From 6a081346617aecc40277f94fc0cb4fbe11843e84 Mon Sep 17 00:00:00 2001 From: Hech <53417823+HeChangHaoGary@users.noreply.github.com> Date: Wed, 6 Mar 2024 07:47:29 +0800 Subject: [PATCH] community[patch], langchain[minor]: Add retriever self_query and score_threshold in DingoDB (#18106) --- docs/api_reference/guide_imports.json | 2 +- .../retrievers/self_query/dingo.ipynb | 496 ++++++++++++++++++ .../langchain_community/vectorstores/dingo.py | 10 +- .../langchain/retrievers/self_query/base.py | 3 + .../langchain/retrievers/self_query/dingo.py | 49 ++ .../retrievers/self_query/test_dingo.py | 99 ++++ 6 files changed, 656 insertions(+), 3 deletions(-) create mode 100644 docs/docs/integrations/retrievers/self_query/dingo.ipynb create mode 100644 libs/langchain/langchain/retrievers/self_query/dingo.py create mode 100644 libs/langchain/tests/unit_tests/retrievers/self_query/test_dingo.py diff --git a/docs/api_reference/guide_imports.json b/docs/api_reference/guide_imports.json index 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"PsychicLoader": {"Psychic": "https://python.langchain.com/docs/integrations/document_loaders/psychic"}, "TencentCOSDirectoryLoader": {"Tencent COS Directory": "https://python.langchain.com/docs/integrations/document_loaders/tencent_cos_directory"}, "GitHubIssuesLoader": {"GitHub": "https://python.langchain.com/docs/integrations/document_loaders/github"}, "UnstructuredOrgModeLoader": {"Org-mode": "https://python.langchain.com/docs/integrations/document_loaders/org_mode"}, "LarkSuiteDocLoader": {"LarkSuite (FeiShu)": "https://python.langchain.com/docs/integrations/document_loaders/larksuite"}, "load_summarize_chain": {"LarkSuite (FeiShu)": "https://python.langchain.com/docs/integrations/document_loaders/larksuite", "LLM Caching integrations": "https://python.langchain.com/docs/integrations/llms/llm_caching", "Set env var OPENAI_API_KEY or load from a .env file": "https://python.langchain.com/docs/use_cases/summarization"}, "IuguLoader": {"Iugu": 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"https://python.langchain.com/docs/integrations/toolkits/vectorstore"}, "VectorStoreInfo": {"Vectorstore": "https://python.langchain.com/docs/integrations/toolkits/vectorstore"}, "create_vectorstore_router_agent": {"Vectorstore": "https://python.langchain.com/docs/integrations/toolkits/vectorstore"}, "VectorStoreRouterToolkit": {"Vectorstore": "https://python.langchain.com/docs/integrations/toolkits/vectorstore"}, "reduce_openapi_spec": {"OpenAPI": "https://python.langchain.com/docs/integrations/toolkits/openapi"}, "RequestsWrapper": {"OpenAPI": "https://python.langchain.com/docs/integrations/toolkits/openapi"}, "create_openapi_agent": {"OpenAPI": "https://python.langchain.com/docs/integrations/toolkits/openapi"}, "OpenAPIToolkit": {"OpenAPI": "https://python.langchain.com/docs/integrations/toolkits/openapi"}, "GitLabToolkit": {"Gitlab": "https://python.langchain.com/docs/integrations/toolkits/gitlab"}, "GitLabAPIWrapper": {"Gitlab": 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"TimeWeightedVectorStoreRetriever": {"Generative Agents in LangChain": "https://python.langchain.com/docs/use_cases/more/agents/agent_simulations/characters"}, "LLMBashChain": {"Bash chain": "https://python.langchain.com/docs/use_cases/more/code_writing/llm_bash"}, "BashOutputParser": {"Bash chain": "https://python.langchain.com/docs/use_cases/more/code_writing/llm_bash"}, "BashProcess": {"Bash chain": "https://python.langchain.com/docs/use_cases/more/code_writing/llm_bash"}, "LLMSymbolicMathChain": {"LLM Symbolic Math ": "https://python.langchain.com/docs/use_cases/more/code_writing/llm_symbolic_math"}, "LLMSummarizationCheckerChain": {"Summarization checker chain": "https://python.langchain.com/docs/use_cases/more/self_check/llm_summarization_checker"}, "LLMCheckerChain": {"Self-checking chain": "https://python.langchain.com/docs/use_cases/more/self_check/llm_checker"}, "ElasticsearchDatabaseChain": {"Set env var OPENAI_API_KEY or load from a .env