Added: model.py

Adds scripts to construct the model

model.py

@@ -0,0 +1,78 @@
+import os
+import subprocess
+from dotenv import load_dotenv
+load_dotenv()
+try:
+    os.environ["HUGGINGFACEHUB_API_TOKEN"] = os.getenv("HUGGINGFACEHUB_API_TOKEN")
+    PINECONE_API_KEY = os.getenv("PINECONE_API_KEY")
+except:
+    PINECONE_API_KEY = subprocess.check_output(["bash", "-c", "echo ${{ secrets.PINECONE_API_KEY }}"]).decode("utf-8").strip()
+
+
+from langchain.embeddings import HuggingFaceEmbeddings
+import pinecone
+import torch
+from langchain import PromptTemplate, LLMChain,HuggingFacePipeline
+from langchain.vectorstores import Pinecone
+from langchain.chains.question_answering import load_qa_chain
+from langchain.chains import RetrievalQA
+from transformers import pipeline
+
+def get_llm(model_name,pinecone_index,llm):
+    # model_name = "bert-large-uncased" #"t5-large"
+    model_kwargs = {'device': 'cuda' if torch.cuda.is_available() else 'cpu'}
+
+    embeddings = HuggingFaceEmbeddings(model_name=model_name, model_kwargs=model_kwargs)
+
+
+    pinecone.init(
+        api_key=PINECONE_API_KEY,
+        environment="us-east-1-aws"
+    )
+
+    index = pinecone.Index(pinecone_index)
+    print(index.describe_index_stats())
+
+    docsearch = Pinecone(index, embeddings.embed_query,"text")
+
+    # print("About to load the model")
+
+    instruct_pipeline = pipeline(model=llm, torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto", 
+        return_full_text=True, do_sample=False, max_new_tokens=128)
+    llm = HuggingFacePipeline(pipeline=instruct_pipeline)
+    # print("Loaded the LLM")
+
+    # print("Prompting")
+
+    template = """Context: {context}
+
+    Question: {question}
+
+    Answer: Let's go step by step."""
+
+    prompt = PromptTemplate(template=template, input_variables=["question","context"])
+    llm_chain = LLMChain(prompt=prompt, llm=llm)
+    return llm_chain, docsearch
+
+
+if __name__ == "__main__":
+    model_name = "bert-large-uncased"
+    pinecone_index = "bert-large-uncased"
+    llm = "databricks/dolly-v2-3b"
+    llm_chain, docsearch = get_llm(model_name,pinecone_index,llm)
+    print(":"*40)
+    questions = ["what is the name of the first Hindi newspaper published in Bihar?",
+                "what is the capital of Bihar?",
+                "Brief about the Gupta Dynasty"]
+    for question in questions:
+        context = docsearch.similarity_search(question, k=3,metadata=False)
+        content = ""
+        for i in context:
+            content= content + f"{i.__dict__['page_content']}"
+        print(f"{question}")
+        response = llm_chain.predict(question=question,context=content)
+        print(f"{response}\n{'--'*25}")
+
+
+
+