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[community][graph]: Update KuzuQAChain and docs (#21218)

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This PR makes some small updates for `KuzuQAChain` for graph QA.

- Updated Cypher generation prompt (we now support `WHERE EXISTS`) and
generalize it more
- Support different LLMs for Cypher generation and QA
- Update docs and examples
This commit is contained in:
Prashanth Rao 2024-05-13 20:17:14 -04:00 committed by GitHub
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commit 63c3a0e56c
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326
docs/docs/integrations/graphs/kuzu_db.ipynb
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@ -7,11 +7,12 @@
"source": [
"# Kuzu\n",
"\n",
">[Kùzu](https://kuzudb.com) is an in-process property graph database management system. \n",
">\n",
">This notebook shows how to use LLMs to provide a natural language interface to [Kùzu](https://kuzudb.com) database with `Cypher` graph query language.\n",
">\n",
">[Cypher](https://en.wikipedia.org/wiki/Cypher_(query_language)) is a declarative graph query language that allows for expressive and efficient data querying in a property graph."
">[Kùzu](https://kuzudb.com) is an embeddable property graph database management system built for query speed and scalability.\n",
"> \n",
"> Kùzu has a permissive (MIT) open source license and implements [Cypher](https://en.wikipedia.org/wiki/Cypher_(query_language)), a declarative graph query language that allows for expressive and efficient data querying in a property graph.\n",
"> It uses columnar storage and its query processor contains novel join algorithms that allow it to scale to very large graphs without sacrificing query performance.\n",
"> \n",
"> This notebook shows how to use LLMs to provide a natural language interface to [Kùzu](https://kuzudb.com) database with Cypher."
]
},
{
@ -21,7 +22,8 @@
"source": [
"## Setting up\n",
"\n",
"Install the python package:\n",
"Kùzu is an embedded database (it runs in-process), so there are no servers to manage.\n",
"Simply install it via its Python package:\n",
"\n",
"```bash\n",
"pip install kuzu\n",
@ -32,7 +34,7 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
@ -52,16 +54,16 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<kuzu.query_result.QueryResult at 0x1066ff410>"
"<kuzu.query_result.QueryResult at 0x103a72290>"
]
},
"execution_count": 2,
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
@ -84,16 +86,16 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<kuzu.query_result.QueryResult at 0x107016210>"
"<kuzu.query_result.QueryResult at 0x103a9e750>"
]
},
"execution_count": 3,
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
@ -132,7 +134,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
@ -143,7 +145,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
@ -152,11 +154,15 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"chain = KuzuQAChain.from_llm(ChatOpenAI(temperature=0), graph=graph, verbose=True)"
"chain = KuzuQAChain.from_llm(\n",
" llm=ChatOpenAI(temperature=0, model=\"gpt-3.5-turbo-16k\"),\n",
" graph=graph,\n",
" verbose=True,\n",
")"
]
},
{
@ -166,12 +172,13 @@
"source": [
"## Refresh graph schema information\n",
"\n",
"If the schema of database changes, you can refresh the schema information needed to generate Cypher statements."
"If the schema of database changes, you can refresh the schema information needed to generate Cypher statements.\n",
"You can also display the schema of the Kùzu graph as demonstrated below."
]
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
@ -180,7 +187,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 10,
"metadata": {},
"outputs": [
{
@ -205,78 +212,7 @@
"source": [
"## Querying the graph\n",
"\n",
"We can now use the `KuzuQAChain` to ask question of the graph"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new chain...\u001b[0m\n",
"Generated Cypher:\n",
"\u001b[32;1m\u001b[1;3mMATCH (p:Person)-[:ActedIn]->(m:Movie {name: 'The Godfather: Part II'}) RETURN p.name\u001b[0m\n",
"Full Context:\n",
"\u001b[32;1m\u001b[1;3m[{'p.name': 'Al Pacino'}, {'p.name': 'Robert De Niro'}]\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'Al Pacino and Robert De Niro both played in The Godfather: Part II.'"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain.run(\"Who played in The Godfather: Part II?\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new chain...\u001b[0m\n",
"Generated Cypher:\n",
"\u001b[32;1m\u001b[1;3mMATCH (p:Person {name: 'Robert De Niro'})-[:ActedIn]->(m:Movie)\n",
"RETURN m.name\u001b[0m\n",
"Full Context:\n",
"\u001b[32;1m\u001b[1;3m[{'m.name': 'The Godfather: Part II'}]\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'Robert De Niro played in The Godfather: Part II.'"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain.run(\"Robert De Niro played in which movies?\")"
"We can now use the `KuzuQAChain` to ask questions of the graph."
]
},
{
@ -290,12 +226,13 @@
"text": [
"\n",
"\n",
"\u001b[1m> Entering new chain...\u001b[0m\n",
"\u001b[1m> Entering new KuzuQAChain chain...\u001b[0m\n",
"Generated Cypher:\n",
"\u001b[32;1m\u001b[1;3mMATCH (p:Person {name: 'Robert De Niro'})-[:ActedIn]->(m:Movie)\n",
"RETURN p.birthDate\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mMATCH (p:Person)-[:ActedIn]->(m:Movie)\n",
