lets see

app.py

@@ -1,96 +1,79 @@
-from gradio_client import Client
 import gradio as gr
-import requests
-# from langchain.chains import RetrievalQA
-# import pinecone
-# from langchain.vectorstores import Pinecone
 import os
-# import openai
-# import time
-# from langchain.embeddings.huggingface import HuggingFaceEmbeddings
-# import transformers
-# from langchain.chains import RetrievalQA
-
-API_URL = "https://api-inference.huggingface.co/models/HuggingFaceH4/zephyr-7b-beta"
-headers = {"Authorization": f"Bearer {os.environ.get('API_KEY')}"}
-retrieval = Client("https://ishaan-mital-ncert-helper-vector-db.hf.space/--replicas/7f5fz9pvt/")
-llm = Client("https://library-samples-zephyr-7b.hf.space/--replicas/b7p4f/")
-# embed_model_id = 'sentence-transformers/all-MiniLM-L6-v2'
-
-# embed_model = HuggingFaceEmbeddings(
-#     model_name=embed_model_id,
-# )
-
-
-# pinecone.init(
-#     api_key=os.environ.get('PINECONE_API_KEY'),
-#     environment=os.environ.get('PINECONE_ENVIRONMENT')
-# )
-
-# index_name='llama-rag'
-# index = pinecone.Index(index_name)
-# index.describe_index_stats()
-# text_field = 'text'
-
-# vectorstore = Pinecone(
-#     index, embed_model.embed_query, text_field
-# )
-
-
-# headers = {"Authorization": "Bearer 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"}
-# generate_text = transformers.pipeline(
-#     model="HuggingFaceH4/zephyr-7b-beta",
-#     return_full_text=True,  # langchain expects the full text
-#     task='text-generation',
-#     # we pass model parameters here too
-#     temperature=0.7,  # 'randomness' of outputs, 0.0 is the min and 1.0 the max
-#     max_new_tokens=512,  # mex number of tokens to generate in the output
-#     repetition_penalty=1.1,  # without this output begins repeating
-#     do_sample=True
-# )
-	
-# from langchain.llms import HuggingFacePipeline
-
-# llm = HuggingFacePipeline(pipeline=generate_text)
-
-# rag_pipeline = RetrievalQA.from_chain_type(
-#     llm=llm, chain_type='stuff',
-#     retriever=vectorstore.as_retriever()
-# )
-def query(payload):
-	response = requests.post(API_URL, headers=headers, json=payload)
-	return response.json()
-
-def main(question):
-    print(question)
-    context = retrieval.predict(question)
-    # try:
-    print(context)
-    answer = llm.predict(
-        f'Question: {question} and context: {context}', 
-        "NCERT Helper!!",	# str  in 'System prompt' Textbox component
-        2048,	# float (numeric value between 1 and 2048) in 'Max new tokens' Slider component
-        0.1,	# float (numeric value between 0.1 and 4.0) in 'Temperature' Slider component
-        0.05,	# float (numeric value between 0.05 and 1.0) in 'Top-p (nucleus sampling)' Slider component
-        1,	# float (numeric value between 1 and 1000) in 'Top-k' Slider component
-        1,	# float (numeric value between 1.0 and 2.0) in 'Repetition penalty' Slider component
-        api_name="/chat"
-        )
-    # except:
-    #     answer = llm.predict(
-    #         f'Question: {question}', 
-    #         "NCERT Helper!!",	# str  in 'System prompt' Textbox component
-    #         2048,	# float (numeric value between 1 and 2048) in 'Max new tokens' Slider component
-    #         0.1,	# float (numeric value between 0.1 and 4.0) in 'Temperature' Slider component
-    #         0.05,	# float (numeric value between 0.05 and 1.0) in 'Top-p (nucleus sampling)' Slider component
-    #         1,	# float (numeric value between 1 and 1000) in 'Top-k' Slider component
-    #         1,	# float (numeric value between 1.0 and 2.0) in 'Repetition penalty' Slider component
-    #         api_name="/chat"
-    #         ) 
-    # answer = query({"inputs": {"question": question, "context": context}})
-    return answer
+import pinecone
+import time
+# from torch import cuda
+from langchain.embeddings.huggingface import HuggingFaceEmbeddings
+# import PyPDF2
+# import re
+from langchain.vectorstores import Pinecone
+from langchain import HuggingFaceHub, LLMChain
+from langchain.prompts import PromptTemplate
+from langchain.chains import RetrievalQA
+
+embed_model_id = 'sentence-transformers/all-MiniLM-L6-v2'
+# device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu'
+
+embed_model = HuggingFaceEmbeddings(
+    model_name=embed_model_id,
+    # model_kwargs={'device': device},
+    # encode_kwargs={'device': device, 'batch_size': 32}
+)
+
+# get API key from app.pinecone.io and environment from console
+pinecone.init(
+    api_key=os.environ.get('PINECONE_API_KEY'),
+    environment=os.environ.get('PINECONE_ENVIRONMENT')
+)
+docs = [
+    "this is one document",
+    "and another document"
+]
+
+embeddings = embed_model.embed_documents(docs)
+
+index_name = 'llama-rag'
+
+# if index_name not in pinecone.list_indexes():
+#     pinecone.create_index(
+#         index_name,
+#         dimension=len(embeddings[0]),
+#         metric='cosine'
+#     )
+#     # wait for index to finish initialization
+#     while not pinecone.describe_index(index_name).status['ready']:
+#         time.sleep(1)
+
+index = pinecone.Index(index_name)
+index.describe_index_stats()
+
 
-demo = gr.Interface(main, inputs = "text", outputs = "text")
 
+
+text_field = 'text'  # field in metadata that contains text content
+
+vectorstore = Pinecone(
+    index, embed_model.embed_query, text_field
+)
+
+
+hub = HuggingFaceHub(repo_id = "HuggingFaceH4/zephyr-7b-beta",huggingfacehub_api_token="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")
+print(hub)
+prompt = PromptTemplate(
+    input_variables=["question"],
+    template="Question: {question}\nAnswer:",
+)
+
+rag_pipeline = RetrievalQA.from_chain_type(
+    llm=hub, chain_type='stuff',
+    retriever=vectorstore.as_retriever()
+)
+
+def question(question):
+    answer = rag_pipeline(question)
+    return answer
+
+demo = gr.Interface(fn=question, inputs="text", outputs="text")
+    
 if __name__ == "__main__":
     demo.launch()

requirements.txt

@@ -3,9 +3,9 @@ gradio
 gradio_client
 gtts
 # openai==0.28
-# pydantic==1.10.9
-# langchain
-# pinecone-client==2.2.2
+pydantic==1.10.9
+langchain
+pinecone-client==2.2.2
 # faiss-cpu
-# sentence_transformers
+sentence_transformers
 # transformers