Postgres Chat Memory Node#
Verwenden Sie den Postgres Chat Memory Node, um Postgres als Memory Server zum Speichern des Chatverlaufs zu verwenden.
Auf dieser Seite finden Sie eine Liste der Operationen, die der Postgres Chat Memory Node unterstützt, sowie Links zu weiteren Ressourcen.
Anmeldedaten
Sie finden Authentifizierungsinformationen für diesen Node hier.
Parameter resolution in sub-nodes
Sub-nodes behave differently to other nodes when processing multiple items using an expression.
Most nodes, including root nodes, take any number of items as input, process these items, and output the results. You can use expressions to refer to input items, and the node resolves the expression for each item in turn. For example, given an input of five name values, the expression {{ $json.name }} resolves to each name in turn.
In sub-nodes, the expression always resolves to the first item. For example, given an input of five name values, the expression {{ $json.name }} always resolves to the first name.
Node Parameter#
- Session Key: Geben Sie den Schlüssel ein, der zum Speichern des Speichers in den Workflow-Daten verwendet werden soll.
- Table Name: Geben Sie den Namen der Tabelle ein, in der der Chatverlauf gespeichert werden soll. Das System erstellt die Tabelle, falls sie nicht vorhanden ist.
- Context Window Length: Geben Sie die Anzahl der vorherigen Interaktionen an, die für den Kontext berücksichtigt werden sollen.
Verwandte Ressourcen#
Weitere Informationen zum Dienst finden Sie in der LangChain Postgres Chat Message History Dokumentation.
View Localmind Automate's Advanced AI documentation.
Einzelne Speicherinstanz#
If you add more than one Postgres Chat Memory node to your workflow, all nodes access the same memory instance by default. Be careful when doing destructive actions that override existing memory contents, such as the override all messages operation in the Chat Memory Manager node. If you want more than one memory instance in your workflow, set different session IDs in different memory nodes.
AI glossary#
- completion: Completions are the responses generated by a model like GPT.
- hallucinations: Hallucination in AI is when an LLM (large language model) mistakenly perceives patterns or objects that don't exist.
- vector database: A vector database stores mathematical representations of information. Use with embeddings and retrievers to create a database that your AI can access when answering questions.
- vector store: A vector store, or vector database, stores mathematical representations of information. Use with embeddings and retrievers to create a database that your AI can access when answering questions.