mirror of
https://github.com/hwchase17/langchain
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couchbase: Add standard and semantic caches (#23607)
Thank you for contributing to LangChain! **Description:** Add support for caching (standard + semantic) LLM responses using Couchbase - [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/ Additional guidelines: - Make sure optional dependencies are imported within a function. - Please do not add dependencies to pyproject.toml files (even optional ones) unless they are required for unit tests. - Most PRs should not touch more than one package. - Changes should be backwards compatible. - If you are adding something to community, do not re-import it in langchain. If no one reviews your PR within a few days, please @-mention one of baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17. --------- 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
GitHub
parent
8d82a0d483
commit
f1618ec540
@ -12,7 +12,7 @@
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": 1,
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"id": "10ad9224",
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"metadata": {
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"ExecuteTime": {
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@ -1809,7 +1809,6 @@
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"cell_type": "markdown",
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"id": "0c69d84d",
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"metadata": {
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"jp-MarkdownHeadingCollapsed": true,
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"tags": []
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},
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"source": [
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@ -1891,7 +1890,6 @@
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"cell_type": "markdown",
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"id": "5da41b77",
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"metadata": {
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"jp-MarkdownHeadingCollapsed": true,
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"tags": []
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},
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"source": [
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@ -2149,6 +2147,7 @@
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},
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{
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"cell_type": "markdown",
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"id": "2ac1a8c7",
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"metadata": {},
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"source": [
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"## SingleStoreDB Semantic Cache\n",
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@ -2173,6 +2172,353 @@
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7019c991-0101-4f9c-b212-5729a5471293",
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"metadata": {},
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"source": [
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"## Couchbase Caches\n",
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"\n",
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"Use [Couchbase](https://couchbase.com/) as a cache for prompts and responses."
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]
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},
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{
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"cell_type": "markdown",
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"id": "d6aac680-ba32-4c19-8864-6471cf0e7d5a",
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"metadata": {},
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"source": [
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"### Couchbase Cache\n",
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"\n",
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"The standard cache that looks for an exact match of the user prompt."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "9b4764e4-c75f-4185-b326-524287a826be",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Create couchbase connection object\n",
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"from datetime import timedelta\n",
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"\n",
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"from couchbase.auth import PasswordAuthenticator\n",
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"from couchbase.cluster import Cluster\n",
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"from couchbase.options import ClusterOptions\n",
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"from langchain_couchbase.cache import CouchbaseCache\n",
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"from langchain_openai import ChatOpenAI\n",
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"\n",
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"COUCHBASE_CONNECTION_STRING = (\n",
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" \"couchbase://localhost\" # or \"couchbases://localhost\" if using TLS\n",
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")\n",
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"DB_USERNAME = \"Administrator\"\n",
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"DB_PASSWORD = \"Password\"\n",
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"\n",
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"auth = PasswordAuthenticator(DB_USERNAME, DB_PASSWORD)\n",
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"options = ClusterOptions(auth)\n",
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"cluster = Cluster(COUCHBASE_CONNECTION_STRING, options)\n",
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"\n",
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"# Wait until the cluster is ready for use.\n",
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"cluster.wait_until_ready(timedelta(seconds=5))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "4b5e73c5-92c1-4eab-84e2-77924ea9c123",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Specify the bucket, scope and collection to store the cached documents\n",
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"BUCKET_NAME = \"langchain-testing\"\n",
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"SCOPE_NAME = \"_default\"\n",
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"COLLECTION_NAME = \"_default\"\n",
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"\n",
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"set_llm_cache(\n",
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" CouchbaseCache(\n",
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" cluster=cluster,\n",
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" bucket_name=BUCKET_NAME,\n",
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" scope_name=SCOPE_NAME,\n",
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" collection_name=COLLECTION_NAME,\n",
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" )\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "db8d28cc-8d93-47b4-8326-57a29a06fb3c",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"CPU times: user 22.2 ms, sys: 14 ms, total: 36.2 ms\n",
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"Wall time: 938 ms\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"\"\\n\\nWhy couldn't the bicycle stand up by itself? Because it was two-tired!\""
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"%%time\n",
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"# The first time, it is not yet in the cache, so it should take longer\n",
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"llm.invoke(\"Tell me a joke\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "b470dc81-2e7f-4743-9435-ce9071394eea",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"CPU times: user 53 ms, sys: 29 ms, total: 82 ms\n",
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"Wall time: 84.2 ms\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"\"\\n\\nWhy couldn't the bicycle stand up by itself? Because it was two-tired!\""
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"%%time\n",
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"# The second time, it is in the cache, so it should be much faster\n",
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"llm.invoke(\"Tell me a joke\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "43626f33-d184-4260-b641-c9341cef5842",
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"metadata": {},
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"source": [
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"### Couchbase Semantic Cache\n",
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"Semantic caching allows users to retrieve cached prompts based on semantic similarity between the user input and previously cached inputs. Under the hood it uses Couchbase as both a cache and a vectorstore. This needs an appropriate Vector Search Index defined to work. Please look at the usage example on how to set up the index."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "6b470c03-d7fe-4270-89e1-638251619a53",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Create Couchbase connection object\n",
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"from datetime import timedelta\n",
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"\n",
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"from couchbase.auth import PasswordAuthenticator\n",
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"from couchbase.cluster import Cluster\n",
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"from couchbase.options import ClusterOptions\n",
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"from langchain_couchbase.cache import CouchbaseSemanticCache\n",
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"from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n",
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"\n",
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"COUCHBASE_CONNECTION_STRING = (\n",
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" \"couchbase://localhost\" # or \"couchbases://localhost\" if using TLS\n",
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")\n",
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"DB_USERNAME = \"Administrator\"\n",
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"DB_PASSWORD = \"Password\"\n",
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"\n",
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"auth = PasswordAuthenticator(DB_USERNAME, DB_PASSWORD)\n",
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"options = ClusterOptions(auth)\n",
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"cluster = Cluster(COUCHBASE_CONNECTION_STRING, options)\n",
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"\n",
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"# Wait until the cluster is ready for use.\n",
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"cluster.wait_until_ready(timedelta(seconds=5))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f831bc4c-f330-4bd7-9b80-76771d91827e",
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"metadata": {},
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"source": [
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"Notes:\n",
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"- The search index for the semantic cache needs to be defined before using the semantic cache. \n",
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"- The optional parameter, `score_threshold` in the Semantic Cache that you can use to tune the results of the semantic search.\n",
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"\n",
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"### How to Import an Index to the Full Text Search service?\n",
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" - [Couchbase Server](https://docs.couchbase.com/server/current/search/import-search-index.html)\n",
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" - Click on Search -> Add Index -> Import\n",
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" - Copy the following Index definition in the Import screen\n",
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" - Click on Create Index to create the index.\n",
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" - [Couchbase Capella](https://docs.couchbase.com/cloud/search/import-search-index.html)\n",
