GraphQAChain Not Recognizing Knowledge Triples in LangChain

[kartikey-codes]

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Example Code

from langchain.chains import GraphQAChain
from langchain_community.graphs import NetworkxEntityGraph
from langchain_together import ChatTogether

# Initialize the chat model with API key and model details
chat_model = ChatTogether(
    together_api_key="your_api_key_here",  # Ensure to replace with your actual API key
    model="meta-llama/Llama-3-70b-chat-hf",
)

# Load the graph from a GML file
gml_file_path = "./graph.gml"
loaded_graph = NetworkxEntityGraph.from_gml(gml_file_path)
print(loaded_graph.get_triples())

# Set up the GraphQAChain with the loaded graph and invoke it with a query
chain = GraphQAChain.from_llm(chat_model, graph=loaded_graph, verbose=True)
question = "What can you tell me about the number of nodes?"
response = chain.invoke(question)
print("Response:", response)

Description

Description

I'm using GraphQAChain from langchain.chains and NetworkxEntityGraph from langchain_community.graphs to load and query a graph defined in a GML file. This graph contains various triples detailing dependencies and statuses of different backends. However, when querying the number of nodes using the chain, it responds with incorrect information, stating that there are no nodes in the graph.

Expected Behavior:
The GraphQAChain should accurately interpret the loaded triples and provide correct data about the number of nodes or relevant information stored within the graph structure.

Current Behavior:
Despite the graph apparently loading triples correctly (as shown in the console output), the GraphQAChain reports that no data is available to count the number of nodes, contradicting the visible loaded data.

System Info

[dosubot[bot]]

Hey @kartikey-codes! I'm here to help you with any bugs, questions, or contributions you have. Let's tackle this together!

I found a similar unsolved discussion that might be relevant to your issue: Issue with GraphQAChain: Inability to Recognize Context Beyond 2-3 Graph Triplets ^[1].

The GraphQAChain from langchain.chains might not be recognizing the knowledge triples in the NetworkxEntityGraph when querying the number of nodes because the triples may not have been added correctly to the graph. Ensure that the triples are being parsed and added to the graph properly using the add_triple method.

Here is an example of how to add triples to the NetworkxEntityGraph:

from langchain.chains import GraphQAChain
from langchain_community.graphs.networkx_graph import NetworkxEntityGraph, parse_triples

# Create an instance of NetworkxEntityGraph
graph = NetworkxEntityGraph()

# Example knowledge string
knowledge_str = "(subject1, predicate1, object1)<|>(subject2, predicate2, object2)"

# Parse the knowledge triples
triples = parse_triples(knowledge_str)

# Add each triple to the graph
for triple in triples:
    graph.add_triple(triple)

# Now you can query the number of nodes
number_of_nodes = graph.get_number_of_nodes()
print(f"Number of nodes: {number_of_nodes}")

Ensure that the knowledge_str is correctly formatted and that the parse_triples function is correctly parsing the triples. If the triples are not added to the graph, the get_number_of_nodes method will not return the expected result.

Additionally, make sure that the GraphQAChain is properly configured to use the NetworkxEntityGraph instance. The GraphQAChain uses the get_entities function to extract entities and then retrieves the knowledge associated with those entities from the graph. If the entities are not correctly extracted or the knowledge is not properly retrieved, the chain may not function as expected.

To automate the process of creating a graph from text, you can use the GraphIndexCreator class, which leverages a language model to extract knowledge triples from text and add them to the graph:

from langchain_community.graphs.index_creator import GraphIndexCreator
from langchain_core.language_models import YourLanguageModel

# Initialize your language model
llm = YourLanguageModel()

# Create an instance of GraphIndexCreator
index_creator = GraphIndexCreator(llm=llm)

# Example text
text = "Descartes likes to drive antique scooters and play the mandolin."

# Create the graph from text
graph = index_creator.from_text(text)

# Now you can query the number of nodes
number_of_nodes = graph.get_number_of_nodes()
print(f"Number of nodes: {number_of_nodes}")

This approach ensures that the triples are correctly extracted and added to the graph, allowing the GraphQAChain to function as expected ^[2][3][4].

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