DashVector Reader¶

If you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.

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%pip install llama-index-readers-dashvector

%pip install llama-index-readers-dashvector

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!pip install llama-index

!pip install llama-index

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import logging
import sys
import os

logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))

import logging import sys import os logging.basicConfig(stream=sys.stdout, level=logging.INFO) logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))

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api_key = os.environ["DASHVECTOR_API_KEY"]

api_key = os.environ["DASHVECTOR_API_KEY"]

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from llama_index.readers.dashvector import DashVectorReader

reader = DashVectorReader(api_key=api_key)

from llama_index.readers.dashvector import DashVectorReader reader = DashVectorReader(api_key=api_key)

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import numpy as np

# the id_to_text_map specifies a mapping from the ID specified in DashVector to your text.
id_to_text_map = {
    "id1": "text blob 1",
    "id2": "text blob 2",
}

# the query_vector is an embedding representation of your query_vector
query_vector = [n1, n2, n3, ...]

import numpy as np # the id_to_text_map specifies a mapping from the ID specified in DashVector to your text. id_to_text_map = { "id1": "text blob 1", "id2": "text blob 2", } # the query_vector is an embedding representation of your query_vector query_vector = [n1, n2, n3, ...]

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# NOTE: Required args are index_name, id_to_text_map, vector.
# In addition, we can pass through the metadata filter that meet the SQL syntax.
# See the Python client: https://pypi.org/project/dashvector/ for more details.
documents = reader.load_data(
    collection_name="quickstart",
    id_to_text_map=id_to_text_map,
    top_k=3,
    vector=query_vector,
    filter="key = 'value'",
)

# NOTE: Required args are index_name, id_to_text_map, vector. # In addition, we can pass through the metadata filter that meet the SQL syntax. # See the Python client: https://pypi.org/project/dashvector/ for more details. documents = reader.load_data( collection_name="quickstart", id_to_text_map=id_to_text_map, top_k=3, vector=query_vector, filter="key = 'value'", )

Create index¶

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from llama_index.core import ListIndex
from IPython.display import Markdown, display

index = ListIndex.from_documents(documents)

from llama_index.core import ListIndex from IPython.display import Markdown, display index = ListIndex.from_documents(documents)

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# set Logging to DEBUG for more detailed outputs
query_engine = index.as_query_engine()
response = query_engine.query("<query_text>")

# set Logging to DEBUG for more detailed outputs query_engine = index.as_query_engine() response = query_engine.query("")

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display(Markdown(f"<b>{response}</b>"))

display(Markdown(f"{response}"))