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requirements.txt

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+langchain==0.0.188
+openai
+llama_index==0.6.12
+pypdf
+PyCryptodome
+ratelimit

validation.py

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+
+from llama_index import SimpleDirectoryReader, LLMPredictor, PromptHelper, StorageContext, ServiceContext, GPTVectorStoreIndex, load_index_from_storage
+from langchain.chat_models import ChatOpenAI
+import gradio as gr
+import sys
+import os
+import openai
+from ratelimit import limits, sleep_and_retry
+from langchain import HuggingFaceHub
+
+
+# fixing bugs
+# 1. open ai key: https://stackoverflow.com/questions/76425556/tenacity-retryerror-retryerrorfuture-at-0x7f89bc35eb90-state-finished-raised
+# 2. rate limit error in lang_chain default version - install langchain==0.0.188. https://github.com/jerryjliu/llama_index/issues/924
+# 3. added true Config variable in langchain: https://github.com/pydantic/pydantic/issues/3320
+# 4. deploy on huggingfaces https://huggingface.co/welcome
+#   create huggingfaces token https://huggingface.co/settings/tokens
+#   login: huggingface-cli login
+#   add requirements.txt file  https://huggingface.co/docs/hub/spaces-dependencies
+
+os.environ["OPENAI_API_KEY"] = os.environ.get("openai_key")
+openai.api_key = os.environ["OPENAI_API_KEY"]
+
+# Define the rate limit for API calls (requests per second)
+RATE_LIMIT = 3
+
+# Implement the rate limiting decorator
+@sleep_and_retry
+@limits(calls=RATE_LIMIT, period=1)
+def create_service_context():
+
+    #constraint parameters
+    max_input_size = 4096
+    num_outputs = 512
+    max_chunk_overlap = 20
+    chunk_size_limit = 600
+
+    #allows the user to explicitly set certain constraint parameters
+    prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)
+
+    #LLMPredictor is a wrapper class around LangChain's LLMChain that allows easy integration into LlamaIndex
+    llm_predictor = LLMPredictor(llm=ChatOpenAI(temperature=0.5, model_name="gpt-3.5-turbo", max_tokens=num_outputs))
+
+    #constructs service_context
+    service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, prompt_helper=prompt_helper)
+    return service_context
+
+
+# Implement the rate limiting decorator
+@sleep_and_retry
+@limits(calls=RATE_LIMIT, period=1)
+def data_ingestion_indexing(directory_path):
+
+    #loads data from the specified directory path
+    documents = SimpleDirectoryReader(directory_path).load_data()
+
+    #when first building the index
+    index = GPTVectorStoreIndex.from_documents(
+        documents, service_context=create_service_context()
+    )
+
+    #persist index to disk, default "storage" folder
+    index.storage_context.persist()
+
+    return index
+
+def data_querying(input_text):
+
+    #rebuild storage context
+    storage_context = StorageContext.from_defaults(persist_dir="./storage")
+
+    #loads index from storage
+    index = load_index_from_storage(storage_context, service_context=create_service_context())
+
+    #queries the index with the input text
+    response = index.as_query_engine().query(input_text)
+
+    return response.response
+
+iface = gr.Interface(fn=data_querying,
+                     inputs=gr.components.Textbox(lines=30, label="Enter your question"),
+                     outputs=gr.components.Textbox(lines=40, label="Response", style="height: 400px; overflow-y: scroll;"),  
+                     title="Therapy Validation GPT 0.1 pre alpha")
+
+#passes in data directory
+index = data_ingestion_indexing("book-validation")
+iface.launch(inline=True)