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community[patch]: Add function response to graph cypher qa chain (#22690)

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LLMs struggle with Graph RAG, because it's different from vector RAG in
a way that you don't provide the whole context, only the answer and the
LLM has to believe. However, that doesn't really work a lot of the time.
However, if you wrap the context as function response the accuracy is
much better.

btw... `union[LLMChain, Runnable]` is linting fun, that's why so many
ignores
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Tomaz Bratanic 2024-06-10 13:52:17 -07:00 committed by GitHub
parent 34edfe4a16
commit 76a193decc
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3 changed files with 212 additions and 40 deletions
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152
docs/docs/integrations/graphs/neo4j_cypher.ipynb
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@ -164,10 +164,10 @@
"text": [ "text": [
"Node properties:\n", "Node properties:\n",
"- **Movie**\n", "- **Movie**\n",
" - `runtime: INTEGER` Min: 120, Max: 120\n", " - `runtime`: INTEGER Min: 120, Max: 120\n",
" - `name: STRING` Available options: ['Top Gun']\n", " - `name`: STRING Available options: ['Top Gun']\n",
"- **Actor**\n", "- **Actor**\n",
" - `name: STRING` Available options: ['Tom Cruise', 'Val Kilmer', 'Anthony Edwards', 'Meg Ryan']\n", " - `name`: STRING Available options: ['Tom Cruise', 'Val Kilmer', 'Anthony Edwards', 'Meg Ryan']\n",
"Relationship properties:\n", "Relationship properties:\n",
"\n", "\n",
"The relationships:\n", "The relationships:\n",
@ -225,7 +225,7 @@
"WHERE m.name = 'Top Gun'\n", "WHERE m.name = 'Top Gun'\n",
"RETURN a.name\u001b[0m\n", "RETURN a.name\u001b[0m\n",
"Full Context:\n", "Full Context:\n",
"\u001b[32;1m\u001b[1;3m[{'a.name': 'Anthony Edwards'}, {'a.name': 'Meg Ryan'}, {'a.name': 'Val Kilmer'}, {'a.name': 'Tom Cruise'}]\u001b[0m\n", "\u001b[32;1m\u001b[1;3m[{'a.name': 'Tom Cruise'}, {'a.name': 'Val Kilmer'}, {'a.name': 'Anthony Edwards'}, {'a.name': 'Meg Ryan'}]\u001b[0m\n",
"\n", "\n",
"\u001b[1m> Finished chain.\u001b[0m\n" "\u001b[1m> Finished chain.\u001b[0m\n"
] ]
@ -234,7 +234,7 @@
"data": { "data": {
"text/plain": [ "text/plain": [
"{'query': 'Who played in Top Gun?',\n", "{'query': 'Who played in Top Gun?',\n",
" 'result': 'Anthony Edwards, Meg Ryan, Val Kilmer, Tom Cruise played in Top Gun.'}" " 'result': 'Tom Cruise, Val Kilmer, Anthony Edwards, and Meg Ryan played in Top Gun.'}"
] ]
}, },
"execution_count": 8, "execution_count": 8,
@ -286,7 +286,7 @@
"WHERE m.name = 'Top Gun'\n", "WHERE m.name = 'Top Gun'\n",
"RETURN a.name\u001b[0m\n", "RETURN a.name\u001b[0m\n",
"Full Context:\n", "Full Context:\n",
"\u001b[32;1m\u001b[1;3m[{'a.name': 'Anthony Edwards'}, {'a.name': 'Meg Ryan'}]\u001b[0m\n", "\u001b[32;1m\u001b[1;3m[{'a.name': 'Tom Cruise'}, {'a.name': 'Val Kilmer'}]\u001b[0m\n",
