Implemented Milvus translator for self-querying (#10162)

- Implemented the MilvusTranslator for self-querying using Milvus vector
store
- Made unit tests to test its functionality
- Documented the Milvus self-querying

docs/extras/modules/data_connection/retrievers/self_query/milvus_self_query.ipynb

@@ -0,0 +1,375 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Self-querying with Milvus\n",
+    "\n",
+    "In the walkthrough we'll demo the `SelfQueryRetriever` with a `Milvus` vector store."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating a Milvus vectorstore\n",
+    "First we'll want to create a Milvus VectorStore and seed it with some data. We've created a small demo set of documents that contain summaries of movies.\n",
+    "\n",
+    "I have used the cloud version of Milvus, thus I need `uri` and `token` as well.\n",
+    "\n",
+    "NOTE: The self-query retriever requires you to have `lark` installed (`pip install lark`). We also need the `pymilvus` package."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip install lark"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip install pymilvus"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We want to use `OpenAIEmbeddings` so we have to get the OpenAI API Key."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "OPENAI_API_KEY = \"Use your OpenAI key:)\"\n",
+    "\n",
+    "os.environ[\"OPENAI_API_KEY\"] = OPENAI_API_KEY"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.schema import Document\n",
+    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain.vectorstores import Milvus\n",
+    "\n",
+    "embeddings = OpenAIEmbeddings()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docs = [\n",
+    "    Document(page_content=\"A bunch of scientists bring back dinosaurs and mayhem breaks loose\", metadata={\"year\": 1993, \"rating\": 7.7, \"genre\": \"action\"}),\n",
+    "    Document(page_content=\"Leo DiCaprio gets lost in a dream within a dream within a dream within a ...\", metadata={\"year\": 2010,\"genre\": \"thriller\", \"rating\": 8.2}),\n",
+    "    Document(page_content=\"A bunch of normal-sized women are supremely wholesome and some men pine after them\", metadata={\"year\": 2019, \"rating\": 8.3, \"genre\": \"drama\"}),\n",
+    "    Document(page_content=\"Three men walk into the Zone, three men walk out of the Zone\", metadata={\"year\": 1979, \"rating\": 9.9, \"genre\": \"science fiction\"}),\n",
+    "    Document(\n",
+    "        page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea',\n",
+    "        metadata={\"year\": 2006, \"genre\": \"thriller\", 'rating': 9.0},\n",
+    "    ),\n",
+    "    Document(page_content=\"Toys come alive and have a blast doing so\", metadata={\"year\": 1995, \"genre\": \"animated\", \"rating\": 9.3 }),\n",
+    "]\n",
+    "\n",
+    "vector_store = Milvus.from_documents(\n",
+    "    docs,\n",
+    "    embedding=embeddings,\n",
+    "    connection_args={\"uri\": 'Use your uri:)', \"token\":'Use your token:)'}\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating our self-querying retriever\n",
+    "Now we can instantiate our retriever. To do this we'll need to provide some information upfront about the metadata fields that our documents support and a short description of the document contents."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.llms import OpenAI\n",
+    "from langchain.retrievers.self_query.base import SelfQueryRetriever\n",
+    "from langchain.chains.query_constructor.base import AttributeInfo\n",
+    "\n",
+    "metadata_field_info = [\n",
+    "    AttributeInfo(\n",
+    "        name=\"genre\",\n",
+    "        description=\"The genre of the movie\",\n",
+    "        type=\"string\",\n",
+    "    ),\n",
+    "    AttributeInfo(\n",
+    "        name=\"year\",\n",
+    "        description=\"The year the movie was released\",\n",
+    "        type=\"integer\",\n",
+    "    ),\n",
+    "    AttributeInfo(\n",
+    "        name=\"rating\", description=\"A 1-10 rating for the movie\", type=\"float\"\n",
+    "    ),\n",
+    "]\n",
+    "document_content_description = \"Brief summary of a movie\"\n",
+    "llm = OpenAI(temperature=0)\n",
+    "retriever = SelfQueryRetriever.from_llm(\n",
+    "    llm, vector_store, document_content_description, metadata_field_info, verbose=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Testing it out\n",
+    "And now we can try actually using our retriever!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "query='dinosaur' filter=None limit=None\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'rating': 7.7, 'genre': 'action'}),\n",
+       " Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'rating': 9.3, 'genre': 'animated'}),\n",
+       " Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'rating': 9.9, 'genre': 'science fiction'}),\n",
+       " Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'year': 2006, 'rating': 9.0, 'genre': 'thriller'})]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# This example only specifies a relevant query\n",
+    "retriever.get_relevant_documents(\"What are some movies about dinosaurs\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "query=' ' filter=Comparison(comparator=<Comparator.GT: 'gt'>, attribute='rating', value=9) limit=None\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'rating': 9.3, 'genre': 'animated'}),\n",
+       " Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'rating': 9.9, 'genre': 'science fiction'})]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# This example specifies a filter\n",
+    "retriever.get_relevant_documents(\"What are some highly rated movies (above 9)?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "query='toys' filter=Comparison(comparator=<Comparator.GT: 'gt'>, attribute='rating', value=9) limit=None\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'rating': 9.3, 'genre': 'animated'}),\n",
+       " Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'rating': 9.9, 'genre': 'science fiction'})]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# This example only specifies a query and a filter\n",
+    "retriever.get_relevant_documents(\"I want to watch a movie about toys rated higher than 9\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "query=' ' filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='thriller'), Comparison(comparator=<Comparator.GTE: 'gte'>, attribute='rating', value=9)]) limit=None\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'year': 2006, 'rating': 9.0, 'genre': 'thriller'})]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# This example specifies a composite filter\n",
+    "retriever.get_relevant_documents(\"What's a highly rated (above or equal 9) thriller film?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "query='dinosaur' filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.GT: 'gt'>, attribute='year', value=1990), Comparison(comparator=<Comparator.LT: 'lt'>, attribute='year', value=2005), Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='action')]) limit=None\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'rating': 7.7, 'genre': 'action'})]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# This example specifies a query and composite filter\n",
+    "retriever.get_relevant_documents(\n",
+    "    \"What's a movie after 1990 but before 2005 that's all about dinosaurs, \\\n",
+    "    and preferably has a lot of action\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Filter k\n",
+    "\n",
+    "We can also use the self query retriever to specify `k`: the number of documents to fetch.\n",
+    "\n",
+    "We can do this by passing `enable_limit=True` to the constructor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "retriever = SelfQueryRetriever.from_llm(\n",
+    "    llm, \n",
+    "    vector_store, \n",
+    "    document_content_description, \n",
+    "    metadata_field_info, \n",
+    "    verbose=True,\n",
+    "    enable_limit=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "query='dinosaur' filter=None limit=2\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'rating': 7.7, 'genre': 'action'}),\n",
+       " Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'rating': 9.3, 'genre': 'animated'})]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# This example only specifies a relevant query\n",
+    "retriever.get_relevant_documents(\"What are two movies about dinosaurs?\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

