Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKQA over structured dataSQLRetrieval-augmented generation (RAG)Interacting with APIsChatbotsExtractionSummarizationTaggingWeb scrapingSynthetic data generationGraph queryingDiffbot Graph TransformerArangoDB QA chainNeo4j DB QA chainFalkorDBQAChainHugeGraph QA ChainKuzuQAChainMemgraph QA chainNebulaGraphQAChainNetworkX Graph QAGraphSparqlQAChainNeptune Open Cypher QA ChainGraph queryingMemgraph QA chainOn this pageMemgraph QA chainThis notebook shows how to use LLMs to provide a natural language interface to a Memgraph database. To complete this tutorial, you will need Docker and Python 3.x installed.To follow along with this tutorial, ensure you have a running Memgraph instance. You can download and run it in a local Docker container by executing the following script:docker run \    -it \    -p 7687:7687 \    -p 7444:7444 \    -p 3000:3000 \    -e MEMGRAPH="--bolt-server-name-for-init=Neo4j/" \    -v mg_lib:/var/lib/memgraph memgraph/memgraph-platformYou will need to wait a few seconds for the database to start. If the process completes successfully, you should see something like this:mgconsole X.XConnected to 'memgraph://127.0.0.1:7687'Type :help for shell usageQuit the shell by typing Ctrl-D(eof) or :quitmemgraph>Now you can start playing with Memgraph!Begin by installing and importing all the necessary packages. We'll use the package manager called pip, along with the --user flag, to ensure proper permissions. If you've installed Python 3.4 or a later version, pip is included by default. You can install all the required packages using the following command:pip install langchain openai neo4j gqlalchemy --userYou can either run the provided code blocks in this notebook or use a separate Python file to experiment with Memgraph and LangChain.from langchain.chat_models import ChatOpenAIfrom langchain.chains import GraphCypherQAChainfrom
This notebook shows how to use LLMs to provide a natural language interface to a Memgraph database. To complete this tutorial, you will need Docker and Python 3.x installed.
This notebook shows how to use LLMs to provide a natural language interface to a Memgraph database. To complete this tutorial, you will need Docker and Python 3.x installed. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKQA over structured dataSQLRetrieval-augmented generation (RAG)Interacting with APIsChatbotsExtractionSummarizationTaggingWeb scrapingSynthetic data generationGraph queryingDiffbot Graph TransformerArangoDB QA chainNeo4j DB QA chainFalkorDBQAChainHugeGraph QA ChainKuzuQAChainMemgraph QA chainNebulaGraphQAChainNetworkX Graph QAGraphSparqlQAChainNeptune Open Cypher QA ChainGraph queryingMemgraph QA chainOn this pageMemgraph QA chainThis notebook shows how to use LLMs to provide a natural language interface to a Memgraph database. To complete this tutorial, you will need Docker and Python 3.x installed.To follow along with this tutorial, ensure you have a running Memgraph instance. You can download and run it in a local Docker container by executing the following script:docker run \    -it \    -p 7687:7687 \    -p 7444:7444 \    -p 3000:3000 \    -e MEMGRAPH="--bolt-server-name-for-init=Neo4j/" \    -v mg_lib:/var/lib/memgraph memgraph/memgraph-platformYou will need to wait a few seconds for the database to start. If the process completes successfully, you should see something like this:mgconsole X.XConnected to 'memgraph://127.0.0.1:7687'Type :help for shell usageQuit the shell by typing Ctrl-D(eof) or :quitmemgraph>Now you can start playing with Memgraph!Begin by installing and importing all the necessary packages. We'll use the package manager called pip, along with the --user flag, to ensure proper permissions. If you've installed Python 3.4 or a later version, pip is included by default. You can install all the required packages using the following command:pip install langchain openai neo4j gqlalchemy --userYou can either run the provided code blocks in this notebook or use a separate Python file to experiment with Memgraph and LangChain.from langchain.chat_models import ChatOpenAIfrom langchain.chains import GraphCypherQAChainfrom