feat: init ai activity chat session

apps/api/src/services/ai/base.py

@@ -0,0 +1,82 @@
+from langchain.agents import AgentExecutor
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings
+from langchain.vectorstores import Chroma
+from langchain_core.messages import BaseMessage
+from langchain.agents.openai_functions_agent.base import OpenAIFunctionsAgent
+from langchain.prompts import MessagesPlaceholder
+from langchain.memory import ConversationBufferMemory
+from langchain_core.messages import SystemMessage
+from langchain.agents.openai_functions_agent.agent_token_buffer_memory import (
+    AgentTokenBufferMemory,
+)
+from langchain.chat_models import ChatOpenAI
+from langchain.agents.agent_toolkits import (
+    create_retriever_tool,
+)
+
+import chromadb
+
+from config.config import get_learnhouse_config
+
+client = chromadb.Client()
+
+
+chat_history = []
+
+
+def ask_ai(
+    question: str,
+    chat_history: list[BaseMessage],
+    text_reference: str,
+    message_for_the_prompt: str,
+):
+    # Get API Keys
+    LH_CONFIG = get_learnhouse_config()
+    openai_api_key = LH_CONFIG.ai_config.openai_api_key
+
+    # split it into chunks
+    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+    documents = text_splitter.create_documents([text_reference])
+    texts = text_splitter.split_documents(documents)
+
+    # create the open-source embedding function
+    embedding_function = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
+
+    # load it into Chroma and use it as a retriever
+    db = Chroma.from_documents(texts, embedding_function)
+    tool = create_retriever_tool(
+        db.as_retriever(),
+        "find_context_text",
+        "Find associated text to get context about a course or a lecture",
+    )
+    tools = [tool]
+
+    llm = ChatOpenAI(
+        temperature=0, api_key=openai_api_key
+    )
+
+    memory_key = "history"
+
+    memory = AgentTokenBufferMemory(memory_key=memory_key, llm=llm)
+
+
+    system_message = SystemMessage(content=(message_for_the_prompt))
+
+    prompt = OpenAIFunctionsAgent.create_prompt(
+        system_message=system_message,
+        extra_prompt_messages=[MessagesPlaceholder(variable_name=memory_key)],
+    )
+
+    agent = OpenAIFunctionsAgent(llm=llm, tools=tools, prompt=prompt)
+
+
+    agent_executor = AgentExecutor(
+        agent=agent,
+        tools=tools,
+        memory=memory,
+        verbose=True,
+        return_intermediate_steps=True,
+    )
+
+    return agent_executor({"input": question})