gpt4all/gpt4all-bindings/python/docs/gpt4all_chat.md

GPT4All Chat UI

The GPT4All Chat Client lets you easily interact with any local large language model.

It is optimized to run 7-13B parameter LLMs on the CPU's of any computer running OSX/Windows/Linux.

Running LLMs on CPU

The GPT4All Chat UI supports models from all newer versions of ggML, llama.cpp including the LLaMA, MPT and GPT-J architectures. The falcon and replit architectures will soon also be supported.

GPT4All maintains an official list of recommended models located in models.json. You can pull request new models to it and if accepted they will show up in the official download dialog.

Sideloading any ggML model

If a model is compatible with the gpt4all-backend, you can sideload it into GPT4All Chat by:

1. Downloading your model in ggML format. It should be a 3-8 GB file similar to the ones here.
2. Identifying your GPT4All Chat downloads folder. This is the path listed at the bottom of the download dialog.
3. Prefixing your downloaded model with string ggml- and placing it into the GPT4All Chat downloads folder.
4. Restarting your chat app. Your model should appear in the download dialog.

Plugins

GPT4All Chat Plugins allow you to expand the capabilities of Local LLMs.

LocalDocs Beta Plugin (Chat With Your Data)

LocalDocs is a GPT4All plugin that allows you to chat with your local files and data. It allows you to utilize powerful local LLMs to chat with private data without any data leaving your computer or server. When using LocalDocs, your LLM will cite the sources that most likely contributed to a given output. Note, even an LLM equipped with LocalDocs can hallucinate. If the LocalDocs plugin decides to utilize your documents to help answer a prompt, you will see references appear below the response.

GPT4All-Snoozy with LocalDocs. Try GPT4All-Groovy for a faster experience!

Enabling LocalDocs

1. Install the latest version of GPT4All Chat from GPT4All Website.
2. Go to Settings > LocalDocs tab.
3. Configure a collection (folder) on your computer that contains the files your LLM should have access to. You can alter the contents of the folder/directory at anytime. As you add more files to your collection, your LLM will dynamically be able to access them.
4. Spin up a chat session with any LLM (including external ones like ChatGPT but warning data will leave your machine!)
5. At the top right, click the database icon and select which collection you want your LLM to know about during your chat session.

LocalDocs Capabilities

LocalDocs allows your LLM to have context about the contents of your documentation collection. Not all prompts/question will utilize your document collection for context. If LocalDocs was used in your LLMs response, you will see references to the document snippets that LocalDocs used.

LocalDocs can:

• Query your documents based upon your prompt / question. If your documents contain answers that may help answer your question/prompt LocalDocs will try to utilize snippets of your documents to provide context.

LocalDocs cannot:

• Answer general metadata queries (e.g. What documents do you know about?, Tell me about my documents)
• Summarize a single document (e.g. Summarize my magna carta PDF.)

See the Troubleshooting section for common issues.

How LocalDocs Works

LocalDocs works by maintaining an index of all data in the directory your collection is linked to. This index consists of small chunks of each document that the LLM can receive as additional input when you ask it a question. The general technique this plugin uses is called Retrieval Augmented Generation.

These document chunks help your LLM respond to queries with knowledge about the contents of your data. The number of chunks and the size of each chunk can be configured in the LocalDocs plugin settings tab. For indexing speed purposes, LocalDocs uses pre-deep-learning n-gram and TF-IDF based retrieval when deciding what document chunks your LLM should use as context. You'll find its of comparable quality with embedding based retrieval approaches but magnitudes faster to ingest data.

LocalDocs supports the following file types:

["txt", "doc", "docx", "pdf", "rtf", "odt", "html", "htm", "xls", "xlsx", "csv", "ods", "ppt", "pptx", "odp", "xml", "json", "log", "md", "org", "tex", "asc", "wks",
"wpd", "wps", "wri", "xhtml", "xht", "xslt", "yaml", "yml", "dtd", "sgml", "tsv", "strings", "resx",
"plist", "properties", "ini", "config", "bat", "sh", "ps1", "cmd", "awk", "sed", "vbs", "ics", "mht",
"mhtml", "epub", "djvu", "azw", "azw3", "mobi", "fb2", "prc", "lit", "lrf", "tcr", "pdb", "oxps",
"xps", "pages", "numbers", "key", "keynote", "abw", "zabw", "123", "wk1", "wk3", "wk4", "wk5", "wq1",
"wq2", "xlw", "xlr", "dif", "slk", "sylk", "wb1", "wb2", "wb3", "qpw", "wdb", "wks", "wku", "wr1",
"wrk", "xlk", "xlt", "xltm", "xltx", "xlsm", "xla", "xlam", "xll", "xld", "xlv", "xlw", "xlc", "xlm",
"xlt", "xln"]

Troubleshooting and FAQ

My LocalDocs plugin isn't using my documents

• Make sure LocalDocs is enabled for your chat session (the DB icon on the top-right should have a border)
• Try to modify your prompt to be more specific and use terminology that is in your document. This will increase the likelihood that LocalDocs matches document snippets for your question.
• If your document collection is large, wait 1-2 minutes for it to finish indexing.

LocalDocs Roadmap

• Embedding based semantic search for retrieval.
• Customize model fine-tuned with retrieval in the loop.
• Plugin compatibility with chat client server mode.

Server Mode

GPT4All Chat comes with a built-in server mode allowing you to programmatically interact with any supported local LLM through a very familiar HTTP API. You can find the API documentation here.

Enabling server mode in the chat client will spin-up on an HTTP server running on localhost port 4891 (the reverse of 1984). You can enable the webserver via GPT4All Chat > Settings > Enable web server.

Begin using local LLMs in your AI powered apps by changing a single line of code: the base path for requests.

import openai

openai.api_base = "http://localhost:4891/v1"
#openai.api_base = "https://api.openai.com/v1"

openai.api_key = "not needed for a local LLM"

# Set up the prompt and other parameters for the API request
prompt = "Who is Michael Jordan?"

# model = "gpt-3.5-turbo"
#model = "mpt-7b-chat"
model = "gpt4all-j-v1.3-groovy"

# Make the API request
response = openai.Completion.create(
    model=model,
    prompt=prompt,
    max_tokens=50,
    temperature=0.28,
    top_p=0.95,
    n=1,
    echo=True,
    stream=False
)

# Print the generated completion
print(response)

which gives the following response

{
  "choices": [
    {
      "finish_reason": "stop",
      "index": 0,
      "logprobs": null,
      "text": "Who is Michael Jordan?\nMichael Jordan is a former professional basketball player who played for the Chicago Bulls in the NBA. He was born on December 30, 1963, and retired from playing basketball in 1998."
    }
  ],
  "created": 1684260896,
  "id": "foobarbaz",
  "model": "gpt4all-j-v1.3-groovy",
  "object": "text_completion",
  "usage": {
    "completion_tokens": 35,
    "prompt_tokens": 39,
    "total_tokens": 74
  }
}