Looking to Build GraphRAG? Start with This Practical Guide from our star, Pavan

In this practical guide, Pavan demonstrates how to build a GraphRAG architecture using LangExtract, @neo4j, Qdrant, and @ollama.

You’ll learn how to: → Extract entities and relationships from unstructured text with LangExtract → Build a knowledge graph in @neo4j → Use Qdrant for semantic retrieval → Combine graph traversal with vector search for more context-aware responses → Run the entire pipeline locally with @ollama

If you’re exploring GraphRAG or building production-ready retrieval systems, this is a great walkthrough.

Read the article: https://blog.stackademic.com/a-practical-graphrag-architecture-using-langextract-neo4j-qdrant-and-ollama-0e4c86908c41…

A Practical GraphRAG Architecture Using LangExtract, Neo4j, Qdrant, and Ollama

Source: https://blog.stackademic.com/a-practical-graphrag-architecture-using-langextract-neo4j-qdrant-and-ollama-0e4c86908c41?gi=f57d40028cea Today, we are going to build a complete GraphRAG system using 100% local LLMs powered by Ollama, Neo4j, Qdrant, and LangExtract. Unlike traditional RAG systems that retrieve document chunks, this architecture transforms raw text into a knowledge graph and retrieves semantically relevant entities connected through relationships. The real highlight of this solution is LangExtract, which automatically extracts entities and relationships from unstructured text and converts them into graph-ready knowledge. By combining vector retrieval from Qdrant with graph traversal in Neo4j, we create a retrieval pipeline that understands both meaning and context. Let’s dive into the architecture and see how each component works together to deliver graph-grounded answers.

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created by author M K Pavan Kumar

Architecture Deep Dive

The architecture begins with unstructured raw text entering the LangExtract layer. Unlike conventional RAG pipelines that immediately split documents into chunks and generate embeddings, this system first transforms the text into structured knowledge. LangExtract, powered by an Ollama-hosted language model, identifies entities and their semantic relationships directly from the source text. In the medication example, entities such as medications, dosages, frequencies, and medical conditions are extracted while preserving their relationships through a common grouping mechanism. This stage effectively converts natural language into a structured representation that captures factual connections rather than merely storing text passages.

Once extraction is complete, the generated entities and relationships are transformed into graph components. Every extracted concept becomes a graph node with a unique identifier, while semantic relationships such as dosage, frequency, or condition become explicit edges connecting those nodes. This conversion process creates a formal knowledge representation where information is organized around entities and their relationships instead of document boundaries. The graph structure preserves contextual meaning that would normally be lost in traditional chunk-based retrieval systems.

The extracted graph is then ingested into Neo4j, which serves as the system’s primary knowledge repository. Each entity is stored as a graph node, and each semantic relationship is stored as a native graph edge. Neo4j becomes the authoritative source of structured knowledge and enables efficient traversal across connected concepts. Rather than retrieving isolated text snippets, the system can later explore multi-hop relationships between entities, allowing it to uncover contextual information that extends beyond the original extraction point.

After graph construction, the architecture generates embeddings for every graph node. Instead of embedding entire documents or chunks, the embedding model operates directly on entity names and concepts. Each entity receives a vector representation that captures its semantic meaning in embedding space. This design choice creates a semantic index over graph entities rather than textual content, enabling retrieval to focus on concepts rather than passages.

These entity embeddings are then stored in Qdrant alongside their corresponding Neo4j node identifiers. Qdrant functions as a high-performance semantic retrieval layer, while Neo4j remains the structured knowledge layer. The payload stored in Qdrant contains the Neo4j node ID, creating a direct mapping between vector search results and graph entities. This separation of responsibilities allows Qdrant to excel at semantic similarity search while Neo4j handles relationship traversal and graph reasoning.

During query time, a user submits a natural language question. The same embedding model converts the query into a vector representation. This query embedding is then searched against Qdrant to identify the most semantically relevant graph entities. Unlike traditional RAG systems that return document chunks, the retrieval process returns node identifiers corresponding to entities within the knowledge graph. These nodes act as semantic entry points into the graph and represent the concepts most closely related to the user’s intent.

The retrieved node identifiers are subsequently used to query Neo4j. Rather than stopping at the retrieved entities, the system performs graph expansion by traversing neighboring nodes and relationships. This traversal retrieves a relevant subgraph containing both the matched entities and their connected context. The expansion process allows the architecture to gather supporting information that may not have been directly retrieved through vector similarity alone. As a result, the retrieved context contains both semantic relevance and structural relationships.

The resulting subgraph is then transformed into a format suitable for language model consumption. Nodes are collected into a structured list, while relationships are converted into readable triples that express explicit connections between entities. For example, relationships such as “Lisinopril dosage 10mg” or “Metformin condition diabetes” become structured contextual statements. This formatting stage bridges the gap between graph databases and language models by converting graph structures into interpretable textual context.