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" - Copy the index definition to a new file `index.json`\n",
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" - Import the file in Capella using the instructions in the documentation.\n",
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" - Click on Create Index to create the index.\n",
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"\n",
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"#### Example index for the vector search. \n",
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" ```\n",
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" {\n",
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" \"type\": \"fulltext-index\",\n",
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" \"name\": \"langchain-testing._default.semantic-cache-index\",\n",
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" \"sourceType\": \"gocbcore\",\n",
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" \"sourceName\": \"langchain-testing\",\n",
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" \"planParams\": {\n",
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" \"maxPartitionsPerPIndex\": 1024,\n",
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" \"indexPartitions\": 16\n",
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" },\n",
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" \"params\": {\n",
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" \"doc_config\": {\n",
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" \"docid_prefix_delim\": \"\",\n",
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" \"docid_regexp\": \"\",\n",
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" \"mode\": \"scope.collection.type_field\",\n",
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" \"type_field\": \"type\"\n",
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" },\n",
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" \"mapping\": {\n",
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" \"analysis\": {},\n",
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" \"default_analyzer\": \"standard\",\n",
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" \"default_datetime_parser\": \"dateTimeOptional\",\n",
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" \"default_field\": \"_all\",\n",
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" \"default_mapping\": {\n",
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" \"dynamic\": true,\n",
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" \"enabled\": false\n",
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" },\n",
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" \"default_type\": \"_default\",\n",
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" \"docvalues_dynamic\": false,\n",
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" \"index_dynamic\": true,\n",
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" \"store_dynamic\": true,\n",
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" \"type_field\": \"_type\",\n",
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" \"types\": {\n",
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" \"_default.semantic-cache\": {\n",
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" \"dynamic\": false,\n",
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" \"enabled\": true,\n",
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" \"properties\": {\n",
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" \"embedding\": {\n",
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" \"dynamic\": false,\n",
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" \"enabled\": true,\n",
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" \"fields\": [\n",
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" {\n",
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" \"dims\": 1536,\n",
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" \"index\": true,\n",
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" \"name\": \"embedding\",\n",
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" \"similarity\": \"dot_product\",\n",
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" \"type\": \"vector\",\n",
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" \"vector_index_optimized_for\": \"recall\"\n",
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" }\n",
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" ]\n",
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" },\n",
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" \"metadata\": {\n",
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" \"dynamic\": true,\n",
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" \"enabled\": true\n",
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" },\n",
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" \"text\": {\n",
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" \"dynamic\": false,\n",
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" \"enabled\": true,\n",
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" \"fields\": [\n",
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" {\n",
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" \"index\": true,\n",
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" \"name\": \"text\",\n",
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" \"store\": true,\n",
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" \"type\": \"text\"\n",
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" }\n",
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" ]\n",
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" }\n",
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" }\n",
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" }\n",
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" }\n",
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" },\n",
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" \"store\": {\n",
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" \"indexType\": \"scorch\",\n",
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" \"segmentVersion\": 16\n",
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" }\n",
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" },\n",
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" \"sourceParams\": {}\n",
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" }\n",
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" ```"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "ae0766c8-ea34-4604-b0dc-cf2bbe8077f4",
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"metadata": {},
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"outputs": [],
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"source": [
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"BUCKET_NAME = \"langchain-testing\"\n",
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"SCOPE_NAME = \"_default\"\n",
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"COLLECTION_NAME = \"semantic-cache\"\n",
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"INDEX_NAME = \"semantic-cache-index\"\n",
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"embeddings = OpenAIEmbeddings()\n",
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"\n",
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"cache = CouchbaseSemanticCache(\n",
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" cluster=cluster,\n",
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" embedding=embeddings,\n",
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" bucket_name=BUCKET_NAME,\n",
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" scope_name=SCOPE_NAME,\n",
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" collection_name=COLLECTION_NAME,\n",
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" index_name=INDEX_NAME,\n",
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" score_threshold=0.8,\n",
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")\n",
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"\n",
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"set_llm_cache(cache)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "a2e82743-10ea-4319-b43e-193475ae5449",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\n",
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"The average lifespan of a dog is around 12 years, but this can vary depending on the breed, size, and overall health of the individual dog. Some smaller breeds may live longer, while larger breeds may have shorter lifespans. Proper care, diet, and exercise can also play a role in extending a dog's lifespan.\n",
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"CPU times: user 826 ms, sys: 2.46 s, total: 3.28 s\n",
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"Wall time: 2.87 s\n"
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]
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}
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],
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"source": [
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"%%time\n",
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"# The first time, it is not yet in the cache, so it should take longer\n",
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"print(llm.invoke(\"How long do dogs live?\"))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "c36f4e29-d872-4334-a1f1-0e6d10c5d9f2",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\n",
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"The average lifespan of a dog is around 12 years, but this can vary depending on the breed, size, and overall health of the individual dog. Some smaller breeds may live longer, while larger breeds may have shorter lifespans. Proper care, diet, and exercise can also play a role in extending a dog's lifespan.\n",
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"CPU times: user 9.82 ms, sys: 2.61 ms, total: 12.4 ms\n",
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"Wall time: 311 ms\n"
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]
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}
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],
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"source": [
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"%%time\n",
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"# The second time, it is in the cache, so it should be much faster\n",
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"print(llm.invoke(\"What is the expected lifespan of a dog?\"))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ae1f5e1c-085e-4998-9f2d-b5867d2c3d5b",
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@ -2228,7 +2574,9 @@
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"| langchain_core.caches | [InMemoryCache](https://api.python.langchain.com/en/latest/caches/langchain_core.caches.InMemoryCache.html) |\n",
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"| langchain_elasticsearch.cache | [ElasticsearchCache](https://api.python.langchain.com/en/latest/cache/langchain_elasticsearch.cache.ElasticsearchCache.html) |\n",
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"| langchain_mongodb.cache | [MongoDBAtlasSemanticCache](https://api.python.langchain.com/en/latest/cache/langchain_mongodb.cache.MongoDBAtlasSemanticCache.html) |\n",
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"| langchain_mongodb.cache | [MongoDBCache](https://api.python.langchain.com/en/latest/cache/langchain_mongodb.cache.MongoDBCache.html) |\n"
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"| langchain_mongodb.cache | [MongoDBCache](https://api.python.langchain.com/en/latest/cache/langchain_mongodb.cache.MongoDBCache.html) |\n",
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"| langchain_couchbase.cache | [CouchbaseCache](https://api.python.langchain.com/en/latest/cache/langchain_couchbase.cache.CouchbaseCache.html) |\n",
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"| langchain_couchbase.cache | [CouchbaseSemanticCache](https://api.python.langchain.com/en/latest/cache/langchain_couchbase.cache.CouchbaseSemanticCache.html) |\n"
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]
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},
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{
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@ -2256,7 +2604,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.12"
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"version": "3.10.13"
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}
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},
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"nbformat": 4,
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@ -27,3 +27,65 @@ See a [usage example](/docs/integrations/document_loaders/couchbase).