"WHERE m.name = 'The Godfather: Part II'\n",
"RETURN p.name\u001b[0m\n",
"Full Context:\n",
"\u001b[32;1m\u001b[1;3m[{'p.birthDate': '1943-08-17'}]\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m[{'p.name': 'Al Pacino'}, {'p.name': 'Robert De Niro'}]\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
@ -303,7 +240,8 @@
{
"data": {
"text/plain": [
"'Robert De Niro was born on August 17, 1943.'"
"{'query': 'Who acted in The Godfather: Part II?',\n",
" 'result': 'Al Pacino, Robert De Niro acted in The Godfather: Part II.'}"
]
},
"execution_count": 11,
@ -312,7 +250,7 @@
}
],
"source": [
"chain.run(\"Robert De Niro is born in which year?\")"
"chain.invoke(\"Who acted in The Godfather: Part II?\")"
]
},
{
@ -326,13 +264,87 @@
"text": [
"\n",
"\n",
"\u001b[1m> Entering new chain...\u001b[0m\n",
"\u001b[1m> Entering new KuzuQAChain chain...\u001b[0m\n",
"Generated Cypher:\n",
"\u001b[32;1m\u001b[1;3mMATCH (p:Person)-[:ActedIn]->(m:Movie{name:'The Godfather: Part II'})\n",
"WITH p, m, p.birthDate AS birthDate\n",
"ORDER BY birthDate ASC\n",
"LIMIT 1\n",
"RETURN p.name\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mMATCH (p:Person)-[:ActedIn]->(m:Movie)\n",
"WHERE p.name = 'Robert De Niro'\n",
"RETURN m.name\u001b[0m\n",
"Full Context:\n",
"\u001b[32;1m\u001b[1;3m[{'m.name': 'The Godfather: Part II'}]\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"{'query': 'Robert De Niro played in which movies?',\n",
" 'result': 'Robert De Niro played in The Godfather: Part II.'}"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain.invoke(\"Robert De Niro played in which movies?\")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new KuzuQAChain chain...\u001b[0m\n",
"Generated Cypher:\n",
"\u001b[32;1m\u001b[1;3mMATCH (:Person)-[:ActedIn]->(:Movie {name: 'Godfather: Part II'})\n",
"RETURN count(*)\u001b[0m\n",
"Full Context:\n",
"\u001b[32;1m\u001b[1;3m[{'COUNT_STAR()': 0}]\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"{'query': 'How many actors played in the Godfather: Part II?',\n",
" 'result': \"I don't know the answer.\"}"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain.invoke(\"How many actors played in the Godfather: Part II?\")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new KuzuQAChain chain...\u001b[0m\n",
"Generated Cypher:\n",
"\u001b[32;1m\u001b[1;3mMATCH (p:Person)-[:ActedIn]->(m:Movie {name: 'The Godfather: Part II'})\n",
"RETURN p.name\n",
"ORDER BY p.birthDate ASC\n",
"LIMIT 1\u001b[0m\n",
"Full Context:\n",
"\u001b[32;1m\u001b[1;3m[{'p.name': 'Al Pacino'}]\u001b[0m\n",
"\n",
@ -342,16 +354,114 @@
{
"data": {
"text/plain": [
"'The oldest actor who played in The Godfather: Part II is Al Pacino.'"
"{'query': 'Who is the oldest actor who played in The Godfather: Part II?',\n",
" 'result': 'Al Pacino is the oldest actor who played in The Godfather: Part II.'}"
]
},
"execution_count": 12,
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain.run(\"Who is the oldest actor who played in The Godfather: Part II?\")"
"chain.invoke(\"Who is the oldest actor who played in The Godfather: Part II?\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Use separate LLMs for Cypher and answer generation\n",
"\n",
"You can specify `cypher_llm` and `qa_llm` separately to use different LLMs for Cypher generation and answer generation."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/prrao/code/langchain/.venv/lib/python3.11/site-packages/langchain_core/_api/deprecation.py:119: LangChainDeprecationWarning: The class `LLMChain` was deprecated in LangChain 0.1.17 and will be removed in 0.3.0. Use RunnableSequence, e.g., `prompt | llm` instead.\n",
" warn_deprecated(\n"
]
}
],
"source": [
"chain = KuzuQAChain.from_llm(\n",
" cypher_llm=ChatOpenAI(temperature=0, model=\"gpt-3.5-turbo-16k\"),\n",
" qa_llm=ChatOpenAI(temperature=0, model=\"gpt-4\"),\n",
" graph=graph,\n",
" verbose=True,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new KuzuQAChain chain...\u001b[0m\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/prrao/code/langchain/.venv/lib/python3.11/site-packages/langchain_core/_api/deprecation.py:119: LangChainDeprecationWarning: The method `Chain.run` was deprecated in langchain 0.1.0 and will be removed in 0.2.0. Use invoke instead.\n",
" warn_deprecated(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Generated Cypher:\n",
"\u001b[32;1m\u001b[1;3mMATCH (:Person)-[:ActedIn]->(:Movie {name: 'The Godfather: Part II'})\n",
"RETURN count(*)\u001b[0m\n",
"Full Context:\n",
"\u001b[32;1m\u001b[1;3m[{'COUNT_STAR()': 2}]\u001b[0m\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/prrao/code/langchain/.venv/lib/python3.11/site-packages/langchain_core/_api/deprecation.py:119: LangChainDeprecationWarning: The method `Chain.__call__` was deprecated in langchain 0.1.0 and will be removed in 0.2.0. Use invoke instead.\n",
" warn_deprecated(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"{'query': 'How many actors played in The Godfather: Part II?',\n",
" 'result': 'Two actors played in The Godfather: Part II.'}"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain.invoke(\"How many actors played in The Godfather: Part II?\")"
]
}
],
@ -371,7 +481,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.12"
"version": "3.11.7"
}
},
"nbformat": 4,

