectorStore +from langchain_core.documents import Document +from redis_kv import RedisKVStore +import os +from openai import OpenAI +from langchain_community.embeddings import HuggingFaceBgeEmbeddings +from langchain_huggingface import HuggingFaceEndpointEmbeddings + +# 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("LLM_MODEL", "meta-llama/Llama-3.3-70B-Instruct") +LLM_ENDPOINT=os.getenv("LLM_ENDPOINT", "http://localhost:8086") +MAX_TOKENS = os.getenv("MAX_TOKENS", 1024) +TEMPERATURE = os.getenv("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 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 comany 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 serach 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 + + From 94f16efb3c7f6d2128d38d9a72b580480ad34ed5 Mon Sep 17 00:00:00 2001 From: minmin-intel Date: Fri, 14 Mar 2025 03:09:54 +0000 Subject: [PATCH 03/20] add dataprep compose yaml Signed-off-by: minmin-intel --- FinanceAgent/README.md | 2 +- .../intel/hpu/gaudi/compose.yaml | 56 ++++++------ .../intel/hpu/gaudi/dataprep_compose.yaml | 83 ++++++++++++++++++ FinanceAgent/tests/step1_build_images.sh | 67 ++++++++++++++ FinanceAgent/tests/test.py | 5 ++ FinanceAgent/tests/test_compose_on_gaudi.sh | 87 +++++++++++++++++++ 6 files changed, 271 insertions(+), 29 deletions(-) create mode 100644 FinanceAgent/docker_compose/intel/hpu/gaudi/dataprep_compose.yaml create mode 100644 FinanceAgent/tests/step1_build_images.sh diff --git a/FinanceAgent/README.md b/FinanceAgent/README.md index d6d1007997..36cb8b3a33 100644 --- a/FinanceAgent/README.md +++ b/FinanceAgent/README.md @@ -49,7 +49,7 @@ The commands below will upload some example files into the knowledge base. You c First, launch the redis databases and the dataprep microservice. ```bash - +docker compose -f $WORKDIR/GenAIExamples/FinanceAgent/docker_compose/intel/hpu/gaudi/dataprep.yaml up -d ``` ### 3.3 Launch the multi-agent system diff --git a/FinanceAgent/docker_compose/intel/hpu/gaudi/compose.yaml b/FinanceAgent/docker_compose/intel/hpu/gaudi/compose.yaml index 2f3a4fafce..51efdd6551 100644 --- a/FinanceAgent/docker_compose/intel/hpu/gaudi/compose.yaml +++ b/FinanceAgent/docker_compose/intel/hpu/gaudi/compose.yaml @@ -31,34 +31,34 @@ services: https_proxy: ${https_proxy} port: 9095 - worker-research-agent: - image: opea/agent:latest - container_name: research-agent-endpoint - volumes: - - ${TOOLSET_PATH}:/home/user/tools/ - - ${PROMPT_PATH}:/home/user/prompts/ - ports: - - "9096:9096" - ipc: host - environment: - ip_address: ${ip_address} - strategy: react_llama - with_memory: false - recursion_limit: ${recursion_limit_worker} - llm_engine: vllm - HUGGINGFACEHUB_API_TOKEN: ${HUGGINGFACEHUB_API_TOKEN} - llm_endpoint_url: ${LLM_ENDPOINT_URL} - model: ${LLM_MODEL_ID} - temperature: ${temperature} - max_new_tokens: ${max_new_tokens} - stream: false - tools: /home/user/tools/research_agent_tools.yaml - custom_prompt: /home/user/prompts/research_prompt.py - require_human_feedback: false - no_proxy: ${no_proxy} - http_proxy: ${http_proxy} - https_proxy: ${https_proxy} - port: 9096 + # worker-research-agent: + # image: opea/agent:latest + # container_name: research-agent-endpoint + # volumes: + # - ${TOOLSET_PATH}:/home/user/tools/ + # - ${PROMPT_PATH}:/home/user/prompts/ + # ports: + # - "9096:9096" + # ipc: host + # environment: + # ip_address: ${ip_address} + # strategy: react_llama + # with_memory: false + # recursion_limit: ${recursion_limit_worker} + # llm_engine: vllm + # HUGGINGFACEHUB_API_TOKEN: ${HUGGINGFACEHUB_API_TOKEN} + # llm_endpoint_url: ${LLM_ENDPOINT_URL} + # model: ${LLM_MODEL_ID} + # temperature: ${temperature} + # max_new_tokens: ${max_new_tokens} + # stream: false + # tools: /home/user/tools/research_agent_tools.yaml + # custom_prompt: /home/user/prompts/research_prompt.py + # require_human_feedback: false + # no_proxy: ${no_proxy} + # http_proxy: ${http_proxy} + # https_proxy: ${https_proxy} + # port: 9096 supervisor-react-agent: image: opea/agent:latest diff --git a/FinanceAgent/docker_compose/intel/hpu/gaudi/dataprep_compose.yaml b/FinanceAgent/docker_compose/intel/hpu/gaudi/dataprep_compose.yaml new file mode 100644 index 0000000000..c8189d3e70 --- /dev/null +++ b/FinanceAgent/docker_compose/intel/hpu/gaudi/dataprep_compose.yaml @@ -0,0 +1,83 @@ +# Copyright (C) 2025 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + +services: + tei-embedding-serving: + image: ghcr.io/huggingface/text-embeddings-inference:cpu-1.5 + container_name: tei-embedding-serving + entrypoint: /bin/sh -c "apt-get update && apt-get install -y curl && text-embeddings-router --json-output --model-id ${EMBEDDING_MODEL_ID} --auto-truncate" + ports: + - "${TEI_EMBEDDER_PORT:-12000}:80" + volumes: + - "./data:/data" + shm_size: 1g + environment: + no_proxy: ${no_proxy} + http_proxy: ${http_proxy} + https_proxy: ${https_proxy} + host_ip: ${host_ip} + HF_TOKEN: ${HF_TOKEN} + healthcheck: + test: ["CMD", "curl", "-f", "http://${host_ip}:${TEI_EMBEDDER_PORT}/health"] + interval: 10s + timeout: 6s + retries: 48 + + redis-vector-db: + image: redis/redis-stack:7.2.0-v9 + container_name: redis-vector-db + ports: + - "${REDIS_PORT1:-6379}:6379" + - "${REDIS_PORT2:-8001}:8001" + environment: + - no_proxy=${no_proxy} + - http_proxy=${http_proxy} + - https_proxy=${https_proxy} + healthcheck: + test: ["CMD", "redis-cli", "ping"] + timeout: 10s + retries: 3 + start_period: 10s + + redis-kv-store: + image: redis/redis-stack:7.2.0-v9 + container_name: redis-kv-store + ports: + - "${REDIS_PORT3:-6380}:6379" + - "${REDIS_PORT4:-8002}:8001" + environment: + - no_proxy=${no_proxy} + - http_proxy=${http_proxy} + - https_proxy=${https_proxy} + healthcheck: + test: ["CMD", "redis-cli", "ping"] + timeout: 10s + retries: 3 + start_period: 10s + + dataprep-redis-finance: + image: ${REGISTRY:-opea}/dataprep:${TAG:-latest} + container_name: dataprep-redis-server-finance + depends_on: + redis-vector-db: + condition: service_healthy + redis-kv-store: + condition: service_healthy + tei-embedding-serving: + condition: service_healthy + ports: + - "${DATAPREP_PORT:-11108}:5000" + environment: + no_proxy: ${no_proxy} + http_proxy: ${http_proxy} + https_proxy: ${https_proxy} + DATAPREP_COMPONENT_NAME: ${DATAPREP_COMPONENT_NAME} + REDIS_URL_VECTOR: ${REDIS_URL_VECTOR} + REDIS_URL_KV: ${REDIS_URL_KV} + TEI_EMBEDDING_ENDPOINT: ${TEI_EMBEDDING_ENDPOINT} + LLM_ENDPOINT: ${LLM_ENDPOINT} + LLM_MODEL: ${LLM_MODEL} + HUGGINGFACEHUB_API_TOKEN: ${HF_TOKEN} + HF_TOKEN: ${HF_TOKEN} + LOGFLAG: true + diff --git a/FinanceAgent/tests/step1_build_images.sh b/FinanceAgent/tests/step1_build_images.sh new file mode 100644 index 0000000000..e11153f8e9 --- /dev/null +++ b/FinanceAgent/tests/step1_build_images.sh @@ -0,0 +1,67 @@ +#!/bin/bash +# Copyright (C) 2024 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + +set -e +WORKPATH=$(dirname "$PWD") +export WORKDIR=$WORKPATH/../../ +echo "WORKDIR=${WORKDIR}" +export ip_address=$(hostname -I | awk '{print $1}') + + +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_agent_docker_image() { + for CI + cd $WORKDIR/GenAIExamples/AgentQnA/docker_image_build/ + get_genai_comps + cd $WORKDIR/GenAIComps/FinanceAgent/docker_image_build/ + 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)") + git checkout ${VLLM_VER} &> /dev/null + docker build --no-cache -f Dockerfile.hpu -t opea/vllm-gaudi:ci --shm-size=128g . --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy + if [ $? -ne 0 ]; then + echo "opea/vllm-gaudi:ci failed" + exit 1 + else + echo "opea/vllm-gaudi:ci successful" + fi +} + + +function main() { + echo "==================== Build agent docker image ====================" + # build_agent_docker_image + build_agent_image_local + echo "==================== Build agent docker image completed ====================" + + echo "==================== Build vllm docker image ====================" + build_vllm_docker_image + echo "==================== Build vllm docker image completed ====================" + + docker image ls | grep vllm +} + +main diff --git a/FinanceAgent/tests/test.py b/FinanceAgent/tests/test.py index 2f0bbd557f..ab83f4f08d 100644 --- a/FinanceAgent/tests/test.py +++ b/FinanceAgent/tests/test.py @@ -79,11 +79,16 @@ def test_supervisor_agent_single_turn(args): parser.add_argument("--stream", action="store_true", help="streaming mode") parser.add_argument("--prompt", type=str, help="prompt message") parser.add_argument("--agent_role", type=str, default="supervisor", help="supervisor or worker") + parser.add_argument("--multi-turn", action="store_true", help="multi-turn conversation") args, _ = parser.parse_known_args() print(args) if args.agent_role == "supervisor": + if args.multi_turn: + test_chat_completion_multi_turn(args) + else: + test_supervisor_agent_single_turn(args) test_chat_completion_multi_turn(args) elif args.agent_role == "worker": test_worker_agent(args) diff --git a/FinanceAgent/tests/test_compose_on_gaudi.sh b/FinanceAgent/tests/test_compose_on_gaudi.sh index e69de29bb2..59ad1cebbc 100644 --- a/FinanceAgent/tests/test_compose_on_gaudi.sh +++ b/FinanceAgent/tests/test_compose_on_gaudi.sh @@ -0,0 +1,87 @@ +#!/bin/bash +# Copyright (C) 2024 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 +set -xe + +WORKPATH=$(dirname "$PWD") +export WORKDIR=$WORKPATH/../../ +echo "WORKDIR=${WORKDIR}" +export ip_address=$(hostname -I | awk '{print $1}') +export HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN} +export TOOLSET_PATH=$WORKDIR/GenAIExamples/AgentQnA/tools/ + + +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 +} + +function stop_llm(){ + cd $WORKPATH/docker_compose/intel/hpu/gaudi/ + container_list=$(cat tgi_gaudi.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 $cid -f && sleep 1s; fi + done + + cid=$(docker ps -aq --filter "name=vllm-gaudi-server") + echo "Stopping container $cid" + if [[ ! -z "$cid" ]]; then docker rm $cid -f && sleep 1s; fi + + cid=$(docker ps -aq --filter "name=test-comps-vllm-gaudi-service") + echo "Stopping container $cid" + if [[ ! -z "$cid" ]]; then docker rm $cid -f && sleep 1s; fi + +} + +function start_dataprep(){ + docker compose -f $WORKPATH/docker_compose/intel/hpu/gaudi/dataprep.yaml up -d +} + +function stop_dataprep() { + echo "Stopping databases" + cid=$(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 + +} + + +echo "workpath: $WORKPATH" +echo "=================== Stop containers ====================" +stop_llm +stop_agent_docker +stop_databases + +cd $WORKPATH/tests + +echo "=================== #1 Building docker images====================" +bash step1_build_images.sh +echo "=================== #1 Building docker images completed====================" + +echo "=================== #2 Start dataprep====================" +start_dataprep +echo "=================== #2 Dataprep started====================" + +echo "=================== #3 Ingest data and validate retrieval====================" +bash step3_ingest_data_and_validate_retrieval.sh +echo "=================== #3 Data ingestion and validation completed====================" + +echo "=================== #4 Start agents ====================" +bash step4_launch_and_validate_agent_gaudi.sh +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!!" From acf9796100c5f920c65140118677dbe5deffba28 Mon Sep 17 00:00:00 2001 From: minmin-intel Date: Fri, 14 Mar 2025 19:21:05 +0000 Subject: [PATCH 04/20] add test scripts Signed-off-by: minmin-intel --- .../intel/hpu/gaudi/compose.yaml | 17 +- .../intel/hpu/gaudi/dataprep_compose.yaml | 6 +- .../intel/hpu/gaudi/launch_agents.sh | 25 ++ FinanceAgent/tests/test_compose_on_gaudi.sh | 256 +++++++++++++++--- FinanceAgent/tests/test_redis_finance.py | 70 +++++ FinanceAgent/tools/finqa_tools.py | 16 +- FinanceAgent/tools/utils.py | 13 +- 7 files changed, 339 insertions(+), 64 deletions(-) create mode 100644 FinanceAgent/docker_compose/intel/hpu/gaudi/launch_agents.sh create mode 100644 FinanceAgent/tests/test_redis_finance.py diff --git a/FinanceAgent/docker_compose/intel/hpu/gaudi/compose.yaml b/FinanceAgent/docker_compose/intel/hpu/gaudi/compose.yaml index 51efdd6551..ba243dbbec 100644 --- a/FinanceAgent/docker_compose/intel/hpu/gaudi/compose.yaml +++ b/FinanceAgent/docker_compose/intel/hpu/gaudi/compose.yaml @@ -20,8 +20,8 @@ services: HUGGINGFACEHUB_API_TOKEN: ${HUGGINGFACEHUB_API_TOKEN} llm_endpoint_url: ${LLM_ENDPOINT_URL} model: ${LLM_MODEL_ID} - temperature: ${temperature} - max_new_tokens: ${max_new_tokens} + temperature: ${TEMPERATURE} + max_new_tokens: ${MAX_TOKENS} stream: false tools: /home/user/tools/finqa_agent_tools.yaml custom_prompt: /home/user/prompts/finqa_prompt.py @@ -29,6 +29,9 @@ services: no_proxy: ${no_proxy} http_proxy: ${http_proxy} https_proxy: ${https_proxy} + REDIS_URL_VECTOR: $REDIS_URL_VECTOR + REDIS_URL_KV: $REDIS_URL_KV + TEI_EMBEDDING_ENDPOINT: $TEI_EMBEDDING_ENDPOINT port: 9095 # worker-research-agent: @@ -49,8 +52,8 @@ services: # HUGGINGFACEHUB_API_TOKEN: ${HUGGINGFACEHUB_API_TOKEN} # llm_endpoint_url: ${LLM_ENDPOINT_URL} # model: ${LLM_MODEL_ID} - # temperature: ${temperature} - # max_new_tokens: ${max_new_tokens} + # temperature: ${TEMPERATURE} + # max_new_tokens: ${MAX_TOKENS} # stream: false # tools: /home/user/tools/research_agent_tools.yaml # custom_prompt: /home/user/prompts/research_prompt.py @@ -65,7 +68,7 @@ services: container_name: supervisor-agent-endpoint depends_on: - worker-finqa-agent - - worker-research-agent + # - worker-research-agent volumes: - ${TOOLSET_PATH}:/home/user/tools/ - ${PROMPT_PATH}:/home/user/prompts/ @@ -81,8 +84,8 @@ services: HUGGINGFACEHUB_API_TOKEN: ${HUGGINGFACEHUB_API_TOKEN} llm_endpoint_url: ${LLM_ENDPOINT_URL} model: ${LLM_MODEL_ID} - temperature: ${temperature} - max_new_tokens: ${max_new_tokens} + temperature: ${TEMPERATURE} + max_new_tokens: ${MAX_TOKENS} stream: true tools: /home/user/tools/supervisor_agent_tools.yaml custom_prompt: /home/user/prompts/supervisor_prompt.py diff --git a/FinanceAgent/docker_compose/intel/hpu/gaudi/dataprep_compose.yaml b/FinanceAgent/docker_compose/intel/hpu/gaudi/dataprep_compose.yaml index c8189d3e70..c04445c302 100644 --- a/FinanceAgent/docker_compose/intel/hpu/gaudi/dataprep_compose.yaml +++ b/FinanceAgent/docker_compose/intel/hpu/gaudi/dataprep_compose.yaml @@ -7,7 +7,7 @@ services: container_name: tei-embedding-serving entrypoint: /bin/sh -c "apt-get update && apt-get install -y curl && text-embeddings-router --json-output --model-id ${EMBEDDING_MODEL_ID} --auto-truncate" ports: - - "${TEI_EMBEDDER_PORT:-12000}:80" + - "${TEI_EMBEDDER_PORT:-10221}:80" volumes: - "./data:/data" shm_size: 1g @@ -56,7 +56,7 @@ services: start_period: 10s dataprep-redis-finance: - image: ${REGISTRY:-opea}/dataprep:${TAG:-latest} + image: ${REGISTRY:-opea}/dataprep:${TAG:-comps} ### CHange back to latest container_name: dataprep-redis-server-finance depends_on: redis-vector-db: @@ -66,7 +66,7 @@ services: tei-embedding-serving: condition: service_healthy ports: - - "${DATAPREP_PORT:-11108}:5000" + - "${DATAPREP_PORT:-6007}:5000" environment: no_proxy: ${no_proxy} http_proxy: ${http_proxy} diff --git a/FinanceAgent/docker_compose/intel/hpu/gaudi/launch_agents.sh b/FinanceAgent/docker_compose/intel/hpu/gaudi/launch_agents.sh new file mode 100644 index 0000000000..6e002a2936 --- /dev/null +++ b/FinanceAgent/docker_compose/intel/hpu/gaudi/launch_agents.sh @@ -0,0 +1,25 @@ + +export ip_address=$(hostname -I | awk '{print $1}') +export HUGGINGFACEHUB_API_TOKEN=${HF_TOKEN} +export TOOLSET_PATH=$WORKDIR/GenAIExamples/FinanceAgent/tools/ +echo "TOOLSET_PATH=${TOOLSET_PATH}" +export PROMPT_PATH=$WORKDIR/GenAIExamples/FinanceAgent/prompts/ +export recursion_limit_worker=12 +export recursion_limit_supervisor=10 + +vllm_port=8086 +export LLM_MODEL_ID="meta-llama/Llama-3.3-70B-Instruct" +export LLM_ENDPOINT_URL="http://${ip_address}:${vllm_port}" +export TEMPERATURE=0.5 +export MAX_TOKENS=4096 + +export WORKER_FINQA_AGENT_URL="http://${ip_address}:9095" +export WORKER_RESEARCH_AGENT_URL="http://${ip_address}:9096" + +export TEI_EMBEDDING_ENDPOINT="http://${ip_address}:10221" +export REDIS_URL_VECTOR="redis://${ip_address}:6379" +export REDIS_URL_KV="redis://${ip_address}:6380" + + + +docker compose -f compose.yaml up -d diff --git a/FinanceAgent/tests/test_compose_on_gaudi.sh b/FinanceAgent/tests/test_compose_on_gaudi.sh index 59ad1cebbc..d7d0826a1c 100644 --- a/FinanceAgent/tests/test_compose_on_gaudi.sh +++ b/FinanceAgent/tests/test_compose_on_gaudi.sh @@ -3,45 +3,147 @@ # SPDX-License-Identifier: Apache-2.0 set -xe -WORKPATH=$(dirname "$PWD") +export WORKPATH=$(dirname "$PWD") export WORKDIR=$WORKPATH/../../ echo "WORKDIR=${WORKDIR}" export ip_address=$(hostname -I | awk '{print $1}') -export HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN} -export TOOLSET_PATH=$WORKDIR/GenAIExamples/AgentQnA/tools/ +#### env vars for LLM endpoint ############# +model=meta-llama/Llama-3.3-70B-Instruct +vllm_port=8086 +vllm_image=$vllm_image +HF_CACHE_DIR=/data2/huggingface +vllm_volume=${HF_CACHE_DIR} +####################################### -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 $cid -f && sleep 1s; fi - done +#### env vars for dataprep ############# +export host_ip=${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}" +####################################### + +#### env vars for agent ############# + + +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 stop_llm(){ - cd $WORKPATH/docker_compose/intel/hpu/gaudi/ - container_list=$(cat tgi_gaudi.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 +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)") + 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=0,1,2,3 -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 ${model} --max-seq-len-to-capture 16384 --tensor-parallel-size 4 + sleep 5s + echo "Waiting vllm gaudi ready" + n=0 + until [[ "$n" -ge 100 ]] || [[ $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" ${LOG_PATH}/vllm-gaudi-service.log; then + echo "container vllm-gaudi-server not found" + exit 1 + fi + sleep 5s done + sleep 5s + echo "Service started successfully" +} - cid=$(docker ps -aq --filter "name=vllm-gaudi-server") - echo "Stopping container $cid" - if [[ ! -z "$cid" ]]; then docker rm $cid -f && sleep 1s; fi - cid=$(docker ps -aq --filter "name=test-comps-vllm-gaudi-service") +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.yaml up -d + 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() { @@ -51,37 +153,105 @@ function stop_dataprep() { } +function start_agents() { + echo "Starting Agent services" + cd $WORKDIR/GenAIExamples/FinanceAgent/docker_compose/intel/hpu/gaudi/ + bash launch_agents.sh + sleep 2m +} + + +function validate_agent_service() { + # # test worker finqa agent + echo "======================Testing worker rag agent======================" + export agent_port="9095" + prompt="What is Gap's revenue in 2024?" + local CONTENT=$(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 [ "$EXIT_CODE" == "1" ]; then + docker logs finqa-agent-endpoint + exit 1 + fi + + # # # test worker research agent + # echo "======================Testing worker sql agent======================" + # export agent_port="9096" + # prompt="How many employees are there in the company?" + # local CONTENT=$(python3 $WORKDIR/GenAIExamples/AgentQnA/tests/test.py --prompt "$prompt" --agent_role "worker" --ext_port $agent_port) + # local EXIT_CODE=$(validate "$CONTENT" "8" "sql-agent-endpoint") + # echo $CONTENT + # # echo $EXIT_CODE + # local EXIT_CODE="${EXIT_CODE:0-1}" + # if [ "$EXIT_CODE" == "1" ]; then + # docker logs sql-agent-endpoint + # exit 1 + # fi + + # test supervisor react agent + echo "======================Testing supervisor agent: single turns ======================" + export agent_port="9090" + local CONTENT=$(python3 $WORKDIR/GenAIExamples/FinanceAgent/tests/test.py --agent_role "supervisor" --ext_port $agent_port --stream) + # echo $CONTENT + # local EXIT_CODE=$(validate "$CONTENT" "Iron" "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 +# echo "=================== Stop containers ====================" +# stop_llm stop_agent_docker -stop_databases +# stop_dataprep cd $WORKPATH/tests -echo "=================== #1 Building docker images====================" -bash step1_build_images.sh -echo "=================== #1 Building docker images completed====================" +# 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 dataprep====================" -start_dataprep -echo "=================== #2 Dataprep started====================" +# echo "=================== #2 Start vllm endpoint====================" +# start_vllm_service_70B +# echo "=================== #2 vllm endpoint started====================" -echo "=================== #3 Ingest data and validate retrieval====================" -bash step3_ingest_data_and_validate_retrieval.sh -echo "=================== #3 Data ingestion and validation completed====================" +# echo "=================== #3 Start dataprep and ingest data ====================" +# start_dataprep +# ingest_validate_dataprep +# echo "=================== #3 Data ingestion and validation completed====================" echo "=================== #4 Start agents ====================" -bash step4_launch_and_validate_agent_gaudi.sh +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 "=================== #5 Stop microservices ====================" +# stop_agent_docker +# stop_dataprep +# stop_llm +# echo "=================== #5 Microservices stopped====================" -echo y | docker system prune +# echo y | docker system prune -echo "ALL DONE!!" +# echo "ALL DONE!!" diff --git a/FinanceAgent/tests/test_redis_finance.py b/FinanceAgent/tests/test_redis_finance.py new file mode 100644 index 0000000000..f98360d69a --- /dev/null +++ b/FinanceAgent/tests/test_redis_finance.py @@ -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.") \ No newline at end of file diff --git a/FinanceAgent/tools/finqa_tools.py b/FinanceAgent/tools/finqa_tools.py index aff31a82ab..55ea015d87 100644 --- a/FinanceAgent/tools/finqa_tools.py +++ b/FinanceAgent/tools/finqa_tools.py @@ -1,4 +1,4 @@ -from utils import * +from tools.utils import * def get_context_bm25_llm(query, company, year, quarter = ""): k = 5 @@ -29,7 +29,7 @@ def get_context_bm25_llm(query, company, year, quarter = ""): # get unique results context = get_unique_docs(chunks+tables) - print("Context:\n", context) + print("Context:\n", context[:500]) if context: query = f"{query} for {company} in {year} {quarter}" @@ -38,6 +38,7 @@ def get_context_bm25_llm(query, company, year, quarter = ""): response = parse_response(response) else: response = f"No relevant information found for {company} in {year} {quarter}." + print("Search result:\n", response) return response @@ -75,9 +76,14 @@ def search_full_doc(query, company): if __name__ == "__main__": - company="Gap" - year="2024" - quarter="Q4" + # company="Gap" + # year="2024" + # quarter="Q4" + + company="Costco" + year="2025" + quarter="Q2" + collection_name=f"chunks_{company}" search_metadata = ("company", company) diff --git a/FinanceAgent/tools/utils.py b/FinanceAgent/tools/utils.py index 9e7d953813..fb0679f6e7 100644 --- a/FinanceAgent/tools/utils.py +++ b/FinanceAgent/tools/utils.py @@ -1,14 +1,14 @@ from langchain_community.retrievers import BM25Retriever from langchain_redis import RedisConfig, RedisVectorStore from langchain_core.documents import Document -from redis_kv import RedisKVStore +from tools.redis_kv import RedisKVStore import os from openai import OpenAI from langchain_community.embeddings import HuggingFaceBgeEmbeddings from langchain_huggingface import HuggingFaceEndpointEmbeddings # Embedding model -EMBED_MODEL = os.getenv("EMBED_MODEL", "BAAI/bge-base-en-v1.5") +EMBED_MODEL = os.getenv("EMBED_MODEL", "BAAI/bge-base-en-v1.5") TEI_EMBEDDING_ENDPOINT = os.getenv("TEI_EMBEDDING_ENDPOINT", "") # Redis URL @@ -16,10 +16,11 @@ REDIS_URL_KV = os.getenv("REDIS_URL_KV", "redis://localhost:6380/") # LLM config -LLM_MODEL=os.getenv("LLM_MODEL", "meta-llama/Llama-3.3-70B-Instruct") -LLM_ENDPOINT=os.getenv("LLM_ENDPOINT", "http://localhost:8086") -MAX_TOKENS = os.getenv("MAX_TOKENS", 1024) -TEMPERATURE = os.getenv("TEMPERATURE", 0.2) +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: From 00592bdb8779a7e4e1673574d50cadf2c4f6c27c Mon Sep 17 00:00:00 2001 From: minmin-intel Date: Fri, 14 Mar 2025 22:21:05 +0000 Subject: [PATCH 05/20] finqa and supervisor agent tested Signed-off-by: minmin-intel --- .../intel/hpu/gaudi/dataprep_compose.yaml | 2 +- .../intel/hpu/gaudi/launch_agents.sh | 4 +- FinanceAgent/docker_image_build/build.yaml | 12 ++-- FinanceAgent/tests/step1_build_images.sh | 67 ------------------- FinanceAgent/tests/test.py | 1 - .../tools/supervisor_agent_tools.yaml | 32 ++++----- 6 files changed, 25 insertions(+), 93 deletions(-) delete mode 100644 FinanceAgent/tests/step1_build_images.sh diff --git a/FinanceAgent/docker_compose/intel/hpu/gaudi/dataprep_compose.yaml b/FinanceAgent/docker_compose/intel/hpu/gaudi/dataprep_compose.yaml index c04445c302..a1f78bebab 100644 --- a/FinanceAgent/docker_compose/intel/hpu/gaudi/dataprep_compose.yaml +++ b/FinanceAgent/docker_compose/intel/hpu/gaudi/dataprep_compose.yaml @@ -56,7 +56,7 @@ services: start_period: 10s dataprep-redis-finance: - image: ${REGISTRY:-opea}/dataprep:${TAG:-comps} ### CHange back to latest + image: ${REGISTRY:-opea}/dataprep:${TAG:-latest} container_name: dataprep-redis-server-finance depends_on: redis-vector-db: diff --git a/FinanceAgent/docker_compose/intel/hpu/gaudi/launch_agents.sh b/FinanceAgent/docker_compose/intel/hpu/gaudi/launch_agents.sh index 6e002a2936..cc2d979f21 100644 --- a/FinanceAgent/docker_compose/intel/hpu/gaudi/launch_agents.sh +++ b/FinanceAgent/docker_compose/intel/hpu/gaudi/launch_agents.sh @@ -13,8 +13,8 @@ export LLM_ENDPOINT_URL="http://${ip_address}:${vllm_port}" export TEMPERATURE=0.5 export MAX_TOKENS=4096 -export WORKER_FINQA_AGENT_URL="http://${ip_address}:9095" -export WORKER_RESEARCH_AGENT_URL="http://${ip_address}:9096" +export WORKER_FINQA_AGENT_URL="http://${ip_address}:9095/v1/chat/completions" +export WORKER_RESEARCH_AGENT_URL="http://${ip_address}:9096/v1/chat/completions" export TEI_EMBEDDING_ENDPOINT="http://${ip_address}:10221" export REDIS_URL_VECTOR="redis://${ip_address}:6379" diff --git a/FinanceAgent/docker_image_build/build.yaml b/FinanceAgent/docker_image_build/build.yaml index ac0d56dc4d..867cc3a8c4 100644 --- a/FinanceAgent/docker_image_build/build.yaml +++ b/FinanceAgent/docker_image_build/build.yaml @@ -20,9 +20,9 @@ services: 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} + # agent-ui: + # build: + # context: ../ui + # dockerfile: ./docker/Dockerfile + # extends: agent + # image: ${REGISTRY:-opea}/agent-ui:${TAG:-latest} diff --git a/FinanceAgent/tests/step1_build_images.sh b/FinanceAgent/tests/step1_build_images.sh deleted file mode 100644 index e11153f8e9..0000000000 --- a/FinanceAgent/tests/step1_build_images.sh +++ /dev/null @@ -1,67 +0,0 @@ -#!