Update space

app.py

@@ -1,61 +1,182 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+from transformers import AutoTokenizer
+import os
 
-"""
-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 torch
+from llama_index.llms.huggingface import HuggingFaceLLM
+
+# Optional quantization to 4bit
+from transformers import BitsAndBytesConfig
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+from llama_index.core import Settings
+
+import faiss
+from llama_index.core import (
+    load_index_from_storage,
+    StorageContext,
+)
+from llama_index.vector_stores.faiss import FaissVectorStore
+from llama_index.core.tools import QueryEngineTool, ToolMetadata
+
+import json
+from typing import Sequence, List
+
+from llama_index.core.llms import ChatMessage
+from llama_index.core.tools import BaseTool, FunctionTool
+from llama_index.core.agent import ReActAgent
+
+import nest_asyncio
+
+from llama_index.core.tools import QueryEngineTool, ToolMetadata
+
+HF_TOKEN = os.environ.get("HF_TOKEN", None)
+
+DESCRIPTION = '''
+<div>
+<h1 style="text-align: center;">Mistral 7B Instruct v0.3</h1>
+<p>This Space demonstrates the Agent based RAG on multiple documents using Gemma 2b it and llama index</p>
+</div>
+'''
+
+tokenizer = AutoTokenizer.from_pretrained(
+    "google/gemma-1.1-2b-it",
+    token=HF_TOKEN,
+)
+
+stopping_ids = [
+    tokenizer.eos_token_id,
+    tokenizer.convert_tokens_to_ids("<|eot_id|>"),
+]
+
+
+quantization_config = BitsAndBytesConfig(
+    load_in_4bit = True,
+    bnb_4bit_compute_dtype=torch.float16,
+    bnb_4bit_quant_type = "nf4",
+    bnb_4bit_use_double_quant = True,
+)
+
+llm = HuggingFaceLLM(
+    model_name = "google/gemma-1.1-2b-it",
+    model_kwargs = {
+        "token": HF_TOKEN,
+        "torch_dtype": torch.bfloat16,  # comment this line and uncomment below to use 4bit
+        #"quantization_config": quantization_config
+    },
+    generate_kwargs = {
+        "do_sample": True,
+        "temperature": 0.6,
+        "top_p": 0.9,
+    },
+    tokenizer_name = "google/gemma-1.1-2b-it",
+    tokenizer_kwargs = {"token": HF_TOKEN},
+    stopping_ids = stopping_ids,
+)
 
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-large-en-v1.5")
 
-    messages.append({"role": "user", "content": message})
 
-    response = ""
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+# dimensions of bge-large-en-v1.5 obtained from https://huggingface.co/BAAI/bge-large-en-v1.5
+d = 1024
+faiss_index = faiss.IndexFlatL2(d)
 
-        response += token
-        yield response
+
+nest_asyncio.apply()
+
+# bge embedding model
+Settings.embed_model = embed_model
+
+# GPU - Llama-3-8B-Instruct model
+# CPU - Gemma 1.1 2B it instruct
+Settings.llm = llm
+
+# rebuild storage context
+
+geoVectorStore = FaissVectorStore.from_persist_dir("./geoindex/")
+
+geoStorageContext = StorageContext.from_defaults(
+    vector_store=geoVectorStore, persist_dir="./geoindex/")
+
+geoindex = load_index_from_storage(storage_context=geoStorageContext)
+
+bioVectorStore = FaissVectorStore.from_persist_dir("./bioindex/")
+
+bioStorageContext = StorageContext.from_defaults(
+    vector_store=bioVectorStore, persist_dir="./bioindex/")
+
+bioindex = load_index_from_storage(storage_context=geoStorageContext)
+
+geo_engine = geoindex.as_query_engine(similarity_top_k=3)
+bio_engine = bioindex.as_query_engine(similarity_top_k=3)
+
+query_engine_tools = [
+    QueryEngineTool(
+        query_engine=geo_engine,
+        metadata=ToolMetadata(
+            name="geography",
+            description=(
+                "This is a geography textbook, it provides information about geography. "
+                "Use a detailed plain text question as input to the tool."
+            ),
+        ),
+    ),
+    QueryEngineTool(
+        query_engine=bio_engine,
+        metadata=ToolMetadata(
+            name="biology",
+            description=(
+                "This is a biology textbook it provides information about biology. "
+                "Use a detailed plain text question as input to the tool."
+            ),
+        ),
+    ),
+]
+
+agent = ReActAgent.from_tools(
+    query_engine_tools,
+    llm=llm,
+    verbose=False,
+)
+
+def respond(
+    message,
+    # history: list[tuple[str, str]],
+    # system_message,
+    # max_tokens,
+    # temperature,
+    # top_p,
+):
+    prompt=f'''Analyze the question: {message} and use appropriate tool to get the relevant context and answer the question, do not answer on your own and output only Observation'''
+    response = agent.chat(prompt)
+    return print(str(response))
 
 """
 For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
 """
 demo = gr.ChatInterface(
     respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
+    # additional_inputs=[
+    #     gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
+    #     gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
+    #     gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
+    #     gr.Slider(
+    #         minimum=0.1,
+    #         maximum=1.0,
+    #         value=0.95,
+    #         step=0.05,
+    #         label="Top-p (nucleus sampling)",
+    #     ),
+    # ],
+    examples=[
+        ["What are different types of rural settlement?"],
+        ["Explain Urbanisation in India?"],
+        ["What was the level of urbanisation in India in 2011?"],
+        ["List the religious and cultural towns in India?"],
     ],
+    cache_examples=False,
 )
 
 

requirements.txt

@@ -1 +1,11 @@
-huggingface_hub==0.22.2
+huggingface_hub==0.22.2
+llama-index
+llama-index-llms-huggingface
+llama-index-embeddings-huggingface
+transformers
+accelerate
+bitsandbytes
+llama-index-readers-file
+pymupdf
+llama-index-vector-stores-faiss
+faiss-cpu