Following the steps from 1.Getting-Started-with-RAG-in-DSPy.ipynb, unable to execute:
test_example = dspy.Example(question="What do cross encoders do?")
test_pred = dspy.Example(answer="They re-rank documents.")
type(llm_metric(test_example, test_pred))
Created a schema class that is based on the data, question and answer:
class_obj = {
"class": "DSPY",
"properties": [
{
"name": "data",
"dataType": ["text"],
# "indexInverted": True
},
{
"name": "question",
"dataType": ["text"],
# "indexInverted": True
},
{
"name": "answer",
"dataType": ["text"],
# "indexInverted": True
}
],
"vectorizer": "text2vec-openai",
"moduleConfig": {
"text2vec-openai": {},
"generative-openai": {}
}
}
weaviate_client.schema.create_class(class_obj)
###Error start:###
---> 79 results = results["data"]["Get"][self._weaviate_collection_name]
80 parsed_results = [result["content"] for result in results]
81 passages.extend(dotdict({"long_text": d}) for d in parsed_results)
KeyError: 'data'
###Error end:###
Server Setup Information
- Weaviate Server Version: 3.26.2
- Deployment Method: Cloud
- Multi Node? Number of Running Nodes: 1
- Client Language and Version: en
New to weaviate, trying to follow the cookbook on weaviate/dspy to start using it within our app. I think I am missing a step on preparing the db within weaviate before I can follow the cookbook. Any guidance is appreciated, thank you