diff --git a/README.md b/README.md index 032bb633..ee27a902 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,7 @@ # privateGPT Ask questions to your documents without an internet connection, using the power of LLMs. 100% private, no data leaves your execution environment at any point. You can ingest documents and ask questions without an internet connection! -Built with [LangChain](https://github.com/hwchase17/langchain) and [GPT4All](https://github.com/nomic-ai/gpt4all) and [LlamaCpp](https://github.com/ggerganov/llama.cpp) +Built with [LangChain](https://github.com/hwchase17/langchain), [GPT4All](https://github.com/nomic-ai/gpt4all), [LlamaCpp](https://github.com/ggerganov/llama.cpp), [Chroma](https://www.trychroma.com/) and [SentenceTransformers](https://www.sbert.net/). demo @@ -12,20 +12,19 @@ In order to set your environment up to run the code here, first install all requ pip install -r requirements.txt ``` -Then, download the 2 models and place them in a directory of your choice. +Then, download the LLM model and place it in a directory of your choice: - LLM: default to [ggml-gpt4all-j-v1.3-groovy.bin](https://gpt4all.io/models/ggml-gpt4all-j-v1.3-groovy.bin). If you prefer a different GPT4All-J compatible model, just download it and reference it in your `.env` file. -- Embedding: default to [ggml-model-q4_0.bin](https://huggingface.co/Pi3141/alpaca-native-7B-ggml/resolve/397e872bf4c83f4c642317a5bf65ce84a105786e/ggml-model-q4_0.bin). If you prefer a different compatible Embeddings model, just download it and reference it in your `.env` file. Rename `example.env` to `.env` and edit the variables appropriately. ``` MODEL_TYPE: supports LlamaCpp or GPT4All PERSIST_DIRECTORY: is the folder you want your vectorstore in -LLAMA_EMBEDDINGS_MODEL: (absolute) Path to your LlamaCpp supported embeddings model MODEL_PATH: Path to your GPT4All or LlamaCpp supported LLM -MODEL_N_CTX: Maximum token limit for both embeddings and LLM models +MODEL_N_CTX: Maximum token limit for the LLM model +EMBEDDINGS_MODEL_NAME: SentenceTransformers embeddings model name (see https://www.sbert.net/docs/pretrained_models.html) ``` -Note: because of the way `langchain` loads the `LLAMA` embeddings, you need to specify the absolute path of your embeddings model binary. This means it will not work if you use a home directory shortcut (eg. `~/` or `$HOME/`). +Note: because of the way `langchain` loads the `SentenceTransformers` embeddings, the first time you run the script it will require internet connection to download the embeddings model itself. ## Test dataset This repo uses a [state of the union transcript](https://github.com/imartinez/privateGPT/blob/main/source_documents/state_of_the_union.txt) as an example. @@ -55,11 +54,11 @@ Run the following command to ingest all the data. python ingest.py ``` -It will create a `db` folder containing the local vectorstore. Will take time, depending on the size of your documents. +It will create a `db` folder containing the local vectorstore. Will take 20-30 seconds per document, depending on the size of the document. You can ingest as many documents as you want, and all will be accumulated in the local embeddings database. If you want to start from an empty database, delete the `db` folder. -Note: during the ingest process no data leaves your local environment. You could ingest without an internet connection. +Note: during the ingest process no data leaves your local environment. You could ingest without an internet connection, except for the first time you run the ingest script, when the embeddings model is downloaded. ## Ask questions to your documents, locally! In order to ask a question, run a command like: @@ -83,7 +82,7 @@ Type `exit` to finish the script. # How does it work? Selecting the right local models and the power of `LangChain` you can run the entire pipeline locally, without any data leaving your environment, and with reasonable performance. -- `ingest.py` uses `LangChain` tools to parse the document and create embeddings locally using `LlamaCppEmbeddings`. It then stores the result in a local vector database using `Chroma` vector store. +- `ingest.py` uses `LangChain` tools to parse the document and create embeddings locally using `HuggingFaceEmbeddings` (`SentenceTransformers`). It then stores the result in a local vector database using `Chroma` vector store. - `privateGPT.py` uses a local LLM based on `GPT4All-J` or `LlamaCpp` to understand questions and create answers. The context for the answers is extracted from the local vector store using a similarity search to locate the right piece of context from the docs. - `GPT4All-J` wrapper was introduced in LangChain 0.0.162. diff --git a/example.env b/example.env index 149eca2e..82907845 100644 --- a/example.env +++ b/example.env @@ -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 \ No newline at end of file diff --git a/ingest.py b/ingest.py index 0ad60f0d..d28edd50 100644 --- a/ingest.py +++ b/ingest.py @@ -19,7 +19,7 @@ from langchain.document_loaders import ( 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 @@ -72,22 +72,23 @@ 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 diff --git a/privateGPT.py b/privateGPT.py index 4c603a27..ae08bb93 100644 --- a/privateGPT.py +++ b/privateGPT.py @@ -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()]