refactoring of coding examples

examples/code_agent.py

@@ -0,0 +1,64 @@
+from pathlib import Path
+from dotenv import load_dotenv
+from just_agents.interfaces.IAgent import build_agent, IAgent
+from just_agents.llm_session import LLMSession
+from llm_sandbox.micromamba import MicromambaSession
+from llm_sandbox.docker import SandboxDockerSession
+from docker.types import Mount
+import os
+
+load_dotenv(override=True)
+
+"""
+This example shows how to use a Chain Of Thought code agent to run python code and bash commands, it uses volumes and is based on Chain Of Thought Agent class.
+"""
+
+def make_mounts():
+    examples_dir = Path(__file__).parent.absolute()
+    assert examples_dir.exists(), f"Examples directory {str(examples_dir)} does not exist, check the current working directory"
+    input_dir =  examples_dir / "input"
+    output_dir =  examples_dir / "output"
+    return [
+        Mount(target="/input", source=str(input_dir), type="bind"),
+        Mount(target="/output", source=str(output_dir), type="bind")
+    ]
+
+def run_bash_command(command: str):
+    """
+    command: str # command to run in bash, for example install software inside micromamba environment
+    """
+    mounts = make_mounts()
+
+    with  MicromambaSession(image="ghcr.io/longevity-genie/just-agents/biosandbox:main", 
+                            lang="python", 
+                            keep_template=True, 
+                            verbose=True,
+                            mounts=mounts
+                            ) as session:
+        result = session.execute_command(command=command)
+        return result
+
+
+def run_python_code(code: str):
+    """
+    code: str # python code to run in micromamba environment
+    """
+    mounts = make_mounts()
+
+    with  MicromambaSession(image="ghcr.io/longevity-genie/just-agents/biosandbox:main", 
+                            lang="python", 
+                            keep_template=True, 
+                            verbose=True,
+                            mounts=mounts
+                            ) as session:
+        result = session.run(code)
+        return result
+
+if __name__ == "__main__":
+    examples_dir = Path(__file__).parent.absolute()
+    assert examples_dir.exists(), f"Examples directory {str(examples_dir)} does not exist, check the current working directory"
+
+    assistant: LLMSession= build_agent(examples_dir / "code_agent.yaml")
+    result, thoughts = assistant.query("Get FGF2 human protein sequence with biopython from uniprot and save it as FGF2.fasta")
+    print("Thoughts: ", thoughts)
+    print("Result: ", result)

examples/code_agent.yaml

@@ -1,23 +1,26 @@
-class: "ChainOfThoughtAgent" #TODO: still work in progress
+class: "ChainOfThoughtAgent"
 system_prompt: "You are a bioinformatician AI assistant. 
 Your role is to help with bioinformatics tasks and generate plans or code as needed. 
 Please adhere to the following guidelines strictly:
 1. Always maintain your role as a bioinformatician.
-2. You are working on an Ubuntu 24.04 system with base micromamba installed, no other software is installed by default.
+2. You are working on an Ubuntu 24.04 system with base micromamba environment installed which includes biopython, requests, polars, genomepy, pyensembl, plotly and GEOparse libraries installed. 
+However no other software is installed by default.
 3. You use run_bash_command tool to install new dependencies. You do not need to activate base micromamba environment, it is already preactivated when you run commands.
 4. Use run_python_code tool to run python code. The code will be run in the base micromamba environment in which the dependencies are installed with run_bash_command.
 5. Use information provided in the input to write detailed plans or bash code to accomplish the given goal or task.
-6. Do not include loading data as a separate step in your plans.
+6. Do not include loading data as a separate step in your plans. If input data is provided it will be in the /input directory.
 7. When writing code:
    - Use full absolute paths for all files.
    - Install dependencies and software using micromamba, pip with the -y flag.
    - Use default values for unspecified parameters.
    - Only use software directly installed with micromamba or pip.
    - Do not repeat steps already completed in the history.
+   - If you create files and folders with results save them inside /output directory unless other is specified explicitly.
 8. Pay attention to the number of input files and do not miss any.
 9. Do not create or activate the micromamba environment 'base', it is already activated by default.
 10. Be aware of file name changes or outputs from previous steps when provided with history.
 11. If execution errors occur, fix the code based on the error information provided.
+12. When you are ready to give the final answer, explain the results obtained and files and folders created in the /output (if any).
 System constraints:
 - You are working on an Ubuntu 24.04 system.
 - You have a micromamba environment named 'base'.
@@ -56,9 +59,9 @@ thought_max_tokes: 500
 max_steps: 25
 final_max_tokens: 1500
 tools:
-  - package: "just_agents_coding.tools"
+  - package: "examples.code_agent"
     function: "run_bash_command"
-  - package: "just_agents_coding.tools"
+  - package: "examples.code_agent"
     function: "run_python_code"
 options:
   model: "gpt-4o-mini"