"\n", "\n",
"\u001b[1m> Finished chain.\u001b[0m\n" "\u001b[1m> Finished chain.\u001b[0m\n"
] ]
@ -295,7 +295,7 @@
"data": { "data": {
"text/plain": [ "text/plain": [
"{'query': 'Who played in Top Gun?',\n", "{'query': 'Who played in Top Gun?',\n",
" 'result': 'Anthony Edwards, Meg Ryan played in Top Gun.'}" " 'result': 'Tom Cruise, Val Kilmer played in Top Gun.'}"
] ]
}, },
"execution_count": 10, "execution_count": 10,
@ -346,11 +346,11 @@
"WHERE m.name = 'Top Gun'\n", "WHERE m.name = 'Top Gun'\n",
"RETURN a.name\u001b[0m\n", "RETURN a.name\u001b[0m\n",
"Full Context:\n", "Full Context:\n",
"\u001b[32;1m\u001b[1;3m[{'a.name': 'Anthony Edwards'}, {'a.name': 'Meg Ryan'}, {'a.name': 'Val Kilmer'}, {'a.name': 'Tom Cruise'}]\u001b[0m\n", "\u001b[32;1m\u001b[1;3m[{'a.name': 'Tom Cruise'}, {'a.name': 'Val Kilmer'}, {'a.name': 'Anthony Edwards'}, {'a.name': 'Meg Ryan'}]\u001b[0m\n",
"\n", "\n",
"\u001b[1m> Finished chain.\u001b[0m\n", "\u001b[1m> Finished chain.\u001b[0m\n",
"Intermediate steps: [{'query': \"MATCH (a:Actor)-[:ACTED_IN]->(m:Movie)\\nWHERE m.name = 'Top Gun'\\nRETURN a.name\"}, {'context': [{'a.name': 'Anthony Edwards'}, {'a.name': 'Meg Ryan'}, {'a.name': 'Val Kilmer'}, {'a.name': 'Tom Cruise'}]}]\n", "Intermediate steps: [{'query': \"MATCH (a:Actor)-[:ACTED_IN]->(m:Movie)\\nWHERE m.name = 'Top Gun'\\nRETURN a.name\"}, {'context': [{'a.name': 'Tom Cruise'}, {'a.name': 'Val Kilmer'}, {'a.name': 'Anthony Edwards'}, {'a.name': 'Meg Ryan'}]}]\n",
"Final answer: Anthony Edwards, Meg Ryan, Val Kilmer, Tom Cruise played in Top Gun.\n" "Final answer: Tom Cruise, Val Kilmer, Anthony Edwards, and Meg Ryan played in Top Gun.\n"
] ]
} }
], ],
@ -406,10 +406,10 @@
"data": { "data": {
"text/plain": [ "text/plain": [
"{'query': 'Who played in Top Gun?',\n", "{'query': 'Who played in Top Gun?',\n",
" 'result': [{'a.name': 'Anthony Edwards'},\n", " 'result': [{'a.name': 'Tom Cruise'},\n",
" {'a.name': 'Meg Ryan'},\n",
" {'a.name': 'Val Kilmer'},\n", " {'a.name': 'Val Kilmer'},\n",
" {'a.name': 'Tom Cruise'}]}" " {'a.name': 'Anthony Edwards'},\n",
" {'a.name': 'Meg Ryan'}]}"
] ]
}, },
"execution_count": 14, "execution_count": 14,
@ -482,7 +482,7 @@
"\n", "\n",
"\u001b[1m> Entering new GraphCypherQAChain chain...\u001b[0m\n", "\u001b[1m> Entering new GraphCypherQAChain chain...\u001b[0m\n",
"Generated Cypher:\n", "Generated Cypher:\n",
"\u001b[32;1m\u001b[1;3mMATCH (:Movie {name:\"Top Gun\"})<-[:ACTED_IN]-()\n", "\u001b[32;1m\u001b[1;3mMATCH (m:Movie {name:\"Top Gun\"})<-[:ACTED_IN]-()\n",
"RETURN count(*) AS numberOfActors\u001b[0m\n", "RETURN count(*) AS numberOfActors\u001b[0m\n",
"Full Context:\n", "Full Context:\n",
"\u001b[32;1m\u001b[1;3m[{'numberOfActors': 4}]\u001b[0m\n", "\u001b[32;1m\u001b[1;3m[{'numberOfActors': 4}]\u001b[0m\n",
@ -494,7 +494,7 @@
"data": { "data": {
