273 lines
10 KiB
Python
273 lines
10 KiB
Python
from typing import Any, Optional, Type, List, Coroutine
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from collections import Counter
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from uuid import NAMESPACE_OID, uuid5
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import string
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from cognee.infrastructure.engine import DataPoint
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from cognee.tasks.storage import add_data_points
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from cognee.modules.graph.utils.convert_node_to_data_point import get_all_subclasses
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from cognee.modules.retrieval.base_retriever import BaseRetriever
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from cognee.modules.retrieval.utils.brute_force_triplet_search import brute_force_triplet_search
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from cognee.modules.retrieval.utils.completion import generate_completion
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from cognee.modules.retrieval.utils.stop_words import DEFAULT_STOP_WORDS
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from cognee.shared.logging_utils import get_logger
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from cognee.modules.retrieval.utils.extract_uuid_from_node import extract_uuid_from_node
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from cognee.modules.retrieval.utils.models import CogneeUserInteraction
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from cognee.modules.engine.models.node_set import NodeSet
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from cognee.infrastructure.databases.graph import get_graph_engine
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logger = get_logger("GraphCompletionRetriever")
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class GraphCompletionRetriever(BaseRetriever):
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"""
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Retriever for handling graph-based completion searches.
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This class provides methods to retrieve graph nodes and edges, resolve them into a
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human-readable format, and generate completions based on graph context. Public methods
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include:
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- resolve_edges_to_text
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- get_triplets
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- get_context
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- get_completion
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"""
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def __init__(
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self,
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user_prompt_path: str = "graph_context_for_question.txt",
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system_prompt_path: str = "answer_simple_question.txt",
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top_k: Optional[int] = 5,
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node_type: Optional[Type] = None,
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node_name: Optional[List[str]] = None,
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save_interaction: bool = False,
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):
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"""Initialize retriever with prompt paths and search parameters."""
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self.save_interaction = save_interaction
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self.user_prompt_path = user_prompt_path
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self.system_prompt_path = system_prompt_path
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self.top_k = top_k if top_k is not None else 5
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self.node_type = node_type
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self.node_name = node_name
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def _get_nodes(self, retrieved_edges: list) -> dict:
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"""Creates a dictionary of nodes with their names and content."""
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nodes = {}
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for edge in retrieved_edges:
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for node in (edge.node1, edge.node2):
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if node.id not in nodes:
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text = node.attributes.get("text")
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if text:
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name = self._get_title(text)
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content = text
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else:
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name = node.attributes.get("name", "Unnamed Node")
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content = node.attributes.get("description", name)
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nodes[node.id] = {"node": node, "name": name, "content": content}
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return nodes
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async def resolve_edges_to_text(self, retrieved_edges: list) -> str:
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"""
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Converts retrieved graph edges into a human-readable string format.
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Parameters:
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-----------
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- retrieved_edges (list): A list of edges retrieved from the graph.
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Returns:
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--------
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- str: A formatted string representation of the nodes and their connections.
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"""
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nodes = self._get_nodes(retrieved_edges)
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node_section = "\n".join(
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f"Node: {info['name']}\n__node_content_start__\n{info['content']}\n__node_content_end__\n"
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for info in nodes.values()
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)
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connection_section = "\n".join(
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f"{nodes[edge.node1.id]['name']} --[{edge.attributes['relationship_type']}]--> {nodes[edge.node2.id]['name']}"
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for edge in retrieved_edges
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)
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return f"Nodes:\n{node_section}\n\nConnections:\n{connection_section}"
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async def get_triplets(self, query: str) -> list:
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"""
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Retrieves relevant graph triplets based on a query string.
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Parameters:
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-----------
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- query (str): The query string used to search for relevant triplets in the graph.
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Returns:
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--------
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- list: A list of found triplets that match the query.
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"""
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subclasses = get_all_subclasses(DataPoint)
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vector_index_collections = []
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for subclass in subclasses:
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if "metadata" in subclass.model_fields:
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metadata_field = subclass.model_fields["metadata"]
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if hasattr(metadata_field, "default") and metadata_field.default is not None:
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if isinstance(metadata_field.default, dict):
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index_fields = metadata_field.default.get("index_fields", [])
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for field_name in index_fields:
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vector_index_collections.append(f"{subclass.__name__}_{field_name}")
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found_triplets = await brute_force_triplet_search(
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query,
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top_k=self.top_k,
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collections=vector_index_collections or None,
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node_type=self.node_type,
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node_name=self.node_name,
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)
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return found_triplets
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async def get_context(self, query: str) -> str | tuple[str, list]:
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"""
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Retrieves and resolves graph triplets into context based on a query.
