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couchbase: Add ttl support to caches & chat_message_history (#26214)

Browse Source 
**Description:** Add support to delete documents automatically from the
caches & chat message history by adding a new optional parameter, `ttl`.


- [x] **Add tests and docs**: If you're adding a new integration, please
include
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.


- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/

---------

Co-authored-by: Nithish Raghunandanan <nithishr@users.noreply.github.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
This commit is contained in:
Nithish Raghunandanan
2024-09-21 01:44:29 +02:00
committed by GitHub
parent c6c508ee96
commit 2d21274bf6
10 changed files with 901 additions and 417 deletions
Download Patch File Download Diff File Expand all files Collapse all files

91
docs/docs/integrations/llm_caching.ipynb
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@@ -14,7 +14,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 2,
"id": "88486f6f",
"metadata": {},
"outputs": [],
@@ -30,7 +30,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 3,
"id": "10ad9224",
"metadata": {
"ExecuteTime": {
@@ -2400,7 +2400,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 4,
"id": "9b4764e4-c75f-4185-b326-524287a826be",
"metadata": {},
"outputs": [],
@@ -2430,7 +2430,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 5,
"id": "4b5e73c5-92c1-4eab-84e2-77924ea9c123",
"metadata": {},
"outputs": [],
@@ -2452,7 +2452,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 6,
"id": "db8d28cc-8d93-47b4-8326-57a29a06fb3c",
"metadata": {},
"outputs": [
@@ -2483,7 +2483,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 7,
"id": "b470dc81-2e7f-4743-9435-ce9071394eea",
"metadata": {},
"outputs": [
@@ -2491,17 +2491,17 @@
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 53 ms, sys: 29 ms, total: 82 ms\n",
"Wall time: 84.2 ms\n"
"CPU times: user 25.9 ms, sys: 15.3 ms, total: 41.3 ms\n",
"Wall time: 144 ms\n"
]
},
{
"data": {
"text/plain": [
"\"\\n\\nWhy couldn't the bicycle stand up by itself? Because it was two-tired!\""
"\"\\n\\nWhy don't scientists trust atoms?\\n\\nBecause they make up everything.\""
]
},
"execution_count": 5,
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
@@ -2512,6 +2512,35 @@
"llm.invoke(\"Tell me a joke\")"
]
},
{
"cell_type": "markdown",
"id": "1dca39d8-233a-45ba-ad7d-0920dfbc4a50",
"metadata": {},
"source": [
"### Specifying a Time to Live (TTL) for the Cached entries\n",
"The Cached documents can be deleted after a specified time automatically by specifying a `ttl` parameter along with the initialization of the Cache."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "a3edcc6f-2ccd-45ba-8ca4-f65b5e51b461",
"metadata": {},
"outputs": [],
"source": [
"from datetime import timedelta\n",
"\n",
"set_llm_cache(\n",
" CouchbaseCache(\n",
" cluster=cluster,\n",
" bucket_name=BUCKET_NAME,\n",
" scope_name=SCOPE_NAME,\n",
" collection_name=COLLECTION_NAME,\n",
" ttl=timedelta(minutes=5),\n",
" )\n",
")"
]
},
{
"cell_type": "markdown",
"id": "43626f33-d184-4260-b641-c9341cef5842",
@@ -2523,7 +2552,7 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 9,
"id": "6b470c03-d7fe-4270-89e1-638251619a53",
"metadata": {},
"outputs": [],
@@ -2653,7 +2682,7 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 10,
"id": "ae0766c8-ea34-4604-b0dc-cf2bbe8077f4",
"metadata": {},
"outputs": [],
@@ -2679,7 +2708,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 11,
"id": "a2e82743-10ea-4319-b43e-193475ae5449",
"metadata": {},
"outputs": [
@@ -2703,7 +2732,7 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 12,
"id": "c36f4e29-d872-4334-a1f1-0e6d10c5d9f2",
"metadata": {},
"outputs": [
@@ -2725,6 +2754,38 @@
"print(llm.invoke(\"What is the expected lifespan of a dog?\"))"
]
},
{
"cell_type": "markdown",
"id": "f6f674fa-70b5-4cf9-a208-992aad2c3c89",
"metadata": {},
"source": [
"### Specifying a Time to Live (TTL) for the Cached entries\n",
"The Cached documents can be deleted after a specified time automatically by specifying a `ttl` parameter along with the initialization of the Cache."
