dosubot[bot]
commented
1 year ago 🤖
Hello,
Thank you for your detailed question. Based on your use case, it seems like you want to use a chat_engine with a Graph Index in LlamaIndex, and you have multiple sources of information. You also want to retain history and query from history.
To achieve this, you can use the ContextChatEngine class provided in the llama_index.chat_engine.context module. This class uses a retriever to retrieve a context, sets the context in the system prompt, and then uses an LLM to generate a response, for a fluid chat experience.
Here is an example of how you can use the ContextChatEngine:
from llama_index.chat_engine.context import ContextChatEngine
from llama_index.indices.knowledge_graph.retrievers import KnowledgeGraphRAGRetriever
from llama_index.indices.knowledge_graph.base import KnowledgeGraphIndex
from llama_index.llms.base import LLM
from llama_index.memory import BaseMemory
from llama_index.llm_predictor.base import LLMPredictor
from llama_index.indices.service_context import ServiceContext
# Initialize your retriever, LLM, and memory
index = KnowledgeGraphIndex()
retriever = KnowledgeGraphRAGRetriever(index)
llm = LLM()
memory = BaseMemory()
# Initialize your service context
service_context = ServiceContext.from_defaults()
# Create an instance of ContextChatEngine
context_chat_engine = ContextChatEngine.from_defaults(
retriever=retriever,
service_context=service_context,
memory=memory
)
# Use the chat method to interact with the chat engine
response = context_chat_engine.chat("Hello, how are you?")In this example, we create an instance of ContextChatEngine with the desired retriever, LLM, and memory. We then use the chat method to interact with the chat engine. The chat method takes a message and an optional chat history as arguments, and returns an AgentChatResponse.
Please note that this is a simplified example and you would need to implement the KnowledgeGraphIndex, KnowledgeGraphRAGRetriever, LLM, and BaseMemory based on your specific requirements. The KnowledgeGraphRAGRetriever is used to retrieve information from a knowledge graph index, which is useful when you have multiple sources of information and need to retain history and query from history.
You can find more information about the ContextChatEngine class in the llama_index/chat_engine/context.py file in the LlamaIndex repository.
I hope this helps! If you have any further questions, please feel free to ask.
Best, Dosu
Sources
- [Question]: What's the best way to add conversation history to SubQuestionQueryEngine?
- [Question]: I have a list of vectorstore indices how do i create one chat engine using those indices
- Support to add conversation history in the prompt
- llama_index/chat_engine/types.py
- llama_index/chat_engine/context.py
- llama_index/indices/knowledge_graph/retrievers.py
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Question Validation
Question
I have gone through the document but my use case is to use a graph index. In short, I have multiple sources of information i.e. Database, File, and Directory. Hence I created a ListIndex for each and combine them to store it as a graph index. Now the issue is Graph index does not have
chat_engineoption i.e.index.as_chat_engine(...). It only hasquery_engine.I need the
chat_engineoption because I want to retain history and query from history which acts like memory while is same is not available forquery_engine. Example below:Here
message_historyis an array ofChatMessage.Any recommendations, please? What is the best way to have index from different sources and use
chat_engineto have a memory as well?