In Python, you can use the LangChain framework to simplify the process of using your own data with ChatGPT. Once you've installed the necessary Python packages, you can follow these steps to use your custom data with ChatGPT:
- Add OpenAI API key;
- Use the OpenAI LLM (or chat model);
- Load Document(s) With Your Custom Data;
- Create an Index (to efficiently compare and match queries against the indexed documents);
- Get the user prompt via CLI (to use as a query);
- Query the Index (to search and retrieve relevant information or documents from the indexed collection);
- Print the response.
A simple implementation of this could be like the following Python script:
# chatgpt.py
import os
import sys
from langchain.document_loaders import TextLoader
from langchain.indexes import VectorstoreIndexCreator
from langchain.llms import OpenAI
# 1: add API key as environment variable
os.environ["OPENAI_API_KEY"] = "<YOUR_OPENAI_API_KEY>"
# 2: use the OpenAI LLM (with the `model_name` optionally)
llm = OpenAI(model_name="text-davinci-003")
# 3: load your data document
loader = TextLoader("data.txt")
# 4: create an index
index = VectorstoreIndexCreator().from_loaders([loader])
# 5: get prompt specified via command line
query = sys.argv[1]
# 6: query the index
# 7: output the response
print(index.query(query, llm=llm))
This will make the GPT language model use the custom data you provide via the "data.txt
" file as a source document to generate answers from. Alternatively, you can use the DirectoryLoader
to load data from a directory:
# ...
from langchain.document_loaders import DirectoryLoader
# ...
# 2: load your data document
loader = DirectoryLoader(".", glob="*.txt")
# ...
LangChain provides a number of document loaders that you can use. To give you a very basic example of how this would work, let's suppose that your custom data is a list of the following numbers:
1 2 3 4 5
Prompting GPT to, for example, "list all odd numbers
would result in the following:
$ python chatgpt.py "list all odd numbers"
1, 3, 5
As you can see from this example, the response of ChatGPT is limited to the data you provided it. This is, of course, a very basic example to get answers based on the data you provide. However, it provides a good foundation to build on.
Please note that, by default, this will not have the context from the outside world. To do that, you would need to add some additional code.
This post was published (and was last revised ) by Daniyal Hamid. Daniyal currently works as the Head of Engineering in Germany and has 20+ years of experience in software engineering, design and marketing. Please show your love and support by sharing this post.