27
libs/community/langchain_community/chains/graph_qa/kuzu.py
View File

@ -92,15 +92,36 @@ class KuzuQAChain(Chain):
@classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
llm: Optional[BaseLanguageModel] = None,
*,
qa_prompt: BasePromptTemplate = CYPHER_QA_PROMPT,
cypher_prompt: BasePromptTemplate = KUZU_GENERATION_PROMPT,
cypher_llm: Optional[BaseLanguageModel] = None,
qa_llm: Optional[BaseLanguageModel] = None,
**kwargs: Any,
) -> KuzuQAChain:
"""Initialize from LLM."""
qa_chain = LLMChain(llm=llm, prompt=qa_prompt)
cypher_generation_chain = LLMChain(llm=llm, prompt=cypher_prompt)
if not cypher_llm and not llm:
raise ValueError("Either `llm` or `cypher_llm` parameters must be provided")
if not qa_llm and not llm:
raise ValueError(
"Either `llm` or `qa_llm` parameters must be provided along with"
" `cypher_llm`"
)
if cypher_llm and qa_llm and llm:
raise ValueError(
"You can specify up to two of 'cypher_llm', 'qa_llm'"
", and 'llm', but not all three simultaneously."
)
qa_chain = LLMChain(
llm=qa_llm or llm, # type: ignore[arg-type]
prompt=qa_prompt,
)
cypher_generation_chain = LLMChain(
llm=cypher_llm or llm, # type: ignore[arg-type]
prompt=cypher_prompt,
)
return cls(
qa_chain=qa_chain,

10
libs/community/langchain_community/chains/graph_qa/prompts.py
View File

@ -75,13 +75,11 @@ NGQL_GENERATION_PROMPT = PromptTemplate(
KUZU_EXTRA_INSTRUCTIONS = """
Instructions:
Generate the Kùzu dialect of Cypher with the following rules in mind:
1. Do not use a `WHERE EXISTS` clause to check the existence of a property.
2. Do not omit the relationship pattern. Always use `()-[]->()` instead of `()->()`.
3. Do not include any notes or comments even if the statement does not produce the expected result.
```\n"""
1. Do not omit the relationship pattern. Always use `()-[]->()` instead of `()->()`.
2. Do not include triple backticks ``` in your response. Return only Cypher.
3. Do not return any notes or comments in your response.
\n"""
KUZU_GENERATION_TEMPLATE = CYPHER_GENERATION_TEMPLATE.replace(
"Generate Cypher", "Generate Kùzu Cypher"