Update app.py

app.py

@@ -1,44 +1,4 @@
-# import gradio as gr
 
-# from langchain_community.llms import LlamaCpp
-# from langchain.prompts import PromptTemplate
-# from langchain.chains import LLMChain
-# from langchain_core.callbacks import StreamingStdOutCallbackHandler
-# from langchain.retrievers import TFIDFRetriever
-# from langchain.chains import RetrievalQA
-# from langchain.memory import ConversationBufferMemory
-# from langchain_community.chat_models import ChatLlamaCpp
-
-
-# callbacks = [StreamingStdOutCallbackHandler()]
-# print("creating ll started")
-# llm = ChatLlamaCpp(
-#     model_path="finbro-v0.1.0-llama-3-8B-instruct-1m.gguf",
-#     n_batch=8,
-#     temperature=0.85,
-#     max_tokens=256,
-#     top_p=0.95,
-#     top_k = 10,
-#     callback_manager=callbacks,
-#     n_ctx=2048,
-#     verbose=True,  # Verbose is required to pass to the callback manager
-# )
-# print("creating llm ended")
-
-
-
-
-
-
-# def greet(question, model_type):
-#     print(f"question is {question}")
-#     out_gen = "testsetestestetsetsets"
-#     return out_gen
-
-# demo = gr.Interface(fn=greet, inputs=["text", gr.Dropdown(
-#             ["With memory", "Without memory"], label="Memory status", info="With using memory, the output will be slow but strong"
-#         ),], outputs="text")
-# demo.launch(debug=True, share=True)
 
 
 import gradio as gr
@@ -51,6 +11,11 @@ from langchain.retrievers import TFIDFRetriever
 from langchain.chains import RetrievalQA
 from langchain.memory import ConversationBufferMemory
 from langchain_community.chat_models import ChatLlamaCpp
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnablePassthrough
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import Chroma
+from langchain_core.prompts import PromptTemplate
 
 callbacks = [StreamingStdOutCallbackHandler()]
 print("creating ll started")
@@ -66,34 +31,80 @@ llm = ChatLlamaCpp(
     n_ctx=2048,
     verbose=True,  # Verbose is required to pass to the callback manager
 )
-# print("creating ll ended")
+print("creating ll ended")
+
+# for without memory
+template = """You are the Finiantial expert:
+
+### Instruction:
+{question}
+
+### Input:
 
 
+### Response:
+"""
+
+prompt = PromptTemplate(template=template, input_variables=["question"])
+print("test2")
+llm_chain_model = LLMChain(prompt=prompt, llm=llm)
+
+# for retriver
+
+
+def format_docs(docs):
+    return "\n\n".join(doc.page_content for doc in docs)
+
+model_name = "BAAI/bge-base-en-v1.5"
+model_kwargs = {"device":'cpu'}
+encode_kwargs = {'normalize_embeddings':True}
+
+hf = HuggingFaceEmbeddings(
+    model_name = model_name,
+    model_kwargs = model_kwargs,
+    encode_kwargs = encode_kwargs
+)
+
+
+vectorstore = Chroma(
+    collection_name="example_collection",
+    embedding_function=hf,
+    persist_directory="./chroma_langchain_db",  # Where to save data locally, remove if not neccesary
+)
+
+retriever = vectorstore.as_retriever(search_type="similarity", search_kwargs={"k": 6})
+template = """you are the financial ai assistant
+
+{context}
+
+Question: {question}
+
+Helpful Answer:"""
+custom_rag_prompt = PromptTemplate.from_template(template)
+
+rag_chain = (
+    {"context": retriever | format_docs, "question": RunnablePassthrough()}
+    | custom_rag_prompt
+    | llm
+    | StrOutputParser()
+)
+print("retriver done")
 
 
 
 
 def greet(question, model_type):
-    print("prompt started ")
+
     print(f"question is {question}")
-    template = """You are the Finiantial expert:
-    
-    ### Instruction:
-    {question}
-    
-    ### Input:
-    
-    
-    ### Response:
-    """
-    print("test1")
-    prompt = PromptTemplate(template=template, input_variables=["question"])
-    print("test2")
-    llm_chain_model = LLMChain(prompt=prompt, llm=llm)
-    print("test3")
-    out_gen = llm_chain_model.run(question)  
-    print("test4")
-    print(f"out is: {out_gen}")
+    if model_type == "With memory":
+        out_gen = rag_chain.invoke("give me suggestion for investment")
+        print("test5")
+        print(f"out is: {out_gen}")
+    else:
+        print("test3")
+        out_gen = llm_chain_model.run(question)  
+        print("test4")
+        print(f"out is: {out_gen}")
     return out_gen
 
 demo = gr.Interface(fn=greet, inputs=["text", gr.Dropdown(