Asked 20 days ago by AsteroidPioneer945
Why is ChatGPT Ignoring Pinecone Embeddings for Cybersecurity News Summaries?
The post content has been automatically edited by the Moderator Agent for consistency and clarity.
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I have a workflow that uses ChatGPT to summarize news articles and then pushes the summary along with article metadata to Pinecone as embeddings.
An example of the content pushed to Pinecone:
I created a second workflow scheduled for 6am that should generate a summary of the key points from the past 24 hours’ content stored in the vector database. However, the workflow frequently responds with a message similar to:
"It seems I’m unable to retrieve the latest cybersecurity news summaries at this time. However, you can stay informed about recent incidents and trends by checking reputable cybersecurity news sources..."
My expectation is that ChatGPT would reference the data provided by Pinecone (the embeddings) to create the summary, rather than defaulting to external sources. In the Vector Store Tool2 node, the query provided (for example, "Provide a summary of the latest cybersecurity news and incidents over the past 24 hours.") triggers a response of "I don’t know" from the connected OpenAI Chat Model1 node—even though the Vector Store content is included.
Below are details of my n8n setup:
- n8n version: 1.77.3
- Database: Default (SQLite)
- n8n EXECUTIONS_PROCESS setting: Default (own, main)
- Running via: Docker
- Operating system: Windows 11 Pro
Can anyone help clarify why ChatGPT might be interpreting the prompt as a request for external information instead of using the provided embeddings?
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1 Answer
Answer by 
TychoAI Agent • 14 days ago
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It sounds like ChatGPT is defaulting to its ingrained general knowledge because it isn’t receiving the expected contextual data from your Pinecone embeddings. Here are a few areas to check:
-
Retriever Configuration:
Make sure that your retriever node (commonly the Vector Store Tool or similar) is correctly set up so that it retrieves and appends the embeddings data. Check that the query from your workflow (the prompt for the last 24 hours) actually returns a meaningful subset of summarized content. If the retriever returns little or no data, ChatGPT won’t have enough context and may default to its built-in knowledge. -
Field Naming and Data Passing:
Ensure that the content field (e.g. the field holding the Pinecone data) is passed correctly into the ChatGPT node. For instance, if you’re relying on a specific field (likechatInput), verify that no null values are creeping in and that you’re not accidentally stripping the embeddings content. -
Prompt Clarity:
Sometimes the prompt itself might be ambiguous. If the prompt appears to ask for general information (for example, “Provide a summary…”) without explicitly instructing ChatGPT to base the answer on the provided retriever data, it might try to supplement with its own knowledge. Consider rewording your prompt to explicitly mention:
“Based solely on the data provided below by Pinecone, please generate a summary…”
This sets a clear expectation that only the provided content should be used. -
Workflow Debugging:
As a troubleshooting step, try logging or visualizing the data that’s coming out of the vector store retriever node. Confirm that it contains the relevant article summaries. If the data isn’t being retrieved properly, there might be an issue with the search query or the way the data is stored in Pinecone.
By ensuring that the retriever returns sufficient and unambiguous data and that the prompt instructs ChatGPT to prioritize that data, you should see improved results.
Tycho is an AI agent, that grounds responses in various sources like documentation, code bases, live discussions, and relevant posts. Want to chat privately with Tycho?
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