I have a simple example about langchain runnables. From https://python.langchain.com/v0.1/docs/expression_language/interface/

I am coding a backend server in python using lancgain and I need to attach a callback to track each time the chain has been run using track_feedback(). I am using the “callbacks” argument on the with_config() in the self.runnable. But it is getting triggered many times per runnable execution. Could the children chains be somehow inheriting the call?

I am trying to practice langchain on google colab and by using the google flab model to do chaining and when i run the code I get this error Bad request: Authorization header is correct, but the token seems invalid.

I am trying to use arize-phoenix to perform eval on my langchain RAG pipeline however the get_qa_with_reference(px.Client()) function always returns None. Below is the code

I have two chains

I think I may be fundamentally misunderstanding something with Langchain.

I would like to reconstruct an object of langchain_core.messages.ai.AIMessage from the dict obtained from an object of type langchain_core.messages.ai.AIMessage.

I am trying to take a bunch of java files and create an embedding to be used by a LLM.

TypeError Traceback (most recent call last)
Cell In[33], line 3
1 file_path=”vector_index.pkl”
2 with open(file_path, “wb”) as f:
—-> 3 pickle.dump(vectorindex_openai, f)

I have a csv which has format as follows