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```python
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from langchain_community.document_loaders.couchbase import CouchbaseLoader
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```
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## LLM Caches
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### CouchbaseCache
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Use Couchbase as a cache for prompts and responses.
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|
||||
See a [usage example](/docs/integrations/llm_caching/#couchbase-cache).
|
||||
|
||||
To import this cache:
|
||||
```python
|
||||
from langchain_couchbase.cache import CouchbaseCache
|
||||
```
|
||||
|
||||
To use this cache with your LLMs:
|
||||
```python
|
||||
from langchain_core.globals import set_llm_cache
|
||||
|
||||
cluster = couchbase_cluster_connection_object
|
||||
|
||||
set_llm_cache(
|
||||
CouchbaseCache(
|
||||
cluster=cluster,
|
||||
bucket_name=BUCKET_NAME,
|
||||
scope_name=SCOPE_NAME,
|
||||
collection_name=COLLECTION_NAME,
|
||||
)
|
||||
)
|
||||
```
|
||||
|
||||
|
||||
### CouchbaseSemanticCache
|
||||
Semantic caching allows users to retrieve cached prompts based on the semantic similarity between the user input and previously cached inputs. Under the hood it uses Couchbase as both a cache and a vectorstore.
|
||||
The CouchbaseSemanticCache needs a Search Index defined to work. Please look at the [usage example](/docs/integrations/vectorstores/couchbase) on how to set up the index.
|
||||
|
||||
See a [usage example](/docs/integrations/llm_caching/#couchbase-semantic-cache).
|
||||
|
||||
To import this cache:
|
||||
```python
|
||||
from langchain_couchbase.cache import CouchbaseSemanticCache
|
||||
```
|
||||
|
||||
To use this cache with your LLMs:
|
||||
```python
|
||||
from langchain_core.globals import set_llm_cache
|
||||
|
||||
# use any embedding provider...
|
||||
from langchain_openai.Embeddings import OpenAIEmbeddings
|
||||
|
||||
embeddings = OpenAIEmbeddings()
|
||||
cluster = couchbase_cluster_connection_object
|
||||
|
||||
set_llm_cache(
|
||||
CouchbaseSemanticCache(
|
||||
cluster=cluster,
|
||||
embedding = embeddings,
|
||||
bucket_name=BUCKET_NAME,
|
||||
scope_name=SCOPE_NAME,
|
||||
collection_name=COLLECTION_NAME,
|
||||
index_name=INDEX_NAME,
|
||||
)
|
||||
)
|
||||
```
|
||||
|
||||
@ -2,4 +2,6 @@ from langchain_couchbase.vectorstores import CouchbaseVectorStore
|
||||
|
||||
__all__ = [
|
||||
"CouchbaseVectorStore",
|
||||
"CouchbaseCache",
|
||||
"CouchbaseSemanticCache",
|
||||
]
|
||||
|
||||
350
libs/partners/couchbase/langchain_couchbase/cache.py
Normal file
350
libs/partners/couchbase/langchain_couchbase/cache.py
Normal file
@ -0,0 +1,350 @@
|
||||
"""
|
||||
LangChain Couchbase Caches
|
||||
|
||||
Functions "_hash", "_loads_generations" and "_dumps_generations"
|
||||
are duplicated in this utility from modules:
|
||||
- "libs/community/langchain_community/cache.py"
|
||||
"""
|
||||
|
||||
import hashlib
|
||||
import json
|
||||
import logging
|
||||
from typing import Any, Dict, Optional, Union
|
||||
|
||||
from couchbase.cluster import Cluster
|
||||
from langchain_core.caches import RETURN_VAL_TYPE, BaseCache
|
||||
from langchain_core.embeddings import Embeddings
|
||||
from langchain_core.load.dump import dumps
|
||||
from langchain_core.load.load import loads
|
||||
from langchain_core.outputs import Generation
|
||||
|
||||
from langchain_couchbase.vectorstores import CouchbaseVectorStore
|
||||
|
||||
logger = logging.getLogger(__file__)
|
||||
|
||||
|
||||
def _hash(_input: str) -> str:
|
||||
"""Use a deterministic hashing approach."""
|
||||
return hashlib.md5(_input.encode()).hexdigest()
|
||||
|
||||
|
||||
def _dumps_generations(generations: RETURN_VAL_TYPE) -> str:
|
||||
"""
|
||||
Serialization for generic RETURN_VAL_TYPE, i.e. sequence of `Generation`
|
||||
|
||||
Args:
|
||||
generations (RETURN_VAL_TYPE): A list of language model generations.
|
||||
|
||||
Returns:
|
||||
str: a single string representing a list of generations.
|
||||
|
||||
This function (+ its counterpart `_loads_generations`) rely on
|
||||
the dumps/loads pair with Reviver, so are able to deal
|
||||
with all subclasses of Generation.
|
||||
|
||||
Each item in the list can be `dumps`ed to a string,
|
||||
then we make the whole list of strings into a json-dumped.
|
||||
"""
|
||||
return json.dumps([dumps(_item) for _item in generations])
|
||||
|
||||
|
||||
def _loads_generations(generations_str: str) -> Union[RETURN_VAL_TYPE, None]:
|
||||
"""
|
||||
Deserialization of a string into a generic RETURN_VAL_TYPE
|
||||
(i.e. a sequence of `Generation`).
|
||||
|
||||
See `_dumps_generations`, the inverse of this function.
|
||||
|
||||
Args:
|
||||
generations_str (str): A string representing a list of generations.
|
||||
|
||||
Compatible with the legacy cache-blob format
|
||||
Does not raise exceptions for malformed entries, just logs a warning
|
||||
and returns none: the caller should be prepared for such a cache miss.
|
||||
|
||||
Returns:
|
||||
RETURN_VAL_TYPE: A list of generations.
