Kompletny lokalny pipeline GraphRAG wykorzystujący LangExtract, Neo4j, Qdrant i Ollama

To rozwiązanie to system GraphRAG, który funkcjonuje wyłącznie na lokalnych modelach. Składnikiem jest Ollama do obsługi modeli językowych i embeddingów, Neo4j do zarządzania grafem, Qdrant do wyszukiwania wektorowego oraz LangExtract do wyodrębniania struktury z tekstu. Zamiast pobierać fragmenty dokumentów w sposób typowy dla zwykłych systemów RAG, przekształca surowy tekst w graf wiedzy, wyszukuje entity semantycznie bliskie pytaniu, a następnie śledzi powiązania pomiędzy nimi.

Główną częścią tego rozwiązania jest LangExtract, który znajduje entity i relacje w tekstach niestrukturyzowanych i zwraca je w formie, którą może przechowywać graf. Qdrant wprowadza znaczenie poprzez podobieństwo wektorowe, natomiast Neo4j dostarcza kontekstu poprzez przeszukiwanie grafu. Razem tworzą one pipeline wyszukiwania, który bierze pod uwagę zarówno temat pytania, jak i sposób powiązania faktów.

Jak funkcjonuje architektura

Tekst surowy trafia najpierw do warstwy LangExtract. Tradycyjne procesy natychmiast dzieliłyby dokumenty na fragmenty i je umieszczały. Tutaj tekst jest najpierw przekształcany w ustrukturyzowaną wiedzę. LangExtract, wspierany modelem hostowanym w Ollama, wyodrębnia entity oraz relacje pomiędzy nimi. Przykładem jest data dotycząca leków: wydobywane są informacje o lekach, dawkach, częstotliwości przyjmowania oraz stanach chorobowych, a ich powiązania są zachowywane dzięki wspólnemu grupowaniu. Język naturalny staje się w ten sposób ustrukturyzowanym zapisem faktów, a nie zbiorem przechowywanych fragmentów tekstu.

Następnie wyekstrahowane entity i relacje stają się elementami grafu. Każdy pojęcie przekształca się w węzeł o unikalnym ID, a relacje takie jak dawka, częstotliwość czy stan zdrowia stają się wyraźnymi krawędziami łączącymi węzły. Informacje są teraz organizowane wokół entity i ich powiązań, a nie wokół granic dokumentów, co pozwala zachować kontekst, który przy wyszukiwaniu opartym na fragmentach zwykle by został utracony.

Następnie graf jest ładowany do Neo4j, głównego systemu przechowywania strukturyzowanych informacji. Entity stają się węzłami, a relacje – natywnymi krawędziami. Ponieważ Neo4j może efektywnie przemieszczać się między powiązanymi pojęciami, system może później śledzić wieloetapowe ścieżki i ujawniać kontekst znajdujący się poza punktem, w którym rozpoczęto ekstrakcję.

Gdy graf zostanie utworzony, dla każdego węzła tworzy się embedding. Model embeddingu odczytuje bezpośrednio nazwy entytetów i koncepcje, a nie całe dokumenty czy ich fragmenty. Rezultatem jest semantyczny indeks entytetów grafu, dzięki czemu wyszukiwanie koncentruje się na koncepcjach, a nie na fragmentach tekstu.

Ty wektory trafiają do Qdrant wraz z odpowiadającymi im ID węzłów Neo4j. Qdrant pełni rolę szybkiej warstwy semantycznej, podczas gdy Neo4j pozostaje strukturalną bazą danych. Ponieważ każdy pakiet danych Qdrant zawiera ID węzła Neo4j, trafienie wektora odpowiada bezpośrednio konkretnej entytetowi grafu. Taka podziałka umożliwia każdej bazie danych wykonywanie tego, w czym jest najlepsza: wyszukiwanie podobieństw w jednej, a przeszukiwanie relacji i rozumowanie grafowe w drugiej.

Podczas wyszukiwania użytkownik zadaje pytanie w języku naturalnym. Ten sam model embeddingu przekształca je w wektor, który jest wyszukiwany w Qdrant w celu znalezienia najbardziej istotnych entytetów grafu. Zamiast fragmentów dokumentów, wyszukiwanie zwraca identyfikatory węzłów. Te węzły stanowią semantyczne punkty wejścia do grafu, reprezentując koncepcje najbliższe temu, czego szuka użytkownik.

