ERROR:root:2 validation errors for LLMChatStartEvent messages.0 Input should be a valid dictionary or instance of ChatMessage [type=model_type, input_value=ChatMessage(role=<Message... additional_kwargs=None), input_type=ChatMessage]
messages = [
ChatMessage(
role=MessageRole.SYSTEM,
content=PromptTemplate((
"Você é responsável por reformular e criar uma nova questão a partir de uma questão existente.\n" +
"A questão criada deverá manter a mesma qualidade e relevância da questão original.\n" +
"É necessário reforumular o conteúdo para apresentar variações em estilo, complexidade e contexto.\n" +
"A questão original é a seguinte:\n" +
"{question}\n" +
"As alternativas são:\n" +
"{alternatives}\n"
# "Os comentários do professor são:\n" +
# "{comments}"
)).format(
question=parsed_question_text,
alternatives=alternative_text,
comments=question_comments
)
),
ChatMessage(
role=MessageRole.USER,
content=PromptTemplate((
"Reformule a questão original e crie uma nova questão.\n" +
"Retorne com a nova questão e as alternativas."
))
)
]redis_client: TtlRedis = TtlRedis(
host=os.getenv("REDIS_CLUSTER_ADDRESS", "localhost"),
password=os.getenv("REDIS_CLUSTER_PASSWORD", None),
port=os.getenv("REDIS_CLUSTER_PORT", 6379),
ttl=86400,
)
# Initialize Redis vector store
vector_store = RedisVectorStore(
redis_client=redis_client,
schema=await create_schema(user_id, application_id)
)
index = VectorStoreIndex.from_vector_store(vector_store)
# Check if the index exists; create if it doesn't
if not vector_store.index_exists():
# try loading from s3 since it is not in redis{"error":"Wrong input: Vector inserting error: expected dim: 1536, got 1024"}email_docs = process_emails_sync(filtered_unprocessed_emails, user) docstore = MongoDocumentStore.from_uri(uri=LLAMAINDEX_MONGODB_STORAGE_SRV) parser = SentenceSplitter() nodes = parser.get_nodes_from_documents(my_docs) docstore.add_documents(nodes) Settings.llm = OpenAI(model=ModelType.OPENAI_GPT_4_o_MINI.value) Settings.embed_model = OpenAIEmbedding(api_key=OPENAI_API_KEY) client = qdrant_client.QdrantClient(url=QDRANT_API_URL, api_key=QDRANT_API_TOKEN) vector_store = QdrantVectorStore(client=client, collection_name=LLAMAINDEX_QDRANT_COLLECTION_NAME) index_store = MongoIndexStore.from_uri(uri=LLAMAINDEX_MONGODB_STORAGE_SRV) storage_context = StorageContext.from_defaults(vector_store=vector_store, index_store=index_store, docstore=docstore) index = VectorStoreIndex(nodes, storage_context=storage_context, show_progress=True) index.storage_context.persist()
index_id because a new index is created every time I run the code above. How to pass the index_id to the store so it updates existing index? Please note that I am already using doc_id correctly to ensure upserting of documents. VectorStoreIndex(nodes, storage_context=storage_context, show_progress=True, index_id="<index_id>") but that approach didn't work. try:
# Load appropriate data based on query type
json_data = load_marketing_data() if is_logo_query else load_json_data()
result = await w.run(
query=user_message.content,
list_of_dict=json_data,
llm=llm,
table_name=DEFAULT_TABLE_NAME,
timeout=10
)input() or a websocket and then resume operations. But my use case needs to be asynhronous.summary_index = DocumentSummaryIndex.from_documents(
documents=documents,
transformations=[splitter],
response_synthesizer = response_synthesizer,
extractors=[sentiment_extractor],
)class SentimentExtractor(BaseExtractor):
def __init__(self, me, str):
print("here")
self.me = me
self.str = str
async def aextract(self, nodes: Sequence) -> List[Dict]:
metadata_list = []
for node in nodes:
generated_sentiment = {"sentiment": "Positive"} # Replace with actual LLM call
metadata_list.append(generated_sentiment)
return metadata_listsentiment_extractor = SentimentExtractor(me = "zsdfsadf", str="hello")ValueError: "SentimentExtractor" object has no field "me"