Getting 404 exception while using GraphCypherQAChain

[vaibhavjain11]

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Example Code

from langchain.chains import GraphCypherQAChain
from langchain_community.graphs import Neo4jGraph
from langchain_core.prompts import PromptTemplate
from langchain_openai import AzureOpenAI
from src import generate_token
import os

client = AzureOpenAI(
    # (H2M access is only provided for UAT)
    api_key=generate_token.CoinToken.get_coin_token(),
    temperature=0
)

host = 'URL'
user = 'USER'
password = 'PASSWORD'

graph = Neo4jGraph(host, username=user, password=password)

CYPHER_GENERATION_TEMPLATE = """Task:Generate Cypher statement to query a graph database.
Instructions:
Use only the provided relationship types and properties in the schema.
Do not use any other relationship types or properties that are not provided.
Schema:
{schema}
Note: Do not include any explanations or apologies in your responses.
Do not respond to any questions that might ask anything else than for you to construct a Cypher statement.
Do not include any text except the generated Cypher statement.

The question is:
{question} 

Important: In the generated Cypher query, the RETURN statement must explicitly include the property values used in the query's filtering condition, alongside the main information requested from the original question.

"""
CYPHER_GENERATION_PROMPT = PromptTemplate(
    input_variables=["schema", "question"], template=CYPHER_GENERATION_TEMPLATE
)
chain = GraphCypherQAChain.from_llm(cypher_prompt=CYPHER_GENERATION_PROMPT, llm=client, graph=graph, verbose=True, validate_cypher=True)

result = chain.invoke({"query" : "Find all health care providers specialized in Dermatology"})

Description

Trying to get natural language answer using langchain

I am facing error when running the above code
`

Entering new GraphCypherQAChain chain...
Traceback (most recent call last):
File "C:\Users\PycharmProjects\TTSTransactions\src\prompt\chat.py", line 59, in
calc()
File "C:\Users\PycharmProjects\TTSTransactions\src\prompt\chat.py", line 51, in calc
result = chain.invoke({"query" : "Find all health care providers specialized in Dermatology"})
File "C:\Users\PycharmProjects\TTSTransactions.venv\Lib\site-packages\langchain\chains\base.py", line 164, in invoke
raise e
File "C:\Users\PycharmProjects\TTSTransactions.venv\Lib\site-packages\langchain\chains\base.py", line 154, in invoke
self._call(inputs, run_manager=run_manager)
File "C:\Users\PycharmProjects\TTSTransactions.venv\Lib\site-packages\langchain_community\chains\graph_qa\cypher.py", line 322, in _call
generated_cypher = self.cypher_generation_chain.run(args, callbacks=callbacks)
File "C:\Users\PycharmProjects\TTSTransactions.venv\Lib\site-packages\langchain_core_api\deprecation.py", line 170, in warning_emitting_wrapper
return wrapped(*args, **kwargs)
File "C:\Users\PycharmProjects\TTSTransactions.venv\Lib\site-packages\langchain\chains\base.py", line 599, in run
return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
File "C:\Users\PycharmProjects\TTSTransactions.venv\Lib\site-packages\langchain_core_api\deprecation.py", line 170, in warning_emitting_wrapper
return wrapped(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\PycharmProjects\TTSTransactions.venv\Lib\site-packages\langchain\chains\base.py", line 381, in call
return self.invoke(
File "C:\Users\PycharmProjects\TTSTransactions.venv\Lib\site-packages\langchain\chains\base.py", line 164, in invoke
raise e
File "C:\Users\PycharmProjects\TTSTransactions.venv\Lib\site-packages\langchain\chains\base.py", line 154, in invoke
self._call(inputs, run_manager=run_manager)
File "C:\Users\PycharmProjects\TTSTransactions.venv\Lib\site-packages\langchain\chains\llm.py", line 126, in _call
response = self.generate([inputs], run_manager=run_manager)
File "C:\Users\PycharmProjects\TTSTransactions.venv\Lib\site-packages\langchain\chains\llm.py", line 138, in generate
return self.llm.generate_prompt(
File "C:\Users\PycharmProjects\TTSTransactions.venv\Lib\site-packages\langchain_core\language_models\llms.py", line 751, in generate_prompt
return self.generate(prompt_strings, stop=stop, callbacks=callbacks, **kwargs)
File "C:\Users\PycharmProjects\TTSTransactions.venv\Lib\site-packages\langchain_core\language_models\llms.py", line 945, in generate
output = self._generate_helper(
File "C:\Users\PycharmProjects\TTSTransactions.venv\Lib\site-packages\langchain_core\language_models\llms.py", line 788, in _generate_helper
raise e
File "C:\Users\PycharmProjects\TTSTransactions.venv\Lib\site-packages\langchain_core\language_models\llms.py", line 775, in _generate_helper
self._generate(
File "C:\Users\PycharmProjects\TTSTransactions.venv\Lib\site-packages\langchain_openai\llms\base.py", line 360, in _generate
response = self.client.create(prompt=_prompts, **params)
File "C:\Users\PycharmProjects\TTSTransactions.venv\Lib\site-packages\openai_utils_utils.py", line 274, in wrapper
return func(*args, **kwargs)
File "C:\Users\PycharmProjects\TTSTransactions.venv\Lib\site-packages\openai\resources\completions.py", line 528, in create
return self._post(
File "C:\Users\PycharmProjects\TTSTransactions.venv\Lib\site-packages\openai_base_client.py", line 1261, in post
return cast(ResponseT, self.request(cast_to, opts, stream=stream, stream_cls=stream_cls))
File "C:\Users\PycharmProjects\TTSTransactions.venv\Lib\site-packages\openai_base_client.py", line 937, in request
return self._request(
File "C:\Users\PycharmProjects\TTSTransactions.venv\Lib\site-packages\openai_base_client.py", line 1041, in _request
raise self._make_status_error_from_response(err.response) from None
openai.NotFoundError: Error code: 404