"text/plain": [ "text/plain": [
"{'query': 'How many people played in Top Gun?',\n", "{'query': 'How many people played in Top Gun?',\n",
" 'result': 'There were 4 actors who played in Top Gun.'}" " 'result': 'There were 4 actors in Top Gun.'}"
] ]
}, },
"execution_count": 16, "execution_count": 16,
@ -548,7 +548,7 @@
"WHERE m.name = 'Top Gun'\n", "WHERE m.name = 'Top Gun'\n",
"RETURN a.name\u001b[0m\n", "RETURN a.name\u001b[0m\n",
"Full Context:\n", "Full Context:\n",
"\u001b[32;1m\u001b[1;3m[{'a.name': 'Anthony Edwards'}, {'a.name': 'Meg Ryan'}, {'a.name': 'Val Kilmer'}, {'a.name': 'Tom Cruise'}]\u001b[0m\n", "\u001b[32;1m\u001b[1;3m[{'a.name': 'Tom Cruise'}, {'a.name': 'Val Kilmer'}, {'a.name': 'Anthony Edwards'}, {'a.name': 'Meg Ryan'}]\u001b[0m\n",
"\n", "\n",
"\u001b[1m> Finished chain.\u001b[0m\n" "\u001b[1m> Finished chain.\u001b[0m\n"
] ]
@ -557,7 +557,7 @@
"data": { "data": {
"text/plain": [ "text/plain": [
"{'query': 'Who played in Top Gun?',\n", "{'query': 'Who played in Top Gun?',\n",
" 'result': 'Anthony Edwards, Meg Ryan, Val Kilmer, and Tom Cruise played in Top Gun.'}" " 'result': 'Tom Cruise, Val Kilmer, Anthony Edwards, and Meg Ryan played in Top Gun.'}"
] ]
}, },
"execution_count": 18, "execution_count": 18,
@ -661,7 +661,7 @@
"WHERE m.name = 'Top Gun'\n", "WHERE m.name = 'Top Gun'\n",
"RETURN a.name\u001b[0m\n", "RETURN a.name\u001b[0m\n",
"Full Context:\n", "Full Context:\n",
"\u001b[32;1m\u001b[1;3m[{'a.name': 'Anthony Edwards'}, {'a.name': 'Meg Ryan'}, {'a.name': 'Val Kilmer'}, {'a.name': 'Tom Cruise'}]\u001b[0m\n", "\u001b[32;1m\u001b[1;3m[{'a.name': 'Tom Cruise'}, {'a.name': 'Val Kilmer'}, {'a.name': 'Anthony Edwards'}, {'a.name': 'Meg Ryan'}]\u001b[0m\n",
"\n", "\n",
"\u001b[1m> Finished chain.\u001b[0m\n" "\u001b[1m> Finished chain.\u001b[0m\n"
] ]
@ -670,7 +670,7 @@
"data": { "data": {
"text/plain": [ "text/plain": [
"{'query': 'Who played in Top Gun?',\n", "{'query': 'Who played in Top Gun?',\n",
" 'result': 'Anthony Edwards, Meg Ryan, Val Kilmer, Tom Cruise played in Top Gun.'}" " 'result': 'Tom Cruise, Val Kilmer, Anthony Edwards, and Meg Ryan played in Top Gun.'}"
] ]
}, },
"execution_count": 22, "execution_count": 22,
@ -683,12 +683,116 @@
] ]
}, },
{ {
"cell_type": "code", "cell_type": "markdown",
"execution_count": null, "id": "81093062-eb7f-4d96-b1fd-c36b8f1b9474",
"id": "3fa3f3d5-f7e7-4ca9-8f07-ca22b897f192",
"metadata": {}, "metadata": {},
"outputs": [], "source": [
"source": [] "## Provide context from database results as tool/function output\n",
"\n",
"You can use the `use_function_response` parameter to pass context from database results to an LLM as a tool/function output. This method improves the response accuracy and relevance of an answer as the LLM follows the provided context more closely.\n",
"_You will need to use an LLM with native function calling support to use this feature_."
