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iam using document summary index for my context chat engine, now my chat chat engine only

iam using document summary index for my context chat engine, now my chat chat engine only answers about summary or it can answer based on orginal document if some information is not captured in summary

43 comments

kkapa.ai

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kkapa.ai

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The Document Summary Index in LlamaIndex is designed to generate a summary of the text in your documents. It stores all of the documents and returns all of them to your query engine. This means that the responses from your chat engine when using a Document Summary Index will be based on the summaries of the documents, not the original documents themselves.

However, the Document Summary Index is built using a response synthesizer, which can be customized. In the default mode of building the index, the response synthesizer is set to "tree_summarize". If you want the chat engine to answer based on the original document, you might need to customize the response synthesizer or use a different type of index that suits your needs.

Here is an example of how to build a Document Summary Index:

Plain Text

# LLM (gpt-3.5-turbo)
chatgpt = OpenAI(temperature=0, model="gpt-3.5-turbo")
splitter = SentenceSplitter(chunk_size=1024)

# default mode of building the index
response_synthesizer = get_response_synthesizer(
    response_mode="tree_summarize", use_async=True
)
doc_summary_index = DocumentSummaryIndex.from_documents(
    city_docs,
    llm=chatgpt,
    transformations=[splitter],
    response_synthesizer=response_synthesizer,
    show_progress=True,
)

(Source: GitHub - jerryjliu/llama_index ())

Remember, the Document Summary Index is best suited to queries where you are trying to generate a summary of the text in your documents. If you need more detailed responses, you might need to use a different type of index or adjust your response synthesizer.

kkapa.ai

(Source: LlamaIndex Documentation - Indexing ())
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TTech explorer

@kapa.ai I want to get some python and linux codes . Which chatengine should I use

kkapa.ai

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kkapa.ai

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Based on the information provided in the extracts, you can use any of the three types of chat engines provided by LlamaIndex to interact with your data. These are:

Query Engines (): These allow you to ask questions over your data and return a response along with reference context retrieved and passed to the LLM.

Chat Engines (): These allow for a conversation with your data, enabling multiple back-and-forth interactions instead of a single question-and-answer.

Agents (): These are automated decision-makers powered by an LLM that interact with the world via a set of tools. Agents can take an arbitrary number of steps to complete a given task, dynamically deciding on the best course of action rather than following pre-determined steps.

However, the specific type of chat engine you should use depends on the nature of your data and the specific use case. If your data consists of Python and Linux codes and you want to ask specific questions about these codes, a Query Engine might be suitable. If you want to have a more interactive conversation about the codes, a Chat Engine might be more appropriate. If you want an automated system to make decisions based on the codes, an Agent might be the best choice.