file": "https://python.langchain.com/docs/use_cases/qa_structured/sql", "Elasticsearch": "https://python.langchain.com/docs/use_cases/qa_structured/integrations/elasticsearch", "SQL": "https://python.langchain.com/docs/use_cases/sql/sql"}, "SQLRecordManager": {"Indexing": "https://python.langchain.com/docs/modules/data_connection/indexing"}, "index": {"Indexing": "https://python.langchain.com/docs/modules/data_connection/indexing"}, "BaseLoader": {"Indexing": "https://python.langchain.com/docs/modules/data_connection/indexing"}, "InMemoryStore": {"Caching": "https://python.langchain.com/docs/modules/data_connection/text_embedding/caching_embeddings", "MultiVector Retriever": "https://python.langchain.com/docs/modules/data_connection/retrievers/multi_vector", "Parent Document Retriever": "https://python.langchain.com/docs/modules/data_connection/retrievers/parent_document_retriever"}, "LocalFileStore": {"Caching": "https://python.langchain.com/docs/modules/data_connection/text_embedding/caching_embeddings"}, "RedisStore": {"Caching": "https://python.langchain.com/docs/modules/data_connection/text_embedding/caching_embeddings"}, "CacheBackedEmbeddings": {"Caching": "https://python.langchain.com/docs/modules/data_connection/text_embedding/caching_embeddings"}, "EnsembleRetriever": {"Ensemble Retriever": "https://python.langchain.com/docs/modules/data_connection/retrievers/ensemble"}, "MultiVectorRetriever": {"MultiVector Retriever": "https://python.langchain.com/docs/modules/data_connection/retrievers/multi_vector"}, "JsonKeyOutputFunctionsParser": {"MultiVector Retriever": "https://python.langchain.com/docs/modules/data_connection/retrievers/multi_vector", "prompt_llm_parser.md": "https://python.langchain.com/docs/expression_language/cookbook/prompt_llm_parser"}, "ParentDocumentRetriever": {"Parent Document Retriever": "https://python.langchain.com/docs/modules/data_connection/retrievers/parent_document_retriever"}, "SentenceTransformersTokenTextSplitter": {"Split by tokens ": "https://python.langchain.com/docs/modules/data_connection/document_transformers/text_splitters/split_by_token"}, "NLTKTextSplitter": {"Split by tokens ": "https://python.langchain.com/docs/modules/data_connection/document_transformers/text_splitters/split_by_token"}, "ChatMessageHistory": {"Message Memory in Agent backed by a database": "https://python.langchain.com/docs/modules/memory/agent_with_memory_in_db"}, "BaseMemory": {"Custom Memory": "https://python.langchain.com/docs/modules/memory/custom_memory"}, "ConversationKGMemory": {"Conversation Knowledge Graph": "https://python.langchain.com/docs/modules/memory/types/kg"}, "ConversationTokenBufferMemory": {"Conversation Token Buffer": "https://python.langchain.com/docs/modules/memory/types/token_buffer"}, "tracing_enabled": {"Multiple callback handlers": "https://python.langchain.com/docs/modules/callbacks/multiple_callbacks"}, "FileCallbackHandler": {"Logging to file": "https://python.langchain.com/docs/modules/callbacks/filecallbackhandler"}, "AsyncCallbackHandler": {"Async callbacks": "https://python.langchain.com/docs/modules/callbacks/async_callbacks"}, "StructuredTool": {"Multi-Input Tools": "https://python.langchain.com/docs/modules/agents/tools/multi_input_tool", "Defining Custom Tools": "https://python.langchain.com/docs/modules/agents/tools/custom_tools"}, "AsyncCallbackManagerForToolRun": {"Defining Custom Tools": "https://python.langchain.com/docs/modules/agents/tools/custom_tools"}, "CallbackManagerForToolRun": {"Defining Custom Tools": "https://python.langchain.com/docs/modules/agents/tools/custom_tools"}, "ToolException": {"Defining Custom Tools": "https://python.langchain.com/docs/modules/agents/tools/custom_tools"}, "format_tool_to_openai_function": {"Tools as OpenAI Functions": "https://python.langchain.com/docs/modules/agents/tools/tools_as_openai_functions"}, "RequestsGetTool": {"Tool Input Schema": "https://python.langchain.com/docs/modules/agents/tools/tool_input_validation"}, "HumanApprovalCallbackHandler": {"Human-in-the-loop Tool Validation": "https://python.langchain.com/docs/modules/agents/tools/human_approval"}, "XMLAgent": {"XML Agent": "https://python.langchain.com/docs/modules/agents/agent_types/xml_agent", "Agents": "https://python.langchain.com/docs/expression_language/cookbook/agent"}, "DocstoreExplorer": {"ReAct document