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README.md

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 # 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)
+
+<img width="902" alt="demo" src="https://user-images.githubusercontent.com/721666/236942256-985801c9-25b9-48ef-80be-3acbb4575164.png">
+
+# Environment Setup
+
+In order to set your environment up to run the code here, first install all requirements:
+
+```shell
+pip install -r requirements.txt
+```
+
+Then, download the 2 models and place them 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
+```
+
+Note: because of the way `langchain` loads the `LLAMMA` 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/`).
+
+## 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.
+
+## Instructions for ingesting your own dataset
+
+Put any and all of your .txt, .pdf, or .csv files into the source_documents directory
+
+Run the following command to ingest all the data.
+
+```shell
+python ingest.py
+```
+
+It will create a `db` folder containing the local vectorstore. Will take time, depending on the size of your documents.
+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.
+
+## Ask questions to your documents, locally!
+In order to ask a question, run a command like:
+
+```shell
+python privateGPT.py
+```
+
+And wait for the script to require your input. 
+
+```shell
+> Enter a query:
+```
+
+Hit enter. You'll need to wait 20-30 seconds (depending on your machine) while the LLM model consumes the prompt and prepares the answer. Once done, it will print the answer and the 4 sources it used as context from your documents; you can then ask another question without re-running the script, just wait for the prompt again. 
+
+Note: you could turn off your internet connection, and the script inference would still work. No data gets out of your local environment.
+
+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. 
+- `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.
+
+# Disclaimer
+This is a test project to validate the feasibility of a fully private solution for question answering using LLMs and Vector embeddings. It is not production ready, and it is not meant to be used in production. The models selection is not optimized for performance, but for privacy; but it is possible to use different models and vectorstores to improve performance.