refine agent directories. (opea-project#1109)

.github/workflows/docker/compose/agent-compose.yaml

@@ -3,7 +3,7 @@
 
 # this file should be run in the root of the repo
 services:
-  agent-langchain:
+  agent:
     build:
-      dockerfile: comps/agent/langchain/Dockerfile
-    image: ${REGISTRY:-opea}/agent-langchain:${TAG:-latest}
+      dockerfile: comps/agent/src/Dockerfile
+    image: ${REGISTRY:-opea}/agent:${TAG:-latest}

comps/agent/langchain/Dockerfile → comps/agent/src/Dockerfile

@@ -21,19 +21,19 @@ COPY comps /home/user/comps
 
 RUN pip install --no-cache-dir --upgrade pip setuptools && \
     if [ ${ARCH} = "cpu" ]; then \
-        pip install --no-cache-dir --extra-index-url https://download.pytorch.org/whl/cpu -r /home/user/comps/agent/langchain/requirements.txt; \
+        pip install --no-cache-dir --extra-index-url https://download.pytorch.org/whl/cpu -r /home/user/comps/agent/src/requirements.txt; \
     else \
-        pip install --no-cache-dir -r /home/user/comps/agent/langchain/requirements.txt; \
+        pip install --no-cache-dir -r /home/user/comps/agent/src/requirements.txt; \
     fi
 
 ENV PYTHONPATH=$PYTHONPATH:/home/user
 
 USER root
 
-RUN mkdir -p /home/user/comps/agent/langchain/status && chown -R user /home/user/comps/agent/langchain/status
+RUN mkdir -p /home/user/comps/agent/src/status && chown -R user /home/user/comps/agent/src/status
 
 USER user
 
-WORKDIR /home/user/comps/agent/langchain/
+WORKDIR /home/user/comps/agent/src/
 
 ENTRYPOINT ["python", "agent.py"]

comps/agent/langchain/README.md → comps/agent/src/README.md

@@ -11,7 +11,7 @@ We currently support the following types of agents. Please refer to the example
 1. ReAct: use `react_langchain` or `react_langgraph` or `react_llama` as strategy. First introduced in this seminal [paper](https://arxiv.org/abs/2210.03629). The ReAct agent engages in "reason-act-observe" cycles to solve problems. Please refer to this [doc](https://python.langchain.com/v0.2/docs/how_to/migrate_agent/) 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.
 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](src/strategy/sqlagent/README.md).
+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](integrations/strategy/sqlagent/README.md).
 
 **Note**:
 
@@ -60,7 +60,7 @@ Examples of how to register tools can be found in [Section 4](#-4-provide-your-o
 
 ```bash
 cd GenAIComps/ # back to GenAIComps/ folder
-docker build -t opea/agent-langchain:latest -f comps/agent/langchain/Dockerfile . --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy
+docker build -t opea/agent:latest -f comps/agent/src/Dockerfile . --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy
 ```
 
 #### 2.2.1 Start Agent microservices with TGI
@@ -78,7 +78,7 @@ docker run -d --runtime=habana --name "comps-tgi-gaudi-service" -p 8080:80 -v ./
 docker logs comps-tgi-gaudi-service
 
 # Agent: react_llama strategy
-docker run -d --runtime=runc --name="comps-langchain-agent-endpoint" -v $WORKPATH/comps/agent/langchain/tools:/home/user/comps/agent/langchain/tools -p 9090:9090 --ipc=host -e HUGGINGFACEHUB_API_TOKEN=${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 recursion_limit=15 -e require_human_feedback=false -e tools=/home/user/comps/agent/langchain/tools/custom_tools.yaml opea/agent-langchain:latest
+docker run -d --runtime=runc --name="comps-langchain-agent-endpoint" -v $WORKPATH/comps/agent/langchain/tools:/home/user/comps/agent/langchain/tools -p 9090:9090 --ipc=host -e HUGGINGFACEHUB_API_TOKEN=${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 recursion_limit=15 -e require_human_feedback=false -e tools=/home/user/comps/agent/langchain/tools/custom_tools.yaml opea/agent:latest
 
 # check status
 docker logs comps-langchain-agent-endpoint
@@ -105,7 +105,7 @@ docker run -d --runtime=habana --rm --name "comps-vllm-gaudi-service" -p 8080:80
 docker logs comps-vllm-gaudi-service
 