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "7e127d0a-5049-47e0-abf4-6424ad7d9fec",
"metadata": {},
"outputs": [],
"source": [
"from datetime import timedelta\n",
"\n",
"set_llm_cache(\n",
" CouchbaseSemanticCache(\n",
" cluster=cluster,\n",
" embedding=embeddings,\n",
" bucket_name=BUCKET_NAME,\n",
" scope_name=SCOPE_NAME,\n",
" collection_name=COLLECTION_NAME,\n",
" index_name=INDEX_NAME,\n",
" score_threshold=0.8,\n",
" ttl=timedelta(minutes=5),\n",
" )\n",
")"
]
},
{
"cell_type": "markdown",
"id": "ae1f5e1c-085e-4998-9f2d-b5867d2c3d5b",
@@ -2802,7 +2863,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.3"
"version": "3.10.13"
}
},
"nbformat": 4,

677
docs/docs/integrations/memory/couchbase_chat_message_history.ipynb
View File

@@ -1,325 +1,354 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "a283d2fd-e26e-4811-a486-d3cf0ecf6749",
"metadata": {},
"source": [
"# Couchbase\n",
"> Couchbase is an award-winning distributed NoSQL cloud database that delivers unmatched versatility, performance, scalability, and financial value for all of your cloud, mobile, AI, and edge computing applications. Couchbase embraces AI with coding assistance for developers and vector search for their applications.\n",
"\n",
"This notebook goes over how to use the `CouchbaseChatMessageHistory` class to store the chat message history in a Couchbase cluster\n"
]
},
{
"cell_type": "markdown",
"id": "ff868a6c-3e17-4c3d-8d32-67b01f4d7bcc",
"metadata": {},
"source": [
"## Set Up Couchbase Cluster\n",
"To run this demo, you need a Couchbase Cluster. \n",
"\n",
"You can work with both [Couchbase Capella](https://www.couchbase.com/products/capella/) and your self-managed Couchbase Server."
]
},
{
"cell_type": "markdown",
"id": "41fa85e7-6968-45e4-a445-de305d80f332",
"metadata": {},
"source": [
"## Install Dependencies\n",
"`CouchbaseChatMessageHistory` lives inside the `langchain-couchbase` package. "
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "b744ca05-b8c6-458c-91df-f50ca2c20b3c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"%pip install --upgrade --quiet langchain-couchbase"
]
},
{
"cell_type": "markdown",
"id": "41f29205-6452-493b-ba18-8a3b006bcca4",
"metadata": {},
"source": [
"## Create Couchbase Connection Object\n",
"We create a connection to the Couchbase cluster initially and then pass the cluster object to the Vector Store. \n",
"\n",
"Here, we are connecting using the username and password. You can also connect using any other supported way to your cluster. \n",
"\n",
"For more information on connecting to the Couchbase cluster, please check the [Python SDK documentation](https://docs.couchbase.com/python-sdk/current/hello-world/start-using-sdk.html#connect)."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "f394908e-f5fe-408a-84d7-b97fdebcfa26",
"metadata": {},
"outputs": [],
"source": [
"COUCHBASE_CONNECTION_STRING = (\n",
" \"couchbase://localhost\" # or \"couchbases://localhost\" if using TLS\n",
")\n",
"DB_USERNAME = \"Administrator\"\n",
"DB_PASSWORD = \"Password\""
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "ad4dce21-d80c-465a-b709-fd366ba5ce35",
"metadata": {},
"outputs": [],
"source": [
"from datetime import timedelta\n",
"\n",
"from couchbase.auth import PasswordAuthenticator\n",
"from couchbase.cluster import Cluster\n",
"from couchbase.options import ClusterOptions\n",
"\n",
"auth = PasswordAuthenticator(DB_USERNAME, DB_PASSWORD)\n",
"options = ClusterOptions(auth)\n",
"cluster = Cluster(COUCHBASE_CONNECTION_STRING, options)\n",
"\n",
"# Wait until the cluster is ready for use.\n",
"cluster.wait_until_ready(timedelta(seconds=5))"
]
},
{
"cell_type": "markdown",
"id": "e3d0210c-e2e6-437a-86f3-7397a1899fef",
"metadata": {},
"source": [
"We will now set the bucket, scope, and collection names in the Couchbase cluster that we want to use for storing the message history.\n",
"\n",
"Note that the bucket, scope, and collection need to exist before using them to store the message history."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "e8c7f846-a5c4-4465-a40e-4a9a23ac71bd",
"metadata": {},
"outputs": [],
"source": [
"BUCKET_NAME = \"langchain-testing\"\n",
"SCOPE_NAME = \"_default\"\n",
"COLLECTION_NAME = \"conversational_cache\""
]
},
{
"cell_type": "markdown",
"id": "283959e1-6af7-4768-9211-5b0facc6ef65",
"metadata": {},
"source": [
"## Usage\n",
"In order to store the messages, you need the following:\n",
"- Couchbase Cluster object: Valid connection to the Couchbase cluster\n",
"- bucket_name: Bucket in cluster to store the chat message history\n",
"- scope_name: Scope in bucket to store the message history\n",
"- collection_name: Collection in scope to store the message history\n",
"- session_id: Unique identifier for the session\n",
"\n",
"Optionally you can configure the following:\n",
