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app.py

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+import os
+import zipfile
+import gradio as gr
+
+from langchain_community.llms import HuggingFaceEndpoint
+from langchain_community.vectorstores import Chroma # Light-weight and in memory
+from langchain.chains import RetrievalQA
+
+
+# Authentication for Huggingface API
+
+HF_TOKEN = os.getenv("HF_TOKEN")
+os.environ["HF_TOKEN"] = HF_TOKEN
+os.environ["HUGGINGFACEHUB_API_TOKEN"] = HF_TOKEN
+
+
+# Initialization of LLM
+
+llm = HuggingFaceEndpoint(
+    repo_id="HuggingFaceH4/zephyr-7b-beta",
+    task="text-generation",
+    max_new_tokens = 512,
+    top_k = 30,
+    temperature = 0.1,
+    repetition_penalty = 1.03,
+)
+
+## Embeddings
+
+from langchain_community.embeddings import HuggingFaceEmbeddings
+#modelPath = "sentence-transformers/all-MiniLM-l6-v2"
+modelPath ="mixedbread-ai/mxbai-embed-large-v1"
+
+# Create a dictionary with model configuration options, specifying to use the CPU for computations
+model_kwargs = {'device':'cpu'} # cuda/cpu
+
+# Create a dictionary with encoding options, specifically setting 'normalize_embeddings' to False
+encode_kwargs = {'normalize_embeddings': False}
+
+embedding =  HuggingFaceEmbeddings(
+    model_name=modelPath,     # Provide the pre-trained model's path
+    model_kwargs=model_kwargs, # Pass the model configuration options
+    encode_kwargs=encode_kwargs # Pass the encoding options
+)
+
+# Upload the vector db from previous step and unzip
+
+with zipfile.ZipFile('docs.zip', 'r') as zip_ref:
+    zip_ref.extractall()
+
+persist_directory = 'docs/chroma/' 
+
+vectordb = Chroma(
+    persist_directory=persist_directory,
+    embedding_function=embedding
+)
+
+
+title = "Q&A  on enterprise data"
+description = "Implementation of Open Source RAG on Private Document"
+
+def quena(question):
+  qa_chain = RetrievalQA.from_chain_type(llm, retriever=vectordb.as_retriever(), return_source_documents=True)
+  result = qa_chain.invoke({"query": question})
+  return result["result"]
+    
+demo=gr.Interface(fn=quena,
+                  inputs=gr.Textbox(lines=10,placeholder='''Write your question inside double quotation..Type the Sample Question:\n
+                  What are the procedures to move from research to production environment?? Reply in step-wise pointers.'''),
+                  outputs="text",
+                  title=title,
+                  description=description,)
+# Launch the demo!
+demo.launch()
+
+
+

docs/chroma/47e3871d-a5de-4728-9d16-1de800020642/data_level0.bin

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docs/chroma/47e3871d-a5de-4728-9d16-1de800020642/header.bin

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docs/chroma/47e3871d-a5de-4728-9d16-1de800020642/link_lists.bin

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requirements.txt

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+sentence-transformers
+langchain
+chromadb
+gradio