捷豹向量存储

docker pull jaguardb/jaguardb
docker run -d -p 8888:8888 -p 8080:8080 --name jaguardb jaguardb/jaguardb
pip install -U llama-index
pip install -U jaguardb-http-client

curl -fsSL http://jaguardb.com/install.sh | sh\n
pip install -U llama-index
pip install -U jaguardb-http-client

%pip install llama-index-vector-stores-jaguar

!pip install -U jaguardb-http-client

Collecting jaguardb-http-client
  Using cached jaguardb_http_client-3.4.1-py2.py3-none-any.whl (15 kB)
Installing collected packages: jaguardb-http-client
Successfully installed jaguardb-http-client-3.4.1

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core import StorageContext
from llama_index.vector_stores.jaguar import JaguarVectorStore
from jaguardb_http_client.JaguarHttpClient import JaguarHttpClient

url = "http://127.0.0.1:8080/fwww/"
pod = "vdb"
store = "llamaindex_jaguar_store"
vector_index = "v"
vector_type = "cosine_fraction_float"
# vector_type = "cosine_fraction_short"  # half of memory usage compared to float
# vector_type = "cosine_fraction_byte" # quarter of memory usage compared to float
vector_dimension = 1536  # per OpenAIEmbedding model
jaguarstore = JaguarVectorStore(
    pod,
    store,
    vector_index,
    vector_type,
    vector_dimension,
    url,
)

true_or_false = jaguarstore.login()
print(f"login result is {true_or_false}")

login result is True

metadata_str = "author char(32), category char(16)"
text_size = 1024
jaguarstore.create(metadata_str, text_size)

documents = SimpleDirectoryReader("../data/paul_graham/").load_data()
print(f"loading {len(documents)} doument(s)")

loading 1 doument(s)

### make a storage context using our vector store
storage_context = StorageContext.from_defaults(vector_store=jaguarstore)

### clear all vectors in the vector store
jaguarstore.clear()

### make an index with the documents,storage context
index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context
)

### You could add more documents to the vector store:
# jaguarstore.add_documents(some_docs)
# jaguarstore.add_documents(more_docs, text_tag="tag to these documents")

### print number of documents in jaguar vector store
num = jaguarstore.count()
print(f"There are {num} vectors in jaguar vector store")

There are 22 vectors in jaguar vector store

query_engine = index.as_query_engine()
q = "What did the author do growing up?"
print(f"Question: {q}")
response = query_engine.query(q)
print(f"Answer: {str(response)}\n")

q = "What did the author do after his time at Viaweb?"
print(f"Question: {q}")
response = query_engine.query(q)
print(f"Answer: {str(response)}")

Question: What did the author do growing up?
Answer: The author mentioned that growing up, they worked on two main things outside of school: writing and programming. They wrote short stories and tried writing programs on an IBM 1401 computer.

Question: What did the author do after his time at Viaweb?
Answer: After his time at Viaweb, the author started a company to put art galleries online. However, this idea did not turn out to be successful as art galleries did not want to be online.

qkwargs = {
    "args": "day_cutoff=365,day_decay_rate=0.01",
    "where": "category='startup' or category=''",
}
query_engine_filter = index.as_query_engine(vector_store_kwargs=qkwargs)
q = "What was the author's life style?"
print(f"Question: {q}")
response = query_engine_filter.query(q)
print(f"Answer: {str(response)}")

Question: What was the author's life style?
Answer: The author's lifestyle involved attending the Accademia as a student and painting still lives in their bedroom at night. They also wrote essays and had a messy life, which they thought would be interesting and encouraging to others.

### remove all the data in the vector store if you want
jaguarstore.clear()

### delete the whole vector in the database if you want
jaguarstore.drop()

### disconnect from jaguar server and cleanup resources
jaguarstore.logout()