Практычныя прытамулі: Практычная архітектура GraphRAG з выкарыстоўваннем LangExtract, Neo4j

Існавайце гэта як перапрацоўаны варыянт ідэй з кнігі «Практычная архітектура GraphRAG з выкарыстоўванням LangExtract, Neo4j, Qdrant і Ollama» для працавікаў: чыстыя этапы, аранжаваныя блакі коду і прыметкі з восстанавлення, якія застаюцца пасля перадачы задання. Этап Аналізу работае найкраща, калі яго спрыяглядаць як мерыемую плошчу. Запісайце адну ідеальную транскрыпцыю, адзін прыклад неудачы і прыметкі з вярнення да пачатковага стану прычаму расшырэння масштаба. Спрыяглядайце гэты этап як кантракт межа вхіднымі даннымі і перакананымі выходнымі рэзультатамі. Дайце назвы артыфактам, задаце критэрыя успеху і адмовіцеся ад беззвучнага частковага завершэння.

Дзеясны аналіз архітектуры

Для стадіі глыбокага аналізу архітектуры неабяжна прадварыце з’явіць вхідныя данні, адпаведнага адпаведальнага за крок і критэрыі завершэння пры змяне коду. Аперацыйныя працавнікі павінны магчымае перазапускаць крок з вядомай точкі контролю, не прабуючы спадарацца пра схованы стан. Запісваюць час выконання і кост токенаў або запытаў разам з функцыйнаімі рэзультатамі. Відразлівая візуабельнасць костаў з самага пачатку запобегае неспакою, калі процес пераходзіць з дэмавай версіі ў спяльныя сераўы. Аддзеляюць стварэнне кліента ад цыклу перадачы паведамленняў, ўнаследке чаго можна змяніць прадстаўніка без перапісвання машыны стану дыялогу.

Этапы рэалізацыі

У стадії практычнага адаптавання неабяжна ўзначыць вхідныя даны, адпаведальнага за крок і критэрыя завершэння пры зміне коду. Аператары должны магчымаць перзапуск кроку з вядомай точкі контролю, не спрабоўваючы з’ясаваць захаваны стан. Конфігурацыю трэба залічыць паза кодам прыкладнення. Файлы сераўіса, хранільнікі секрэтных данных і флагі функцыйяў должны знаходзіцца ў аднам месцы, якое аператары можаць пераглядаць, не чытаяўшы весь структураны код. Трэба аддзеліць стварэнне кліента ад цыклу перадачы паведамленняў, ўпрымкнучы такі спосаб, каб можна было заменіць прадаўцоў без перапісвання машыны стану дыялогу.

def __init__(self,env_path: str = ".env",ollama_model_extract: str = "gemma3:latest",
            ollama_model_answer: str = "gemma3:latest",ollama_embedding_model: str = "embeddinggemma:latest",
            ollama_host: str | None = None,vector_dimension: int = 768,
    ):

        load_dotenv(env_path)

        self.qdrant_key = os.getenv("QDRANT_KEY")
        self.qdrant_url = os.getenv("QDRANT_URL")
        self.neo4j_uri = os.getenv("NEO4J_URI")
        self.neo4j_username = os.getenv("NEO4J_USERNAME")
        self.neo4j_password = os.getenv("NEO4J_PASSWORD")

        self.neo4j_driver = GraphDatabase.driver(
            self.neo4j_uri, auth=(self.neo4j_username, self.neo4j_password)
        )

        self.qdrant_client = QdrantClient(
            url=self.qdrant_url,
            api_key=self.qdrant_key,
        )

        # Ollama client for local embeddings (embeddinggemma:latest)
        self.ollama_client = ollama.Client(host=ollama_host) if ollama_host else ollama.Client()

        # langextract needs a plain URL string, not a client object
        self.ollama_url = ollama_host or os.environ.get("OLLAMA_HOST", "http://localhost:11434")

        # Model / config knobs
        self.ollama_model_extract = ollama_model_extract
        self.ollama_model_answer = ollama_model_answer
        self.ollama_embedding_model = ollama_embedding_model
        self.vector_dimension = vector_dimension

def extract_graph_components(self, raw_data: str):
        """Extract medication entities and relationships using langextract + Ollama."""

        prompt_description = textwrap.dedent("""
            Extract medications with their details, using attributes to group related information:
            1. Extract entities in the order they appear in the text
            2. Each entity must have a 'medication_group' attribute linking it to its medication
            3. All details about a medication should share the same medication_group value
            """).strip()

        examples = [
            lx.data.ExampleData(
                text=(
                    "Patient takes Aspirin 100mg daily for heart health and"
                    " Simvastatin 20mg at bedtime."
                ),
                extractions=[
                    lx.data.Extraction(
                        extraction_class="medication",
                        extraction_text="Aspirin",
                        attributes={"medication_group": "Aspirin"},
                    ),
                    lx.data.Extraction(
                        extraction_class="dosage",
                        extraction_text="100mg",
                        attributes={"medication_group": "Aspirin"},
                    ),
                    lx.data.Extraction(
                        extraction_class="frequency",
                        extraction_text="daily",
                        attributes={"medication_group": "Aspirin"},
                    ),
                    lx.data.Extraction(
                        extraction_class="condition",
                        extraction_text="heart health",
                        attributes={"medication_group": "Aspirin"},
                    ),
                    lx.data.Extraction(
                        extraction_class="medication",
                        extraction_text="Simvastatin",
                        attributes={"medication_group": "Simvastatin"},
                    ),
                    lx.data.Extraction(
                        extraction_class="dosage",
                        extraction_text="20mg",
                        attributes={"medication_group": "Simvastatin"},
                    ),
                    lx.data.Extraction(
                        extraction_class="frequency",
                        extraction_text="at bedtime",
                        attributes={"medication_group": "Simvastatin"},
                    ),
                ],
            )
        ]

        result = lx.extract(
            text_or_documents=raw_data,
            prompt_description=prompt_description,
            examples=examples,
            model_id=self.ollama_model_extract,
            model_url=self.ollama_url,
            resolver_params={"format_handler": lx_ollama.OLLAMA_FORMAT_HANDLER},
            max_char_buffer=4000,
            show_progress=True,
        )

        return self._convert_extractions_to_graph(result.extractions)

def _convert_extractions_to_graph(self, extractions: list):
        """Convert langextract's flat, grouped extractions into (nodes, relationships)."""

        groups: dict[str, list] = {}
        for ext in extractions:
            if not ext.attributes or "medication_group" not in ext.attributes:
                continue
            group_name = ext.attributes["medication_group"]
            groups.setdefault(group_name, []).append(ext)

        nodes: dict[str, str] = {}
        relationships: list[dict] = []

        for group_name, group_extractions in groups.items():
            anchor_ext = next(
                (e for e in group_extractions if e.extraction_class == "medication"),
                None,
            )

            # Fall back to the group name itself if no explicit "medication"
            # extraction was found in this group, so we still get an anchor.
            anchor_text = anchor_ext.extraction_text if anchor_ext else group_name

            if anchor_text not in nodes:
                nodes[anchor_text] = str(uuid.uuid4())

            for ext in group_extractions:
                if ext is anchor_ext:
                    continue

                target_text = ext.extraction_text
                if target_text not in nodes:
                    nodes[target_text] = str(uuid.uuid4())

                relationships.append(
                    {
                        "source": nodes[anchor_text],
                        "target": nodes[target_text],
                        "type": ext.extraction_class,
                    }
                )

        return nodes, relationships

def ingest_to_neo4j(self, nodes: dict, relationships: list):
        """
        Ingest nodes and relationships into Neo4j.
        """
        with self.neo4j_driver.session() as session:
            # Create nodes in Neo4j
            for name, node_id in nodes.items():
                session.run(
                    "CREATE (n:Entity {id: $id, name: $name})",
                    id=node_id,
                    name=name,
                )

            # Create relationships in Neo4j, using the semantic type
            # (dosage/frequency/condition/etc.) as the actual relationship
            # label instead of a generic "RELATIONSHIP" type.
            for relationship in relationships:
                rel_type = self._sanitize_relationship_type(relationship["type"])
                session.run(
                    "MATCH (a:Entity {id: $source_id}), (b:Entity {id: $target_id}) "
                    f"CREATE (a)-[:{rel_type} {{type: $type}}]->(b)",
                    source_id=relationship["source"],
                    target_id=relationship["target"],
                    type=relationship["type"],
                )

        return nodes

@staticmethod
    def _sanitize_relationship_type(raw_type: str) -> str:
        """
        Cypher relationship types can't be passed as query parameters, so
        they have to be interpolated into the query string directly. Since
        raw_type comes from LLM-extracted text, sanitize it to a safe
        UPPER_SNAKE_CASE identifier before interpolation, to avoid Cypher
        injection or syntax errors from unexpected characters.
        """
        safe = "".join(ch if ch.isalnum() else "_" for ch in raw_type.strip())
        safe = safe.upper().strip("_") or "RELATIONSHIP"
        if safe[0].isdigit():
            safe = f"REL_{safe}"
        return safe

def create_collection(self, collection_name: str, vector_dimension: int = None):
        vector_dimension = vector_dimension or self.vector_dimension

        try:
            # Try to fetch the collection status
            self.qdrant_client.get_collection(collection_name)
            print(f"Skipping creating collection; '{collection_name}' already exists.")
        except Exception as e:
            # If collection does not exist, an error will be thrown, so we create the collection
            if "Not found: Collection" in str(e) or "doesn't exist" in str(e) or "404" in str(e):
                print(f"Collection '{collection_name}' not found. Creating it now...")

                self.qdrant_client.create_collection(
                    collection_name=collection_name,
                    vectors_config=models.VectorParams(
                        size=vector_dimension, distance=models.Distance.COSINE
                    ),
                )

                print(f"Collection '{collection_name}' created successfully.")
            else:
                print(f"Error while checking collection: {e}")

def ollama_embeddings(self, text: str) -> list[float]:
        response = self.ollama_client.embeddings(
            model=self.ollama_embedding_model,
            prompt=text,
        )
        return response["embedding"]

def ingest_to_qdrant(self, collection_name: str, raw_data: str, node_id_mapping: dict):
        names = list(node_id_mapping.keys())
        embeddings = [self.ollama_embeddings(name) for name in names]

        self.qdrant_client.upsert(
            collection_name=collection_name,
            points=[
                {
                    "id": str(uuid.uuid4()),
                    "vector": embedding,
                    "payload": {"id": node_id_mapping[name], "name": name},
                }
                for name, embedding in zip(names, embeddings)
            ],
        )

def retriever_search(self, collection_name: str, query: str, top_k: int = 5):
        retriever = QdrantNeo4jRetriever(
            driver=self.neo4j_driver,
            client=self.qdrant_client,
            collection_name=collection_name,
            id_property_external="id",
            id_property_neo4j="id",
        )

        results = retriever.search(
            query_vector=self.ollama_embeddings(query), top_k=top_k
        )

        return results

def fetch_related_graph(self, entity_ids: list):
        query = """
        MATCH (e:Entity)-[r1]-(n1)-[r2]-(n2)
        WHERE e.id IN $entity_ids
        RETURN e, r1 as r, n1 as related, r2, n2
        UNION
        MATCH (e:Entity)-[r]-(related)
        WHERE e.id IN $entity_ids
        RETURN e, r, related, null as r2, null as n2
        """
        with self.neo4j_driver.session() as session:
            result = session.run(query, entity_ids=entity_ids)
            subgraph = []
            for record in result:
                subgraph.append(
                    {
                        "entity": record["e"],
                        "relationship": record["r"],
                        "related_node": record["related"],
                    }
                )
                if record["r2"] and record["n2"]:
                    subgraph.append(
                        {
                            "entity": record["related"],
                            "relationship": record["r2"],
                            "related_node": record["n2"],
                        }
                    )
        return subgraph

def format_graph_context(self, subgraph: list):
        nodes = set()
        edges = []

        for entry in subgraph:
            entity = entry["entity"]
            related = entry["related_node"]
            relationship = entry["relationship"]

            nodes.add(entity["name"])
            nodes.add(related["name"])

            edges.append(f"{entity['name']} {relationship['type']} {related['name']}")

        return {"nodes": list(nodes), "edges": edges}

def graphRAG_run(self, graph_context: dict, user_query: str):
        nodes_str = ", ".join(graph_context["nodes"])
        edges_str = "; ".join(graph_context["edges"])
        prompt = f"""
        You are an intelligent assistant with access to the following knowledge graph:

        Nodes: {nodes_str}

        Edges: {edges_str}

        Using this graph, Answer the following question:

        User Query: "{user_query}"
        """

        try:
            response = chat(
                model=self.ollama_model_answer,
                messages=[
                    {
                        "role": "system",
                        "content": "Provide the answer for the following question:",
                    },
                    {"role": "user", "content": prompt},
                ],
            )
            return response.message.content

        except Exception as e:
            return f"Error querying LLM: {str(e)}"

def create_and_ingest(self, raw_data: str, query: str, collection_name: str = "medicationGraphRAGstore"):
        print("Creating collection...")
        self.create_collection(collection_name, self.vector_dimension)
        print("Collection created/verified")

        print("Extracting graph components...")
        nodes, relationships = self.extract_graph_components(raw_data)
        print("Nodes:", nodes)
        print("Relationships:", relationships)

        print("Ingesting to Neo4j...")
        node_id_mapping = self.ingest_to_neo4j(nodes, relationships)
        print("Neo4j ingestion complete")

        print("Ingesting to Qdrant...")
        self.ingest_to_qdrant(collection_name, raw_data, node_id_mapping)
        print("Qdrant ingestion complete")

def run_pipeline(self, raw_data: str, query: str, collection_name: str = "medicationGraphRAGstore"):

        # run only the first time, comment this for subsequent runs
        # self.create_and_ingest(raw_data, query, collection_name)

        print("Starting retriever search...")
        retriever_result = self.retriever_search(collection_name, query)
        print("Retriever results:", retriever_result)

        print("Extracting entity IDs...")
        entity_ids = [
            item.content.split("'id': '")[1].split("'")[0]
            for item in retriever_result.items
        ]
        print("Entity IDs:", entity_ids)

        print("Fetching related graph...")
        subgraph = self.fetch_related_graph(entity_ids)
        print("Subgraph:", subgraph)

        print("Formatting graph context...")
        graph_context = self.format_graph_context(subgraph)
        print("Graph context:", graph_context)

        print("Running GraphRAG...")
        answer = self.graphRAG_run(graph_context, query)
        print("Final Answer:", answer)

        return answer

def close(self):
        self.neo4j_driver.close()

if __name__ == "__main__":
    print("Script started")

    graph_rag = MedicationGraphRAG(env_path="../.env")

    # Example-1
    # raw_data = textwrap.dedent("""
    #     The patient was prescribed Lisinopril and Metformin last month.
    #     He takes the Lisinopril 10mg daily for hypertension, but often misses
    #     his Metformin 500mg dose which should be taken twice daily for diabetes.
    #     """).strip()

    # Example-2
    raw_data = textwrap.dedent("""
    The patient is a 62-year-old man with a history of multiple chronic conditions
    being managed through an extensive medication regimen. He was prescribed
    Lisinopril, Metformin, Atorvastatin, Aspirin, Levothyroxine, and Sertraline
    over the course of the past year, with his treatment plan adjusted several
    times based on follow-up visits.

    He takes Lisinopril 10mg daily for hypertension, but often misses his
    Metformin 500mg dose which should be taken twice daily for diabetes. His
    cardiologist also started him on Atorvastatin 40mg at bedtime for high
    cholesterol after his last lipid panel showed elevated LDL levels. To reduce
    his risk of cardiovascular events, he was additionally prescribed Aspirin
    81mg daily for heart disease prevention, which he takes alongside his
    breakfast each morning.

    Following a routine thyroid screening, he was found to have an underactive
    thyroid and was started on Levothyroxine 75mcg every morning for
    hypothyroidism, to be taken on an empty stomach before any other medications.
    More recently, after reporting persistent low mood and difficulty sleeping
    during a wellness visit, his primary care physician added Sertraline 50mg
    daily for depression, with plans to reassess the dosage after eight weeks.

    Despite the number of prescriptions, the patient has had difficulty
    maintaining consistency with his Metformin and occasionally forgets his
    evening Atorvastatin dose, which his care team is now addressing through a
    simplified pill organizer and reminder system.
    """).strip()

    # Sample Questions
    #1. "What is the dosage and frequency for Lisinopril?"
    #2. "What is the dosage and frequency for Metformin?"
    #3. "Which medications does the patient take once daily versus twice daily?"
    #4. "What medication is prescribed for hypothyroidism, and at what dose?"
    #5. "List all medications related to cardiovascular conditions and their dosages."
    #6. "How often does the patient take Aspirin?"
    #7. "What condition is Levothyroxine prescribed for?"
    #8. "What time of day should Levothyroxine be taken, and why?"
    #9. "Which medications does the patient have trouble taking consistently?"
    #10. "What is the dosage and frequency for Sertraline?"

    query = "List all medications related to cardiovascular conditions and their dosages."

    answer = graph_rag.run_pipeline(raw_data, query, collection_name="medicationGraphRAGstore")

    graph_rag.close()

import os
import uuid
import textwrap

import ollama
from dotenv import load_dotenv, find_dotenv
from ollama import chat
from neo4j import GraphDatabase
from qdrant_client import QdrantClient, models
from neo4j_graphrag.retrievers import QdrantNeo4jRetriever

import langextract as lx
from langextract.providers import ollama as lx_ollama

load_dotenv(find_dotenv())


class MedicationGraphRAG:

    def __init__(self,env_path: str = ".env",ollama_model_extract: str = "gemma3:latest",
            ollama_model_answer: str = "gemma3:latest",ollama_embedding_model: str = "embeddinggemma:latest",
            ollama_host: str | None = None,vector_dimension: int = 768,
    ):

        load_dotenv(env_path)

        self.qdrant_key = os.getenv("QDRANT_KEY")
        self.qdrant_url = os.getenv("QDRANT_URL")
        self.neo4j_uri = os.getenv("NEO4J_URI")
        self.neo4j_username = os.getenv("NEO4J_USERNAME")
        self.neo4j_password = os.getenv("NEO4J_PASSWORD")

        self.neo4j_driver = GraphDatabase.driver(
            self.neo4j_uri, auth=(self.neo4j_username, self.neo4j_password)
        )

        self.qdrant_client = QdrantClient(
            url=self.qdrant_url,
            api_key=self.qdrant_key,
        )

        # Ollama client for local embeddings (embeddinggemma:latest)
        self.ollama_client = ollama.Client(host=ollama_host) if ollama_host else ollama.Client()

        # langextract needs a plain URL string, not a client object
        self.ollama_url = ollama_host or os.environ.get("OLLAMA_HOST", "http://localhost:11434")

        # Model / config knobs
        self.ollama_model_extract = ollama_model_extract
        self.ollama_model_answer = ollama_model_answer
        self.ollama_embedding_model = ollama_embedding_model
        self.vector_dimension = vector_dimension

    def extract_graph_components(self, raw_data: str):
        """Extract medication entities and relationships using langextract + Ollama."""

        prompt_description = textwrap.dedent("""
            Extract medications with their details, using attributes to group related information:
            1. Extract entities in the order they appear in the text
            2. Each entity must have a 'medication_group' attribute linking it to its medication
            3. All details about a medication should share the same medication_group value
            """).strip()

        examples = [
            lx.data.ExampleData(
                text=(
                    "Patient takes Aspirin 100mg daily for heart health and"
                    " Simvastatin 20mg at bedtime."
                ),
                extractions=[
                    lx.data.Extraction(
                        extraction_class="medication",
                        extraction_text="Aspirin",
                        attributes={"medication_group": "Aspirin"},
                    ),
                    lx.data.Extraction(
                        extraction_class="dosage",
                        extraction_text="100mg",
                        attributes={"medication_group": "Aspirin"},
                    ),
                    lx.data.Extraction(
                        extraction_class="frequency",
                        extraction_text="daily",
                        attributes={"medication_group": "Aspirin"},
                    ),
                    lx.data.Extraction(
                        extraction_class="condition",
                        extraction_text="heart health",
                        attributes={"medication_group": "Aspirin"},
                    ),
                    lx.data.Extraction(
                        extraction_class="medication",
                        extraction_text="Simvastatin",
                        attributes={"medication_group": "Simvastatin"},
                    ),
                    lx.data.Extraction(
                        extraction_class="dosage",
                        extraction_text="20mg",
                        attributes={"medication_group": "Simvastatin"},
                    ),
                    lx.data.Extraction(
                        extraction_class="frequency",
                        extraction_text="at bedtime",
                        attributes={"medication_group": "Simvastatin"},
                    ),
                ],
            )
        ]

        result = lx.extract(
            text_or_documents=raw_data,
            prompt_description=prompt_description,
            examples=examples,
            model_id=self.ollama_model_extract,
            model_url=self.ollama_url,
            resolver_params={"format_handler": lx_ollama.OLLAMA_FORMAT_HANDLER},
            max_char_buffer=4000,
            show_progress=True,
        )

        return self._convert_extractions_to_graph(result.extractions)

    def _convert_extractions_to_graph(self, extractions: list):
        """Convert langextract's flat, grouped extractions into (nodes, relationships)."""

        groups: dict[str, list] = {}
        for ext in extractions:
            if not ext.attributes or "medication_group" not in ext.attributes:
                continue
            group_name = ext.attributes["medication_group"]
            groups.setdefault(group_name, []).append(ext)

        nodes: dict[str, str] = {}
        relationships: list[dict] = []

        for group_name, group_extractions in groups.items():
            anchor_ext = next(
                (e for e in group_extractions if e.extraction_class == "medication"),
                None,
            )

            # Fall back to the group name itself if no explicit "medication"
            # extraction was found in this group, so we still get an anchor.
            anchor_text = anchor_ext.extraction_text if anchor_ext else group_name

            if anchor_text not in nodes:
                nodes[anchor_text] = str(uuid.uuid4())

            for ext in group_extractions:
                if ext is anchor_ext:
                    continue

                target_text = ext.extraction_text
                if target_text not in nodes:
                    nodes[target_text] = str(uuid.uuid4())

                relationships.append(
                    {
                        "source": nodes[anchor_text],
                        "target": nodes[target_text],
                        "type": ext.extraction_class,
                    }
                )

        return nodes, relationships

    def ingest_to_neo4j(self, nodes: dict, relationships: list):
        """
        Ingest nodes and relationships into Neo4j.
        """
        with self.neo4j_driver.session() as session:
            # Create nodes in Neo4j
            for name, node_id in nodes.items():
                session.run(
                    "CREATE (n:Entity {id: $id, name: $name})",
                    id=node_id,
                    name=name,
                )