store": "https://python.langchain.com/docs/modules/agents/agent_types/react_docstore"}, "ReadOnlySharedMemory": {"Shared memory across agents and tools": "https://python.langchain.com/docs/modules/agents/how_to/sharedmemory_for_tools"}, "BaseMultiActionAgent": {"Custom multi-action agent": "https://python.langchain.com/docs/modules/agents/how_to/custom_multi_action_agent"}, "FinalStreamingStdOutCallbackHandler": {"Streaming final agent output": "https://python.langchain.com/docs/modules/agents/how_to/streaming_stdout_final_only"}, "LangChainTracer": {"Async API": "https://python.langchain.com/docs/modules/agents/how_to/async_agent"}, "HumanInputChatModel": {"Human input chat model": "https://python.langchain.com/docs/modules/model_io/models/chat/human_input_chat_model"}, "CallbackManagerForLLMRun": {"Custom LLM": "https://python.langchain.com/docs/modules/model_io/models/llms/custom_llm"}, "LLM": {"Custom LLM": "https://python.langchain.com/docs/modules/model_io/models/llms/custom_llm"}, "HumanInputLLM": {"Human input LLM": "https://python.langchain.com/docs/modules/model_io/models/llms/human_input_llm"}, "OutputFixingParser": {"Retry parser": "https://python.langchain.com/docs/modules/model_io/output_parsers/retry"}, "RetryOutputParser": {"Retry parser": "https://python.langchain.com/docs/modules/model_io/output_parsers/retry"}, "RetryWithErrorOutputParser": {"Retry parser": 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"https://python.langchain.com/docs/modules/model_io/prompts/example_selectors/custom_example_selector"}, "NGramOverlapExampleSelector": {"Select by n-gram overlap": "https://python.langchain.com/docs/modules/model_io/prompts/example_selectors/ngram_overlap"}, "FewShotChatMessagePromptTemplate": {"Few-shot examples for chat models": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/few_shot_examples_chat"}, "ChatMessagePromptTemplate": {"Types of `MessagePromptTemplate`": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/msg_prompt_templates"}, "MultiPromptChain": {"Router": "https://python.langchain.com/docs/modules/chains/foundational/router"}, "LLMRouterChain": {"Router": "https://python.langchain.com/docs/modules/chains/foundational/router"}, "RouterOutputParser": {"Router": "https://python.langchain.com/docs/modules/chains/foundational/router"}, "EmbeddingRouterChain": {"Router": 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"https://python.langchain.com/docs/integrations/document_loaders/google_translate"}} diff --git a/docs/docs/integrations/retrievers/self_query/dingo.ipynb b/docs/docs/integrations/retrievers/self_query/dingo.ipynb new file mode 100644 index 00000000000..ee7f8be1b0c --- /dev/null +++ b/docs/docs/integrations/retrievers/self_query/dingo.ipynb @@ -0,0 +1,496 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1446a9ca", + "metadata": {}, + "source": [ + "# DingoDB\n", + "\n", + ">[DingoDB](https://dingodb.readthedocs.io/en/latest/) is a distributed multi-mode vector database, which combines the characteristics of data lakes and vector databases, and can store data of any type and size (Key-Value, PDF, audio, video, etc.). It has real-time low-latency processing capabilities to achieve rapid insight and response, and can efficiently conduct instant analysis and process multi-modal data.\n", + "\n", + "In the walkthrough, we'll demo the `SelfQueryRetriever` with a `DingoDB` vector store." + ] + }, + { + "cell_type": "markdown", + "id": "43c61487", + "metadata": {}, + "source": [ + "## Creating a DingoDB index\n", + "First we'll want to create a `DingoDB` vector store and seed it with some data. We've created a small demo set of documents that contain summaries of movies.\n", + "\n", + "To use DingoDB, you should have a [DingoDB instance up and running](https://github.com/dingodb/dingo-deploy/blob/main/README.md).\n", + "\n", + "**Note:** The self-query retriever requires you to have `lark` package installed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f84c227f", + "metadata": {}, + "outputs": [], + "source": [ + "%pip install --upgrade --quiet dingodb\n", + "# or install latest:\n", + "%pip install --upgrade --quiet git+https://git@github.com/dingodb/pydingo.git" + ] + }, + { + "cell_type": "markdown", + "id": "5fdf04ae", + "metadata": {}, + "source": [ + "We want to use `OpenAIEmbeddings` so we have to get the OpenAI API Key." