참고소스 수정본

참고/neo4j-graphrag-python-main/examples/customize/embeddings/azure_openai_embeddings.py

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+"""This example demonstrate how to embed a text into a vector
+using OpenAI models and API.
+"""
+
+from neo4j_graphrag.embeddings import AzureOpenAIEmbeddings
+
+embedder = AzureOpenAIEmbeddings(
+    model="text-embedding-ada-002",
+    azure_endpoint="https://my-endpoint.openai.azure.com/",
+    api_key="<my key>",
+    api_version="<update version>",
+)
+res = embedder.embed_query("my question")
+print(res[:10])

참고/neo4j-graphrag-python-main/examples/customize/embeddings/bedrock_embeddings.py

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+from botocore.exceptions import ClientError, NoCredentialsError, PartialCredentialsError
+
+from neo4j_graphrag.embeddings import BedrockEmbeddings
+
+# AWS credentials are read from environment or ~/.aws/credentials
+embedder = BedrockEmbeddings(
+    model_id="amazon.titan-embed-text-v2:0",
+    dimensions=1024,
+    region_name="us-east-1",
+)
+
+try:
+    res = embedder.embed_query("my question")
+    print(res[:10])
+except NoCredentialsError:
+    print(
+        "AWS credentials not found. Run 'aws configure' or set environment variables."
+    )
+except PartialCredentialsError as e:
+    print(f"Incomplete AWS credentials: {e}")
+except ClientError as e:
+    print(f"AWS API error: {e}")

참고/neo4j-graphrag-python-main/examples/customize/embeddings/cohere_embeddings.py

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+from neo4j_graphrag.embeddings import CohereEmbeddings
+
+# set api key here on in the CO_API_KEY env var
+api_key = None
+
+embeder = CohereEmbeddings(
+    model="embed-english-v3.0",
+    api_key=api_key,
+)
+res = embeder.embed_query("my question")
+print(res[:10])

참고/neo4j-graphrag-python-main/examples/customize/embeddings/custom_embeddings.py

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+import random
+from typing import Any
+
+from neo4j_graphrag.embeddings import Embedder
+
+
+class CustomEmbeddings(Embedder):
+    def __init__(self, dimension: int = 10, **kwargs: Any):
+        super().__init__(**kwargs)
+        self.dimension = dimension
+
+    def embed_query(self, input: str) -> list[float]:
+        return [random.random() for _ in range(self.dimension)]
+
+
+llm = CustomEmbeddings(dimensions=1024)
+res = llm.embed_query("text")
+print(res[:10])

참고/neo4j-graphrag-python-main/examples/customize/embeddings/mistalai_embeddings.py

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+"""This example demonstrate how to embed a text into a vector
+using MistralAI models and API.
+"""
+
+from neo4j_graphrag.embeddings import MistralAIEmbeddings
+
+# set api key here on in the MISTRAL_API_KEY env var
+api_key = None
+
+embeder = MistralAIEmbeddings(model="mistral-embed", api_key=api_key)
+res = embeder.embed_query("my question")
+print(res[:10])

참고/neo4j-graphrag-python-main/examples/customize/embeddings/ollama_embeddings.py

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+"""This example demonstrate how to embed a text into a vector
+using a local model served by Ollama.
+"""
+
+from neo4j_graphrag.embeddings import OllamaEmbeddings
+
+embeder = OllamaEmbeddings(
+    model="<model_name>",
+    # host="...",  # if using a remote server
+)
+res = embeder.embed_query("my question")
+print(res[:10])

참고/neo4j-graphrag-python-main/examples/customize/embeddings/openai_embeddings.py

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+"""This example demonstrate how to embed a text into a vector
+using OpenAI models and API.
+"""
+
+from neo4j_graphrag.embeddings import OpenAIEmbeddings
+
+# set api key here on in the OPENAI_API_KEY env var
+api_key = None
+
+embeder = OpenAIEmbeddings(model="text-embedding-ada-002", api_key=api_key)
+res = embeder.embed_query("my question")
+print(res[:10])

참고/neo4j-graphrag-python-main/examples/customize/embeddings/vertexai_embeddings.py

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+"""This example demonstrate how to embed a text into a vector
+using Google models and the VertexAI API.
+"""
+
+from neo4j_graphrag.embeddings import VertexAIEmbeddings
+
+embeder = VertexAIEmbeddings(model="text-embedding-005")
+res = embeder.embed_query("my question")
+print(res[:10])