Merge pull request #140 from langchain-ai/harrison/add-other-bots

cr

backend/app/agent.py

@@ -24,8 +24,10 @@
     TOOLS,
     AvailableTools,
     get_retrieval_tool,
+    get_retriever,
 )
 from app.chatbot import get_chatbot_executor
+from app.retrieval import get_retrieval_executor
 
 
 class AgentType(str, Enum):
@@ -149,15 +151,13 @@ def get_chatbot(
 class ConfigurableChatBot(RunnableBinding):
     llm: LLMType
     system_message: str = DEFAULT_SYSTEM_MESSAGE
-    assistant_id: Optional[str] = None
     user_id: Optional[str] = None
 
     def __init__(
         self,
         *,
         llm: LLMType = LLMType.GPT_35_TURBO,
         system_message: str = DEFAULT_SYSTEM_MESSAGE,
-        assistant_id: Optional[str] = None,
         kwargs: Optional[Mapping[str, Any]] = None,
         config: Optional[Mapping[str, Any]] = None,
         **others: Any,
@@ -179,6 +179,61 @@ def __init__(
     .configurable_fields(
         llm=ConfigurableField(id="llm_type", name="LLM Type"),
         system_message=ConfigurableField(id="system_message", name="System Message"),
+    )
+    .with_types(input_type=Sequence[AnyMessage], output_type=Sequence[AnyMessage])
+)
+
+
+class ConfigurableRetrieval(RunnableBinding):
+    llm_type: LLMType
+    system_message: str = DEFAULT_SYSTEM_MESSAGE
+    assistant_id: Optional[str] = None
+    user_id: Optional[str] = None
+
+    def __init__(
+        self,
+        *,
+        llm_type: LLMType = LLMType.GPT_35_TURBO,
+        system_message: str = DEFAULT_SYSTEM_MESSAGE,
+        assistant_id: Optional[str] = None,
+        kwargs: Optional[Mapping[str, Any]] = None,
+        config: Optional[Mapping[str, Any]] = None,
+        **others: Any,
+    ) -> None:
+        others.pop("bound", None)
+        retriever = get_retriever(assistant_id)
+        checkpointer = RedisCheckpoint()
+        if llm_type == LLMType.GPT_35_TURBO:
+            llm = get_openai_llm()
+        elif llm_type == LLMType.GPT_4:
+            llm = get_openai_llm(gpt_4=True)
+        elif llm_type == LLMType.AZURE_OPENAI:
+            llm = get_openai_llm(azure=True)
+        elif llm_type == LLMType.CLAUDE2:
+            llm = get_anthropic_llm()
+        elif llm_type == LLMType.BEDROCK_CLAUDE2:
+            llm = get_anthropic_llm(bedrock=True)
+        elif llm_type == LLMType.GEMINI:
+            llm = get_google_llm()
+        elif llm_type == LLMType.MIXTRAL:
+            llm = get_mixtral_fireworks()
+        else:
+            raise ValueError("Unexpected llm type")
+        chatbot = get_retrieval_executor(llm, retriever, system_message, checkpointer)
+        super().__init__(
+            llm_type=llm_type,
+            system_message=system_message,
+            bound=chatbot,
+            kwargs=kwargs or {},
+            config=config or {},
+        )
+
+
+chat_retrieval = (
+    ConfigurableRetrieval(llm_type=LLMType.GPT_35_TURBO, checkpoint=RedisCheckpoint())
+    .configurable_fields(
+        llm_type=ConfigurableField(id="llm_type", name="LLM Type"),
+        system_message=ConfigurableField(id="system_message", name="System Message"),
         assistant_id=ConfigurableField(
             id="assistant_id", name="Assistant ID", is_shared=True
         ),
@@ -216,6 +271,7 @@ def __init__(
         default_key="assistant",
         prefix_keys=True,
         chatbot=chatbot,
+        chat_retrieval=chat_retrieval,
     )
     .with_types(input_type=Sequence[AnyMessage], output_type=Sequence[AnyMessage])
 )

