Merge pull request #20 from EURAC-EEBgroup/pre/beta

Pre/beta

CHANGELOG.md

@@ -1,3 +1,31 @@
+## [1.3.0-beta.1](https://github.com/EURAC-EEBgroup/brick-llm/compare/v1.2.0...v1.3.0-beta.1) (2025-01-02)
+
+
+### Features
+
+* get_sensor implementation ([e100a88](https://github.com/EURAC-EEBgroup/brick-llm/commit/e100a886acb536077d4ec69bae07f7f84626178d))
+* get_sensor_presence edge ([b7a3e59](https://github.com/EURAC-EEBgroup/brick-llm/commit/b7a3e597e107844fd484068111037756d25d1cd4))
+* implementing ollama 3.2 through ChatOllama models ([bf3cf6b](https://github.com/EURAC-EEBgroup/brick-llm/commit/bf3cf6bab72add3f449a3932d51e360a76c18da3))
+
+
+### Bug Fixes
+
+* extract answer content in local generation ([a8f7e39](https://github.com/EURAC-EEBgroup/brick-llm/commit/a8f7e3954c25b1d2584a40c7329a64f9b77f199f))
+* poetry run pre-commit ([edf4664](https://github.com/EURAC-EEBgroup/brick-llm/commit/edf4664deda8a8faaa4e0b1d5ce92276f2bea137))
+* prompt correction ([cf5babd](https://github.com/EURAC-EEBgroup/brick-llm/commit/cf5babd71b703f4df2764b7e410524923df8253b))
+* prompt correction ([3e8d61a](https://github.com/EURAC-EEBgroup/brick-llm/commit/3e8d61a9e008e8e4f24153ab31bf781f910a6cd0))
+* prompt engineering ([a7ae3bc](https://github.com/EURAC-EEBgroup/brick-llm/commit/a7ae3bcfbf69fa369ef7a0a21c39596aa9c13791))
+
+
+### chore
+
+* added missing dependencies and linting ([4e4169c](https://github.com/EURAC-EEBgroup/brick-llm/commit/4e4169c053f48a3bd2bf3252a052c64565558ae2))
+
+
+### Docs
+
+* added env note ([23c57e2](https://github.com/EURAC-EEBgroup/brick-llm/commit/23c57e249931c71130ae8d1b880bee1950b1501d))
+
 ## [1.2.0](https://github.com/EURAC-EEBgroup/brick-llm/compare/v1.1.2...v1.2.0) (2024-11-11)
 
 

README.md

@@ -43,7 +43,7 @@ pip install poetry
 poetry install
 
 # Install pre-commit hooks
-pre-commit install
+poetry runpre-commit install
 ```
 
 </details>
@@ -52,7 +52,7 @@ pre-commit install
 
 Here's a simple example of how to use BrickLLM:
 
-> [!NOTE]  
+> [!NOTE]
 > You must first create a [.env](.env.example) file with the API keys of the specified LLM provider (if not local) and load them in the environment
 
 ``` python

brickllm/__init__.py

@@ -3,6 +3,7 @@
 from .schemas import (
     ElemListSchema,
     RelationshipsSchema,
+    SensorSchema,
     TTLSchema,
     TTLToBuildingPromptSchema,
 )
@@ -17,4 +18,5 @@
     "StateLocal",
     "GraphConfig",
     "custom_logger",
+    "SensorSchema",
 ]

brickllm/edges/__init__.py

@@ -1,4 +1,5 @@
+from .check_sensor_presence import check_sensor_presence
 from .validate_condition import validate_condition
 from .validate_condition_local import validate_condition_local
 
-__all__ = ["validate_condition", "validate_condition_local"]
+__all__ = ["validate_condition", "validate_condition_local", "check_sensor_presence"]

