mirror of https://github.com/hwchase17/langchain
Merge branch 'master' into add-openai-tts
Robby
4 weeks ago
committed by
GitHub
commit
33269e3cda
@ -0,0 +1,392 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "14d3fd06",
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"metadata": {
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"id": "14d3fd06"
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},
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"source": [
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"# Hybrid Search\n",
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"\n",
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"The standard search in LangChain is done by vector similarity. However, a number of vectorstores implementations (Astra DB, ElasticSearch, Neo4J, AzureSearch, ...) also support more advanced search combining vector similarity search and other search techniques (full-text, BM25, and so on). This is generally referred to as \"Hybrid\" search.\n",
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"\n",
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"**Step 1: Make sure the vectorstore you are using supports hybrid search**\n",
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"\n",
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"At the moment, there is no unified way to perform hybrid search in LangChain. Each vectorstore may have their own way to do it. This is generally exposed as a keyword argument that is passed in during `similarity_search`. By reading the documentation or source code, figure out whether the vectorstore you are using supports hybrid search, and, if so, how to use it.\n",
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"\n",
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"**Step 2: Add that parameter as a configurable field for the chain**\n",
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"\n",
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"This will let you easily call the chain and configure any relevant flags at runtime. See [this documentation](/docs/how_to/configure) for more information on configuration.\n",
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"\n",
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"**Step 3: Call the chain with that configurable field**\n",
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"\n",
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"Now, at runtime you can call this chain with configurable field.\n",
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"\n",
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"## Code Example\n",
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"\n",
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"Let's see a concrete example of what this looks like in code. We will use the Cassandra/CQL interface of Astra DB for this example.\n",
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"\n",
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"Install the following Python package:"
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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": null,
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"id": "c2efe35eea197769",
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||||
"metadata": {
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"id": "c2efe35eea197769",
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||||
"outputId": "527275b4-076e-4b22-945c-e41a59188116"
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},
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"outputs": [],
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"source": [
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"!pip install \"cassio>=0.1.7\""
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]
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},
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{
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"cell_type": "markdown",
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"id": "b4ef96d44341cd84",
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"metadata": {
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"collapsed": false,
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"id": "b4ef96d44341cd84"
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},
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"source": [
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"Get the [connection secrets](https://docs.datastax.com/en/astra/astra-db-vector/get-started/quickstart.html).\n",
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"\n",
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"Initialize cassio:"
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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": null,
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"id": "cb2cef097277c32e",
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"metadata": {
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||||
"id": "cb2cef097277c32e",
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||||
"outputId": "4c3d05a0-319a-44a0-8ec3-0a9c78453132"
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},
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"outputs": [],
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"source": [
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"import cassio\n",
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"\n",
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"cassio.init(\n",
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" database_id=\"Your database ID\",\n",
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" token=\"Your application token\",\n",
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" keyspace=\"Your key space\",\n",
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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": "e1e51444877f45eb",
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"metadata": {
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"collapsed": false,
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"id": "e1e51444877f45eb"
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},
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"source": [
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"Create the Cassandra VectorStore with a standard [index analyzer](https://docs.datastax.com/en/astra/astra-db-vector/cql/use-analyzers-with-cql.html). The index analyzer is needed to enable term matching."
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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": null,
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"id": "7345de3c",
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"metadata": {
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"id": "7345de3c",
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"outputId": "d38bcee0-0134-4ac6-8d35-afcce282481b"
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},
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"outputs": [],
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"source": [
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"from cassio.table.cql import STANDARD_ANALYZER\n",
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"from langchain_community.vectorstores import Cassandra\n",
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"from langchain_openai import OpenAIEmbeddings\n",
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"\n",
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"embeddings = OpenAIEmbeddings()\n",
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"vectorstore = Cassandra(\n",
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" embedding=embeddings,\n",
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" table_name=\"test_hybrid\",\n",
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" body_index_options=[STANDARD_ANALYZER],\n",
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" session=None,\n",
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" keyspace=None,\n",
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")\n",
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"\n",
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"vectorstore.add_texts(\n",
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" [\n",
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" \"In 2023, I visited Paris\",\n",
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" \"In 2022, I visited New York\",\n",
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" \"In 2021, I visited New Orleans\",\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": "markdown",
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"id": "73887f23bbab978c",
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"metadata": {
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"collapsed": false,
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"id": "73887f23bbab978c"
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},
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"source": [
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"If we do a standard similarity search, we get all the documents:"
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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": null,
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"id": "3c2a39fa",
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"metadata": {
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"id": "3c2a39fa",
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"outputId": "5290085b-896c-4c81-9b40-c315331b7009"
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[Document(page_content='In 2022, I visited New York'),\n",
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"Document(page_content='In 2023, I visited Paris'),\n",
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"Document(page_content='In 2021, I visited New Orleans')]"
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]
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},
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"execution_count": null,
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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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"vectorstore.as_retriever().invoke(\"What city did I visit last?\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "78d4c3c79e67d8c3",
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"metadata": {
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"collapsed": false,
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"id": "78d4c3c79e67d8c3"
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},
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"source": [
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"The Astra DB vectorstore `body_search` argument can be used to filter the search on the term `new`."
