GPT for your wiki pages
In order to set your environment up to run the code here, first install all requirements:
pip3 install -r requirements.txtThen, download the LLM model and place it in a directory of your choice:
.env file.Copy the example.env template into .env
cp example.env .envand edit the variables appropriately in the .env file.
MODEL_TYPE: supports LlamaCpp or GPT4All
PERSIST_DIRECTORY: is the folder you want your vectorstore in
MODEL_PATH: Path to your GPT4All or LlamaCpp supported LLM
MODEL_N_CTX: Maximum token limit for the LLM model
MODEL_N_BATCH: Number of tokens in the prompt that are fed into the model at a time. Optimal value differs a lot depending on the model (8 works well for GPT4All, and 1024 is better for LlamaCpp)
EMBEDDINGS_MODEL_NAME: SentenceTransformers embeddings model name (see https://www.sbert.net/docs/pretrained_models.html)
TARGET_SOURCE_CHUNKS: The amount of chunks (sources) that will be used to answer a question
SOURCE_DIRECTORY: Directory for the documentsNote: 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.
This repo contains within synthetically produced text files containing description of different projects. These files can be found under v1.0_fake_products.
Put any and all your files into the source_documents directory
The supported extensions are:
.csv: CSV,.docx: Word Document,.doc: Word Document,.enex: EverNote,.eml: Email,.epub: EPub,.html: HTML File,.md: Markdown,.msg: Outlook Message,.odt: Open Document Text,.pdf: Portable Document Format (PDF),.pptx : PowerPoint Document,.ppt : PowerPoint Document,.txt: Text file (UTF-8),Run the following command to ingest all the data.
python src/ingest.pyIt 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, except for the first time you run the ingest script, when the embeddings model is downloaded.
In order to ask a question, run a command like:
python localrun.pyAnd wait for the script to require your input.
> 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.
To launch a webservice that runs on local host port 8080, run the following command:
python v1.1_microservice/server.pyThe webserver has 2 requests that you can make. One is a GET request to /ping which will return the string "pong". This will be to do any healthchecks on the service. And lastly there is a POST request to /invocations that will take in a query in the body of the response and return the response from the large language model. Here is an example of the request body:
{"query":"can you explain what the connectify project is?"}and here is an example of the response:
{
"result": {
"answer": " Connectify, also known as CN or UPd - it's a software solution that turns your computer into an efficient virtual router for seamless internet connectivity. It offers features like hotspot creation and network management to enhance productivity while enabling sharing of data with other devices on the same LAN/WAN network (HS).",
"Documents": {
"v1.0_fake_products\\connectify.txt": "Key Activities:\n- Continuous improvement and feature updates of Connectify (UPD)\n- Gathering user feedback for product enhancements (FB)\n- Providing comprehensive training and customer support (TS)\n\nValue Propositions:\n- Transform your computer into a virtual router with a simple interface (UI)\n- Share internet connections with other devices seamlessly (HS)\n- Ensure data security and privacy with advanced encryption (AS)"
}
},
"timestamp": "15/06/2023 16:48:06",
"isSuccess": true,
"jobDurationSeconds": 99.0
}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 HuggingFaceEmbeddings (SentenceTransformers). It then stores the result in a local vector database using Chroma vector store.DocuDive.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.To use this software, you must have Python 3.10 or later installed. Earlier versions of Python will not compile.
If you encounter an error while building a wheel during the pip install process, you may need to install a C++ compiler on your computer.
To install a C++ compiler on Windows 10/11, follow these steps:
gcc component.When running a Mac with Intel hardware (not M1), you may run into clang: error: the clang compiler does not support '-march=native' during pip install.
If so set your archflags during pip install. eg: _ARCHFLAGS="-arch x8664" pip3 install -r requirements.txt