brickllm/edges/check_sensor_presence.py

@@ -0,0 +1,42 @@
+from typing import Any, Dict, Literal
+
+from ..logger import custom_logger
+from ..utils import get_hierarchical_info
+
+
+def check_sensor_presence(
+    state: Dict[str, Any]
+) -> Literal["get_sensors", "schema_to_ttl"]:
+    """
+    Check if the sensors are present in the building structure.
+
+    Args:
+        state (Dict[str, Any]): The current state containing the sensor structure.
+
+    Returns:
+        Literal["get_sensors", "schema_to_ttl"]: The next node to visit.
+    """
+
+    custom_logger.eurac("📡 Checking for sensor presence")
+
+    elem_list = state.get("elem_list", [])
+
+    parents, children = get_hierarchical_info("Point")
+
+    children_dict = {}
+    for child in children:
+        children_dict[child] = get_hierarchical_info(child)[1]
+
+    # Flatten the dictionary in a list
+    children_list = [elem for sublist in children_dict.values() for elem in sublist]
+
+    is_sensor = False
+
+    for elem in elem_list:
+        if elem in children_list:
+            is_sensor = True
+
+    if is_sensor:
+        return "get_sensors"
+    else:
+        return "schema_to_ttl"

brickllm/graphs/brickschema_graph.py

@@ -4,7 +4,7 @@
 from langgraph.graph import END, START, StateGraph
 
 from .. import GraphConfig, State
-from ..edges import validate_condition
+from ..edges import check_sensor_presence, validate_condition
 from ..nodes import (
     get_elem_children,
     get_elements,
@@ -30,18 +30,23 @@ def build_graph(self):
         self.workflow.add_node("get_elem_children", get_elem_children)
         self.workflow.add_node("get_relationships", get_relationships)
         self.workflow.add_node("schema_to_ttl", schema_to_ttl)
+        # self.workflow.add_node("sensor_presence", sensor_presence)
         self.workflow.add_node("validate_schema", validate_schema)
         self.workflow.add_node("get_sensors", get_sensors)
 
         # Add edges to define the flow logic
         self.workflow.add_edge(START, "get_elements")
         self.workflow.add_edge("get_elements", "get_elem_children")
         self.workflow.add_edge("get_elem_children", "get_relationships")
-        self.workflow.add_edge("get_relationships", "schema_to_ttl")
+        self.workflow.add_conditional_edges(
+            "get_relationships",
+            check_sensor_presence,
+            {"get_sensors": "get_sensors", "schema_to_ttl": "schema_to_ttl"},
+        )
+        self.workflow.add_edge("get_sensors", "schema_to_ttl")
         self.workflow.add_edge("schema_to_ttl", "validate_schema")
         self.workflow.add_conditional_edges("validate_schema", validate_condition)
-        self.workflow.add_edge("get_relationships", "get_sensors")
-        self.workflow.add_edge("get_sensors", END)
+        self.workflow.add_edge("validate_schema", END)
 
     def run(
         self, input_data: Dict[str, Any], stream: bool = False

brickllm/helpers/__init__.py

@@ -3,6 +3,7 @@
     get_elem_children_instructions,
     get_elem_instructions,
     get_relationships_instructions,
+    get_sensors_instructions,
     prompt_template_local,
     schema_to_ttl_instructions,
     ttl_example,
@@ -18,4 +19,5 @@
     "ttl_example",
     "prompt_template_local",
     "ttl_to_user_prompt",
+    "get_sensors_instructions",
 ]

brickllm/helpers/llm_models.py

@@ -4,6 +4,7 @@
 from langchain_anthropic import ChatAnthropic
 from langchain_community.llms import Ollama
 from langchain_fireworks import ChatFireworks
+from langchain_ollama import ChatOllama
 from langchain_openai import ChatOpenAI
 
 
@@ -23,6 +24,8 @@ def _get_model(model: Union[str, BaseChatModel]) -> BaseChatModel:
 
     if model == "openai":
         return ChatOpenAI(temperature=0, model="gpt-4o")
+    elif model == "ollama3.2":
+        return ChatOllama(model="llama3.2")
     elif model == "anthropic":
         return ChatAnthropic(temperature=0, model_name="claude-3-sonnet-20240229")
     elif model == "fireworks":
@@ -31,5 +34,10 @@ def _get_model(model: Union[str, BaseChatModel]) -> BaseChatModel:
         )
     elif model == "llama3.1:8b-brick":
         return Ollama(model="llama3.1:8b-brick-v8")
+    elif model == "llama32-3B-brick":
+        return Ollama(model="hf.co/Giudice7/llama32-3B-brick-demo:latest")
+
     else:
-        raise ValueError(f"Unsupported model type: {model}")
+        raise ValueError(
+            f"Unsupported model type: {model}. Load your own BaseChatModel if this one is not supported."
+        )