How could I use AzureOpenAI to build LlamaIndex? about pgvecto.rs

[gaocegege]

Hi,

You could refer to this doc: https://docs.llamaindex.ai/en/stable/examples/customization/llms/AzureOpenAI/

api_key = "<api-key>"
azure_endpoint = "https://<your-resource-name>.openai.azure.com/"
api_version = "2023-07-01-preview"

llm = AzureOpenAI(
    model="gpt-35-turbo-16k",
    deployment_name="my-custom-llm",
    api_key=api_key,
    azure_endpoint=azure_endpoint,
    api_version=api_version,
)

# You need to deploy your own embedding model as well as your own chat completion model
embed_model = AzureOpenAIEmbedding(
    model="text-embedding-ada-002",
    deployment_name="my-custom-embedding",
    api_key=api_key,
    azure_endpoint=azure_endpoint,
    api_version=api_version,
)

from llama_index.core import Settings

Settings.llm = llm
Settings.embed_model = embed_model

# load documents
documents = SimpleDirectoryReader("./src/getting-started/overview.md").load_data()

# initialize without metadata filter
from llama_index.storage.storage_context import StorageContext

vector_store = PGVectoRsStore(client=client)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context
)

[JulianChenlol]

Hi,

You could refer to this doc: https://docs.llamaindex.ai/en/stable/examples/customization/llms/AzureOpenAI/

api_key = "<api-key>"
azure_endpoint = "https://<your-resource-name>.openai.azure.com/"
api_version = "2023-07-01-preview"

llm = AzureOpenAI(
    model="gpt-35-turbo-16k",
    deployment_name="my-custom-llm",
    api_key=api_key,
    azure_endpoint=azure_endpoint,
    api_version=api_version,
)

# You need to deploy your own embedding model as well as your own chat completion model
embed_model = AzureOpenAIEmbedding(
    model="text-embedding-ada-002",
    deployment_name="my-custom-embedding",
    api_key=api_key,
    azure_endpoint=azure_endpoint,
    api_version=api_version,
)

from llama_index.core import Settings

Settings.llm = llm
Settings.embed_model = embed_model

# load documents
documents = SimpleDirectoryReader("./src/getting-started/overview.md").load_data()

# initialize without metadata filter
from llama_index.storage.storage_context import StorageContext

vector_store = PGVectoRsStore(client=client)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context
)

Thank you! It works! This a good template.