"- session_id_key: Field in the chat message documents to store the `session_id`\n",
"- message_key: Field in the chat message documents to store the message content\n",
"- create_index: Used to specify if the index needs to be created on the collection. By default, an index is created on the `message_key` and the `session_id_key` of the documents"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "43c3b2d5-aae2-44a9-9e9f-f10adf054cfa",
"metadata": {},
"outputs": [],
"source": [
"from langchain_couchbase.chat_message_histories import CouchbaseChatMessageHistory\n",
"\n",
"message_history = CouchbaseChatMessageHistory(\n",
" cluster=cluster,\n",
" bucket_name=BUCKET_NAME,\n",
" scope_name=SCOPE_NAME,\n",
" collection_name=COLLECTION_NAME,\n",
" session_id=\"test-session\",\n",
")\n",
"\n",
"message_history.add_user_message(\"hi!\")\n",
"\n",
"message_history.add_ai_message(\"how are you doing?\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "e7e348ef-79e9-481c-aeef-969ae03dea6a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[HumanMessage(content='hi!'), AIMessage(content='how are you doing?')]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"message_history.messages"
]
},
{
"cell_type": "markdown",
"id": "c8b942a7-93fa-4cd9-8414-d047135c2733",
"metadata": {},
"source": [
"## Chaining\n",
"The chat message history class can be used with [LCEL Runnables](https://python.langchain.com/docs/how_to/message_history/)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8a9f0d91-d1d6-481d-8137-ea11229f485a",
"metadata": {},
"outputs": [],
"source": [
"import getpass\n",
"import os\n",
"\n",
"from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
"from langchain_core.runnables.history import RunnableWithMessageHistory\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "946d45aa-5a61-49ae-816b-1c3949c56d9a",
"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",
"# Create the LCEL runnable\n",
"chain = prompt | ChatOpenAI()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "20dfd838-b549-42ed-b3ba-ac005f7e024c",
"metadata": {},
"outputs": [],
"source": [
"chain_with_history = RunnableWithMessageHistory(\n",
" chain,\n",
" lambda session_id: CouchbaseChatMessageHistory(\n",
" cluster=cluster,\n",
" bucket_name=BUCKET_NAME,\n",
" scope_name=SCOPE_NAME,\n",
" collection_name=COLLECTION_NAME,\n",
" session_id=session_id,\n",
" ),\n",
" input_messages_key=\"question\",\n",
" history_messages_key=\"history\",\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "17bd09f4-896d-433d-bb9a-369a06e7aa8a",
"metadata": {},
"outputs": [],
"source": [
"# This is where we configure the session id\n",
"config = {\"configurable\": {\"session_id\": \"testing\"}}"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "4bda1096-2fc2-40d7-a046-0d5d8e3a8f75",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='Hello Bob! How can I assist you today?', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 22, 'total_tokens': 32}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-a0f8a29e-ddf4-4e06-a1fe-cf8c325a2b72-0', usage_metadata={'input_tokens': 22, 'output_tokens': 10, 'total_tokens': 32})"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain_with_history.invoke({\"question\": \"Hi! I'm bob\"}, config=config)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "1cfb31da-51bb-4c5f-909a-b7118b0ae08d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='Your name is Bob.', response_metadata={'token_usage': {'completion_tokens': 5, 'prompt_tokens': 43, 'total_tokens': 48}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-f764a9eb-999e-4042-96b6-fe47b7ae4779-0', usage_metadata={'input_tokens': 43, 'output_tokens': 5, 'total_tokens': 48})"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain_with_history.invoke({\"question\": \"Whats my name\"}, config=config)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
"cells": [
{
"cell_type": "markdown",
"id": "a283d2fd-e26e-4811-a486-d3cf0ecf6749",
"metadata": {},
"source": [
"# Couchbase\n",
"> Couchbase is an award-winning distributed NoSQL cloud database that delivers unmatched versatility, performance, scalability, and financial value for all of your cloud, mobile, AI, and edge computing applications. Couchbase embraces AI with coding assistance for developers and vector search for their applications.\n",
"\n",
"This notebook goes over how to use the `CouchbaseChatMessageHistory` class to store the chat message history in a Couchbase cluster\n"
]
},
{
"cell_type": "markdown",
"id": "ff868a6c-3e17-4c3d-8d32-67b01f4d7bcc",
"metadata": {},
"source": [
"## Set Up Couchbase Cluster\n",
"To run this demo, you need a Couchbase Cluster. \n",
"\n",
"You can work with both [Couchbase Capella](https://www.couchbase.com/products/capella/) and your self-managed Couchbase Server."
