I am trying to use an LLM azure open ai model, the chat works but the .complete

1. First, make sure you've installed the necessary package:

pip install llama-index-llms-azure-openai

1. Then, you need to set up your Azure OpenAI client. Here's an example of how to do it:

from llama_index.llms.azure_openai import AzureOpenAI

llm = AzureOpenAI(
    engine="your-deployment-name",
    model="gpt-35-turbo-16k",  # or your specific model
    temperature=0.0,
    azure_endpoint="<https://your-resource-name.openai.azure.com/",>
    api_key="your-api-key",
    api_version="2023-07-01-preview",
)

1. If you prefer to use environment variables instead of passing the API key directly, you can set them up like this:

import os

os.environ["OPENAI_API_KEY"] = "your-api-key"
os.environ["AZURE_OPENAI_ENDPOINT"] = "<https://your-resource-name.openai.azure.com/">
os.environ["OPENAI_API_VERSION"] = "2023-07-01-preview"

• Your API key is correct and has the necessary permissions.
• The endpoint URL is correct.
• The engine name (deployment name) is correct.
• The API version is up to date.

1. .complete method:
   
   • This method is used for text completion tasks.
   • It takes a single string prompt as input.
   • It's typically used with traditional language models that generate text based on a given prompt.

1. .chat method:
   
   • This method is designed for conversational tasks.
   • It takes a sequence of ChatMessage objects as input, which can include different roles (system, user, assistant).
   • It's used with chat-based models that can maintain context across multiple messages.

1. Input format:
   
   • .complete takes a single string prompt.
   • .chat takes a list of ChatMessage objects, allowing for more structured conversations.

1. Use case:
   
   • .complete is better for single-turn interactions or text generation tasks.
   • .chat is more suitable for multi-turn conversations or when you need to provide system instructions and maintain context.

1. Model compatibility:
   
   • Some models are specifically designed for chat interactions, while others are for text completion.
   • However, LlamaIndex provides compatibility layers. For example, the .complete method is still available for models of type chat-completions. In these cases, your input text is converted to a message with role="user".

1. Output:
   
   • .complete typically returns a CompletionResponse object.
   • .chat returns a ChatResponse object.

Using .complete

Using .chat

1. API Key Configuration:
   Make sure you've properly set up your API key. You can do this in two ways:

   import os
   os.environ["OPENAI_API_KEY"] = "your-api-key"
   

   from llama_index.llms.azure_openai import AzureOpenAI

   llm = AzureOpenAI(
       engine="your-deployment-name",
       model="your-model-name",
       api_key="your-api-key",
       azure_endpoint="<https://your-resource-name.openai.azure.com/">
   )
   

1. Model Configuration:
   Ensure that you're using the correct model name and engine (deployment name) for both chat and completion tasks. Some models might be configured differently for chat and completion.

1. Completion to Chat Conversion:
   As mentioned in the LlamaIndex Azure OpenAI Documentation, the .complete method is still available for models of type chat-completions. In these cases, your input text is converted to a message with role="user". This conversion might be causing issues if not handled correctly.

1. Check for Overrides:
   Make sure you haven't accidentally overridden the .complete method or its authentication mechanism in your code.

1. Version Compatibility:
   Ensure that you're using compatible versions of LlamaIndex and the Azure OpenAI SDK.

1. Explicitly pass the API key in the .complete method call:
   
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   response = llm.complete("Your prompt here", api_key="your-api-key")

1. Use the chat method with a single message to mimic completion:
   
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   from llama_index.core.llms import ChatMessage
   
   response = llm.chat([ChatMessage(role="user", content="Your prompt here")])

1. Check if the issue persists with a different Azure OpenAI model or deployment.

1. Limit the context window:
   You can artificially limit the context window to prevent it from growing too large. This approach is mentioned in several examples in the LlamaIndex documentation. For instance:

   from llama_index.core import Settings
   Settings.context_window = 3900  # or any other suitable value
   

1. Use a retriever with limited top-k:
   When setting up your query engine or agent, you can limit the number of retrieved documents:

   query_engine = index.as_query_engine(similarity_top_k=2)
   

1. Implement a chunking strategy:
   If you're dealing with large documents, you might want to implement a chunking strategy that breaks down the content into smaller, manageable pieces. The TopicNodeParser in LlamaIndex can help with this:

   from llama_index.node_parser.topic import TopicNodeParser
   
   parser = TopicNodeParser(chunk_size=1000)  # Adjust chunk_size as needed
   

1. Use a model with a larger context window:
   If possible, consider using a model with a larger context window. However, this should be a last resort if the other strategies don't suffice.

1. Implement a conversation memory management strategy:
   You might need to implement a strategy to summarize or prune the conversation history periodically to prevent it from growing too large.

1. Adjust the ReActAgent configuration:
   You may need to adjust the configuration of the ReActAgent to limit the number of reasoning steps or the amount of information it accumulates during its process.

from llama_index.core import Settings
from llama_index.core.agent import ReActAgent

# Limit the context window
Settings.context_window = 100000  # Adjust as needed

# Create the agent with limited tools and adjusted configuration
agent = ReActAgent.from_tools(
    await ctx.get("tools"),
    llm=llm_4o_2,
    verbose=True,
    max_reasoning_steps=5,  # Adjust as needed
    context_window=100000  # Explicitly set context window for the agent
)

# Use the agent with potentially chunked or limited input
response = agent.chat(ev.question)

1. Check the scores of retrieved nodes:
   Rerankers typically modify the scores of the retrieved nodes. You can compare the scores before and after applying the reranker to see if they've changed.

   # Assuming you have a retriever and a reranker set up
   retrieved_nodes = retriever.retrieve(query)
   
   print("Before reranking:")
   for node in retrieved_nodes:
       print(f"Node ID: {node.node.node_id}, Score: {node.score}")
   
   reranked_nodes = reranker.postprocess_nodes(retrieved_nodes, query)
   
   print("\nAfter reranking:")
   for node in reranked_nodes:
       print(f"Node ID: {node.node.node_id}, Score: {node.score}")
   

1. Compare the order of retrieved nodes:
   Rerankers often change the order of the retrieved nodes based on their relevance. You can check if the order has changed after applying the reranker.

1. Use verbose mode:
   Some rerankers, like the JinaRerank, have a verbose mode that can provide additional information about the reranking process. For example:

   from llama_index.postprocessor.jinaai_rerank import JinaRerank
   
   jina_rerank = JinaRerank(api_key=api_key, top_n=2, verbose=True)
   

1. Check the number of retrieved nodes:
   Some rerankers, like the one in this example from the LlamaIndex documentation, limit the number of returned nodes. You can check if the number of nodes has changed after reranking:

   query_engine = index.as_query_engine(
       similarity_top_k=10, node_postprocessors=[jina_rerank]
   )
   response = query_engine.query("Your query here")
   
   print(f"Number of retrieved nodes after reranking: {len(response.source_nodes)}")
   

1. Examine the content of retrieved nodes:
   After reranking, the most relevant nodes should be at the top. You can print out the content of the top nodes to see if they seem more relevant to your query.

   for i, node in enumerate(response.source_nodes[:2]):  # Check top 2 nodes
       print(f"Node {i+1}:")
       print(f"Text: {node.text}")
       print(f"Score: {node.score}")
       print("\n")