TypeError: cannot pickle '_thread.RLock' object #108
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Hi @david101-hunter, the error that you see happens because the FAISS vector store object cannot be serialized using Pickle. This change might have happened recently or could arise from the new embedding class you pass to the object. BackgroundZenML uses the concept of materializers to save (serialize) and load (deserialize) objects from artifact stores. This is important because steps in a pipeline might need inputs from other steps which might be running in different environments. ZenML takes the output of a step, saves it using the logic from the compatible materializer and then loads it in whatever step needs it. Custom MaterializerBy default, if you don't specify a materializer for an output of the step, ZenML chooses to use pickle to serialize the object. This is not recommended for production use cases (see Is Pickle safe?). In this case, pickle can't serialize a part of the object which means you should create a custom materializer. In other words, you have to tell ZenML how to save and load an object of type FAISS. This way, whenever a FAISS object is encountered, ZenML will use your logic to interact with that object. Help on saving FAISS objectsYou can read this section here on FAISS docs on how to best serialize their objects. https://python.langchain.com/v0.1/docs/integrations/vectorstores/faiss/#saving-and-loading You can read the ZenML docs on how to create a custom materializer using the logic above. ContributingYou can create this materializer for this project yourself and if it works, feel free to contribute it to this project and to the ZenML OSS repo. Let me know if anything is unclear. |
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Thanks for your explanation. I'm starting to try with the information that you provide. |
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@david101-hunter what version of langchain are you using? |
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@strickvl I use version langchain: 0.1.20 |
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This project, e.g., has the version pinned to |
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I used |
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Maybe @wjayesh knows more about the original set of requirements, but I just know that higher versions of |
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@wjayesh can you provide full requirements? |
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I'm able to get the requirements file to resolve as follows: Everything gets resolved correctly and without any errors, using the 0.0.325 version of langchain. |
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Thanks. What can I do after I run this pipeline?. Maybe that's a stupid question, but I'm curious about it |
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@david101-hunter
In short, what happens after the pipeline is run, is you get an agent that you can release to whatever system needs it. In our case, it could act like a support rep but you can design it in any other way. |
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Thanks
Do ZenML official document provide tutorial about it? or you can provide some key words? |
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You can use this page here to understand how to consume artifacts from ZenML https://docs.zenml.io/user-guide/starter-guide/manage-artifacts#consuming-artifacts-produced-by-other-pipelines once you have the agent object, you can just create a web server using a framework of your choice and create a function at some path that uses this object and runs the input query on it. |
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@wjayesh I don't quite understand. I loaded agent object like. How FastAPI will work with this information? |
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@wjayesh I need your help |
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@wjayesh now, I still have not used this agent. I tried method: ask(), ... It's not wokring
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I am sorry. the function should be |
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@wjayesh , OllamaEndpointNotFoundError: Ollama call failed with status code 404. Maybe your model is not found and you should This ís locally model. I don't set authenticaiton. why it raise 404. I use docker to build gemma:2b from ollama
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@wjayesh Can you say something? Please. |
I'm follow this tutorials: creating-a-production-ready-llm-powered-agent-with-zenml
I have changed
Here is full logs
How can I fix?
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