Method execute

scrapegraphai/nodes/rag_node.py:43–106

(self, state: dict)

        )

    def execute(self, state: dict) -> dict:
        self.logger.info(f"--- Executing {self.node_name} Node ---")

        try:
            from qdrant_client import QdrantClient
            from qdrant_client.models import Distance, PointStruct, VectorParams
        except ImportError:
            raise ImportError(
                "qdrant_client is not installed. Please install it using 'pip install qdrant-client'."
            )

        if self.node_config.get("client_type") in ["memory", None]:
            client = QdrantClient(":memory:")
        elif self.node_config.get("client_type") == "local_db":
            client = QdrantClient(path="path/to/db")
        elif self.node_config.get("client_type") == "image":
            client = QdrantClient(url="http://localhost:6333")
        else:
            raise ValueError("client_type provided not correct")

        docs = [elem.get("summary") for elem in state.get("docs")]
        ids = list(range(1, len(state.get("docs")) + 1))

        if state.get("embeddings"):
            import openai

            openai_client = openai.Client()

            files = state.get("documents")

            array_of_embeddings = []
            i = 0

            for file in files:
                embeddings = openai_client.embeddings.create(
                    input=file, model=state.get("embeddings").get("model")
                )
                i += 1
                points = PointStruct(
                    id=i,
                    vector=embeddings,
                    payload={"text": file},
                )

                array_of_embeddings.append(points)

            collection_name = "collection"

            client.create_collection(
                collection_name,
                vectors_config=VectorParams(
                    size=1536,
                    distance=Distance.COSINE,
                ),
            )
            client.upsert(collection_name, points)

            state["vectorial_db"] = client