File size: 2,135 Bytes
1cc9481 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 | from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import Chroma
from langchain_google_genai import GoogleGenerativeAIEmbeddings
from langchain.prompts import PromptTemplate
from langchain.chains.question_answering import load_qa_chain
from langchain_google_genai import ChatGoogleGenerativeAI
import google.generativeai as genai
import os
from dotenv import load_dotenv
def list_available_models():
models = genai.models.list_models()
print("Available models:")
for model in models:
print(f"Name: {model.name}")
print(f"Description: {model.description}")
print(f"Supported methods: {', '.join(model.supported_methods)}")
print("\n")
def get_response(file, query):
# Load environment variables
load_dotenv()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=400)
context = '\n\n'.join(str(p.page_content) for p in file)
data = text_splitter.split_text(context)
# Specify the correct model name based on your requirements
model_name = 'models/chat-bison-001'
# Specify the API key directly in the code
google_api_key = os.getenv("GOOGLE_API_KEY")
embeddings = GoogleGenerativeAIEmbeddings(model=model_name, google_api_key=google_api_key)
searcher = Chroma.from_texts(data, embeddings).as_retriever()
ques = 'Which country has maximum GDP?'
records = searcher.get_relevent_documents(ques)
prompt_template = """
You have to give the correct answer to the question from the provided context and make sure you give all details\n
Context: {context}\n
Question: {question}\n
Answer:
"""
prompt = PromptTemplate(template=prompt_template, input_variable=['context', 'question'])
model = ChatGoogleGenerativeAI(model=model_name, temperature=0.5)
chain = load_qa_chain(model, chain_type='stuff', prompt=prompt)
response = chain(
{
'input_document': records,
'question': query
},
return_only_output=True
)
return response['output_text']
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