diff --git a/docs/modules/indexes/retrievers/examples/qdrant_self_query.ipynb b/docs/modules/indexes/retrievers/examples/qdrant_self_query.ipynb new file mode 100644 index 00000000..231e2671 --- /dev/null +++ b/docs/modules/indexes/retrievers/examples/qdrant_self_query.ipynb @@ -0,0 +1,396 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "13afcae7", + "metadata": {}, + "source": [ + "# Self-querying with Qdrant\n", + "\n", + ">[Qdrant](https://qdrant.tech/documentation/) (read: quadrant ) is a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload. `Qdrant` is tailored to extended filtering support. It makes it useful \n", + "\n", + "In the notebook we'll demo the `SelfQueryRetriever` wrapped around a Qdrant vector store. " + ] + }, + { + "cell_type": "markdown", + "id": "68e75fb9", + "metadata": {}, + "source": [ + "## Creating a Qdrant vectorstore\n", + "First we'll want to create a Chroma VectorStore and seed it with some data. We've created a small demo set of documents that contain summaries of movies.\n", + "\n", + "NOTE: The self-query retriever requires you to have `lark` installed (`pip install lark`). We also need the `qdrant-client` package." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "63a8af5b", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "#!pip install lark qdrant-client" + ] + }, + { + "cell_type": "markdown", + "id": "83811610-7df3-4ede-b268-68a6a83ba9e2", + "metadata": {}, + "source": [ + "We want to use `OpenAIEmbeddings` so we have to get the OpenAI API Key." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "dd01b61b-7d32-4a55-85d6-b2d2d4f18840", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# import os\n", + "# import getpass\n", + "\n", + "# os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "cb4a5787", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from langchain.schema import Document\n", + "from langchain.embeddings.openai import OpenAIEmbeddings\n", + "from langchain.vectorstores import Qdrant\n", + "\n", + "embeddings = OpenAIEmbeddings()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "bcbe04d9", + "metadata": { + "tags": [] + }, + "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\": \"science fiction\"}),\n", + " Document(page_content=\"Leo DiCaprio gets lost in a dream within a dream within a dream within a ...\", metadata={\"year\": 2010, \"director\": \"Christopher Nolan\", \"rating\": 8.2}),\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, \"director\": \"Satoshi Kon\", \"rating\": 8.6}),\n", + " Document(page_content=\"A bunch of normal-sized women are supremely wholesome and some men pine after them\", metadata={\"year\": 2019, \"director\": \"Greta Gerwig\", \"rating\": 8.3}),\n", + " Document(page_content=\"Toys come alive and have a blast doing so\", metadata={\"year\": 1995, \"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, \"director\": \"Andrei Tarkovsky\", \"genre\": \"science fiction\", \"rating\": 9.9})\n", + "]\n", + "vectorstore = Qdrant.from_documents(\n", + " docs, \n", + " embeddings, \n", + " location=\":memory:\", # Local mode with in-memory storage only\n", + " collection_name=\"my_documents\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "5ecaab6d", + "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, + "id": "86e34dbf", + "metadata": { + "tags": [] + }, + "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 or list[string]\", \n", + " ),\n", + " AttributeInfo(\n", + " name=\"year\",\n", + " description=\"The year the movie was released\", \n", + " type=\"integer\", \n", + " ),\n", + " AttributeInfo(\n", + " name=\"director\",\n", + " description=\"The name of the movie director\", \n", + " type=\"string\", \n", + " ),\n", + " AttributeInfo(\n", + " name=\"rating\",\n", + " description=\"A 1-10 rating for the movie\",\n", + " type=\"float\"\n", + " ),\n", + "]\n", + "document_content_description = \"Brief summary of a movie\"\n", + "llm = OpenAI(temperature=0)\n", + "retriever = SelfQueryRetriever.from_llm(llm, vectorstore, document_content_description, metadata_field_info, verbose=True)" + ] + }, + { + "cell_type": "markdown", + "id": "ea9df8d4", + "metadata": {}, + "source": [ + "## Testing it out\n", + "And now we can try actually using our retriever!" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "38a126e9", + "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': 'science fiction'}),\n", + " Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, '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, 'director': 'Andrei Tarkovsky', '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, 'director': 'Satoshi Kon', 'rating': 8.6})]" + ] + }, + "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": 7, + "id": "fc3f1e6e", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "query=' ' filter=Comparison(comparator=, attribute='rating', value=8.5) limit=None\n" + ] + }, + { + "data": { + "text/plain": [ + "[Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'rating': 9.9, 'director': 'Andrei Tarkovsky', '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, 'director': 'Satoshi Kon', 'rating': 8.6})]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# This example only specifies a filter\n", + "retriever.get_relevant_documents(\"I want to watch a movie rated higher than 8.5\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "b19d4da0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "query='women' filter=Comparison(comparator=, attribute='director', value='Greta Gerwig') limit=None\n" + ] + }, + { + "data": { + "text/plain": [ + "[Document(page_content='A bunch of normal-sized women are supremely wholesome and some men pine after them', metadata={'year': 2019, 'director': 'Greta Gerwig', 'rating': 8.3})]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# This example specifies a query and a filter\n", + "retriever.get_relevant_documents(\"Has Greta Gerwig directed any movies about women\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "f900e40e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "query=' ' filter=Operation(operator=, arguments=[Comparison(comparator=, attribute='rating', value=8.5), Comparison(comparator=, attribute='genre', value='science fiction')]) limit=None\n" + ] + }, + { + "data": { + "text/plain": [ + "[Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'rating': 9.9, 'director': 'Andrei Tarkovsky', 'genre': 'science fiction'})]" + ] + }, + "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 8.5) science fiction film?\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "12a51522", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "query='toys' filter=Operation(operator=, arguments=[Comparison(comparator=, attribute='year', value=1990), Comparison(comparator=, attribute='year', value=2005), Comparison(comparator=, attribute='genre', value='animated')]) limit=None\n" + ] + }, + { + "data": { + "text/plain": [ + "[Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'})]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# This example specifies a query and composite filter\n", + "retriever.get_relevant_documents(\"What's a movie after 1990 but before 2005 that's all about toys, and preferably is animated\")" + ] + }, + { + "cell_type": "markdown", + "id": "39bd1de1-b9fe-4a98-89da-58d8a7a6ae51", + "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, + "id": "bff36b88-b506-4877-9c63-e5a1a8d78e64", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "retriever = SelfQueryRetriever.from_llm(\n", + " llm, \n", + " vectorstore, \n", + " document_content_description, \n", + " metadata_field_info, \n", + " enable_limit=True,\n", + " verbose=True\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "2758d229-4f97-499c-819f-888acaf8ee10", + "metadata": { + "tags": [] + }, + "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': 'science fiction'}),\n", + " Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, '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 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/modules/indexes/vectorstores/examples/qdrant.ipynb