            # Create relationships in Neo4j, using the semantic type
            # (dosage/frequency/condition/etc.) as the actual relationship
            # label instead of a generic "RELATIONSHIP" type.
            for relationship in relationships:
                rel_type = self._sanitize_relationship_type(relationship["type"])
                session.run(
                    "MATCH (a:Entity {id: $source_id}), (b:Entity {id: $target_id}) "
                    f"CREATE (a)-[:{rel_type} {{type: $type}}]->(b)",
                    source_id=relationship["source"],
                    target_id=relationship["target"],
                    type=relationship["type"],
                )

        return nodes

    @staticmethod
    def _sanitize_relationship_type(raw_type: str) -> str:
        """
        Cypher relationship types can't be passed as query parameters, so
        they have to be interpolated into the query string directly. Since
        raw_type comes from LLM-extracted text, sanitize it to a safe
        UPPER_SNAKE_CASE identifier before interpolation, to avoid Cypher
        injection or syntax errors from unexpected characters.
        """
        safe = "".join(ch if ch.isalnum() else "_" for ch in raw_type.strip())
        safe = safe.upper().strip("_") or "RELATIONSHIP"
        if safe[0].isdigit():
            safe = f"REL_{safe}"
        return safe

    def create_collection(self, collection_name: str, vector_dimension: int = None):
        vector_dimension = vector_dimension or self.vector_dimension

        try:
            # Try to fetch the collection status
            self.qdrant_client.get_collection(collection_name)
            print(f"Skipping creating collection; '{collection_name}' already exists.")
        except Exception as e:
            # If collection does not exist, an error will be thrown, so we create the collection
            if "Not found: Collection" in str(e) or "doesn't exist" in str(e) or "404" in str(e):
                print(f"Collection '{collection_name}' not found. Creating it now...")

                self.qdrant_client.create_collection(
                    collection_name=collection_name,
                    vectors_config=models.VectorParams(
                        size=vector_dimension, distance=models.Distance.COSINE
                    ),
                )

                print(f"Collection '{collection_name}' created successfully.")
            else:
                print(f"Error while checking collection: {e}")

    def ollama_embeddings(self, text: str) -> list[float]:
        response = self.ollama_client.embeddings(
            model=self.ollama_embedding_model,
            prompt=text,
        )
        return response["embedding"]

    def ingest_to_qdrant(self, collection_name: str, raw_data: str, node_id_mapping: dict):
        names = list(node_id_mapping.keys())
        embeddings = [self.ollama_embeddings(name) for name in names]

        self.qdrant_client.upsert(
            collection_name=collection_name,
            points=[
                {
                    "id": str(uuid.uuid4()),
                    "vector": embedding,
                    "payload": {"id": node_id_mapping[name], "name": name},
                }
                for name, embedding in zip(names, embeddings)
            ],
        )

    def retriever_search(self, collection_name: str, query: str, top_k: int = 5):
        retriever = QdrantNeo4jRetriever(
            driver=self.neo4j_driver,
            client=self.qdrant_client,
            collection_name=collection_name,
            id_property_external="id",
            id_property_neo4j="id",
        )

        results = retriever.search(
            query_vector=self.ollama_embeddings(query), top_k=top_k
        )

        return results

    def fetch_related_graph(self, entity_ids: list):
        query = """
        MATCH (e:Entity)-[r1]-(n1)-[r2]-(n2)
        WHERE e.id IN $entity_ids
        RETURN e, r1 as r, n1 as related, r2, n2
        UNION
        MATCH (e:Entity)-[r]-(related)
        WHERE e.id IN $entity_ids
        RETURN e, r, related, null as r2, null as n2
        """
        with self.neo4j_driver.session() as session:
            result = session.run(query, entity_ids=entity_ids)
            subgraph = []
            for record in result:
                subgraph.append(
                    {
                        "entity": record["e"],
                        "relationship": record["r"],
                        "related_node": record["related"],
                    }
                )
                if record["r2"] and record["n2"]:
                    subgraph.append(
                        {
                            "entity": record["related"],
                            "relationship": record["r2"],
                            "related_node": record["n2"],
                        }
                    )
        return subgraph

    def format_graph_context(self, subgraph: list):
        nodes = set()
        edges = []

        for entry in subgraph:
            entity = entry["entity"]
            related = entry["related_node"]
            relationship = entry["relationship"]

            nodes.add(entity["name"])
            nodes.add(related["name"])

            edges.append(f"{entity['name']} {relationship['type']} {related['name']}")

        return {"nodes": list(nodes), "edges": edges}

    def graphRAG_run(self, graph_context: dict, user_query: str):
        nodes_str = ", ".join(graph_context["nodes"])
        edges_str = "; ".join(graph_context["edges"])
        prompt = f"""
        You are an intelligent assistant with access to the following knowledge graph:

        Nodes: {nodes_str}

        Edges: {edges_str}

        Using this graph, Answer the following question:

        User Query: "{user_query}"
        """

        try:
            response = chat(
                model=self.ollama_model_answer,
                messages=[
                    {
                        "role": "system",
                        "content": "Provide the answer for the following question:",
                    },
                    {"role": "user", "content": prompt},
                ],
            )
            return response.message.content

        except Exception as e:
            return f"Error querying LLM: {str(e)}"

    def create_and_ingest(self, raw_data: str, query: str, collection_name: str = "medicationGraphRAGstore"):
        print("Creating collection...")
        self.create_collection(collection_name, self.vector_dimension)
        print("Collection created/verified")

        print("Extracting graph components...")
        nodes, relationships = self.extract_graph_components(raw_data)
        print("Nodes:", nodes)
        print("Relationships:", relationships)

        print("Ingesting to Neo4j...")
        node_id_mapping = self.ingest_to_neo4j(nodes, relationships)
        print("Neo4j ingestion complete")

        print("Ingesting to Qdrant...")
        self.ingest_to_qdrant(collection_name, raw_data, node_id_mapping)
        print("Qdrant ingestion complete")

    def run_pipeline(self, raw_data: str, query: str, collection_name: str = "medicationGraphRAGstore"):

        # run only the first time, comment this for subsequent runs
        # self.create_and_ingest(raw_data, query, collection_name)

        print("Starting retriever search...")
        retriever_result = self.retriever_search(collection_name, query)
        print("Retriever results:", retriever_result)

        print("Extracting entity IDs...")
        entity_ids = [
            item.content.split("'id': '")[1].split("'")[0]
            for item in retriever_result.items
        ]
        print("Entity IDs:", entity_ids)

        print("Fetching related graph...")
        subgraph = self.fetch_related_graph(entity_ids)
        print("Subgraph:", subgraph)

        print("Formatting graph context...")
        graph_context = self.format_graph_context(subgraph)
        print("Graph context:", graph_context)

        print("Running GraphRAG...")
        answer = self.graphRAG_run(graph_context, query)
        print("Final Answer:", answer)

        return answer

    def close(self):
        self.neo4j_driver.close()

if __name__ == "__main__":
    print("Script started")

    graph_rag = MedicationGraphRAG(env_path="../.env")

    # Example-1
    # raw_data = textwrap.dedent("""
    #     The patient was prescribed Lisinopril and Metformin last month.
    #     He takes the Lisinopril 10mg daily for hypertension, but often misses
    #     his Metformin 500mg dose which should be taken twice daily for diabetes.
    #     """).strip()

    # Example-2
    raw_data = textwrap.dedent("""
    The patient is a 62-year-old man with a history of multiple chronic conditions
    being managed through an extensive medication regimen. He was prescribed
    Lisinopril, Metformin, Atorvastatin, Aspirin, Levothyroxine, and Sertraline
    over the course of the past year, with his treatment plan adjusted several
    times based on follow-up visits.

    He takes Lisinopril 10mg daily for hypertension, but often misses his
    Metformin 500mg dose which should be taken twice daily for diabetes. His
    cardiologist also started him on Atorvastatin 40mg at bedtime for high
    cholesterol after his last lipid panel showed elevated LDL levels. To reduce
    his risk of cardiovascular events, he was additionally prescribed Aspirin
    81mg daily for heart disease prevention, which he takes alongside his
    breakfast each morning.

    Following a routine thyroid screening, he was found to have an underactive
    thyroid and was started on Levothyroxine 75mcg every morning for
    hypothyroidism, to be taken on an empty stomach before any other medications.
    More recently, after reporting persistent low mood and difficulty sleeping
    during a wellness visit, his primary care physician added Sertraline 50mg
    daily for depression, with plans to reassess the dosage after eight weeks.

    Despite the number of prescriptions, the patient has had difficulty
    maintaining consistency with his Metformin and occasionally forgets his
    evening Atorvastatin dose, which his care team is now addressing through a
    simplified pill organizer and reminder system.
    """).strip()

    # Sample Questions
    #1. "What is the dosage and frequency for Lisinopril?"
    #2. "What is the dosage and frequency for Metformin?"
    #3. "Which medications does the patient take once daily versus twice daily?"
    #4. "What medication is prescribed for hypothyroidism, and at what dose?"
    #5. "List all medications related to cardiovascular conditions and their dosages."
    #6. "How often does the patient take Aspirin?"
    #7. "What condition is Levothyroxine prescribed for?"
    #8. "What time of day should Levothyroxine be taken, and why?"
    #9. "Which medications does the patient have trouble taking consistently?"
    #10. "What is the dosage and frequency for Sertraline?"

    query = "List all medications related to cardiovascular conditions and their dosages."

    answer = graph_rag.run_pipeline(raw_data, query, collection_name="medicationGraphRAGstore")

    graph_rag.close()

Адкліканне:

Для стадіі The Run неабяжна практычна апранаванне вхідных дадзеных, адміністратара крока і крэтэрыяў завершэння прычым змене коду. Аперацыйныя працавнікі павінны магчымае перапрацаваць крок з вядомай точкі контролю, не падозрываючы схованы статус. Неабяжна задокументаваць як шлях успеху, так і шлях вярнення. Перапрабавкі, людзкіе перакрыцця і обробка некоректных паведамленняў є часткай продукту, а не чымсь, што дадаецца пазней. Аддзельнае стварэнне кліента ад цыклу паведамленняў дазволяе змініць прадаўцаў без перапісвання машыны стану размовы.

query = "List all medications related to cardiovascular conditions and their dosages."

