Initial commit

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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.gitignore

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README.md

@@ -4,7 +4,7 @@ emoji: 🏢
 colorFrom: pink
 colorTo: gray
 sdk: gradio
-sdk_version: 4.31.2
+sdk_version: 4.31.0
 app_file: app.py
 pinned: false
 ---

app.py

@@ -0,0 +1,88 @@
+__import__('pysqlite3')
+import sys
+sys.modules['sqlite3'] = sys.modules.pop('pysqlite3')
+
+from langchain_chroma import Chroma
+from langchain_openai import OpenAIEmbeddings
+from langchain_core.messages import HumanMessage
+from langchain.chains import create_history_aware_retriever, create_retrieval_chain
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain_chroma import Chroma
+from langchain_openai import OpenAIEmbeddings
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder, PromptTemplate
+from langchain_openai import ChatOpenAI
+from dotenv import load_dotenv
+import gradio as gr
+
+load_dotenv()
+
+# OpenAI's GPT model to use
+GPT_VERSION = "gpt-3.5-turbo-0125"
+
+# Prompt used to contextualize a question inside the context of a conversation
+CONTEXTUALIZATION_PROMPT = """Given a chat history and the latest user question \
+which might reference context in the chat history, formulate a standalone question \
+which can be understood without the chat history. Do NOT answer the question, \
+just reformulate it if needed and otherwise return it as is."""
+
+# Prompt used for building an answer to a question using the retrieved context
+SYSTEM_PROMPT = """You are a marketing assistant for question-answering tasks for a company called Tryolabs. \
+You must answer the question using pieces of retrieved context consisting of blogposts written by Tryolabs. \
+Answer the question in a friendly manner, trying to refer to Tryolabs' content to engage the user and encourage \
+to make contact with the company. \
+When you can, include properly formatted hyperlinks to the content. TRY providing references to different content \
+within the same response. Extract the hyperlink from the context. \
+If you don't know the answer to the question, encourage the user to contact us for more information. \
+Tryolabs' contact email is [email protected]. \
+Keep your answers concise. \
+ALWAYS respond with Markdown. \
+You MUST only use information found on the pieces of context given. You MUST NOT attribute any inventions to \
+Tryolabs unless explicitly mentioned on the context. \
+
+{context}"""
+
+llm = ChatOpenAI(model=GPT_VERSION)
+
+vectorstore = Chroma(persist_directory="./chroma_db", embedding_function=OpenAIEmbeddings())
+
+retriever = vectorstore.as_retriever(search_type="mmr", search_kwargs={'k': 6, 'lambda_mult': 0.3})
+
+contextualize_q_prompt = ChatPromptTemplate.from_messages(
+    [
+        ("system", CONTEXTUALIZATION_PROMPT),
+        MessagesPlaceholder("chat_history"),
+        ("human", "{input}"),
+    ]
+)
+
+history_aware_retriever = create_history_aware_retriever(llm, retriever, contextualize_q_prompt)
+
+custom_rag_prompt = ChatPromptTemplate.from_messages(
+    [
+        ("system", SYSTEM_PROMPT),
+        MessagesPlaceholder("chat_history"),
+        ("human", "{input}")
+    ]
+)
+
+document_prompt = PromptTemplate(
+    input_variables=['page_content', 'title', 'link'], 
+    template="Title: {title}, Link to blogpost: {link}, Content: {page_content}"
+)
+qa_chain = create_stuff_documents_chain(llm, custom_rag_prompt, document_prompt=document_prompt)
+rag_chain = create_retrieval_chain(history_aware_retriever, qa_chain)
+
+
+def predict(message, history):
+    chat_history = []
+
+    for m, a in history:
+        chat_history.append(HumanMessage(content=m))
+        chat_history.append(a)
+
+    ai_response = rag_chain.invoke({"input": message, "chat_history": chat_history})
+
+    return ai_response["answer"]
+
+
+gr.ChatInterface(predict).launch()

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requirements.txt

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