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Jaguar Vector Database

  1. It is a distributed vector database
  2. The β€œZeroMove” feature of JaguarDB enables instant horizontal scalability
  3. Multimodal: embeddings, text, images, videos, PDFs, audio, time series, and geospatial
  4. All-masters: allows both parallel reads and writes
  5. Anomaly detection capabilities
  6. RAG support: combines LLM with proprietary and real-time data
  7. Shared metadata: sharing of metadata across multiple vector indexes
  8. Distance metrics: Euclidean, Cosine, InnerProduct, Manhatten, Chebyshev, Hamming, Jeccard, Minkowski


There are two requirements for running the examples in this file.

  1. You must install and set up the JaguarDB server and its HTTP gateway server. Please refer to the instructions in: For quick setup in docker environment: docker pull jaguardb/jaguardb_with_http docker run -d -p 8888:8888 -p 8080:8080 --name jaguardb_with_http jaguardb/jaguardb_with_http

  2. You must install the http client package for JaguarDB:

        pip install -U jaguardb-http-client

RAG With Langchain​

This section demonstrates chatting with LLM together with Jaguar in the langchain software stack.

from langchain.chains import RetrievalQAWithSourcesChain
from langchain_community.document_loaders import TextLoader
from import Jaguar
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_openai import ChatOpenAI, OpenAI, OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter

Load a text file into a set of documents
loader = TextLoader("../../modules/state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=300)
docs = text_splitter.split_documents(documents)

Instantiate a Jaguar vector store
### Jaguar HTTP endpoint
url = ""

### Use OpenAI embedding model
embeddings = OpenAIEmbeddings()

### Pod is a database for vectors
pod = "vdb"

### Vector store name
store = "langchain_rag_store"

### Vector index name
vector_index = "v"

### Type of the vector index
# cosine: distance metric
# fraction: embedding vectors are decimal numbers
# float: values stored with floating-point numbers
vector_type = "cosine_fraction_float"

### Dimension of each embedding vector
vector_dimension = 1536

### Instantiate a Jaguar store object
vectorstore = Jaguar(
pod, store, vector_index, vector_type, vector_dimension, url, embeddings

Login must be performed to authorize the client.
The environment variable JAGUAR_API_KEY or file $HOME/.jagrc
should contain the API key for accessing JaguarDB servers.

Create vector store on the JaguarDB database server.
This should be done only once.
# Extra metadata fields for the vector store
metadata = "category char(16)"

# Number of characters for the text field of the store
text_size = 4096

# Create a vector store on the server
vectorstore.create(metadata, text_size)

Add the texts from the text splitter to our vectorstore
# or tag the documents:
# vectorstore.add_documents(more_docs, text_tag="tags to these documents")

""" Get the retriever object """
retriever = vectorstore.as_retriever()
# retriever = vectorstore.as_retriever(search_kwargs={"where": "m1='123' and m2='abc'"})

template = """You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.
Question: {question}
Context: {context}
prompt = ChatPromptTemplate.from_template(template)

""" Obtain a Large Language Model """
LLM = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)

""" Create a chain for the RAG flow """
rag_chain = (
{"context": retriever, "question": RunnablePassthrough()}
| prompt
| StrOutputParser()

resp = rag_chain.invoke("What did the president say about Justice Breyer?")

Interaction With Jaguar Vector Store​

Users can interact directly with the Jaguar vector store for similarity search and anomaly detection.

from import Jaguar
from langchain_openai import OpenAIEmbeddings

# Instantiate a Jaguar vector store object
url = ""
pod = "vdb"
store = "langchain_test_store"
vector_index = "v"
vector_type = "cosine_fraction_float"
vector_dimension = 10
embeddings = OpenAIEmbeddings()
vectorstore = Jaguar(
pod, store, vector_index, vector_type, vector_dimension, url, embeddings

# Login for authorization

# Create the vector store with two metadata fields
# This needs to be run only once.
metadata_str = "author char(32), category char(16)"
vectorstore.create(metadata_str, 1024)

# Add a list of texts
texts = ["foo", "bar", "baz"]
metadatas = [
{"author": "Adam", "category": "Music"},
{"author": "Eve", "category": "Music"},
{"author": "John", "category": "History"},
ids = vectorstore.add_texts(texts=texts, metadatas=metadatas)

# Search similar text
output = vectorstore.similarity_search(
metadatas=["author", "category"],
assert output[0].page_content == "foo"
assert output[0].metadata["author"] == "Adam"
assert output[0].metadata["category"] == "Music"
assert len(output) == 1

# Search with filtering (where)
where = "author='Eve'"
output = vectorstore.similarity_search(
metadatas=["author", "category"],
assert output[0].page_content == "bar"
assert output[0].metadata["author"] == "Eve"
assert output[0].metadata["category"] == "Music"
assert len(output) == 1

# Anomaly detection
result = vectorstore.is_anomalous(
query="dogs can jump high",
assert result is False

# Remove all data in the store
assert vectorstore.count() == 0

# Remove the store completely

# Logout

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