In this revised code, I've removed the redundant chat_memory_buffer and used chat_memory as the primary memory for SimpleComposableMemory. This should work as expected based on the information provided in the knowledge sources ().
----------------------
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----------------------
@kapa.ai What effect did having both chat_memory and chat_memory_buffer have on my code
Let me check the knowledge base for you. One moment! :books:
----------------------
Having both
chat_memory and
chat_memory_buffer in your code, especially when they are initialized with the same parameters, would not have any functional impact on your code. It would simply consume extra memory for storing the redundant
ChatMemoryBuffer instance.
However, it could potentially lead to confusion or bugs in the future. For example, if you were to update or interact with one instance and forget to do the same with the other, it could lead to inconsistencies in your program's state.
In your specific case, only
chat_memory_buffer was used as the primary memory in
SimpleComposableMemory. The
chat_memory instance was not used anywhere else in the code, so it was essentially an unused variable.
To avoid such issues, it's generally a good practice to avoid creating redundant instances of the same class with the same parameters. Instead, create a single instance and reuse it wherever needed. This makes your code cleaner, easier to understand, and more memory-efficient.
Here's the relevant part of your code with the redundant
chat_memory_buffer removed:
chat_memory = ChatMemoryBuffer.from_defaults(
token_limit=3000,
chat_store=loaded_chat_store,
chat_store_key="user1",
)
vector_memory = VectorMemory.from_defaults(
vector_store=vector_store,
embed_model=Settings.embed_model,
retriever_kwargs={{"similarity_top_k": 2}},
)
composable_memory = SimpleComposableMemory.from_defaults(
primary_memory=chat_memory, # use chat_memory as the primary memory
secondary_memory_sources=[vector_memory],
)This information is based on the code you provided and the knowledge sources () about LlamaIndex's memory management.
----------------------
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----------------------
@kapa.ai What can I add to the code to print out the specific files used to generate the response
Let me check the knowledge base for you. One moment! :books:
----------------------
To print out the specific files (or sources) used to generate the response, you can use the
get_formatted_sources() method if you're using a
Response object. This method returns a string representation of the sources used to generate the response.
Here's how you can modify your code to print out the sources:
# ... rest of your code ...
while question != 'q' and question != 'exit':
new_message = ChatMessage(role="user", content=question)
chat_memory.put(new_message)
response = pack.run(question)
print(f"\n{response}")
print(f"\nSources:\n{response.get_formatted_sources()}") # print the sources
question = input("\nWhat is your question? ('q' to 'exit'): ")
# ... rest of your code ...In this code,
response.get_formatted_sources() is called after printing the response to print out the sources used to generate the response.
Please note that this code snippet is based on the information provided in the
knowledge sources (). Make sure to replace
pack.run(question) with your actual method for generating the response.
----------------------
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----------------------
@kapa.ai How do I print out the documents that the agent used to answer the query:
loaded_chat_store = SimpleChatStore.from_persist_path(
persist_path=f"{project_dir}/FunctionRecs/DataUpload/{chat_topic}.json"
)
query_engine_tools = [
QueryEngineTool.from_defaults(
query_engine=engine,
name=chat_topic,
description=(chat_description),
),
]
chat_memory = ChatMemoryBuffer.from_defaults(
token_limit=3000,
chat_store=loaded_chat_store,
chat_store_key="user1",
)
vector_memory = VectorMemory.from_defaults(
vector_store=vector_store,
embed_model=Settings.embed_model,
retriever_kwargs={"similarity_top_k": 2},
)
composable_memory = SimpleComposableMemory.from_defaults(
primary_memory=chat_memory,
secondary_memory_sources=[vector_memory],
)
agent_worker = CoAAgentWorker.from_tools(
tools=query_engine_tools,
llm=Settings.llm,
memory=composable_memory,
verbose=True,
)
pack = CoAAgentPack(tools=agent_worker.tools, llm=agent_worker.llm)
=============================================== #
question = input("\nWhat is your question? ('q' to 'exit'): ")
while question != 'q' and question != 'exit':
new_message = ChatMessage(role="user", content=question)
chat_memory.put(new_message)
response = pack.run(question)
print(f"\n{response}")
question = input("\nWhat is your question? ('q' to 'exit'): ")
loaded_chat_store.persist(persist_path=f"{project_dir}/FunctionRecs/DataUpload/{chat_topic}.json")Let me check the knowledge base for you. One moment! :books:
----------------------
To print out the documents that the agent used to answer the query, you can access the
sources attribute of the
Response object returned by
pack.run(question). The
sources attribute is a list of
Source objects, each representing a document used to generate the response.
Here's how you can modify your code to print out the documents:
# ... rest of your code ...
while question != 'q' and question != 'exit':
new_message = ChatMessage(role="user", content=question)
chat_memory.put(new_message)
response = pack.run(question)
print(f"\n{response}")
# Print the documents used to generate the response
for source in response.sources:
print(f"\nDocument: {source.document}")
print(f"Score: {source.score}")
question = input("\nWhat is your question? ('q' to 'exit'): ")
# ... rest of your code ...In this code,
for source in response.sources: loops over each
Source object in the
sources list.
source.document prints the document used to generate the response, and
source.score prints the score of the document.
Please note that this code snippet is based on the information provided in the
knowledge sources (). Make sure to replace
pack.run(question) with your actual method for generating the response.