/bin/bash -# Copyright (C) 2024 Intel Corporation -# SPDX-License-Identifier: Apache-2.0 - -set -e -WORKPATH=$(dirname "$PWD") -export WORKDIR=$WORKPATH/../../ -echo "WORKDIR=${WORKDIR}" -export ip_address=$(hostname -I | awk '{print $1}') - - -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_agent_docker_image() { - for CI - cd $WORKDIR/GenAIExamples/AgentQnA/docker_image_build/ - get_genai_comps - cd $WORKDIR/GenAIComps/FinanceAgent/docker_image_build/ - 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)") - git checkout ${VLLM_VER} &> /dev/null - docker build --no-cache -f Dockerfile.hpu -t opea/vllm-gaudi:ci --shm-size=128g . --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy - if [ $? -ne 0 ]; then - echo "opea/vllm-gaudi:ci failed" - exit 1 - else - echo "opea/vllm-gaudi:ci successful" - fi -} - - -function main() { - echo "==================== Build agent docker image ====================" - # build_agent_docker_image - build_agent_image_local - echo "==================== Build agent docker image completed ====================" - - echo "==================== Build vllm docker image ====================" - build_vllm_docker_image - echo "==================== Build vllm docker image completed ====================" - - docker image ls | grep vllm -} - -main diff --git a/FinanceAgent/tests/test.py b/FinanceAgent/tests/test.py index ab83f4f08d..a8c73bbd88 100644 --- a/FinanceAgent/tests/test.py +++ b/FinanceAgent/tests/test.py @@ -89,7 +89,6 @@ def test_supervisor_agent_single_turn(args): test_chat_completion_multi_turn(args) else: test_supervisor_agent_single_turn(args) - test_chat_completion_multi_turn(args) elif args.agent_role == "worker": test_worker_agent(args) else: diff --git a/FinanceAgent/tools/supervisor_agent_tools.yaml b/FinanceAgent/tools/supervisor_agent_tools.yaml index 5b8817c5a1..9c9789674e 100644 --- a/FinanceAgent/tools/supervisor_agent_tools.yaml +++ b/FinanceAgent/tools/supervisor_agent_tools.yaml @@ -7,20 +7,20 @@ finqa_agent: 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_agent: - description: generate high-lelvel summary of a financial document. - callable_api: supervisor_tools.py:summarize_doc - args_schema: - doc_title: - type: str - description: the description of the document, should include company name and time, for example, Apple 2023 Q4 earnings call. - return_output: summary +# summarization_agent: +# description: generate high-lelvel summary of a financial document. +# callable_api: supervisor_tools.py:summarize_doc +# args_schema: +# doc_title: +# type: str +# description: the description of the document, should include company name and time, for example, Apple 2023 Q4 earnings call. +# 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 +# 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 From 2c2010981dcda7a46a194215d536645c13d08371 Mon Sep 17 00:00:00 2001 From: rbrugaro Date: Thu, 27 Mar 2025 10:06:58 -0700 Subject: [PATCH 06/20] Summarization agent and updated supervisor (#1722) Signed-off-by: Rita Brugarolas Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- FinanceAgent/README.md | 22 +++- .../intel/hpu/gaudi/compose.yaml | 24 ++++ .../intel/hpu/gaudi/dataprep_compose.yaml | 5 +- .../intel/hpu/gaudi/launch_agents.sh | 10 ++ FinanceAgent/prompts/finqa_prompt.py | 3 + FinanceAgent/prompts/supervisor_prompt.py | 3 + FinanceAgent/tests/test.py | 5 +- FinanceAgent/tests/test_compose_on_gaudi.sh | 2 +- FinanceAgent/tests/test_redis_finance.py | 2 +- FinanceAgent/tools/finqa_agent_tools.yaml | 5 +- FinanceAgent/tools/finqa_tools.py | 38 +++--- FinanceAgent/tools/redis_kv.py | 2 +- FinanceAgent/tools/research_agent_tools.yaml | 2 + FinanceAgent/tools/sum_agent_tools.py | 113 ++++++++++++++++++ .../tools/supervisor_agent_tools.yaml | 22 ++-- FinanceAgent/tools/supervisor_tools.py | 15 ++- FinanceAgent/tools/utils.py | 106 ++++++++-------- 17 files changed, 280 insertions(+), 99 deletions(-) create mode 100644 FinanceAgent/tools/sum_agent_tools.py diff --git a/FinanceAgent/README.md b/FinanceAgent/README.md index 36cb8b3a33..e37d3df0e2 100644 --- a/FinanceAgent/README.md +++ b/FinanceAgent/README.md @@ -1,16 +1,20 @@ # Finance Agent -## 1. Overview +## 1. Overview ## 2. Getting started -### 2.1 Dowload repos + +### 2.1 Download repos + ```bash mkdir /path/to/your/workspace/ export WORKDIR=/path/to/your/workspace/ genaicomps genaiexamples ``` + ### 2.2 Set up env vars + ```bash export HF_CACHE_DIR=/path/to/your/model/cache/ export HF_TOKEN= @@ -18,16 +22,20 @@ export HF_TOKEN= ``` ### 2.3 Build docker images + Build docker images for dataprep, agent, agent-ui. + ```bash # use docker image build ``` If deploy on Gaudi, also need to build vllm image. + ```bash cd $WORKDIR git clone https://github.com/HabanaAI/vllm-fork.git # get the latest release tag of vllm gaudi +cd vllm-fork VLLM_VER=$(git describe --tags "$(git rev-list --tags --max-count=1)") echo "Check out vLLM tag ${VLLM_VER}" git checkout ${VLLM_VER} @@ -35,8 +43,11 @@ docker build --no-cache -f Dockerfile.hpu -t opea/vllm-gaudi:latest --shm-size=1 ``` ## 3. Deploy with docker compose + ### 3.1 Launch vllm endpoint + Below is the command to launch a vllm endpoint on Gaudi that serves `meta-llama/Llama-3.3-70B-Instruct` model on 4 Gaudi cards. + ```bash export vllm_port=8086 export vllm_volume=$HF_CACHE_DIR @@ -44,14 +55,17 @@ export max_length=16384 export model="meta-llama/Llama-3.3-70B-Instruct" docker run -d --runtime=habana --rm --name "vllm-gaudi-server" -e HABANA_VISIBLE_DEVICES=all -p $vllm_port:8000 -v $vllm_volume:/data -e HF_TOKEN=$HF_TOKEN -e HUGGING_FACE_HUB_TOKEN=$HF_TOKEN -e HF_HOME=/data -e OMPI_MCA_btl_vader_single_copy_mechanism=none -e PT_HPU_ENABLE_LAZY_COLLECTIVES=true -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e no_proxy=$no_proxy -e VLLM_SKIP_WARMUP=true --cap-add=sys_nice --ipc=host opea/vllm-gaudi:comps --model ${model} --max-seq-len-to-capture $max_length --tensor-parallel-size 4 ``` + ### 3.2 Prepare knowledge base + The commands below will upload some example files into the knowledge base. You can also upload files through UI. First, launch the redis databases and the dataprep microservice. + ```bash -docker compose -f $WORKDIR/GenAIExamples/FinanceAgent/docker_compose/intel/hpu/gaudi/dataprep.yaml up -d +docker compose -f $WORKDIR/GenAIExamples/FinanceAgent/docker_compose/intel/hpu/gaudi/dataprep_compose.yaml up -d ``` ### 3.3 Launch the multi-agent system -### 3.4 Validate agents +### 3.4 Validate agents diff --git a/FinanceAgent/docker_compose/intel/hpu/gaudi/compose.yaml b/FinanceAgent/docker_compose/intel/hpu/gaudi/compose.yaml index ba243dbbec..9a06b965ac 100644 --- a/FinanceAgent/docker_compose/intel/hpu/gaudi/compose.yaml +++ b/FinanceAgent/docker_compose/intel/hpu/gaudi/compose.yaml @@ -95,4 +95,28 @@ services: https_proxy: ${https_proxy} WORKER_FINQA_AGENT_URL: $WORKER_FINQA_AGENT_URL WORKER_RESEARCH_AGENT_URL: $WORKER_RESEARCH_AGENT_URL + #WORKER_SUM_AGENT_URL: $WORKER_SUM_AGENT_URL + DOCSUM_ENDPOINT: $DOCSUM_ENDPOINT + REDIS_URL_VECTOR: $REDIS_URL_VECTOR + REDIS_URL_KV: $REDIS_URL_KV + TEI_EMBEDDING_ENDPOINT: $TEI_EMBEDDING_ENDPOINT port: 9090 + + docsum-vllm-gaudi: + image: opea/llm-docsum:latest + container_name: docsum-vllm-gaudi + ports: + - ${DOCSUM_PORT:-9000}:9000 + ipc: host + environment: + no_proxy: ${no_proxy} + http_proxy: ${http_proxy} + https_proxy: ${https_proxy} + LLM_ENDPOINT: ${LLM_ENDPOINT} + LLM_MODEL_ID: ${LLM_MODEL_ID} + HF_TOKEN: ${HF_TOKEN} + LOGFLAG: ${LOGFLAG:-False} + MAX_INPUT_TOKENS: ${MAX_INPUT_TOKENS} + MAX_TOTAL_TOKENS: ${MAX_TOTAL_TOKENS} + DocSum_COMPONENT_NAME: ${DocSum_COMPONENT_NAME:-OpeaDocSumvLLM} + restart: unless-stopped diff --git a/FinanceAgent/docker_compose/intel/hpu/gaudi/dataprep_compose.yaml b/FinanceAgent/docker_compose/intel/hpu/gaudi/dataprep_compose.yaml index a1f78bebab..5e4333c7d2 100644 --- a/FinanceAgent/docker_compose/intel/hpu/gaudi/dataprep_compose.yaml +++ b/FinanceAgent/docker_compose/intel/hpu/gaudi/dataprep_compose.yaml @@ -22,7 +22,7 @@ services: interval: 10s timeout: 6s retries: 48 - + redis-vector-db: image: redis/redis-stack:7.2.0-v9 container_name: redis-vector-db @@ -54,7 +54,7 @@ services: timeout: 10s retries: 3 start_period: 10s - + dataprep-redis-finance: image: ${REGISTRY:-opea}/dataprep:${TAG:-latest} container_name: dataprep-redis-server-finance @@ -80,4 +80,3 @@ services: HUGGINGFACEHUB_API_TOKEN: ${HF_TOKEN} HF_TOKEN: ${HF_TOKEN} LOGFLAG: true - diff --git a/FinanceAgent/docker_compose/intel/hpu/gaudi/launch_agents.sh b/FinanceAgent/docker_compose/intel/hpu/gaudi/launch_agents.sh index cc2d979f21..9a97cfd631 100644 --- a/FinanceAgent/docker_compose/intel/hpu/gaudi/launch_agents.sh +++ b/FinanceAgent/docker_compose/intel/hpu/gaudi/launch_agents.sh @@ -1,9 +1,13 @@ +# Copyright (C) 2025 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + export ip_address=$(hostname -I | awk '{print $1}') export HUGGINGFACEHUB_API_TOKEN=${HF_TOKEN} export TOOLSET_PATH=$WORKDIR/GenAIExamples/FinanceAgent/tools/ echo "TOOLSET_PATH=${TOOLSET_PATH}" export PROMPT_PATH=$WORKDIR/GenAIExamples/FinanceAgent/prompts/ +echo "PROMPT_PATH=${PROMPT_PATH}" export recursion_limit_worker=12 export recursion_limit_supervisor=10 @@ -16,10 +20,16 @@ export MAX_TOKENS=4096 export WORKER_FINQA_AGENT_URL="http://${ip_address}:9095/v1/chat/completions" export WORKER_RESEARCH_AGENT_URL="http://${ip_address}:9096/v1/chat/completions" +export EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5" export TEI_EMBEDDING_ENDPOINT="http://${ip_address}:10221" export REDIS_URL_VECTOR="redis://${ip_address}:6379" export REDIS_URL_KV="redis://${ip_address}:6380" +export MAX_INPUT_TOKENS=2048 +export MAX_TOTAL_TOKENS=4096 +export DocSum_COMPONENT_NAME="OpeaDocSumvLLM" +export DOCSUM_ENDPOINT="http://${ip_address}:9000/v1/docsum" + docker compose -f compose.yaml up -d diff --git a/FinanceAgent/prompts/finqa_prompt.py b/FinanceAgent/prompts/finqa_prompt.py index 877aa342ed..9dda6dc22c 100644 --- a/FinanceAgent/prompts/finqa_prompt.py +++ b/FinanceAgent/prompts/finqa_prompt.py @@ -1,3 +1,6 @@ +# 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: diff --git a/FinanceAgent/prompts/supervisor_prompt.py b/FinanceAgent/prompts/supervisor_prompt.py index cf5883da80..404ba1a481 100644 --- a/FinanceAgent/prompts/supervisor_prompt.py +++ b/FinanceAgent/prompts/supervisor_prompt.py @@ -1,3 +1,6 @@ +# 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. diff --git a/FinanceAgent/tests/test.py b/FinanceAgent/tests/test.py index a8c73bbd88..5162a58f67 100644 --- a/FinanceAgent/tests/test.py +++ b/FinanceAgent/tests/test.py @@ -57,6 +57,7 @@ def test_chat_completion_multi_turn(args): 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 = [ @@ -67,9 +68,7 @@ def test_supervisor_agent_single_turn(args): for query in query_list: thread_id = f"{uuid.uuid4()}" add_message_and_run(url, query, thread_id, stream=args.stream) - print("="*50) - - + print("=" * 50) if __name__ == "__main__": diff --git a/FinanceAgent/tests/test_compose_on_gaudi.sh b/FinanceAgent/tests/test_compose_on_gaudi.sh index d7d0826a1c..e232008887 100644 --- a/FinanceAgent/tests/test_compose_on_gaudi.sh +++ b/FinanceAgent/tests/test_compose_on_gaudi.sh @@ -150,7 +150,7 @@ 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() { diff --git a/FinanceAgent/tests/test_redis_finance.py b/FinanceAgent/tests/test_redis_finance.py index f98360d69a..b62d4deb2d 100644 --- a/FinanceAgent/tests/test_redis_finance.py +++ b/FinanceAgent/tests/test_redis_finance.py @@ -67,4 +67,4 @@ def test_get(url): url = f"http://localhost:{port}/v1/dataprep/get" test_get(url) else: - raise ValueError("Invalid test_option value. Please choose from ingest, get, delete.") \ No newline at end of file + raise ValueError("Invalid test_option value. Please choose from ingest, get, delete.") diff --git a/FinanceAgent/tools/finqa_agent_tools.yaml b/FinanceAgent/tools/finqa_agent_tools.yaml index bcc5cfd1c0..c118e222a2 100644 --- a/FinanceAgent/tools/finqa_agent_tools.yaml +++ b/FinanceAgent/tools/finqa_agent_tools.yaml @@ -1,3 +1,6 @@ +# 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 @@ -14,4 +17,4 @@ search_knowledge_base: 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 \ No newline at end of file + return_output: retrieved_data diff --git a/FinanceAgent/tools/finqa_tools.py b/FinanceAgent/tools/finqa_tools.py index 55ea015d87..57a0ff95d0 100644 --- a/FinanceAgent/tools/finqa_tools.py +++ b/FinanceAgent/tools/finqa_tools.py @@ -1,24 +1,28 @@ +# Copyright (C) 2025 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + from tools.utils import * -def get_context_bm25_llm(query, company, year, quarter = ""): + +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}" + 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}" + 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") @@ -28,7 +32,7 @@ def get_context_bm25_llm(query, company, year, quarter = ""): tables = [] # get unique results - context = get_unique_docs(chunks+tables) + context = get_unique_docs(chunks + tables) print("Context:\n", context[:500]) if context: @@ -50,7 +54,7 @@ def search_full_doc(query, company): 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) @@ -60,8 +64,8 @@ def search_full_doc(query, company): doc = docs[0] doc_title = doc.page_content print(f"Most similar doc title: {doc_title}") - - kvstore= RedisKVStore(redis_uri=REDIS_URL_KV) + + 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"] @@ -80,17 +84,17 @@ def search_full_doc(query, company): # year="2024" # quarter="Q4" - company="Costco" - year="2025" - quarter="Q2" + company = "Costco" + year = "2025" + quarter = "Q2" - collection_name=f"chunks_{company}" + 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("=" * 50) print("testing retrieve full doc") query = f"{company} {year} {quarter} earning call" - search_full_doc(query, company) \ No newline at end of file + search_full_doc(query, company) diff --git a/FinanceAgent/tools/redis_kv.py b/FinanceAgent/tools/redis_kv.py index 9f88d02a6b..3ded5bffdb 100644 --- a/FinanceAgent/tools/redis_kv.py +++ b/FinanceAgent/tools/redis_kv.py @@ -143,4 +143,4 @@ def from_host_and_port( port (int): Redis port """ url = f"redis://{host}:{port}".format(host=host, port=port) - return cls(redis_uri=url) \ No newline at end of file + return cls(redis_uri=url) diff --git a/FinanceAgent/tools/research_agent_tools.yaml b/FinanceAgent/tools/research_agent_tools.yaml index e69de29bb2..4057dc0163 100644 --- a/FinanceAgent/tools/research_agent_tools.yaml +++ b/FinanceAgent/tools/research_agent_tools.yaml @@ -0,0 +1,2 @@ +# Copyright (C) 2025 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 diff --git a/FinanceAgent/tools/sum_agent_tools.py b/FinanceAgent/tools/sum_agent_tools.py new file mode 100644 index 0000000000..6c502c13cf --- /dev/null +++ b/FinanceAgent/tools/sum_agent_tools.py @@ -0,0 +1,113 @@ +# Copyright (C) 2025 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + +import json + +import requests +from tools.redis_kv import RedisKVStore +from tools.utils import * + + +def get_summary_else_doc(query, 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: + print(f"Company not found in knowledge base: {company}") + return company + print(f"Company {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}") + doc_length = doc["doc_length"] + print(f"Doc length: {doc_length}") + if "summary" not in doc: + content = doc["full_doc"] + print(f"is_summary: {False}") + is_summary = False + return doc_title, content, is_summary + content = doc["summary"] + is_summary = True + print(f"Summary already exists in KV store: {is_summary}") + 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(doc_title, doc_dict, collection=f"full_doc_{company}") + + +def summarize(doc_name, company): + ip_address = os.environ.get("ip_address") + # docsum_url = f"http://{ip_address}:9000/v1/docsum" + docsum_url = os.environ.get("DOCSUM_ENDPOINT") + print(f"Docsum Endpoint URL: {docsum_url}") + + doc_title, sum, is_summary = get_summary_else_doc(doc_name, company) + print(f"Summary or full doc from get_summary_else_doc: {sum[:100]} \n -------\n") + 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.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" + summarize(doc_name, company) + print("=" * 50) diff --git a/FinanceAgent/tools/supervisor_agent_tools.yaml b/FinanceAgent/tools/supervisor_agent_tools.yaml index 9c9789674e..0b451557e3 100644 --- a/FinanceAgent/tools/supervisor_agent_tools.yaml +++ b/FinanceAgent/tools/supervisor_agent_tools.yaml @@ -1,3 +1,6 @@ +# 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 @@ -7,14 +10,17 @@ finqa_agent: 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_agent: -# description: generate high-lelvel summary of a financial document. -# callable_api: supervisor_tools.py:summarize_doc -# args_schema: -# doc_title: -# type: str -# description: the description of the document, should include company name and time, for example, Apple 2023 Q4 earnings call. -# return_output: summary +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. diff --git a/FinanceAgent/tools/supervisor_tools.py b/FinanceAgent/tools/supervisor_tools.py index 68b9215142..5bddc16bcd 100644 --- a/FinanceAgent/tools/supervisor_tools.py +++ b/FinanceAgent/tools/supervisor_tools.py @@ -1,7 +1,12 @@ -import requests +# Copyright (C) 2025 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + import os -def finqa_agent(query:str): +import requests + + +def finqa_agent(query: str): url = os.environ.get("WORKER_FINQA_AGENT_URL") print(url) proxies = {"http": ""} @@ -11,10 +16,8 @@ def finqa_agent(query:str): response = requests.post(url, json=payload, proxies=proxies) return response.json()["text"] -def summarize_doc(doc_title): - pass -def research_agent(company:str): +def research_agent(company: str): url = os.environ.get("WORKER_RESEARCH_AGENT_URL") print(url) proxies = {"http": ""} @@ -22,4 +25,4 @@ def research_agent(company:str): "messages": company, } response = requests.post(url, json=payload, proxies=proxies) - return response.json()["text"] \ No newline at end of file + return response.json()["text"] diff --git a/FinanceAgent/tools/utils.py b/FinanceAgent/tools/utils.py index fb0679f6e7..ba3820ca54 100644 --- a/FinanceAgent/tools/utils.py +++ b/FinanceAgent/tools/utils.py @@ -1,14 +1,18 @@ -from langchain_community.retrievers import BM25Retriever -from langchain_redis import RedisConfig, RedisVectorStore -from langchain_core.documents import Document -from tools.redis_kv import RedisKVStore +# Copyright (C) 2025 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + import os -from openai import OpenAI + 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 +from tools.redis_kv import RedisKVStore # Embedding model -EMBED_MODEL = os.getenv("EMBED_MODEL", "BAAI/bge-base-en-v1.5") +EMBED_MODEL = os.getenv("EMBED_MODEL", "BAAI/bge-base-en-v1.5") TEI_EMBEDDING_ENDPOINT = os.getenv("TEI_EMBEDDING_ENDPOINT", "") # Redis URL @@ -16,19 +20,19 @@ 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") +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="""\ +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. +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". """ @@ -42,6 +46,7 @@ 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 get_embedder(): if TEI_EMBEDDING_ENDPOINT: # create embeddings using TEI endpoint service @@ -54,10 +59,9 @@ def get_embedder(): embedder = HuggingFaceBgeEmbeddings(model_name=EMBED_MODEL) return embedder + def generate_answer(prompt): - """ - Use vllm endpoint to generate the answer - """ + """Use vllm endpoint to generate the answer.""" # send request to vllm endpoint client = OpenAI( base_url=f"{LLM_ENDPOINT}/v1", @@ -70,12 +74,8 @@ def generate_answer(prompt): } completion = client.chat.completions.create( - model=LLM_MODEL, - messages=[ - {"role": "user", "content": prompt} - ], - **params - ) + model=LLM_MODEL, messages=[{"role": "user", "content": prompt}], **params + ) # get response response = completion.choices[0].message.content @@ -99,15 +99,16 @@ def get_company_list(): 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(): @@ -119,6 +120,7 @@ def get_company_name_in_kb(company, company_list): else: return "Failed to parse LLM response." + def get_docs_matching_metadata(metadata, collection_name): """ metadata: ("company_year", "3M_2023") @@ -126,8 +128,8 @@ def get_docs_matching_metadata(metadata, collection_name): """ key = metadata[0] value = metadata[1] - kvstore= RedisKVStore(redis_uri=REDIS_URL_KV) - collection = kvstore.get_all(collection_name) # collection is a dict + kvstore = RedisKVStore(redis_uri=REDIS_URL_KV) + collection = kvstore.get_all(collection_name) # collection is a dict matching_docs = [] for idx in collection: @@ -138,7 +140,8 @@ def get_docs_matching_metadata(metadata, collection_name): 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 = [] @@ -146,24 +149,17 @@ def convert_docs(docs): 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 - ) - + 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 - ) + 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}") @@ -189,21 +185,21 @@ def bm25_search(query, metadata, company, doc_type="chunks", k=10): 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}") + 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}") + 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}") + 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}") + metadata = ("company", f"{company}") docs = bm25_search(query, metadata, company, k=k, doc_type=doc_type) docs = docs + docs1 @@ -216,11 +212,13 @@ def bm25_search_broad(query, company, year, quarter, k=10, doc_type="chunks"): 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)) @@ -229,17 +227,17 @@ def similarity_search(vector_store, k, query, company, year, quarter=None): 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 comany and year") + + 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 serach with company.....") + + 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 @@ -248,6 +246,7 @@ def similarity_search(vector_store, k, query, company, year, quarter=None): else: return docs + def get_index_name(doc_type: str, metadata: dict): company = metadata["company"] if doc_type == "chunks": @@ -262,6 +261,7 @@ def get_index_name(doc_type: str, metadata: dict): 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 @@ -273,20 +273,20 @@ def get_content(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 = "" @@ -340,5 +340,3 @@ def get_vectorstore_titles(index_name): embedder = get_embedder() vector_store = RedisVectorStore(embedder, config=config) return vector_store - - From 434bbdf3fcf0fe3fd0b17883201ad8ca5ff5c01b Mon Sep 17 00:00:00 2001 From: minmin-intel Date: Wed, 2 Apr 2025 19:13:53 +0000 Subject: [PATCH 07/20] fix summarization tool error Signed-off-by: minmin-intel --- .../intel/hpu/gaudi/compose.yaml | 1 - FinanceAgent/tests/test.py | 2 +- FinanceAgent/tests/test_compose_on_gaudi.sh | 27 +++++-- FinanceAgent/tools/sum_agent_tools.py | 79 ++++++++++--------- FinanceAgent/tools/utils.py | 16 +++- 5 files changed, 75 insertions(+), 50 deletions(-) diff --git a/FinanceAgent/docker_compose/intel/hpu/gaudi/compose.yaml b/FinanceAgent/docker_compose/intel/hpu/gaudi/compose.yaml index 9a06b965ac..084f98b29d 100644 --- a/FinanceAgent/docker_compose/intel/hpu/gaudi/compose.yaml +++ b/FinanceAgent/docker_compose/intel/hpu/gaudi/compose.yaml @@ -95,7 +95,6 @@ services: https_proxy: ${https_proxy} WORKER_FINQA_AGENT_URL: $WORKER_FINQA_AGENT_URL WORKER_RESEARCH_AGENT_URL: $WORKER_RESEARCH_AGENT_URL - #WORKER_SUM_AGENT_URL: $WORKER_SUM_AGENT_URL DOCSUM_ENDPOINT: $DOCSUM_ENDPOINT REDIS_URL_VECTOR: $REDIS_URL_VECTOR REDIS_URL_KV: $REDIS_URL_KV diff --git a/FinanceAgent/tests/test.py b/FinanceAgent/tests/test.py index 5162a58f67..e8fed1c85f 100644 --- a/FinanceAgent/tests/test.py +++ b/FinanceAgent/tests/test.py @@ -63,7 +63,7 @@ def test_supervisor_agent_single_turn(args): query_list = [ "What was Gap's revenue growth in 2024?", "Can you summarize Costco's 2025 Q2 earnings call?", - "Should I increase investment in Costco?", + # "Should I increase investment in Costco?", ] for query in query_list: thread_id = f"{uuid.uuid4()}" diff --git a/FinanceAgent/tests/test_compose_on_gaudi.sh b/FinanceAgent/tests/test_compose_on_gaudi.sh index e232008887..e14262dc03 100644 --- a/FinanceAgent/tests/test_compose_on_gaudi.sh +++ b/FinanceAgent/tests/test_compose_on_gaudi.sh @@ -7,12 +7,14 @@ export WORKPATH=$(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=$vllm_image -HF_CACHE_DIR=/data2/huggingface +# HF_CACHE_DIR=/data2/huggingface vllm_volume=${HF_CACHE_DIR} ####################################### @@ -29,7 +31,6 @@ export EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5" export TEI_EMBEDDING_ENDPOINT="http://${ip_address}:${TEI_EMBEDDER_PORT}" ####################################### -#### env vars for agent ############# function get_genai_comps() { @@ -163,7 +164,7 @@ function start_agents() { function validate_agent_service() { # # test worker finqa agent - echo "======================Testing worker rag 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 $agent_port) @@ -194,8 +195,8 @@ function validate_agent_service() { echo "======================Testing supervisor agent: single turns ======================" export agent_port="9090" local CONTENT=$(python3 $WORKDIR/GenAIExamples/FinanceAgent/tests/test.py --agent_role "supervisor" --ext_port $agent_port --stream) - # echo $CONTENT - # local EXIT_CODE=$(validate "$CONTENT" "Iron" "react-agent-endpoint") + echo $CONTENT + # local EXIT_CODE=$(validate "$CONTENT" "" "react-agent-endpoint") # echo $EXIT_CODE # local EXIT_CODE="${EXIT_CODE:0-1}" # if [ "$EXIT_CODE" == "1" ]; then @@ -203,6 +204,16 @@ function validate_agent_service() { # exit 1 # fi + echo "======================Testing supervisor agent: multi turns ======================" + local CONTENT=$(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() { @@ -220,7 +231,7 @@ echo "workpath: $WORKPATH" # echo "=================== Stop containers ====================" # stop_llm stop_agent_docker -# stop_dataprep +stop_dataprep cd $WORKPATH/tests @@ -237,8 +248,8 @@ cd $WORKPATH/tests # echo "=================== #2 vllm endpoint started====================" # echo "=================== #3 Start dataprep and ingest data ====================" -# start_dataprep -# ingest_validate_dataprep +start_dataprep +ingest_validate_dataprep # echo "=================== #3 Data ingestion and validation completed====================" echo "=================== #4 Start agents ====================" diff --git a/FinanceAgent/tools/sum_agent_tools.py b/FinanceAgent/tools/sum_agent_tools.py index 6c502c13cf..5760650d80 100644 --- a/FinanceAgent/tools/sum_agent_tools.py +++ b/FinanceAgent/tools/sum_agent_tools.py @@ -4,22 +4,16 @@ import json import requests -from tools.redis_kv import RedisKVStore -from tools.utils import * +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): - 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: - print(f"Company not found in knowledge base: {company}") - return company - print(f"Company {company}") +def get_summary_else_doc(query, company): # search most similar doc title index_name = f"titles_{company}" vector_store = get_vectorstore_titles(index_name) @@ -30,18 +24,22 @@ def get_summary_else_doc(query, company): 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}") - doc_length = doc["doc_length"] - print(f"Doc length: {doc_length}") - if "summary" not in doc: - content = doc["full_doc"] - print(f"is_summary: {False}") + 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 - content = doc["summary"] - is_summary = True - print(f"Summary already exists in KV store: {is_summary}") return doc_title, content, is_summary @@ -55,23 +53,25 @@ def save_doc_summary(summary, doc_title, company): company: The company associated with the document. """ kvstore = RedisKVStore(redis_uri=REDIS_URL_KV) - doc_dict = kvstore.get(doc_title, f"full_doc_{company}") + # doc_dict = kvstore.get(doc_title, f"full_doc_{company}") - # Add the summary to the dictionary - doc_dict["summary"] = summary + # # Add the summary to the dictionary + # doc_dict["summary"] = summary # Save the updated value back to the store - kvstore.put(doc_title, doc_dict, collection=f"full_doc_{company}") + kvstore.put(f"{doc_title}_summary", {"summary":summary}, collection=f"full_doc_{company}") def summarize(doc_name, company): - ip_address = os.environ.get("ip_address") - # docsum_url = f"http://{ip_address}:9000/v1/docsum" 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) - print(f"Summary or full doc from get_summary_else_doc: {sum[:100]} \n -------\n") + if not doc_title: + return(f"Cannot find documents related to {doc_title} in KV store.") + if not is_summary: data = { "messages": sum, @@ -86,7 +86,7 @@ def summarize(doc_name, company): try: print("Computing Summary with OPEA DocSum...") resp = requests.post(url=docsum_url, data=json.dumps(data), headers=headers) - ret = resp.text + 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}" @@ -99,15 +99,16 @@ def summarize(doc_name, company): if __name__ == "__main__": - company = "Gap" - year = "2024" - quarter = "Q4" + # company = "Gap" + # year = "2024" + # quarter = "Q4" - # company="Costco" - # year="2025" - # quarter="Q2" + company="Costco" + year="2025" + quarter="Q2" print("testing summarize") doc_name = f"{company} {year} {quarter} earning call" - summarize(doc_name, company) + ret = summarize(doc_name, company) + print("Summary: ", ret) print("=" * 50) diff --git a/FinanceAgent/tools/utils.py b/FinanceAgent/tools/utils.py index ba3820ca54..7b70289ac3 100644 --- a/FinanceAgent/tools/utils.py +++ b/FinanceAgent/tools/utils.py @@ -9,7 +9,10 @@ from langchain_huggingface import HuggingFaceEndpointEmbeddings from langchain_redis import RedisConfig, RedisVectorStore from openai import OpenAI -from tools.redis_kv import RedisKVStore +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") @@ -46,6 +49,17 @@ 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: From 2fa357c664b6891c3f4c36105616434368ceecf8 Mon Sep 17 00:00:00 2001 From: lkk <33276950+lkk12014402@users.noreply.github.com> Date: Thu, 3 Apr 2025 22:09:04 +0800 Subject: [PATCH 08/20] Add financial research agent. (#1738) Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- .../intel/hpu/gaudi/compose.yaml | 55 +++---- FinanceAgent/prompts/research_prompt.py | 52 ++++++ FinanceAgent/tests/test_compose_on_gaudi.sh | 26 +-- FinanceAgent/tools/research_agent_tools.yaml | 45 +++++ FinanceAgent/tools/research_tools.py | 154 ++++++++++++++++++ 5 files changed, 291 insertions(+), 41 deletions(-) create mode 100644 FinanceAgent/prompts/research_prompt.py create mode 100644 FinanceAgent/tools/research_tools.py diff --git a/FinanceAgent/docker_compose/intel/hpu/gaudi/compose.yaml b/FinanceAgent/docker_compose/intel/hpu/gaudi/compose.yaml index 084f98b29d..f5b2937af0 100644 --- a/FinanceAgent/docker_compose/intel/hpu/gaudi/compose.yaml +++ b/FinanceAgent/docker_compose/intel/hpu/gaudi/compose.yaml @@ -34,34 +34,33 @@ services: TEI_EMBEDDING_ENDPOINT: $TEI_EMBEDDING_ENDPOINT port: 9095 - # worker-research-agent: - # image: opea/agent:latest - # container_name: research-agent-endpoint - # volumes: - # - ${TOOLSET_PATH}:/home/user/tools/ - # - ${PROMPT_PATH}:/home/user/prompts/ - # ports: - # - "9096:9096" - # ipc: host - # environment: - # ip_address: ${ip_address} - # strategy: react_llama - # with_memory: false - # recursion_limit: ${recursion_limit_worker} - # llm_engine: vllm - # HUGGINGFACEHUB_API_TOKEN: ${HUGGINGFACEHUB_API_TOKEN} - # llm_endpoint_url: ${LLM_ENDPOINT_URL} - # model: ${LLM_MODEL_ID} - # temperature: ${TEMPERATURE} - # max_new_tokens: ${MAX_TOKENS} - # stream: false - # tools: /home/user/tools/research_agent_tools.yaml - # custom_prompt: /home/user/prompts/research_prompt.py - # require_human_feedback: false - # no_proxy: ${no_proxy} - # http_proxy: ${http_proxy} - # https_proxy: ${https_proxy} - # port: 9096 + worker-research-agent: + image: opea/agent:latest + container_name: research-agent-endpoint + volumes: + - ${TOOLSET_PATH}:/home/user/tools/ + - ${PROMPT_PATH}:/home/user/prompts/ + ports: + - "9096:9096" + ipc: host + environment: + ip_address: ${ip_address} + strategy: react_llama + with_memory: false + recursion_limit: ${recursion_limit_worker} + llm_engine: vllm + HUGGINGFACEHUB_API_TOKEN: ${HUGGINGFACEHUB_API_TOKEN} + llm_endpoint_url: ${LLM_ENDPOINT_URL} + model: ${LLM_MODEL_ID} + stream: false + tools: /home/user/tools/research_agent_tools.yaml + custom_prompt: /home/user/prompts/research_prompt.py + require_human_feedback: false + no_proxy: ${no_proxy} + http_proxy: ${http_proxy} + https_proxy: ${https_proxy} + FINNHUB_API_KEY: ${FINNHUB_API_KEY} + port: 9096 supervisor-react-agent: image: opea/agent:latest diff --git a/FinanceAgent/prompts/research_prompt.py b/FinanceAgent/prompts/research_prompt.py new file mode 100644 index 0000000000..4d01cb264f --- /dev/null +++ b/FinanceAgent/prompts/research_prompt.py @@ -0,0 +1,52 @@ +# Copyright (C) 2025 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + +REACT_AGENT_LLAMA_PROMPT = """\ +You are a Market Analyst, one must possess strong analytical and problem-solving abilities, collect necessary financial information and aggregate them based on client's requirement. For coding tasks, only use the functions you have been provided with. Reply TERMINATE when the task is done. + + +You have access to the following tools: +{tools} + +For a comprehensive analysis, you can use all the tools provided to retrieve information available for the company. + +**Procedure:** +1. Read the question carefully. To get the company news, financial histories, some related tools need `start_date` and `end_date, 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. +2. Analyze the positive developments and potential concerns of the company with 2-4 most important factors respectively and keep them concise. Most factors should be inferred from company related news. +3. Make a rough prediction (e.g. up/down by 2-3%) of the NVDA stock price movement for next week. +4. Provide a summary analysis to support your prediction. +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. 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", ...}}}} +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"}} + +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:** +* 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} +""" diff --git a/FinanceAgent/tests/test_compose_on_gaudi.sh b/FinanceAgent/tests/test_compose_on_gaudi.sh index e14262dc03..c85d99f209 100644 --- a/FinanceAgent/tests/test_compose_on_gaudi.sh +++ b/FinanceAgent/tests/test_compose_on_gaudi.sh @@ -177,19 +177,19 @@ function validate_agent_service() { exit 1 fi - # # # test worker research agent - # echo "======================Testing worker sql agent======================" - # export agent_port="9096" - # prompt="How many employees are there in the company?" - # local CONTENT=$(python3 $WORKDIR/GenAIExamples/AgentQnA/tests/test.py --prompt "$prompt" --agent_role "worker" --ext_port $agent_port) - # local EXIT_CODE=$(validate "$CONTENT" "8" "sql-agent-endpoint") - # echo $CONTENT - # # echo $EXIT_CODE - # local EXIT_CODE="${EXIT_CODE:0-1}" - # if [ "$EXIT_CODE" == "1" ]; then - # docker logs sql-agent-endpoint - # exit 1 - # fi + # # test worker research agent + echo "======================Testing worker research agent======================" + export agent_port="9096" + prompt="generate NVDA financial research report" + local CONTENT=$(python3 $WORKDIR/GenAIExamples/AgentQnA/tests/test.py --prompt "$prompt" --agent_role "worker" --ext_port $agent_port) + local EXIT_CODE=$(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 ======================" diff --git a/FinanceAgent/tools/research_agent_tools.yaml b/FinanceAgent/tools/research_agent_tools.yaml index 4057dc0163..582330e7be 100644 --- a/FinanceAgent/tools/research_agent_tools.yaml +++ b/FinanceAgent/tools/research_agent_tools.yaml @@ -1,2 +1,47 @@ # 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: + 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 + diff --git a/FinanceAgent/tools/research_tools.py b/FinanceAgent/tools/research_tools.py new file mode 100644 index 0000000000..f7a135b646 --- /dev/null +++ b/FinanceAgent/tools/research_tools.py @@ -0,0 +1,154 @@ +# Copyright (C) 2025 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 typing import Annotated + +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 + + +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, +) -> str: + """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") + + +if __name__ == "__main__": + + print(get_company_profile("AAPL")) + # print(FinnHubUtils.get_basic_financials_history("AAPL", "annual", "2019-01-01", "2021-01-01")) + # print(FinnHubUtils.get_basic_financials("AAPL")) From 8567ec6ffaa53ee8236f936a5cc7c60ab9f5d9a5 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 3 Apr 2025 14:13:17 +0000 Subject: [PATCH 09/20] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- FinanceAgent/tools/research_agent_tools.yaml | 1 - FinanceAgent/tools/sum_agent_tools.py | 14 +++++++------- FinanceAgent/tools/utils.py | 3 +++ 3 files changed, 10 insertions(+), 8 deletions(-) diff --git a/FinanceAgent/tools/research_agent_tools.yaml b/FinanceAgent/tools/research_agent_tools.yaml index 582330e7be..6b79bae012 100644 --- a/FinanceAgent/tools/research_agent_tools.yaml +++ b/FinanceAgent/tools/research_agent_tools.yaml @@ -44,4 +44,3 @@ get_current_date: description: get current date. callable_api: research_tools.py:get_current_date return_output: current_date - diff --git a/FinanceAgent/tools/sum_agent_tools.py b/FinanceAgent/tools/sum_agent_tools.py index 5760650d80..9b53701e72 100644 --- a/FinanceAgent/tools/sum_agent_tools.py +++ b/FinanceAgent/tools/sum_agent_tools.py @@ -4,6 +4,7 @@ import json import requests + try: from tools.redis_kv import RedisKVStore from tools.utils import * @@ -12,7 +13,6 @@ from utils import * - def get_summary_else_doc(query, company): # search most similar doc title index_name = f"titles_{company}" @@ -59,7 +59,7 @@ def save_doc_summary(summary, doc_title, company): # 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}") + kvstore.put(f"{doc_title}_summary", {"summary": summary}, collection=f"full_doc_{company}") def summarize(doc_name, company): @@ -67,10 +67,10 @@ def summarize(doc_name, company): 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.") + return f"Cannot find documents related to {doc_title} in KV store." if not is_summary: data = { @@ -103,9 +103,9 @@ def summarize(doc_name, company): # year = "2024" # quarter = "Q4" - company="Costco" - year="2025" - quarter="Q2" + company = "Costco" + year = "2025" + quarter = "Q2" print("testing summarize") doc_name = f"{company} {year} {quarter} earning call" diff --git a/FinanceAgent/tools/utils.py b/FinanceAgent/tools/utils.py index 7b70289ac3..bfc25e7532 100644 --- a/FinanceAgent/tools/utils.py +++ b/FinanceAgent/tools/utils.py @@ -9,6 +9,7 @@ from langchain_huggingface import HuggingFaceEndpointEmbeddings from langchain_redis import RedisConfig, RedisVectorStore from openai import OpenAI + try: from tools.redis_kv import RedisKVStore except ImportError: @@ -49,6 +50,7 @@ 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() @@ -61,6 +63,7 @@ def format_company_name(company): print(f"Company: {company}") return company + def get_embedder(): if TEI_EMBEDDING_ENDPOINT: # create embeddings using TEI endpoint service From ce604d14fdc77c668b5f8abd9c0e197ab0a52731 Mon Sep 17 00:00:00 2001 From: lkk12014402 Date: Mon, 7 Apr 2025 13:20:20 +0000 Subject: [PATCH 10/20] update research agent prompt and tool. --- FinanceAgent/prompts/research_prompt.py | 5 +++-- FinanceAgent/tools/research_agent_tools.yaml | 21 ++++++++++++++++++++ 2 files changed, 24 insertions(+), 2 deletions(-) diff --git a/FinanceAgent/prompts/research_prompt.py b/FinanceAgent/prompts/research_prompt.py index 4d01cb264f..8d5383f44b 100644 --- a/FinanceAgent/prompts/research_prompt.py +++ b/FinanceAgent/prompts/research_prompt.py @@ -2,6 +2,7 @@ # SPDX-License-Identifier: Apache-2.0 REACT_AGENT_LLAMA_PROMPT = """\ + You are a Market Analyst, one must possess strong analytical and problem-solving abilities, collect necessary financial information and aggregate them based on client's requirement. For coding tasks, only use the functions you have been provided with. Reply TERMINATE when the task is done. @@ -11,9 +12,9 @@ For a comprehensive analysis, you can use all the tools provided to retrieve information available for the company. **Procedure:** -1. Read the question carefully. To get the company news, financial histories, some related tools need `start_date` and `end_date, 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. +1. Read the question carefully. You need get accurate `start_date` and `end_date` value first 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. 2. Analyze the positive developments and potential concerns of the company with 2-4 most important factors respectively and keep them concise. Most factors should be inferred from company related news. -3. Make a rough prediction (e.g. up/down by 2-3%) of the NVDA stock price movement for next week. +3. Make a rough prediction (e.g. up/down by 2-3%) of the company stock price movement for next week. 4. Provide a summary analysis to support your prediction. 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. diff --git a/FinanceAgent/tools/research_agent_tools.yaml b/FinanceAgent/tools/research_agent_tools.yaml index 6b79bae012..ad33995bda 100644 --- a/FinanceAgent/tools/research_agent_tools.yaml +++ b/FinanceAgent/tools/research_agent_tools.yaml @@ -28,6 +28,27 @@ get_company_news: 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 From 4a905517b7a03ad5740e9793a1cdf0d06cc1a75d Mon Sep 17 00:00:00 2001 From: lkk12014402 Date: Wed, 9 Apr 2025 14:11:55 +0000 Subject: [PATCH 11/20] update research agent tools and prompt. --- FinanceAgent/prompts/research_prompt.py | 54 ++- FinanceAgent/tools/research_agent_tools.yaml | 57 ++++ FinanceAgent/tools/research_tools.py | 325 ++++++++++++++++++- 3 files changed, 418 insertions(+), 18 deletions(-) diff --git a/FinanceAgent/prompts/research_prompt.py b/FinanceAgent/prompts/research_prompt.py index 8d5383f44b..34c0985d72 100644 --- a/FinanceAgent/prompts/research_prompt.py +++ b/FinanceAgent/prompts/research_prompt.py @@ -3,21 +3,52 @@ REACT_AGENT_LLAMA_PROMPT = """\ -You are a Market Analyst, one must possess strong analytical and problem-solving abilities, collect necessary financial information and aggregate them based on client's requirement. For coding tasks, only use the functions you have been provided with. Reply TERMINATE when the task is done. +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 a comprehensive analysis, you can use all the tools provided to retrieve information available for the company. +For writing a comprehensive analysis financial research report, you can use all the tools provided to retrieve information available for the company. -**Procedure:** -1. Read the question carefully. You need get accurate `start_date` and `end_date` value first 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. -2. Analyze the positive developments and potential concerns of the company with 2-4 most important factors respectively and keep them concise. Most factors should be inferred from company related news. -3. Make a rough prediction (e.g. up/down by 2-3%) of the company stock price movement for next week. -4. Provide a summary analysis to support your prediction. -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. +**Procedures:** +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. Analyze Key Financial Statements: + - **Balance Sheet Analysis**: Use the `analyze_balance_sheet` tool to obtain insights into the company's financial stability and liquidity. + - **Income Statement Analysis**: Employ the `analyze_income_stmt` tool to assess the company's profitability over the fiscal year. + - **Cash Flow Statement Analysis**: Apply the `analyze_cash_flow` tool to evaluate the cash inflows and outflows, providing a clear view of the company's operational efficiency. +4. Perform Additional Analyses: + - **Business Highlights**: Use the `get_company_news` tool to summarize the company's operational achievements and strategic milestones over the year. + - **Company Description**: Utilize the `get_company_profile` tool to provide a detailed overview of the company's business model, industry positioning, and strategic initiatives. + - **Risk Assessment**: Employ all the tool output financial information to identify and summarize the top three risks facing the company. +5. Share Performance: analysis the stock performance using the `get_share_performance` tool. +6. Generate Report: Finally, compile all the analyzed data, insights, and visuals into a comprehensive financial report, which contains the following paragraphs: + - income summarization + - market position + - business overview + - risk assessment + - competitors analysis + - share performance analysis +7. All the paragraphs should combine between 400 and 450 words. +8. Read the execution history if any to understand the tools that have been called and the information that has been gathered. +9. Reason about the information gathered so far and decide if you can answer the question or if you need to call more tools. +10. Do not repeat your thinking process!!! Only gather all information when you generate report. **Output format:** You should output your thought process. Finish thinking first. Output tool calls or your answer at the end. @@ -25,8 +56,8 @@ 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"}} +If you can generate the financial, provide the report in the following format: +FINAL ANSWER: {{"answer": "your financial research report here"}} Follow these guidelines when formulating your answer: 1. If the question contains a false premise or assumption, answer “invalid question”. @@ -34,6 +65,7 @@ 3. Give concise, factual and relevant answers. **IMPORTANT:** +* Do not generate history messages repeatly. * 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. diff --git a/FinanceAgent/tools/research_agent_tools.yaml b/FinanceAgent/tools/research_agent_tools.yaml index ad33995bda..bcf4e2bac2 100644 --- a/FinanceAgent/tools/research_agent_tools.yaml +++ b/FinanceAgent/tools/research_agent_tools.yaml @@ -65,3 +65,60 @@ 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 diff --git a/FinanceAgent/tools/research_tools.py b/FinanceAgent/tools/research_tools.py index f7a135b646..4293330458 100644 --- a/FinanceAgent/tools/research_tools.py +++ b/FinanceAgent/tools/research_tools.py @@ -1,4 +1,4 @@ -# Copyright (C) 2025 Intel Corporation +# Copyright (C) 2024 Intel Corporation # SPDX-License-Identifier: Apache-2.0 import json @@ -6,7 +6,8 @@ import random from collections import defaultdict from datetime import date, datetime, timedelta -from typing import Annotated +from typing import Annotated, Any, List, Optional +from textwrap import dedent import pandas as pd @@ -25,6 +26,164 @@ 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) @@ -55,7 +214,7 @@ def get_company_news( "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, -) -> str: +): """Retrieve market news related to designated company.""" news = finnhub_client.company_news(symbol, _from=start_date, to=end_date) if len(news) == 0: @@ -147,8 +306,160 @@ def get_current_date(): return date.today().strftime("%Y-%m-%d") -if __name__ == "__main__": +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 - print(get_company_profile("AAPL")) - # print(FinnHubUtils.get_basic_financials_history("AAPL", "annual", "2019-01-01", "2021-01-01")) - # print(FinnHubUtils.get_basic_financials("AAPL")) From dfbd7a7af79c9e92d47aebd1b40636584de43e80 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Wed, 9 Apr 2025 14:07:35 +0000 Subject: [PATCH 12/20] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- FinanceAgent/prompts/research_prompt.py | 2 +- FinanceAgent/tools/research_tools.py | 85 +++++++++++++------------ 2 files changed, 45 insertions(+), 42 deletions(-) diff --git a/FinanceAgent/prompts/research_prompt.py b/FinanceAgent/prompts/research_prompt.py index 34c0985d72..2ad0ee41d5 100644 --- a/FinanceAgent/prompts/research_prompt.py +++ b/FinanceAgent/prompts/research_prompt.py @@ -65,7 +65,7 @@ 3. Give concise, factual and relevant answers. **IMPORTANT:** -* Do not generate history messages repeatly. +* 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. diff --git a/FinanceAgent/tools/research_tools.py b/FinanceAgent/tools/research_tools.py index 4293330458..f54527a3ee 100644 --- a/FinanceAgent/tools/research_tools.py +++ b/FinanceAgent/tools/research_tools.py @@ -6,8 +6,8 @@ import random from collections import defaultdict from datetime import date, datetime, timedelta -from typing import Annotated, Any, List, Optional from textwrap import dedent +from typing import Annotated, Any, List, Optional import pandas as pd @@ -35,9 +35,9 @@ import requests from pydantic import BaseModel - FINANCIAL_DATASETS_BASE_URL = "https://api.financialdatasets.ai/" + class FinancialDatasetsAPIWrapper(BaseModel): """Wrapper for financial datasets API.""" @@ -65,8 +65,7 @@ def get_income_statements( period: str, limit: Optional[int], ) -> Optional[dict]: - """ - Get the income statements for a stock `ticker` over a `period` of time. + """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. @@ -96,8 +95,7 @@ def get_balance_sheets( period: str, limit: Optional[int], ) -> List[dict]: - """ - Get the balance sheets for a stock `ticker` over a `period` of time. + """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. @@ -127,8 +125,7 @@ def get_cash_flow_statements( period: str, limit: Optional[int], ) -> List[dict]: - """ - Get the cash flow statements for a stock `ticker` over a `period` of time. + """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. @@ -183,7 +180,6 @@ def run(self, mode: str, ticker: str, **kwargs: Any) -> str: 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) @@ -310,21 +306,24 @@ 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 + 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. + """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, + mode="get_balance_sheets", + ticker=symbol, + period=period, + limit=limit, ) df_string = "Balance sheet:\n" + balance_sheet @@ -347,19 +346,21 @@ def analyze_balance_sheet( 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 + 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. + """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, + mode="get_income_statements", + ticker=symbol, + period=period, + limit=limit, ) df_string = "Income statement:\n" + income_stmt @@ -379,19 +380,21 @@ def analyze_income_stmt( """ ) - # 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 + 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. + """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. """ @@ -419,29 +422,30 @@ def analyze_cash_flow( 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: +) -> 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 + 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}' + 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"} @@ -449,7 +453,7 @@ def get_share_performance( response = requests.get(url, headers=headers) # parse prices from the response - prices = response.json().get('prices') + prices = response.json().get("prices") df_string = "Past 60 days Stock prices:\n" + json.dumps(prices) @@ -462,4 +466,3 @@ def get_share_performance( prompt = combine_prompt(instruction, df_string) return prompt - From 0363ad819b6b5332c51281a4af2ef53df2177149 Mon Sep 17 00:00:00 2001 From: lkk12014402 Date: Thu, 10 Apr 2025 12:19:25 +0000 Subject: [PATCH 13/20] update