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app/.chainlit/config.toml

@@ -0,0 +1,78 @@
+[project]
+# Whether to enable telemetry (default: true). No personal data is collected.
+enable_telemetry = true
+
+# List of environment variables to be provided by each user to use the app.
+user_env = []
+
+# Duration (in seconds) during which the session is saved when the connection is lost
+session_timeout = 3600
+
+# Enable third parties caching (e.g LangChain cache)
+cache = false
+
+# Follow symlink for asset mount (see https://github.com/Chainlit/chainlit/issues/317)
+# follow_symlink = false
+
+[features]
+# Show the prompt playground
+prompt_playground = true
+
+# Authorize users to upload files with messages
+multi_modal = true
+
+# Allows user to use speech to text
+[features.speech_to_text]
+    enabled = false
+    # See all languages here https://github.com/JamesBrill/react-speech-recognition/blob/HEAD/docs/API.md#language-string
+    # language = "en-US"
+
+[UI]
+# Name of the app and chatbot.
+name = "Chatbot"
+
+# Show the readme while the conversation is empty.
+show_readme_as_default = true
+
+# Description of the app and chatbot. This is used for HTML tags.
+# description = ""
+
+# Large size content are by default collapsed for a cleaner ui
+default_collapse_content = true
+
+# The default value for the expand messages settings.
+default_expand_messages = false
+
+# Hide the chain of thought details from the user in the UI.
+hide_cot = false
+
+# Link to your github repo. This will add a github button in the UI's header.
+# github = ""
+
+# Specify a CSS file that can be used to customize the user interface.
+# The CSS file can be served from the public directory or via an external link.
+# custom_css = "/public/test.css"
+
+# Override default MUI light theme. (Check theme.ts)
+[UI.theme.light]
+    #background = "#FAFAFA"
+    #paper = "#FFFFFF"
+
+    [UI.theme.light.primary]
+        #main = "#F80061"
+        #dark = "#980039"
+        #light = "#FFE7EB"
+
+# Override default MUI dark theme. (Check theme.ts)
+[UI.theme.dark]
+    #background = "#FAFAFA"
+    #paper = "#FFFFFF"
+
+    [UI.theme.dark.primary]
+        #main = "#F80061"
+        #dark = "#980039"
+        #light = "#FFE7EB"
+
+
+[meta]
+generated_by = "0.7.400"

app/requirements.txt

@@ -4,5 +4,4 @@ openai
 pinecone-client
 cohere
 tiktoken
-context-python
 promptwatch

app/simchat.py

@@ -0,0 +1,102 @@
+import os
+import time
+import pinecone
+import chainlit as cl
+
+from promptwatch import PromptWatch
+from prompts import load_query_gen_prompt, load_simchat_prompt
+
+from chainlit import on_message, on_chat_start
+
+
+
+from langchain.embeddings.cohere import CohereEmbeddings
+from langchain.vectorstores import Pinecone
+from langchain.chains import ConversationalRetrievalChain, LLMChain
+from langchain.chat_models import ChatOpenAI
+from langchain.memory import ConversationTokenBufferMemory
+from langchain.prompts import (
+    ChatPromptTemplate,
+    PromptTemplate,
+    SystemMessagePromptTemplate,
+    HumanMessagePromptTemplate,
+)
+from langchain.prompts.prompt import PromptTemplate
+from langchain.chains.qa_with_sources import load_qa_with_sources_chain
+# from langchain.callbacks import ContextCallbackHandler
+
+ 
+
+index_name = "chat-index-v1"
+
+simchat = load_simchat_prompt()
+query_gen_prompt = load_query_gen_prompt()
+CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(query_gen_prompt)
+pinecone.init(
+            api_key=os.environ.get("PINECONE_API_KEY"),
+            environment='gcp-starter',
+    )
+@cl.on_chat_start
+async def on_chat_start():
+    # token = os.environ["CONTEXT_TOKEN"]
+    # context_callback = ContextCallbackHandler(token)
+    llm = ChatOpenAI(temperature=0.0, verbose=False, openai_api_key = os.environ.get("OPENAI_API_KEY"), streaming=True, 
+                     model_name='gpt-3.5-turbo',
+                    #  callbacks=[context_callback]
+                    )
+    memory = ConversationTokenBufferMemory(llm=llm,memory_key="chat_history", return_messages=True,input_key='question',max_token_limit=1000)
+    embeddings = CohereEmbeddings(model='embed-english-light-v2.0',cohere_api_key=os.environ.get("COHERE_API_KEY"))
+
+    docsearch = Pinecone.from_existing_index(
+                            index_name=index_name, embedding=embeddings
+                            )
+    retriever = docsearch.as_retriever(search_kwargs={"k": 4})
+    
+    messages = [SystemMessagePromptTemplate.from_template(simchat)]
+   
+    messages.append(HumanMessagePromptTemplate.from_template("{question}"))
+    prompt = ChatPromptTemplate.from_messages(messages)
+
+    question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT, verbose=False)
+    doc_chain = load_qa_with_sources_chain(llm, chain_type="stuff", verbose=False, prompt=prompt)
+
+    chain = ConversationalRetrievalChain(
+            retriever=retriever,
+            question_generator=question_generator,
+            combine_docs_chain=doc_chain,
+            verbose=False,
+            memory=memory,
+            rephrase_question=False,
+            # callbacks=[context_callback]
+        )
+    cl.user_session.set("conversation_chain", chain)
+
+    cl.user_session.set(
+        "message_history",
+        [{"role": "system", "content": "You are a helpful assistant."}],
+    )
+    # await cl.Message(content="Selamat datang! saya adalah Nada, asisten virtual anda.").send()
+
+    
+@cl.on_message
+async def on_message(message: cl.Message):
+    
+    
+    message_history = cl.user_session.get("message_history")
+    message_history.append({"role": "user", "content": message.content})
+        
+    chain = cl.user_session.get("conversation_chain")
+    
+    res = await chain.arun({"question": message.content, 'stream': True})
+    
+    msg = cl.Message(content=res)
+    message_history.append({"role": "assistant", "content": msg.content})
+    await msg.send()
+    await msg.update()
+
+
+    print(message.content)
+    print(message.id)
+
+    
+