langchain/docs/extras/integrations/vectorstores/dingo.ipynb

Dingo

Dingo is a distributed multi-mode vector database, which combines the characteristics of data lakes and vector databases, and can store data of any type and size (Key-Value, PDF, audio, video, etc.). It has real-time low-latency processing capabilities to achieve rapid insight and response, and can efficiently conduct instant analysis and process multi-modal data.

This notebook shows how to use functionality related to the DingoDB vector database.

To run, you should have a DingoDB instance up and running.

In [ ]:

!pip install dingodb
or install latest:
!pip install git+https://git@github.com/dingodb/pydingo.git

We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.

In [1]:

import os
import getpass

os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")

OpenAI API Key:········

In [2]:

from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Dingo
from langchain.document_loaders import TextLoader

In [3]:

from langchain.document_loaders import TextLoader

loader = TextLoader("../../../state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)

embeddings = OpenAIEmbeddings()

In [4]:

from dingodb import DingoDB

index_name = "langchain-demo"

dingo_client = DingoDB(user="", password="", host=["127.0.0.1:13000"])
# First, check if our index already exists. If it doesn't, we create it
if index_name not in dingo_client.get_index():
    # we create a new index, modify to your own
    dingo_client.create_index(
      index_name=index_name,
      dimension=1536,
      metric_type='cosine',
      auto_id=False
)

# The OpenAI embedding model `text-embedding-ada-002 uses 1536 dimensions`
docsearch = Dingo.from_documents(docs, embeddings, client=dingo_client, index_name=index_name)

In [ ]:

from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Dingo
from langchain.document_loaders import TextLoader

In [5]:

query = "What did the president say about Ketanji Brown Jackson"
docs = docsearch.similarity_search(query)

In [2]:

print(docs[0].page_content)

Adding More Text to an Existing Index

More text can embedded and upserted to an existing Dingo index using the add_texts function

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vectorstore = Dingo(embeddings, "text", client=dingo_client, index_name=index_name)

vectorstore.add_texts(["More text!"])

Maximal Marginal Relevance Searches

In addition to using similarity search in the retriever object, you can also use mmr as retriever.

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retriever = docsearch.as_retriever(search_type="mmr")
matched_docs = retriever.get_relevant_documents(query)
for i, d in enumerate(matched_docs):
    print(f"\n## Document {i}\n")
    print(d.page_content)

Or use max_marginal_relevance_search directly:

In [ ]:

found_docs = docsearch.max_marginal_relevance_search(query, k=2, fetch_k=10)
for i, doc in enumerate(found_docs):
    print(f"{i + 1}.", doc.page_content, "\n")