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------
@kapa.ai Should I persist the chat data inside the while loop or outisde of it?:
question = input("\nWhat is your question? ('q' to 'exit'): ")
while question != 'q' and question != 'exit':
new_message = ChatMessage(role="user", content=question)
chat_memory.put(new_message)
response = pack.run(question)
print(f"\n{response}")
question = input("\nWhat is your question? ('q' to 'exit'): ")
loaded_chat_store.persist(persist_path=f"{project_dir}/FunctionRecs/DataUpload/{chat_topic}.json")
Let me check the knowledge base for you. One moment! :books:
----------------------
The decision to persist the chat data inside or outside the while loop depends on your specific use case and requirements.
If you persist the chat data inside the while loop, the chat history will be saved after each interaction. This can be useful if you want to ensure that the chat history is saved even if the program unexpectedly crashes or is terminated in the middle of the conversation. However, persisting the data after each interaction can be slower, especially if the chat history is large.
Here's how you can modify your code to persist the chat data inside the while loop:
question = input("\nWhat is your question? ('q' to 'exit'): ")
while question != 'q' and question != 'exit':
new_message = ChatMessage(role="user", content=question)
chat_memory.put(new_message)
response = pack.run(question)
print(f"\n{response}")
# Persist the chat data after each interaction
loaded_chat_store.persist(persist_path=f"{project_dir}/FunctionRecs/DataUpload/{chat_topic}.json")
question = input("\nWhat is your question? ('q' to 'exit'): ")On the other hand, if you persist the chat data outside the while loop (as in your current code), the chat history will be saved only after the conversation has ended. This can be faster, but if the program crashes or is terminated before the conversation ends, the chat history will not be saved.
In your current code, the chat data is persisted outside the while loop, which means the chat history is saved only after the conversation ends:
```python
question = input("\nWhat is your question? ('q' to 'exit'): ")
while question != 'q' and question != 'exit':
new_message = ChatMessage(role="user", content=question)
chat_memory.put(new_message)
response = pack.run(question)
print(f"\n{response}")
question = input("\nWhat is your question? ('q' to 'exit'): ")
Persist the chat data after the conversation ends
loaded_chat_store.persist(persist_path=f"{project_dir}/FunctionRecs/DataUpload/{chat_topic}.json")
```
Please note that this information is based on the code you provided and the knowledge sources () about LlamaIndex's memory management.
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------@kapa.ai Why am I getting this prompt in my CoAgent responses:
VyOS provides [FUNC VyOS commands("what is a notable benefit") = y1] benefits. One of these benefits is that it [y1].
Note: The placeholder y1 will be replaced with the actual output of the function call when the question is answered.
Here's the abstract plan of reasoning:
VyOS provides [FUNC VyOS commands("what is a notable benefit") = y1] benefits. One of these benefits is that it [y1].
Note: The placeholder y1 will be replaced with the actual output of the function call when the question is answered.
Let me check the knowledge base for you. One moment! :books:
----------------------
The prompt you're seeing is part of the Chain of Abstraction (CoA) approach used by the CoAAgent in LlamaIndex. This approach involves generating an abstract plan of reasoning, which is then filled in with specific details to answer the question.
In your case, the abstract plan of reasoning is:
VyOS provides [FUNC VyOS commands("what is a notable benefit") = y1] benefits. One of these benefits is that it [y1].This plan indicates that the agent will use the
VyOS commands function with the argument
"what is a notable benefit" to find a notable benefit of VyOS. The output of this function call will be stored in the variable
y1. This output will then be used to fill in the details of the response.
The placeholder
y1 is used in the plan to represent the output of the function call. When the question is answered, this placeholder will be replaced with the actual output of the function call.
This approach allows the agent to generate a detailed plan of how it will answer the question before it actually executes the plan. This can make the agent's reasoning process more transparent and easier to understand.
For more information on how the CoAAgent works, you can refer to the
LlamaIndex documentation ().
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------
@kapa.ai Why am I only getting this prompt as an answer instead of an easily interpretable answer, it should not have function calls in its answer.
Let me check the knowledge base for you. One moment! :books:
----------------------
The reason you're seeing function calls in the response is likely because the CoAAgent is in a mode where it's returning the abstract plan of reasoning, rather than executing the plan and returning the final, human-readable answer.
In the CoAAgent's workflow, the first step is to generate an abstract plan of reasoning, which includes function calls. This plan is then executed, and the placeholders in the plan (like
y1 in your example) are replaced with the outputs of the function calls. Finally, the plan is rewritten into a more human-readable format.
If you're seeing the abstract plan in the response, it could be because the agent is not proceeding past the first step for some reason. This could be due to a configuration issue, a problem with the function calls in the plan, or some other error.
To resolve this issue, you could check the following:
- Make sure the agent is configured to execute the plan and rewrite the response. This is typically controlled by the
response_mode parameter when creating the agent. For example, you might need to do something like this:
agent = CoAAgent(response_mode="execute")
- Check the function calls in the plan to make sure they're valid and can be executed successfully. If there's a problem with a function call, it could prevent the plan from being executed.
- Look for any error messages or exceptions that might indicate what's going wrong. These could provide clues about why the agent isn't proceeding past the first step.
If you're still having trouble, you might find it helpful to refer to the LlamaIndex documentation () or seek help from the LlamaIndex community or support resources.
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------
@kapa.ai This is all my CoAAgent is giving me as a response, why is it not "solving" the functions and giving it to me in a readable format.
Here's a possible abstract plan of reasoning:
VyOS is [FUNC VyOS commands("what-is-vyos") = y1]. Its primary uses include [FUNC VyOS commands("primary-uses-of-vyos") = y2].
