💬Chat on PDF

Python Implementation

Since we have a PDF default data loader we can use it directly from here.

from genai_stack.model import OpenAIGpt35Model

model = OpenAIGpt35Model.from_kwargs(
 fields={"openai_api_key": "Paste your Open AI key"}
)
model.add_source("pdf", "valid_pdf_path_or_url")
model.predict("<Any question on top of the pdf>")

CLI Implementation

etl.json

{
    "etl": "langchain",
    "source": {
        "name": "PyPDFLoader",
        "fields": {
            "file_path": "/your/pdf/path"
        }
    },
    "vectordb": {
        "name": "chromadb",
        "class_name": "genai_stack"
    }
}

Run the ETL command

genai-stack etl --config_file etl.json

model.json

{
    "model": {
        "name": "gpt4all"
    },
    "retriever": {
        "name": "langchain"
    },
    "vectordb": {
        "name": "chromadb",
        "class_name": "genai_stack"
    }
}

Run the model server

genai-stack start --config_file model.json

You can make predictions on this model server:

import requests

url = "http://127.0.0.1:8082/predict"
res = requests.post(url, data={"query": "<Any question on top of the pdf>"})
print(res.content)