ManticoreSearch VectorStore
ManticoreSearch is an open-source search engine that offers fast, scalable, and user-friendly capabilities. Originating as a fork of Sphinx Search, it has evolved to incorporate modern search engine features and improvements. ManticoreSearch distinguishes itself with its robust performance and ease of integration into various applications.
ManticoreSearch has recently introduced vector search capabilities, starting with search engine version 6.2 and only with manticore-columnar-lib package installed. This feature is a considerable advancement, allowing for the execution of searches based on vector similarity.
As of now, the vector search functionality is only accessible in the developmental (dev) versions of the search engine. Consequently, it is imperative to employ a developmental manticoresearch-dev Python client for utilizing this feature effectively.
Setting up environments
Starting Docker-container with ManticoreSearch and installing manticore-columnar-lib package (optional)
import time
# Start container
containers = !docker ps --filter "name=langchain-manticoresearch-server" -q
if len(containers) == 0:
!docker run -d -p 9308:9308 --name langchain-manticoresearch-server manticoresearch/manticore:dev
time.sleep(20) # Wait for the container to start up
# Get ID of container
container_id = containers[0]
# Install manticore-columnar-lib package as root user
!docker exec -it --user 0 {container_id} apt-get update
!docker exec -it --user 0 {container_id} apt-get install -y manticore-columnar-lib
# Restart container
!docker restart {container_id}
Get:1 http://repo.manticoresearch.com/repository/manticoresearch_jammy_dev jammy InRelease [3525 kB]
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Get:20 http://repo.manticoresearch.com/repository/manticoresearch_jammy_dev jammy/main amd64 Packages [5020 kB]
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manticore-columnar-lib
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Get:1 http://repo.manticoresearch.com/repository/manticoresearch_jammy_dev jammy/main amd64 manticore-columnar-lib amd64 2.2.5-240217-a5342a1 [1990 kB]
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debconf: delaying package configuration, since apt-utils is not installed
Selecting previously unselected package manticore-columnar-lib.
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Installing ManticoreSearch python client
%pip install --upgrade --quiet manticoresearch-dev
[1m[[0m[34;49mnotice[0m[1;39;49m][0m[39;49m A new release of pip is available: [0m[31;49m23.2.1[0m[39;49m -> [0m[32;49m24.0[0m
[1m[[0m[34;49mnotice[0m[1;39;49m][0m[39;49m To update, run: [0m[32;49mpip install --upgrade pip[0m
Note: you may need to restart the kernel to use updated packages.
We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.
from langchain_community.embeddings import GPT4AllEmbeddings
from langchain_community.vectorstores import ManticoreSearch, ManticoreSearchSettings
from langchain_text_splitters import CharacterTextSplitter
from langchain_community.document_loaders import TextLoader
loader = TextLoader("../../modules/paul_graham_essay.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=100, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = GPT4AllEmbeddings()
API Reference:TextLoader
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``````output
bert_load_from_file: gguf version = 2
bert_load_from_file: gguf alignment = 32
bert_load_from_file: gguf data offset = 695552
bert_load_from_file: model name = BERT
bert_load_from_file: model architecture = bert
bert_load_from_file: model file type = 1
bert_load_from_file: bert tokenizer vocab = 30522
for d in docs:
d.metadata = {"some": "metadata"}
settings = ManticoreSearchSettings(table="manticoresearch_vector_search_example")
docsearch = ManticoreSearch.from_documents(docs, embeddings, config=settings)
query = "Robert Morris is"
docs = docsearch.similarity_search(query)
print(docs)
[Document(page_content='Computer Science is an uneasy alliance between two halves, theory and systems. The theory people prove things, and the systems people build things. I wanted to build things. I had plenty of respect for theory — indeed, a sneaking suspicion that it was the more admirable of the two halves — but building things seemed so much more exciting.', metadata={'some': 'metadata'}), Document(page_content="I applied to 3 grad schools: MIT and Yale, which were renowned for AI at the time, and Harvard, which I'd visited because Rich Draves went there, and was also home to Bill Woods, who'd invented the type of parser I used in my SHRDLU clone. Only Harvard accepted me, so that was where I went.", metadata={'some': 'metadata'}), Document(page_content='For my undergraduate thesis, I reverse-engineered SHRDLU. My God did I love working on that program. It was a pleasing bit of code, but what made it even more exciting was my belief — hard to imagine now, but not unique in 1985 — that it was already climbing the lower slopes of intelligence.', metadata={'some': 'metadata'}), Document(page_content="The problem with systems work, though, was that it didn't last. Any program you wrote today, no matter how good, would be obsolete in a couple decades at best. People might mention your software in footnotes, but no one would actually use it. And indeed, it would seem very feeble work. Only people with a sense of the history of the field would even realize that, in its time, it had been good.", metadata={'some': 'metadata'})]
Related
- Vector store conceptual guide
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