|
||||
"""
|
||||
try:
|
||||
generations = [loads(_item_str) for _item_str in json.loads(generations_str)]
|
||||
return generations
|
||||
except (json.JSONDecodeError, TypeError):
|
||||
# deferring the (soft) handling to after the legacy-format attempt
|
||||
pass
|
||||
|
||||
try:
|
||||
gen_dicts = json.loads(generations_str)
|
||||
# not relying on `_load_generations_from_json` (which could disappear):
|
||||
generations = [Generation(**generation_dict) for generation_dict in gen_dicts]
|
||||
logger.warning(
|
||||
f"Legacy 'Generation' cached blob encountered: '{generations_str}'"
|
||||
)
|
||||
return generations
|
||||
except (json.JSONDecodeError, TypeError):
|
||||
logger.warning(
|
||||
f"Malformed/unparsable cached blob encountered: '{generations_str}'"
|
||||
)
|
||||
return None
|
||||
|
||||
|
||||
class CouchbaseCache(BaseCache):
|
||||
"""Couchbase LLM Cache
|
||||
LLM Cache that uses Couchbase as the backend
|
||||
"""
|
||||
|
||||
PROMPT = "prompt"
|
||||
LLM = "llm"
|
||||
RETURN_VAL = "return_val"
|
||||
|
||||
def _check_bucket_exists(self) -> bool:
|
||||
"""Check if the bucket exists in the linked Couchbase cluster"""
|
||||
bucket_manager = self._cluster.buckets()
|
||||
try:
|
||||
bucket_manager.get_bucket(self._bucket_name)
|
||||
return True
|
||||
except Exception:
|
||||
return False
|
||||
|
||||
def _check_scope_and_collection_exists(self) -> bool:
|
||||
"""Check if the scope and collection exists in the linked Couchbase bucket
|
||||
Raises a ValueError if either is not found"""
|
||||
scope_collection_map: Dict[str, Any] = {}
|
||||
|
||||
# Get a list of all scopes in the bucket
|
||||
for scope in self._bucket.collections().get_all_scopes():
|
||||
scope_collection_map[scope.name] = []
|
||||
|
||||
# Get a list of all the collections in the scope
|
||||
for collection in scope.collections:
|
||||
scope_collection_map[scope.name].append(collection.name)
|
||||
|
||||
# Check if the scope exists
|
||||
if self._scope_name not in scope_collection_map.keys():
|
||||
raise ValueError(
|
||||
f"Scope {self._scope_name} not found in Couchbase "
|
||||
f"bucket {self._bucket_name}"
|
||||
)
|
||||
|
||||
# Check if the collection exists in the scope
|
||||
if self._collection_name not in scope_collection_map[self._scope_name]:
|
||||
raise ValueError(
|
||||
f"Collection {self._collection_name} not found in scope "
|
||||
f"{self._scope_name} in Couchbase bucket {self._bucket_name}"
|
||||
)
|
||||
|
||||
return True
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
cluster: Cluster,
|
||||
bucket_name: str,
|
||||
scope_name: str,
|
||||
collection_name: str,
|
||||
**kwargs: Dict[str, Any],
|
||||
) -> None:
|
||||
"""Initialize the Couchbase LLM Cache
|
||||
Args:
|
||||
cluster (Cluster): couchbase cluster object with active connection.
|
||||
bucket_name (str): name of the bucket to store documents in.
|
||||
scope_name (str): name of the scope in bucket to store documents in.
|
||||
collection_name (str): name of the collection in the scope to store
|
||||
documents in.
|
||||
"""
|
||||
if not isinstance(cluster, Cluster):
|
||||
raise ValueError(
|
||||
f"cluster should be an instance of couchbase.Cluster, "
|
||||
f"got {type(cluster)}"
|
||||
)
|
||||
|
||||
self._cluster = cluster
|
||||
|
||||
self._bucket_name = bucket_name
|
||||
self._scope_name = scope_name
|
||||
self._collection_name = collection_name
|
||||
|
||||
# Check if the bucket exists
|
||||
if not self._check_bucket_exists():
|
||||
raise ValueError(
|
||||
f"Bucket {self._bucket_name} does not exist. "
|
||||
" Please create the bucket before searching."
|
||||
)
|
||||
|
||||
try:
|
||||
self._bucket = self._cluster.bucket(self._bucket_name)
|
||||
self._scope = self._bucket.scope(self._scope_name)
|
||||
self._collection = self._scope.collection(self._collection_name)
|
||||
except Exception as e:
|
||||
raise ValueError(
|
||||
"Error connecting to couchbase. "
|
||||
"Please check the connection and credentials."
|
||||
) from e
|
||||
|
||||
# Check if the scope and collection exists. Throws ValueError if they don't
|
||||
try:
|
||||
self._check_scope_and_collection_exists()
|
||||
except Exception as e:
|
||||
raise e
|
||||
|
||||
def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]:
|
||||
"""Look up from cache based on prompt and llm_string."""
|
||||
try:
|
||||
doc = self._collection.get(
|
||||
self._generate_key(prompt, llm_string)
|
||||
).content_as[dict]
|
||||
return _loads_generations(doc[self.RETURN_VAL])
|
||||
except Exception:
|
||||
return None
|
||||
|
||||
def _generate_key(self, prompt: str, llm_string: str) -> str:
|
||||
"""Generate the key based on prompt and llm_string."""
|
||||
return _hash(prompt + llm_string)
|
||||
|
||||
def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None:
|
||||
"""Update cache based on prompt and llm_string."""
|
||||
doc = {
|
||||
self.PROMPT: prompt,
|
||||
self.LLM: llm_string,
|
||||
self.RETURN_VAL: _dumps_generations(return_val),
|
||||
}
|
||||
try:
|
||||
self._collection.upsert(
|
||||
key=self._generate_key(prompt, llm_string), value=doc
|
||||
)
|
||||
except Exception:
|
||||
logger.error("Error updating cache")
|
||||
|
||||
def clear(self, **kwargs: Any) -> None:
|
||||
"""Clear the cache.
|
||||
This will delete all documents in the collection. This requires an index on the
|
||||
collection.
|
||||
"""
|
||||
try:
|
||||
query = f"DELETE FROM `{self._collection_name}`"
|
||||
self._scope.query(query).execute()
|
||||
except Exception:
|
||||
logger.error("Error clearing cache. Please check if you have an index.")
