Agent Microservice

1. Overview

This agent microservice is built on Langchain/Langgraph frameworks. Agents integrate the reasoning capabilities of large language models (LLMs) with the ability to take actionable steps, creating a more sophisticated system that can understand and process information, evaluate situations, take appropriate actions, communicate responses, and track ongoing situations.

1.1 Supported agent types

We currently support the following types of agents. Please refer to the example config yaml (links in the table in Section 1.2) for each agent strategy to see what environment variables need to be set up.

1. ReAct: use react_langchain or react_langgraph or react_llama as strategy. First introduced in this seminal paper. The ReAct agent engages in “reason-act-observe” cycles to solve problems. Please refer to this doc to understand the differences between the langchain and langgraph versions of react agents. See table below to understand the validated LLMs for each react strategy. We recommend using react_llama as it has the most features enabled, including agent memory, multi-turn conversations and assistants APIs.

2. RAG agent: use rag_agent or rag_agent_llama strategy. This agent is specifically designed for improving RAG performance. It has the capability to rephrase query, check relevancy of retrieved context, and iterate if context is not relevant. See table below to understand the validated LLMs for each rag agent strategy.

3. Plan and execute: plan_execute strategy. This type of agent first makes a step-by-step plan given a user request, and then execute the plan sequentially (or in parallel, to be implemented in future). If the execution results can solve the problem, then the agent will output an answer; otherwise, it will replan and execute again.

4. SQL agent: use sql_agent_llama or sql_agent strategy. This agent is specifically designed and optimized for answering questions aabout data in SQL databases. Users need to specify db_name and db_path for the agent to access the SQL database. For more technical details read descriptions here.

Note:

1. Due to the limitations in support for tool calling by TGI and vllm, we have developed subcategories of agent strategies (rag_agent_llama, react_llama and sql_agent_llama) specifically designed for open-source LLMs served with TGI and vllm.

2. Currently only react_llama agent supports memory and multi-turn conversations.

3. For advanced developers who want to implement their own agent strategies, please refer to Section 5 below.

1.2 LLM engine

Agents use LLM for reasoning and planning. We support 2 options of LLM engine:

1. Open-source LLMs served with vllm. Follow the instructions in Section 2.2.

2. OpenAI LLMs via API calls. To use OpenAI llms, specify llm_engine=openai and export OPENAI_API_KEY=<your-openai-key>

Agent type	strategy arg	Validated LLMs (serving SW)	Notes	Example config yaml
ReAct	react_langchain	llama3.1-70B-Instruct (vllm-gaudi)	Only allows tools with one input variable	react_langchain yaml
ReAct	react_langgraph	GPT-4o-mini, llama3.1-70B-Instruct (vllm-gaudi)	if using vllm, need to specify --enable-auto-tool-choice --tool-call-parser ${model_parser}, refer to vllm docs for more info, only one tool call in each LLM output due to the limitations of llama3.1 modal and vllm tool call parser.	react_langgraph yaml
ReAct	react_llama	llama3.1-70B-Instruct, llama3.3-70B-Instruct(vllm-gaudi)	Recommended for open-source LLMs, supports multiple tools and parallel tool calls.	react_llama yaml
RAG agent	rag_agent	GPT-4o-mini		rag_agent yaml
RAG agent	rag_agent_llama	llama3.1-70B-Instruct, llama3.3-70B-Instruct (vllm-gaudi)	Recommended for open-source LLMs, only allows 1 tool with input variable to be “query”	rag_agent_llama yaml
Plan and execute	plan_execute	GPT-4o-mini, llama3.1-70B-Instruct (vllm-gaudi)	use --guided-decoding-backend lm-format-enforcer when launching vllm.	plan_execute yaml
SQL agent	sql_agent_llama	llama3.1-70B-Instruct, llama3.3-70B-Instruct (vllm-gaudi)	database query tool is natively integrated using Langchain’s QuerySQLDataBaseTool. User can also register their own tools with this agent.	sql_agent_llama yaml
SQL agent	sql_agent	GPT-4o-mini	database query tool is natively integrated using Langchain’s QuerySQLDataBaseTool. User can also register their own tools with this agent.	sql_agent yaml

1.3 Tools

The tools are registered with a yaml file. We support the following types of tools:

1. Endpoint: user to provide url

2. User-defined python functions. This is usually used to wrap endpoints with request post or simple pre/post-processing.

3. Langchain tool modules.

Examples of how to register tools can be found in Section 4 below.