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Parameters:
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-----------
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- query (str): The query string used to retrieve context from the graph triplets.
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Returns:
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--------
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- str: A string representing the resolved context from the retrieved triplets, or an
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empty string if no triplets are found.
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"""
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triplets = await self.get_triplets(query)
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if len(triplets) == 0:
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logger.warning("Empty context was provided to the completion")
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return "", triplets
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context = await self.resolve_edges_to_text(triplets)
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return context, triplets
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async def get_completion(self, query: str, context: Optional[Any] = None) -> Any:
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"""
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Generates a completion using graph connections context based on a query.
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Parameters:
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-----------
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- query (str): The query string for which a completion is generated.
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- context (Optional[Any]): Optional context to use for generating the completion; if
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not provided, context is retrieved based on the query. (default None)
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Returns:
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--------
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- Any: A generated completion based on the query and context provided.
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"""
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triplets = None
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if context is None:
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context, triplets = await self.get_context(query)
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completion = await generate_completion(
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query=query,
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context=context,
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user_prompt_path=self.user_prompt_path,
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system_prompt_path=self.system_prompt_path,
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)
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if self.save_interaction and context and triplets and completion:
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await self.save_qa(
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question=query, answer=completion, context=context, triplets=triplets
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)
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return [completion]
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def _top_n_words(self, text, stop_words=None, top_n=3, separator=", "):
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"""Concatenates the top N frequent words in text."""
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if stop_words is None:
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stop_words = DEFAULT_STOP_WORDS
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words = [word.lower().strip(string.punctuation) for word in text.split()]
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if stop_words:
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words = [word for word in words if word and word not in stop_words]
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top_words = [word for word, freq in Counter(words).most_common(top_n)]
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return separator.join(top_words)
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def _get_title(self, text: str, first_n_words: int = 7, top_n_words: int = 3) -> str:
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"""Creates a title, by combining first words with most frequent words from the text."""
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first_n_words = text.split()[:first_n_words]
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top_n_words = self._top_n_words(text, top_n=top_n_words)
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return f"{' '.join(first_n_words)}... [{top_n_words}]"
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async def save_qa(self, question: str, answer: str, context: str, triplets: List) -> None:
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"""
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Saves a question and answer pair for later analysis or storage.
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Parameters:
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-----------
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- question (str): The question text.
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- answer (str): The answer text.
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- context (str): The context text.
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- triplets (List): A list of triples retrieved from the graph.
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"""
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nodeset_name = "Interactions"
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interactions_node_set = NodeSet(
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id=uuid5(NAMESPACE_OID, name=nodeset_name), name=nodeset_name
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)
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source_id = uuid5(NAMESPACE_OID, name=(question + answer + context))
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cognee_user_interaction = CogneeUserInteraction(
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id=source_id,
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question=question,
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answer=answer,
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context=context,
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belongs_to_set=interactions_node_set,
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)
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await add_data_points(data_points=[cognee_user_interaction], update_edge_collection=False)
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relationships = []
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relationship_name = "used_graph_element_to_answer"
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for triplet in triplets:
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target_id_1 = extract_uuid_from_node(triplet.node1)
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target_id_2 = extract_uuid_from_node(triplet.node2)
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if target_id_1 and target_id_2:
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relationships.append(
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(
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source_id,
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target_id_1,
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relationship_name,
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{
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"relationship_name": relationship_name,
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"source_node_id": source_id,
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"target_node_id": target_id_1,
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"ontology_valid": False,
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"feedback_weight": 0,
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},
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)
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)
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relationships.append(
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(
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source_id,
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target_id_2,
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relationship_name,
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{
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"relationship_name": relationship_name,
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"source_node_id": source_id,
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"target_node_id": target_id_2,
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"ontology_valid": False,
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"feedback_weight": 0,
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},
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)
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)
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if len(relationships) > 0:
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graph_engine = await get_graph_engine()
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await graph_engine.add_edges(relationships)
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