|
||||
|
||||
|
||||
class CouchbaseSemanticCache(BaseCache, CouchbaseVectorStore):
|
||||
"""Couchbase Semantic Cache
|
||||
Cache backed by a Couchbase Server with Vector Store support
|
||||
"""
|
||||
|
||||
LLM = "llm_string"
|
||||
RETURN_VAL = "return_val"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
cluster: Cluster,
|
||||
embedding: Embeddings,
|
||||
bucket_name: str,
|
||||
scope_name: str,
|
||||
collection_name: str,
|
||||
index_name: str,
|
||||
score_threshold: Optional[float] = None,
|
||||
) -> None:
|
||||
"""Initialize the Couchbase LLM Cache
|
||||
Args:
|
||||
cluster (Cluster): couchbase cluster object with active connection.
|
||||
embedding (Embeddings): embedding model to use.
|
||||
bucket_name (str): name of the bucket to store documents in.
|
||||
scope_name (str): name of the scope in bucket to store documents in.
|
||||
collection_name (str): name of the collection in the scope to store
|
||||
documents in.
|
||||
index_name (str): name of the Search index to use.
|
||||
score_threshold (float): score threshold to use for filtering results.
|
||||
"""
|
||||
if not isinstance(cluster, Cluster):
|
||||
raise ValueError(
|
||||
f"cluster should be an instance of couchbase.Cluster, "
|
||||
f"got {type(cluster)}"
|
||||
)
|
||||
|
||||
self._cluster = cluster
|
||||
|
||||
self._bucket_name = bucket_name
|
||||
self._scope_name = scope_name
|
||||
self._collection_name = collection_name
|
||||
|
||||
# Check if the bucket exists
|
||||
if not self._check_bucket_exists():
|
||||
raise ValueError(
|
||||
f"Bucket {self._bucket_name} does not exist. "
|
||||
" Please create the bucket before searching."
|
||||
)
|
||||
|
||||
try:
|
||||
self._bucket = self._cluster.bucket(self._bucket_name)
|
||||
self._scope = self._bucket.scope(self._scope_name)
|
||||
self._collection = self._scope.collection(self._collection_name)
|
||||
except Exception as e:
|
||||
raise ValueError(
|
||||
"Error connecting to couchbase. "
|
||||
"Please check the connection and credentials."
|
||||
) from e
|
||||
|
||||
# Check if the scope and collection exists. Throws ValueError if they don't
|
||||
try:
|
||||
self._check_scope_and_collection_exists()
|
||||
except Exception as e:
|
||||
raise e
|
||||
|
||||
self.score_threshold = score_threshold
|
||||
|
||||
# Initialize the vector store
|
||||
super().__init__(
|
||||
cluster=cluster,
|
||||
bucket_name=bucket_name,
|
||||
scope_name=scope_name,
|
||||
collection_name=collection_name,
|
||||
embedding=embedding,
|
||||
index_name=index_name,
|
||||
)
|
||||
|
||||
def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]:
|
||||
"""Look up from cache based on the semantic similarity of the prompt"""
|
||||
search_results = self.similarity_search_with_score(
|
||||
prompt, k=1, search_options={f"metadata.{self.LLM}": llm_string}
|
||||
)
|
||||
if search_results:
|
||||
selected_doc, score = search_results[0]
|
||||
else:
|
||||
return None
|
||||
|
||||
# Check if the score is above the threshold if a threshold is provided
|
||||
if self.score_threshold:
|
||||
if score < self.score_threshold:
|
||||
return None
|
||||
|
||||
# Note that the llm_string might not match the vector search result.
|
||||
# So if the llm_string does not match, do not return the result.
|
||||
if selected_doc.metadata["llm_string"] != llm_string:
|
||||
return None
|
||||
|
||||
return _loads_generations(selected_doc.metadata[self.RETURN_VAL])
|
||||
|
||||
def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None:
|
||||
"""Update cache based on the prompt and llm_string"""
|
||||
try:
|
||||
self.add_texts(
|
||||
texts=[prompt],
|
||||
metadatas=[
|
||||
{
|
||||
self.LLM: llm_string,
|
||||
self.RETURN_VAL: _dumps_generations(return_val),
|
||||
}
|
||||
],
|
||||
)
|
||||
except Exception:
|
||||
logger.error("Error updating cache")
|
||||
|
||||
def clear(self, **kwargs: Any) -> None:
|
||||
"""Clear the cache.
|
||||
This will delete all documents in the collection.
|
||||
This requires an index on the collection.
|
||||
"""
|
||||
try:
|
||||
query = f"DELETE FROM `{self._collection_name}`"
|
||||
self._scope.query(query).execute()
|
||||
except Exception:
|
||||
logger.error("Error clearing cache. Please check if you have an index.")
|
||||
451
libs/partners/couchbase/poetry.lock
generated
451
libs/partners/couchbase/poetry.lock
generated
@ -1,4 +1,4 @@
|
||||
# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
|
||||
# This file is automatically @generated by Poetry 1.8.1 and should not be changed by hand.
|
||||
|
||||
[[package]]
|
||||
name = "annotated-types"
|
||||
@ -16,13 +16,13 @@ typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""}
|
||||
|
||||
[[package]]
|
||||
name = "certifi"
|
||||
version = "2024.2.2"
|
||||
version = "2024.7.4"
|
||||
description = "Python package for providing Mozilla's CA Bundle."