Console:
Starting retriever search...
Retriever results: items=[RetrieverResultItem(content="<Record node=<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:19' labels=frozenset({'Entity'}) properties={'name': 'on an empty stomach before any other medications', 'id': '1c9fb28e-c3e1-4515-afa0-01939beac419'}> score=0.5062605>", metadata=None), RetrieverResultItem(content="<Record node=<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:0' labels=frozenset({'Entity'}) properties={'name': 'Atorvastatin', 'id': '1db81626-c3de-4cab-b5dd-091327785182'}> score=0.47961158>", metadata=None), RetrieverResultItem(content="<Record node=<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:6' labels=frozenset({'Entity'}) properties={'name': 'heart disease prevention', 'id': '44573186-869f-4d5d-ba04-fe254ec4ed21'}> score=0.45777896>", metadata=None), RetrieverResultItem(content="<Record node=<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:8' labels=frozenset({'Entity'}) properties={'name': 'Lisinopril', 'id': '04f27c55-38a3-46a9-a411-4faf07836a56'}> score=0.45199984>", metadata=None), RetrieverResultItem(content="<Record node=<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:7' labels=frozenset({'Entity'}) properties={'name': 'Levothyroxine', 'id': '60654f2d-2ec8-4f4e-be02-cd57b7c5abfb'}> score=0.4511963>", metadata=None)] metadata={'__retriever': 'QdrantNeo4jRetriever'}
Extracting entity IDs...
Entity IDs: ['1c9fb28e-c3e1-4515-afa0-01939beac419', '1db81626-c3de-4cab-b5dd-091327785182', '44573186-869f-4d5d-ba04-fe254ec4ed21', '04f27c55-38a3-46a9-a411-4faf07836a56', '60654f2d-2ec8-4f4e-be02-cd57b7c5abfb']
Fetching related graph...
Subgraph: [{'entity': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:6' labels=frozenset({'Entity'}) properties={'name': 'heart disease prevention', 'id': '44573186-869f-4d5d-ba04-fe254ec4ed21'}>, 'relationship': <Relationship element_id='5:a53f7783-1308-4f3d-9a27-45cede6b6376:5' nodes=(<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:4' labels=frozenset({'Entity'}) properties={'name': 'Aspirin', 'id': '10939c85-6fc1-41b4-9b80-abe1662eda11'}>, <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:6' labels=frozenset({'Entity'}) properties={'name': 'heart disease prevention', 'id': '44573186-869f-4d5d-ba04-fe254ec4ed21'}>) type='CONDITION' properties={'type': 'condition'}>, 'related_node': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:4' labels=frozenset({'Entity'}) properties={'name': 'Aspirin', 'id': '10939c85-6fc1-41b4-9b80-abe1662eda11'}>}, {'entity': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:4' labels=frozenset({'Entity'}) properties={'name': 'Aspirin', 'id': '10939c85-6fc1-41b4-9b80-abe1662eda11'}>, 'relationship': <Relationship element_id='5:a53f7783-1308-4f3d-9a27-45cede6b6376:4' nodes=(<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:4' labels=frozenset({'Entity'}) properties={'name': 'Aspirin', 'id': '10939c85-6fc1-41b4-9b80-abe1662eda11'}>, <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:10' labels=frozenset({'Entity'}) properties={'name': 'daily', 'id': '06da6faa-baa5-4657-a961-5091f58e3058'}>) type='FREQUENCY' properties={'type': 'frequency'}>, 'related_node': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:10' labels=frozenset({'Entity'}) properties={'name': 'daily', 'id': '06da6faa-baa5-4657-a961-5091f58e3058'}>}, {'entity': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:6' labels=frozenset({'Entity'}) properties={'name': 'heart disease prevention', 'id': '44573186-869f-4d5d-ba04-fe254ec4ed21'}>, 'relationship': <Relationship element_id='5:a53f7783-1308-4f3d-9a27-45cede6b6376:5' nodes=(<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:4' labels=frozenset({'Entity'}) properties={'name': 'Aspirin', 'id': '10939c85-6fc1-41b4-9b80-abe1662eda11'}>, <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:6' labels=frozenset({'Entity'}) properties={'name': 'heart disease prevention', 'id': '44573186-869f-4d5d-ba04-fe254ec4ed21'}>) type='CONDITION' properties={'type': 'condition'}>, 'related_node': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:4' labels=frozenset({'Entity'}) properties={'name': 'Aspirin', 'id': '10939c85-6fc1-41b4-9b80-abe1662eda11'}>}, {'entity': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:4' labels=frozenset({'Entity'}) properties={'name': 'Aspirin', 'id': '10939c85-6fc1-41b4-9b80-abe1662eda11'}>, 'relationship': <Relationship element_id='5:a53f7783-1308-4f3d-9a27-45cede6b6376:3' nodes=(<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:4' labels=frozenset({'Entity'}) properties={'name': 'Aspirin', 'id': '10939c85-6fc1-41b4-9b80-abe1662eda11'}>, <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:5' labels=frozenset({'Entity'}) properties={'name': '81mg', 'id': '9d1d3859-cf6f-4a22-81b8-7d80e3cc8e89'}>) type='DOSAGE' properties={'type': 'dosage'}>, 'related_node': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:5' labels=frozenset({'Entity'}) properties={'name': '81mg', 'id': '9d1d3859-cf6f-4a22-81b8-7d80e3cc8e89'}>}, {'entity': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:8' labels=frozenset({'Entity'}) properties={'name': 'Lisinopril', 'id': '04f27c55-38a3-46a9-a411-4faf07836a56'}>, 'relationship': <Relationship element_id='5:a53f7783-1308-4f3d-9a27-45cede6b6376:7' nodes=(<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:8' labels=frozenset({'Entity'}) properties={'name': 'Lisinopril', 'id': '04f27c55-38a3-46a9-a411-4faf07836a56'}>, <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:10' labels=frozenset({'Entity'}) properties={'name': 'daily', 'id': '06da6faa-baa5-4657-a961-5091f58e3058'}>) type='FREQUENCY' properties={'type': 'frequency'}>, 'related_node': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:10' labels=frozenset({'Entity'}) properties={'name': 'daily', 'id': '06da6faa-baa5-4657-a961-5091f58e3058'}>}, {'entity': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:10' labels=frozenset({'Entity'}) properties={'name': 'daily', 'id': '06da6faa-baa5-4657-a961-5091f58e3058'}>, 'relationship': <Relationship element_id='5:a53f7783-1308-4f3d-9a27-45cede6b6376:17' nodes=(<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:20' labels=frozenset({'Entity'}) properties={'name': 'Sertraline', 'id': '42844c4c-ce96-4bd9-8d37-d8adad5a75fe'}>, <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:10' labels=frozenset({'Entity'}) properties={'name': 'daily', 'id': '06da6faa-baa5-4657-a961-5091f58e3058'}>) type='FREQUENCY' properties={'type': 'frequency'}>, 'related_node': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:20' labels=frozenset({'Entity'}) properties={'name': 'Sertraline', 'id': '42844c4c-ce96-4bd9-8d37-d8adad5a75fe'}>}, {'entity': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:8' labels=frozenset({'Entity'}) properties={'name': 'Lisinopril', 'id': '04f27c55-38a3-46a9-a411-4faf07836a56'}>, 'relationship': <Relationship element_id='5:a53f7783-1308-4f3d-9a27-45cede6b6376:7' nodes=(<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:8' labels=frozenset({'Entity'}) properties={'name': 'Lisinopril', 'id': '04f27c55-38a3-46a9-a411-4faf07836a56'}>, <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:10' labels=frozenset({'Entity'}) properties={'name': 'daily', 'id': '06da6faa-baa5-4657-a961-5091f58e3058'}>) type='FREQUENCY' properties={'type': 'frequency'}>, 'related_node': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:10' labels=frozenset({'Entity'}) properties={'name': 'daily', 'id': '06da6faa-baa5-4657-a961-5091f58e3058'}>}, {'entity': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:10' labels=frozenset({'Entity'}) properties={'name': 'daily', 'id': '06da6faa-baa5-4657-a961-5091f58e3058'}>, 'relationship': <Relationship element_id='5:a53f7783-1308-4f3d-9a27-45cede6b6376:4' nodes=(<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:4' labels=frozenset({'Entity'}) properties={'name': 'Aspirin', 'id': '10939c85-6fc1-41b4-9b80-abe1662eda11'}>, <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:10' labels=frozenset({'Entity'}) properties={'name': 'daily', 'id': '06da6faa-baa5-4657-a961-5091f58e3058'}>) type='FREQUENCY' properties={'type': 'frequency'}>, 'related_node': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:4' labels=frozenset({'Entity'}) properties={'name': 'Aspirin', 'id': '10939c85-6fc1-41b4-9b80-abe1662eda11'}>}, {'entity': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:19' labels=frozenset({'Entity'}) properties={'name': 'on an empty stomach before any other medications', 'id': '1c9fb28e-c3e1-4515-afa0-01939beac419'}>, 'relationship': <Relationship element_id='5:a53f7783-1308-4f3d-9a27-45cede6b6376:15' nodes=(<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:7' labels=frozenset({'Entity'}) properties={'name': 'Levothyroxine', 'id': '60654f2d-2ec8-4f4e-be02-cd57b7c5abfb'}>, <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:19' labels=frozenset({'Entity'}) properties={'name': 'on an empty stomach before any other medications', 'id': '1c9fb28e-c3e1-4515-afa0-01939beac419'}>) type='ROUTE' properties={'type': 'route'}>, 'related_node': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:7' labels=frozenset({'Entity'}) properties={'name': 'Levothyroxine', 'id': '60654f2d-2ec8-4f4e-be02-cd57b7c5abfb'}>}, {'entity': <Node 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labels=frozenset({'Entity'}) properties={'name': 'Levothyroxine', 'id': '60654f2d-2ec8-4f4e-be02-cd57b7c5abfb'}>}]
Formatting graph context...
Graph context: {'nodes': ['on an empty stomach before any other medications', 'hypertension', 'Atorvastatin', 'Lisinopril', 'Levothyroxine', 'at bedtime', 'high cholesterol', 'Aspirin', '75mcg', '81mg', 'daily', '10mg', 'heart disease prevention', 'every morning', 'Sertraline', 'hypothyroidism', '40mg'], 'edges': ['heart disease prevention condition Aspirin', 'Aspirin frequency daily', 'heart disease prevention condition Aspirin', 'Aspirin dosage 81mg', 'Lisinopril frequency daily', 'daily frequency Sertraline', 'Lisinopril frequency daily', 'daily frequency Aspirin', 'on an empty stomach before any other medications route Levothyroxine', 'Levothyroxine condition hypothyroidism', 'on an empty stomach before any other medications route Levothyroxine', 'Levothyroxine frequency every morning', 'on an empty stomach before any other medications route Levothyroxine', 'Levothyroxine dosage 75mcg', 'Atorvastatin condition high cholesterol', 'Atorvastatin frequency at bedtime', 'Atorvastatin dosage 40mg', 'heart disease prevention condition Aspirin', 'Levothyroxine route on an empty stomach before any other medications', 'Levothyroxine condition hypothyroidism', 'Levothyroxine frequency every morning', 'Levothyroxine dosage 75mcg', 'Lisinopril condition hypertension', 'Lisinopril frequency daily', 'Lisinopril dosage 10mg', 'on an empty stomach before any other medications route Levothyroxine']}
Running GraphRAG...
Final Answer: Here’s a list of medications related to cardiovascular conditions and their dosages based on the knowledge graph:

*   **Aspirin:** 81mg (frequency: daily) - for heart disease prevention.
*   **Atorvastatin:** 40mg (frequency: at bedtime) - for high cholesterol.
*   **Lisinopril:** 10mg (frequency: daily) - for hypertension.

query = "What medication is prescribed for hypothyroidism, and at what dose?"