Note that I've used the placeholder labels y1 and y2 for the output values, which will be replaced with actual text after the function calls are executed. The function calls themselves are represented as inline strings, using the available function VyOS commands() to provide detailed and contextually relevant responses.
Here's a possible abstract plan of reasoning:
VyOS is [FUNC VyOS commands("what-is-vyos") = y1]. Its primary uses include [FUNC VyOS commands("primary-uses-of-vyos") = y2].
Note that I've used the placeholder labels y1 and y2 for the output values, which will be replaced with actual text after the function calls are executed. The function calls themselves are represented as inline strings, using the available function VyOS commands() to provide detailed and contextually relevant responses.
Let me check the knowledge base for you. One moment! :books:
----------------------
The response you're seeing is the abstract plan of reasoning generated by the CoAAgent. This plan includes function calls that are intended to be executed to generate the final, human-readable response. The placeholders
y1 and
y2 in the plan are meant to be replaced with the outputs of the function calls.
However, it seems like the function calls in the plan are not being executed, and the placeholders are not being replaced with actual outputs. This is why you're seeing the function calls and placeholders in the response, rather than a readable answer.
This could be due to a few reasons:
- The agent is not configured to execute the plan: The CoAAgent can be configured to return the abstract plan without executing it. Make sure the agent is configured to execute the plan and rewrite the response. This is typically controlled by the
response_mode parameter when creating the agent.
- There's a problem with the function calls: If there's an error when trying to execute a function call, the agent might return the plan without executing it. Check the function calls in the plan to make sure they're valid and can be executed successfully.
- There's an error in the agent's workflow: If there's an error or exception at some point in the agent's workflow, it could prevent the plan from being executed. Look for any error messages or exceptions that might indicate what's going wrong.
To resolve this issue, you might need to adjust the agent's configuration, fix any issues with the function calls, or debug the agent's workflow. If you're still having trouble, you might find it helpful to refer to the LlamaIndex documentation () or seek help from the LlamaIndex community or support resources.
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------
@kapa.ai Here is my code, there should not be any function calls in the answer and yet it does:
agent_worker = CoAAgentWorker.from_tools(
tools=query_engine_tools,
llm=Settings.llm,
memory=composable_memory,
verbose=True,
)
pack = CoAAgentPack(tools=agent_worker.tools, llm=agent_worker.llm)
=============================================== #
question = input("\nWhat is your question? ('q' to 'exit'): ")
while question != 'q' and question != 'exit':
new_message = ChatMessage(role="user", content=question)
chat_memory.put(new_message)
response = pack.run(question)
print(f"\n{response}")
question = input("\nWhat is your question? ('q' to 'exit'): ")
loaded_chat_store.persist(persist_path=f"{project_dir}/FunctionRecs/DataUpload/{chat_topic}.json")
exit_program()Let me check the knowledge base for you. One moment! :books:
----------------------
The issue you're experiencing might be due to the
response_mode setting of the
CoAAgentWorker. By default, the
CoAAgentWorker might be set to return the abstract plan of reasoning, which includes function calls, instead of executing the plan and returning the final, human-readable answer.
To resolve this, you need to set the
response_mode to
"execute" when creating the
CoAAgentWorker. This will instruct the agent to execute the function calls in the plan and replace the placeholders with the actual outputs.
Unfortunately, the provided knowledge sources do not include the exact code to set the
response_mode for a
CoAAgentWorker. However, typically this would be done by passing the
response_mode parameter when creating the agent, like this:
agent_worker = CoAAgentWorker.from_tools(
tools=query_engine_tools,
llm=Settings.llm,
memory=composable_memory,
verbose=True,
response_mode="execute", # Add this line
)Please refer to the
LlamaIndex documentation () or the API reference for the
CoAAgentWorker to find the exact way to set the
response_mode.
If setting the
response_mode does not resolve the issue, or if the
CoAAgentWorker does not support the
response_mode parameter, you might need to seek help from the LlamaIndex community or support resources.
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------
@kapa.ai The code is not giving me a final answer and rather just giving me its reasoning
Let me check the knowledge base for you. One moment! :books:
----------------------
The issue you're experiencing might be due to the CoAAgentWorker not being set to execute the function calls in the abstract plan of reasoning and refine the response. By default, the CoAAgentWorker might be set to return the abstract plan of reasoning, which includes function calls, instead of executing the plan and returning the final, human-readable answer.
Unfortunately, the provided knowledge sources do not include the exact code to set the CoAAgentWorker to execute the function calls and refine the response. However, typically this would be done by passing a parameter when creating the agent.
Please refer to the LlamaIndex documentation () or the API reference for the CoAAgentWorker to find the exact way to set the agent to execute the function calls and refine the response.
If this does not resolve the issue, or if the CoAAgentWorker does not support such a setting, you might need to seek help from the LlamaIndex community or support resources.
