Initial commit

.gitignore

@@ -0,0 +1,2 @@
+chroma_db/*
+__pycache__/*

README.md

@@ -1,8 +1,8 @@
 ---
-title: Chatbot G Pdf
-emoji: 📚
-colorFrom: blue
-colorTo: blue
+title: GAIA Chatbot - level 3
+emoji: 🌍
+colorFrom: red
+colorTo: purple
 sdk: gradio
 sdk_version: 4.16.0
 app_file: app.py
@@ -10,4 +10,32 @@ pinned: false
 license: mit
 ---
 
+# Run on a space
+
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+Simply push your code on a huggingface space.
+
+# Run locally
+
+You must have python (3.8)[https://www.python.org/downloads/].
+
+Check https://www.gradio.app/guides/quickstart for more details about Gradio.
+
+## Install dependencies
+
+`pip install gradio`
+
+`pip install -r requirements.txt`
+
+## Add Mistral API Key to your environement variables
+
+in `~/.profile` or `~/.bashrc`
+
+`export MISTRAL_API_KEY=YOUR_API_KEY`
+
+## Run your code
+
+`python3 app.py`
+
+## Open your browser to `http://127.0.0.1:7860`

app.py

@@ -0,0 +1,155 @@
+import os
+import json
+import gradio as gr
+from llama_index import (
+    VectorStoreIndex,
+    download_loader,
+)
+import chromadb
+
+from llama_index.llms import MistralAI
+from llama_index.embeddings import MistralAIEmbedding
+from llama_index.vector_stores import ChromaVectorStore
+from llama_index.storage.storage_context import StorageContext
+from llama_index import ServiceContext
+
+title = "Gaia Mistral Chat RAG PDF Demo"
+description = "Example of an assistant with Gradio, RAG from PDF documents and Mistral AI via its API"
+placeholder = (
+    "Vous pouvez me posez une question sur ce contexte, appuyer sur Entrée pour valider"
+)
+placeholder_url = "Extract text from this url"
+llm_model = "mistral-small"
+
+env_api_key = os.environ.get("MISTRAL_API_KEY")
+query_engine = None
+
+# Define LLMs
+llm = MistralAI(api_key=env_api_key, model=llm_model)
+embed_model = MistralAIEmbedding(model_name="mistral-embed", api_key=env_api_key)
+
+# create client and a new collection
+db = chromadb.PersistentClient(path="./chroma_db")
+chroma_collection = db.get_or_create_collection("quickstart")
+
+# set up ChromaVectorStore and load in data
+vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
+storage_context = StorageContext.from_defaults(vector_store=vector_store)
+service_context = ServiceContext.from_defaults(
+    chunk_size=1024, llm=llm, embed_model=embed_model
+)
+
+PDFReader = download_loader("PDFReader")
+loader = PDFReader()
+
+index = VectorStoreIndex(
+    [], service_context=service_context, storage_context=storage_context
+)
+query_engine = index.as_query_engine(similarity_top_k=5)
+
+
+def get_documents_in_db():
+    print("Fetching documents in DB")
+    docs = []
+    for item in chroma_collection.get(include=["metadatas"])["metadatas"]:
+        docs.append(json.loads(item["_node_content"])["metadata"]["file_name"])
+    docs = list(set(docs))
+    print(f"Found {len(docs)} documents")
+    out = "**List of files in db:**\n"
+    for d in docs:
+        out += " - " + d + "\n"
+
+    return out
+
+
+def empty_db():
+    ids = chroma_collection.get()["ids"]
+    chroma_collection.delete(ids)
+    return get_documents_in_db()
+
+
+def load_file(file):
+    documents = loader.load_data(file=file)
+
+    for doc in documents:
+        index.insert(doc)
+
+    return (
+        gr.Textbox(visible=False),
+        gr.Textbox(value=f"Document encoded ! You can ask questions", visible=True),
+        get_documents_in_db(),
+    )
+
+
+def load_document(input_file):
+    file_name = input_file.name.split("/")[-1]
+    return gr.Textbox(value=f"Document loaded: {file_name}", visible=True)
+
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+        """ # Welcome to Gaia Level 3 Demo 
+    
+        Add a file before interacting with the Chat.
+        This demo allows you to interact with a pdf file and then ask questions to Mistral APIs.
+        Mistral will answer with the context extracted from your uploaded file.
+
+        *The files will stay in the database unless there is 48h of inactivty or you re-build the space.*
+        """
+    )
+
+    gr.Markdown(""" ### 1 / Extract data from PDF """)
+
+    with gr.Row():
+        with gr.Column():
+            input_file = gr.File(
+                label="Load a pdf",
+                file_types=[".pdf"],
+                file_count="single",
+                type="filepath",
+                interactive=True,
+            )
+            file_msg = gr.Textbox(
+                label="Loaded documents:", container=False, visible=False
+            )
+
+            input_file.upload(
+                fn=load_document,
+                inputs=[
+                    input_file,
+                ],
+                outputs=[file_msg],
+                concurrency_limit=20,
+            )
+
+            file_btn = gr.Button(value="Encode file ✅", interactive=True)
+            btn_msg = gr.Textbox(container=False, visible=False)
+
+            with gr.Row():
+                db_list = gr.Markdown(value=get_documents_in_db)
+                delete_btn = gr.Button(value="Empty db 🗑️", interactive=True, scale=0)
+
+            file_btn.click(
+                load_file,
+                inputs=[input_file],
+                outputs=[file_msg, btn_msg, db_list],
+                show_progress="full",
+            )
+            delete_btn.click(empty_db, outputs=[db_list], show_progress="minimal")
+
+    gr.Markdown(""" ### 2 / Ask a question about this context """)
+
+    chatbot = gr.Chatbot()
+    msg = gr.Textbox(placeholder=placeholder)
+    clear = gr.ClearButton([msg, chatbot])
+
+    def respond(message, chat_history):
+        response = query_engine.query(message)
+        chat_history.append((message, str(response)))
+        return chat_history
+
+    msg.submit(respond, [msg, chatbot], [chatbot])
+
+demo.title = title
+
+demo.launch()

requirements.txt

@@ -0,0 +1,5 @@
+pypdf
+mistralai
+llama-index
+gradio
+chromadb