how to use llama index for rewriting

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The post asks how to use LlamaIndex for rewriting. The comments indicate that the community members do not have specific information on using LlamaIndex for rewriting, but suggest referring to the LlamaIndex documentation for more details. The comments provide some general information on using LlamaIndex, such as loading documents, constructing an index, and querying the index. The comments also mention that the maximum response size can be set by the user, with an example of setting it to 256 tokens. However, the actual maximum limit may depend on the specific model and configuration being used.

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kkapa.ai

Please stand by @3bs as I search the knowledge base. I normally take about 20 seconds or so. :wave:

kkapa.ai

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Hmm, I don't know enough to give you a confident answer yet. The provided extracts do not contain specific information on how to use LlamaIndex for rewriting. However, you can refer to the LlamaIndex documentation () for more detailed information and usage examples.
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33bs

how to use the llama index for rewriting a full document .

33bs

@kapa.ai how to use the llama index for rewriting a full document .

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

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I'm sorry, but the provided extracts do not contain specific information on how to use LlamaIndex for rewriting a full document. However, LlamaIndex is a data framework designed to ingest, structure, and access data for various LLM applications. It can be used for various tasks such as question-answering, summarization, and as a component in a chatbot.

For more specific use-cases or tutorials, you may want to refer to the LlamaIndex documentation ().
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33bs

@kapa.ai the response is not well organized why i want it to be more organized and in a good shape

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

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I apologize for any confusion. Based on the provided extracts, here's a more organized guide on how to use LlamaIndex:

Load Documents: The first step is to load your data. This data is represented in the form of Document objects. You can load data either manually or through a data loader. Here's an example of how to load data from a directory:

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    from llama_index import SimpleDirectoryReader
    documents = SimpleDirectoryReader('./data').load_data()

You can also construct documents manually:

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    from llama_index import Document
    text_list = [text1, text2, ...]
    documents = [Document(text=t) for t in text_list]

Parse Documents into Nodes: After loading the documents, you can convert them into Node objects. A Node represents a “chunk” of a source Document.

Construct Index: You can construct an index from the Nodes or Documents. LlamaIndex supports different vector stores as the storage backend for VectorStoreIndex.

Query the Index: LlamaIndex gives you the ability to query your data for any downstream LLM use case, whether it’s question-answering, summarization, or a component in a chatbot.

Here's an example of how to retrieve data from a Document Summary Index:

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    from llama_index.indices.document_summary import DocumentSummaryIndexRetriever
    retriever = DocumentSummaryIndexRetriever(doc_summary_index)
    retrieved_nodes = retriever.retrieve("What are the sports teams in Toronto?")

Document Management: Most LlamaIndex index structures allow for insertion, deletion, update, and refresh operations. You can insert a new Document into any index data structure, after building the index initially.

Here's an example of how to insert documents:

kkapa.ai

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    from llama_index import ListIndex, Document
    index = ListIndex([])
    text_chunks = ['text_chunk_1', 'text_chunk_2', 'text_chunk_3']
    doc_chunks = [Document(text=t, id_=f"doc_id_{i}") for i, t in enumerate(text_chunks)]
    for doc_chunk in doc_chunks:
        index.insert(doc_chunk)

Please note that the provided extracts do not contain specific information on how to use LlamaIndex for rewriting a full document. For more specific use-cases or tutorials, you may want to refer to the LlamaIndex documentation ().
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Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
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33bs

@kapa.ai what is the maximum limit of a response could be for llama index

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

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Based on the provided extracts, the maximum limit of a response in LlamaIndex can be set by the user. In the example provided, the number of output tokens is set to 256. Here's the relevant code snippet:

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# set number of output tokens
num_output = 256

This means that the model will generate a maximum of 256 tokens in response to a given prompt. However, this value can be adjusted according to your specific needs.

Please note that the actual maximum limit might depend on the specific model and configuration you are using. For more detailed information, you can refer to the LlamaIndex documentation ().
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Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
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33bs

@kapa.ai i am using vectore index and llm predictor and llama index what is the maximum limit of a response could be

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

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In the provided extracts, when using the MockLLM in LlamaIndex, the max_tokens parameter is used to set a "worst case" prediction for the number of tokens in each LLM response. Here's the relevant code snippet:

Plain Text

from llama_index.llms import MockLLM

llm = MockLLM(max_tokens=256)

In this example, max_tokens is set to 256, meaning each LLM response will contain exactly that number of tokens. If max_tokens is not specified, then it will simply predict back the prompt.

However, the actual maximum limit might depend on the specific model and configuration you are using. For more detailed information, you can refer to the LlamaIndex documentation ().
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Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
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33bs

max_input = 4096
tokens = 512
chunk_size = 600
max_chunk_overlap = 0.2
promptHelper = PromptHelper(max_input, tokens, max_chunk_overlap, chunk_size_limit=chunk_size)

# Define LLm
llmPredictor = LLMPredictor(llm=OpenAI(temperature=0, model_name="text-davinci-003", max_tokens=tokens))

# Load data
docs = SimpleDirectoryReader(path).load_data()

# Create vector index
service_context = ServiceContext.from_defaults(llm_predictor=llmPredictor, prompt_helper=promptHelper)

vectorIndex = GPTVectorStoreIndex.from_documents(documents=docs, service_context=service_context)
vectorIndex.storage_context.persist(persist_dir='index_store')
return vectorIndex
query_engine = index.as_query_engine(response_mode='refine')

here what could be the maximum reponse size

33bs

@kapa.ai max_input = 4096
tokens = 512
chunk_size = 600
max_chunk_overlap = 0.2
promptHelper = PromptHelper(max_input, tokens, max_chunk_overlap, chunk_size_limit=chunk_size)

# Define LLm
llmPredictor = LLMPredictor(llm=OpenAI(temperature=0, model_name="text-davinci-003", max_tokens=tokens))

# Load data
docs = SimpleDirectoryReader(path).load_data()

# Create vector index
service_context = ServiceContext.from_defaults(llm_predictor=llmPredictor, prompt_helper=promptHelper)

vectorIndex = GPTVectorStoreIndex.from_documents(documents=docs, service_context=service_context)
vectorIndex.storage_context.persist(persist_dir='index_store')
return vectorIndex
query_engine = index.as_query_engine(response_mode='refine')

here what could be the maximum reponse size

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

@3bs That's a great question, but unfortunately I am only capable of responding to a maximum of three questions per thread. Don't hesitate to create a new thread for any further questions you have though. I'm here to help!

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how to use llama index for rewriting