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Update code to use sentence-transformers through huggingfaceembeddings

This commit is contained in:
Iván Martínez 2023-05-17 00:32:41 +02:00
parent 8a5b2f453b
commit 23d24c88e9
3 changed files with 13 additions and 12 deletions
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2
example.env
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@ -1,5 +1,5 @@
PERSIST_DIRECTORY=db
LLAMA_EMBEDDINGS_MODEL=models/ggml-model-q4_0.bin
MODEL_TYPE=GPT4All
MODEL_PATH=models/ggml-gpt4all-j-v1.3-groovy.bin
EMBEDDINGS_MODEL_NAME=all-MiniLM-L6-v2
MODEL_N_CTX=1000

15
ingest.py
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@ -6,7 +6,7 @@ from dotenv import load_dotenv
from langchain.document_loaders import TextLoader, PDFMinerLoader, CSVLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.embeddings import LlamaCppEmbeddings
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.docstore.document import Document
from constants import CHROMA_SETTINGS
@ -38,22 +38,23 @@ def main():
# Load environment variables
persist_directory = os.environ.get('PERSIST_DIRECTORY')
source_directory = os.environ.get('SOURCE_DIRECTORY', 'source_documents')
llama_embeddings_model = os.environ.get('LLAMA_EMBEDDINGS_MODEL')
model_n_ctx = os.environ.get('MODEL_N_CTX')
embeddings_model_name = os.environ.get('EMBEDDINGS_MODEL_NAME')
# Load documents and split in chunks
print(f"Loading documents from {source_directory}")
chunk_size = 500
chunk_overlap = 50
documents = load_documents(source_directory)
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
texts = text_splitter.split_documents(documents)
print(f"Loaded {len(documents)} documents from {source_directory}")
print(f"Split into {len(texts)} chunks of text (max. 500 tokens each)")
print(f"Split into {len(texts)} chunks of text (max. {chunk_size} characters each)")
# Create embeddings
llama = LlamaCppEmbeddings(model_path=llama_embeddings_model, n_ctx=model_n_ctx)
embeddings = HuggingFaceEmbeddings(model_name=embeddings_model_name)
# Create and store locally vectorstore
db = Chroma.from_documents(texts, llama, persist_directory=persist_directory, client_settings=CHROMA_SETTINGS)
db = Chroma.from_documents(texts, embeddings, persist_directory=persist_directory, client_settings=CHROMA_SETTINGS)
db.persist()
db = None

8
privateGPT.py
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@ -1,6 +1,6 @@
from dotenv import load_dotenv
from langchain.chains import RetrievalQA
from langchain.embeddings import LlamaCppEmbeddings
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.vectorstores import Chroma
from langchain.llms import GPT4All, LlamaCpp
@ -8,7 +8,7 @@ import os
load_dotenv()
llama_embeddings_model = os.environ.get("LLAMA_EMBEDDINGS_MODEL")
embeddings_model_name = os.environ.get("EMBEDDINGS_MODEL_NAME")
persist_directory = os.environ.get('PERSIST_DIRECTORY')
model_type = os.environ.get('MODEL_TYPE')
@ -18,8 +18,8 @@ model_n_ctx = os.environ.get('MODEL_N_CTX')
from constants import CHROMA_SETTINGS
def main():
llama = LlamaCppEmbeddings(model_path=llama_embeddings_model, n_ctx=model_n_ctx)
db = Chroma(persist_directory=persist_directory, embedding_function=llama, client_settings=CHROMA_SETTINGS)
embeddings = HuggingFaceEmbeddings(model_name=embeddings_model_name)
db = Chroma(persist_directory=persist_directory, embedding_function=embeddings, client_settings=CHROMA_SETTINGS)
retriever = db.as_retriever()
# Prepare the LLM
callbacks = [StreamingStdOutCallbackHandler()]