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community[patch]: Add function response to graph cypher qa chain (#22690)
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
This commit is contained in:
Tomaz Bratanic
GitHub
parent
34edfe4a16
commit
76a193decc
@ -164,10 +164,10 @@
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"text": [
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"Node properties:\n",
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"- **Movie**\n",
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" - `runtime: INTEGER` Min: 120, Max: 120\n",
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" - `name: STRING` Available options: ['Top Gun']\n",
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" - `runtime`: INTEGER Min: 120, Max: 120\n",
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" - `name`: STRING Available options: ['Top Gun']\n",
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"- **Actor**\n",
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" - `name: STRING` Available options: ['Tom Cruise', 'Val Kilmer', 'Anthony Edwards', 'Meg Ryan']\n",
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" - `name`: STRING Available options: ['Tom Cruise', 'Val Kilmer', 'Anthony Edwards', 'Meg Ryan']\n",
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"Relationship properties:\n",
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"\n",
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"The relationships:\n",
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@ -225,7 +225,7 @@
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"WHERE m.name = 'Top Gun'\n",
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"RETURN a.name\u001b[0m\n",
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"Full Context:\n",
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"\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",
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"\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",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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@ -234,7 +234,7 @@
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"data": {
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"text/plain": [
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"{'query': 'Who played in Top Gun?',\n",
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" 'result': 'Anthony Edwards, Meg Ryan, Val Kilmer, Tom Cruise played in Top Gun.'}"
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" 'result': 'Tom Cruise, Val Kilmer, Anthony Edwards, and Meg Ryan played in Top Gun.'}"
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]
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},
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"execution_count": 8,
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@ -286,7 +286,7 @@
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"WHERE m.name = 'Top Gun'\n",
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"RETURN a.name\u001b[0m\n",
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"Full Context:\n",
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"\u001b[32;1m\u001b[1;3m[{'a.name': 'Anthony Edwards'}, {'a.name': 'Meg Ryan'}]\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3m[{'a.name': 'Tom Cruise'}, {'a.name': 'Val Kilmer'}]\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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@ -295,7 +295,7 @@
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"data": {
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"text/plain": [
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"{'query': 'Who played in Top Gun?',\n",
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" 'result': 'Anthony Edwards, Meg Ryan played in Top Gun.'}"
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" 'result': 'Tom Cruise, Val Kilmer played in Top Gun.'}"
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]
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},
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"execution_count": 10,
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@ -346,11 +346,11 @@
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"WHERE m.name = 'Top Gun'\n",
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"RETURN a.name\u001b[0m\n",
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"Full Context:\n",
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"\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",
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"\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",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n",
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"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",
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"Final answer: Anthony Edwards, Meg Ryan, Val Kilmer, Tom Cruise played in Top Gun.\n"
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"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",
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"Final answer: Tom Cruise, Val Kilmer, Anthony Edwards, and Meg Ryan played in Top Gun.\n"
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]
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}
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],
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@ -406,10 +406,10 @@
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"data": {
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"text/plain": [
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"{'query': 'Who played in Top Gun?',\n",
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" 'result': [{'a.name': 'Anthony Edwards'},\n",
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" {'a.name': 'Meg Ryan'},\n",
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" 'result': [{'a.name': 'Tom Cruise'},\n",
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" {'a.name': 'Val Kilmer'},\n",
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" {'a.name': 'Tom Cruise'}]}"
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" {'a.name': 'Anthony Edwards'},\n",
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" {'a.name': 'Meg Ryan'}]}"
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]
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},
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"execution_count": 14,
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@ -482,7 +482,7 @@
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"\n",
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"\u001b[1m> Entering new GraphCypherQAChain chain...\u001b[0m\n",
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"Generated Cypher:\n",
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"\u001b[32;1m\u001b[1;3mMATCH (:Movie {name:\"Top Gun\"})<-[:ACTED_IN]-()\n",
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"\u001b[32;1m\u001b[1;3mMATCH (m:Movie {name:\"Top Gun\"})<-[:ACTED_IN]-()\n",
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"RETURN count(*) AS numberOfActors\u001b[0m\n",
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"Full Context:\n",
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"\u001b[32;1m\u001b[1;3m[{'numberOfActors': 4}]\u001b[0m\n",
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@ -494,7 +494,7 @@
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"data": {
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"text/plain": [
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"{'query': 'How many people played in Top Gun?',\n",
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" 'result': 'There were 4 actors who played in Top Gun.'}"
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" 'result': 'There were 4 actors in Top Gun.'}"
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]
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},
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"execution_count": 16,
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@ -548,7 +548,7 @@
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"WHERE m.name = 'Top Gun'\n",
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"RETURN a.name\u001b[0m\n",
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"Full Context:\n",
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"\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",
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"\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",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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@ -557,7 +557,7 @@
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"data": {
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"text/plain": [
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"{'query': 'Who played in Top Gun?',\n",
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" 'result': 'Anthony Edwards, Meg Ryan, Val Kilmer, and Tom Cruise played in Top Gun.'}"
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" 'result': 'Tom Cruise, Val Kilmer, Anthony Edwards, and Meg Ryan played in Top Gun.'}"
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]
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},
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"execution_count": 18,
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@ -661,7 +661,7 @@
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"WHERE m.name = 'Top Gun'\n",
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"RETURN a.name\u001b[0m\n",