Console:
Starting retriever search...
Retriever results: items=[RetrieverResultItem(content="<Record node=<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:18' labels=frozenset({'Entity'}) properties={'name': 'hypothyroidism', 'id': '95b91440-5ff1-41eb-a036-b57880094b05'}> score=0.6616618>", metadata=None), RetrieverResultItem(content="<Record node=<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:7' labels=frozenset({'Entity'}) properties={'name': 'Levothyroxine', 'id': '60654f2d-2ec8-4f4e-be02-cd57b7c5abfb'}> score=0.5983002>", metadata=None), RetrieverResultItem(content="<Record node=<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:12' labels=frozenset({'Entity'}) properties={'name': 'Metformin', 'id': 'd14d70ef-3780-4ebe-b803-1ab8bd8ec847'}> score=0.4675771>", metadata=None), RetrieverResultItem(content="<Record node=<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:19' labels=frozenset({'Entity'}) properties={'name': 'on an empty stomach before any other medications', 'id': '1c9fb28e-c3e1-4515-afa0-01939beac419'}> score=0.4620626>", metadata=None), RetrieverResultItem(content="<Record node=<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:9' labels=frozenset({'Entity'}) properties={'name': '10mg', 'id': 'cddff401-0958-4093-b196-ad270b7aa797'}> score=0.4382253>", metadata=None)] metadata={'__retriever': 'QdrantNeo4jRetriever'}
Extracting entity IDs...
Entity IDs: ['95b91440-5ff1-41eb-a036-b57880094b05', '60654f2d-2ec8-4f4e-be02-cd57b7c5abfb', 'd14d70ef-3780-4ebe-b803-1ab8bd8ec847', '1c9fb28e-c3e1-4515-afa0-01939beac419', 'cddff401-0958-4093-b196-ad270b7aa797']
Fetching related graph...
Subgraph: [{'entity': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:9' labels=frozenset({'Entity'}) properties={'name': '10mg', 'id': 'cddff401-0958-4093-b196-ad270b7aa797'}>, 'relationship': <Relationship element_id='5:a53f7783-1308-4f3d-9a27-45cede6b6376:6' nodes=(<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:8' labels=frozenset({'Entity'}) properties={'name': 'Lisinopril', 'id': '04f27c55-38a3-46a9-a411-4faf07836a56'}>, <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:9' labels=frozenset({'Entity'}) properties={'name': '10mg', 'id': 'cddff401-0958-4093-b196-ad270b7aa797'}>) type='DOSAGE' properties={'type': 'dosage'}>, 'related_node': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:8' labels=frozenset({'Entity'}) properties={'name': 'Lisinopril', 'id': '04f27c55-38a3-46a9-a411-4faf07836a56'}>}, {'entity': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:8' labels=frozenset({'Entity'}) properties={'name': 'Lisinopril', 'id': '04f27c55-38a3-46a9-a411-4faf07836a56'}>, 'relationship': <Relationship element_id='5:a53f7783-1308-4f3d-9a27-45cede6b6376:8' nodes=(<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:8' labels=frozenset({'Entity'}) properties={'name': 'Lisinopril', 'id': '04f27c55-38a3-46a9-a411-4faf07836a56'}>, <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:11' labels=frozenset({'Entity'}) properties={'name': 'hypertension', 'id': '1bc0479e-5e00-4ede-8a9d-704e56f58814'}>) type='CONDITION' properties={'type': 'condition'}>, 'related_node': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:11' labels=frozenset({'Entity'}) properties={'name': 'hypertension', 'id': '1bc0479e-5e00-4ede-8a9d-704e56f58814'}>}, {'entity': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:9' labels=frozenset({'Entity'}) properties={'name': '10mg', 'id': 'cddff401-0958-4093-b196-ad270b7aa797'}>, 'relationship': <Relationship element_id='5:a53f7783-1308-4f3d-9a27-45cede6b6376:6' nodes=(<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:8' labels=frozenset({'Entity'}) properties={'name': 'Lisinopril', 'id': '04f27c55-38a3-46a9-a411-4faf07836a56'}>, <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:9' labels=frozenset({'Entity'}) properties={'name': '10mg', 'id': 'cddff401-0958-4093-b196-ad270b7aa797'}>) type='DOSAGE' properties={'type': 'dosage'}>, 'related_node': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:8' labels=frozenset({'Entity'}) properties={'name': 'Lisinopril', 'id': '04f27c55-38a3-46a9-a411-4faf07836a56'}>}, {'entity': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:8' labels=frozenset({'Entity'}) properties={'name': 'Lisinopril', 'id': '04f27c55-38a3-46a9-a411-4faf07836a56'}>, 'relationship': <Relationship element_id='5:a53f7783-1308-4f3d-9a27-45cede6b6376:7' nodes=(<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:8' labels=frozenset({'Entity'}) properties={'name': 'Lisinopril', 'id': '04f27c55-38a3-46a9-a411-4faf07836a56'}>, <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:10' labels=frozenset({'Entity'}) properties={'name': 'daily', 'id': '06da6faa-baa5-4657-a961-5091f58e3058'}>) type='FREQUENCY' properties={'type': 'frequency'}>, 'related_node': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:10' labels=frozenset({'Entity'}) properties={'name': 'daily', 'id': '06da6faa-baa5-4657-a961-5091f58e3058'}>}, {'entity': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:18' labels=frozenset({'Entity'}) properties={'name': 'hypothyroidism', 'id': '95b91440-5ff1-41eb-a036-b57880094b05'}>, 'relationship': <Relationship element_id='5:a53f7783-1308-4f3d-9a27-45cede6b6376:14' nodes=(<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:7' labels=frozenset({'Entity'}) properties={'name': 'Levothyroxine', 'id': '60654f2d-2ec8-4f4e-be02-cd57b7c5abfb'}>, <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:18' labels=frozenset({'Entity'}) properties={'name': 'hypothyroidism', 'id': '95b91440-5ff1-41eb-a036-b57880094b05'}>) type='CONDITION' properties={'type': 'condition'}>, 'related_node': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:7' labels=frozenset({'Entity'}) properties={'name': 'Levothyroxine', 'id': '60654f2d-2ec8-4f4e-be02-cd57b7c5abfb'}>}, {'entity': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:7' labels=frozenset({'Entity'}) properties={'name': 'Levothyroxine', 'id': '60654f2d-2ec8-4f4e-be02-cd57b7c5abfb'}>, 'relationship': <Relationship element_id='5:a53f7783-1308-4f3d-9a27-45cede6b6376:15' nodes=(<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:7' labels=frozenset({'Entity'}) properties={'name': 'Levothyroxine', 'id': '60654f2d-2ec8-4f4e-be02-cd57b7c5abfb'}>, <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:19' labels=frozenset({'Entity'}) properties={'name': 'on an empty stomach before any other medications', 'id': '1c9fb28e-c3e1-4515-afa0-01939beac419'}>) type='ROUTE' properties={'type': 'route'}>, 'related_node': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:19' labels=frozenset({'Entity'}) properties={'name': 'on an empty stomach before any other medications', 'id': '1c9fb28e-c3e1-4515-afa0-01939beac419'}>}, {'entity': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:18' labels=frozenset({'Entity'}) properties={'name': 'hypothyroidism', 'id': '95b91440-5ff1-41eb-a036-b57880094b05'}>, 'relationship': <Relationship element_id='5:a53f7783-1308-4f3d-9a27-45cede6b6376:14' nodes=(<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:7' labels=frozenset({'Entity'}) properties={'name': 'Levothyroxine', 'id': '60654f2d-2ec8-4f4e-be02-cd57b7c5abfb'}>, <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:18' labels=frozenset({'Entity'}) properties={'name': 'hypothyroidism', 'id': '95b91440-5ff1-41eb-a036-b57880094b05'}>) type='CONDITION' properties={'type': 'condition'}>, 'related_node': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:7' labels=frozenset({'Entity'}) properties={'name': 'Levothyroxine', 'id': '60654f2d-2ec8-4f4e-be02-cd57b7c5abfb'}>}, {'entity': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:7' labels=frozenset({'Entity'}) properties={'name': 'Levothyroxine', 'id': '60654f2d-2ec8-4f4e-be02-cd57b7c5abfb'}>, 'relationship': <Relationship element_id='5:a53f7783-1308-4f3d-9a27-45cede6b6376:13' nodes=(<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:7' labels=frozenset({'Entity'}) properties={'name': 'Levothyroxine', 'id': '60654f2d-2ec8-4f4e-be02-cd57b7c5abfb'}>, <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:17' labels=frozenset({'Entity'}) properties={'name': 'every morning', 'id': '7fb1c7e5-e414-43ec-8606-474b08dc9173'}>) type='FREQUENCY' properties={'type': 'frequency'}>, 'related_node': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:17' labels=frozenset({'Entity'}) properties={'name': 'every morning', 'id': 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labels=frozenset({'Entity'}) properties={'name': 'Levothyroxine', 'id': '60654f2d-2ec8-4f4e-be02-cd57b7c5abfb'}>, 'relationship': <Relationship element_id='5:a53f7783-1308-4f3d-9a27-45cede6b6376:12' nodes=(<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:7' labels=frozenset({'Entity'}) properties={'name': 'Levothyroxine', 'id': '60654f2d-2ec8-4f4e-be02-cd57b7c5abfb'}>, <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:16' labels=frozenset({'Entity'}) properties={'name': '75mcg', 'id': '75b6f7a1-2f53-419c-9365-5f026dcb1e25'}>) type='DOSAGE' properties={'type': 'dosage'}>, 'related_node': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:16' labels=frozenset({'Entity'}) properties={'name': '75mcg', 'id': '75b6f7a1-2f53-419c-9365-5f026dcb1e25'}>}, {'entity': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:19' labels=frozenset({'Entity'}) properties={'name': 'on an empty stomach before any other medications', 'id': '1c9fb28e-c3e1-4515-afa0-01939beac419'}>, 'relationship': <Relationship element_id='5:a53f7783-1308-4f3d-9a27-45cede6b6376:15' nodes=(<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:7' labels=frozenset({'Entity'}) properties={'name': 'Levothyroxine', 'id': '60654f2d-2ec8-4f4e-be02-cd57b7c5abfb'}>, <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:19' labels=frozenset({'Entity'}) properties={'name': 'on an empty stomach before any other medications', 'id': '1c9fb28e-c3e1-4515-afa0-01939beac419'}>) type='ROUTE' properties={'type': 'route'}>, 'related_node': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:7' labels=frozenset({'Entity'}) properties={'name': 'Levothyroxine', 'id': '60654f2d-2ec8-4f4e-be02-cd57b7c5abfb'}>}, {'entity': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:7' labels=frozenset({'Entity'}) properties={'name': 'Levothyroxine', 'id': '60654f2d-2ec8-4f4e-be02-cd57b7c5abfb'}>, 'relationship': <Relationship element_id='5:a53f7783-1308-4f3d-9a27-45cede6b6376:14' nodes=(<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:7' labels=frozenset({'Entity'}) properties={'name': 'Levothyroxine', 'id': '60654f2d-2ec8-4f4e-be02-cd57b7c5abfb'}>, <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:18' labels=frozenset({'Entity'}) properties={'name': 'hypothyroidism', 'id': '95b91440-5ff1-41eb-a036-b57880094b05'}>) type='CONDITION' properties={'type': 'condition'}>, 'related_node': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:18' labels=frozenset({'Entity'}) properties={'name': 'hypothyroidism', 'id': '95b91440-5ff1-41eb-a036-b57880094b05'}>}, {'entity': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:19' labels=frozenset({'Entity'}) properties={'name': 'on an empty stomach before any other medications', 'id': '1c9fb28e-c3e1-4515-afa0-01939beac419'}>, 'relationship': <Relationship element_id='5:a53f7783-1308-4f3d-9a27-45cede6b6376:15' nodes=(<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:7' labels=frozenset({'Entity'}) properties={'name': 'Levothyroxine', 'id': '60654f2d-2ec8-4f4e-be02-cd57b7c5abfb'}>, <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:19' labels=frozenset({'Entity'}) properties={'name': 'on an empty stomach before any other medications', 'id': '1c9fb28e-c3e1-4515-afa0-01939beac419'}>) type='ROUTE' properties={'type': 'route'}>, 'related_node': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:7' labels=frozenset({'Entity'}) properties={'name': 'Levothyroxine', 'id': '60654f2d-2ec8-4f4e-be02-cd57b7c5abfb'}>}, {'entity': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:7' labels=frozenset({'Entity'}) properties={'name': 'Levothyroxine', 'id': '60654f2d-2ec8-4f4e-be02-cd57b7c5abfb'}>, 'relationship': <Relationship element_id='5:a53f7783-1308-4f3d-9a27-45cede6b6376:13' nodes=(<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:7' labels=frozenset({'Entity'}) properties={'name': 