๐ฌChat 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)
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