templates: Add neo4j semantic layer with ollama template (#17192)

A template with JSON-based agent using Mixtral via Ollama.

---------

Co-authored-by: Erick Friis <erick@langchain.dev>

templates/neo4j-semantic-ollama/neo4j_semantic_ollama/agent.py

@@ -0,0 +1,110 @@
+import os
+from typing import List, Tuple
+
+from langchain.agents import AgentExecutor
+from langchain.agents.format_scratchpad import format_log_to_messages
+from langchain.agents.output_parsers import (
+    ReActJsonSingleInputOutputParser,
+)
+from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain.pydantic_v1 import BaseModel, Field
+from langchain.schema import AIMessage, HumanMessage
+from langchain.tools.render import render_text_description_and_args
+from langchain_community.chat_models import ChatOllama
+
+from neo4j_semantic_ollama.information_tool import InformationTool
+from neo4j_semantic_ollama.memory_tool import MemoryTool
+from neo4j_semantic_ollama.recommendation_tool import RecommenderTool
+from neo4j_semantic_ollama.smalltalk_tool import SmalltalkTool
+
+llm = ChatOllama(
+    model="mixtral",
+    temperature=0,
+    base_url=os.environ["OLLAMA_BASE_URL"],
+    streaming=True,
+)
+chat_model_with_stop = llm.bind(stop=["\nObservation"])
+tools = [InformationTool(), RecommenderTool(), MemoryTool(), SmalltalkTool()]
+
+# Inspiration taken from hub.pull("hwchase17/react-json")
+system_message = f"""Answer the following questions as best you can.
+You can answer directly if the user is greeting you or similar.
+Otherise, you have access to the following tools:
+
+{render_text_description_and_args(tools).replace('{', '{{').replace('}', '}}')}
+
+The way you use the tools is by specifying a json blob.
+Specifically, this json should have a `action` key (with the name of the tool to use)
+and a `action_input` key (with the input to the tool going here).
+The only values that should be in the "action" field are: {[t.name for t in tools]}
+The $JSON_BLOB should only contain a SINGLE action, 
+do NOT return a list of multiple actions.
+Here is an example of a valid $JSON_BLOB:
+```
+{{{{
+    "action": $TOOL_NAME,
+    "action_input": $INPUT
+}}}}
+```
+The $JSON_BLOB must always be enclosed with triple backticks!
+
+ALWAYS use the following format:
+Question: the input question you must answer
+Thought: you should always think about what to do
+Action:```
+$JSON_BLOB
+```
+Observation: the result of the action... 
+(this Thought/Action/Observation can repeat N times)
+Thought: I now know the final answer
+Final Answer: the final answer to the original input question
+
+Begin! Reminder to always use the exact characters `Final Answer` when responding.'
+"""
+
+prompt = ChatPromptTemplate.from_messages(
+    [
+        (
+            "user",
+            system_message,
+        ),
+        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"],
+        "agent_scratchpad": lambda x: format_log_to_messages(x["intermediate_steps"]),
+        "chat_history": lambda x: _format_chat_history(x["chat_history"])
+        if x.get("chat_history")
+        else [],
+    }
+    | prompt
+    | chat_model_with_stop
+    | ReActJsonSingleInputOutputParser()
+)
+
+
+# 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
+)