backend/app/retrieval.py

@@ -1,23 +1,120 @@
+import json
+
 from langchain_core.language_models.base import LanguageModelLike
-from langchain_core.messages import SystemMessage
+from langchain_core.messages import (
+    SystemMessage,
+    HumanMessage,
+    AIMessage,
+    FunctionMessage,
+)
+from langchain_core.runnables import chain
+from langchain_core.retrievers import BaseRetriever
 from langgraph.checkpoint import BaseCheckpointSaver
 from langgraph.graph import END
 from langgraph.graph.message import MessageGraph
+from langchain_core.prompts import PromptTemplate
+
+
+search_prompt = PromptTemplate.from_template(
+    """Given the conversation below, come up with a search query to look up.
+
+This search query can be either a few words or question
+
+Return ONLY this search query, nothing more.
+
+>>> Conversation:
+{conversation}
+>>> END OF CONVERSATION
+
+Remember, return ONLY the search query that will help you when formulating a response to the above conversation."""
+)
+
+
+response_prompt_template = """{instructions}
+
+Respond to the user using ONLY the context provided below. Do not make anything up.
+
+{context}"""
+
+
+@chain
+def get_search_query(llm, messages):
+    convo = []
+    for m in messages:
+        if isinstance(m, AIMessage):
+            if "function_call" not in m.additional_kwargs:
+                convo.append(f"AI: {m.content}")
+        if isinstance(m, HumanMessage):
+            convo.append(f"Human: {m.content}")
+    conversation = "\n".join(convo)
+    prompt = search_prompt.invoke({"conversation": conversation})
+    response = llm.invoke(prompt)
+    return response.content
 
 
 def get_retrieval_executor(
     llm: LanguageModelLike,
+    retriever: BaseRetriever,
     system_message: str,
     checkpoint: BaseCheckpointSaver,
 ):
     def _get_messages(messages):
-        return [SystemMessage(content=system_message)] + messages
+        chat_history = []
+        for m in messages:
+            if isinstance(m, AIMessage):
+                if "function_call" not in m.additional_kwargs:
+                    chat_history.append(m)
+            if isinstance(m, HumanMessage):
+                chat_history.append(m)
+        content = messages[-1].content
+        return [
+            SystemMessage(
+                content=response_prompt_template.format(
+                    instructions=system_message, context=content
+                )
+            )
+        ] + chat_history
+
+    def invoke_retrieval(messages):
+        if len(messages) == 1:
+            human_input = messages[-1].content
+            return AIMessage(
+                content="",
+                additional_kwargs={
+                    "function_call": {
+                        "name": "retrieval",
+                        "arguments": json.dumps({"query": human_input}),
+                    }
+                },
+            )
+        else:
+            search_query = get_search_query.invoke({"llm": llm, "messages": messages})
+            return AIMessage(
+                content="",
+                additional_kwargs={
+                    "function_call": {
+                        "name": "retrieval",
+                        "arguments": json.dumps({"query": search_query}),
+                    }
+                },
+            )
+
+    def retrieve(messages):
+        params = messages[-1].additional_kwargs["function_call"]
+        query = json.loads(params["arguments"])["query"]
+        response = retriever.invoke(query)
+        content = "\n".join([d.page_content for d in response])
+        return FunctionMessage(name="retrieval", content=content)
 
-    chatbot = _get_messages | llm
+    response = _get_messages | llm
 
     workflow = MessageGraph()
-    workflow.add_node("chatbot", chatbot)
-    workflow.set_entry_point("chatbot")
-    workflow.add_edge("chatbot", END)
+    workflow.add_node("invoke_retrieval", invoke_retrieval)
+    workflow.add_node("retrieve", retrieve)
+    workflow.add_node("response", response)
+    workflow.set_entry_point("invoke_retrieval")
+    workflow.add_edge("invoke_retrieval", "retrieve")
+    workflow.add_edge("retrieve", "response")
+    workflow.add_edge("response", END)
     app = workflow.compile(checkpointer=checkpoint)
     return app

backend/app/tools.py

@@ -33,11 +33,15 @@ class PythonREPLInput(BaseModel):
 If the user is referencing particular files, that is often a good hint that information may be here."""
 
 
+def get_retriever(assistant_id: str):
+    return vstore.as_retriever(
+        search_kwargs={"filter": RedisFilter.tag("namespace") == assistant_id}
+    )
+
+
 def get_retrieval_tool(assistant_id: str, description: str):
     return create_retriever_tool(
-        vstore.as_retriever(
-            search_kwargs={"filter": RedisFilter.tag("namespace") == assistant_id}
-        ),
+        get_retriever(assistant_id),
         "Retriever",
         description,
     )