Process finished with exit code 1
`

Can some provide any leads on solution ?

System Info

pip install langchain

[dosubot[bot]]

Hey @vaibhavjain11! How's it going? I'm here to help you out with any bugs or questions you have. Let's get that issue sorted together!

To resolve the 404 exception when using GraphCypherQAChain, ensure that your Neo4j instance is running and accessible at the specified URL. Here are the steps to troubleshoot and resolve the issue:

1. Verify Neo4j Instance:
   Ensure that your Neo4j instance is running and accessible at the URL you provided (host). You can start a local Neo4j instance using Docker if needed:

   docker run \
       --name neo4j \
       -p 7474:7474 -p 7687:7687 \
       -d \
       -e NEO4J_AUTH=neo4j/pleaseletmein \
       -e NEO4J_PLUGINS=\[\"apoc\"\]  \
       neo4j:latest

2. Check Credentials:
   Verify that the username and password you provided are correct.

3. Network Accessibility:
   Ensure that the necessary ports (7474 for HTTP and 7687 for Bolt) are open and not blocked by a firewall.

4. Test Connection:
   Use the following test code to ensure that your Neo4j database is correctly instantiated and connected:

   import os
   from langchain_community.graphs import Neo4jGraph
   
   def test_connect_neo4j() -> None:
       """Test that Neo4j database is correctly instantiated and connected."""
       url = os.environ.get("NEO4J_URI", "bolt://localhost:7687")
       username = os.environ.get("NEO4J_USERNAME", "neo4j")
       password = os.environ.get("NEO4J_PASSWORD", "pleaseletmein")
       assert url is not None
       assert username is not None
       assert password is not None
   
       graph = Neo4jGraph(
           url=url,
           username=username,
           password=password,
       )
   
       output = graph.query(
           """
       RETURN "test" AS output
       """
       )
       expected_output = [{"output": "test"}]
       assert output == expected_output
   
   test_connect_neo4j()

5. Azure OpenAI Configuration:
   Ensure that your Azure OpenAI client is correctly configured with the necessary environment variables:

   import os
   
   os.environ["OPENAI_API_VERSION"] = "2023-12-01-preview"
   os.environ["AZURE_OPENAI_ENDPOINT"] = "https://your-resource-name.openai.azure.com"
   os.environ["AZURE_OPENAI_API_KEY"] = "<your Azure OpenAI API key>"
   os.environ["AZURE_OPENAI_AD_TOKEN"] = "<your Azure AD token>"