Następnie te identyfikatory służą do wysyłania zapytania do Neo4j. Zamiast zatrzymać się na dopasowanych entytetach, system rozszerza poszukiwania o sąsiednie węzły i relacje, gromadząc istotny podgraf zawierający zarówno dopasowania, jak i otaczający je kontekst. Takie rozszerzanie pozwala na zebranie dodatkowych faktów, których sam podobieństwo wektorowe nie ujawniłoby, dzięki czemu kontekst łączy w sobie zarówno semantyczną istotność, jak i strukturę.

Podgraf jest następnie przekształcany w formę przydatną dla modelu językowego. Węzły są grupowane w uporządkowaną listę, a relacje zamieniane na czytelne trójki przedstawiające wyraźne powiązania, takie jak dawka Lisinoprilu 10 mg lub stosowanie Metforminu przy cukrzycy. Ten krok łączy bazy danych grafowych z modelami językowymi, przekształcając strukturę grafu w tekst, który model może zinterpretować.

Ostatecznie sformatowany kontekst grafu trafia do modelu językowego wielowymiarowego. Model otrzymuje entity i relacje, a nie wydobyte fragmenty tekstu, dzięki czemu może analizować podane fakty i nie musi wnioskować o powiązaniach na podstawie rozproszonych fragmentów. Ponieważ kontekst pochodzi z przeszukiwania grafu, jest również bardziej wyjaśnialny i śledzalny niż zwykłe przeszukiwanie wektorowe.

Łącznie stanowi to hybrydowy system wyszukiwania. LangExtract zajmuje się wydobywaniem wiedzy, Neo4j zarządza strukturalnymi relacjami, Qdrant wyszukuje entity w sposób semantyczny, a LLM analizuje kontekst oparty na grafie. Połączenie podobieństwa z przeszukiwaniem po łańcuchach umożliwia odpowiadanie na pytania na podstawie powiązanej wiedzy, a nie izolowanych fragmentów tekstu. Dzięki temu podejście jest szczególnie skuteczne w dziedzinach takich jak ochrona zdrowia, finanse, prawo, zarządzanie wiedzą przedsiębiorstw i badania naukowe, gdzie relacje między entity często mają większe znaczenie niż pojedynczy akapit.

Metoda po metodzie

__init__() jest punktem wejścia całego systemu. Odczytuje konfigurację z zmiennych środowiskowych, obejmującą dane uwierzytelniające do Neo4j, szczegóły połączenia z Qdrant oraz ustawienia Ollamy. Nawiązuje połączenia z Neo4j i Qdrant, których wykorzystuje reszta procesu, a także tworzy klienta Ollamy używanego do ekstrakcji, generowania wektorów i syntezy odpowiedzi. Nazwy modeli oraz wymiary wektorów są przechowywane jako parametry, co umożliwia dostosowanie konfiguracji.

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

extract_graph_components() przekształca surowy tekst w ustrukturyzowaną wiedzę. Dostarcza instrukcji ekstrakcji wraz z przykładami, które kierują LangExtract na właściwe entity i relacje. Model hostowany przez Ollamę następnie przetwarza dane wejściowe i wyodrębnia leki, dawki, częstotliwość stosowania oraz choroby. Wynikiem jest nadal zbiór wyekstrahowanych elementów, a nie graf, dlatego metoda przekazuje je dalej w celu budowy grafu.

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)

_convert_extractions_to_graph() przekształca dane wydobyte w formę grafu. Grupuje powiązane elementy ze sobą i wybiera główną jednostkę w każdej grupie jako węzeł kotwiczący. Każdy węzeł otrzymuje unikalny identyfikator, aby Neo4j i Qdrant zachowywały spójność. Następnie krawędzie łączą węzły kotwiczące z ich atrybutami, zachowując przy tym oryginalne znaczenie. Wynikiem jest zbiór węzłów i krawędzi gotowy do użycia w bazie danych grafowych.

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

ingest_to_neo4j() zapisuje graf do Neo4j, przy czym każda entytet jest przechowywana jako węzeł, a każda relacja jako krawędź. Taka struktura umożliwia efektywne przemierzanie i dalsze badanie grafu. Każdy węzeł zachowuje swój unikalny identyfikator, który pozwala mu łączyć się z wynikami wyszukiwania wektorowego. Gdy metoda zostanie zakończona, graf staje się strukturalną bazą wiedzy systemu.

