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Vishwas
Offline, last seen 4 months ago
Joined September 25, 2024
Hello guys, When I execute this piece of code
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from llama_index.core.postprocessor import SentenceTransformerRerank
rerank = SentenceTransformerRerank(
            model="cross-encoder/ms-marco-MiniLM-L-2-v2", top_n=3
        )


Then this error will occur.
ImportError: ('Cannot import sentence-transformers or torch package,', 'please pip install torch sentence-transformers')

Even though I have already installed these packages properly, the error still occurs.
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Vishwas
·

TTS

Hello guys,
Is it possible to voice chat with OpenAI (any gpt model) without converting speech-to-text or text-to-speech?
I was thinking of sending a prompt in audio format and having GPT also give a response in audio format.
3 comments
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Vishwas
·

Hi, @Logan M

Hi,
Can we use VectorStore in the llama_index Ollama (LLaMA 3 model)? using Qdrant
My task is to chat with my own document, which is uploaded on the Quadrant server, chat using Ollama (LLaMA 3 model).
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Hello, instead of SentenceTransformerRerank, which node postprocessor is the best?
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Hello guys, @Logan M , @WhiteFang_Jr
I am facing some issue with torch module in llama-index.
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ImportError: libcufft.so.11: failed to map segment from shared object

how can i fixed this issue
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Hello guys, i used below code.

rerank = SentenceTransformerRerank(
model="cross-encoder/ms-marco-MiniLM-L-2-v2", top_n=3
)
response_synthesizer = get_response_synthesizer(streaming=True)
try:
vector_retriever = self._vector_index.as_retriever(
similarity_top_k=10)
keyword_retriever = self._keyword_index.as_retriever()
custom_retriever = CustomRetriever(
vector_retriever, keyword_retriever)
custom_query_engine = RetrieverQueryEngine(
retriever=custom_retriever, response_synthesizer=response_synthesizer, node_postprocessors=[rerank])
response = custom_query_engine.query(query)
answer = generatorize_response(response.response_gen)

But getting the error :
tenacity.RetryError: RetryError[<Future at 0x7c4b17a48610 state=finished raised APIRemovedInV1>]

in this line of execution(response = custom_query_engine.query(query).
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