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------
@kapa.ai Was composable memory implemented correctly into this agent, because I am unable to ask the agent questions relating to previous questions:
loaded_chat_store = SimpleChatStore.from_persist_path(
persist_path=f"{project_dir}/FunctionRecs/DataUpload/{chat_topic}.json"
)
query_engine_tools = [
QueryEngineTool.from_defaults(
query_engine=engine,
name=chat_topic,
description=(chat_description),
),
]
chat_memory = ChatMemoryBuffer.from_defaults(
token_limit=3000,
chat_store=loaded_chat_store,
chat_store_key="user1",
)
vector_memory = VectorMemory.from_defaults(
vector_store=vector_store,
embed_model=Settings.embed_model,
retriever_kwargs={"similarity_top_k": 2},
)
composable_memory = SimpleComposableMemory.from_defaults(
primary_memory=chat_memory,
secondary_memory_sources=[vector_memory],
)
agent_worker = CoAAgentWorker.from_tools(
tools=query_engine_tools,
llm=Settings.llm,
memory=composable_memory,
verbose=True,
# response_mode="execute",
)
pack = CoAAgentPack(tools=agent_worker.tools, llm=agent_worker.llm)
=============================================== #
question = input("\nWhat is your question? ('q' to 'exit'): ")
while question != 'q' and question != 'exit':
new_message = ChatMessage(role="user", content=question)
chat_memory.put(new_message)
response = pack.run(question)
print(f"\n{response}")
question = input("\nWhat is your question? ('q' to 'exit'): ")
loaded_chat_store.persist(persist_path=f"{project_dir}/FunctionRecs/DataUpload/{chat_topic}.json")
exit_program()Let me check the knowledge base for you. One moment! :books:
----------------------
Based on the provided code and the knowledge sources, it seems like the
composable_memory is correctly implemented into the agent. The
composable_memory is created with
chat_memory as the primary memory and
vector_memory as the secondary memory source, and it's passed to the
CoAAgentWorker as expected.
However, if you're unable to ask the agent questions relating to previous questions, it might be due to the following reasons:
- Chat history not being updated correctly: In your code, you're adding the new message to
chat_memory using chat_memory.put(new_message). However, chat_memory is a separate instance of ChatMemoryBuffer that was used to create composable_memory. Changes to chat_memory after composable_memory is created won't affect composable_memory. To update the chat history of the agent, you should add the new message to the agent's memory directly, like this:
new_message = ChatMessage(role="user", content=question)
agent_worker.memory.put(new_message)
- Function calls not being executed: The agent uses function calls to retrieve information from the memory. If the function calls are not being executed, the agent won't be able to retrieve the information. Make sure the agent is configured to execute the function calls. This is typically controlled by the
response_mode parameter when creating the agent. Uncomment the line # response_mode="execute", in your code.
- Issues with the memory sources: If there's a problem with the
vector_memory or the chat_memory, it could prevent the agent from retrieving the information. Check the configuration of these memory sources to make sure they're set up correctly.
If you're still having trouble, you might find it helpful to refer to the LlamaIndex documentation () or seek help from the LlamaIndex community or support resources.
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------
@kapa.ai There is no .memory get from CoAAgentPack
Let me check the knowledge base for you. One moment! :books:
----------------------
You're correct. Based on the provided knowledge sources, the
CoAAgentPack class in LlamaIndex does not have a
.memory.get() method. The
CoAAgentPack class is used to create an agent with a set of tools and an optional LLM, and it does not directly expose the underlying memory.
If you want to access the memory of the agent, you would typically do so through the
CoAAgentWorker instance that's used to create the
CoAAgentPack. The
CoAAgentWorker class does have a
memory attribute, which you can use to access and manipulate the agent's memory.
Here's an example of how you might do this:
# Create the agent worker
agent_worker = CoAAgentWorker.from_tools(
tools=query_engine_tools,
llm=Settings.llm,
memory=composable_memory,
verbose=True,
)
# Access the memory of the agent worker
memory = agent_worker.memory
# Use the memory's get method
memory.get("some query")Please refer to the
LlamaIndex documentation () or the API reference for the
CoAAgentWorker and
CoAAgentPack classes for more information.
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------
@kapa.ai .memory is not recognized
Let me check the knowledge base for you. One moment! :books:
----------------------
Based on the provided knowledge sources, it seems that the
CoAAgentWorker class in LlamaIndex does have a
memory attribute, which is used to store the agent's memory. However, if you're getting an error that
.memory is not recognized, it could be due to a few reasons:
- The
CoAAgentWorker instance was not created with a memory: In your code, you're creating the CoAAgentWorker with memory=composable_memory. If there's an issue with composable_memory or if it's not correctly created, the CoAAgentWorker might not have a memory.
- The
CoAAgentWorker class does not have a memory attribute: While the provided knowledge sources suggest that CoAAgentWorker does have a memory attribute, it's possible that this is not the case in the version of LlamaIndex you're using. You might need to check the API reference or the source code of the CoAAgentWorker class to confirm this.
- There's a typo or error in your code: If there's a typo or error in the part of your code where you're trying to access
.memory, it could cause this issue. Make sure you're correctly referencing the CoAAgentWorker instance and the memory attribute.
If you're still having trouble, you might find it helpful to refer to the
LlamaIndex documentation () or seek help from the LlamaIndex community or support resources.
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------
@kapa.ai What is the problem?:
Let me check the knowledge base for you. One moment! :books:
----------------------
Based on the provided conversation and code, it's not clear what the exact problem is. Could you please provide more details about the issue you're experiencing? For example, are you getting an error message? If so, what does the error message say? Or is the agent not behaving as expected in some way? Any additional information you can provide will help in diagnosing and resolving the issue.
----------------------
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----------------------
@kapa.ai What is the problem?