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

_sanitize_relationship_type() istnieje, ponieważ nazwy relacji pochodzą z wyjścia modeli językowych i mogą zawierać nieważne znaki lub nietypowe formaty. Metoda ta oczyszcza i standaryzuje te etykiety przed ich przesłaniem do Neo4j, tworząc bezpieczne identyfikatory w wielkich literach, które spełniają zasady języka Cypher. Chroni również przed nieprawidłowo sformatowanymi nazwami oraz problemami w zapytaniach, które mogłyby one spowodować. Choć metoda jest prosta, pomaga utrzymać poprawność grafu.

@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

create_collection() upewnia się, że kolekcja Qdrant istnieje przed przechowywaniem jakichkolwiek wektorów. Sprawdza, czy nazwana kolekcja jest już obecna, a jeśli nie – tworzy ją, konfigurując wymiar wektorów oraz miarę podobieństwa używaną przy wyszukiwaniu. Takie wcześniejsze sprawdzenie zapobiega niepotrzebnemu tworzeniu kolekcji i przygotowuje warstwę wektorową przed rozpoczęciem procesu importu.

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}")

ollama_embeddings() generuje zwarte wektory przy użyciu modelu embeddingów Ollamy. Każda entytet grafu oraz każde zapytanie użytkownika przechodzą przez niego. Wektory wyrażają znaczenie w formie liczbowej, dzięki czemu można je efektywnie porównywać, co umożliwia wyszukiwanie podobieństw w Qdrant. W praktyce ta metoda stanowi most pomiędzy językiem naturalnym a wyszukiwaniem wektorowym.

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

ingest_to_qdrant() tworzy embedding dla każdej entity, gdy już istnieją węzły grafu, i przechowuje go w Qdrant. Obok każdego wektora przechowuje metadane, a mianowicie identyfikator węzła Neo4j oraz nazwę entity. To powiązanie łączy bazę danych wektorową z bazą danych grafowych, dzięki czemu wyniki wyszukiwania można później przekształcić z powrotem na entity grafowe. Ta metoda buduje warstwę wyszukiwania semantycznego w architekturze.

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)
            ],
        )

retriever_search() przeprowadza semantyczne wyszukiwanie w momencie zapytania. Zapytanie jest uwzględniane i porównywane z wektorami w Qdrant. W rezultacie otrzymuje się entity grafu podobne do zapytania, a nie fragmenty tekstu, które stanowią punkty wejścia do grafu wiedzy. Wyszukiwanie jest zatem kierowane przez koncepcje, a nie przez dokumenty.

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

fetch_related_graph() prosi Neo4j o informacje o elementach znajdujących się w otoczeniu właśnie odnalezionych entytetów. Przechodzi po grafie, aby zebrać sąsiednie węzły oraz relacje łączące je z odnalezionymi entytetami. Ta ekspansja dodaje kontekst, którego może nie dostrzec algorytm porównywania wektorowego, i zapewnia sensowny podgraf zamiast izolowanych entytetów. Ten krok umożliwia GraphRAGowi rozumowanie na podstawie relacji.

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

format_graph_context() przygotowuje podgraf dla modelu, ponieważ surowy wynik grafu nie nadaje się bezpośrednio do użycia przez model językowy. Przekształca tę strukturę w czytelny tekst składający się z węzłów oraz opisów relacji, przedstawionych jako łatwe do zrozumienia trójki opisujące, w jaki sposób łączą się poszczególne entity. Ten format zachowuje strukturę grafu, jednocześnie ułatwiając modelowi językowemu jej zrozumienie. Jest to ostatni etap przygotowania kontekstu przed udzieleniem odpowiedzi.

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}

graphRAG_run() generuje ostateczną odpowiedź. Łączy sformatowany kontekst grafu z pytaniem użytkownika, tworząc prompt. Model otrzymuje informacje oparte na grafie, a nie surowe fragmenty dokumentów, dzięki czemu może analizować relacje i powiązane fakty, a jego odpowiedź opiera się na pozyskanej wiedzy graficznej. To właśnie jest warstwa rozumowania na końcu procesu przetwarzania.

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)}"

create_and_ingest() koordynuje cały proces pobierania danych: tworzy kolekcję wektorową, wyodrębnia komponenty grafu, przechowuje je w Neo4j, generuje embeddingi oraz indeksuje entity w Qdrant. Jego uruchomienie przekształca surowy tekst w bazę wiedzy GraphRAG nadającą się do wyszukiwania. Zazwyczaj jest wykonywany raz, podczas przygotowywania danych, po czym system może odpowiadać na zapytania.