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "2be8f51c-e80a-4a60-ab1c-266450fc17cd",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new GraphCypherQAChain chain...\u001b[0m\n",
"Generated Cypher:\n",
"\u001b[32;1m\u001b[1;3mMATCH (a:Actor)-[:ACTED_IN]->(m:Movie)\n",
"WHERE m.name = 'Top Gun'\n",
"RETURN a.name\u001b[0m\n",
"Full Context:\n",
"\u001b[32;1m\u001b[1;3m[{'a.name': 'Tom Cruise'}, {'a.name': 'Val Kilmer'}, {'a.name': 'Anthony Edwards'}, {'a.name': 'Meg Ryan'}]\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"{'query': 'Who played in Top Gun?',\n",
" 'result': 'The main actors in Top Gun are Tom Cruise, Val Kilmer, Anthony Edwards, and Meg Ryan.'}"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain = GraphCypherQAChain.from_llm(\n",
" llm=ChatOpenAI(temperature=0, model=\"gpt-3.5-turbo\"),\n",
" graph=graph,\n",
" verbose=True,\n",
" use_function_response=True,\n",
")\n",
"chain.invoke({\"query\": \"Who played in Top Gun?\"})"
]
},
{
"cell_type": "markdown",
"id": "48a75785-5bc9-49a7-a41b-88bf3ef9d312",
"metadata": {},
"source": [
"You can provide custom system message when using the function response feature by providing `function_response_system` to instruct the model on how to generate answers.\n",
"\n",
"_Note that `qa_prompt` will have no effect when using `use_function_response`_"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "ddf0a61e-f104-4dbb-abbf-e65f3f57dd9a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new GraphCypherQAChain chain...\u001b[0m\n",
"Generated Cypher:\n",
"\u001b[32;1m\u001b[1;3mMATCH (a:Actor)-[:ACTED_IN]->(m:Movie)\n",
"WHERE m.name = 'Top Gun'\n",
"RETURN a.name\u001b[0m\n",
"Full Context:\n",
"\u001b[32;1m\u001b[1;3m[{'a.name': 'Tom Cruise'}, {'a.name': 'Val Kilmer'}, {'a.name': 'Anthony Edwards'}, {'a.name': 'Meg Ryan'}]\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"{'query': 'Who played in Top Gun?',\n",
" 'result': \"Arrr matey! In the film Top Gun, ye be seein' Tom Cruise, Val Kilmer, Anthony Edwards, and Meg Ryan sailin' the high seas of the sky! Aye, they be a fine crew of actors, they be!\"}"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain = GraphCypherQAChain.from_llm(\n",
" llm=ChatOpenAI(temperature=0, model=\"gpt-3.5-turbo\"),\n",
" graph=graph,\n",
" verbose=True,\n",
" use_function_response=True,\n",
" function_response_system=\"Respond as a pirate!\",\n",
")\n",
"chain.invoke({\"query\": \"Who played in Top Gun?\"})"
]
} }
], ],
"metadata": { "metadata": {

92
libs/community/langchain_community/chains/graph_qa/cypher.py
View File

@ -2,14 +2,27 @@
from __future__ import annotations from __future__ import annotations
import re import re
from typing import Any, Dict, List, Optional from typing import Any, Dict, List, Optional, Union
from langchain.chains.base import Chain from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain from langchain.chains.llm import LLMChain
from langchain_core.callbacks import CallbackManagerForChainRun from langchain_core.callbacks import CallbackManagerForChainRun
from langchain_core.language_models import BaseLanguageModel from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts import BasePromptTemplate from langchain_core.messages import (
AIMessage,
BaseMessage,
SystemMessage,
ToolMessage,
)
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import (
BasePromptTemplate,
ChatPromptTemplate,
HumanMessagePromptTemplate,
MessagesPlaceholder,
)
from langchain_core.pydantic_v1 import Field from langchain_core.pydantic_v1 import Field
from langchain_core.runnables import Runnable
from langchain_community.chains.graph_qa.cypher_utils import ( from langchain_community.chains.graph_qa.cypher_utils import (
CypherQueryCorrector, CypherQueryCorrector,
@ -23,6 +36,12 @@ from langchain_community.graphs.graph_store import GraphStore
INTERMEDIATE_STEPS_KEY = "intermediate_steps" INTERMEDIATE_STEPS_KEY = "intermediate_steps"
FUNCTION_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!
"""
def extract_cypher(text: str) -> str: def extract_cypher(text: str) -> str:
"""Extract Cypher code from a text. """Extract Cypher code from a text.