6. Update Code:
   Ensure your code is correctly set up to handle the connection and query:

   from langchain.chains import GraphCypherQAChain
   from langchain_community.graphs import Neo4jGraph
   from langchain_core.prompts import PromptTemplate
   from langchain_openai import AzureOpenAI
   import os
   
   client = AzureOpenAI(
       api_key=os.getenv("AZURE_OPENAI_API_KEY"),
       temperature=0
   )
   
   host = 'bolt://localhost:7687'
   user = 'neo4j'
   password = 'pleaseletmein'
   
   graph = Neo4jGraph(url=host, username=user, password=password)
   
   CYPHER_GENERATION_TEMPLATE = """Task:Generate Cypher statement to query a graph database.
   Instructions:
   Use only the provided relationship types and properties in the schema.
   Do not use any other relationship types or properties that are not provided.
   Schema:
   {schema}
   Note: Do not include any explanations or apologies in your responses.
   Do not respond to any questions that might ask anything else than for you to construct a Cypher statement.
   Do not include any text except the generated Cypher statement.
   
   The question is:
   {question} 
   
   Important: In the generated Cypher query, the RETURN statement must explicitly include the property values used in the query's filtering condition, alongside the main information requested from the original question.
   
   """
   CYPHER_GENERATION_PROMPT = PromptTemplate(
       input_variables=["schema", "question"], template=CYPHER_GENERATION_TEMPLATE
   )
   chain = GraphCypherQAChain.from_llm(cypher_prompt=CYPHER_GENERATION_PROMPT, llm=client, graph=graph, verbose=True, validate_cypher=True)
   
   result = chain.invoke({"query": "Find all health care providers specialized in Dermatology"})
   print(result)

By following these steps, you should be able to resolve the 404 exception and successfully run your GraphCypherQAChain code ^[1][2][3][4].

To continue talking to Dosu, mention @dosu.

[vaibhavjain11]

@dosu My database connection is working fine when I am creating a separate file to test my database connection. Also When I am writing asking a general question using azure open ai, below code is working

`client = AzureOpenAI(
azure_endpoint='',
# (H2M access is only provided for UAT)
api_key=get_coin_token(),
api_version="2023-05-15"
)

completion = client.chat.completions.create(
model='',
messages=[
{
"role": "system",
"content": "You are an expert on financial institutions.",
},
{
"role": "user",
"content": "what is hedge funds",
},
],
user=os.getenv("USERNAME"),
temperature=0.2,
stream=False
)

print(completion)`

But when using langchain with neo4j then issue is coming.

[dosubot[bot]]

The 404 exception when using GraphCypherQAChain with Neo4j and Azure OpenAI is likely due to a misconfiguration or an incorrect endpoint being accessed. Here are some steps to troubleshoot and resolve the issue:

1. Verify Neo4j Configuration:
   Ensure that your Neo4j instance is correctly set up and accessible. Here is a sample setup for the Neo4j instance:

   from langchain.chains import GraphCypherQAChain
   from langchain_community.graphs import Neo4jGraph
   from langchain_openai import ChatOpenAI
   
   # Ensure the Neo4j instance is running and accessible
   graph = Neo4jGraph(
       url="bolt://localhost:7687", username="neo4j", password="password"
   )
   
   # Refresh the graph schema to ensure it is up-to-date
   graph.refresh_schema()
   
   # Initialize the GraphCypherQAChain with the correct LLM and graph
   chain = GraphCypherQAChain.from_llm(
       ChatOpenAI(temperature=0), graph=graph, verbose=True
   )
   
   # Run a sample query to test the setup
   result = chain.invoke({"query": "Who played in Top Gun?"})
   print(result)

2. Check Azure OpenAI Configuration:
   Ensure that your Azure OpenAI client is correctly configured. Here is an example configuration:

   from langchain_openai import AzureOpenAI
   
   client = AzureOpenAI(
       azure_endpoint='<end point>',
       api_key=get_coin_token(),
       api_version="2023-05-15"
   )