 # Agent
-docker run -d --runtime=runc --name="comps-langchain-agent-endpoint" -v $WORKPATH/comps/agent/langchain/tools:/home/user/comps/agent/langchain/tools -p 9090:9090 --ipc=host -e HUGGINGFACEHUB_API_TOKEN=${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=vllm -e recursion_limit=15 -e require_human_feedback=false -e tools=/home/user/comps/agent/langchain/tools/custom_tools.yaml opea/agent-langchain:latest
+docker run -d --runtime=runc --name="comps-langchain-agent-endpoint" -v $WORKPATH/comps/agent/langchain/tools:/home/user/comps/agent/langchain/tools -p 9090:9090 --ipc=host -e HUGGINGFACEHUB_API_TOKEN=${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=vllm -e recursion_limit=15 -e require_human_feedback=false -e tools=/home/user/comps/agent/langchain/tools/custom_tools.yaml opea/agent:latest
 
 # check status
 docker logs comps-langchain-agent-endpoint
@@ -114,7 +114,7 @@ docker logs comps-langchain-agent-endpoint
 > debug mode
 >
 > ```bash
-> docker run --rm --runtime=runc --name="comps-langchain-agent-endpoint" -v ./comps/agent/langchain/:/home/user/comps/agent/langchain/ -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=${ip_address} -e strategy=react_llama -e llm_endpoint_url=http://${ip_address}:8080 -e llm_engine=vllm -e recursion_limit=15 -e require_human_feedback=false -e tools=/home/user/comps/agent/langchain/tools/custom_tools.yaml opea/agent-langchain:latest
+> docker run --rm --runtime=runc --name="comps-langchain-agent-endpoint" -v ./comps/agent/langchain/:/home/user/comps/agent/langchain/ -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=${ip_address} -e strategy=react_llama -e llm_endpoint_url=http://${ip_address}:8080 -e llm_engine=vllm -e recursion_limit=15 -e require_human_feedback=false -e tools=/home/user/comps/agent/langchain/tools/custom_tools.yaml opea/agent:latest
 > ```
 
 ## 🚀 3. Validate Microservice
@@ -189,7 +189,7 @@ def opea_rag_query(query):
 
 ```bash
 # Agent
-docker run -d --runtime=runc --name="comps-langchain-agent-endpoint" -v my_tools:/home/user/comps/agent/langchain/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/langchain/tools/custom_tools.yaml opea/agent-langchain:latest
+docker run -d --runtime=runc --name="comps-langchain-agent-endpoint" -v my_tools:/home/user/comps/agent/langchain/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/langchain/tools/custom_tools.yaml opea/agent:latest
 ```
 
 - validate with my_tools
@@ -205,5 +205,5 @@ data: [DONE]
 
 ## 5. Customize agent strategy
 
-For advanced developers who want to implement their own agent strategies, you can add a separate folder in `src\strategy`, implement your agent by inherit the `BaseAgent` class, and add your strategy into the `src\agent.py`. The architecture of this agent microservice is shown in the diagram below as a reference.
+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.
 ![Architecture Overview](agent_arch.jpg)

comps/agent/langchain/agent.py → comps/agent/src/agent.py

@@ -14,10 +14,10 @@
 sys.path.append(comps_path)
 
 from comps import CustomLogger, GeneratedDoc, LLMParamsDoc, ServiceType, opea_microservices, register_microservice
-from comps.agent.langchain.src.agent import instantiate_agent
-from comps.agent.langchain.src.global_var import assistants_global_kv, threads_global_kv
-from comps.agent.langchain.src.thread import instantiate_thread_memory, thread_completion_callback
-from comps.agent.langchain.src.utils import get_args
+from comps.agent.src.integrations.agent import instantiate_agent
+from comps.agent.src.integrations.global_var import assistants_global_kv, threads_global_kv
+from comps.agent.src.integrations.thread import instantiate_thread_memory, thread_completion_callback
+from comps.agent.src.integrations.utils import get_args
 from comps.cores.proto.api_protocol import (
     AssistantsObject,
     ChatCompletionRequest,

comps/agent/langchain/test.py → comps/agent/src/test.py

@@ -8,11 +8,11 @@
 
 import pandas as pd
 import requests
-from src.utils import get_args
+from integrations.utils import get_args
 