@ -104,6 +123,31 @@ def construct_schema(
) )
def get_function_response(
question: str, context: List[Dict[str, Any]]
) -> List[BaseMessage]:
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
class GraphCypherQAChain(Chain): class GraphCypherQAChain(Chain):
"""Chain for question-answering against a graph by generating Cypher statements. """Chain for question-answering against a graph by generating Cypher statements.
@ -121,7 +165,7 @@ class GraphCypherQAChain(Chain):
graph: GraphStore = Field(exclude=True) graph: GraphStore = Field(exclude=True)
cypher_generation_chain: LLMChain cypher_generation_chain: LLMChain
qa_chain: LLMChain qa_chain: Union[LLMChain, Runnable]
graph_schema: str graph_schema: str
input_key: str = "query" #: :meta private: input_key: str = "query" #: :meta private:
output_key: str = "result" #: :meta private: output_key: str = "result" #: :meta private:
@ -133,6 +177,8 @@ class GraphCypherQAChain(Chain):
"""Whether or not to return the result of querying the graph directly.""" """Whether or not to return the result of querying the graph directly."""
cypher_query_corrector: Optional[CypherQueryCorrector] = None cypher_query_corrector: Optional[CypherQueryCorrector] = None
"""Optional cypher validation tool""" """Optional cypher validation tool"""
use_function_response: bool = False
"""Whether to wrap the database context as tool/function response"""
@property @property
def input_keys(self) -> List[str]: def input_keys(self) -> List[str]:
@ -163,12 +209,14 @@ class GraphCypherQAChain(Chain):
qa_prompt: Optional[BasePromptTemplate] = None, qa_prompt: Optional[BasePromptTemplate] = None,
cypher_prompt: Optional[BasePromptTemplate] = None, cypher_prompt: Optional[BasePromptTemplate] = None,
cypher_llm: Optional[BaseLanguageModel] = None, cypher_llm: Optional[BaseLanguageModel] = None,
qa_llm: Optional[BaseLanguageModel] = None, qa_llm: Optional[Union[BaseLanguageModel, Any]] = None,
exclude_types: List[str] = [], exclude_types: List[str] = [],
include_types: List[str] = [], include_types: List[str] = [],
validate_cypher: bool = False, validate_cypher: bool = False,
qa_llm_kwargs: Optional[Dict[str, Any]] = None, qa_llm_kwargs: Optional[Dict[str, Any]] = None,
cypher_llm_kwargs: Optional[Dict[str, Any]] = None, cypher_llm_kwargs: Optional[Dict[str, Any]] = None,
use_function_response: bool = False,
function_response_system: str = FUNCTION_RESPONSE_SYSTEM,
**kwargs: Any, **kwargs: Any,
) -> GraphCypherQAChain: ) -> GraphCypherQAChain:
"""Initialize from LLM.""" """Initialize from LLM."""
@ -205,7 +253,22 @@ class GraphCypherQAChain(Chain):
cypher_prompt if cypher_prompt is not None else CYPHER_GENERATION_PROMPT cypher_prompt if cypher_prompt is not None else CYPHER_GENERATION_PROMPT
) )
qa_chain = LLMChain(llm=qa_llm or llm, **use_qa_llm_kwargs) # type: ignore[arg-type] qa_llm = qa_llm or llm
if use_function_response:
try:
qa_llm.bind_tools({}) # type: ignore[union-attr]
response_prompt = ChatPromptTemplate.from_messages(
[
SystemMessage(content=function_response_system),
HumanMessagePromptTemplate.from_template("{question}"),
MessagesPlaceholder(variable_name="function_response"),
]
)
qa_chain = response_prompt | qa_llm | StrOutputParser() # type: ignore
except (NotImplementedError, AttributeError):
raise ValueError("Provided LLM does not support native tools/functions")
else:
qa_chain = LLMChain(llm=qa_llm, **use_qa_llm_kwargs) # type: ignore[arg-type]
cypher_generation_chain = LLMChain( cypher_generation_chain = LLMChain(
llm=cypher_llm or llm, # type: ignore[arg-type] llm=cypher_llm or llm, # type: ignore[arg-type]
@ -217,7 +280,6 @@ class GraphCypherQAChain(Chain):