b/docs/modules/indexes/vectorstores/examples/qdrant.ipynb index 45273241..526d08a9 100644 --- a/docs/modules/indexes/vectorstores/examples/qdrant.ipynb +++ b/docs/modules/indexes/vectorstores/examples/qdrant.ipynb @@ -401,17 +401,18 @@ }, { "cell_type": "markdown", + "id": "525e3582", + "metadata": {}, "source": [ "### Metadata filtering\n", "\n", "Qdrant has an [extensive filtering system](https://qdrant.tech/documentation/concepts/filtering/) with rich type support. It is also possible to use the filters in Langchain, by passing an additional param to both the `similarity_search_with_score` and `similarity_search` methods." - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", + "id": "1c2c58dc", + "metadata": {}, "source": [ "```python\n", "from qdrant_client.http import models as rest\n", @@ -419,10 +420,7 @@ "query = \"What did the president say about Ketanji Brown Jackson\"\n", "found_docs = qdrant.similarity_search_with_score(query, filter=rest.Filter(...))\n", "```" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", @@ -683,7 +681,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.11.3" } }, "nbformat": 4, diff --git a/langchain/chains/query_constructor/base.py b/langchain/chains/query_constructor/base.py index 51dc12c6..fd48b86e 100644 --- a/langchain/chains/query_constructor/base.py +++ b/langchain/chains/query_constructor/base.py @@ -33,6 +33,7 @@ class StructuredQueryOutputParser(BaseOutputParser[StructuredQuery]): def parse(self, text: str) -> StructuredQuery: try: expected_keys = ["query", "filter"] + allowed_keys = ["query", "filter", "limit"] parsed = parse_and_check_json_markdown(text, expected_keys) if len(parsed["query"]) == 0: parsed["query"] = " " @@ -40,10 +41,10 @@ class StructuredQueryOutputParser(BaseOutputParser[StructuredQuery]): parsed["filter"] = None else: parsed["filter"] = self.ast_parse(parsed["filter"]) + if not parsed.get("limit"): + parsed.pop("limit", None) return StructuredQuery( - query=parsed["query"], - filter=parsed["filter"], - limit=parsed.get("limit"), + **{k: v for k, v in parsed.items() if k in allowed_keys} ) except Exception as e: raise OutputParserException( diff --git a/langchain/chains/query_constructor/ir.py b/langchain/chains/query_constructor/ir.py index 2aca4280..f131de3a 100644 --- a/langchain/chains/query_constructor/ir.py +++ b/langchain/chains/query_constructor/ir.py @@ -3,7 +3,7 @@ from __future__ import annotations from abc import ABC, abstractmethod from enum import Enum -from typing import Any, List, Optional, Sequence +from typing import Any, List, Optional, Sequence, Union from pydantic import BaseModel @@ -14,6 +14,20 @@ class Visitor(ABC): allowed_comparators: Optional[Sequence[Comparator]] = None allowed_operators: Optional[Sequence[Operator]] = None + def _validate_func(self, func: Union[Operator, Comparator]) -> None: + if isinstance(func, Operator) and self.allowed_operators is not None: + if func not in self.allowed_operators: + raise ValueError( + f"Received disallowed operator {func}. Allowed " + f"comparators are {self.allowed_operators}" + ) + if isinstance(func, Comparator) and self.allowed_comparators is not None: + if func not in self.allowed_comparators: + raise ValueError( + f"Received disallowed comparator {func}. Allowed " + f"comparators are {self.allowed_comparators}" + ) + @abstractmethod def visit_operation(self, operation: Operation) -> Any: """Translate an Operation.""" diff --git a/langchain/retrievers/self_query/base.py b/langchain/retrievers/self_query/base.py index ccd69f6c..6fed65b4 100644 --- a/langchain/retrievers/self_query/base.py +++ b/langchain/retrievers/self_query/base.py @@ -10,23 +10,28 @@ from langchain.chains.query_constructor.ir import StructuredQuery, Visitor from langchain.chains.query_constructor.schema import AttributeInfo from langchain.retrievers.self_query.chroma import ChromaTranslator from langchain.retrievers.self_query.pinecone import PineconeTranslator +from langchain.retrievers.self_query.qdrant import QdrantTranslator from langchain.retrievers.self_query.weaviate import WeaviateTranslator from langchain.schema import BaseRetriever, Document -from langchain.vectorstores import Chroma, Pinecone, VectorStore, Weaviate +from langchain.vectorstores import Chroma, Pinecone, Qdrant, VectorStore, Weaviate -def _get_builtin_translator(vectorstore_cls: Type[VectorStore]) -> Visitor: +def _get_builtin_translator(vectorstore: VectorStore) -> Visitor: """Get the translator class corresponding to the vector store class.""" + vectorstore_cls = vectorstore.