Finally, the formatted graph context is provided to the LLM. Instead of relying on retrieved document chunks, the model receives a graph-grounded representation consisting of entities and relationships. This enables the LLM to reason over explicit facts and connections rather than inferring relationships from fragmented text passages. Because the context originates from graph traversal, the model gains access to structured knowledge that is inherently more explainable and traceable than standard vector-based retrieval.

The overall architecture can therefore be viewed as a hybrid retrieval system where LangExtract performs knowledge extraction, Neo4j manages structured relationships, Qdrant provides semantic entity retrieval, and the LLM performs graph-grounded reasoning. The combination of vector similarity and graph traversal creates a retrieval mechanism that is both semantically aware and structurally informed, allowing the system to answer questions using connected knowledge rather than isolated text fragments. This approach significantly improves the retrieval of relational information and makes it particularly effective for domains such as healthcare, finance, legal systems, enterprise knowledge management, and scientific research where understanding relationships between entities is often more important than retrieving individual passages of text.

Implementation Walkthrough

\_\_init\_\_\(\)

The constructor serves as the entry point for the entire GraphRAG system. It loads all required configuration values such as Neo4j credentials, Qdrant connection details, and Ollama settings from environment variables. The method establishes connections to both Neo4j and Qdrant so they can be reused throughout the pipeline. It also initializes the Ollama client that will be used for entity extraction, embedding generation, and answer synthesis. Finally, it stores model names and vector dimensions, allowing the architecture to remain flexible and configurable.

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\(\)

This method is responsible for converting raw unstructured text into structured knowledge. It defines extraction instructions and provides examples that guide LangExtract in identifying entities and their relationships. Using an Ollama-hosted LLM, LangExtract processes the input text and extracts concepts such as medications, dosages, frequencies, and conditions. The output at this stage is still a collection of extracted elements rather than a graph. The method then forwards these extractions for graph construction, creating the foundation for the knowledge graph.

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\(\)

Once entities are extracted, this method transforms them into a graph-friendly structure. It groups related information together and identifies the primary entity that acts as the anchor node. Unique identifiers are generated for every node to ensure consistency across Neo4j and Qdrant. Relationships are then created between anchor entities and their associated attributes, preserving semantic meaning. The final output consists of graph nodes and edges that are ready for ingestion into a graph database.

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\(\)

This method persists the generated graph structure into Neo4j. Each extracted entity is stored as a graph node, while semantic relationships are stored as graph edges. By storing data in this format, Neo4j can later perform efficient graph traversals and relationship exploration. The method ensures that every node maintains a unique identifier, allowing it to be linked with vector search results. Once completed, the graph becomes the system’s structured knowledge repository.

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\(\)

Since relationship names originate from LLM-generated extractions, they may contain invalid characters or formats. This method cleans and standardizes relationship labels before they are inserted into Neo4j. The transformation converts labels into safe uppercase identifiers that comply with Cypher requirements. It also protects the system from malformed relationship names and potential query issues. Although small, this method plays an important role in maintaining graph integrity.

@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\(\)

Before vectors can be stored, a collection must exist in Qdrant. This method checks whether the specified collection is already available and creates it if necessary. It also configures vector dimensions and similarity metrics that will be used during retrieval. By performing this validation step, the system avoids unnecessary collection recreation. The method ensures that the vector storage layer is properly prepared before ingestion begins.

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\(\)

This method generates dense vector representations using Ollama’s embedding model. Every graph entity and user query eventually passes through this function. The generated embeddings capture semantic meaning in a numerical form that can be compared efficiently. These vectors enable similarity search within Qdrant. In many ways, this method acts as the bridge between natural language and vector retrieval.

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\(\)

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After graph nodes are created, this method generates embeddings for each entity and stores them in Qdrant. Along with the embedding, it stores metadata such as the Neo4j node identifier and entity name. This mapping creates a direct connection between the vector database and graph database. During retrieval, vector search results can therefore be translated back into graph entities. The method effectively builds the semantic search layer of the architecture.

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\(\)

This method performs semantic retrieval during query execution. The user query is first converted into an embedding and then compared against vectors stored in Qdrant. Rather than retrieving text chunks, the search returns graph entities that are semantically similar to the query. These entities serve as entry points into the knowledge graph. The result is a retrieval process that is concept-driven rather than document-driven.

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\(\)

Once relevant entities are identified, this method queries Neo4j to retrieve their surrounding context. It performs graph traversal to collect neighboring nodes and relationships connected to the retrieved entities. This expansion process enriches the retrieved information with additional context that may not have been directly matched during vector search. As a result, the system gains access to a meaningful subgraph instead of isolated entities. This step is what gives GraphRAG its relational reasoning capabilities.

