Added graph intefrace, added neo4j + networkx structure and updates to the notebook
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4 changed files with 29 additions and 2 deletions
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@ -13,3 +13,8 @@ async def content_to_cog_layers(text_input: str,system_prompt_path:str, response
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return await llm_client.acreate_structured_output(text_input,system_prompt_path, response_model)
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return await llm_client.acreate_structured_output(text_input,system_prompt_path, response_model)
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if __name__ == "__main__":
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content_to_cog_layers("test", "test", ContentPrediction)
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@ -1,7 +1,7 @@
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from typing import Type
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from typing import Type
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from pydantic import BaseModel
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from pydantic import BaseModel
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from cognitive_architecture.infrastructure.llm.get_llm_client import get_llm_client
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from cognitive_architecture.infrastructure.llm.get_llm_client import get_llm_client
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from cognitive_architecture.shared.data_models import CognitiveLayer
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async def content_to_cog_layers(text_input: str,system_prompt_path:str, response_model: Type[BaseModel]):
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async def content_to_cog_layers(text_input: str,system_prompt_path:str, response_model: Type[BaseModel]):
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llm_client = get_llm_client()
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llm_client = get_llm_client()
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@ -12,6 +12,10 @@ async def content_to_cog_layers(text_input: str,system_prompt_path:str, response
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return await llm_client.acreate_structured_output(text_input,system_prompt_path, response_model)
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return await llm_client.acreate_structured_output(text_input,system_prompt_path, response_model)
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if __name__ == "__main__":
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content_to_cog_layers("test", "test", response_model=CognitiveLayer)
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@ -2,10 +2,15 @@
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from typing import Type
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from typing import Type
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from pydantic import BaseModel
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from pydantic import BaseModel
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from cognitive_architecture.infrastructure.llm.get_llm_client import get_llm_client
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from cognitive_architecture.infrastructure.llm.get_llm_client import get_llm_client
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from cognitive_architecture.shared.data_models import KnowledgeGraph
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async def generate_graph(text_input:str,system_prompt_path:str, response_model: Type[BaseModel]):
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async def generate_graph(text_input:str,system_prompt_path:str, response_model: Type[BaseModel]):
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doc_path = "cognitive_architecture/infrastructure/llm/prompts/generate_graph_prompt.txt"
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doc_path = "cognitive_architecture/infrastructure/llm/prompts/generate_graph_prompt.txt"
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llm_client = get_llm_client()
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llm_client = get_llm_client()
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return await llm_client.generate_graph(text_input,system_prompt_path, response_model)
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return await llm_client.generate_graph(text_input,system_prompt_path, response_model)
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if __name__ == "__main__":
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generate_graph("test", "test", response_model=KnowledgeGraph)
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@ -166,3 +166,16 @@ class ContentPrediction(BaseModel):
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label: Union[TextContent, AudioContent, ImageContent, VideoContent, MultimediaContent, Model3DContent, ProceduralContent]
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label: Union[TextContent, AudioContent, ImageContent, VideoContent, MultimediaContent, Model3DContent, ProceduralContent]
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class CognitiveLayerSubgroup(BaseModel):
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""" CognitiveLayerSubgroup in a general layer """
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id: int
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name:str
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description: str
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class CognitiveLayer(BaseModel):
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"""Cognitive layer"""
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category_name:str
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cognitive_layers: List[CognitiveLayerSubgroup] = Field(..., default_factory=list)
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