1.4 Agent APIs

We support two sets of APIs that are OpenAI compatible:

1. OpenAI compatible chat completions API. Example usage with Python code below.

url = f"http://{ip_address}:{agent_port}/v1/chat/completions"

# single-turn, not streaming -> if agent is used as a worker agent (i.e., tool for supervisor agent)
payload = {"messages": query, "stream": false}
resp = requests.post(url=url, json=payload, proxies=proxies, stream=False)

# multi-turn, streaming -> to interface with users
query = {"role": "user", "messages": user_message, "thread_id": thread_id, "stream": stream}
content = json.dumps(query)
resp = requests.post(url=url, data=content, proxies=proxies, stream=True)
for line in resp.iter_lines(decode_unicode=True):
    print(line)

2. OpenAI compatible assistants APIs.

   See example Python code here. There are 4 steps:

   Step 1. create an assistant: /v1/assistants

   Step 2. create a thread: /v1/threads

   Step 3. send a message to the thread: /v1/threads/{thread_id}/messages

   Step 4. run the assistant: /v1/threads/{thread_id}/runs

Note:

1. Currently only reract_llama agent is enabled for assistants APIs.

2. Not all keywords of OpenAI APIs are supported yet.

1.5 Agent memory

We currently supports two types of memory.

1. checkpointer: agent memory stored in RAM, so is volatile, the memory contains agent states within a thread. Used to enable multi-turn conversations between the user and the agent. Both chat completions API and assistants APIs support this type of memory.

2. store: agent memory stored in Redis database, contains agent states in all threads. Only assistants APIs support this type of memory. Used to enable multi-turn conversations. In future we will explore algorithms to take advantage of the info contained in previous conversations to improve agent’s performance.

Note: Currently only react_llama agent supports memory and multi-turn conversations.

How to enable agent memory?

Specify with_memory=True. If want to use persistent memory, specify memory_type=store, and you need to launch a Redis database using the command below.

# you can change the port from 6379 to another one.
docker run -d -it -p 6379:6379 --rm --name "test-persistent-redis" --net=host --ipc=host --name redis-vector-db redis/redis-stack:7.2.0-v9

Examples of python code for multi-turn conversations using agent memory:

1. chat completions API with checkpointer

2. assistants APIs with persistent store memory

To run the two examples above, first launch the agent microservice using this docker compose yaml.

🚀2. Start Agent Microservice

2.1 Build docker image for agent microservice

cd GenAIComps/ # back to GenAIComps/ folder
docker build -t opea/agent:latest -f comps/agent/src/Dockerfile . --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy

2.2 Start Agent microservices with vllm

export ip_address=$(hostname -I | awk '{print $1}')
export model="meta-llama/Meta-Llama-3.1-70B-Instruct"
export HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN}
export HF_TOKEN=${HUGGINGFACEHUB_API_TOKEN}
export vllm_volume=${YOUR_LOCAL_DIR_FOR_MODELS}

# build vLLM image
git clone https://github.com/HabanaAI/vllm-fork.git
cd ./vllm-fork
git checkout v0.6.4.post2+Gaudi-1.19.0
docker build -f Dockerfile.hpu -t opea/vllm-gaudi:latest --shm-size=128g . --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy

# vllm serving on 4 Gaudi2 cards
docker run -d --runtime=habana --rm --name "comps-vllm-gaudi-service" -p 8080:8000 -v $vllm_volume:/data -e HF_TOKEN=$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 VLLM_SKIP_WARMUP=true --cap-add=sys_nice --ipc=host opea/vllm-gaudi:latest --model ${model} --max-seq-len-to-capture 16384 --enable-auto-tool-choice --tool-call-parser llama3_json --guided-decoding-backend lm-format-enforcer --tensor-parallel-size 4

# check status
docker logs comps-vllm-gaudi-service

# Agent
docker run -d --runtime=runc --name="comps-agent-endpoint" -v $WORKPATH/comps/agent/src/tools:/home/user/comps/agent/src/tools -p 9090:9090 --ipc=host -e HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN} -e model=${model} -e ip_address=${ip_address} -e strategy=react_llama -e with_memory=true -e llm_endpoint_url=http://${ip_address}:8080 -e llm_engine=vllm -e recursion_limit=15 -e require_human_feedback=false -e tools=/home/user/comps/agent/src/tools/custom_tools.yaml opea/agent:latest

# check status
docker logs comps-agent-endpoint

debug mode

docker run --rm --runtime=runc --name="comps-agent-endpoint" -v ./comps/agent/src/:/home/user/comps/agent/src/ -p 9090:9090 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN} -e model=${model} -e ip_address=${ip_address} -e strategy=react_llama -e with_memory=true -e llm_endpoint_url=http://${ip_address}:8080 -e llm_engine=vllm -e recursion_limit=15 -e require_human_feedback=false -e tools=/home/user/comps/agent/src/tools/custom_tools.yaml opea/agent:latest

🚀 3. Validate Microservice

Once microservice starts, user can use below script to invoke.