Here is an example of how to configure a chat engine:

```python
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.llms.openai import OpenAI

llm = OpenAI(model="gpt-4")
data = SimpleDirectoryReader(input_dir="./data/paul_graham/").load_data()
index = VectorStoreIndex.from_documents(data)

kkapa.ai

chat_engine = index.as_chat_engine(chat_mode="best", llm=llm, verbose=True)

response = chat_engine.chat("What are the first programs Paul Graham tried writing?")
print(response)

``
(Source: **GitHub - jerryjliu/llama_index** (<https://github.com/jerryjliu/llama_index/blob/main/docs/examples/chat_engine/chat_engine_best.ipynb>))

Remember to replace the

input_dir with the directory containing your Python and Linux codes, and replace the question in the chat` method with your specific question.
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TTech explorer

@kapa.ai how can I use code llama as code interpreter without any index. I just want to ask a query and get some code

kkapa.ai

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kkapa.ai

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To use Llama as a code interpreter without any index, you can use the OpenAI class provided by LlamaIndex. This class is a wrapper around the OpenAI API and can be used to generate responses to queries directly without the need for an index.

Here is an example of how you can use it:

Plain Text

from llama_index.llms.openai import OpenAI

# Initialize the OpenAI class with the model of your choice
llm = OpenAI(model="gpt-3.5-turbo")

# Ask a query
response = llm.complete("Write a Python function to calculate the factorial of a number.")

# Print the response
print(response.text)

In this example, replace "Write a Python function to calculate the factorial of a number." with your specific query.

Please note that you need to have the OpenAI API key set in your environment variables for this to work. You can do this by running the following line before initializing the OpenAI class:

Plain Text

import os
os.environ['OPENAI_API_KEY'] = "INSERT OPENAI KEY"

Replace "INSERT OPENAI KEY" with your actual OpenAI API key.

(Source: GitHub - jerryjliu/llama_index ())
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TTech explorer

@kapa.ai but I want to use local model without any open ai API key

kkapa.ai

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kkapa.ai

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To use a local model without an OpenAI API key, you can use the Ollama class provided by LlamaIndex. This class allows you to run a local model such as Llama2 locally.

Here is an example of how you can use it:

Plain Text

from llama_index.llms.ollama import Ollama
from llama_index.core import Settings

# Set the local model
Settings.llm = Ollama(model="llama2", request_timeout=60.0)

# Now you can use the local model to generate responses
response = Settings.llm.complete("Write a Python function to calculate the factorial of a number.")

# Print the response
print(response.text)

In this example, replace "Write a Python function to calculate the factorial of a number." with your specific query.

Please note that you need to have Ollama installed and running for this to work. You can install Ollama following the instructions in the Ollama README ().

(Source: LlamaIndex Documentation - Using a local LLM ())
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TTech explorer

@kapa.ai instead ollama can I use llamacpp for code generation ?

kkapa.ai

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kkapa.ai

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Yes, you can use LlamaCPP for code generation. LlamaCPP is a wrapper around the llama-cpp-python library, which allows you to use the Llama2 model locally.

Here is an example of how you can use it:

Plain Text

from llama_index.llms.llama_cpp import LlamaCPP

# Initialize the LlamaCPP class
llm = LlamaCPP(
    model_url=None,  # You can pass in the URL to a GGML model to download it automatically
    model_path=None,  # Optionally, you can set the path to a pre-downloaded model instead of model_url
    temperature=0.1,
    max_new_tokens=256,
    context_window=3900,  # Llama2 has a context window of 4096 tokens, but we set it lower to allow for some wiggle room
    generate_kwargs={},  # kwargs to pass to __call__()
    model_kwargs={"n_gpu_layers": 1},  # kwargs to pass to __init__(), set to at least 1 to use GPU
    verbose=True,
)

# Ask a query
response = llm.complete("Write a Python function to calculate the factorial of a number.")

# Print the response
print(response.text)

In this example, replace "Write a Python function to calculate the factorial of a number." with your specific query.

Please note that you need to have the llama-cpp-python library installed for this to work. You can install it using pip:

Plain Text

!pip install llama-cpp-python

(Source: GitHub - jerryjliu/llama_index ())
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TTech explorer

@kapa.ai but it's not generating code , it's just completing text

kkapa.ai

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

kkapa.ai

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I apologize for the confusion. The complete method of the LlamaCPP class is indeed used for text completion, not specifically for code generation. However, you can use it to generate code by providing a prompt that asks for code. The LLM will then generate a completion for the prompt that could be code if the prompt asks for it.

Here's an example:

Plain Text

response = llm.complete("Write a Python function to calculate the factorial of a number.")
print(response.text)

In this case, the prompt is asking for a Python function, so the completion generated by the LLM should be a Python function.

However, please note that the quality and correctness of the generated code will depend on the capabilities of the underlying LLM. The LLM might not always generate correct or optimal code, especially for more complex prompts.

If you're looking for a tool specifically designed for code generation, you might need to use a different tool or service that specializes in this area.

(Source: GitHub - jerryjliu/llama_index ())
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TTech explorer

@kapa.ai my chat engine is responding with only top k similarity everytime. If I ask apart from above top k give me another results it's unable to do

kkapa.ai

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

kkapa.ai

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The behavior you're observing is due to the similarity_top_k parameter that is set when creating the query engine. This parameter determines the number of top similar results that the engine will return for a given query.

If you want to get more results, you can increase the value of similarity_top_k. However, please note that increasing this value will also increase the computational cost and may slow down the response time.

Here is an example of how to create a query engine with a higher similarity_top_k value:

Plain Text

query_engine = base_index.as_query_engine(similarity_top_k=10)
response = query_engine.query("Your query here")
print(response)

In this example, replace "Your query here" with your specific query.

If you want to get different results for the same query, you might need to implement a mechanism to keep track of the results that have already been returned and exclude them in subsequent queries. This would require customizing the retrieval and ranking process, which is beyond the capabilities of the basic query engine provided by LlamaIndex.

(Source: GitHub - jerryjliu/llama_index ())
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TTech explorer

@kapa.ai What if I want my query engine to pick another 10 results apart from which it picked earlier

kkapa.ai

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

kkapa.ai

----------------------
The LlamaIndex query engine, as it is currently designed, does not support retrieving different sets of results for the same query out of the box. The similarity_top_k parameter determines the number of top similar results that the engine will return for a given query, but it does not support excluding previously returned results.

