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Jaguar 向量数据库 (Jaguar Vector Database)

  1. 这是一个分布式向量数据库 (It is a distributed vector database)
  2. JaguarDB 的“零移动”功能支持即时水平扩展 (The “ZeroMove” feature of JaguarDB enables instant horizontal scalability)
  3. 多模态：嵌入、文本、图像、视频、PDF、音频、时间序列和地理空间 (Multimodal: embeddings, text, images, videos, PDFs, audio, time series, and geospatial)
  4. 全主节点：允许并行读取和写入 (All-masters: allows both parallel reads and writes)
  5. 异常检测能力 (Anomaly detection capabilities)
  6. RAG 支持：将 LLM 与专有数据和实时数据结合使用 (RAG support: combines LLM with proprietary and real-time data)
  7. 共享元数据：在多个向量索引之间共享元数据 (Shared metadata: sharing of metadata across multiple vector indexes)
  8. 距离度量：欧几里得、余弦、内积、曼哈顿、切比雪夫、汉明、杰卡德、闵可夫斯基 (Distance metrics: Euclidean, Cosine, InnerProduct, Manhatten, Chebyshev, Hamming, Jeccard, Minkowski)

先决条件​

运行此文件中的示例有两个要求。(There are two requirements for running the examples in this file.)

  1. 您必须安装并设置 JaguarDB 服务器及其 HTTP 网关服务器。请参考以下说明：www.jaguardb.com。在 docker 环境中快速设置：docker pull jaguardb/jaguardb_with_http docker run -d -p 8888:8888 -p 8080:8080 --name jaguardb_with_http jaguardb/jaguardb_with_http(You must install and set up the JaguarDB server and its HTTP gateway server. Please refer to the instructions in: www.jaguardb.com 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. 您必须安装 JaguarDB 的 http 客户端包(You must install the http client package for JaguarDB)

        pip install -U jaguardb-http-client

  3. 您需要使用 pip install -qU langchain-community 安装 langchain-community 才能使用此集成(You'll need to install langchain-community with pip install -qU langchain-community to use this integration)

使用 Langchain 的 RAG​

本节演示了在 langchain 软件栈中与 LLM 和 Jaguar 一起聊天。(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 langchain_community.vectorstores.jaguar 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("../../how_to/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 = "http://192.168.5.88:8080/fwww/"

### 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.
"""
vectorstore.login()


"""
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
"""
vectorstore.add_documents(docs)
# 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}
Answer:
"""
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
    | LLM
    | StrOutputParser()
)

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

与 Jaguar 向量存储交互​

用户可以直接与 Jaguar 向量存储交互，进行相似性搜索和异常检测。(Users can interact directly with the Jaguar vector store for similarity search and anomaly detection.)

from langchain_community.vectorstores.jaguar import Jaguar
from langchain_openai import OpenAIEmbeddings

# Instantiate a Jaguar vector store object
url = "http://192.168.3.88:8080/fwww/"
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
vectorstore.login()

# 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(
    query="foo",
    k=1,
    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(
    query="foo",
    k=3,
    fetch_k=9,
    where=where,
    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
vectorstore.clear()
assert vectorstore.count() == 0

# Remove the store completely
vectorstore.drop()

# Logout
vectorstore.logout()
API 参考：Jaguar | OpenAIEmbeddings

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