 
 def test_agent_local(args):
-    from src.agent import instantiate_agent
+    from integrations.agent import instantiate_agent
 
     if args.q == 0:
         df = pd.DataFrame({"query": ["What is the Intel OPEA Project?"]})
@@ -148,7 +148,7 @@ def process_request(api, query, is_stream=False):
 
 
 def test_ut(args):
-    from src.tools import get_tools_descriptions
+    from integrations.tools import get_tools_descriptions
 
     tools = get_tools_descriptions("tools/custom_tools.py")
     for tool in tools:

comps/agent/langchain/test_assistant_api.py → comps/agent/src/test_assistant_api.py

@@ -5,7 +5,7 @@
 import json
 
 import requests
-from src.utils import get_args
+from integrations.utils import get_args
 
 
 def test_assistants_http(args):

tests/agent/sql_agent_test/test_sql_agent.sh

@@ -15,7 +15,7 @@ LOG_PATH="$WORKPATH/tests"
 export WORKDIR=$(dirname "$WORKPATH")
 echo $WORKDIR
 
-export agent_image="opea/agent-langchain:comps"
+export agent_image="opea/agent:comps"
 export agent_container_name="test-comps-agent-endpoint"
 
 export ip_address=$(hostname -I | awk '{print $1}')
@@ -29,7 +29,7 @@ export LLM_MODEL_ID="meta-llama/Meta-Llama-3.1-70B-Instruct"
 export LLM_ENDPOINT_URL="http://${ip_address}:${vllm_port}"
 export temperature=0.01
 export max_new_tokens=4096
-export TOOLSET_PATH=$WORKPATH/comps/agent/langchain/tools/ # $WORKPATH/tests/agent/sql_agent_test/
+export TOOLSET_PATH=$WORKPATH/comps/agent/src/tools/ # $WORKPATH/tests/agent/sql_agent_test/
 echo "TOOLSET_PATH=${TOOLSET_PATH}"
 export recursion_limit=15
 export db_name=california_schools
@@ -75,12 +75,12 @@ function build_docker_images() {
     echo "Building the docker images"
     cd $WORKPATH
     echo $WORKPATH
-    docker build --no-cache -t $agent_image --build-arg http_proxy=$http_proxy --build-arg https_proxy=$https_proxy -f comps/agent/langchain/Dockerfile .
+    docker build --no-cache -t $agent_image --build-arg http_proxy=$http_proxy --build-arg https_proxy=$https_proxy -f comps/agent/src/Dockerfile .
     if [ $? -ne 0 ]; then
-        echo "opea/agent-langchain built fail"
+        echo "opea/agent built fail"
         exit 1
     else
-        echo "opea/agent-langchain built successful"
+        echo "opea/agent built successful"
     fi
 }
 

tests/agent/test_agent_langchain_on_intel_hpu.sh

@@ -20,7 +20,7 @@ ls $vllm_volume
 
 export WORKPATH=$WORKPATH
 
-export agent_image="opea/agent-langchain:comps"
+export agent_image="opea/agent:comps"
 export agent_container_name="test-comps-agent-endpoint"
 
 export model=meta-llama/Meta-Llama-3.1-70B-Instruct
@@ -31,20 +31,20 @@ export LLM_MODEL_ID="meta-llama/Meta-Llama-3.1-70B-Instruct"
 export LLM_ENDPOINT_URL="http://${ip_address}:${vllm_port}"
 export temperature=0.01
 export max_new_tokens=4096
-export TOOLSET_PATH=$WORKPATH/comps/agent/langchain/tools/
+export TOOLSET_PATH=$WORKPATH/comps/agent/src/tools/
 echo "TOOLSET_PATH=${TOOLSET_PATH}"
 export recursion_limit=15
 
 function build_docker_images() {
     echo "Building the docker images"
     cd $WORKPATH
     echo $WORKPATH
-    docker build --no-cache -t $agent_image --build-arg http_proxy=$http_proxy --build-arg https_proxy=$https_proxy -f comps/agent/langchain/Dockerfile .
+    docker build --no-cache -t $agent_image --build-arg http_proxy=$http_proxy --build-arg https_proxy=$https_proxy -f comps/agent/src/Dockerfile .
     if [ $? -ne 0 ]; then
-        echo "opea/agent-langchain built fail"
+        echo "opea/agent built fail"
         exit 1
     else
-        echo "opea/agent-langchain built successful"
+        echo "opea/agent built successful"
     fi
 }
 