ut. --- .../intel/hpu/gaudi/compose.yaml | 1 + .../intel/hpu/gaudi/launch_agents.sh | 3 +- FinanceAgent/prompts/research_prompt.py | 35 +++++----------- FinanceAgent/tests/test.py | 6 ++- FinanceAgent/tests/test_compose_on_gaudi.sh | 40 ++++++++++--------- 5 files changed, 39 insertions(+), 46 deletions(-) diff --git a/FinanceAgent/docker_compose/intel/hpu/gaudi/compose.yaml b/FinanceAgent/docker_compose/intel/hpu/gaudi/compose.yaml index f5b2937af0..5748ec8f81 100644 --- a/FinanceAgent/docker_compose/intel/hpu/gaudi/compose.yaml +++ b/FinanceAgent/docker_compose/intel/hpu/gaudi/compose.yaml @@ -60,6 +60,7 @@ services: http_proxy: ${http_proxy} https_proxy: ${https_proxy} FINNHUB_API_KEY: ${FINNHUB_API_KEY} + FINANCIAL_DATASETS_API_KEY: ${FINANCIAL_DATASETS_API_KEY} port: 9096 supervisor-react-agent: diff --git a/FinanceAgent/docker_compose/intel/hpu/gaudi/launch_agents.sh b/FinanceAgent/docker_compose/intel/hpu/gaudi/launch_agents.sh index 9a97cfd631..55dcbb7d3d 100644 --- a/FinanceAgent/docker_compose/intel/hpu/gaudi/launch_agents.sh +++ b/FinanceAgent/docker_compose/intel/hpu/gaudi/launch_agents.sh @@ -30,6 +30,7 @@ export MAX_TOTAL_TOKENS=4096 export DocSum_COMPONENT_NAME="OpeaDocSumvLLM" export DOCSUM_ENDPOINT="http://${ip_address}:9000/v1/docsum" - +export FINNHUB_API_KEY=${FINNHUB_API_KEY} +export FINANCIAL_DATASETS_API_KEY=${FINANCIAL_DATASETS_API_KEY} docker compose -f compose.yaml up -d diff --git a/FinanceAgent/prompts/research_prompt.py b/FinanceAgent/prompts/research_prompt.py index 2ad0ee41d5..c78ab34250 100644 --- a/FinanceAgent/prompts/research_prompt.py +++ b/FinanceAgent/prompts/research_prompt.py @@ -26,38 +26,23 @@ For writing a comprehensive analysis financial research report, you can use all the tools provided to retrieve information available for the company. -**Procedures:** +**Pay attention to the followings:** 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. Analyze Key Financial Statements: - - **Balance Sheet Analysis**: Use the `analyze_balance_sheet` tool to obtain insights into the company's financial stability and liquidity. - - **Income Statement Analysis**: Employ the `analyze_income_stmt` tool to assess the company's profitability over the fiscal year. - - **Cash Flow Statement Analysis**: Apply the `analyze_cash_flow` tool to evaluate the cash inflows and outflows, providing a clear view of the company's operational efficiency. -4. Perform Additional Analyses: - - **Business Highlights**: Use the `get_company_news` tool to summarize the company's operational achievements and strategic milestones over the year. - - **Company Description**: Utilize the `get_company_profile` tool to provide a detailed overview of the company's business model, industry positioning, and strategic initiatives. - - **Risk Assessment**: Employ all the tool output financial information to identify and summarize the top three risks facing the company. -5. Share Performance: analysis the stock performance using the `get_share_performance` tool. -6. Generate Report: Finally, compile all the analyzed data, insights, and visuals into a comprehensive financial report, which contains the following paragraphs: - - income summarization - - market position - - business overview - - risk assessment - - competitors analysis - - share performance analysis -7. All the paragraphs should combine between 400 and 450 words. -8. Read the execution history if any to understand the tools that have been called and the information that has been gathered. -9. Reason about the information gathered so far and decide if you can answer the question or if you need to call more tools. -10. Do not repeat your thinking process!!! Only gather all information when you generate report. +3. Use tools one by one for clarity, especially when asking for instructions. +4. Provide stock performace, 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. Finish thinking first. Output tool calls or your answer at the end. -When making tool calls, you should use the following 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 can generate the financial, provide the report in the following format: -FINAL ANSWER: {{"answer": "your financial research report here"}} +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”. diff --git a/FinanceAgent/tests/test.py b/FinanceAgent/tests/test.py index e8fed1c85f..151a8a27b7 100644 --- a/FinanceAgent/tests/test.py +++ b/FinanceAgent/tests/test.py @@ -29,7 +29,10 @@ def process_request(url, query, is_stream=False): def test_worker_agent(args): url = f"http://{args.ip_addr}:{args.ext_port}/v1/chat/completions" - query = {"role": "user", "messages": args.prompt, "stream": "false"} + 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) @@ -79,6 +82,7 @@ def test_supervisor_agent_single_turn(args): 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) diff --git a/FinanceAgent/tests/test_compose_on_gaudi.sh b/FinanceAgent/tests/test_compose_on_gaudi.sh index c85d99f209..cbb97894c0 100644 --- a/FinanceAgent/tests/test_compose_on_gaudi.sh +++ b/FinanceAgent/tests/test_compose_on_gaudi.sh @@ -59,7 +59,8 @@ function build_vllm_docker_image() { 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=$(git describe --tags "$(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 @@ -75,11 +76,11 @@ 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=0,1,2,3 -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 5s + 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 100 ]] || [[ $ready == true ]]; do + until [[ "$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" ${LOG_PATH}/vllm-gaudi-service.log; then @@ -89,9 +90,9 @@ function start_vllm_service_70B() { echo "container vllm-gaudi-server not found" exit 1 fi - sleep 5s + sleep 10s done - sleep 5s + sleep 10s echo "Service started successfully" } @@ -181,7 +182,7 @@ function validate_agent_service() { echo "======================Testing worker research agent======================" export agent_port="9096" prompt="generate NVDA financial research report" - local CONTENT=$(python3 $WORKDIR/GenAIExamples/AgentQnA/tests/test.py --prompt "$prompt" --agent_role "worker" --ext_port $agent_port) + local CONTENT=$(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 @@ -214,6 +215,7 @@ function validate_agent_service() { # docker logs react-agent-endpoint # exit 1 # fi + } function stop_agent_docker() { @@ -228,23 +230,23 @@ function stop_agent_docker() { echo "workpath: $WORKPATH" -# echo "=================== Stop containers ====================" -# stop_llm +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 +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 +start_vllm_service_70B # echo "=================== #2 vllm endpoint started====================" # echo "=================== #3 Start dataprep and ingest data ====================" @@ -257,12 +259,12 @@ 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 "=================== #5 Stop microservices ====================" +stop_agent_docker +stop_dataprep +stop_llm +echo "=================== #5 Microservices stopped====================" -# echo y | docker system prune +echo y | docker system prune -# echo "ALL DONE!!" +echo "ALL DONE!!" From 2a9b58af235a72e9f9af9c198707183dec4e79cf Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 10 Apr 2025 12:15:20 +0000 Subject: [PATCH 14/20] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- FinanceAgent/prompts/research_prompt.py | 4 ++-- FinanceAgent/tests/test.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/FinanceAgent/prompts/research_prompt.py b/FinanceAgent/prompts/research_prompt.py index c78ab34250..7c3c925753 100644 --- a/FinanceAgent/prompts/research_prompt.py +++ b/FinanceAgent/prompts/research_prompt.py @@ -26,11 +26,11 @@ 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 followings:** +**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 performace, because the financial report is used for stock investment analysis. +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. diff --git a/FinanceAgent/tests/test.py b/FinanceAgent/tests/test.py index 151a8a27b7..a6cd69583c 100644 --- a/FinanceAgent/tests/test.py +++ b/FinanceAgent/tests/test.py @@ -82,7 +82,7 @@ def test_supervisor_agent_single_turn(args): 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') + parser.add_argument("--tool_choice", nargs="+", help="limit tools") args, _ = parser.parse_known_args() print(args) From 3a2068b19aa96d771206a154e4fdac93f42cdf7f Mon Sep 17 00:00:00 2001 From: Rita Brugarolas Date: Thu, 10 Apr 2025 21:29:01 +0000 Subject: [PATCH 15/20] set missing env var Signed-off-by: rbrugaro --- FinanceAgent/tests/test_compose_on_gaudi.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/FinanceAgent/tests/test_compose_on_gaudi.sh b/FinanceAgent/tests/test_compose_on_gaudi.sh index cbb97894c0..c189affc73 100644 --- a/FinanceAgent/tests/test_compose_on_gaudi.sh +++ b/FinanceAgent/tests/test_compose_on_gaudi.sh @@ -14,7 +14,7 @@ model=meta-llama/Llama-3.3-70B-Instruct vllm_image=opea/vllm-gaudi:latest vllm_port=8086 vllm_image=$vllm_image -# HF_CACHE_DIR=/data2/huggingface +HF_CACHE_DIR=/data2/huggingface vllm_volume=${HF_CACHE_DIR} ####################################### From b599fdc92c85883135086c0cf3e09e4b75ddab1f Mon Sep 17 00:00:00 2001 From: Rita Brugarolas Date: Thu, 10 Apr 2025 22:28:54 +0000 Subject: [PATCH 16/20] README edits Signed-off-by: Rita Brugarolas --- FinanceAgent/README.md | 40 ++++++++++++++++++++++++++++++++++++++-- 1 file changed, 38 insertions(+), 2 deletions(-) diff --git a/FinanceAgent/README.md b/FinanceAgent/README.md index e37d3df0e2..457ee192ca 100644 --- a/FinanceAgent/README.md +++ b/FinanceAgent/README.md @@ -26,7 +26,8 @@ export HF_TOKEN= Build docker images for dataprep, agent, agent-ui. ```bash -# use docker image build +cd GenAIExamples/FinanceAgent/docker_image_build +docker compose -f build.yaml build --no-cache ``` If deploy on Gaudi, also need to build vllm image. @@ -65,7 +66,42 @@ First, launch the redis databases and the dataprep microservice. ```bash docker compose -f $WORKDIR/GenAIExamples/FinanceAgent/docker_compose/intel/hpu/gaudi/dataprep_compose.yaml up -d ``` - +Validate datat ingest data and retrieval from database: +```bash +python $WORKPATH/tests/test_redis_finance.py --port $DATAPREP_PORT --test_option ingest +python $WORKPATH/tests/test_redis_finance.py --port $DATAPREP_PORT --test_option get +``` ### 3.3 Launch the multi-agent system +```bash +cd $WORKDIR/GenAIExamples/FinanceAgent/docker_compose/intel/hpu/gaudi/ +bash launch_agents.sh +``` + ### 3.4 Validate agents + +FinQA Agent: +```bash +export agent_port="9095" +prompt="What is Gap's revenue in 2024?" +python3 $WORKDIR/GenAIExamples/FinanceAgent/tests/test.py --prompt "$prompt" --agent_role "worker" --ext_port $agent_port +``` + +Research Agent: +```bash +export agent_port="9096" +prompt="generate NVDA financial research report" +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" +``` +Supervisor ReAct Agent: +```bash +export agent_port="9090" +python3 $WORKDIR/GenAIExamples/FinanceAgent/tests/test.py --agent_role "supervisor" --ext_port $agent_port --stream +``` + +Supervisor ReAct Agent Multi turn: +```bash +python3 $WORKDIR/GenAIExamples/FinanceAgent/tests/test.py --agent_role "supervisor" --ext_port $agent_port --multi-turn --stream + +``` + \ No newline at end of file From 9d33629c32616b7b82f6e9fe25f6c1f6b0aababd Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 10 Apr 2025 22:22:02 +0000 Subject: [PATCH 17/20] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- FinanceAgent/README.md | 17 ++++++++++++----- 1 file changed, 12 insertions(+), 5 deletions(-) diff --git a/FinanceAgent/README.md b/FinanceAgent/README.md index 457ee192ca..fbb3ad34d2 100644 --- a/FinanceAgent/README.md +++ b/FinanceAgent/README.md @@ -26,7 +26,7 @@ export HF_TOKEN= Build docker images for dataprep, agent, agent-ui. ```bash -cd GenAIExamples/FinanceAgent/docker_image_build +cd GenAIExamples/FinanceAgent/docker_image_build docker compose -f build.yaml build --no-cache ``` @@ -66,42 +66,49 @@ First, launch the redis databases and the dataprep microservice. ```bash docker compose -f $WORKDIR/GenAIExamples/FinanceAgent/docker_compose/intel/hpu/gaudi/dataprep_compose.yaml up -d ``` + Validate datat ingest data and retrieval from database: -```bash + +```bash python $WORKPATH/tests/test_redis_finance.py --port $DATAPREP_PORT --test_option ingest python $WORKPATH/tests/test_redis_finance.py --port $DATAPREP_PORT --test_option get ``` + ### 3.3 Launch the multi-agent system + ```bash cd $WORKDIR/GenAIExamples/FinanceAgent/docker_compose/intel/hpu/gaudi/ bash launch_agents.sh ``` - ### 3.4 Validate agents FinQA Agent: -```bash + +```bash export agent_port="9095" prompt="What is Gap's revenue in 2024?" python3 $WORKDIR/GenAIExamples/FinanceAgent/tests/test.py --prompt "$prompt" --agent_role "worker" --ext_port $agent_port ``` Research Agent: + ```bash export agent_port="9096" prompt="generate NVDA financial research report" 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" ``` + Supervisor ReAct Agent: + ```bash export agent_port="9090" python3 $WORKDIR/GenAIExamples/FinanceAgent/tests/test.py --agent_role "supervisor" --ext_port $agent_port --stream ``` Supervisor ReAct Agent Multi turn: + ```bash python3 $WORKDIR/GenAIExamples/FinanceAgent/tests/test.py --agent_role "supervisor" --ext_port $agent_port --multi-turn --stream ``` - \ No newline at end of file From e6ed987b0b02bde096576f8b5bfd04b54c8a853c Mon Sep 17 00:00:00 2001 From: minmin-intel Date: Fri, 11 Apr 2025 00:10:43 +0000 Subject: [PATCH 18/20] update readme Signed-off-by: minmin-intel --- FinanceAgent/README.md | 32 ++++++++++++------ FinanceAgent/assets/finance_agent_arch.png | Bin 0 -> 58438 bytes .../intel/hpu/gaudi/launch_dataprep.sh | 12 +++++++ .../intel/hpu/gaudi/launch_vllm.sh | 4 +++ .../intel/hpu/gaudi/vllm_compose.yaml | 32 ++++++++++++++++++ FinanceAgent/tests/test_compose_on_gaudi.sh | 2 +- 6 files changed, 71 insertions(+), 11 deletions(-) create mode 100644 FinanceAgent/assets/finance_agent_arch.png create mode 100644 FinanceAgent/docker_compose/intel/hpu/gaudi/launch_dataprep.sh create mode 100644 FinanceAgent/docker_compose/intel/hpu/gaudi/launch_vllm.sh create mode 100644 FinanceAgent/docker_compose/intel/hpu/gaudi/vllm_compose.yaml diff --git a/FinanceAgent/README.md b/FinanceAgent/README.md index fbb3ad34d2..e0805876a9 100644 --- a/FinanceAgent/README.md +++ b/FinanceAgent/README.md @@ -1,6 +1,20 @@ # Finance Agent ## 1. Overview +The architecture of this Finance Agent example is shown in the figure below. The agent has 3 main functions: +1. Summarize long financial documents and provide key points. +2. Answer questions over financial documents, such as SEC filings. +3. Conduct research of a public company and provide an investment report of the company. + +![Finance Agent Architecture](assets/finance_agent_arch.png) + +The `dataprep` microservice can ingest financial documents in two formats: +1. PDF documents stored locally, such as SEC filings saved in local directory. +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)). + +Please note: +1. Each financial document should be about one company. +2. URLs ending in `.htm` are not supported. ## 2. Getting started @@ -50,11 +64,8 @@ docker build --no-cache -f Dockerfile.hpu -t opea/vllm-gaudi:latest --shm-size=1 Below is the command to launch a vllm endpoint on Gaudi that serves `meta-llama/Llama-3.3-70B-Instruct` model on 4 Gaudi cards. ```bash -export vllm_port=8086 -export vllm_volume=$HF_CACHE_DIR -export max_length=16384 -export model="meta-llama/Llama-3.3-70B-Instruct" -docker run -d --runtime=habana --rm --name "vllm-gaudi-server" -e HABANA_VISIBLE_DEVICES=all -p $vllm_port:8000 -v $vllm_volume:/data -e HF_TOKEN=$HF_TOKEN -e HUGGING_FACE_HUB_TOKEN=$HF_TOKEN -e HF_HOME=/data -e OMPI_MCA_btl_vader_single_copy_mechanism=none -e PT_HPU_ENABLE_LAZY_COLLECTIVES=true -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e no_proxy=$no_proxy -e VLLM_SKIP_WARMUP=true --cap-add=sys_nice --ipc=host opea/vllm-gaudi:comps --model ${model} --max-seq-len-to-capture $max_length --tensor-parallel-size 4 +cd $WORKDIR/GenAIExamples/FinanceAgent/docker_compose/intel/hpu/gaudi +bash launch_vllm.sh ``` ### 3.2 Prepare knowledge base @@ -64,20 +75,21 @@ The commands below will upload some example files into the knowledge base. You c First, launch the redis databases and the dataprep microservice. ```bash -docker compose -f $WORKDIR/GenAIExamples/FinanceAgent/docker_compose/intel/hpu/gaudi/dataprep_compose.yaml up -d +# inside $WORKDIR/GenAIExamples/FinanceAgent/docker_compose/intel/hpu/gaudi/ +bash launch_dataprep.sh ``` Validate datat ingest data and retrieval from database: ```bash -python $WORKPATH/tests/test_redis_finance.py --port $DATAPREP_PORT --test_option ingest -python $WORKPATH/tests/test_redis_finance.py --port $DATAPREP_PORT --test_option get +python $WORKPATH/tests/test_redis_finance.py --port 6007 --test_option ingest +python $WORKPATH/tests/test_redis_finance.py --port 6007 --test_option get ``` ### 3.3 Launch the multi-agent system ```bash -cd $WORKDIR/GenAIExamples/FinanceAgent/docker_compose/intel/hpu/gaudi/ +# inside $WORKDIR/GenAIExamples/FinanceAgent/docker_compose/intel/hpu/gaudi/ bash launch_agents.sh ``` @@ -96,7 +108,7 @@ Research Agent: ```bash export agent_port="9096" prompt="generate NVDA financial research report" -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" +python3 $WORKDIR/GenAIExamples/FinanceAgent/tests/test.py --prompt "$prompt" --agent_role "worker" --ext_port $agent_port --tool_choice "get_current_date" --tool_choice "get_share_performance" ``` Supervisor ReAct Agent: diff --git a/FinanceAgent/assets/finance_agent_arch.png b/FinanceAgent/assets/finance_agent_arch.png new file mode 100644 index 0000000000000000000000000000000000000000..5f0f1ad5b0c7f980ca059a0797cbfaf2bc67718f GIT binary patch literal 58438 zcmdSAcTiMY(>^-n41xk845H*98G#`ih>}5a6hS~TL(Yf@$dGde$p}c!No2@5NJbdq zkaPC8(Q|&s^S<9#b^o|^tFBe2^z6M>_tV|a>eai~njj?wDFWPkxF8USKt}qR3J8P+ z2Z7LPu+e}gE*@Cdz+Wg1DpF5C#r;(4!2h6|NytlpKwrY}E{!liAk=oH7i!Oc{`}cF zFx=AFn~+oF8Ivo$Iq^gdsYfyaJ;P_<5=Q8_up|6~MBGR%?PR~l~ 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b/FinanceAgent/docker_compose/intel/hpu/gaudi/launch_dataprep.sh @@ -0,0 +1,12 @@ +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}" + +docker compose -f dataprep_compose.yaml up -d \ No newline at end of file diff --git a/FinanceAgent/docker_compose/intel/hpu/gaudi/launch_vllm.sh b/FinanceAgent/docker_compose/intel/hpu/gaudi/launch_vllm.sh new file mode 100644 index 0000000000..f07185574b --- /dev/null +++ b/FinanceAgent/docker_compose/intel/hpu/gaudi/launch_vllm.sh @@ -0,0 +1,4 @@ +export LLM_MODEL_ID="meta-llama/Llama-3.3-70B-Instruct" +export MAX_LEN=16384 + +docker compose -f vllm_compose.yaml up -d \ No newline at end of file diff --git a/FinanceAgent/docker_compose/intel/hpu/gaudi/vllm_compose.yaml b/FinanceAgent/docker_compose/intel/hpu/gaudi/vllm_compose.yaml new file mode 100644 index 0000000000..30dbbc0bf6 --- /dev/null +++ b/FinanceAgent/docker_compose/intel/hpu/gaudi/vllm_compose.yaml @@ -0,0 +1,32 @@ + +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 $MAX_LEN \ No newline at end of file diff --git a/FinanceAgent/tests/test_compose_on_gaudi.sh b/FinanceAgent/tests/test_compose_on_gaudi.sh index c189affc73..18615646b4 100644 --- a/FinanceAgent/tests/test_compose_on_gaudi.sh +++ b/FinanceAgent/tests/test_compose_on_gaudi.sh @@ -14,7 +14,7 @@ model=meta-llama/Llama-3.3-70B-Instruct vllm_image=opea/vllm-gaudi:latest vllm_port=8086 vllm_image=$vllm_image -HF_CACHE_DIR=/data2/huggingface +HF_CACHE_DIR=${model_cache:-"/data2/huggingface"} vllm_volume=${HF_CACHE_DIR} ####################################### From 727584c81668d5f6e5a654d618f1a61fb4871c00 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 11 Apr 2025 00:03:47 +0000 Subject: [PATCH 19/20] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- FinanceAgent/README.md | 8 ++++++-- .../docker_compose/intel/hpu/gaudi/launch_dataprep.sh | 5 ++++- .../docker_compose/intel/hpu/gaudi/launch_vllm.sh | 5 ++++- .../docker_compose/intel/hpu/gaudi/vllm_compose.yaml | 5 ++++- 4 files changed, 18 insertions(+), 5 deletions(-) diff --git a/FinanceAgent/README.md b/FinanceAgent/README.md index e0805876a9..2cd27aeb04 100644 --- a/FinanceAgent/README.md +++ b/FinanceAgent/README.md @@ -1,18 +1,22 @@ # Finance Agent ## 1. Overview + The architecture of this Finance Agent example is shown in the figure below. The agent has 3 main functions: + 1. Summarize long financial documents and provide key points. 2. Answer questions over financial documents, such as SEC filings. 3. Conduct research of a public company and provide an investment report of the company. ![Finance Agent Architecture](assets/finance_agent_arch.png) -The `dataprep` microservice can ingest financial documents in two formats: +The `dataprep` microservice can ingest financial documents in two formats: + 1. PDF documents stored locally, such as SEC filings saved in local directory. 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)). -Please note: +Please note: + 1. Each financial document should be about one company. 2. URLs ending in `.htm` are not supported. diff --git a/FinanceAgent/docker_compose/intel/hpu/gaudi/launch_dataprep.sh b/FinanceAgent/docker_compose/intel/hpu/gaudi/launch_dataprep.sh index 71d3bac692..9bb006c191 100644 --- a/FinanceAgent/docker_compose/intel/hpu/gaudi/launch_dataprep.sh +++ b/FinanceAgent/docker_compose/intel/hpu/gaudi/launch_dataprep.sh @@ -1,3 +1,6 @@ +# Copyright (C) 2025 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + export host_ip=${ip_address} export DATAPREP_PORT="6007" export TEI_EMBEDDER_PORT="10221" @@ -9,4 +12,4 @@ export DATAPREP_COMPONENT_NAME="OPEA_DATAPREP_REDIS_FINANCE" export EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5" export TEI_EMBEDDING_ENDPOINT="http://${ip_address}:${TEI_EMBEDDER_PORT}" -docker compose -f dataprep_compose.yaml up -d \ No newline at end of file +docker compose -f dataprep_compose.yaml up -d diff --git a/FinanceAgent/docker_compose/intel/hpu/gaudi/launch_vllm.sh b/FinanceAgent/docker_compose/intel/hpu/gaudi/launch_vllm.sh index f07185574b..5d8d58641b 100644 --- a/FinanceAgent/docker_compose/intel/hpu/gaudi/launch_vllm.sh +++ b/FinanceAgent/docker_compose/intel/hpu/gaudi/launch_vllm.sh @@ -1,4 +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 \ No newline at end of file +docker compose -f vllm_compose.yaml up -d diff --git a/FinanceAgent/docker_compose/intel/hpu/gaudi/vllm_compose.yaml b/FinanceAgent/docker_compose/intel/hpu/gaudi/vllm_compose.yaml index 30dbbc0bf6..8ca62e1e46 100644 --- a/FinanceAgent/docker_compose/intel/hpu/gaudi/vllm_compose.yaml +++ b/FinanceAgent/docker_compose/intel/hpu/gaudi/vllm_compose.yaml @@ -1,4 +1,7 @@ +# Copyright (C) 2025 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + services: vllm-service: image: ${REGISTRY:-opea}/vllm-gaudi:${TAG:-latest} @@ -29,4 +32,4 @@ services: 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 \ No newline at end of file + command: --model $LLM_MODEL_ID --tensor-parallel-size 4 --host 0.0.0.0 --port 8000 --max-seq-len-to-capture $MAX_LEN From baa827b86a22a4c5884208006049a5f62875b4ad Mon Sep 17 00:00:00 2001 From: lkk12014402 Date: Fri, 11 Apr 2025 06:13:44 +0000 Subject: [PATCH 20/20] add ui doc. --- FinanceAgent/README.md | 15 +++++++++++++++ FinanceAgent/assets/opea-agent-setting.png | Bin 0 -> 117291 bytes FinanceAgent/assets/opea-agent-test.png | Bin 0 -> 386674 bytes .../intel/hpu/gaudi/compose.yaml | 12 ++++++++++++ 4 files changed, 27 insertions(+) create mode 100644 FinanceAgent/assets/opea-agent-setting.png create mode 100644 FinanceAgent/assets/opea-agent-test.png diff --git a/FinanceAgent/README.md b/FinanceAgent/README.md index 2cd27aeb04..c652e06e02 100644 --- a/FinanceAgent/README.md +++ b/FinanceAgent/README.md @@ -128,3 +128,18 @@ Supervisor ReAct Agent Multi turn: python3 $WORKDIR/GenAIExamples/FinanceAgent/tests/test.py --agent_role "supervisor" --ext_port $agent_port --multi-turn --stream ``` + +## How to interact with the agent system with UI + +The UI microservice is launched in the previous step with the other microservices. +To see the UI, open a web browser to `http://${ip_address}:5175` to access the UI. Note the `ip_address` here is the host IP of the UI microservice. + +1. `create Admin Account` with a random value + +2. use an opea agent endpoint, for example, the `Research Agent` endpoint `http://$ip_address:9096/v1`, which is a openai compatible api + +![opea-agent-setting](assets/opea-agent-setting.png) + +3. test opea agent with ui + +![opea-agent-test](assets/opea-agent-test.png) diff --git a/FinanceAgent/assets/opea-agent-setting.png b/FinanceAgent/assets/opea-agent-setting.png new file mode 100644 index 0000000000000000000000000000000000000000..0d511966435b9c638625647dcc88b08a2ca6216f GIT binary patch literal 117291 zcmdqJg;!h4`ag`80tH%Hin|@$tyn3vSb-J`?(Xgotfdt9;*YOKj7HPsmWnrR3{MLn&YBBEnp(3=$hXFwvP^VLAWn)`GS@z zOLB8_Gi^6A=k#+9gYv(thlJeWK7R7Pe06)DZE&j{=0#& z9vJ`cJ)lrIz<=MtRLE)eEA78~`SgR}|8w0G{3}eBIhASMcIEIy)2Y{jbODr={>JXTgVLy)6l6aMWkna;TZpew_ZPe~cb@8zvoyEHLl> zxHVl)9TXH4s{z7`s8IM%JLoBJ#8pnLGieJz5ZAv*S=r48-ob_dOqwL-LD$>g^`Ieb z0;6!U{S_m%_sBOJ7hE+BF8Ga)H 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z-pDqGs;ODGQ_tLNgSAcZVtE1iHSats<5mr31{%-aygzqBnIVCKcyUG5n8@5-b2fi|s47PEXNh0A!V-au{pZglo~vab{dtwog~j*h#T8d7W|*jbU^$2{Ecbm zEx#Nsi2q)&c{SvjVVCt2|AuF|R_9n`h82JRO4U=FjpYA6VR30jS=K;)#jU?rP2yii t>ydsYs@r9{5>&+8A6zK)pyRnel2wH`YuTCFCE~B{%PPy1NIib`e*gfF7~=o{ literal 0 HcmV?d00001 diff --git a/FinanceAgent/docker_compose/intel/hpu/gaudi/compose.yaml b/FinanceAgent/docker_compose/intel/hpu/gaudi/compose.yaml index 5748ec8f81..cc02847be9 100644 --- a/FinanceAgent/docker_compose/intel/hpu/gaudi/compose.yaml +++ b/FinanceAgent/docker_compose/intel/hpu/gaudi/compose.yaml @@ -119,3 +119,15 @@ services: MAX_TOTAL_TOKENS: ${MAX_TOTAL_TOKENS} DocSum_COMPONENT_NAME: ${DocSum_COMPONENT_NAME:-OpeaDocSumvLLM} restart: unless-stopped + + agent-ui: + image: opea/agent-ui:latest + container_name: agent-ui + environment: + host_ip: ${host_ip} + no_proxy: ${no_proxy} + http_proxy: ${http_proxy} + https_proxy: ${https_proxy} + ports: + - "5175:8080" + ipc: host