initial commit

app.py

@@ -1,53 +1,53 @@
 from gradio_client import Client
 import gradio as gr
 import requests
-# from langchain.chains import RetrievalQA
-# import pinecone
-# from langchain.vectorstores import Pinecone
+from langchain.chains import RetrievalQA
+import pinecone
+from langchain.vectorstores import Pinecone
 import os
-# from langchain.embeddings.huggingface import HuggingFaceEmbeddings
-# import time
+from langchain.embeddings.huggingface import HuggingFaceEmbeddings
+import time
 
 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/149bl5mjn/")
+# retrieval = Client("https://ishaan-mital-ncert-helper-vector-db.hf.space/--replicas/149bl5mjn/")
 
-# embed_model_id = 'sentence-transformers/all-MiniLM-L6-v2'
-# # device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu'
+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}
-# )
+embed_model = HuggingFaceEmbeddings(
+    model_name=embed_model_id,
+    # model_kwargs={'device': device},
+    # encode_kwargs={'device': device, 'batch_size': 32}
+)
 
 
-# pinecone.init(
-#     api_key=os.environ.get('PINECONE_API_KEY'),
-#     environment=os.environ.get('PINECONE_ENVIRONMENT')
-# )
+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)
-# text_field = 'text'  # field in metadata that contains text content
-# docs = [
-#     "this is one document",
-#     "and another document"
-# ]
+index_name = 'llama-rag'
+index = pinecone.Index(index_name)
+text_field = 'text'  # field in metadata that contains text content
+docs = [
+    "this is one document",
+    "and another document"
+]
 
-# embeddings = embed_model.embed_documents(docs)
-# 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)
-# vectorstore = Pinecone(
-#     index, embed_model.embed_query, text_field
-# )
+embeddings = embed_model.embed_documents(docs)
+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)
+vectorstore = Pinecone(
+    index, embed_model.embed_query, text_field
+)
 
 def call_llm_api(input_text,context):
     payload = {
@@ -56,19 +56,19 @@ def call_llm_api(input_text,context):
     response = requests.post(API_URL, headers=headers, json=payload)
     return response.json()  # Adjust as needed based on your API response format
 	
-# rag_pipeline = RetrievalQA.from_chain_type(
-#     llm=call_llm_api, chain_type='stuff',
-#     retriever=vectorstore.as_retriever()
-# )
+rag_pipeline = RetrievalQA.from_chain_type(
+    llm=call_llm_api, chain_type='stuff',
+    retriever=vectorstore.as_retriever()
+)
 
 
 def main(question):
-    # return rag_pipeline(question)
-    global chatbot
-    context = retrieval.predict(question, api_name = "/predict")
-    answer=call_llm_api(question,context)
-    # chatbot = answer[1]
-    return answer[0]
+    return rag_pipeline(question)
+    # global chatbot
+    # context = retrieval.predict(question, api_name = "/predict")
+    # answer=call_llm_api(question,context)
+    # # chatbot = answer[1]
+    # return answer[0]
 
 demo = gr.Interface(main, inputs = "text", outputs = "text")
 

requirements.txt

@@ -2,8 +2,8 @@ hugchat
 gradio
 gradio_client
 gtts
-# pydantic==1.10.9
-# langchain
-# pinecone-client==2.2.2
-# faiss-cpu
-# sentence_transformers
+pydantic==1.10.9
+langchain
+pinecone-client==2.2.2
+faiss-cpu
+sentence_transformers