'Levothyroxine', 'id': '60654f2d-2ec8-4f4e-be02-cd57b7c5abfb'}>, <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:17' labels=frozenset({'Entity'}) properties={'name': 'every morning', 'id': '7fb1c7e5-e414-43ec-8606-474b08dc9173'}>) type='FREQUENCY' properties={'type': 'frequency'}>, 'related_node': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:17' labels=frozenset({'Entity'}) properties={'name': 'every morning', 'id': '7fb1c7e5-e414-43ec-8606-474b08dc9173'}>}, {'entity': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:19' labels=frozenset({'Entity'}) properties={'name': 'on an empty stomach before any other medications', 'id': '1c9fb28e-c3e1-4515-afa0-01939beac419'}>, 'relationship': <Relationship element_id='5:a53f7783-1308-4f3d-9a27-45cede6b6376:15' nodes=(<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:7' labels=frozenset({'Entity'}) properties={'name': 'Levothyroxine', 'id': '60654f2d-2ec8-4f4e-be02-cd57b7c5abfb'}>, <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:19' labels=frozenset({'Entity'}) properties={'name': 'on an empty stomach before any other medications', 'id': '1c9fb28e-c3e1-4515-afa0-01939beac419'}>) type='ROUTE' properties={'type': 'route'}>, 'related_node': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:7' labels=frozenset({'Entity'}) properties={'name': 'Levothyroxine', 'id': '60654f2d-2ec8-4f4e-be02-cd57b7c5abfb'}>}, {'entity': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:7' labels=frozenset({'Entity'}) properties={'name': 'Levothyroxine', 'id': '60654f2d-2ec8-4f4e-be02-cd57b7c5abfb'}>, 'relationship': <Relationship element_id='5:a53f7783-1308-4f3d-9a27-45cede6b6376:12' nodes=(<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:7' labels=frozenset({'Entity'}) properties={'name': 'Levothyroxine', 'id': '60654f2d-2ec8-4f4e-be02-cd57b7c5abfb'}>, <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:16' labels=frozenset({'Entity'}) properties={'name': '75mcg', 'id': '75b6f7a1-2f53-419c-9365-5f026dcb1e25'}>) type='DOSAGE' properties={'type': 'dosage'}>, 'related_node': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:16' labels=frozenset({'Entity'}) properties={'name': '75mcg', 'id': '75b6f7a1-2f53-419c-9365-5f026dcb1e25'}>}, {'entity': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:7' labels=frozenset({'Entity'}) properties={'name': 'Levothyroxine', 'id': '60654f2d-2ec8-4f4e-be02-cd57b7c5abfb'}>, 'relationship': <Relationship element_id='5:a53f7783-1308-4f3d-9a27-45cede6b6376:15' nodes=(<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:7' labels=frozenset({'Entity'}) properties={'name': 'Levothyroxine', 'id': '60654f2d-2ec8-4f4e-be02-cd57b7c5abfb'}>, <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:19' labels=frozenset({'Entity'}) properties={'name': 'on an empty stomach before any other medications', 'id': '1c9fb28e-c3e1-4515-afa0-01939beac419'}>) type='ROUTE' properties={'type': 'route'}>, 'related_node': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:19' labels=frozenset({'Entity'}) properties={'name': 'on an empty stomach before any other medications', 'id': '1c9fb28e-c3e1-4515-afa0-01939beac419'}>}, {'entity': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:7' labels=frozenset({'Entity'}) properties={'name': 'Levothyroxine', 'id': '60654f2d-2ec8-4f4e-be02-cd57b7c5abfb'}>, 'relationship': <Relationship element_id='5:a53f7783-1308-4f3d-9a27-45cede6b6376:14' nodes=(<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:7' labels=frozenset({'Entity'}) properties={'name': 'Levothyroxine', 'id': '60654f2d-2ec8-4f4e-be02-cd57b7c5abfb'}>, <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:18' labels=frozenset({'Entity'}) properties={'name': 'hypothyroidism', 'id': '95b91440-5ff1-41eb-a036-b57880094b05'}>) type='CONDITION' properties={'type': 'condition'}>, 'related_node': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:18' labels=frozenset({'Entity'}) properties={'name': 'hypothyroidism', 'id': '95b91440-5ff1-41eb-a036-b57880094b05'}>}, {'entity': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:7' labels=frozenset({'Entity'}) properties={'name': 'Levothyroxine', 'id': '60654f2d-2ec8-4f4e-be02-cd57b7c5abfb'}>, 'relationship': <Relationship element_id='5:a53f7783-1308-4f3d-9a27-45cede6b6376:13' nodes=(<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:7' labels=frozenset({'Entity'}) properties={'name': 'Levothyroxine', 'id': '60654f2d-2ec8-4f4e-be02-cd57b7c5abfb'}>, <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:17' labels=frozenset({'Entity'}) properties={'name': 'every morning', 'id': '7fb1c7e5-e414-43ec-8606-474b08dc9173'}>) type='FREQUENCY' properties={'type': 'frequency'}>, 'related_node': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:17' labels=frozenset({'Entity'}) properties={'name': 'every morning', 'id': '7fb1c7e5-e414-43ec-8606-474b08dc9173'}>}, {'entity': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:7' labels=frozenset({'Entity'}) properties={'name': 'Levothyroxine', 'id': '60654f2d-2ec8-4f4e-be02-cd57b7c5abfb'}>, 'relationship': <Relationship element_id='5:a53f7783-1308-4f3d-9a27-45cede6b6376:12' nodes=(<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:7' labels=frozenset({'Entity'}) properties={'name': 'Levothyroxine', 'id': '60654f2d-2ec8-4f4e-be02-cd57b7c5abfb'}>, <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:16' labels=frozenset({'Entity'}) properties={'name': '75mcg', 'id': '75b6f7a1-2f53-419c-9365-5f026dcb1e25'}>) type='DOSAGE' properties={'type': 'dosage'}>, 'related_node': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:16' labels=frozenset({'Entity'}) properties={'name': '75mcg', 'id': '75b6f7a1-2f53-419c-9365-5f026dcb1e25'}>}, {'entity': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:9' labels=frozenset({'Entity'}) properties={'name': '10mg', 'id': 'cddff401-0958-4093-b196-ad270b7aa797'}>, 'relationship': <Relationship element_id='5:a53f7783-1308-4f3d-9a27-45cede6b6376:6' nodes=(<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:8' labels=frozenset({'Entity'}) properties={'name': 'Lisinopril', 'id': '04f27c55-38a3-46a9-a411-4faf07836a56'}>, <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:9' labels=frozenset({'Entity'}) properties={'name': '10mg', 'id': 'cddff401-0958-4093-b196-ad270b7aa797'}>) type='DOSAGE' properties={'type': 'dosage'}>, 'related_node': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:8' labels=frozenset({'Entity'}) properties={'name': 'Lisinopril', 'id': '04f27c55-38a3-46a9-a411-4faf07836a56'}>}, {'entity': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:12' labels=frozenset({'Entity'}) properties={'name': 'Metformin', 'id': 'd14d70ef-3780-4ebe-b803-1ab8bd8ec847'}>, 'relationship': <Relationship element_id='5:a53f7783-1308-4f3d-9a27-45cede6b6376:11' nodes=(<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:12' labels=frozenset({'Entity'}) properties={'name': 'Metformin', 'id': 'd14d70ef-3780-4ebe-b803-1ab8bd8ec847'}>, <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:15' labels=frozenset({'Entity'}) properties={'name': 'diabetes', 'id': '9cbb512f-ca6b-47de-bb12-378ac35826bd'}>) type='CONDITION' properties={'type': 'condition'}>, 'related_node': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:15' labels=frozenset({'Entity'}) properties={'name': 'diabetes', 'id': '9cbb512f-ca6b-47de-bb12-378ac35826bd'}>}, {'entity': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:12' labels=frozenset({'Entity'}) properties={'name': 'Metformin', 'id': 'd14d70ef-3780-4ebe-b803-1ab8bd8ec847'}>, 'relationship': <Relationship element_id='5:a53f7783-1308-4f3d-9a27-45cede6b6376:10' nodes=(<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:12' labels=frozenset({'Entity'}) properties={'name': 'Metformin', 'id': 'd14d70ef-3780-4ebe-b803-1ab8bd8ec847'}>, <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:14' labels=frozenset({'Entity'}) properties={'name': 'twice daily', 'id': '73d5a502-05be-4bb7-916a-10b8ff5130fa'}>) type='FREQUENCY' properties={'type': 'frequency'}>, 'related_node': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:14' labels=frozenset({'Entity'}) properties={'name': 'twice daily', 'id': '73d5a502-05be-4bb7-916a-10b8ff5130fa'}>}, {'entity': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:12' labels=frozenset({'Entity'}) properties={'name': 'Metformin', 'id': 'd14d70ef-3780-4ebe-b803-1ab8bd8ec847'}>, 'relationship': <Relationship element_id='5:a53f7783-1308-4f3d-9a27-45cede6b6376:9' nodes=(<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:12' labels=frozenset({'Entity'}) properties={'name': 'Metformin', 'id': 'd14d70ef-3780-4ebe-b803-1ab8bd8ec847'}>, <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:13' labels=frozenset({'Entity'}) properties={'name': '500mg', 'id': '6c38fe32-0ca3-4bcc-a072-05e302aaa8e7'}>) type='DOSAGE' properties={'type': 'dosage'}>, 'related_node': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:13' labels=frozenset({'Entity'}) properties={'name': '500mg', 'id': '6c38fe32-0ca3-4bcc-a072-05e302aaa8e7'}>}, {'entity': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:18' labels=frozenset({'Entity'}) properties={'name': 'hypothyroidism', 'id': '95b91440-5ff1-41eb-a036-b57880094b05'}>, 'relationship': <Relationship element_id='5:a53f7783-1308-4f3d-9a27-45cede6b6376:14' nodes=(<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:7' labels=frozenset({'Entity'}) properties={'name': 'Levothyroxine', 'id': '60654f2d-2ec8-4f4e-be02-cd57b7c5abfb'}>, <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:18' labels=frozenset({'Entity'}) properties={'name': 'hypothyroidism', 'id': '95b91440-5ff1-41eb-a036-b57880094b05'}>) type='CONDITION' properties={'type': 'condition'}>, 'related_node': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:7' labels=frozenset({'Entity'}) properties={'name': 'Levothyroxine', 'id': '60654f2d-2ec8-4f4e-be02-cd57b7c5abfb'}>}, {'entity': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:19' labels=frozenset({'Entity'}) properties={'name': 'on an empty stomach before any other medications', 'id': '1c9fb28e-c3e1-4515-afa0-01939beac419'}>, 'relationship': <Relationship element_id='5:a53f7783-1308-4f3d-9a27-45cede6b6376:15' nodes=(<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:7' labels=frozenset({'Entity'}) properties={'name': 'Levothyroxine', 'id': '60654f2d-2ec8-4f4e-be02-cd57b7c5abfb'}>, <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:19' labels=frozenset({'Entity'}) properties={'name': 'on an empty stomach before any other medications', 'id': '1c9fb28e-c3e1-4515-afa0-01939beac419'}>) type='ROUTE' properties={'type': 'route'}>, 'related_node': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:7' labels=frozenset({'Entity'}) properties={'name': 'Levothyroxine', 'id': '60654f2d-2ec8-4f4e-be02-cd57b7c5abfb'}>}]
Formatting graph context...
Graph context: {'nodes': ['Lisinopril', '75mcg', 'twice daily', 'diabetes', 'on an empty stomach before any other medications', 'hypertension', 'daily', 'Metformin', '500mg', '10mg', 'hypothyroidism', 'Levothyroxine', 'every morning'], 'edges': ['10mg dosage Lisinopril', 'Lisinopril condition hypertension', '10mg dosage Lisinopril', 'Lisinopril frequency daily', 'hypothyroidism condition Levothyroxine', 'Levothyroxine route on an empty stomach before any other medications', 'hypothyroidism condition Levothyroxine', 'Levothyroxine frequency every morning', 'hypothyroidism condition Levothyroxine', 'Levothyroxine dosage 75mcg', 'on an empty stomach before any other medications route Levothyroxine', 'Levothyroxine condition hypothyroidism', 'on an empty stomach before any other medications route Levothyroxine', 'Levothyroxine frequency every morning', 'on an empty stomach before any other medications route Levothyroxine', 'Levothyroxine dosage 75mcg', 'Levothyroxine route on an empty stomach before any other medications', 'Levothyroxine condition hypothyroidism', 'Levothyroxine frequency every morning', 'Levothyroxine dosage 75mcg', '10mg dosage Lisinopril', 'Metformin condition diabetes', 'Metformin frequency twice daily', 'Metformin dosage 500mg', 'hypothyroidism condition Levothyroxine', 'on an empty stomach before any other medications route Levothyroxine']}
Running GraphRAG...
Final Answer: Levothyroxine 75mcg is prescribed for hypothyroidism, taken every morning.