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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": null,
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"id": "56393baa",
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"metadata": {
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"id": "56393baa",
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"outputId": "d1c939f3-342f-4df4-94a3-d25429b5a25e"
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[Document(page_content='In 2022, I visited New York'),\n",
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"Document(page_content='In 2021, I visited New Orleans')]"
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]
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},
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"execution_count": null,
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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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"vectorstore.as_retriever(search_kwargs={\"body_search\": \"new\"}).invoke(\n",
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" \"What city did I visit last?\"\n",
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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": "88ae97ed",
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"metadata": {
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"id": "88ae97ed"
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},
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"source": [
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"We can now create the chain that we will use to do question-answering over"
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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": null,
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"id": "62707b4f",
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"metadata": {
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"id": "62707b4f"
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},
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"outputs": [],
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"source": [
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"from langchain_core.output_parsers import StrOutputParser\n",
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"from langchain_core.prompts import ChatPromptTemplate\n",
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"from langchain_core.runnables import (\n",
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" ConfigurableField,\n",
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" RunnablePassthrough,\n",
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")\n",
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"from langchain_openai import ChatOpenAI"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b6778ffa",
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"metadata": {
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"id": "b6778ffa"
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},
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"source": [
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"This is basic question-answering chain set up."
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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": null,
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"id": "44a865f6",
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"metadata": {
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"id": "44a865f6"
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},
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"outputs": [],
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"source": [
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"template = \"\"\"Answer the question based only on the following context:\n",
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"{context}\n",
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"Question: {question}\n",
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"\"\"\"\n",
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"prompt = ChatPromptTemplate.from_template(template)\n",
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"\n",
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"model = ChatOpenAI()\n",
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"\n",
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"retriever = vectorstore.as_retriever()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "72125166",
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"metadata": {
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"id": "72125166"
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},
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"source": [
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"Here we mark the retriever as having a configurable field. All vectorstore retrievers have `search_kwargs` as a field. This is just a dictionary, with vectorstore specific fields"
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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": null,
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"id": "babbadff",
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"metadata": {
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"id": "babbadff"
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},
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"outputs": [],
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"source": [
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"configurable_retriever = retriever.configurable_fields(\n",
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" search_kwargs=ConfigurableField(\n",
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" id=\"search_kwargs\",\n",
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" name=\"Search Kwargs\",\n",
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" description=\"The search kwargs to use\",\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": "markdown",
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"id": "2d481b70",
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"metadata": {
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"id": "2d481b70"
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||||
},
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"source": [
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"We can now create the chain using our configurable retriever"
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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": null,
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"id": "210b0446",
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||||
"metadata": {
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"id": "210b0446"
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},
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"outputs": [],
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||||
"source": [
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"chain = (\n",
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" {\"context\": configurable_retriever, \"question\": RunnablePassthrough()}\n",
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" | prompt\n",
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" | model\n",
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" | StrOutputParser()\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": null,
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"id": "a38037b2",
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||||
"metadata": {
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||||
"id": "a38037b2",
|
||||
"outputId": "1ea14996-5965-4a5e-9678-b9c35ce5c6de"
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||||
},
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||||
"outputs": [
|
||||
{
|
||||
"data": {
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"text/plain": [
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"Paris"
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]
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},
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"execution_count": null,
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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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"chain.invoke(\"What city did I visit last?\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7f6458c3",
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||||
"metadata": {
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"id": "7f6458c3"
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||||
},
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"source": [
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"We can now invoke the chain with configurable options. `search_kwargs` is the id of the configurable field. The value is the search kwargs to use for Astra DB."
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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": null,
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"id": "9gYLqBTH8BFz",
|
||||
"metadata": {
|
||||
"id": "9gYLqBTH8BFz",
|
||||
"outputId": "4358a2e6-f306-48f1-dd5c-781ac8a33e89"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"New York"
|
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]
|
||||
},
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"execution_count": null,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
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||||
"source": [
|
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"chain.invoke(\n",
|
||||
" \"What city did I visit last?\",\n",
|
||||
" config={\"configurable\": {\"search_kwargs\": {\"body_search\": \"new\"}}},\n",
|
||||
")"
|
||||
]
|
||||
}
|
||||
],
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"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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||||
},
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"file_extension": ".py",
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"mimetype": "text/x-python",
|
||||
"name": "python",
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||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.1"
|
||||
}
|
||||
},
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||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
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