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "727dce3d", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "OPENAI_API_KEY = \"\"\n", + "\n", + "os.environ[\"OPENAI_API_KEY\"] = OPENAI_API_KEY" + ] + }, + { + "cell_type": "markdown", + "id": "c39cd415", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "cb4a5787", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.schema import Document\n", + "from langchain_community.vectorstores import Dingo\n", + "from langchain_openai import OpenAIEmbeddings\n", + "\n", + "embeddings = OpenAIEmbeddings()\n", + "# create new index\n", + "from dingodb import DingoDB\n", + "\n", + "index_name = \"langchain_demo\"\n", + "\n", + "dingo_client = DingoDB(user=\"\", password=\"\", host=[\"172.30.14.221:13000\"])\n", + "# First, check if our index already exists. If it doesn't, we create it\n", + "if (\n", + " index_name not in dingo_client.get_index()\n", + " and index_name.upper() not in dingo_client.get_index()\n", + "):\n", + " # we create a new index, modify to your own\n", + " dingo_client.create_index(\n", + " index_name=index_name, dimension=1536, metric_type=\"cosine\", auto_id=False\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "bcbe04d9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6 ['1183188982475', '1183189117163', '1183189148854', '1183189172623', '1183189196391', '1183189220159'] [{'year': 1993, 'rating': 7.7, 'genre': '\"action\", \"science fiction\"', 'text': 'A bunch of scientists bring back dinosaurs and mayhem breaks loose'}, {'year': 2010, 'director': 'Christopher Nolan', 'rating': 8.2, 'text': 'Leo DiCaprio gets lost in a dream within a dream within a dream within a ...'}, {'year': 2006, 'director': 'Satoshi Kon', 'rating': 8.6, 'text': 'A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea'}, {'year': 2019, 'director': 'Greta Gerwig', 'rating': 8.3, 'text': 'A bunch of normal-sized women are supremely wholesome and some men pine after them'}, {'year': 1995, 'genre': 'animated', 'text': 'Toys come alive and have a blast doing so'}, {'year': 1979, 'director': 'Andrei Tarkovsky', 'genre': '\"science fiction\", \"thriller\"', 'rating': 9.9, 'text': 'Three men walk into the Zone, three men walk out of the Zone'}]\n", + "http://172.30.14.221:13000/vector/api/DINGO/langchain_demo\n", + "{'Content-Type': 'application/json'}\n", + "[{\"scalarData\": {\"year\": {\"fieldType\": \"INT64\", \"fields\": [{\"data\": 1993}]}, \"rating\": {\"fieldType\": \"DOUBLE\", \"fields\": [{\"data\": 7.7}]}, \"genre\": {\"fieldType\": \"STRING\", \"fields\": [{\"data\": \"\\\"action\\\", \\\"science fiction\\\"\"}]}, \"text\": {\"fieldType\": \"STRING\", \"fields\": 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], + "source": [ + "docs = [\n", + " Document(\n", + " page_content=\"A bunch of scientists bring back dinosaurs and mayhem breaks loose\",\n", + " metadata={\"year\": 1993, \"rating\": 7.7, \"genre\": '\"action\", \"science fiction\"'},\n", + " ),\n", + " Document(\n", + " page_content=\"Leo DiCaprio gets lost in a dream within a dream within a dream within a ...\",\n", + " metadata={\"year\": 2010, \"director\": \"Christopher Nolan\", \"rating\": 8.2},\n", + " ),\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, \"director\": \"Satoshi Kon\", \"rating\": 8.6},\n", + " ),\n", + " Document(\n", + " page_content=\"A bunch of normal-sized women are supremely wholesome and some men pine after them\",\n", + " metadata={\"year\": 2019, \"director\": \"Greta Gerwig\", \"rating\": 8.3},\n", + " ),\n", + " Document(\n", + " page_content=\"Toys come alive and have a blast doing so\",\n", + " metadata={\"year\": 1995, \"genre\": \"animated\"},\n", + " ),\n", + " Document(\n", + " page_content=\"Three men walk into the Zone, three men walk out of the Zone\",\n", + " metadata={\n", + " \"year\": 1979,\n", + " \"director\": \"Andrei Tarkovsky\",\n", + " \"genre\": '\"science fiction\", \"thriller\"',\n", + " \"rating\": 9.9,\n", + " },\n", + " ),\n", + "]\n", + "vectorstore = Dingo.from_documents(\n", + " docs, embeddings, index_name=index_name, client=dingo_client\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "9dbe93f4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dingo_client.get_index()\n", + "dingo_client.delete_index(\"langchain_demo\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "fa0b2921", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "9" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dingo_client.vector_count(\"langchain_demo\")" + ] + }, + { + "cell_type": "markdown", + "id": "2959e5fc", + "metadata": {}, + "source": [] + }, + { + "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": 6, + "id": "86e34dbf", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.chains.query_constructor.base import AttributeInfo\n", + "from langchain.retrievers.self_query.base import SelfQueryRetriever\n", + "from langchain_openai import OpenAI\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\", 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, vectorstore, document_content_description, metadata_field_info, verbose=True\n", + ")" + ] + }, + { + "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": 7, + "id": "38a126e9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "query='dinosaurs' filter=None limit=None\n" + ] + }, + { + "data": { + "text/plain": [ + "[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'id': 1183188982475, 'text': 'A bunch of scientists bring back dinosaurs and mayhem breaks loose', 'score': 0.13397777, 'year': {'value': 1993}, 'rating': {'value': 7.7}, 'genre': '\"action\", \"science fiction\"'}),\n", + " Document(page_content='Toys come alive and have a blast doing so', metadata={'id': 1183189196391, 'text': 'Toys come alive and have a blast doing so', 'score': 0.18994397, 'year': {'value': 1995}, 'genre': 'animated'}),\n", + " Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'id': 1183189220159, 'text': 'Three men walk into the Zone, three men walk out of the Zone', 'score': 0.23288351, 'year': {'value': 1979}, 'director': 'Andrei Tarkovsky', 'rating': {'value': 9.9}, 'genre': '\"science fiction\", \"thriller\"'}),\n", + " Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'id': 1183189148854, 'text': 'A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', 'score': 0.24421334, 'year': {'value': 2006}, 'director': 'Satoshi Kon', 'rating': {'value': 8.6}})]" + ] + }, + "execution_count": 7, + "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, + "id": "fc3f1e6e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "query=' ' filter=Comparison(comparator=, attribute='rating', value=8.5) limit=None\n", + "comparator= attribute='rating' value=8.5\n" + ] + }, + { + "data": { + "text/plain": [ + "[Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'id': 1183189220159, 'text': 'Three men walk into the Zone, three men walk out of the Zone', 'score': 0.25033575, 'year': {'value': 1979}, 'director': 'Andrei Tarkovsky', 'genre': '\"science fiction\", \"thriller\"', 'rating': {'value': 9.9}}),\n", + " Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'id': 1183189148854, 'text': 'A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', 'score': 0.26431882, 'year': {'value': 2006}, 'director': 'Satoshi Kon', 'rating': {'value': 8.6}})]" + ] + }, + "execution_count": 8, + "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", + "comparator= attribute='director' value='Greta Gerwig'\n" + ] + }, + { + "data": { + "text/plain": [ + "[Document(page_content='A bunch of normal-sized women are supremely wholesome and some men pine after them', metadata={'id': 1183189172623, 'text': 'A bunch of normal-sized women are supremely wholesome and some men pine after them', 'score': 0.19482517, 'year': {'value': 2019}, 'director': 'Greta Gerwig', 'rating': {'value': 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": 13, + "id": "f900e40e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "query='science fiction' filter=Comparison(comparator=, attribute='rating', value=8.5) limit=None\n", + "comparator= attribute='rating' value=8.5\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={'id': 1183189148854, 'text': 'A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', 'score': 0.19805312, 'year': {'value': 2006}, 'director': 'Satoshi Kon', 'rating': {'value': 8.6}}),\n", + " Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'id': 1183189220159, 'text': 'Three men walk into the Zone, three men walk out of the Zone', 'score': 0.225586, 'year': {'value': 1979}, 'director': 'Andrei Tarkovsky', 'rating': {'value': 9.9}, 'genre': '\"science fiction\", \"thriller\"'})]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# This example specifies a composite filter\n", + "retriever.get_relevant_documents(\n", + " \"What's a highly rated (above 8.5) science fiction film?