wehub-resource-sync
122 lines
4.9 KiB
Python
122 lines
4.9 KiB
Python
|
|
"""This example shows how to use ChromaDB as a vector embedding database with llmware with the
|
|
default configuration of using ChromaDB as a local persistent file-based vector db, with options
|
|
for both in-memory and client-server installations.
|
|
|
|
(A) Python Dependencies -
|
|
|
|
As a first step, confirm that you have installed chromadb, e.g., `pip3 install chromadb`
|
|
|
|
(B) Using ChromaDB -
|
|
|
|
Installing ChromaDB via pip installs everything you need.
|
|
However, if you need help, there are many great online sources and communities, e.g.,:
|
|
-- ChromaDB documentation - https://docs.trychroma.com/
|
|
-- Docker - https://hub.docker.com/u/chromadb
|
|
-- please also see the docker-compose-chromadb.yaml script provided in the llmware script repository
|
|
|
|
(C) Configurations -
|
|
|
|
You can configure ChromaDB with environment variables. Here is the list of variable names we currently
|
|
support - for more information see ChromaDBConfig.
|
|
-- CHROMADB_HOST
|
|
-- CHROMADB_PORT
|
|
-- CHROMADB_SSL
|
|
-- CHROMADB_HEADERS
|
|
-- CHROMADB_SERVER_AUTH_PROVIDER
|
|
-- CHROMADB_SERVER_AUTH_CREDENTIALS_PROVIDER
|
|
-- CHROMADB_SERVER_AUTH_CREDENTIALS_PROVIDER
|
|
-- CHROMADB_PASSWORD
|
|
-- CHROMADB_SERVER_AUTH_CREDENTIALS_FILE
|
|
-- CHROMADB_SERVER_AUTH_CREDENTIALS
|
|
-- CHROMADB_SERVER_AUTH_TOKEN_TRANSPORT_HEADER
|
|
"""
|
|
|
|
import os
|
|
|
|
from llmware.setup import Setup
|
|
from llmware.library import Library
|
|
from llmware.retrieval import Query
|
|
from llmware.configs import LLMWareConfig, ChromaDBConfig
|
|
|
|
|
|
def build_lib (library_name, folder="Agreements"):
|
|
|
|
# Step 1 - Create library which is the main 'organizing construct' in llmware
|
|
print ("\nupdate: Step 1 - Creating library: {}".format(library_name))
|
|
|
|
library = Library().create_new_library(library_name)
|
|
|
|
# Step 2 - Pull down the sample files from S3 through the .load_sample_files() command
|
|
# --note: if you need to refresh the sample files, set 'over_write=True'
|
|
print ("update: Step 2 - Downloading Sample Files")
|
|
|
|
sample_files_path = Setup().load_sample_files(over_write=False)
|
|
|
|
# Step 3 - point ".add_files" method to the folder of documents that was just created
|
|
# this method parses the documents, text chunks, and captures in MongoDB
|
|
print("update: Step 3 - Parsing and Text Indexing Files")
|
|
|
|
# options: Agreements | UN-Resolutions-500
|
|
library.add_files(input_folder_path=os.path.join(sample_files_path, folder),
|
|
chunk_size=400, max_chunk_size=600, smart_chunking=1)
|
|
|
|
return library
|
|
|
|
|
|
# start script
|
|
|
|
if __name__ == "__main__":
|
|
|
|
# configs
|
|
LLMWareConfig().set_active_db("sqlite")
|
|
library_name = "chromadb_lib_1"
|
|
|
|
print("update: chromadb - persistent path - ", ChromaDBConfig().get_config("persistent_path"))
|
|
|
|
print("update: Step 1- starting here- building library- parsing PDFs into text chunks")
|
|
|
|
lib = build_lib(library_name)
|
|
|
|
# after building the library the first time, you can skip that step, and load the library directly by
|
|
# uncommenting the line below
|
|
# lib = Library().load_library(library_name)
|
|
|
|
# optional - check the status of the library card and embedding
|
|
lib_card = lib.get_library_card()
|
|
print("update: -- before embedding process - check library card - ", lib_card)
|
|
|
|
print("update: Step 2 - starting to install embeddings")
|
|
|
|
# alt embedding models - "mini-lm-sbert" | industry-bert-contracts | text-embedding-ada-002
|
|
# note: if you want to use text-embedding-ada-002, you will need an OpenAI key and enter into os.environ variable
|
|
# e.g., os.environ["USER_MANAGED_OPENAI_API_KEY"] = "<insert your key>"
|
|
|
|
# batch sizes from 100-500 usually give good performance and work on most environments
|
|
lib.install_new_embedding(embedding_model_name="industry-bert-contracts",vector_db="chromadb",batch_size=100)
|
|
|
|
# optional - check the status of the library card and embedding
|
|
lib_card = lib.get_library_card()
|
|
print("update: -- after embedding process - check updated library card - ", lib_card)
|
|
|
|
# run a query
|
|
# note: embedding_model_name is optional, but useful if you create multiple embeddings on the same library
|
|
# --see other example scripts for multiple embeddings
|
|
|
|
# create query object
|
|
query_chromadb = Query(lib)
|
|
|
|
# run multiple queries using query_chromadb
|
|
my_search_results = query_chromadb.semantic_query("What is the sale bonus?", result_count = 24)
|
|
|
|
for i, qr in enumerate(my_search_results):
|
|
print("update: semantic query results: ", i, qr)
|
|
|
|
# if you want to delete the embedding - uncomment the line below
|
|
# lib.delete_installed_embedding("industry-bert-contracts", "chromadb")
|
|
|
|
# optional - check the embeddings on the library
|
|
emb_record = lib.get_embedding_status()
|
|
for j, entries in enumerate(emb_record):
|
|
print("update: embeddings on library: ", j, entries)
|