"Either `exclude_types` or `include_types` " "Either `exclude_types` or `include_types` "
"can be provided, but not both" "can be provided, but not both"
) )
graph_schema = construct_schema( graph_schema = construct_schema(
kwargs["graph"].get_structured_schema, include_types, exclude_types kwargs["graph"].get_structured_schema, include_types, exclude_types
) )
@ -235,6 +297,7 @@ class GraphCypherQAChain(Chain):
qa_chain=qa_chain, qa_chain=qa_chain,
cypher_generation_chain=cypher_generation_chain, cypher_generation_chain=cypher_generation_chain,
cypher_query_corrector=cypher_query_corrector, cypher_query_corrector=cypher_query_corrector,
use_function_response=use_function_response,
**kwargs, **kwargs,
) )
@ -284,12 +347,17 @@ class GraphCypherQAChain(Chain):
) )
intermediate_steps.append({"context": context}) intermediate_steps.append({"context": context})
if self.use_function_response:
result = self.qa_chain( function_response = get_function_response(question, context)
{"question": question, "context": context}, final_result = self.qa_chain.invoke( # type: ignore
callbacks=callbacks, {"question": question, "function_response": function_response},
) )
final_result = result[self.qa_chain.output_key] else:
result = self.qa_chain.invoke( # type: ignore
{"question": question, "context": context},
callbacks=callbacks,
)
final_result = result[self.qa_chain.output_key] # type: ignore
chain_result: Dict[str, Any] = {self.output_key: final_result} chain_result: Dict[str, Any] = {self.output_key: final_result}
if self.return_intermediate_steps: if self.return_intermediate_steps:

8
libs/community/tests/unit_tests/chains/test_graph_qa.py
View File

@ -60,7 +60,7 @@ def test_graph_cypher_qa_chain_prompt_selection_1() -> None:
qa_prompt=qa_prompt, qa_prompt=qa_prompt,
cypher_prompt=cypher_prompt, cypher_prompt=cypher_prompt,
) )
assert chain.qa_chain.prompt == qa_prompt assert chain.qa_chain.prompt == qa_prompt # type: ignore[union-attr]
assert chain.cypher_generation_chain.prompt == cypher_prompt assert chain.cypher_generation_chain.prompt == cypher_prompt
@ -72,7 +72,7 @@ def test_graph_cypher_qa_chain_prompt_selection_2() -> None:
verbose=True, verbose=True,
return_intermediate_steps=False, return_intermediate_steps=False,
) )
assert chain.qa_chain.prompt == CYPHER_QA_PROMPT assert chain.qa_chain.prompt == CYPHER_QA_PROMPT # type: ignore[union-attr]
assert chain.cypher_generation_chain.prompt == CYPHER_GENERATION_PROMPT assert chain.cypher_generation_chain.prompt == CYPHER_GENERATION_PROMPT
@ -88,7 +88,7 @@ def test_graph_cypher_qa_chain_prompt_selection_3() -> None:
cypher_llm_kwargs={"memory": readonlymemory}, cypher_llm_kwargs={"memory": readonlymemory},
qa_llm_kwargs={"memory": readonlymemory}, qa_llm_kwargs={"memory": readonlymemory},
) )
assert chain.qa_chain.prompt == CYPHER_QA_PROMPT assert chain.qa_chain.prompt == CYPHER_QA_PROMPT # type: ignore[union-attr]
assert chain.cypher_generation_chain.prompt == CYPHER_GENERATION_PROMPT assert chain.cypher_generation_chain.prompt == CYPHER_GENERATION_PROMPT
@ -108,7 +108,7 @@ def test_graph_cypher_qa_chain_prompt_selection_4() -> None:
cypher_llm_kwargs={"prompt": cypher_prompt, "memory": readonlymemory}, cypher_llm_kwargs={"prompt": cypher_prompt, "memory": readonlymemory},
qa_llm_kwargs={"prompt": qa_prompt, "memory": readonlymemory}, qa_llm_kwargs={"prompt": qa_prompt, "memory": readonlymemory},
) )
assert chain.qa_chain.prompt == qa_prompt assert chain.qa_chain.prompt == qa_prompt # type: ignore[union-attr]
assert chain.cypher_generation_chain.prompt == cypher_prompt assert chain.cypher_generation_chain.prompt == cypher_prompt