3.1 Use chat completions API

For multi-turn conversations, first specify a thread_id.

export thread_id=<thread-id>
curl http://${ip_address}:9090/v1/chat/completions -X POST -H "Content-Type: application/json" -d '{
     "messages": "What is OPEA project?",
     "thread_id":${thread_id},
     "stream":true
    }'

# expected output

data: 'The OPEA project is .....</s>' # just showing partial example here.

data: [DONE]

3.2 Use assistants APIs

# step1 create assistant to get `asssistant_id`

curl http://${ip_address}:9090/v1/assistants -X POST -H "Content-Type: application/json" -d '{
     "agent_config": {"llm_engine": "vllm", "llm_endpoint_url": "http://${ip_address}:8080", "tools": "/home/user/comps/agent/src/tools/custom_tools.yaml"}
    }'

## if want to persist your agent messages, set store config like this:
curl http://${ip_address}:9090/v1/assistants -X POST -H "Content-Type: application/json" -d '{
     "agent_config": {"llm_engine": "vllm", "llm_endpoint_url": "http://${ip_address}:8080", "tools": "/home/user/comps/agent/src/tools/custom_tools.yaml","with_memory":true, "memory_type":"store", "store_config":{"redis_uri":"redis://${ip_address}:6379"}}
    }'

# step2 create thread to get `thread_id`

curl http://${ip_address}:9090/v1/threads -X POST -H "Content-Type: application/json" -d '{}'

# step3 create messages

curl http://${ip_address}:9090/v1/threads/{thread_id}/messages -X POST -H "Content-Type: application/json" -d '{"role": "user", "content": "What is OPEA project?"}'


## if agent is set with `memory_type`=store, should add `assistant_id` in the messages for store

curl http://${ip_address}:9090/v1/threads/{thread_id}/messages -X POST -H "Content-Type: application/json" -d '{"role": "user", "content": "What is OPEA project?", "assistant_id": "{assistant_id}"}'

# step4 run

curl http://${ip_address}:9090/v1/threads/{thread_id}/runs -X POST -H "Content-Type: application/json" -d '{"assistant_id": "{assistant_id}"}'

🚀 4. Provide your own tools

• Define tools

mkdir -p my_tools
vim my_tools/custom_tools.yaml

# [tool_name]
#   description: [description of this tool]
#   env: [env variables such as API_TOKEN]
#   pip_dependencies: [pip dependencies, separate by ,]
#   callable_api: [2 options provided - function_call, pre-defined-tools]
#   args_schema:
#     [arg_name]:
#       type: [str, int]
#       description: [description of this argument]
#   return_output: [return output variable name]

example - my_tools/custom_tools.yaml

# Follow example below to add your tool
opea_index_retriever:
  description: Retrieve related information of Intel OPEA project based on input query.
  callable_api: tools.py:opea_rag_query
  args_schema:
    query:
      type: str
      description: Question query
  return_output: retrieved_data

example - my_tools/tools.py

def opea_rag_query(query):
    ip_address = os.environ.get("ip_address")
    url = f"http://{ip_address}:8889/v1/retrievaltool"
    content = json.dumps({"text": query})
    print(url, content)
    try:
        resp = requests.post(url=url, data=content)
        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}"
    return ret

• Launch Agent Microservice with your tools path

# Agent
docker run -d --runtime=runc --name="comps-agent-endpoint" -v my_tools:/home/user/comps/agent/src/tools -p 9090:9090 --ipc=host -e HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN} -e model=${model} -e ip_address=${ip_address} -e strategy=react_llama -e llm_endpoint_url=http://${ip_address}:8080 -e llm_engine=tgi -e recursive_limit=15 -e require_human_feedback=false -e tools=/home/user/comps/agent/src/tools/custom_tools.yaml opea/agent:latest

• validate with my_tools

$ curl http://${ip_address}:9090/v1/chat/completions -X POST -H "Content-Type: application/json" -d '{
     "messages": "What is Intel OPEA project in a short answer?"
    }'
data: 'The Intel OPEA project is a initiative to incubate open source development of trusted, scalable open infrastructure for developer innovation and harness the potential value of generative AI. - - - - Thought:  I now know the final answer. - - - - - - Thought: - - - -'

data: [DONE]

5. Customize agent strategy

For advanced developers who want to implement their own agent strategies, you can add a separate folder in integrations\strategy, implement your agent by inherit the BaseAgent class, and add your strategy into the integrations\agent.py. The architecture of this agent microservice is shown in the diagram below as a reference.