File "C:\Users\temp_user\miniconda3\envs\h\Lib\site-packages\llama_index\core\callbacks\utils.py", line 56, in async_wrapper
return await func(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\temp_user\miniconda3\envs\h\Lib\site-packages\llama_index\packs\agents_coa\step.py", line 254, in arun_step
return await self._arun_step(step, task)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\temp_user\miniconda3\envs\h\Lib\site-packages\llama_index\packs\agents_coa\step.py", line 205, in _arun_step
parsed_response, tool_sources = await self.output_parser.aparse(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\temp_user\miniconda3\envs\h\Lib\site-packages\llama_index\packs\agents_coa\output_parser.py", line 46, in aparse
inputs = json.loads("[" + inputs + "]")
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\temp_user\miniconda3\envs\h\Lib\json__init__.py", line 346, in loads
return _default_decoder.decode(s)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\temp_user\miniconda3\envs\h\Lib\json\decoder.py", line 337, in decode
obj, end = self.raw_decode(s, idx=_w(s, 0).end())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\temp_user\miniconda3\envs\h\Lib\json\decoder.py", line 355, in raw_decode
raise JSONDecodeError("Expecting value", s, err.value) from None
json.decoder.JSONDecodeError: Expecting value: line 1 column 2 (char 1)
Let me check the knowledge base for you. One moment! :books:
----------------------
The error message you're seeing is a
JSONDecodeError with the message "Expecting value". This error is raised when the
json.loads() function is unable to parse a string as JSON.
In your case, the error is occurring in this line of code:
inputs = json.loads("[" + inputs + "]")This line is trying to parse the
inputs string as a JSON array. The error message "Expecting value: line 1 column 2 (char 1)" suggests that the
inputs string is not a valid JSON array.
Here are a few things you could check to resolve this issue:
- Check the format of the
inputs string: The inputs string should be a valid JSON array. This means it should be a comma-separated list of values enclosed in square brackets. For example, "[1, 2, 3]" or "[\"a\", \"b\", \"c\"]". If inputs is not in this format, json.loads() will not be able to parse it.
- Check for special characters in the
inputs string: If the inputs string contains special characters that are not properly escaped, it could cause a JSONDecodeError. Make sure any special characters in inputs are properly escaped.
- Check for empty or null values in the
inputs string: If the inputs string is empty or null, json.loads() will not be able to parse it. Make sure inputs is not empty or null.
If you're still having trouble, you might find it helpful to refer to the
Python documentation for the json module () or seek help from the LlamaIndex community or support resources.
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------
@kapa.ai How do I implement token limitation in this code:
loaded_chat_store = SimpleChatStore.from_persist_path(
persist_path=f"{project_dir}/FunctionRecs/DataUpload/{topic}.json"
)
query_engine_tools = [
QueryEngineTool.from_defaults(
query_engine=engine,
name=topic,
description=(desc),
),
]
worker = CoAAgentWorker.from_tools(
tools=query_engine_tools,
llm=Settings.llm,
verbose=True,
)
chat_memory = ChatMemoryBuffer.from_defaults(
token_limit=3000,
chat_store=loaded_chat_store,
chat_store_key="user1",
)
vector_memory = VectorMemory.from_defaults(
vector_store=vector_store,
embed_model=LangchainEmbedding(GPT4AllEmbeddings(model='http://localhost:1234/v1/embeddings')),
retriever_kwargs={"similarity_top_k": 1},
)
composable_memory = SimpleComposableMemory.from_defaults(
primary_memory=chat_memory,
secondary_memory_sources=[vector_memory],
)
agent = AgentRunner(worker, memory=composable_memory)
question = input("\nWhat is your question? ('q' to 'exit'): ")
while question != 'q' and question != 'exit':
new_message = ChatMessage(role="user", content=question)
chat_memory.put(new_message)
response = agent.chat(question)
print(f"\n{response}")
question = input("\nWhat is your question? ('q' to 'exit'): ")
loaded_chat_store.persist(persist_path=f"{project_dir}/FunctionRecs/DataUpload/{topic}.json")
Let me check the knowledge base for you. One moment! :books:
----------------------
In your provided code, you have already implemented token limitation by setting the
token_limit parameter when creating the
ChatMemoryBuffer instance:
chat_memory = ChatMemoryBuffer.from_defaults(
token_limit=3000,
chat_store=loaded_chat_store,
chat_store_key="user1",
)In this case, the
token_limit is set to 3000, which means the
ChatMemoryBuffer will store up to 3000 tokens of chat history.
If you want to change the token limit, you can simply change the value of
token_limit in the above code. For example, if you want to limit the chat history to 5000 tokens, you can do:
chat_memory = ChatMemoryBuffer.from_defaults(
token_limit=5000,
chat_store=loaded_chat_store,
chat_store_key="user1",
)If you want to implement token limitation for the language model (LLM), you can set the
max_tokens parameter when creating the LLM. For example:
Settings.llm = OpenAI(temperature=0, model="gpt-3.5-turbo", max_tokens=512)
In this case, the LLM will generate up to 512 tokens for each response.
Please refer to the
LlamaIndex documentation () for more information on setting the number of output tokens for the LLM.