|
||||
optional = false
|
||||
python-versions = ">=3.6"
|
||||
files = [
|
||||
{file = "certifi-2024.2.2-py3-none-any.whl", hash = "sha256:dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1"},
|
||||
{file = "certifi-2024.2.2.tar.gz", hash = "sha256:0569859f95fc761b18b45ef421b1290a0f65f147e92a1e5eb3e635f9a5e4e66f"},
|
||||
{file = "certifi-2024.7.4-py3-none-any.whl", hash = "sha256:c198e21b1289c2ab85ee4e67bb4b4ef3ead0892059901a8d5b622f24a1101e90"},
|
||||
{file = "certifi-2024.7.4.tar.gz", hash = "sha256:5a1e7645bc0ec61a09e26c36f6106dd4cf40c6db3a1fb6352b0244e7fb057c7b"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@ -126,13 +126,13 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "codespell"
|
||||
version = "2.2.6"
|
||||
version = "2.3.0"
|
||||
description = "Codespell"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "codespell-2.2.6-py3-none-any.whl", hash = "sha256:9ee9a3e5df0990604013ac2a9f22fa8e57669c827124a2e961fe8a1da4cacc07"},
|
||||
{file = "codespell-2.2.6.tar.gz", hash = "sha256:a8c65d8eb3faa03deabab6b3bbe798bea72e1799c7e9e955d57eca4096abcff9"},
|
||||
{file = "codespell-2.3.0-py3-none-any.whl", hash = "sha256:a9c7cef2501c9cfede2110fd6d4e5e62296920efe9abfb84648df866e47f58d1"},
|
||||
{file = "codespell-2.3.0.tar.gz", hash = "sha256:360c7d10f75e65f67bad720af7007e1060a5d395670ec11a7ed1fed9dd17471f"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
@ -154,36 +154,36 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "couchbase"
|
||||
version = "4.2.1"
|
||||
version = "4.3.0"
|
||||
description = "Python Client for Couchbase"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "couchbase-4.2.1-cp310-cp310-macosx_10_15_x86_64.whl", hash = "sha256:7ad4c4462879f456a9067ac1788e62d852509439bac3538b9bc459a754666481"},
|
||||
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||||
]
|
||||
|
||||
[[package]]
|
||||
@ -238,13 +238,13 @@ jsonpointer = ">=1.9"
|
||||
|
||||
[[package]]
|
||||
name = "jsonpointer"
|
||||
version = "2.4"
|
||||
version = "3.0.0"
|
||||
description = "Identify specific nodes in a JSON document (RFC 6901)"
|
||||
optional = false
|
||||
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*"
|
||||
python-versions = ">=3.7"
|
||||
files = [
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||||
]
|
||||
|
||||
[[package]]
|
||||
@ -273,13 +273,13 @@ url = "../../core"
|
||||
|
||||
[[package]]
|
||||
name = "langsmith"
|
||||
version = "0.1.82"
|
||||
version = "0.1.83"
|
||||
description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.8.1"
|
||||
files = [
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]
|
||||
|
||||
[package.dependencies]
|
||||
@ -292,38 +292,38 @@ requests = ">=2,<3"
|
||||
|
||||
[[package]]
|
||||
name = "mypy"
|
||||
version = "1.10.0"
|
||||
version = "1.10.1"
|
||||
description = "Optional static typing for Python"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
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{file = "pydantic_core-2.20.1-cp39-cp39-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:77bf3ac639c1ff567ae3b47f8d4cc3dc20f9966a2a6dd2311dcc055d3d04fb8a"},
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{file = "pydantic_core-2.20.1-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:9ebfef07dbe1d93efb94b4700f2d278494e9162565a54f124c404a5656d7ff09"},
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{file = "pydantic_core-2.20.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:6b9d9bb600328a1ce523ab4f454859e9d439150abb0906c5a1983c146580ebab"},
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{file = "pydantic_core-2.20.1-cp39-none-win32.whl", hash = "sha256:784c1214cb6dd1e3b15dd8b91b9a53852aed16671cc3fbe4786f4f1db07089e2"},
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{file = "pydantic_core-2.20.1-cp39-none-win_amd64.whl", hash = "sha256:d2fe69c5434391727efa54b47a1e7986bb0186e72a41b203df8f5b0a19a4f669"},
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{file = "pydantic_core-2.20.1-pp310-pypy310_pp73-macosx_10_12_x86_64.whl", hash = "sha256:a45f84b09ac9c3d35dfcf6a27fd0634d30d183205230a0ebe8373a0e8cfa0906"},
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{file = "pydantic_core-2.20.1-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:d02a72df14dfdbaf228424573a07af10637bd490f0901cee872c4f434a735b94"},
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{file = "pydantic_core-2.20.1-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d2b27e6af28f07e2f195552b37d7d66b150adbaa39a6d327766ffd695799780f"},
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{file = "pydantic_core-2.20.1-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:084659fac3c83fd674596612aeff6041a18402f1e1bc19ca39e417d554468482"},
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{file = "pydantic_core-2.20.1-pp310-pypy310_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:242b8feb3c493ab78be289c034a1f659e8826e2233786e36f2893a950a719bb6"},
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{file = "pydantic_core-2.20.1-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:254ec27fdb5b1ee60684f91683be95e5133c994cc54e86a0b0963afa25c8f8a6"},
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{file = "pydantic_core-2.20.1-pp39-pypy39_pp73-macosx_10_12_x86_64.whl", hash = "sha256:407653af5617f0757261ae249d3fba09504d7a71ab36ac057c938572d1bc9331"},
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||||
{file = "pydantic_core-2.20.1-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:c693e916709c2465b02ca0ad7b387c4f8423d1db7b4649c551f27a529181c5ad"},
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{file = "pydantic_core-2.20.1-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5b5ff4911aea936a47d9376fd3ab17e970cc543d1b68921886e7f64bd28308d1"},
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||||
{file = "pydantic_core-2.20.1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:177f55a886d74f1808763976ac4efd29b7ed15c69f4d838bbd74d9d09cf6fa86"},
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||||
{file = "pydantic_core-2.20.1-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:964faa8a861d2664f0c7ab0c181af0bea66098b1919439815ca8803ef136fc4e"},
|
||||
{file = "pydantic_core-2.20.1-pp39-pypy39_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:4dd484681c15e6b9a977c785a345d3e378d72678fd5f1f3c0509608da24f2ac0"},
|
||||
{file = "pydantic_core-2.20.1-pp39-pypy39_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:f6d6cff3538391e8486a431569b77921adfcdef14eb18fbf19b7c0a5294d4e6a"},
|
||||