\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "12a51522", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "query='toys' filter=Operation(operator=, arguments=[Operation(operator=, arguments=[Comparison(comparator=, attribute='year', value=1990), Comparison(comparator=, attribute='year', value=2005)]), Comparison(comparator=, attribute='genre', value='animated')]) limit=None\n", + "operator= arguments=[Operation(operator=, arguments=[Comparison(comparator=, attribute='year', value=1990), Comparison(comparator=, attribute='year', value=2005)]), Comparison(comparator=, attribute='genre', value='animated')]\n" + ] + }, + { + "data": { + "text/plain": [ + "[Document(page_content='Toys come alive and have a blast doing so', metadata={'id': 1183189196391, 'text': 'Toys come alive and have a blast doing so', 'score': 0.133829, 'year': {'value': 1995}, 'genre': 'animated'})]" + ] + }, + "execution_count": 12, + "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 toys, and preferably is animated\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "6fe7536c", + "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": 14, + "id": "3a2937c2", + "metadata": {}, + "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": 15, + "id": "83d233aa", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "query='dinosaurs' filter=None limit=2\n" + ] + }, + { + "data": { + "text/plain": [ + "[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'id': 1183188982475, 'text': 'A bunch of scientists bring back dinosaurs and mayhem breaks loose', 'score': 0.13394928, 'year': {'value': 1993}, 'rating': {'value': 7.7}, 'genre': '\"action\", \"science fiction\"'}),\n", + " Document(page_content='Toys come alive and have a blast doing so', metadata={'id': 1183189196391, 'text': 'Toys come alive and have a blast doing so', 'score': 0.1899159, 'year': {'value': 1995}, 'genre': 'animated'})]" + ] + }, + "execution_count": 15, + "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": "3.10.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/libs/community/langchain_community/vectorstores/dingo.py b/libs/community/langchain_community/vectorstores/dingo.py index 57a61982222..cf6e5b39f94 100644 --- a/libs/community/langchain_community/vectorstores/dingo.py +++ b/libs/community/langchain_community/vectorstores/dingo.py @@ -145,7 +145,7 @@ class Dingo(VectorStore): List of Documents most similar to the query and score for each """ docs_and_scores = self.similarity_search_with_score( - query, k=k, search_params=search_params + query, k=k, search_params=search_params, **kwargs ) return [doc for doc, _ in docs_and_scores] @@ -177,9 +177,15 @@ class Dingo(VectorStore): return [] for res in results[0]["vectorWithDistances"]: + score = res["distance"] + if ( + "score_threshold" in kwargs + and kwargs.get("score_threshold") is not None + ): + if score > kwargs.get("score_threshold"): + continue metadatas = res["scalarData"] id = res["id"] - score = res["distance"] text = metadatas[self._text_key]["fields"][0]["data"] metadata = {"id": id, "text": text, "score": score} for meta_key in metadatas.keys(): diff --git a/libs/langchain/langchain/retrievers/self_query/base.py b/libs/langchain/langchain/retrievers/self_query/base.py index e5819a64a1a..dbc53637fc4 100644 --- a/libs/langchain/langchain/retrievers/self_query/base.py +++ b/libs/langchain/langchain/retrievers/self_query/base.py @@ -7,6 +7,7 @@ from langchain_community.vectorstores import ( Chroma, DashVector, DeepLake, + Dingo, ElasticsearchStore, Milvus, MongoDBAtlasVectorSearch, @@ -39,6 +40,7 @@ from langchain.retrievers.self_query.astradb import AstraDBTranslator 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.dingo