feat: implements the first version of graph based completion in search

cognee/api/v1/search/search_v2.py

@@ -15,12 +15,14 @@ from cognee.tasks.chunks import query_chunks
 from cognee.tasks.graph import query_graph_connections
 from cognee.tasks.summarization import query_summaries
 from cognee.tasks.completion import query_completion
+from cognee.tasks.completion import graph_query_completion
 
 class SearchType(Enum):
     SUMMARIES = "SUMMARIES"
     INSIGHTS = "INSIGHTS"
     CHUNKS = "CHUNKS"
     COMPLETION = "COMPLETION"
+    GRAPH_COMPLETION = "GRAPH_COMPLETION"
 
 async def search(query_type: SearchType, query_text: str, user: User = None,
                  datasets: Union[list[str], str, None] = None) -> list:
@@ -58,6 +60,7 @@ async def specific_search(query_type: SearchType, query: str, user) -> list:
         SearchType.INSIGHTS: query_graph_connections,
         SearchType.CHUNKS: query_chunks,
         SearchType.COMPLETION: query_completion,
+        SearchType.GRAPH_COMPLETION: graph_query_completion
     }
 
     search_task = search_tasks.get(query_type)

cognee/infrastructure/llm/prompts/answer_simple_question_restricted.txt

@@ -0,0 +1 @@
+Answer the question using the provided context. If the provided context is not connected to the question, just answer "The provided knowledge base does not contain the answer to the question". Be as brief as possible.

cognee/infrastructure/llm/prompts/graph_context_for_question.txt

@@ -0,0 +1,2 @@
+The question is: `{{ question }}`
+and here is the context provided with a set of relationships from a knowledge graph separated by \n---\n each represented as node1 -- relation -- node2 triplet: `{{ context }}`

cognee/tasks/completion/__init__.py

@@ -1 +1,2 @@
-from .query_completion import query_completion
+from .query_completion import query_completion
+from .graph_query_completion import graph_query_completion

cognee/tasks/completion/graph_query_completion.py

@@ -0,0 +1,45 @@
+from cognee.infrastructure.databases.vector import get_vector_engine
+from cognee.tasks.completion.exceptions import NoRelevantDataFound
+from cognee.infrastructure.llm.get_llm_client import get_llm_client
+from cognee.infrastructure.llm.prompts import read_query_prompt, render_prompt
+from cognee.modules.retrieval.brute_force_triplet_search import brute_force_triplet_search
+
+
+def retrieved_edges_to_string(retrieved_edges):
+    edge_strings = []
+    for edge in retrieved_edges:
+        node1_string = edge.node1.attributes['text'] or edge.node1.attributes.get('name')
+        node2_string = edge.node2.attributes['text'] or edge.node2.attributes.get('name')
+        edge_string = edge.attributes['relationship_type']
+        edge_str = f"{node1_string} -- {edge_string} -- {node2_string}"
+        edge_strings.append(edge_str)
+    return "\n---\n".join(edge_strings)
+
+async def graph_query_completion(query: str) -> list:
+    """
+    Parameters:
+    - query (str): The query string to compute.
+
+    Returns:
+    - list: Answer to the query.
+    """
+    found_triplets = await brute_force_triplet_search(query, top_k=5)
+
+    if len(found_triplets) == 0:
+        raise NoRelevantDataFound
+
+    args = {
+        "question": query,
+        "context": retrieved_edges_to_string(found_triplets),
+    }
+    user_prompt = render_prompt("graph_context_for_question.txt", args)
+    system_prompt = read_query_prompt("answer_simple_question_restricted.txt")
+
+    llm_client = get_llm_client()
+    computed_answer = await llm_client.acreate_structured_output(
+        text_input=user_prompt,
+        system_prompt=system_prompt,
+        response_model=str,
+    )
+
+    return [computed_answer]

examples/python/dynamic_steps_example.py

@@ -1,8 +1,8 @@
 import cognee
 import asyncio
 import logging
-from cognee.modules.retrieval.brute_force_triplet_search import brute_force_triplet_search
-from cognee.modules.retrieval.brute_force_triplet_search import format_triplets
+
+from cognee.api.v1.search import SearchType
 from cognee.shared.utils import setup_logging
 
 job_1 = """
@@ -184,11 +184,13 @@ async def main(enable_steps):
 
     # Step 4: Query insights
     if enable_steps.get("retriever"):
-        results = await brute_force_triplet_search('Who has the most experience with graphic design?')
-        print(format_triplets(results))
+        search_results = await cognee.search(
+            SearchType.GRAPH_COMPLETION, query_text='Who has experience in design tools?'
+        )
+        print(search_results)
 
 if __name__ == '__main__':
-    setup_logging(logging.ERROR)
+    setup_logging(logging.INFO)
 
     rebuild_kg = True
     retrieve = True