{file = "pydantic_core-2.20.1-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:a6d511cc297ff0883bc3708b465ff82d7560193169a8b93260f74ecb0a5e08a7"},
|
||||
{file = "pydantic_core-2.20.1.tar.gz", hash = "sha256:26ca695eeee5f9f1aeeb211ffc12f10bcb6f71e2989988fda61dabd65db878d4"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@ -618,7 +636,6 @@ files = [
|
||||
{file = "PyYAML-6.0.1-cp311-cp311-win_amd64.whl", hash = "sha256:bf07ee2fef7014951eeb99f56f39c9bb4af143d8aa3c21b1677805985307da34"},
|
||||
{file = "PyYAML-6.0.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:855fb52b0dc35af121542a76b9a84f8d1cd886ea97c84703eaa6d88e37a2ad28"},
|
||||
{file = "PyYAML-6.0.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:40df9b996c2b73138957fe23a16a4f0ba614f4c0efce1e9406a184b6d07fa3a9"},
|
||||
{file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a08c6f0fe150303c1c6b71ebcd7213c2858041a7e01975da3a99aed1e7a378ef"},
|
||||
{file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6c22bec3fbe2524cde73d7ada88f6566758a8f7227bfbf93a408a9d86bcc12a0"},
|
||||
{file = "PyYAML-6.0.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:8d4e9c88387b0f5c7d5f281e55304de64cf7f9c0021a3525bd3b1c542da3b0e4"},
|
||||
{file = "PyYAML-6.0.1-cp312-cp312-win32.whl", hash = "sha256:d483d2cdf104e7c9fa60c544d92981f12ad66a457afae824d146093b8c294c54"},
|
||||
@ -655,13 +672,13 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "requests"
|
||||
version = "2.32.2"
|
||||
version = "2.32.3"
|
||||
description = "Python HTTP for Humans."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "requests-2.32.2-py3-none-any.whl", hash = "sha256:fc06670dd0ed212426dfeb94fc1b983d917c4f9847c863f313c9dfaaffb7c23c"},
|
||||
{file = "requests-2.32.2.tar.gz", hash = "sha256:dd951ff5ecf3e3b3aa26b40703ba77495dab41da839ae72ef3c8e5d8e2433289"},
|
||||
{file = "requests-2.32.3-py3-none-any.whl", hash = "sha256:70761cfe03c773ceb22aa2f671b4757976145175cdfca038c02654d061d6dcc6"},
|
||||
{file = "requests-2.32.3.tar.gz", hash = "sha256:55365417734eb18255590a9ff9eb97e9e1da868d4ccd6402399eaf68af20a760"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@ -717,13 +734,13 @@ pytest = ">=7.0.0,<9.0.0"
|
||||
|
||||
[[package]]
|
||||
name = "tenacity"
|
||||
version = "8.3.0"
|
||||
version = "8.5.0"
|
||||
description = "Retry code until it succeeds"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "tenacity-8.3.0-py3-none-any.whl", hash = "sha256:3649f6443dbc0d9b01b9d8020a9c4ec7a1ff5f6f3c6c8a036ef371f573fe9185"},
|
||||
{file = "tenacity-8.3.0.tar.gz", hash = "sha256:953d4e6ad24357bceffbc9707bc74349aca9d245f68eb65419cf0c249a1949a2"},
|
||||
{file = "tenacity-8.5.0-py3-none-any.whl", hash = "sha256:b594c2a5945830c267ce6b79a166228323ed52718f30302c1359836112346687"},
|
||||
{file = "tenacity-8.5.0.tar.gz", hash = "sha256:8bc6c0c8a09b31e6cad13c47afbed1a567518250a9a171418582ed8d9c20ca78"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
@ -743,24 +760,24 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "typing-extensions"
|
||||
version = "4.11.0"
|
||||
version = "4.12.2"
|
||||
description = "Backported and Experimental Type Hints for Python 3.8+"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "typing_extensions-4.11.0-py3-none-any.whl", hash = "sha256:c1f94d72897edaf4ce775bb7558d5b79d8126906a14ea5ed1635921406c0387a"},
|
||||
{file = "typing_extensions-4.11.0.tar.gz", hash = "sha256:83f085bd5ca59c80295fc2a82ab5dac679cbe02b9f33f7d83af68e241bea51b0"},
|
||||
{file = "typing_extensions-4.12.2-py3-none-any.whl", hash = "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d"},
|
||||
{file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "urllib3"
|
||||
version = "2.2.1"
|
||||
version = "2.2.2"
|
||||
description = "HTTP library with thread-safe connection pooling, file post, and more."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "urllib3-2.2.1-py3-none-any.whl", hash = "sha256:450b20ec296a467077128bff42b73080516e71b56ff59a60a02bef2232c4fa9d"},
|
||||
{file = "urllib3-2.2.1.tar.gz", hash = "sha256:d0570876c61ab9e520d776c38acbbb5b05a776d3f9ff98a5c8fd5162a444cf19"},
|
||||
{file = "urllib3-2.2.2-py3-none-any.whl", hash = "sha256:a448b2f64d686155468037e1ace9f2d2199776e17f0a46610480d311f73e3472"},
|
||||
{file = "urllib3-2.2.2.tar.gz", hash = "sha256:dd505485549a7a552833da5e6063639d0d177c04f23bc3864e41e5dc5f612168"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
|
||||
@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
|
||||
|
||||
[tool.poetry]
|
||||
name = "langchain-couchbase"
|
||||
version = "0.0.1"
|
||||
version = "0.0.2"
|
||||
description = "An integration package connecting Couchbase and LangChain"
|
||||
authors = []
|
||||
readme = "README.md"
|
||||
|
||||
120
libs/partners/couchbase/tests/integration_tests/test_cache.py
Normal file
120
libs/partners/couchbase/tests/integration_tests/test_cache.py
Normal file
@ -0,0 +1,120 @@
|
||||
"""Test Couchbase Cache functionality"""
|
||||
|
||||
import os
|
||||
from datetime import timedelta
|
||||
from typing import Any
|
||||
|
||||
import pytest
|
||||
from couchbase.auth import PasswordAuthenticator
|
||||
from couchbase.cluster import Cluster
|
||||
from couchbase.options import ClusterOptions
|
||||
from langchain_core.globals import get_llm_cache, set_llm_cache
|
||||
from langchain_core.outputs import Generation
|
||||
|
||||
from langchain_couchbase.cache import CouchbaseCache, CouchbaseSemanticCache
|
||||
from tests.utils import FakeEmbeddings, FakeLLM
|
||||
|
||||
CONNECTION_STRING = os.getenv("COUCHBASE_CONNECTION_STRING", "")
|
||||
BUCKET_NAME = os.getenv("COUCHBASE_BUCKET_NAME", "")
|
||||
SCOPE_NAME = os.getenv("COUCHBASE_SCOPE_NAME", "")
|
||||
CACHE_COLLECTION_NAME = os.getenv("COUCHBASE_CACHE_COLLECTION_NAME", "")
|
||||
SEMANTIC_CACHE_COLLECTION_NAME = os.getenv(
|
||||
"COUCHBASE_SEMANTIC_CACHE_COLLECTION_NAME", ""
|
||||
)
|
||||
USERNAME = os.getenv("COUCHBASE_USERNAME", "")
|
||||
PASSWORD = os.getenv("COUCHBASE_PASSWORD", "")
|
||||
INDEX_NAME = os.getenv("COUCHBASE_SEMANTIC_CACHE_INDEX_NAME", "")
|
||||
|
||||
|
||||
def set_all_env_vars() -> bool:
|
||||
"""Check if all environment variables are set"""
|
||||
return all(
|
||||
[
|
||||
CONNECTION_STRING,
|
||||
BUCKET_NAME,
|
||||
SCOPE_NAME,
|
||||
CACHE_COLLECTION_NAME,
|
||||
USERNAME,
|
||||
PASSWORD,
|
||||
INDEX_NAME,
|
||||
]
|
||||
)
|
||||
|
||||
|
||||
def get_cluster() -> Any:
|
||||
"""Get a couchbase cluster object"""
|
||||
auth = PasswordAuthenticator(USERNAME, PASSWORD)
|
||||
options = ClusterOptions(auth)
|
||||
connect_string = CONNECTION_STRING
|
||||
cluster = Cluster(connect_string, options)