3. Debug Intermediate Steps:
   Use the return_intermediate_steps parameter to debug and understand the intermediate steps that might be causing the issue:

   chain = GraphCypherQAChain.from_llm(
       ChatOpenAI(temperature=0), graph=graph, verbose=True, return_intermediate_steps=True
   )
   
   result = chain.invoke({"query": "Who played in Top Gun?"})
   print(f"Intermediate steps: {result['intermediate_steps']}")
   print(f"Final answer: {result['result']}")

4. Cypher Query Generation and Validation:
   Ensure that the Cypher query generation and validation are correctly set up. Here is an example:

   from typing import List, Union
   from langchain.chains.graph_qa.cypher_utils import CypherQueryCorrector, Schema
   from langchain_community.graphs import Neo4jGraph
   from langchain_core.messages import AIMessage, SystemMessage, ToolMessage
   from langchain_core.output_parsers import StrOutputParser
   from langchain_core.prompts import ChatPromptTemplate, HumanMessagePromptTemplate, MessagesPlaceholder
   from langchain_core.pydantic_v1 import BaseModel
   from langchain_core.runnables import RunnablePassthrough
   from langchain_openai import ChatOpenAI
   
   # Connection to Neo4j
   graph = Neo4jGraph()
   
   # Cypher validation tool for relationship directions
   corrector_schema = [
       Schema(el["start"], el["type"], el["end"])
       for el in graph.structured_schema.get("relationships")
   ]
   cypher_validation = CypherQueryCorrector(corrector_schema)
   
   # LLMs
   cypher_llm = ChatOpenAI(model="gpt-4", temperature=0.0)
   qa_llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0.0)
   
   # Generate Cypher statement based on natural language input
   cypher_template = """Based on the Neo4j graph schema below, write a Cypher query that would answer the user's question:
   {schema}
   
   Question: {question}
   Cypher query:"""
   
   cypher_prompt = ChatPromptTemplate.from_messages(
       [
           ("system", "Given an input question, convert it to a Cypher query. No pre-amble."),
           ("human", cypher_template),
       ]
   )
   
   cypher_response = (
       RunnablePassthrough.assign(
           schema=lambda _: graph.get_schema,
       )
       | cypher_prompt
       | cypher_llm.bind(stop=["\nCypherResult:"])
       | StrOutputParser()
   )
   
   response_system = """You are an assistant that helps to form nice and human 
   understandable answers based on the provided information from tools.
   Do not add any other information that wasn't present in the tools, and use 
   very concise style in interpreting results!
   """
   response_prompt = ChatPromptTemplate.from_messages(
       [
           SystemMessage(content=response_system),
           HumanMessagePromptTemplate.from_template("{question}"),
           MessagesPlaceholder(variable_name="function_response"),
       ]
   )
   
   def get_function_response(query: str, question: str) -> List[Union[AIMessage, ToolMessage]]:
       context = graph.query(cypher_validation(query))
       TOOL_ID = "call_H7fABDuzEau48T10Qn0Lsh0D"
       messages = [
           AIMessage(
               content="",
               additional_kwargs={
                   "tool_calls": [
                       {
                           "id": TOOL_ID,
                           "function": {
                               "arguments": '{"question":"' + question + '"}',
                               "name": "GetInformation",
                           },
                           "type": "function",
                       }
                   ]
               },
           ),
           ToolMessage(content=str(context), tool_call_id=TOOL_ID),
       ]
       return messages
   
   chain = (
       RunnablePassthrough.assign(query=cypher_response)
       | RunnablePassthrough.assign(
           function_response=lambda x: get_function_response(x["query"], x["question"])
       )
       | response_prompt
       | qa_llm
       | StrOutputParser()
   )
   
   # Add typing for input
   class Question(BaseModel):
       question: str
   
   chain = chain.with_types(input_type=Question)

By following these steps, you should be able to resolve the 404 exception and successfully run your GraphCypherQAChain code ^[1][2][3][4].

To continue talking to Dosu, mention @dosu.

[vaibhavjain11]

I was using AzureOpenAI but I had to use AzureChatOpenAI . After using AzureChatOpenAI the issue is resolved.