add retrieval agent (#13317)

templates/retrieval-agent/retrieval_agent/chain.py

@@ -0,0 +1,75 @@
+import os
+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.chat_models import AzureChatOpenAI
+from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain.pydantic_v1 import BaseModel, Field
+from langchain.schema.messages import AIMessage, HumanMessage
+from langchain.tools import ArxivQueryRun
+from langchain.tools.render import format_tool_to_openai_function
+from langchain.utilities import ArxivAPIWrapper
+
+
+class ArxivInput(BaseModel):
+    query: str = Field(description="search query to look up")
+
+
+# Create the tool
+arxiv_tool = ArxivQueryRun(api_wrapper=ArxivAPIWrapper(), args_schema=ArxivInput)
+tools = [arxiv_tool]
+llm = AzureChatOpenAI(
+    temperature=0,
+    deployment_name=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
+    openai_api_base=os.environ["AZURE_OPENAI_API_BASE"],
+    openai_api_version=os.environ["AZURE_OPENAI_API_VERSION"],
+    openai_api_key=os.environ["AZURE_OPENAI_API_KEY"],
+)
+assistant_system_message = """You are a helpful research assistant. \
+Lookup relevant information as needed."""
+prompt = ChatPromptTemplate.from_messages(
+    [
+        ("system", assistant_system_message),
+        MessagesPlaceholder(variable_name="chat_history"),
+        ("user", "{input}"),
+        MessagesPlaceholder(variable_name="agent_scratchpad"),
+    ]
+)
+
+llm_with_tools = llm.bind(functions=[format_tool_to_openai_function(t) for t in tools])
+
+
+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"]),
+        "agent_scratchpad": lambda x: format_to_openai_function_messages(
+            x["intermediate_steps"]
+        ),
+    }
+    | prompt
+    | llm_with_tools
+    | OpenAIFunctionsAgentOutputParser()
+)
+
+
+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, verbose=True).with_types(
+    input_type=AgentInput
+)