import DingoDBTranslator from langchain.retrievers.self_query.elasticsearch import ElasticsearchTranslator from langchain.retrievers.self_query.milvus import MilvusTranslator from langchain.retrievers.self_query.mongodb_atlas import MongoDBAtlasTranslator @@ -65,6 +67,7 @@ def _get_builtin_translator(vectorstore: VectorStore) -> Visitor: Pinecone: PineconeTranslator, Chroma: ChromaTranslator, DashVector: DashvectorTranslator, + Dingo: DingoDBTranslator, Weaviate: WeaviateTranslator, Vectara: VectaraTranslator, Qdrant: QdrantTranslator, diff --git a/libs/langchain/langchain/retrievers/self_query/dingo.py b/libs/langchain/langchain/retrievers/self_query/dingo.py new file mode 100644 index 00000000000..76e9ef862d3 --- /dev/null +++ b/libs/langchain/langchain/retrievers/self_query/dingo.py @@ -0,0 +1,49 @@ +from typing import Tuple, Union + +from langchain.chains.query_constructor.ir import ( + Comparator, + Comparison, + Operation, + Operator, + StructuredQuery, + Visitor, +) + + +class DingoDBTranslator(Visitor): + """Translate `DingoDB` internal query language elements to valid filters.""" + + allowed_comparators = ( + Comparator.EQ, + Comparator.NE, + Comparator.LT, + Comparator.LTE, + Comparator.GT, + Comparator.GTE, + ) + """Subset of allowed logical comparators.""" + allowed_operators = (Operator.AND, Operator.OR) + """Subset of allowed logical operators.""" + + def _format_func(self, func: Union[Operator, Comparator]) -> str: + self._validate_func(func) + return f"${func.value}" + + def visit_operation(self, operation: Operation) -> Operation: + return operation + + def visit_comparison(self, comparison: Comparison) -> Comparison: + return comparison + + def visit_structured_query( + self, structured_query: StructuredQuery + ) -> Tuple[str, dict]: + if structured_query.filter is None: + kwargs = {} + else: + kwargs = { + "search_params": { + "langchain_expr": structured_query.filter.accept(self) + } + } + return structured_query.query, kwargs diff --git a/libs/langchain/tests/unit_tests/retrievers/self_query/test_dingo.py b/libs/langchain/tests/unit_tests/retrievers/self_query/test_dingo.py new file mode 100644 index 00000000000..fa6e52b37fa --- /dev/null +++ b/libs/langchain/tests/unit_tests/retrievers/self_query/test_dingo.py @@ -0,0 +1,99 @@ +from typing import Dict, Tuple + +from langchain.chains.query_constructor.ir import ( + Comparator, + Comparison, + Operation, + Operator, + StructuredQuery, +) +from langchain.retrievers.self_query.dingo import DingoDBTranslator + +DEFAULT_TRANSLATOR = DingoDBTranslator() + + +def test_visit_comparison() -> None: + comp = Comparison(comparator=Comparator.LT, attribute="foo", value=["1", "2"]) + expected = Comparison(comparator=Comparator.LT, attribute="foo", value=["1", "2"]) + actual = DEFAULT_TRANSLATOR.visit_comparison(comp) + assert expected == actual + + +def test_visit_operation() -> None: + op = Operation( + operator=Operator.AND, + arguments=[ + Comparison(comparator=Comparator.LT, attribute="foo", value=2), + Comparison(comparator=Comparator.EQ, attribute="bar", value="baz"), + ], + ) + expected = Operation( + operator=Operator.AND, + arguments=[ + Comparison(comparator=Comparator.LT, attribute="foo", value=2), + Comparison(comparator=Comparator.EQ, attribute="bar", value="baz"), + ], + ) + actual = DEFAULT_TRANSLATOR.visit_operation(op) + assert expected == actual + + +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=["1", "2"]) + structured_query = StructuredQuery( + query=query, + filter=comp, + ) + expected = ( + query, + { + "search_params": { + "langchain_expr": Comparison( + comparator=Comparator.LT, attribute="foo", value=["1", "2"] + ) + } + }, + ) + 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"), + ], + ) + structured_query = StructuredQuery( + query=query, + filter=op, + ) + expected = ( + query, + { + "search_params": { + "langchain_expr": Operation( + operator=Operator.AND, + arguments=[ + Comparison(comparator=Comparator.LT, attribute="foo", value=2), + Comparison( + comparator=Comparator.EQ, attribute="bar", value="baz" + ), + ], + ) + } + }, + ) + + actual = DEFAULT_TRANSLATOR.visit_structured_query(structured_query) + assert expected == actual