If you want to retrieve different sets of results for the same query, you would need to implement a custom solution. This could involve keeping track of the results that have already been returned for a given query and then modifying the retrieval and ranking process to exclude these results.

However, this would require a deep understanding of the internals of LlamaIndex and might not be straightforward to implement. It would also likely increase the computational cost and could slow down the response time.

As an alternative, you could consider rephrasing your query or providing additional context to get different results. The LlamaIndex query engine uses the entire query to retrieve and rank results, so changing the query or the context can lead to different results.

(Source: GitHub - jerryjliu/llama_index ())
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TTech explorer

@kapa.ai can I use any rerank parameters to change my results

kkapa.ai

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

kkapa.ai

----------------------
Yes, you can use different parameters in the re-ranking process to change the results returned by your query engine. The re-ranker in LlamaIndex allows you to specify the number of top n nodes that will be returned after re-ranking.

Here is an example of how to use the LLMRerank class with a custom top_n value:

Plain Text

from llama_index.core.postprocessor import LLMRerank
from llama_index.llms.gemini import Gemini

reranker = LLMRerank(
    top_n=5,  # number of nodes after reranking
    llm=Gemini(api_key=GOOGLE_API_KEY),
)

In this example, the top_n parameter is set to 5, which means that the re-ranker will return the top 5 nodes after re-ranking. You can adjust this value to get more or fewer results.

Please note that if the top_n value remains the same as the original number of nodes, the re-ranker will only re-rank the nodes and not change the number of nodes. Otherwise, it will re-rank the nodes and return the top n nodes.

Also, remember that the re-ranking process is designed to improve the relevance of the results to the query, not to provide completely different results. If you want to get different results for the same query, you might need to consider other approaches, such as changing the query or the retrieval process.

(Source: GitHub - jerryjliu/llama_index ())
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TTech explorer

@kapa.ai how can I make my chat engine respond precisely instead of large text. I need only short answers

kkapa.ai

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

kkapa.ai

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To make your chat engine respond with shorter answers, you can use the stream_chat method of the ChatEngine class. This method returns a StreamingResponse object that allows you to control the length of the response by consuming the response tokens one by one.

Here is an example of how to use it:

Plain Text

chat_engine = index.as_chat_engine()
streaming_response = chat_engine.stream_chat("Tell me a joke.")
for token in streaming_response.response_gen:
    print(token, end="")

In this example, the stream_chat method is used to start a chat and get a StreamingResponse object. The response_gen attribute of this object is a generator that yields the response tokens one by one. By controlling the number of tokens you consume from this generator, you can control the length of the response.

Please note that this method allows you to control the length of the response, but it does not guarantee that the response will make sense if it is cut off prematurely. You might need to implement additional logic to ensure that the response is coherent and complete.

(Source: LlamaIndex Documentation - Chat Engine Usage Pattern ())
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TTech explorer

@kapa.ai , my chat engine is returning source documents which are not relevant. How to correct them

kkapa.ai

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

kkapa.ai

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If your chat engine is returning irrelevant source documents, there are a few strategies you can consider:

Improve your Index: The quality of the index plays a crucial role in the relevance of the results. Make sure your documents are well-indexed. You might need to re-index your documents or consider using a different type of index that is more suitable for your data.

Refine your Queries: Make sure your queries are clear and specific. Vague or ambiguous queries can lead to irrelevant results.

Adjust Parameters: You can adjust the parameters of the query engine, such as similarity_top_k, to change the number of results that are considered.

Use a Reranker: You can use a reranker to re-rank the results based on their relevance to the query. This can help to bring the most relevant results to the top.

Here is an example of how to use a reranker:

Plain Text

from llama_index.core.postprocessor import LLMRerank
from llama_index.llms.gemini import Gemini

reranker = LLMRerank(
    top_n=5,  # number of nodes after reranking
    llm=Gemini(api_key=GOOGLE_API_KEY),
)

In this example, the top_n parameter is set to 5, which means that the reranker will return the top 5 nodes after re-ranking. You can adjust this value to get more or fewer results.

Please note that these strategies might not always be sufficient to eliminate all irrelevant results. The relevance of the results also depends on the capabilities of the underlying LLM and the quality of the data.

(Source: GitHub - jerryjliu/llama_index ())
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TTech explorer

@kapa.ai how to improve index

kkapa.ai

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

kkapa.ai

----------------------
Improving the index in LlamaIndex involves several steps:

Choose the Right Index Type: LlamaIndex offers several different index types, each with its own strengths and weaknesses. Choose the one that best fits your data and use case. For example, if your data is structured and you want to perform keyword lookups, you might use a Keyword Table Index. If your data is unstructured text, you might use a VectorStoreIndex or a Document Summary Index.

Fine-tune Your Embeddings: If you're using an index that relies on embeddings, such as the VectorStoreIndex, you can improve the index by fine-tuning your embeddings for better retrieval performance. LlamaIndex provides guides on how to fine-tune embeddings for better retrieval performance.

Use High-Quality Data: The quality of your index is directly related to the quality of your data. Make sure your data is clean, relevant, and well-structured.

Regularly Update Your Index: As your data changes, your index should change too. Regularly update your index to ensure it accurately reflects your current data.

Here is an example of how to build an index in LlamaIndex:

Plain Text

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader

documents = SimpleDirectoryReader("data").load_data()
index = VectorStoreIndex.from_documents(documents)

In this example, replace "data" with the path to your data.

(Source: LlamaIndex Documentation - Indexing ())
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TTech explorer

@kapa.ai my document summary index, when I save to local directory, I don't see summary rather I see orginal document information in docstore

kkapa.ai

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

kkapa.ai

Sorry @Tech explorer, I seem to be having a bit of trouble. Don't worry though, I've pinged my support and they will try to fix me up! :technologist:

Perhaps try again in 15mins?

TTech explorer

@kapa.ai how can I use instructor embeddings for vector store index

kkapa.ai

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

kkapa.ai

Sorry @Tech explorer, I seem to be having a bit of trouble. Don't worry though, I've pinged my support and they will try to fix me up! :technologist:

Perhaps try again in 15mins?

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