query_engine = PodClip_index.as_query_engine()
response = query_engine.query("What is Weaviate?")HuggingFaceLLM does not require an OpenAI key for its operation. However, if you're using a VectorStoreIndex or any other component that relies on OpenAI's models for generating embeddings or other operations, you would need to provide the OpenAI key.import os os.environ["OPENAI_API_KEY"] = "your_openai_api_key"
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:**********
Trace: query
|_query -> 0.783593 seconds
|_retrieve -> 0.782767 seconds
|_embedding -> 0.744169 seconds
|_synthesize -> 0.000688 seconds
**********vector_store = WeaviateVectorStore(weaviate_client=client, class_name="PodClips")
query_wrapper_prompt = SimpleInputPrompt(
"Below is an instruction that describes a task. "
"Write a response that appropriately completes the request.\n\n"
"### Instruction:\n{query_str}\n\n### Response:"
)
llm = HuggingFaceLLM(
context_window=2048,
max_new_tokens=256,
generate_kwargs={"temperature": 0.25, "do_sample": False},
query_wrapper_prompt=query_wrapper_prompt,
tokenizer_name="Writer/camel-5b-hf",
model_name="Writer/camel-5b-hf",
device_map="auto",
tokenizer_kwargs={"max_length": 2048},
# uncomment this if using CUDA to reduce memory usage
model_kwargs={"torch_dtype": torch.float16}
)
PodClip_index = VectorStoreIndex.from_vector_store(vector_store=vector_store, service_context=service_context)
query_engine = PodClip_index.as_query_engine()retrieve step, specifically in the embedding sub-step. This suggests that the process of generating embeddings for your data is taking a significant amount of time.WeaviateVectorStore for your VectorStoreIndex, it's possible that the delay is due to the interaction with the Weaviate client. You might want to check the connection to your Weaviate instance and ensure that it's functioning correctly and efficiently.HuggingFaceLLM for your language model. If the embedding step involves generating embeddings using this model, the delay could be due to the model's complexity or the size of the data it's processing. You might want to check the configuration of your HuggingFaceLLM and ensure that it's optimized for your use case.LlamaDebugHandler as shown in the Sub Question Query Engine example () from the extracts. This will print the trace of the sub-questions captured by the SUB_QUESTION callback event type, which might provide more insight into what's happening during the retrieve step.# Using the LlamaDebugHandler to print the trace of the sub questions
# captured by the SUB_QUESTION callback event type
llama_debug = LlamaDebugHandler(print_trace_on_end=True)
callback_manager = CallbackManager([llama_debug])
service_context = ServiceContext.from_defaults(callback_manager=callback_manager)
query_engine = SubQuestionQueryEngine.from_defaults(
query_engine_tools=query_engine_tools,
service_context=service_context,
use_async=False,
)Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:HuggingFaceLM object is being used in a context where a predict method is expected, but it doesn't have this method. HuggingFaceLLM is used, which is a different class from HuggingFaceLM. The HuggingFaceLLM class is used to define a language model for the LlamaIndex system. from llama_index.llms import HuggingFaceLLM
llm = HuggingFaceLLM(
context_window=4096,
max_new_tokens=256,
generate_kwargs={"temperature": 0.7, "do_sample": False},
system_prompt=system_prompt,
query_wrapper_prompt=query_wrapper_prompt,
tokenizer_name="StabilityAI/stablelm-tuned-alpha-3b",
model_name="StabilityAI/stablelm-tuned-alpha-3b",
device_map="auto",
stopping_ids=[50278, 50279, 50277, 1, 0],
tokenizer_kwargs={"max_length": 4096},
# uncomment this if using CUDA to reduce memory usage
# model_kwargs={"torch_dtype": torch.float16}
)HuggingFaceLLM instead of HuggingFaceLM) and that it's being used correctly in the context of the SubQuestionQueryEngine.Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:llm = HuggingFaceLLM(
context_window=2048,
max_new_tokens=256,
generate_kwargs={"temperature": 0.25, "do_sample": False},
query_wrapper_prompt=query_wrapper_prompt,
tokenizer_name="Writer/camel-5b-hf",
model_name="Writer/camel-5b-hf",
device_map="auto",
tokenizer_kwargs={"max_length": 2048},
# uncomment this if using CUDA to reduce memory usage
model_kwargs={"torch_dtype": torch.float16}
)predict function in the docs, is there another way to connect this to the query_engine?HuggingFaceLLM class in LlamaIndex is designed to be used as a language model for generating responses to queries. It doesn't have a predict method, but it is used in the context of a ServiceContext and a VectorStoreIndex or ListIndex, which handle the retrieval and synthesis steps of the query process.HuggingFaceLLM with a VectorStoreIndex based on the extracts:from llama_index import VectorStoreIndex, SimpleDirectoryReader, ServiceContext
from llama_index.llms import HuggingFaceLLM
# Define your HuggingFaceLLM
llm = HuggingFaceLLM(
context_window=2048,
max_new_tokens=256,
generate_kwargs={"temperature": 0.25, "do_sample": False},
query_wrapper_prompt=query_wrapper_prompt,
tokenizer_name="Writer/camel-5b-hf",
model_name="Writer/camel-5b-hf",
device_map="auto",
tokenizer_kwargs={"max_length": 2048},
# uncomment this if using CUDA to reduce memory usage
model_kwargs={"torch_dtype": torch.float16}
)
# Create a ServiceContext with your HuggingFaceLLM
service_context = ServiceContext.from_defaults(llm=llm)
# Load your documents
documents = SimpleDirectoryReader("../../data/paul_graham").load_data()
# Create a VectorStoreIndex with your documents and ServiceContext
index = VectorStoreIndex.from_documents(documents, service_context=service_context)
# Create a query engine from your index
query_engine = index.as_query_engine()query_engine handles the process of querying the index, retrieving relevant documents, and using the HuggingFaceLLM to generate responses. You don't need to call a predict method on the HuggingFaceLLM directly.Note: you can ask me a follow up question by @ mentioning me again :speech_balloon: