DummyLLMAdapter WIP

cognee/infrastructure/llm/anthropic/adapter.py

@@ -4,7 +4,6 @@ import instructor
 from tenacity import retry, stop_after_attempt
 import anthropic
 from cognee.infrastructure.llm.llm_interface import LLMInterface
-from cognee.infrastructure.llm.prompts import read_query_prompt
 
 
 class AnthropicAdapter(LLMInterface):
@@ -38,17 +37,3 @@ class AnthropicAdapter(LLMInterface):
             }],
             response_model = response_model,
         )
-
-    def show_prompt(self, text_input: str, system_prompt: str) -> str:
-        """Format and display the prompt for a user query."""
-
-        if not text_input:
-            text_input = "No user input provided."
-        if not system_prompt:
-            raise ValueError("No system prompt path provided.")
-
-        system_prompt = read_query_prompt(system_prompt)
-
-        formatted_prompt = f"""System Prompt:\n{system_prompt}\n\nUser Input:\n{text_input}\n""" if system_prompt else None
-
-        return formatted_prompt

cognee/infrastructure/llm/generic_llm_api/adapter.py

@@ -6,7 +6,6 @@ import instructor
 from tenacity import retry, stop_after_attempt
 import openai
 from cognee.infrastructure.llm.llm_interface import LLMInterface
-from cognee.infrastructure.llm.prompts import read_query_prompt
 from cognee.shared.data_models import MonitoringTool
 from cognee.base_config import get_base_config
 from cognee.infrastructure.llm.config import get_llm_config
@@ -123,13 +122,3 @@ class GenericAPIAdapter(LLMInterface):
             response_model = response_model,
         )
 
-    def show_prompt(self, text_input: str, system_prompt: str) -> str:
-        """Format and display the prompt for a user query."""
-        if not text_input:
-            text_input = "No user input provided."
-        if not system_prompt:
-            raise ValueError("No system prompt path provided.")
-        system_prompt = read_query_prompt(system_prompt)
-
-        formatted_prompt = f"""System Prompt:\n{system_prompt}\n\nUser Input:\n{text_input}\n""" if system_prompt else None
-        return formatted_prompt

cognee/infrastructure/llm/llm_interface.py

@@ -3,6 +3,8 @@
 from typing import Type, Protocol
 from abc import abstractmethod
 from pydantic import BaseModel
+from cognee.infrastructure.llm.prompts import read_query_prompt
+
 class LLMInterface(Protocol):
     """ LLM Interface """
 
@@ -16,5 +18,13 @@ class LLMInterface(Protocol):
 
     @abstractmethod
     def show_prompt(self, text_input: str, system_prompt: str) -> str:
-        """To get structured output, import/call this function"""
+        """Format and display the prompt for a user query."""
-        raise NotImplementedError
+        if not text_input:
+            text_input = "No user input provided."
+        if not system_prompt:
+            raise ValueError("No system prompt path provided.")
+        system_prompt = read_query_prompt(system_prompt)
+
+        formatted_prompt = f"""System Prompt:\n{system_prompt}\n\nUser Input:\n{text_input}\n"""
+
+        return formatted_prompt

cognee/infrastructure/llm/openai/adapter.py

@@ -8,7 +8,6 @@ import instructor
 from pydantic import BaseModel
 
 from cognee.infrastructure.llm.llm_interface import LLMInterface
-from cognee.infrastructure.llm.prompts import read_query_prompt
 
 class OpenAIAdapter(LLMInterface):
     name = "OpenAI"
@@ -121,14 +120,3 @@ class OpenAIAdapter(LLMInterface):
             max_tokens = 300,
             max_retries = 5,
         )
-
-    def show_prompt(self, text_input: str, system_prompt: str) -> str:
-        """Format and display the prompt for a user query."""
-        if not text_input:
-            text_input = "No user input provided."
-        if not system_prompt:
-            raise ValueError("No system prompt path provided.")
-        system_prompt = read_query_prompt(system_prompt)
-
-        formatted_prompt = f"""System Prompt:\n{system_prompt}\n\nUser Input:\n{text_input}\n""" if system_prompt else None
-        return formatted_prompt