@@ -164,8 +164,8 @@ function start_vllm_service_70B() {
     echo "Service started successfully"
 }
 
-function start_react_langchain_agent_service() {
-    echo "Starting react_langchain agent microservice"
+function start_react_agent_service() {
+    echo "Starting react agent microservice"
     docker compose -f $WORKPATH/tests/agent/react_langchain.yaml up -d
     sleep 120s
     docker logs test-comps-agent-endpoint
@@ -174,7 +174,7 @@ function start_react_langchain_agent_service() {
 
 
 function start_react_langgraph_agent_service_openai() {
-    echo "Starting react_langchain agent microservice"
+    echo "Starting react agent microservice"
     docker compose -f $WORKPATH/tests/agent/react_langgraph_openai.yaml up -d
     sleep 120s
     docker logs test-comps-agent-endpoint
@@ -253,7 +253,7 @@ function validate_microservice() {
     #  "query": "What is OPEA?"
     # }')
     CONTENT=$(python3 $WORKPATH/tests/agent/test.py)
-    local EXIT_CODE=$(validate "$CONTENT" "OPEA" "test-agent-langchain")
+    local EXIT_CODE=$(validate "$CONTENT" "OPEA" "test-agent")
     echo "$EXIT_CODE"
     local EXIT_CODE="${EXIT_CODE:0-1}"
     echo "return value is $EXIT_CODE"
@@ -270,7 +270,7 @@ function validate_microservice() {
 function validate_microservice_streaming() {
     echo "Testing agent service - chat completion API"
     CONTENT=$(python3 $WORKPATH/tests/agent/test.py --stream)
-    local EXIT_CODE=$(validate "$CONTENT" "OPEA" "test-agent-langchain")
+    local EXIT_CODE=$(validate "$CONTENT" "OPEA" "test-agent")
     echo "$EXIT_CODE"
     local EXIT_CODE="${EXIT_CODE:0-1}"
     echo "return value is $EXIT_CODE"
@@ -286,16 +286,16 @@ function validate_microservice_streaming() {
 function validate_assistant_api() {
     cd $WORKPATH
     echo "Testing agent service - assistant api"
-    local CONTENT=$(python3 comps/agent/langchain/test_assistant_api.py --ip_addr ${ip_address} --ext_port 9095 --assistants_api_test --query 'What is Intel OPEA project?' 2>&1 | tee ${LOG_PATH}/test-agent-langchain-assistantsapi.log)
-    local EXIT_CODE=$(validate "$CONTENT" "OPEA" "test-agent-langchain-assistantsapi")
+    local CONTENT=$(python3 comps/agent/src/test_assistant_api.py --ip_addr ${ip_address} --ext_port 9095 --assistants_api_test --query 'What is Intel OPEA project?' 2>&1 | tee ${LOG_PATH}/test-agent-assistantsapi.log)
+    local EXIT_CODE=$(validate "$CONTENT" "OPEA" "test-agent-assistantsapi")
     echo "$EXIT_CODE"
     local EXIT_CODE="${EXIT_CODE:0-1}"
     echo "return value is $EXIT_CODE"
     if [ "$EXIT_CODE" == "1" ]; then
         echo "==================TGI logs ======================"
         docker logs comps-tgi-gaudi-service
         echo "==================Agent logs ======================"
-        docker logs comps-langchain-agent-endpoint
+        docker logs comps-agent-endpoint
         exit 1
     fi
 }
@@ -357,7 +357,7 @@ function main() {
     build_vllm_docker_images
 
     # ==================== Tests with 70B model ====================
-    # RAG agent, react_llama, react_langchain, assistant apis
+    # RAG agent, react_llama, react, assistant apis
 
     start_vllm_service_70B
 
@@ -376,8 +376,8 @@ function main() {
     echo "============================================="
 
 
-    # # # test react_langchain
-    start_react_langchain_agent_service
+    # # # test react
+    start_react_agent_service
     echo "=============Testing ReAct Langchain============="
     validate_microservice_streaming
     validate_assistant_api