examples/input/.gitignore

@@ -1 +1,2 @@
-!.gitignore
+!.gitignore
+*

examples/output/.gitignore

@@ -1,2 +1,2 @@
 !.gitignore
-./*
+*

examples/simple_code_agent.py

@@ -0,0 +1,18 @@
+from pathlib import Path
+from dotenv import load_dotenv
+
+from just_agents.interfaces.IAgent import build_agent, IAgent
+load_dotenv(override=True)
+
+"""
+This example shows how to use a simple code agent to run python code and bash commands, it does not use volumes and is based on basic LLMSession class.
+"""
+
+
+if __name__ == "__main__":
+
+    examples_dir = Path(__file__).parent.absolute()
+    assert examples_dir.exists(), f"Examples directory {str(examples_dir)} does not exist, check the current working directory"
+
+    assistant: IAgent = build_agent( examples_dir / "simple_code_agent.yaml")
+    assistant.query("Get FGF2 human protein sequence with biopython from uniprot")

examples/simple_code_agent.yaml

@@ -4,7 +4,8 @@ system_prompt: "You are a bioinformatician AI assistant.
 Your role is to help with bioinformatics tasks and generate plans or code as needed. 
 Please adhere to the following guidelines strictly:
 1. Always maintain your role as a bioinformatician.
-2. You are working on an Ubuntu 24.04 system with base micromamba installed, no other software is installed by default.
+2. You are working on an Ubuntu 24.04 system with base micromamba environment which includes biopython, requests, polars, genomepy, pyensembl, plotly and GEOparse libraries installed. 
+However, no other software is installed by default.
 3. You use run_bash_command tool to install new dependencies. You do not need to activate base micromamba environment, it is already preactivated when you run commands.
 4. Use run_python_code tool to run python code. The code will be run in the base micromamba environment in which the dependencies are installed with run_bash_command.
 5. Use information provided in the input to write detailed plans or bash code to accomplish the given goal or task.

just_agents_coding/containers/biosandbox/Dockerfile

@@ -5,6 +5,11 @@ RUN apt update && \
     apt upgrade -y && \
     apt install -y tar gzip libz-dev software-properties-common python3-software-properties automake nano cmake zip wget gcc git build-essential curl gosu libbz2-dev zlib1g-dev gawk libxml2-dev
 
+RUN mkdir /input && \
+    chown -R $MAMBA_USER:$MAMBA_USER /input
+RUN mkdir /output && \
+    chown -R $MAMBA_USER:$MAMBA_USER /output
+
 USER $MAMBA_USER
 COPY --chown=$MAMBA_USER:$MAMBA_USER env.yaml /tmp/env.yaml
 RUN micromamba install -y -n base -f /tmp/env.yaml && \

just_agents_coding/containers/sandbox/Dockerfile

@@ -5,6 +5,11 @@ RUN apt update && \
     apt upgrade -y && \
     apt install -y tar gzip libz-dev software-properties-common python3-software-properties automake nano cmake zip wget gcc git build-essential curl gosu libbz2-dev zlib1g-dev gawk libxml2-dev
 
+RUN mkdir /input && \
+    chown -R $MAMBA_USER:$MAMBA_USER /input
+RUN mkdir /output && \
+    chown -R $MAMBA_USER:$MAMBA_USER /output
+
 USER $MAMBA_USER
 COPY --chown=$MAMBA_USER:$MAMBA_USER env.yaml /tmp/env.yaml
 RUN micromamba install -y -n base -f /tmp/env.yaml && \

just_agents_coding/tools.py

@@ -1,4 +1,4 @@
-from just_agents_coding.micromamba_session import MicromambaSession
+from llm_sandbox.micromamba import MicromambaSession
 from llm_sandbox.docker import ConsoleOutput
 
 def run_bash_command(command: str):
@@ -18,7 +18,7 @@ def run_python_code(code: str):
         result: ConsoleOutput = session.run(code)
         return result
 
-def copy_files_from_runtime(src: str, dest: str):
+def copy_from_container(src: str, dest: str):
     """
     src: str # path to file in runtime
     dest: str # path to file in host