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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+HFNWAY.pdf filter=lfs diff=lfs merge=lfs -text

HFNWAY.pdf

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8e432749fc200c3460ad76d7acc2ebd02f16d5f7d588b4ede35225274d40503c
+size 4305099

app.py

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+import openai, os
+openai.api_key = "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"
+# openai.api_key = os.environ["OPENAI_API_KEY"]
+from llama_index.core import SimpleDirectoryReader
+
+documents = SimpleDirectoryReader(
+    input_files = ["./HFNWAY.pdf"]
+).load_data()
+
+from llama_index.core.schema import Document
+document = Document(text = "\n\n".join([doc.text for doc in documents]))
+# above step merges it the 218 different documents into 1 document
+
+from llama_index.core import VectorStoreIndex
+from llama_index.core import ServiceContext
+from llama_index.llms.openai import OpenAI
+
+# resp = OpenAI().complete("Lord Ram is ")
+# print(resp)
+# does not need api key
+# the embedding model we use is hugging face  bge small
+# the service context class already hass this
+
+llm = OpenAI(model= "gpt-3.5-turbo", temperature=0.2)
+service_context = ServiceContext.from_defaults(
+    llm = llm, embed_model = "local:BAAI/bge-small-en-v1.5"
+)
+# https://huggingface.co/BAAI/bge-small-en
+index = VectorStoreIndex.from_documents([document], service_context=service_context)
+# pip install llama-index-embeddings-huggingface
+# this cell takes care of chunking embedding, indexing
+
+query_engine = index.as_query_engine()
+
+import gradio as gr
+
+def chat_interface(message, history):
+  
+  # Update history with the current message
+  # Call the query function with the user's message
+  response = str(query_engine.query(message))
+  # Update history with the bot's response
+  
+  history.append([message, response])
+  return response
+
+
+# Initialize an empty history
+history = []
+# Create the chat interface using gr.ChatInterface
+interface = gr.ChatInterface(chat_interface, examples = ["Who is DAAJI?", "What is Heartfulness way of meditation all about?"] , title = "HFN BOT")
+# Launch the interface
+interface.launch()
+
+