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.gitattributes

@@ -33,3 +33,5 @@ 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
+faiss_index filter=lfs diff=lfs merge=lfs -text
+faiss_document_store.db filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,96 @@
+import gradio as gr
+from huggingface_hub import InferenceClient
+
+"""
+For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
+"""
+# client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+
+import os
+import time
+from haystack.document_stores import FAISSDocumentStore
+from haystack.nodes import EmbeddingRetriever
+from haystack.pipelines import Pipeline
+from groq import Groq
+
+api_key = os.getenv("groq_api")
+
+# Load the FAISS index from the saved file
+faiss_index_path = "./faiss_index"  # Replace with your actual path
+document_store = FAISSDocumentStore.load(faiss_index_path)
+
+# Initialize the Retriever
+retriever = EmbeddingRetriever(
+    document_store=document_store,
+    embedding_model="intfloat/multilingual-e5-large-instruct",
+    model_format="sentence_transformers"
+)
+
+def get_detailed_instruct(task_description: str, query: str) -> str:
+    return f'Instruct: {task_description}\nQuery: {query}'
+
+# task = 'Given a web search query, retrieve relevant passages that answer the query'
+
+def respond(text):
+    # Set up the query
+    # query = text
+    task = 'Given a web search query, retrieve relevant passages that answer the query'
+    query = get_detailed_instruct(task, text)
+    # Step 1: Measure retrieval time and retrieve top documents
+    start_time = time.time()
+    retrieval_results = retriever.retrieve(query=query, top_k=10)
+    print(f"Document retrieval completed in {time.time() - start_time:.2f} seconds")
+
+    # Step 2: Extract context from the retrieved documents
+    context = "\n\n".join([doc.content for doc in retrieval_results])
+
+    # Step 3: Initialize Groq Client for LLM (Ensure API key is stored securely)
+    os.environ['GROQ_API_KEY'] = api_key  # Ensure this key is valid
+    client = Groq(api_key=os.environ["GROQ_API_KEY"])
+
+    # Step 4: Send the query and retrieved context to LLM for completion
+    chat_completion = client.chat.completions.create(
+        messages=[
+            {
+                "role": "system",
+                "content": (
+                    "You are a retrieval-augmented generation system (RAG) designed to answer complex and general questions. "
+                    "Do not say that you are RAG and the retrieved documents"
+                    "Please use the retrieved documents to generate accurate and contextually relevant responses. "
+                    "You should find the answer to the query from the retrieved documents."
+                )
+            },
+            {
+                "role": "user",
+                "content": f"Here are the retrieved documents:\n\n{context}\n\nUsing this information, answer the question: {query}",
+            }
+        ],
+        model="llama-3.1-70b-versatile",  # Specify the model you want to use
+        temperature=0
+    )
+
+    # Step 5: Output the LLM's response
+    # print("Response from LLM:")
+    return chat_completion.choices[0].message.content
+
+
+
+title = "Powered by Kazuk"
+description = "Kazuk Team"
+
+css = """
+body {
+    background-color: #690088 !important;
+}
+"""
+
+demo = gr.Interface(fn=respond, 
+        inputs="text", 
+        outputs="text",
+         title=title,
+        description=description,
+        css=css
+        )
+
+if __name__ == "__main__":
+    demo.launch(share=True)

faiss_document_store.db

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a489f7187154252a6938c15d471387d56099bbb97d0a552050c15ab2a8c9acc2
+size 1765376

faiss_index

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3139bdcd2edb189177297a484b2752a9e975f02f6398c9b10ea68f69615bd334
+size 2056237

faiss_index.json

@@ -0,0 +1 @@
+{"similarity": "cosine", "faiss_index_factory_str": "Flat", "embedding_dim": 1024}

requirements.txt

@@ -0,0 +1,7 @@
+huggingface_hub
+haystack
+groq
+Flask
+farm-haystack
+faiss-cpu
+groq