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")

run_pipeline() koordynuje proces pozyskiwania informacji i rozumowania od początku do końca. Zaczyna się od semantycznego wyszukiwania w Qdrant, rozwiązuje problemy związane z entytetami w grafie dopasowań i przemieszcza się po Neo4j. Otrzymany podgraf jest formatowany w taki sposób, aby służył jako kontekst dla modelu rozumowania, a wygenerowana odpowiedź jest przekazywana z powrotem użytkownikowi. Reprezentuje on pełną ścieżkę od pytania do odpowiedzi.

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

close() zajmuje się sprzątaniem. Zamyka połączenie z driverem Neo4j i uwalnia powiązane z nim zasoby, co zapobiega wyciekom połączeń i umożliwia płynne wyłączenie. Jest proste, ale zawsze powinno być wywoływane po zakończeniu przetwarzania.

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

Kod sterujący łączy klasę w całość:

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()

Pełny scenariusz

Wszystkie elementy razem w jednym pliku:

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()

Rozpoczęcie pracy

Trzy przykładowe pytania pokazują, jak działa system. Każde z nich wyświetla wyniki uzyskane za pomocą mechanizmu wyszukiwania, a następnie odpowiedź opartą na wykresie.

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...
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'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-9a27-45cede6b6376:8' 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-9a27-45cede6b6376:8' 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-9a27-45cede6b6376:8' labels=frozenset({'Entity'}) properties={'name': 'Lisinopril', 'id': '04f27c55-38a3-46a9-a411-4faf07836a56'}>, '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-9a27-45cede6b6376:9' labels=frozenset({'Entity'}) properties={'name': '10mg', 'id': 'cddff401-0958-4093-b196-ad270b7aa797'}>}, {'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': ['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'}) 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'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', 'id': '42844c4c-ce96-4bd9-8d37-d8adad5a75fe'}>, 'relationship': <Relationship element_id='5:a53f7783-1308-4f3d-9a27-45cede6b6376:18' 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-9a27-45cede6b6376:22' labels=frozenset({'Entity'}) properties={'name': 'depression', 'id': '3aca7091-a4fa-4b12-ba5a-a8dbbbac5df4'}>) type='CONDITION' properties={'type': 'condition'}>, 'related_node': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:22' labels=frozenset({'Entity'}) properties={'name': 'depression', 'id': '3aca7091-a4fa-4b12-ba5a-a8dbbbac5df4'}>}, {'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', 'id': '42844c4c-ce96-4bd9-8d37-d8adad5a75fe'}>, 'relationship': <Relationship element_id='5:a53f7783-1308-4f3d-9a27-45cede6b6376:16' nodes=(<Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:20' labels=frozenset({'Entity'}) properties={'name': 'Sertraline', 'id': 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'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-9a27-45cede6b6376:4' labels=frozenset({'Entity'}) properties={'name': 'Aspirin', 'id': '10939c85-6fc1-41b4-9b80-abe1662eda11'}>, '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-9a27-45cede6b6376:6' labels=frozenset({'Entity'}) properties={'name': 'heart disease prevention', 'id': '44573186-869f-4d5d-ba04-fe254ec4ed21'}>}, {'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-9a27-45cede6b6376:4' 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-9a27-45cede6b6376:10' labels=frozenset({'Entity'}) properties={'name': 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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

Co pokazuje proces budowy

Wynikiem jest kompletny pipeline GraphRAG, w którym każdy komponent działa lokalnie na Ollama, Neo4j, Qdrant i LangExtract. Tekst nieuporządkowany przekształca się w uporządkowaną wiedzę poprzez wyodrębnianie entytetów i relacji, a ta wiedza przechowywana jest jako graf w Neo4j. Qdrant umożliwia semantyczne wyszukiwanie wśród entytetów grafu oraz łączy wyszukiwanie wektorowe z przemierzaniem grafu. Razem zastępują one wyszukiwanie oparte na fragmentach tekstu wyszukiwaniem uwzględniającym kontekst, kierowanym przez powiązaną wiedzę, dzięki czemu model językowy może rozumieć relacje, a nie tylko izolowane fragmenty tekstu, co przynosi odpowiedzi lepiej uzasadnione i łatwiejsze do wyjaśnienia. Ponieważ cała infrastruktura jest lokalna, deweloperzy mają pełną kontrolę nad swoimi danymi, modelami i infrastrukturą. W miarę wzrostu zastosowań GraphRAG, taki projekt stanowi praktyczny plan budowy systemu wyszukiwania uwzględniającego relacje, opartego na narzędziach otwartego oprogramowania.

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