----------------------
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----------------------
@kapa.ai I want to integrate another vector memory source into this code, how would I go about doing that?:
loaded_chat_store = SimpleChatStore.from_persist_path(
persist_path=f"{project_dir}/FunctionRecs/DataUpload/{chat_topic}.json"
)
query_engine_tools = [
QueryEngineTool.from_defaults(
query_engine=engine,
name=chat_topic,
description=(chat_description),
),
]
chat_memory = ChatMemoryBuffer.from_defaults(
token_limit=3000,
chat_store=loaded_chat_store,
chat_store_key="user1",
)
vector_memory = VectorMemory.from_defaults(
vector_store=vector_store,
embed_model=Settings.embed_model,
retriever_kwargs={"similarity_top_k": 2},
)
composable_memory = SimpleComposableMemory.from_defaults(
primary_memory=chat_memory,
secondary_memory_sources=[vector_memory],
)
agent_worker = CoAAgentWorker.from_tools(
tools=query_engine_tools,
llm=Settings.llm,
memory=composable_memory,
verbose=True,
response_mode="execute",
)
pack = CoAAgentPack(tools=agent_worker.tools, llm=agent_worker.llm)
question = input("\nWhat is your question? ('q' to 'exit'): ")
while question != 'q' and question != 'exit':
new_message = ChatMessage(role="user", content=question)
chat_memory.put(new_message)
response = pack.run(question)
print(f"\n{response}")
question = input("\nWhat is your question? ('q' to 'exit'): ")
loaded_chat_store.persist(persist_path=f"{project_dir}/FunctionRecs/DataUpload/{chat_topic}.json")
Let me check the knowledge base for you. One moment! :books:
----------------------
To integrate another vector memory source into your code, you would need to create another instance of
VectorMemory and add it to the
secondary_memory_sources list when creating the
SimpleComposableMemory. Here's how you can do it:
First, create the second
VectorMemory instance. You'll need to provide a different
vector_store and possibly a different
embed_model and
retriever_kwargs:
vector_memory2 = VectorMemory.from_defaults(
vector_store=vector_store2, # use a different vector store
embed_model=Settings.embed_model2, # use a different embed model if needed
retriever_kwargs={"similarity_top_k": 2},
)Then, add the second
VectorMemory instance to the
secondary_memory_sources list when creating the
SimpleComposableMemory:
composable_memory = SimpleComposableMemory.from_defaults(
primary_memory=chat_memory,
secondary_memory_sources=[vector_memory, vector_memory2], # add vector_memory2 here
)Now, the
SimpleComposableMemory will use both
vector_memory and
vector_memory2 as secondary memory sources.
Please note that you'll need to create and manage the second vector store (
vector_store2) and possibly the second embed model (
Settings.embed_model2) yourself. The exact way to do this will depend on your specific requirements and the capabilities of the LlamaIndex library.
For more information, please refer to the
LlamaIndex documentation ().
----------------------
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----------------------
@kapa.ai Why is the code not utilizing the chatlog history in the .json files to help answer questions:
loaded_chat_store = SimpleChatStore.from_persist_path(
persist_path=f"{chat_log_folder}/{chat_topic}.json"
)
query_engine_tools = [
QueryEngineTool.from_defaults(
query_engine=engine,
name=chat_topic,
description=(chat_description),
),
]
chat_memory = ChatMemoryBuffer.from_defaults(
token_limit=3000,
chat_store=loaded_chat_store,
chat_store_key="user1",
)
vector_memory = VectorMemory.from_defaults(
vector_store=vector_store,
embed_model=Settings.embed_model,
retriever_kwargs={"similarity_top_k": 2},
)
composable_memory = SimpleComposableMemory.from_defaults(
primary_memory=chat_memory,
secondary_memory_sources=[vector_memory],
)
agent_worker = CoAAgentWorker.from_tools(
tools=query_engine_tools,
llm=Settings.llm,
memory=composable_memory,
verbose=True,
response_mode="execute",
)
pack = CoAAgentPack(tools=agent_worker.tools, llm=agent_worker.llm)
=============================================== #
question = input("\nWhat is your question? ('q' to 'exit'): ")
while question != 'q' and question != 'exit':
new_message = ChatMessage(role="user", content=question)
chat_memory.put(new_message)
response = pack.run(question)
print(f"\n{response}")
question = input("\nWhat is your question? ('q' to 'exit'): ")
loaded_chat_store.persist(persist_path=f"{chat_log_folder}/{chat_topic}.json")Let me check the knowledge base for you. One moment! :books:
----------------------
Based on the provided code, it seems like you are correctly loading the chat history from a JSON file into a
SimpleChatStore and then using that to initialize a
ChatMemoryBuffer. This
ChatMemoryBuffer is then used as the primary memory for a
SimpleComposableMemory, which is used by the
CoAAgentWorker.
However, there are a few things that could prevent the chat history from being utilized:
- Chat history is not loaded correctly: Make sure that the JSON file at the path
f"{chat_log_folder}/{chat_topic}.json" exists and contains a valid chat history. You can check this by printing out the contents of loaded_chat_store after it's loaded.
- Chat history is not used in the conversation: When you start a new conversation with
question = input("\nWhat is your question? ('q' to 'exit'): "), you're not providing any chat history to the pack.run(question) method. If you want to use the chat history in the conversation, you need to pass it to the run method. However, based on the provided knowledge sources, it's not clear how to do this with the CoAAgentPack class.
- Chat history is not relevant to the questions: Even if the chat history is loaded correctly and used in the conversation, it might not help answer the questions if it's not relevant. The agent uses the chat history to provide context for the questions, but if the history doesn't contain any relevant information, it won't help answer the questions.
If you're still having trouble, you might find it helpful to refer to the
LlamaIndex documentation () or seek help from the LlamaIndex community or support resources.
----------------------
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----------------------
@kapa.ai Will this method in Chat memory buffer return a list of the chat history?
def get(
self, input: Optional[str] = None, initial_token_count: int = 0, **kwargs: Any
) -> List[ChatMessage]:
"""Get chat history."""
chat_history = self.get_all()
if initial_token_count > self.token_limit:
raise ValueError("Initial token count exceeds token limit")
message_count = len(chat_history)
cur_messages = chat_history[-message_count:]
token_count = self._token_count_for_messages(cur_messages) + initial_token_count
while token_count > self.token_limit and message_count > 1:
message_count -= 1
if chat_history[-message_count].role == MessageRole.TOOL:
# all tool messages should be preceded by an assistant message
# if we remove a tool message, we need to remove the assistant message too
message_count -= 1
if chat_history[-message_count].role == MessageRole.ASSISTANT:
# we cannot have an assistant message at the start of the chat history
# if after removal of the first, we have an assistant message,
# we need to remove the assistant message too
message_count -= 1
cur_messages = chat_history[-message_count:]
token_count = (
self._token_count_for_messages(cur_messages) + initial_token_count
)
Let me check the knowledge base for you. One moment! :books:
----------------------
Yes, the get method in the ChatMemoryBuffer class will return a list of the chat history. This list contains instances of ChatMessage, which represent the messages in the chat history.