libs/langchain/langchain/retrievers/self_query/base.py

@@ -12,6 +12,7 @@ from langchain.retrievers.self_query.chroma import ChromaTranslator
 from langchain.retrievers.self_query.dashvector import DashvectorTranslator
 from langchain.retrievers.self_query.deeplake import DeepLakeTranslator
 from langchain.retrievers.self_query.elasticsearch import ElasticsearchTranslator
+from langchain.retrievers.self_query.milvus import MilvusTranslator
 from langchain.retrievers.self_query.myscale import MyScaleTranslator
 from langchain.retrievers.self_query.pinecone import PineconeTranslator
 from langchain.retrievers.self_query.qdrant import QdrantTranslator
@@ -23,6 +24,7 @@ from langchain.vectorstores import (
     DashVector,
     DeepLake,
     ElasticsearchStore,
+    Milvus,
     MyScale,
     Pinecone,
     Qdrant,
@@ -43,6 +45,7 @@ def _get_builtin_translator(vectorstore: VectorStore) -> Visitor:
         MyScale: MyScaleTranslator,
         DeepLake: DeepLakeTranslator,
         ElasticsearchStore: ElasticsearchTranslator,
+        Milvus: MilvusTranslator,
     }
     if vectorstore_cls not in BUILTIN_TRANSLATORS:
         raise ValueError(

libs/langchain/langchain/retrievers/self_query/milvus.py

@@ -0,0 +1,83 @@
+"""Logic for converting internal query language to a valid Milvus query."""
+from typing import Tuple, Union
+
+from langchain.chains.query_constructor.ir import (
+    Comparator,
+    Comparison,
+    Operation,
+    Operator,
+    StructuredQuery,
+    Visitor,
+)
+
+COMPARATOR_TO_BER = {
+    Comparator.EQ: "==",
+    Comparator.GT: ">",
+    Comparator.GTE: ">=",
+    Comparator.LT: "<",
+    Comparator.LTE: "<=",
+}
+
+UNARY_OPERATORS = [Operator.NOT]
+
+
+def process_value(value: Union[int, float, str]) -> str:
+    # required for comparators involving strings
+    if isinstance(value, str):
+        # If the value is already a string, add double quotes
+        return f'"{value}"'
+    else:
+        # If the valueis not a string, convert it to a string without double quotes
+        return str(value)
+
+
+class MilvusTranslator(Visitor):
+    """Translate Milvus internal query language elements to valid filters."""
+
+    """Subset of allowed logical operators."""
+    allowed_operators = [Operator.AND, Operator.NOT, Operator.OR]
+
+    """Subset of allowed logical comparators."""
+    allowed_comparators = [
+        Comparator.EQ,
+        Comparator.GT,
+        Comparator.GTE,
+        Comparator.LT,
+        Comparator.LTE,
+    ]
+
+    def _format_func(self, func: Union[Operator, Comparator]) -> str:
+        self._validate_func(func)
+        value = func.value
+        if isinstance(func, Comparator):
+            value = COMPARATOR_TO_BER[func]
+        return f"{value}"
+
+    def visit_operation(self, operation: Operation) -> str:
+        if operation.operator in UNARY_OPERATORS and len(operation.arguments) == 1:
+            operator = self._format_func(operation.operator)
+            return operator + "(" + operation.arguments[0].accept(self) + ")"
+        elif operation.operator in UNARY_OPERATORS:
+            raise ValueError(
+                f'"{operation.operator.value}" can have only one argument in Milvus'
+            )
+        else:
+            args = [arg.accept(self) for arg in operation.arguments]
+            operator = self._format_func(operation.operator)
+            return "(" + (" " + operator + " ").join(args) + ")"
+
+    def visit_comparison(self, comparison: Comparison) -> str:
+        comparator = self._format_func(comparison.comparator)
+        processed_value = process_value(comparison.value)
+        attribute = comparison.attribute
+
+        return "( " + attribute + " " + comparator + " " + processed_value + " )"
+
+    def visit_structured_query(
+        self, structured_query: StructuredQuery
+    ) -> Tuple[str, dict]:
+        if structured_query.filter is None:
+            kwargs = {}
+        else:
+            kwargs = {"expr": structured_query.filter.accept(self)}
+        return structured_query.query, kwargs