]
},
{
"cell_type": "markdown",
"id": "41fa85e7-6968-45e4-a445-de305d80f332",
"metadata": {},
"source": [
"## Install Dependencies\n",
"`CouchbaseChatMessageHistory` lives inside the `langchain-couchbase` package. "
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "b744ca05-b8c6-458c-91df-f50ca2c20b3c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"%pip install --upgrade --quiet langchain-couchbase"
]
},
{
"cell_type": "markdown",
"id": "41f29205-6452-493b-ba18-8a3b006bcca4",
"metadata": {},
"source": [
"## Create Couchbase Connection Object\n",
"We create a connection to the Couchbase cluster initially and then pass the cluster object to the Vector Store. \n",
"\n",
"Here, we are connecting using the username and password. You can also connect using any other supported way to your cluster. \n",
"\n",
"For more information on connecting to the Couchbase cluster, please check the [Python SDK documentation](https://docs.couchbase.com/python-sdk/current/hello-world/start-using-sdk.html#connect)."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "f394908e-f5fe-408a-84d7-b97fdebcfa26",
"metadata": {},
"outputs": [],
"source": [
"COUCHBASE_CONNECTION_STRING = (\n",
" \"couchbase://localhost\" # or \"couchbases://localhost\" if using TLS\n",
")\n",
"DB_USERNAME = \"Administrator\"\n",
"DB_PASSWORD = \"Password\""
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "ad4dce21-d80c-465a-b709-fd366ba5ce35",
"metadata": {},
"outputs": [],
"source": [
"from datetime import timedelta\n",
"\n",
"from couchbase.auth import PasswordAuthenticator\n",
"from couchbase.cluster import Cluster\n",
"from couchbase.options import ClusterOptions\n",
"\n",
"auth = PasswordAuthenticator(DB_USERNAME, DB_PASSWORD)\n",
"options = ClusterOptions(auth)\n",
"cluster = Cluster(COUCHBASE_CONNECTION_STRING, options)\n",
"\n",
"# Wait until the cluster is ready for use.\n",
"cluster.wait_until_ready(timedelta(seconds=5))"
]
},
{
"cell_type": "markdown",
"id": "e3d0210c-e2e6-437a-86f3-7397a1899fef",
"metadata": {},
"source": [
"We will now set the bucket, scope, and collection names in the Couchbase cluster that we want to use for storing the message history.\n",
"\n",
"Note that the bucket, scope, and collection need to exist before using them to store the message history."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "e8c7f846-a5c4-4465-a40e-4a9a23ac71bd",
"metadata": {},
"outputs": [],
"source": [
"BUCKET_NAME = \"langchain-testing\"\n",
"SCOPE_NAME = \"_default\"\n",
"COLLECTION_NAME = \"conversational_cache\""
]
},
{
"cell_type": "markdown",
"id": "283959e1-6af7-4768-9211-5b0facc6ef65",
"metadata": {},
"source": [
"## Usage\n",
"In order to store the messages, you need the following:\n",
"- Couchbase Cluster object: Valid connection to the Couchbase cluster\n",
"- bucket_name: Bucket in cluster to store the chat message history\n",
"- scope_name: Scope in bucket to store the message history\n",
"- collection_name: Collection in scope to store the message history\n",
"- session_id: Unique identifier for the session\n",
"\n",
"Optionally you can configure the following:\n",
"- session_id_key: Field in the chat message documents to store the `session_id`\n",
"- message_key: Field in the chat message documents to store the message content\n",
"- create_index: Used to specify if the index needs to be created on the collection. By default, an index is created on the `message_key` and the `session_id_key` of the documents\n",
"- ttl: Used to specify a time in `timedelta` to live for the documents after which they will get deleted automatically from the storage."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "43c3b2d5-aae2-44a9-9e9f-f10adf054cfa",
"metadata": {},
"outputs": [],
"source": [
"from langchain_couchbase.chat_message_histories import CouchbaseChatMessageHistory\n",
"\n",
"message_history = CouchbaseChatMessageHistory(\n",
" cluster=cluster,\n",
" bucket_name=BUCKET_NAME,\n",
" scope_name=SCOPE_NAME,\n",