query = "Which medications does the patient take once daily versus twice daily?"

Console:
Starting retriever search...
Retriever results: items=[RetrieverResultItem(content="<Record node=<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:14' labels=frozenset({'Entity'}) properties={'name': 'twice daily', 'id': '73d5a502-05be-4bb7-916a-10b8ff5130fa'}> score=0.6052238>", metadata=None), RetrieverResultItem(content="<Record node=<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:19' labels=frozenset({'Entity'}) properties={'name': 'on an empty stomach before any other medications', 'id': '1c9fb28e-c3e1-4515-afa0-01939beac419'}> score=0.50138044>", metadata=None), RetrieverResultItem(content="<Record node=<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:10' labels=frozenset({'Entity'}) properties={'name': 'daily', 'id': '06da6faa-baa5-4657-a961-5091f58e3058'}> score=0.424541>", metadata=None), RetrieverResultItem(content="<Record node=<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:15' labels=frozenset({'Entity'}) properties={'name': 'diabetes', 'id': '9cbb512f-ca6b-47de-bb12-378ac35826bd'}> score=0.42364278>", metadata=None), RetrieverResultItem(content="<Record node=<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:12' labels=frozenset({'Entity'}) properties={'name': 'Metformin', 'id': 'd14d70ef-3780-4ebe-b803-1ab8bd8ec847'}> score=0.4210245>", metadata=None)] metadata={'__retriever': 'QdrantNeo4jRetriever'}
Extracting entity IDs...
Entity IDs: ['73d5a502-05be-4bb7-916a-10b8ff5130fa', '1c9fb28e-c3e1-4515-afa0-01939beac419', '06da6faa-baa5-4657-a961-5091f58e3058', '9cbb512f-ca6b-47de-bb12-378ac35826bd', 'd14d70ef-3780-4ebe-b803-1ab8bd8ec847']
Fetching related graph...
Subgraph: [{'entity': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:10' labels=frozenset({'Entity'}) properties={'name': 'daily', 'id': '06da6faa-baa5-4657-a961-5091f58e3058'}>, 'relationship': <Relationship element_id='5:a53f7783-1308-4f3d-9a27-45cede6b6376:17' nodes=(<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:20' labels=frozenset({'Entity'}) properties={'name': 'Sertraline', 'id': '42844c4c-ce96-4bd9-8d37-d8adad5a75fe'}>, <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:10' labels=frozenset({'Entity'}) properties={'name': 'daily', 'id': '06da6faa-baa5-4657-a961-5091f58e3058'}>) type='FREQUENCY' properties={'type': 'frequency'}>, 'related_node': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:20' labels=frozenset({'Entity'}) properties={'name': 'Sertraline', 'id': '42844c4c-ce96-4bd9-8d37-d8adad5a75fe'}>}, {'entity': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:20' labels=frozenset({'Entity'}) properties={'name': 'Sertraline', 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element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:20' labels=frozenset({'Entity'}) properties={'name': 'Sertraline', 'id': '42844c4c-ce96-4bd9-8d37-d8adad5a75fe'}>, <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:10' labels=frozenset({'Entity'}) properties={'name': 'daily', 'id': '06da6faa-baa5-4657-a961-5091f58e3058'}>) type='FREQUENCY' properties={'type': 'frequency'}>, 'related_node': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:20' labels=frozenset({'Entity'}) properties={'name': 'Sertraline', 'id': '42844c4c-ce96-4bd9-8d37-d8adad5a75fe'}>}, {'entity': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:10' labels=frozenset({'Entity'}) properties={'name': 'daily', 'id': '06da6faa-baa5-4657-a961-5091f58e3058'}>, 'relationship': <Relationship element_id='5:a53f7783-1308-4f3d-9a27-45cede6b6376:4' nodes=(<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:4' labels=frozenset({'Entity'}) properties={'name': 'Aspirin', 'id': 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'daily', 'id': '06da6faa-baa5-4657-a961-5091f58e3058'}>) type='FREQUENCY' properties={'type': 'frequency'}>, 'related_node': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:8' labels=frozenset({'Entity'}) properties={'name': 'Lisinopril', 'id': '04f27c55-38a3-46a9-a411-4faf07836a56'}>}, {'entity': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:12' labels=frozenset({'Entity'}) properties={'name': 'Metformin', 'id': 'd14d70ef-3780-4ebe-b803-1ab8bd8ec847'}>, 'relationship': <Relationship element_id='5:a53f7783-1308-4f3d-9a27-45cede6b6376:11' nodes=(<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:12' labels=frozenset({'Entity'}) properties={'name': 'Metformin', 'id': 'd14d70ef-3780-4ebe-b803-1ab8bd8ec847'}>, <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:15' labels=frozenset({'Entity'}) properties={'name': 'diabetes', 'id': '9cbb512f-ca6b-47de-bb12-378ac35826bd'}>) type='CONDITION' properties={'type': 'condition'}>, 'related_node': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:15' labels=frozenset({'Entity'}) properties={'name': 'diabetes', 'id': '9cbb512f-ca6b-47de-bb12-378ac35826bd'}>}, {'entity': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:12' labels=frozenset({'Entity'}) properties={'name': 'Metformin', 'id': 'd14d70ef-3780-4ebe-b803-1ab8bd8ec847'}>, 'relationship': <Relationship element_id='5:a53f7783-1308-4f3d-9a27-45cede6b6376:10' nodes=(<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:12' labels=frozenset({'Entity'}) properties={'name': 'Metformin', 'id': 'd14d70ef-3780-4ebe-b803-1ab8bd8ec847'}>, <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:14' labels=frozenset({'Entity'}) properties={'name': 'twice daily', 'id': '73d5a502-05be-4bb7-916a-10b8ff5130fa'}>) type='FREQUENCY' properties={'type': 'frequency'}>, 'related_node': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:14' labels=frozenset({'Entity'}) properties={'name': 'twice daily', 'id': '73d5a502-05be-4bb7-916a-10b8ff5130fa'}>}, {'entity': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:12' labels=frozenset({'Entity'}) properties={'name': 'Metformin', 'id': 'd14d70ef-3780-4ebe-b803-1ab8bd8ec847'}>, 'relationship': <Relationship element_id='5:a53f7783-1308-4f3d-9a27-45cede6b6376:9' nodes=(<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:12' labels=frozenset({'Entity'}) properties={'name': 'Metformin', 'id': 'd14d70ef-3780-4ebe-b803-1ab8bd8ec847'}>, <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:13' labels=frozenset({'Entity'}) properties={'name': '500mg', 'id': '6c38fe32-0ca3-4bcc-a072-05e302aaa8e7'}>) type='DOSAGE' properties={'type': 'dosage'}>, 'related_node': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:13' labels=frozenset({'Entity'}) properties={'name': '500mg', 'id': '6c38fe32-0ca3-4bcc-a072-05e302aaa8e7'}>}, {'entity': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:14' labels=frozenset({'Entity'}) properties={'name': 'twice daily', 'id': '73d5a502-05be-4bb7-916a-10b8ff5130fa'}>, 'relationship': <Relationship element_id='5:a53f7783-1308-4f3d-9a27-45cede6b6376:10' nodes=(<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:12' labels=frozenset({'Entity'}) properties={'name': 'Metformin', 'id': 'd14d70ef-3780-4ebe-b803-1ab8bd8ec847'}>, <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:14' labels=frozenset({'Entity'}) properties={'name': 'twice daily', 'id': '73d5a502-05be-4bb7-916a-10b8ff5130fa'}>) type='FREQUENCY' properties={'type': 'frequency'}>, 'related_node': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:12' labels=frozenset({'Entity'}) properties={'name': 'Metformin', 'id': 'd14d70ef-3780-4ebe-b803-1ab8bd8ec847'}>}, {'entity': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:15' labels=frozenset({'Entity'}) properties={'name': 'diabetes', 'id': '9cbb512f-ca6b-47de-bb12-378ac35826bd'}>, 'relationship': <Relationship element_id='5:a53f7783-1308-4f3d-9a27-45cede6b6376:11' nodes=(<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:12' labels=frozenset({'Entity'}) properties={'name': 'Metformin', 'id': 'd14d70ef-3780-4ebe-b803-1ab8bd8ec847'}>, <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:15' labels=frozenset({'Entity'}) properties={'name': 'diabetes', 'id': '9cbb512f-ca6b-47de-bb12-378ac35826bd'}>) type='CONDITION' properties={'type': 'condition'}>, 'related_node': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:12' labels=frozenset({'Entity'}) properties={'name': 'Metformin', 'id': 'd14d70ef-3780-4ebe-b803-1ab8bd8ec847'}>}, {'entity': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:19' labels=frozenset({'Entity'}) properties={'name': 'on an empty stomach before any other medications', 'id': '1c9fb28e-c3e1-4515-afa0-01939beac419'}>, 'relationship': <Relationship element_id='5:a53f7783-1308-4f3d-9a27-45cede6b6376:15' nodes=(<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:7' labels=frozenset({'Entity'}) properties={'name': 'Levothyroxine', 'id': '60654f2d-2ec8-4f4e-be02-cd57b7c5abfb'}>, <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:19' labels=frozenset({'Entity'}) properties={'name': 'on an empty stomach before any other medications', 'id': '1c9fb28e-c3e1-4515-afa0-01939beac419'}>) type='ROUTE' properties={'type': 'route'}>, 'related_node': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:7' labels=frozenset({'Entity'}) properties={'name': 'Levothyroxine', 'id': '60654f2d-2ec8-4f4e-be02-cd57b7c5abfb'}>}]
Formatting graph context...
Graph context: {'nodes': ['twice daily', 'daily', '75mcg', '50mg', '81mg', 'Aspirin', 'on an empty stomach before any other medications', 'hypothyroidism', 'hypertension', 'depression', 'Lisinopril', 'Levothyroxine', 'heart disease prevention', 'every morning', 'Sertraline', 'diabetes', '500mg', '10mg', 'Metformin'], 'edges': ['daily frequency Sertraline', 'Sertraline condition depression', 'daily frequency Sertraline', 'Sertraline dosage 50mg', 'daily frequency Aspirin', 'Aspirin condition heart disease prevention', 'daily frequency Aspirin', 'Aspirin dosage 81mg', 'daily frequency Lisinopril', 'Lisinopril condition hypertension', 'daily frequency Lisinopril', 'Lisinopril dosage 10mg', 'twice daily frequency Metformin', 'Metformin condition diabetes', 'twice daily frequency Metformin', 'Metformin dosage 500mg', 'diabetes condition Metformin', 'Metformin frequency twice daily', 'diabetes condition Metformin', 'Metformin dosage 500mg', 'on an empty stomach before any other medications route Levothyroxine', 'Levothyroxine condition hypothyroidism', 'on an empty stomach before any other medications route Levothyroxine', 'Levothyroxine frequency every morning', 'on an empty stomach before any other medications route Levothyroxine', 'Levothyroxine dosage 75mcg', 'daily frequency Sertraline', 'daily frequency Aspirin', 'daily frequency Lisinopril', 'Metformin condition diabetes', 'Metformin frequency twice daily', 'Metformin dosage 500mg', 'twice daily frequency Metformin', 'diabetes condition Metformin', 'on an empty stomach before any other medications route Levothyroxine']}
Running GraphRAG...
Final Answer: Here’s the breakdown of medications taken once daily versus twice daily based on the knowledge graph:

**Once Daily:**

*   Aspirin: daily frequency
*   Sertraline: daily frequency
*   Levothyroxine: every morning frequency

**Twice Daily:**

*   Metformin: twice daily frequency

Вывад:

У стадії Заключнага аналізу неабяжна практычна вызначыць параметры вводу, адпавядаючага за крок адпаведальнага, а таксама крэтыніяты для завершэння пры змены коду. Аперацыйныя працавнікі павінны магчымаць перзапуск кроку з вядомай точкі контролю, не падозрываючы схованы стан. Лепш выбіраць маленькія, тэставаныя елементы замест большых скрыптов. Калі крок не выйшоў, прычына неудачы павінна вказываць на адзін конкрэтны аспект, а не на заплутаны ланцюг задач. Раздзеляйце стварэнне кліента ад цыклу перадачы паведамленняў, каб было можна змяніць прадаўцаў без перапісвання машыны стану дыялогу.