__class__ BUILTIN_TRANSLATORS: Dict[Type[VectorStore], Type[Visitor]] = { Pinecone: PineconeTranslator, Chroma: ChromaTranslator, Weaviate: WeaviateTranslator, + Qdrant: QdrantTranslator, } if vectorstore_cls not in BUILTIN_TRANSLATORS: raise ValueError( f"Self query retriever with Vector Store type {vectorstore_cls}" f" not supported." ) + if isinstance(vectorstore, Qdrant): + return QdrantTranslator(metadata_key=vectorstore.metadata_payload_key) return BUILTIN_TRANSLATORS[vectorstore_cls]() @@ -55,9 +60,8 @@ class SelfQueryRetriever(BaseRetriever, BaseModel): def validate_translator(cls, values: Dict) -> Dict: """Validate translator.""" if "structured_query_translator" not in values: - vectorstore_cls = values["vectorstore"].__class__ values["structured_query_translator"] = _get_builtin_translator( - vectorstore_cls + values["vectorstore"] ) return values @@ -102,7 +106,7 @@ class SelfQueryRetriever(BaseRetriever, BaseModel): **kwargs: Any, ) -> "SelfQueryRetriever": if structured_query_translator is None: - structured_query_translator = _get_builtin_translator(vectorstore.__class__) + structured_query_translator = _get_builtin_translator(vectorstore) chain_kwargs = chain_kwargs or {} if "allowed_comparators" not in chain_kwargs: diff --git a/langchain/retrievers/self_query/chroma.py b/langchain/retrievers/self_query/chroma.py index 02457de3..44be8665 100644 --- a/langchain/retrievers/self_query/chroma.py +++ b/langchain/retrievers/self_query/chroma.py @@ -18,18 +18,7 @@ class ChromaTranslator(Visitor): """Subset of allowed logical operators.""" def _format_func(self, func: Union[Operator, Comparator]) -> str: - if isinstance(func, Operator) and self.allowed_operators is not None: - if func not in self.allowed_operators: - raise ValueError( - f"Received disallowed operator {func}. Allowed " - f"comparators are {self.allowed_operators}" - ) - if isinstance(func, Comparator) and self.allowed_comparators is not None: - if func not in self.allowed_comparators: - raise ValueError( - f"Received disallowed comparator {func}. Allowed " - f"comparators are {self.allowed_comparators}" - ) + self._validate_func(func) return f"${func.value}" def visit_operation(self, operation: Operation) -> Dict: diff --git a/langchain/retrievers/self_query/pinecone.py b/langchain/retrievers/self_query/pinecone.py index 53083df0..8daea9c8 100644 --- a/langchain/retrievers/self_query/pinecone.py +++ b/langchain/retrievers/self_query/pinecone.py @@ -18,18 +18,7 @@ class PineconeTranslator(Visitor): """Subset of allowed logical operators.""" def _format_func(self, func: Union[Operator, Comparator]) -> str: - if isinstance(func, Operator) and self.allowed_operators is not None: - if func not in self.allowed_operators: - raise ValueError( - f"Received disallowed operator {func}. Allowed " - f"comparators are {self.allowed_operators}" - ) - if isinstance(func, Comparator) and self.allowed_comparators is not None: - if func not in self.allowed_comparators: - raise ValueError( - f"Received disallowed comparator {func}. Allowed " - f"comparators are {self.allowed_comparators}" - ) + self._validate_func(func) return f"${func.value}" def visit_operation(self, operation: Operation) -> Dict: diff --git a/langchain/retrievers/self_query/qdrant.py b/langchain/retrievers/self_query/qdrant.py new file mode 100644 index 00000000..90bcd8ae --- /dev/null +++ b/langchain/retrievers/self_query/qdrant.py @@ -0,0 +1,66 @@ +"""Logic for converting internal query language to a valid Qdrant query.""" +from __future__ import annotations + +from typing import TYPE_CHECKING, Tuple + +from langchain.chains.query_constructor.ir import ( + Comparator, + Comparison, + Operation, + Operator, + StructuredQuery, + Visitor, +) + +if TYPE_CHECKING: + from qdrant_client.http import models as rest + + +class QdrantTranslator(Visitor): + """Logic for converting internal query language elements to valid filters.""" + + def __init__(self, metadata_key: str): + self.metadata_key = metadata_key + + def visit_operation(self, operation: Operation) -> rest.Filter: + from qdrant_client.http import models as rest + + args = [arg.accept(self) for arg in operation.arguments] + operator = { + Operator.AND: "must", + Operator.OR: "should", + Operator.NOT: "must_not", + }[operation.operator] + return rest.Filter(**{operator: args}) + + def visit_comparison(self, comparison: Comparison) -> rest.FieldCondition: + from qdrant_client.http import models as rest + + self._validate_func(comparison.comparator) + attribute = self.metadata_key + "." + comparison.attribute + if comparison.comparator == Comparator.EQ: + return rest.FieldCondition( + key=attribute, match=rest.MatchValue(value=comparison.value) + ) + kwargs = {comparison.comparator.value: comparison.value} + return rest.FieldCondition(key=attribute, range=rest.Range(**kwargs)) + + def visit_structured_query( + self, structured_query: StructuredQuery + ) -> Tuple[str, dict]: + try: + from qdrant_client.http import models as rest + except ImportError as e: + raise ImportError( + "Cannot import qdrant_client. Please install with `pip install " + "qdrant-client`." + ) from e + + if structured_query.filter is None: + kwargs = {} + else: + filter = structured_query.filter.accept(self) + if isinstance(filter, rest.FieldCondition): + filter = rest.Filter(must=[filter]) + kwargs = {"filter": filter} + return structured_query.query, kwargs diff --git a/langchain/retrievers/self_query/weaviate.py b/langchain/retrievers/self_query/weaviate.py index e1faf7d4..af6a8acc 100644 --- a/langchain/retrievers/self_query/weaviate.py +++ b/langchain/retrievers/self_query/weaviate.py @@ -19,26 +19,12 @@ class WeaviateTranslator(Visitor): allowed_comparators = [Comparator.EQ] - def _map_func(self, func: Union[Operator, Comparator]) -> str: + def _format_func(self, func: Union[Operator, Comparator]) -> str: + self._validate_func(func) # https://weaviate.io/developers/weaviate/api/graphql/filters map_dict = {Operator.AND: "And", Operator.OR: "Or", Comparator.EQ: "Equal"} return map_dict[func] - def _format_func(self, func: Union[Operator, Comparator]) -> str: - if isinstance(func, Operator) and self.allowed_operators is not None: - if func not in self.allowed_operators: - raise ValueError( - f"Received disallowed operator {func}. Allowed " - f"comparators are {self.allowed_operators}" - ) - if isinstance(func, Comparator) and self.allowed_comparators is not None: - if func not in self.allowed_comparators: - raise ValueError( - f"Received disallowed comparator {func}. Allowed " - f"comparators are {self.allowed_comparators}" - ) - return self._map_func(func) - def visit_operation(self, operation: Operation) -> Dict: args = [arg.accept(self) for arg in operation.arguments] return {"operator": self._format_func(operation.operator), "operands": args} diff --git a/langchain/vectorstores/qdrant.py b/langchain/vectorstores/qdrant.py index 6542a6bc..ce6a1b78 100644 --- a/langchain/vectorstores/qdrant.py +++ b/langchain/vectorstores/qdrant.py @@ -218,7 +218,7 @@ class Qdrant(VectorStore): Returns: List of Documents most similar to the query. """ - results = self.similarity_search_with_score(query, k, filter) + results = self.similarity_search_with_score(query, k, filter=filter) return list(map(itemgetter(0), results)) def similarity_search_with_score( @@ -245,7 +245,6 @@ class Qdrant(VectorStore): qdrant_filter = self._qdrant_filter_from_dict(filter) else: qdrant_filter = filter - results = self.client.search( collection_name=self.collection_name, query_vector=self._embed_query(query),