Can you create endless indexes or something that separate a lot of data sets in the db? For a chatbot service where users can create custom bots with own data. Pinecone has only a very limited index quantity. Has mongo a limit?
Our indexes are very generous and capable of complex compound field aggregation. Here is a document outlining the limitations: www.mongodb.com/docs/manual/reference/limits/#indexes
Hi there @MongoDB, I tried to follow the tutorial but got a key error = embedding at line docs = vectorStore.max_marginal_relevance_search(query, K = 1) Is there a fix for this? Look forward to hearing from you. Thanks
short but very helpful tutorial. I am wondering whether it makes sense to delete all documents in the vector store while introducing new documents. Is there a way to add/replace certain documents? or what's the best way to track those documents since they were splitted from some long article or paragraphs?
Using OllamaEmbeddings and getting errorfull error: {'ok': 0.0, 'errmsg': 'PlanExecutor error during aggregation :: caused by :: vector field is indexed with 1536 dimensions but queried with 4096', 'code': 8, 'codeName': 'UnknownError', '$clusterTime': {'clusterTime': Timestamp(1713602616, 8), 'signature': {'hash': b'\xc7\xf0CZ\xe16he\x17k\xd7F\xa474T\xc6G5*', 'keyId': 7300890704606134274}}, 'operationTime': Timestamp(1713602616, 8)} Can you help me on this?
We’d love to help you build your CZcams channel :) lots of potential This video will do better if you start it with “how to” and categorize it as such - just like in the thumbnail you have right now - use that text for the title and watch this take off!
🔗 Written tutorial → mdb.link/ZvwUzcMvKiI-tutorial
🔗 GitHub repository → trymongodb.com/3H7kO3L
How do you connect the chunks created by text splitter to the original docs it came from?
Awesome, thanks a lot! i enjoyed video about Vector Search and this one is a wonderful addition.
Can you create endless indexes or something that separate a lot of data sets in the db? For a chatbot service where users can create custom bots with own data. Pinecone has only a very limited index quantity. Has mongo a limit?
Our indexes are very generous and capable of complex compound field aggregation. Here is a document outlining the limitations: www.mongodb.com/docs/manual/reference/limits/#indexes
Hi there @MongoDB, I tried to follow the tutorial but got a key error = embedding at line
docs = vectorStore.max_marginal_relevance_search(query, K = 1)
Is there a fix for this? Look forward to hearing from you. Thanks
Used this instead:
docs = vectorStore.similarity_search_with_relevance_scores(query, k=3)
short but very helpful tutorial.
I am wondering whether it makes sense to delete all documents in the vector store while introducing new documents.
Is there a way to add/replace certain documents? or what's the best way to track those documents since they were splitted from some long article or paragraphs?
Using OllamaEmbeddings and getting errorfull error: {'ok': 0.0, 'errmsg': 'PlanExecutor error during aggregation :: caused by :: vector field is indexed with 1536 dimensions but queried with 4096', 'code': 8, 'codeName': 'UnknownError', '$clusterTime': {'clusterTime': Timestamp(1713602616, 8), 'signature': {'hash': b'\xc7\xf0CZ\xe16he\x17k\xd7F\xa474T\xc6G5*', 'keyId': 7300890704606134274}}, 'operationTime': Timestamp(1713602616, 8)}
Can you help me on this?
Hi there, I tried to follow the tutorial but got a key error = embedding. Is there a fix for this? Look forward to hearing from you. Thanks
USe this instead:
docs = vectorStore.similarity_search_with_relevance_scores(query, k=3)
@@SahilKavitake Thanks Sahil 👍
Can we do this with mongodb compass?
How is this compared to superduperdb?
can you please do for nodejs connecting atlas -openaiembeddings-langchain
i will keep using chroma thank you
Help do for Nodejs.
🙏🙏❤️
We’d love to help you build your CZcams channel :) lots of potential
This video will do better if you start it with “how to” and categorize it as such - just like in the thumbnail you have right now - use that text for the title and watch this take off!