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"Full Context:\n",
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"\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",
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"\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",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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@ -670,7 +670,7 @@
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"data": {
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"text/plain": [
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"{'query': 'Who played in Top Gun?',\n",
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" 'result': 'Anthony Edwards, Meg Ryan, Val Kilmer, Tom Cruise played in Top Gun.'}"
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" 'result': 'Tom Cruise, Val Kilmer, Anthony Edwards, and Meg Ryan played in Top Gun.'}"
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]
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},
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"execution_count": 22,
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@ -683,12 +683,116 @@
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3fa3f3d5-f7e7-4ca9-8f07-ca22b897f192",
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"cell_type": "markdown",
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"id": "81093062-eb7f-4d96-b1fd-c36b8f1b9474",
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"metadata": {},
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"outputs": [],
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"source": []
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"source": [
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"## Provide context from database results as tool/function output\n",
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"\n",
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"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",
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"_You will need to use an LLM with native function calling support to use this feature_."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"id": "2be8f51c-e80a-4a60-ab1c-266450fc17cd",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\n",
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"\u001b[1m> Entering new GraphCypherQAChain chain...\u001b[0m\n",
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"Generated Cypher:\n",
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"\u001b[32;1m\u001b[1;3mMATCH (a:Actor)-[:ACTED_IN]->(m:Movie)\n",
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"WHERE m.name = 'Top Gun'\n",
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"RETURN a.name\u001b[0m\n",
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"Full Context:\n",
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"\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",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"{'query': 'Who played in Top Gun?',\n",
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" 'result': 'The main actors in Top Gun are Tom Cruise, Val Kilmer, Anthony Edwards, and Meg Ryan.'}"
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]
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},
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"execution_count": 23,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"chain = GraphCypherQAChain.from_llm(\n",
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" llm=ChatOpenAI(temperature=0, model=\"gpt-3.5-turbo\"),\n",
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" graph=graph,\n",
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" verbose=True,\n",
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" use_function_response=True,\n",
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")\n",
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"chain.invoke({\"query\": \"Who played in Top Gun?\"})"
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]
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},
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{
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"cell_type": "markdown",
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"id": "48a75785-5bc9-49a7-a41b-88bf3ef9d312",
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"metadata": {},
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"source": [
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"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",
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"\n",
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"_Note that `qa_prompt` will have no effect when using `use_function_response`_"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"id": "ddf0a61e-f104-4dbb-abbf-e65f3f57dd9a",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\n",
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"\u001b[1m> Entering new GraphCypherQAChain chain...\u001b[0m\n",
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"Generated Cypher:\n",
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"\u001b[32;1m\u001b[1;3mMATCH (a:Actor)-[:ACTED_IN]->(m:Movie)\n",
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"WHERE m.name = 'Top Gun'\n",
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"RETURN a.name\u001b[0m\n",
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"Full Context:\n",
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"\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",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"{'query': 'Who played in Top Gun?',\n",
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" '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!\"}"
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]
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},
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"execution_count": 24,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"chain = GraphCypherQAChain.from_llm(\n",
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" llm=ChatOpenAI(temperature=0, model=\"gpt-3.5-turbo\"),\n",
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" graph=graph,\n",
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" verbose=True,\n",
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" use_function_response=True,\n",
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" function_response_system=\"Respond as a pirate!\",\n",
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")\n",
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"chain.invoke({\"query\": \"Who played in Top Gun?\"})"
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]
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}
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],
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"metadata": {
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@ -2,14 +2,27 @@
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from __future__ import annotations
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import re
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from typing import Any, Dict, List, Optional
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from typing import Any, Dict, List, Optional, Union
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from langchain.chains.base import Chain
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from langchain.chains.llm import LLMChain
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from langchain_core.callbacks import CallbackManagerForChainRun
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from langchain_core.language_models import BaseLanguageModel
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from langchain_core.prompts import BasePromptTemplate
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from langchain_core.messages import (
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AIMessage,
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BaseMessage,
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SystemMessage,
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ToolMessage,
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)
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from langchain_core.output_parsers import StrOutputParser
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from langchain_core.prompts import (
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BasePromptTemplate,
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ChatPromptTemplate,
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HumanMessagePromptTemplate,
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MessagesPlaceholder,
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)
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from langchain_core.pydantic_v1 import Field
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from langchain_core.runnables import Runnable
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from langchain_community.chains.graph_qa.cypher_utils import (
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CypherQueryCorrector,
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@ -23,6 +36,12 @@ from langchain_community.graphs.graph_store import GraphStore
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INTERMEDIATE_STEPS_KEY = "intermediate_steps"
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FUNCTION_RESPONSE_SYSTEM = """You are an assistant that helps to form nice and human
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understandable answers based on the provided information from tools.