Чэк-ліст для аперацый

Працюючы над чэк-лістам для аперацый, спачатку запісуйце умовы кантракту: неабяжны параметры вводу, сігнал успеху і тое, што выходзіць пад час частковай неудачы. Такі чэк-ліст дапамагае заставаць пасляўныя змены коду чыстымі.

Лепшыя маленькія, тэставаныя елементы чым велікія скрыпты. Калі якісь крок не выйшае, адказка за гэта паталогія должна быць спрямована на адзін конкрэтны элемент, а не на заплутаны ланцюг задач.

Запісвайце ідэнтыфікатор запиту, ідэнтыфікатор моделі та час адказу праз кожны вызов. Без такога лістынга эпізодыяныя паталогіі прадастоўця выглядаюць як багі ў самай аплікацыі.

Раздзеліце правілы фрагментавання дадзеных ад правілаў ўзяць іх. Змена адных не должна прымусіваць перапісвання іншых, калі змянююцыся паказнікі якосці.

Калі дозволяе бюджет, дадзіце тэст на перакананне, які працюе з критычным ланцюгам у процесе CI, выкарыстоўваючы фіксаты, а не рэальныя платныя API.

Зберагаўце настройкі праза код аплікацыі. Файлы сяродавысці, хранільнікі секрэтных дадзеных та флагі функцыйяў должны знаходзіцца ў аднам месцы, якое аператары можаць пераглядаць, не чытаяўшы весь код.

Перш чым запускать даную систему, заморозьце версіі, зафіксавце критычныя моменты для аналізу і паказвайце крокі з вярнення да пачатковага стану. У спільных средах неабяжна наявнае ліміты на частоту запуска, пераконтроўванне прав на выкорыстання ресурсаў і чысткая адпаведальнасць за зміну секрэтных даных. Лепш выбіраць простую надзею на надзейнасць, чым крэатывныя, але еднакратныя дамы.

Прыметкі для 0e4c86908c41: не кладзіце ключы прадаўцаў у репазітарый, задаце ліміт токенаў на кожную сесію і зберагачыце фіксаціі дзейнасці рядом з інструментамі для тэставання, каб пазнейшыя змены моделей заставаліся пораўнанневымі.

Для прыметак па зміцнэнню структуры на стадыі 0 неабяжна з’явіць вакладкі ўводных дадзеных, адпаведальнага за крок і крэатывных крэтарыяў завершэння працы перш чым зменіць код. Аператары должны магчымае перзапускці крок з вядомай точкі контролю, не падозрываючы прыхованы стан системы. Лепш выбіраць маленькія, тэставанневыя елементы, чым велікія скрыпты. Калі крок не выйшаў, прычына нехасабності должна быць адназначная, а не стосавацца цэлага заплутанага процесу.

Дзеянне паўжасткі 0/771: звярніце увагу на час выканання, клас памылкі і колькасць токенаў, выкарыстоўваных для гэтай змяны, а пасля, на аднойчыннай сэтце запытанняў, а не на асобістых спазыраннях, выявіце, чы хацеце застаўіць гэту змяну.

Калі працуеце над першым этапам змян паўжасткі, спачатку запісайце шаблон контракта: неабходныя даны, сігнал успеху і тое, што выканаецца у разе частковай памылкі. Такі список контроля дапамагае заставаць пасляэтапныя змены коду чыстымі. Запісвайце час выканання і колькасць токенаў або вартасць запытку па боку функцыйнаых рэзультаатаў. Відразлівасць вартасці з самага пачатку запобегае неспакойным рахункам, калі праця пераходзіць з дэмаверсіі ў спакульнаныя сераўы.

Дзеянне паўжасткі 1/771: звярніце увагу на час выканання, клас памылкі і колькасць токенаў, выкарыстоўваных для гэтай змяны, а пасля, на аднойчыннай сэтце запытанняў, а не на асобістых спазыраннях, выявіце, чы хацеце застаўіць гэту змяну.

Этап 2 прыткага зміцнення работае наяўна, калі яго спрыяваць як вимерную паверхню. Запісаўце адна «золатая» транскрыпцыя, адзін прыклад неудачы і запіс пра вярненне да пачатковага стану перш чым расширваць сферу дзеяння. Дакументаваць трэба як успішны, так і вярнучыся шляхы. Перапрыбуткі, людзкія контралі і обработка некоректных паведамленняў є часткай продукту, а не наступным этапам дапрацоўкі.

Дзеянні прыткага зміцнення 2/771: вимеравайце час выканання, класію памылак і витрату токенав для гэтага запісу, а потым вынікайце, чы рашыцца застаўляць змяну, адпаведна фіксаванаму набору пытанняў, а не лячэнням.

Для этапа 3 прыткага зміцнення, перш чым зменяць код, задаце вхідныя даны, адпаведальнага за крок і критэрыя завершэння. Аперацыёныя працавнікі павінны магчымае перазваляць крок з вядомай точкі контролю, не спадзяваючыся на скрыты стан. Спрыявайце гэтаму этапу як кантракту межа вхіднымі данымі і падтвердзенымі выходнымі рэзультатамі. Назвайце артыфакты, задаце перакананні на успех і адмовіцеся ад тых падчасных завершэнняў, калі няма інформацыі.

Дзеянне паўжасткі 3/771: звярніце увагу на час выканання, класы памылак і витрату токенаў для гэтага запісу, а пасля, на аднойчынай базе пытанняў, а не на асобістых спазыраннях, выявіце, чы рэшацца застаўляць змяну.

Працюючы над 4-й стадзіяю запісу паўжасткі, спачатку запішыце контракт: неабходныя данні, сігнал успеху і тое, што выканаецца у разе частковай памылки. Такі список дапамагае заставаць пазнейшыя змяны ў кодзе чыстымі. Зберагаюце канфігурацыю праза код аплікацыі. Файлы сераўнавання, хранільнікі секрэтных дадзенняў і флагі функцыйяй должны знаходзіцца ў аднам месцы, куда аператары можу пераглядаць без неабходнасці чытання всей структуры.

Дзеянне паўжасткі 4/771: звярніце увагу на час выканання, класы памылак і витрату токенаў для гэтага запісу, а пасля, на аднойчынай базе пытанняў, а не на асобістых спазыраннях, выявіце, чы рэшацца застаўляць змяну.

Этап 5 прыткага зміцнення работае наяўна, калі яго спрыяваць як вимерную паверхню. Запісаўце адна «золатая» транскрыпцыя, адин прыклад неудачы і запіс пра вярнэнне да пачатковага стану перш чым расширваць сферу дзеяння. Валіце маленькія, тэставаныя елементы замест большых скрыптов. Калі якісь крок не выйшае, прычына неудачы павінна вказываць на адную адпаведальнасць, а не на заплутаны ланцюг задач.

Дзеянне прыткага зміцнення 5/771: вимеравайце час выканання, класію памылак і колькасць выкорыстоўваных токенав для гэтага запісу, а потым вырашайце, чы робіць змяну на адной пазначанай базе пытанняў, а не на адной анекдотычнай інформацыі.

Для 6-й стадзіі процэсу змяржвання неабяжна ўскладнення: перш чым зменіць код, неабяжна ваказаць параметры вхідных дадзеных, адпаведальнага за даны крок і критэрыя завершэння. Аперацыям неабяжна маты можлівасць паўтарнае адканалаванне кроку з вядомага пункта контролю, не спрабоўваючы здагадвацца пра схованы стан. Неабяжна фіксаваць час выкарыстоўвання, а таксама вартасць токенав чы запытак праза функцыйнае рэзультат. Відкрытая інформацыя пра вартасці запобегае неспакою, калі процэс пераходзіць з дамовай среды ў спакульнаваную.

Дзеянні змяржвання 6/771: неабяжна змерыць час выкарыстоўвання, класі каштоўкаў і колькасць токенав для данай стадзіі, а пасля — на базе фіксаванага набору пытанняў, а не на адной лічбе, вырашыць, чы прымножваць змены.

Калі працуеце над 7-й стадзіяю прыемкі з павышэння безпекі, спачатку запісайце угоду: неабяжлівыя данні, сігнал успеху і тое, што выходзіць пад частковы нявыплэн. Такі список контролю дапамагае заставіць пазнейшыя змены коду быць чыстымі.

Документавайце як «шчаслівы» шлях, так і шлях вяснавання. Перапрыбуткі, людзкія контралі і обработка некоректных паведамленняў ёсць частью продукту, а не пазнейшым дапрацоўкам.

Дзялей 7/771 прыемкі з павышэння безпекі: вымерайце час выканання, класыя ошибкі і витрату токенав для гэтай прыемкі, а пасля выберайце, чы робіць змены на адной пазначанай сэткі пытанняў, а не на адной толькі прымітцы.

7-я стадзія прыемкі з павышэння безпекі работае лепей, калі яе спрыямаць як вымеральную паверхню. Зафіксавайце адна «золатая» транскрыпцыю, адны прыклад нявыплэну і прыемку для абраткаў перад расшырэнням масштаба. Спрыяйце гэтай стадзіі як угоды межа даннімі і перакананымі выходамі. Дайце назвы артыфактам, задаце перакананні успеху і адмовіцеся ад тыхоўскага частковага завершэння.

Дзеянне паўжасткі 8/771: звярніце увагу на час выканання, класы памылак і витрату токенаў для гэтага запісу, а пасля, на аднойчынай базе фіксаванага набору пытанняў, а не на індывідуальных прыкладах, выявіце, чы рэшацься застаўляць змяну.

Для 9-го этапа паўжасткі неабходна перад змянай коду чытко визначыць вхідныя даны, адпаведальнага за крок і критэрыя завершэння. Аперацыёныя працавнікі должны магчымае перадзваначыць крок з вядомага пункта контролю, не спрабоўваючы здагадвацца пра схованы стан. Канфігурацыю трэба зберагчы за межамі коду прыемленае, таму што файлы сяродавішча, хранальнікі секрэтных дадзенняў і флагі функцый належаць у аднам месца, якое працавнікі можу аудытаваць, не чытаючы весь код.

Дзеянне паўжасткі 9/771: звярніце увагу на час выканання, класы памылак і витрату токенаў для гэтага запісу, а пасля, на аднойчынай базе фіксаванага набору пытанняў, а не на індывідуальных прыкладах, выявіце, чы рэшацься застаўляць змяну.

Калі працюеце над 10-й стадзіяю практыкы забезпечэння безпекі, спачатку запісайце умовы кантракта: неабяжлівыя данні, сігнал успеху і тое, што выходзіць па частым неудачам. Такі список контроля дапамагае залічыць пазнейшыя змены ў кодзе чыстымі. Валіце маленькія, тэставаныя елементы замест большых скрыптов. Калі якісь крок не выйшае, неудача должна вказваць на адну конкрэтную адпаведальнасць, а не на заплутаны ланцюг задач.

Дзеянні практыкы забезпечэння безпекі 10/771: звярзайце меры часу выканання, класу каштоўкаў і витрачання токенаў для гэтай стадзіяй, а пасля выберыце, чы робіць змены на адной основе фіксаванага набору пытанняў, а не на адной лячбе.