templates: Add neo4j semantic layer template (#15652)

Co-authored-by: Tomaz Bratanic <tomazbratanic@Tomazs-MacBook-Pro.local>
Co-authored-by: Erick Friis <erick@langchain.dev>

templates/neo4j-semantic-layer/neo4j_semantic_layer/agent.py

@@ -0,0 +1,71 @@
+from typing import List, Tuple
+
+from langchain.agents import AgentExecutor
+from langchain.agents.format_scratchpad import format_to_openai_function_messages
+from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
+from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain.pydantic_v1 import BaseModel, Field
+from langchain.schema import AIMessage, HumanMessage
+from langchain.tools.render import format_tool_to_openai_function
+from langchain_community.chat_models import ChatOpenAI
+
+from neo4j_semantic_layer.information_tool import InformationTool
+from neo4j_semantic_layer.memory_tool import MemoryTool
+from neo4j_semantic_layer.recommendation_tool import RecommenderTool
+
+llm = ChatOpenAI(temperature=0, model="gpt-4")
+tools = [InformationTool(), RecommenderTool(), MemoryTool()]
+
+llm_with_tools = llm.bind(functions=[format_tool_to_openai_function(t) for t in tools])
+
+prompt = ChatPromptTemplate.from_messages(
+    [
+        (
+            "system",
+            "You are a helpful assistant that finds information about movies "
+            " and recommends them. If tools require follow up questions, "
+            "make sure to ask the user for clarification. Make sure to include any "
+            "available options that need to be clarified in the follow up questions",
+        ),
+        MessagesPlaceholder(variable_name="chat_history"),
+        ("user", "{input}"),
+        MessagesPlaceholder(variable_name="agent_scratchpad"),
+    ]
+)
+
+
+def _format_chat_history(chat_history: List[Tuple[str, str]]):
+    buffer = []
+    for human, ai in chat_history:
+        buffer.append(HumanMessage(content=human))
+        buffer.append(AIMessage(content=ai))
+    return buffer
+
+
+agent = (
+    {
+        "input": lambda x: x["input"],
+        "chat_history": lambda x: _format_chat_history(x["chat_history"])
+        if x.get("chat_history")
+        else [],
+        "agent_scratchpad": lambda x: format_to_openai_function_messages(
+            x["intermediate_steps"]
+        ),
+    }
+    | prompt
+    | llm_with_tools
+    | OpenAIFunctionsAgentOutputParser()
+)
+
+
+# Add typing for input
+class AgentInput(BaseModel):
+    input: str
+    chat_history: List[Tuple[str, str]] = Field(
+        ..., extra={"widget": {"type": "chat", "input": "input", "output": "output"}}
+    )
+
+
+agent_executor = AgentExecutor(agent=agent, tools=tools).with_types(
+    input_type=AgentInput
+)