" collection_name=COLLECTION_NAME,\n",
" session_id=\"test-session\",\n",
")\n",
"\n",
"message_history.add_user_message(\"hi!\")\n",
"\n",
"message_history.add_ai_message(\"how are you doing?\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "e7e348ef-79e9-481c-aeef-969ae03dea6a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[HumanMessage(content='hi!'), AIMessage(content='how are you doing?')]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"message_history.messages"
]
},
{
"cell_type": "markdown",
"id": "b993fe69-4462-4cb4-ad0a-eb31a1a4d7d9",
"metadata": {},
"source": [
"## Specifying a Time to Live (TTL) for the Chat Messages\n",
"The stored messages can be deleted after a specified time automatically by specifying a `ttl` parameter along with the initialization of the chat message history store."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "d32d9302-de97-4319-a484-c83530bab508",
"metadata": {},
"outputs": [],
"source": [
"from langchain_couchbase.chat_message_histories import CouchbaseChatMessageHistory\n",
"\n",
"message_history = CouchbaseChatMessageHistory(\n",
" cluster=cluster,\n",
" bucket_name=BUCKET_NAME,\n",
" scope_name=SCOPE_NAME,\n",
" collection_name=COLLECTION_NAME,\n",
" session_id=\"test-session\",\n",
" ttl=timedelta(hours=24),\n",
")"
]
},
{
"cell_type": "markdown",
"id": "c8b942a7-93fa-4cd9-8414-d047135c2733",
"metadata": {},
"source": [
"## Chaining\n",
"The chat message history class can be used with [LCEL Runnables](https://python.langchain.com/docs/how_to/message_history/)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "8a9f0d91-d1d6-481d-8137-ea11229f485a",
"metadata": {},
"outputs": [],
"source": [
"import getpass\n",
"import os\n",
"\n",
"from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
"from langchain_core.runnables.history import RunnableWithMessageHistory\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "946d45aa-5a61-49ae-816b-1c3949c56d9a",
"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",
"# Create the LCEL runnable\n",
"chain = prompt | ChatOpenAI()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "20dfd838-b549-42ed-b3ba-ac005f7e024c",
"metadata": {},
"outputs": [],
"source": [
"chain_with_history = RunnableWithMessageHistory(\n",
" chain,\n",
" lambda session_id: CouchbaseChatMessageHistory(\n",
" cluster=cluster,\n",
" bucket_name=BUCKET_NAME,\n",
" scope_name=SCOPE_NAME,\n",
" collection_name=COLLECTION_NAME,\n",
" session_id=session_id,\n",
" ),\n",
" input_messages_key=\"question\",\n",
" history_messages_key=\"history\",\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "17bd09f4-896d-433d-bb9a-369a06e7aa8a",
"metadata": {},
"outputs": [],
"source": [
"# This is where we configure the session id\n",
"config = {\"configurable\": {\"session_id\": \"testing\"}}"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "4bda1096-2fc2-40d7-a046-0d5d8e3a8f75",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='Hello, Bob! How can I assist you today?', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 11, 'prompt_tokens': 22, 'total_tokens': 33}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-62e54e3d-db70-429d-9ee0-e5e8eb2489a1-0', usage_metadata={'input_tokens': 22, 'output_tokens': 11, 'total_tokens': 33})"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain_with_history.invoke({\"question\": \"Hi! I'm bob\"}, config=config)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "1cfb31da-51bb-4c5f-909a-b7118b0ae08d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='Your name is Bob.', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 5, 'prompt_tokens': 44, 'total_tokens': 49}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-d84a570a-45f3-4931-814a-078761170bca-0', usage_metadata={'input_tokens': 44, 'output_tokens': 5, 'total_tokens': 49})"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain_with_history.invoke({\"question\": \"Whats my name\"}, config=config)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}