The method works by first getting all the messages in the chat history with self.get_all(). It then checks if the initial token count exceeds the token limit. If it does, it raises a ValueError.
Next, it calculates the total token count of the current messages and the initial token count. If this total token count exceeds the token limit and there is more than one message in the chat history, it starts removing messages from the end of the chat history until the total token count is within the limit.
The method ensures that tool messages and assistant messages are removed in pairs, as a tool message should always be preceded by an assistant message. It also ensures that the chat history does not start with an assistant message.
Finally, the method returns the remaining messages in the chat history.
For more information, please refer to the LlamaIndex documentation ().
----------------------
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----------------------
@kapa.ai How would I build a basic framework towards implementing multiple agents using CoA and Composable Memory
Let me check the knowledge base for you. One moment! :books:
----------------------
To implement multiple agents using CoA (Chain of Assistants) and Composable Memory in LlamaIndex, you would need to create multiple instances of
CoAAgentWorker and
SimpleComposableMemory. Here's a basic framework on how you can do it:
First, you need to import the necessary modules:
from llama_index.core.memory import SimpleComposableMemory, ChatMemoryBuffer, VectorMemory
from llama_index.core.agent import CoAAgentWorker
from llama_index.core.tools import QueryEngineTool
from llama_index.llms.openai import OpenAI
Next, define your memory modules. For each agent, you would need a
ChatMemoryBuffer and a
VectorMemory:
chat_memory1 = ChatMemoryBuffer.from_defaults(token_limit=3000)
vector_memory1 = VectorMemory.from_defaults(vector_store=None, embed_model=OpenAIEmbedding(), retriever_kwargs={"similarity_top_k": 2})
chat_memory2 = ChatMemoryBuffer.from_defaults(token_limit=3000)
vector_memory2 = VectorMemory.from_defaults(vector_store=None, embed_model=OpenAIEmbedding(), retriever_kwargs={"similarity_top_k": 2})Then, create
SimpleComposableMemory for each agent:
composable_memory1 = SimpleComposableMemory.from_defaults(primary_memory=chat_memory1, secondary_memory_sources=[vector_memory1])
composable_memory2 = SimpleComposableMemory.from_defaults(primary_memory=chat_memory2, secondary_memory_sources=[vector_memory2])
Next, define your tools. In this case, we're using
QueryEngineTool:
query_engine_tool1 = QueryEngineTool.from_defaults(query_engine=engine1, name="agent1", description="Agent 1")
query_engine_tool2 = QueryEngineTool.from_defaults(query_engine=engine2, name="agent2", description="Agent 2")
Then, create
CoAAgentWorker for each agent:
agent_worker1 = CoAAgentWorker.from_tools(tools=[query_engine_tool1], llm=OpenAI(model="gpt-3.5-turbo"), memory=composable_memory1, verbose=True)
agent_worker2 = CoAAgentWorker.from_tools(tools=[query_engine_tool2], llm=OpenAI(model="gpt-3.5-turbo"), memory=composable_memory2, verbose=True)
Now, you have two agents with their own memory and tools. You can use these agents to handle different tasks or serve different users.
Please note that this is a basic framework and you might need to adjust it based on your specific requirements. For more information, please refer to the
LlamaIndex documentation ().
----------------------
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----------------------
@kapa.ai How can I use two agents to work together with one agent to develop a response
Let me check the knowledge base for you. One moment! :books:
----------------------
To have two agents work together to develop a response, you can use the Chain of Assistants (CoA) framework in LlamaIndex. In this framework, one agent (the main agent) can delegate tasks to other agents (assistant agents). Here's a basic example of how you can do it:
First, create the main agent and the assistant agents:
main_agent = CoAAgentWorker.from_tools(tools=main_agent_tools, llm=main_agent_llm, memory=main_agent_memory, verbose=True)
assistant_agent1 = CoAAgentWorker.from_tools(tools=assistant_agent1_tools, llm=assistant_agent1_llm, memory=assistant_agent1_memory, verbose=True)
assistant_agent2 = CoAAgentWorker.from_tools(tools=assistant_agent2_tools, llm=assistant_agent2_llm, memory=assistant_agent2_memory, verbose=True)
Then, add the assistant agents to the main agent:
main_agent.add_assistant(assistant_agent1)
main_agent.add_assistant(assistant_agent2)
Now, when you call the
chat method on the main agent, it can delegate tasks to the assistant agents:
response = main_agent.chat("What is the weather like?")In this case, the main agent will first try to handle the task itself. If it can't handle the task, it will delegate the task to the assistant agents. The assistant agents will then develop responses to the task and send them back to the main agent. The main agent will combine these responses to develop the final response.
Please note that this is a basic example and you might need to adjust it based on your specific requirements. For more information, please refer to the
LlamaIndex documentation ().
