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docs: Updated MongoDB Chat history example notebook to use LCEL format. (#15750)

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- **Description:** Updated the MongoDB example integration notebook to
latest standards
- **Issue:**
[15664](https://github.com/langchain-ai/langchain/issues/15664)
  - **Dependencies:** None
  - **Twitter handle:** @davedecaprio

---------

Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
pull/15933/head
David DeCaprio 6 months ago committed by GitHub
parent 5c73fd5bba
commit ec9642d667
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1 changed files with 178 additions and 24 deletions
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202
docs/docs/integrations/memory/mongodb_chat_message_history.ipynb
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@ -11,7 +11,7 @@
">\n",
">`MongoDB` is developed by MongoDB Inc. and licensed under the Server Side Public License (SSPL). - [Wikipedia](https://en.wikipedia.org/wiki/MongoDB)\n",
"\n",
"This notebook goes over how to use Mongodb to store chat message history.\n"
"This notebook goes over how to use the `MongoDBChatMessageHistory` class to store chat message history in a Mongodb database.\n"
]
},
{
@ -19,76 +19,230 @@
"id": "2d6ed3c8-b70a-498c-bc9e-41b91797d3b7",
"metadata": {},
"source": [
"## Setting up"
"## Setup\n",
"\n",
"The integration lives in the `langchain-community` package, so we need to install that. We also need to install the `pymongo` package.\n",
"\n",
"```bash\n",
"pip install -U --quiet langchain-community pymongo\n",
"```"
]
},
{
"cell_type": "markdown",
"id": "09c33ad3-9ab1-48b5-bead-9a44f3d86eeb",
"metadata": {},
"source": [
"It's also helpful (but not needed) to set up [LangSmith](https://smith.langchain.com/) for best-in-class observability"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5a7f3b3f-d9b8-4577-a7ef-bdd8ecaedb70",
"id": "0976204d-c681-4288-bfe5-a550e0340f35",
"metadata": {},
"outputs": [],
"source": [
"%pip install --upgrade --quiet pymongo"
"# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
"# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
]
},
{
"cell_type": "markdown",
"id": "71a0a5aa-8f12-462a-bcd0-c611d76566f8",
"metadata": {},
"source": [
"## Usage\n",
"\n",
"To use the storage you need to provide only 2 things:\n",
"\n",
"1. Session Id - a unique identifier of the session, like user name, email, chat id etc.\n",
"2. Connection string - a string that specifies the database connection. It will be passed to MongoDB create_engine function.\n",
"\n",
"If you want to customize where the chat histories go, you can also pass:\n",
"1. *database_name* - name of the database to use\n",
"1. *collection_name* - collection to use within that database"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "47a601d2",
"metadata": {},
"id": "0179847d-76b6-43bc-b15c-7fecfcb27ac7",
"metadata": {
"ExecuteTime": {
"end_time": "2023-08-28T10:04:38.077748Z",
"start_time": "2023-08-28T10:04:36.105894Z"
},
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [],
"source": [
"# Provide the connection string to connect to the MongoDB database\n",
"connection_string = \"mongodb://mongo_user:password123@mongo:27017\""
"from langchain_community.chat_message_histories import MongoDBChatMessageHistory\n",
"\n",
"chat_message_history = MongoDBChatMessageHistory(\n",
" session_id=\"test_session\",\n",
" connection_string=\"mongodb://mongo_user:password123@mongo:27017\",\n",
" database_name=\"my_db\",\n",
" collection_name=\"chat_histories\",\n",
")\n",
"\n",
"chat_message_history.add_user_message(\"Hello\")\n",
"chat_message_history.add_ai_message(\"Hi\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "6e7b8653-a8d2-49a7-97ba-4296f7e717e9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[HumanMessage(content='Hello'), AIMessage(content='Hi')]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chat_message_history.messages"
]
},
{
"cell_type": "markdown",
"id": "a8e63850-3e14-46fe-a59e-be6d6bf8fe61",
"id": "e352d786-0811-48ec-832a-9f1c0b70690e",
"metadata": {},
"source": [
"## Example"
"## Chaining\n",
"\n",
"We can easily combine this message history class with [LCEL Runnables](/docs/expression_language/how_to/message_history)\n",
"\n",
"To do this we will want to use OpenAI, so we need to install that. You will also need to set the OPENAI_API_KEY environment variable to your OpenAI key.\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "d15e3302",
"execution_count": 5,
"id": "6558418b-0ece-4d01-9661-56d562d78f7a",
"metadata": {},
"outputs": [],
"source": [
"from langchain.memory import MongoDBChatMessageHistory\n",
"from typing import Optional\n",
"\n",
"message_history = MongoDBChatMessageHistory(\n",
" connection_string=connection_string, session_id=\"test-session\"\n",
")\n",
"from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
"from langchain_core.runnables.history import RunnableWithMessageHistory\n",
"from langchain_openai import ChatOpenAI"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "86ddfd3f-e8cf-477a-a7fd-91be3b8aa928",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"message_history.add_user_message(\"hi!\")\n",
"assert os.environ[\n",
" \"OPENAI_API_KEY\"\n",
"], \"Set the OPENAI_API_KEY environment variable with your OpenAI API key.\""
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "82149122-61d3-490d-9bdb-bb98606e8ba1",
"metadata": {},
"outputs": [],
"source": [
"prompt = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\"system\", \"You are a helpful assistant.\"),\n",
" MessagesPlaceholder(variable_name=\"history\"),\n",
" (\"human\", \"{question}\"),\n",
" ]\n",
")\n",
"\n",
"message_history.add_ai_message(\"whats up?\")"
"chain = prompt | ChatOpenAI()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "64fc465e",
"execution_count": 11,
"id": "2df90853-b67c-490f-b7f8-b69d69270b9c",
"metadata": {},
"outputs": [],
"source": [
"chain_with_history = RunnableWithMessageHistory(\n",
" chain,\n",
" lambda session_id: MongoDBChatMessageHistory(\n",
" session_id=\"test_session\",\n",
" connection_string=\"mongodb://mongo_user:password123@mongo:27017\",\n",
" database_name=\"my_db\",\n",
" collection_name=\"chat_histories\",\n",
" ),\n",
" input_messages_key=\"question\",\n",
" history_messages_key=\"history\",\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "0ce596b8-3b78-48fd-9f92-46dccbbfd58b",
"metadata": {},
"outputs": [],
"source": [
"# This is where we configure the session id\n",
"config = {\"configurable\": {\"session_id\": \"<SESSION_ID>\"}}"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "38e1423b-ba86-4496-9151-25932fab1a8b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='Hi Bob! How can I assist you today?')"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain_with_history.invoke({\"question\": \"Hi! I'm bob\"}, config=config)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "2ee4ee62-a216-4fb1-bf33-57476a84cf16",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[HumanMessage(content='hi!', additional_kwargs={}, example=False),\n",
" AIMessage(content='whats up?', additional_kwargs={}, example=False)]"
"AIMessage(content='Your name is Bob. Is there anything else I can help you with, Bob?')"
]
},
"execution_count": 5,
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"message_history.messages"
"chain_with_history.invoke({\"question\": \"Whats my name\"}, config=config)"
]
}
],

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