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\(\)

The retrieved subgraph is not immediately suitable for LLM consumption. This method converts graph structures into a clean textual representation consisting of nodes and relationship statements. Relationships are transformed into readable triples that explicitly describe how entities are connected. The resulting format retains the structure of the graph while making it understandable for a language model. This serves as the final context preparation stage before answer generation.

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\(\)

This method is responsible for generating the final answer. It combines the formatted graph context with the user’s question and constructs a prompt for the language model. The LLM receives graph-grounded information instead of raw document chunks, allowing it to reason over relationships and connected facts. Once the model processes the prompt, it generates a response based on the retrieved graph knowledge. This is the final reasoning layer of the GraphRAG pipeline.

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\(\)

This method orchestrates the complete ingestion workflow. It creates the vector collection, extracts graph components, stores them in Neo4j, generates embeddings, and indexes entities in Qdrant. Running this method converts raw text into a fully searchable GraphRAG knowledge base. It is typically executed once during the data preparation phase. After completion, the system becomes ready for querying.

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\(\)

This method orchestrates the end-to-end retrieval and reasoning workflow. It starts with semantic retrieval from Qdrant, extracts the corresponding graph entities, and performs graph traversal in Neo4j. The retrieved subgraph is then formatted into LLM-friendly context and passed to the reasoning model. Finally, the generated answer is returned to the user. This method represents the complete GraphRAG execution pipeline from question to answer.

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\(\)

The final method handles resource cleanup. It safely closes the Neo4j driver connection and releases any associated resources. This helps prevent connection leaks and ensures graceful application shutdown. Although simple, it is an important part of maintaining system stability. It should always be called when processing is complete.

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

The Driver Code:

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()

Putting it all together

import osimport uuidimport textwrapimport ollamafrom dotenv import load_dotenv, find_dotenvfrom ollama import chatfrom neo4j import GraphDatabasefrom qdrant_client import QdrantClient, modelsfrom neo4j_graphrag.retrievers import QdrantNeo4jRetrieverimport langextract as lxfrom langextract.providers import ollama as lx_ollamaload_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()

The Run:

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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'}) 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'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'}) 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': 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labels=frozenset({'Entity'}) properties={'name': 'Levothyroxine', 'id': '60654f2d-2ec8-4f4e-be02-cd57b7c5abfb'}>, 'relationship': <Relationship element_id='5:a53f7783-1308-4f3d-9a27-45cede6b6376:13' 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-9a27-45cede6b6376:18' 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-9a27-45cede6b6376:12' nodes=(<Node 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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': 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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', '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': '42844c4c-ce96-4bd9-8d37-d8adad5a75fe'}>, <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:21' labels=frozenset({'Entity'}) properties={'name': '50mg', 'id': '5fe00dee-bd0f-4476-b5a6-b19974033f78'}>) type='DOSAGE' properties={'type': 'dosage'}>, 'related_node': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:21' labels=frozenset({'Entity'}) properties={'name': '50mg', 'id': '5fe00dee-bd0f-4476-b5a6-b19974033f78'}>}, {'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-9a27-45cede6b6376:5' 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': 'daily', 'id': '06da6faa-baa5-4657-a961-5091f58e3058'}>, '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-9a27-45cede6b6376:8' 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': 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properties={'name': 'daily', 'id': '06da6faa-baa5-4657-a961-5091f58e3058'}>) type='FREQUENCY' properties={'type': 'frequency'}>, 'related_node': <Node element_id='4:a53f7783-1308-4f3d-9a27-45cede6b6376:8' 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-9a27-45cede6b6376:6' 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-9a27-45cede6b6376:14' 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-9a27-45cede6b6376:12' 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-9a27-45cede6b6376:14' 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-9a27-45cede6b6376:12' labels=frozenset({'Entity'}) properties={'name': 'Metformin', 'id': 'd14d70ef-3780-4ebe-b803-1ab8bd8ec847'}>, 'relationship': <Relationship 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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

The Conclusion:

In this article, we learned how to build a complete GraphRAG pipeline using 100% local components powered by Ollama, Neo4j, Qdrant, and LangExtract. We explored how unstructured text can be transformed into structured knowledge through entity and relationship extraction, and how that knowledge can be represented as a graph inside Neo4j. We also saw how Qdrant enables semantic retrieval over graph entities, creating a bridge between vector search and graph traversal. By combining these technologies, we moved beyond traditional chunk-based retrieval and enabled context-aware retrieval driven by connected knowledge. The resulting architecture allows an LLM to reason over relationships rather than isolated pieces of text, leading to more grounded and explainable responses. Most importantly, the entire solution runs locally, giving developers complete control over their data, models, and infrastructure. As GraphRAG continues to gain adoption, architectures like this provide a practical blueprint for building intelligent, relationship-aware retrieval systems using open-source technologies.