----------------------
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----------------------
@kapa.ai I am getting this error " File "c:\Users\temp_user\Documents\VScode\h\coa_basic_test.py", line 197, meta_tools = [, SyntaxError: asynchronous comprehension outside of an asynchronous function" on this code:
query_engine_tools = [
QueryEngineTool.from_defaults(
query_engine=engine,
name=chat_topic,
description=(chat_description),
),
]
chat_memory = ChatMemoryBuffer.from_defaults(
token_limit=3000,
chat_store=loaded_chat_store,
chat_store_key="user1",
)
vector_memory = VectorMemory.from_defaults(
vector_store=vector_store,
embed_model=Settings.embed_model,
retriever_kwargs={"similarity_top_k": 2},
)
composable_memory = SimpleComposableMemory.from_defaults(
primary_memory=chat_memory,
secondary_memory_sources=[vector_memory],
)
message_queue = SimpleMessageQueue()
control_plane = ControlPlaneServer(
message_queue=message_queue,
orchestrator=AgentOrchestrator(llm=Settings.llm),
)
tool_service = ToolService(
message_queue=message_queue,
tools=query_engine_tools,
running=True,
step_interval=0.5,
)
define meta-tools here
meta_tools = [
await MetaServiceTool.from_tool_service(
t.metadata.name,
message_queue=message_queue,
tool_service=tool_service,
)
for t in query_engine_tools
]
worker1 = FunctionCallingAgentWorker.from_tools(
meta_tools,
llm=Settings.llm,
)
agent1 = worker1.as_agent()
agent_server_1 = AgentService(
agent=agent1,
message_queue=message_queue,
description="Used to answer questions over Uber and Lyft 10K documents",
service_name="uber_lyft_10k_analyst_agent",
)
agent_main = CoAAgentWorker.from_tools(
tools=query_engine_tools,
llm=Settings.llm,
memory=composable_memory,
message_queue=message_queue,
verbose=True,
response_mode="execute",
)
pack = CoAAgentPack(tools=agent_main.tools, llm=agent_main.llm)
pack.add_agent(agent_server_1)Let me check the knowledge base for you. One moment! :books:
----------------------
The error message you're seeing is due to the use of an asynchronous comprehension (
await MetaServiceTool.from_tool_service(...) for t in query_engine_tools) outside of an asynchronous function.
In Python,
await can only be used inside an
async function. However, in your code, the
await keyword is used in a list comprehension that is not inside an
async function, which is causing the
SyntaxError.
To fix this, you need to move the creation of
meta_tools inside an
async function. Here's an example of how you can do it:
async def create_meta_tools():
return [
await MetaServiceTool.from_tool_service(
t.metadata.name,
message_queue=message_queue,
tool_service=tool_service,
)
for t in query_engine_tools
]
# Later in your code, when you're inside an async function or coroutine:
meta_tools = await create_meta_tools()In this example,
create_meta_tools is an
async function that creates the
meta_tools list. You can then call this function with
await create_meta_tools() inside another
async function or coroutine to get the
meta_tools list.
Please note that you'll need to adjust your code to ensure that
create_meta_tools is called inside an
async function or coroutine. If you're not familiar with asynchronous programming in Python, you might find it helpful to refer to the
Python documentation on coroutines and tasks ().
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------
@kapa.ai What could the error be:
Traceback (most recent call last):
File "c:\Users\temp_user\Documents\VScode\h\coa_basic_test.py", line 206, in <module>
worker1 = FunctionCallingAgentWorker.from_tools(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\temp_user\miniconda3\envs\h\Lib\site-packages\llama_index\core\agent\function_calling\step.py", line 155, in from_tools
return cls(
^^^^
File "C:\Users\temp_user\miniconda3\envs\h\Lib\site-packages\llama_index\core\agent\function_calling\step.py", line 99, in init
raise ValueError(
ValueError: Model name model-id does not support function calling API.
Let me check the knowledge base for you. One moment! :books:
----------------------
The error message indicates that the model you're trying to use with the
FunctionCallingAgentWorker does not support the function calling API.
In LlamaIndex, the
FunctionCallingAgentWorker class is used to create an agent that can call functions on a language model. However, not all language models support the function calling API. The model you're trying to use, identified by 'model-id', does not support this API, hence the
ValueError.
To fix this issue, you need to use a model that supports the function calling API. You can check the documentation of the language model you're using to see if it supports the function calling API. If it doesn't, you'll need to choose a different model that does.
Here's an example of how you might create a
FunctionCallingAgentWorker with a model that supports the function calling API:
from llama_index.llms.openai import OpenAI
llm = OpenAI(model="gpt-3.5-turbo") # replace "gpt-3.5-turbo" with a model that supports the function calling API
worker1 = FunctionCallingAgentWorker.from_tools(
meta_tools,
llm=llm,
)In this example,
OpenAI(model="gpt-3.5-turbo") creates a language model that supports the function calling API. You would replace
"gpt-3.5-turbo" with the ID of a model that supports the function calling API.
For more information, please refer to the
LlamaIndex documentation ().
----------------------
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----------------------
@kapa.ai the ".add_agent: method is not a part of CoAAgentWorker
Let me check the knowledge base for you. One moment! :books:
----------------------
You're correct. The add_agent method is not a part of the CoAAgentWorker class in LlamaIndex.
The CoAAgentWorker class is used to create an agent that can handle tasks using the Chain of Assistants (CoA) framework. However, it does not have a method to add other agents.
If you want to have multiple agents work together, you would typically create multiple instances of CoAAgentWorker and have them communicate with each other through a shared memory or message queue.
For more information on how to use CoAAgentWorker, please refer to the LlamaIndex documentation ().
----------------------
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----------------------
