Adding chat response

app.py

@@ -25,17 +25,18 @@ prompt = PromptTemplate(template = prompt_template, input_variables = ["context"
 
 chain = prompt | gemini
 
-def inference(pdf_path, chunk_size, chunk_overlap):
+index_name = "langchain-test-index"
+
+def store_embeddings(pdf_path, chunk_size, chunk_overlap):
     raw_documents = []
     for path in pdf_path:
         raw_documents.extend(PyPDFLoader(path).load())
+
     text_splitter = CharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
     documents = text_splitter.split_documents(raw_documents)
 
     pc = Pinecone(api_key=os.environ["PINECONE_API_KEY"])
 
-    index_name = "langchain-test-index"
-
     index = pc.Index(host="https://langchain-test-index-la2n80y.svc.aped-4627-b74a.pinecone.io")
 
     if index.list():
@@ -55,6 +56,26 @@ description = "A simple Gradio interface to query PDFs and compare vector databa
 examples = [[["data/amazon-10-k-2024.pdf"], 1000, 100],
             [["data/goog-10-k-2023.pdf"], 1000, 100]]
 
+def inference(query):
+    chroma_db = Chroma(persist_directory="./chroma_db", embedding_function=embeddings)
+    chroma_docs = chroma_db.similarity_search(query)
+    chroma_answer = chain.invoke({"context":chroma_docs, "question": query}, return_only_outputs=True)
+
+    faiss_db = FAISS.load_local("./faiss_db", embeddings, allow_dangerous_deserialization=True)
+    faiss_docs = faiss_db.similarity_search(query)
+    faiss_answer = chain.invoke({"context":faiss_docs, "question": query}, return_only_outputs=True)
+
+    lance_db = LanceDB(embedding=embeddings, uri="./lance_db")
+    lance_docs = lance_db.similarity_search(query)
+    lance_answer = chain.invoke({"context":lance_docs, "question": query}, return_only_outputs=True)
+
+    pinecone_db = PineconeVectorStore(index="langchain-test-index", embedding=embeddings)
+    pinecone_docs = pinecone_db.similarity_search(query)
+    pinecoce_answer = chain.invoke({"context":pinecone_docs, "question": query}, return_only_outputs=True)
+
+    return chroma_answer, faiss_answer, lance_answer, pinecoce_answer
+
+
 with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
     gr.Markdown(f"# {title}\n{description}")
     with gr.Row():
@@ -68,7 +89,22 @@ with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
         with gr.Column():
             message = gr.Textbox(label="Status", type="text")
 
-    submit_btn.click(inference, inputs=[pdf, chunk_size, chunk_overlap], outputs=message)
+    submit_btn.click(store_embeddings, inputs=[pdf, chunk_size, chunk_overlap], outputs=message)
+
+    with gr.Row():
+        with gr.Column():
+            text = gr.Textbox(label="Question", type="text")
+            with gr.Row():
+                chat_clear_btn = gr.ClearButton(components=[text])
+                chat_submit_btn = gr.Button("Submit", variant='primary')
+        with gr.Column():
+            chroma_out = gr.Textbox(label="ChromaDB Response", type="text")
+            faiss_out = gr.Textbox(label="FaissDB Response", type="text")
+            lance_out = gr.Textbox(label="LanceDB Response", type="text")
+            pinecone_out = gr.Textbox(label="PineconeDB Response", type="text")
+
+    chat_submit_btn.click(inference, inputs=[text], outputs=[chroma_out, faiss_out, lance_out,
+                                                             pinecone_out])
 
     examples_obj = gr.Examples(examples=examples, inputs=[pdf, chunk_size, chunk_overlap])