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When using GraphCypherQAChain to fetch documents from Neo4j, the embeddings field is also returned, which consumes all context window tokens
Checked other resources
- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
- [X] I am sure that this is a bug in LangChain rather than my code.
- [X] The bug is not resolved by updating to the latest stable version of LangChain (or the specific integration package).
Example Code
neo4j_uri = "bolt://localhost:7687"
neo4j_user = "neo4j"
neo4j_password = "....."
graph = Neo4jGraph(
url=neo4j_uri,
username=neo4j_user,
password=neo4j_password,
database="....",
enhanced_schema=True,
)
cypher_chain = GraphCypherQAChain.from_llm(
cypher_llm=AzureChatOpenAI(
deployment_name="<.......>",
azure_endpoint="https://.........openai.azure.com/",
openai_api_key=".....",
api_version=".....",
temperature=0
),
qa_llm=AzureChatOpenAI(
deployment_name="......",
azure_endpoint="......",
openai_api_key="....",
api_version=".....",
temperature=0
),
graph=graph,
verbose=True,
)
response = cypher_chain.invoke(
{"query": "How many tasks do i have"}
)
Error Message and Stack Trace (if applicable)
openai.BadRequestError: Error code: 400 - {'error': {'message': "This model's maximum context length is 32768 tokens. However, your messages resulted in 38782 tokens. Please reduce the length of the messages.", 'type': 'invalid_request_error', 'param': 'messages', 'code': 'context_length_exceeded'}}
Description
When employing the GraphCypherQAChain.from_llm function, it generates a Cypher query that outputs all properties, including embeddings. Currently, there is no functionality to selectively include or exclude specific properties from the documents, which results in utilizing the entire context window.
System Info
Packages
langchain-community==0.2.2 neo4j==5.18.0/5.19.0/5.20.0 langchain==0.2.2 langchain-core==0.2.4 langchain-openai==0.1.