|
||||
|
||||
# Wait until the cluster is ready for use.
|
||||
cluster.wait_until_ready(timedelta(seconds=5))
|
||||
|
||||
return cluster
|
||||
|
||||
|
||||
@pytest.fixture()
|
||||
def cluster() -> Any:
|
||||
"""Get a couchbase cluster object"""
|
||||
return get_cluster()
|
||||
|
||||
|
||||
@pytest.mark.skipif(
|
||||
not set_all_env_vars(), reason="Missing Couchbase environment variables"
|
||||
)
|
||||
class TestCouchbaseCache:
|
||||
def test_cache(self, cluster: Any) -> None:
|
||||
"""Test standard LLM cache functionality"""
|
||||
set_llm_cache(
|
||||
CouchbaseCache(
|
||||
cluster=cluster,
|
||||
bucket_name=BUCKET_NAME,
|
||||
scope_name=SCOPE_NAME,
|
||||
collection_name=CACHE_COLLECTION_NAME,
|
||||
)
|
||||
)
|
||||
|
||||
llm = FakeLLM()
|
||||
|
||||
params = llm.dict()
|
||||
params["stop"] = None
|
||||
llm_string = str(sorted([(k, v) for k, v in params.items()]))
|
||||
get_llm_cache().update("foo", llm_string, [Generation(text="fizz")])
|
||||
cache_output = get_llm_cache().lookup("foo", llm_string)
|
||||
assert cache_output == [Generation(text="fizz")]
|
||||
|
||||
get_llm_cache().clear()
|
||||
output = get_llm_cache().lookup("bar", llm_string)
|
||||
assert output != [Generation(text="fizz")]
|
||||
|
||||
def test_semantic_cache(self, cluster: Any) -> None:
|
||||
"""Test semantic LLM cache functionality"""
|
||||
set_llm_cache(
|
||||
CouchbaseSemanticCache(
|
||||
cluster=cluster,
|
||||
embedding=FakeEmbeddings(),
|
||||
index_name=INDEX_NAME,
|
||||
bucket_name=BUCKET_NAME,
|
||||
scope_name=SCOPE_NAME,
|
||||
collection_name=SEMANTIC_CACHE_COLLECTION_NAME,
|
||||
)
|
||||
)
|
||||
|
||||
llm = FakeLLM()
|
||||
|
||||
params = llm.dict()
|
||||
params["stop"] = None
|
||||
llm_string = str(sorted([(k, v) for k, v in params.items()]))
|
||||
get_llm_cache().update(
|
||||
"foo", llm_string, [Generation(text="fizz"), Generation(text="Buzz")]
|
||||
)
|
||||
|
||||
# foo and bar will have the same embedding produced by FakeEmbeddings
|
||||
cache_output = get_llm_cache().lookup("bar", llm_string)
|
||||
assert cache_output == [Generation(text="fizz"), Generation(text="Buzz")]
|
||||
|
||||
# clear the cache
|
||||
get_llm_cache().clear()
|
||||
output = get_llm_cache().lookup("bar", llm_string)
|
||||
assert output != [Generation(text="fizz"), Generation(text="Buzz")]
|
||||
@ -2,6 +2,8 @@ from langchain_couchbase import __all__
|
||||
|
||||
EXPECTED_ALL = [
|
||||
"CouchbaseVectorStore",
|
||||
"CouchbaseCache",
|
||||
"CouchbaseSemanticCache",
|
||||
]
|
||||
|
||||
|
||||
|
||||
@ -1,10 +1,11 @@
|
||||
"""Fake Embedding class for testing purposes."""
|
||||
|
||||
from typing import List
|
||||
from typing import Any, Dict, List, Mapping, Optional, cast
|
||||
|
||||
from langchain_core.callbacks import CallbackManagerForLLMRun
|
||||
from langchain_core.embeddings import Embeddings
|
||||
|
||||
fake_texts = ["foo", "bar", "baz"]
|
||||
from langchain_core.language_models.llms import LLM
|
||||
from langchain_core.pydantic_v1 import validator
|
||||
|
||||
|
||||
class FakeEmbeddings(Embeddings):
|
||||
@ -53,3 +54,57 @@ class ConsistentFakeEmbeddings(FakeEmbeddings):
|
||||
"""Return consistent embeddings for the text, if seen before, or a constant
|
||||
one if the text is unknown."""
|
||||
return self.embed_documents([text])[0]
|
||||
|
||||
|
||||
class FakeLLM(LLM):
|
||||
"""Fake LLM wrapper for testing purposes."""
|
||||
|
||||
queries: Optional[Mapping] = None
|
||||
sequential_responses: Optional[bool] = False
|
||||
response_index: int = 0
|
||||
|
||||
@validator("queries", always=True)
|
||||
def check_queries_required(
|
||||
cls, queries: Optional[Mapping], values: Mapping[str, Any]
|
||||
) -> Optional[Mapping]:
|
||||
if values.get("sequential_response") and not queries:
|
||||
raise ValueError(
|
||||
"queries is required when sequential_response is set to True"
|
||||
)
|
||||
return queries
|
||||
|
||||
def get_num_tokens(self, text: str) -> int:
|
||||
"""Return number of tokens."""
|
||||
return len(text.split())
|
||||
|
||||
@property
|
||||
def _llm_type(self) -> str:
|
||||
"""Return type of llm."""
|
||||
return "fake"
|
||||
|
||||
def _call(
|
||||
self,
|
||||
prompt: str,
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> str:
|
||||
if self.sequential_responses:
|
||||
return self._get_next_response_in_sequence
|
||||
if self.queries is not None:
|
||||
return self.queries[prompt]
|
||||
if stop is None:
|
||||
return "foo"
|
||||
else:
|
||||
return "bar"
|
||||
|
||||
@property
|
||||
def _identifying_params(self) -> Dict[str, Any]:
|
||||
return {}
|
||||
|
||||
@property
|
||||
def _get_next_response_in_sequence(self) -> str:
|
||||
queries = cast(Mapping, self.queries)
|
||||
response = queries[list(queries.keys())[self.response_index]]
|
||||
self.response_index = self.response_index + 1
|
||||
return response
|
||||
|
||||
Loading…
Reference in New Issue
Block a user