cognee/infrastructure/llm/prompts/generate_cog_layers.txt

@@ -1,14 +0,0 @@
-You are tasked with analyzing `{{ data_type }}` files, especially in a multilayer network context for tasks such as analysis, categorization, and feature extraction. Various layers can be incorporated to capture the depth and breadth of information contained within the {{ data_type }}.
-
-These layers can help in understanding the content, context, and characteristics of the `{{ data_type }}`.
-
-Your objective is to extract meaningful layers of information that will contribute to constructing a detailed multilayer network or knowledge graph.
-
-Approach this task by considering the unique characteristics and inherent properties of the data at hand.
-
-VERY IMPORTANT: The context you are working in is `{{ category_name }}` and the specific domain you are extracting data on is `{{ category_name }}`.
-
-Guidelines for Layer Extraction:
-Take into account: The content type, in this case, is: `{{ category_name }}`, should play a major role in how you decompose into layers.
-
-Based on your analysis, define and describe the layers you've identified, explaining their relevance and contribution to understanding the dataset. Your independent identification of layers will enable a nuanced and multifaceted representation of the data, enhancing applications in knowledge discovery, content analysis, and information retrieval.

cognee/modules/data/extraction/extract_cognitive_layers.py

@@ -1,11 +0,0 @@
-from typing import Type, Dict
-from pydantic import BaseModel
-from cognee.infrastructure.llm.prompts import render_prompt
-from cognee.infrastructure.llm.get_llm_client import get_llm_client
-
-async def extract_cognitive_layers(content: str, category: Dict, response_model: Type[BaseModel]):
-    llm_client = get_llm_client()
-
-    system_prompt = render_prompt("generate_cog_layers.txt", category)
-
-    return await llm_client.acreate_structured_output(content, system_prompt, response_model)

profiling/util/DummyLLMAdapter.py

@@ -0,0 +1,47 @@
+import spacy
+import textacy
+from typing import Type
+from uuid import uuid4
+from pydantic import BaseModel
+from cognee.infrastructure.llm.llm_interface import LLMInterface
+from cognee.shared.data_models import SummarizedContent
+from cognee.shared.data_models import KnowledgeGraph, Node, Edge
+
+
+class DummyLLMAdapter(LLMInterface):
+    nlp = spacy.load('en_core_web_sm')
+    async def acreate_structured_output(self,
+                                        text_input: str,
+                                        system_prompt: str,
+                                        response_model: Type[BaseModel]) -> BaseModel:
+        
+        if isinstance(response_model, SummarizedContent):
+            return(dummy_summarize_content(text_input))
+        elif isinstance(response_model, KnowledgeGraph):
+            return(dummy_extract_knowledge_graph(text_input, nlp))
+        else:
+            raise Exception("Currently dummy acreate_structured_input is only implemented for SummarizedContent and KnowledgeGraph")
+
+
+def dummy_extract_knowledge_graph(text, nlp):
+    doc = nlp(text)
+    triples = list(textacy.extract.subject_verb_object_triples(doc))
+
+    nodes = {}
+    edges = []
+    for triple in triples:
+        source = "_".join([str(e) for e in triple.subject])
+        target  = "_".join([str(e) for e in triple.object])
+        nodes[source] = nodes.get(source, Node(id=str(uuid4()), name=source, type="object", description="")   )
+        nodes[target] = nodes.get(target,  Node(id=str(uuid4()), name=target, type="object", description="")  )
+        edge_type = "_".join([str(e) for e in triple.verb])
+        edges.append(Edge(source_node_id=nodes[source].id, target_node_id=nodes[target].id, relationship_name=edge_type))
+    return(KnowledgeGraph(nodes=list(nodes.keys()), edges=edges))
+    
+
+def dummy_summarize_content(text):
+    words = [(word, len(word)) for word in set(text.split(" "))]
+    words = sorted(words, key=lambda x: x[1], reverse=True)
+    summary = " ".join([word for word, _ in words[:100]])
+    description =  " ".join([word for word, _ in words[:10]])
+    return(SummarizedContent(summary=summary, description=description))