libs/langchain/tests/unit_tests/retrievers/self_query/test_milvus.py

@@ -0,0 +1,116 @@
+from typing import Dict, Tuple
+
+from langchain.chains.query_constructor.ir import (
+    Comparator,
+    Comparison,
+    Operation,
+    Operator,
+    StructuredQuery,
+)
+from langchain.retrievers.self_query.milvus import MilvusTranslator
+
+DEFAULT_TRANSLATOR = MilvusTranslator()
+
+
+def test_visit_comparison() -> None:
+    comp = Comparison(comparator=Comparator.LT, attribute="foo", value=4)
+    expected = "( foo < 4 )"
+    actual = DEFAULT_TRANSLATOR.visit_comparison(comp)
+
+    assert expected == actual
+
+
+def test_visit_operation() -> None:
+    # Non-Unary operator
+
+    op = Operation(
+        operator=Operator.AND,
+        arguments=[
+            Comparison(comparator=Comparator.LT, attribute="foo", value=2),
+            Comparison(comparator=Comparator.EQ, attribute="bar", value="baz"),
+            Comparison(comparator=Comparator.LT, attribute="abc", value="4"),
+        ],
+    )
+
+    expected = '(( foo < 2 ) and ( bar == "baz" ) ' 'and ( abc < "4" ))'
+    actual = DEFAULT_TRANSLATOR.visit_operation(op)
+
+    assert expected == actual
+
+    # Unary operator: normal execution
+    op = Operation(
+        operator=Operator.NOT,
+        arguments=[
+            Comparison(comparator=Comparator.LT, attribute="foo", value=2),
+        ],
+    )
+
+    expected = "not(( foo < 2 ))"
+    actual = DEFAULT_TRANSLATOR.visit_operation(op)
+
+    assert expected == actual
+
+    # Unary operator: error
+    op = Operation(
+        operator=Operator.NOT,
+        arguments=[
+            Comparison(comparator=Comparator.LT, attribute="foo", value=2),
+            Comparison(comparator=Comparator.EQ, attribute="bar", value="baz"),
+            Comparison(comparator=Comparator.LT, attribute="abc", value="4"),
+        ],
+    )
+
+    try:
+        DEFAULT_TRANSLATOR.visit_operation(op)
+    except ValueError as e:
+        assert str(e) == '"not" can have only one argument in Milvus'
+    else:
+        assert False, "Expected exception not raised"  # No exception -> test failed
+
+
+def test_visit_structured_query() -> None:
+    query = "What is the capital of France?"
+    structured_query = StructuredQuery(
+        query=query,
+        filter=None,
+    )
+    expected: Tuple[str, Dict] = (query, {})
+
+    actual = DEFAULT_TRANSLATOR.visit_structured_query(structured_query)
+    assert expected == actual
+
+    comp = Comparison(comparator=Comparator.LT, attribute="foo", value=454)
+    structured_query = StructuredQuery(
+        query=query,
+        filter=comp,
+    )
+
+    expected = (
+        query,
+        {"expr": "( foo < 454 )"},
+    )
+
+    actual = DEFAULT_TRANSLATOR.visit_structured_query(structured_query)
+    assert expected == actual
+
+    op = Operation(
+        operator=Operator.AND,
+        arguments=[
+            Comparison(comparator=Comparator.LT, attribute="foo", value=2),
+            Comparison(comparator=Comparator.EQ, attribute="bar", value="baz"),
+            Comparison(comparator=Comparator.LT, attribute="abc", value=50),
+        ],
+    )
+
+    structured_query = StructuredQuery(
+        query=query,
+        filter=op,
+    )
+
+    expected = (
+        query,
+        {"expr": "(( foo < 2 ) " 'and ( bar == "baz" ) ' "and ( abc < 50 ))"},
+    )
+
+    actual = DEFAULT_TRANSLATOR.visit_structured_query(structured_query)
+    assert expected == actual