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Do not add any other information that wasn't present in the tools, and use
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very concise style in interpreting results!
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"""
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def extract_cypher(text: str) -> str:
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"""Extract Cypher code from a text.
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@ -104,6 +123,31 @@ def construct_schema(
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)
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def get_function_response(
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question: str, context: List[Dict[str, Any]]
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) -> List[BaseMessage]:
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TOOL_ID = "call_H7fABDuzEau48T10Qn0Lsh0D"
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messages = [
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AIMessage(
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content="",
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additional_kwargs={
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"tool_calls": [
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{
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"id": TOOL_ID,
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"function": {
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"arguments": '{"question":"' + question + '"}',
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"name": "GetInformation",
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},
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"type": "function",
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}
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]
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},
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),
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ToolMessage(content=str(context), tool_call_id=TOOL_ID),
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]
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return messages
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class GraphCypherQAChain(Chain):
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"""Chain for question-answering against a graph by generating Cypher statements.
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@ -121,7 +165,7 @@ class GraphCypherQAChain(Chain):
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graph: GraphStore = Field(exclude=True)
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cypher_generation_chain: LLMChain
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qa_chain: LLMChain
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qa_chain: Union[LLMChain, Runnable]
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graph_schema: str
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input_key: str = "query" #: :meta private:
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output_key: str = "result" #: :meta private:
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@ -133,6 +177,8 @@ class GraphCypherQAChain(Chain):
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"""Whether or not to return the result of querying the graph directly."""
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cypher_query_corrector: Optional[CypherQueryCorrector] = None
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"""Optional cypher validation tool"""
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use_function_response: bool = False
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"""Whether to wrap the database context as tool/function response"""
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@property
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def input_keys(self) -> List[str]:
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@ -163,12 +209,14 @@ class GraphCypherQAChain(Chain):
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qa_prompt: Optional[BasePromptTemplate] = None,
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cypher_prompt: Optional[BasePromptTemplate] = None,
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cypher_llm: Optional[BaseLanguageModel] = None,
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qa_llm: Optional[BaseLanguageModel] = None,
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qa_llm: Optional[Union[BaseLanguageModel, Any]] = None,
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exclude_types: List[str] = [],
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include_types: List[str] = [],
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validate_cypher: bool = False,
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qa_llm_kwargs: Optional[Dict[str, Any]] = None,
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cypher_llm_kwargs: Optional[Dict[str, Any]] = None,
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use_function_response: bool = False,
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function_response_system: str = FUNCTION_RESPONSE_SYSTEM,
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**kwargs: Any,
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) -> GraphCypherQAChain:
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"""Initialize from LLM."""
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@ -205,7 +253,22 @@ class GraphCypherQAChain(Chain):
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cypher_prompt if cypher_prompt is not None else CYPHER_GENERATION_PROMPT
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)
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qa_chain = LLMChain(llm=qa_llm or llm, **use_qa_llm_kwargs) # type: ignore[arg-type]
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qa_llm = qa_llm or llm
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if use_function_response:
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try:
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qa_llm.bind_tools({}) # type: ignore[union-attr]
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response_prompt = ChatPromptTemplate.from_messages(
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[
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SystemMessage(content=function_response_system),
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HumanMessagePromptTemplate.from_template("{question}"),
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MessagesPlaceholder(variable_name="function_response"),
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]
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)
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qa_chain = response_prompt | qa_llm | StrOutputParser() # type: ignore
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except (NotImplementedError, AttributeError):
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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(
|
||||
llm=cypher_llm or llm, # type: ignore[arg-type]
|
||||
@ -217,7 +280,6 @@ class GraphCypherQAChain(Chain):
|
||||
"Either `exclude_types` or `include_types` "
|
||||
"can be provided, but not both"
|
||||
)
|
||||
|
||||
graph_schema = construct_schema(
|
||||
kwargs["graph"].get_structured_schema, include_types, exclude_types
|
||||
)
|
||||
@ -235,6 +297,7 @@ class GraphCypherQAChain(Chain):
|
||||
qa_chain=qa_chain,
|
||||
cypher_generation_chain=cypher_generation_chain,
|
||||
cypher_query_corrector=cypher_query_corrector,
|
||||
use_function_response=use_function_response,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
@ -284,12 +347,17 @@ class GraphCypherQAChain(Chain):
|
||||
)
|
||||
|
||||
intermediate_steps.append({"context": context})
|
||||
|
||||
result = self.qa_chain(
|
||||
{"question": question, "context": context},
|
||||
callbacks=callbacks,
|
||||
)
|
||||
final_result = result[self.qa_chain.output_key]
|
||||
if self.use_function_response:
|
||||
function_response = get_function_response(question, context)
|
||||
final_result = self.qa_chain.invoke( # type: ignore
|
||||
{"question": question, "function_response": function_response},
|
||||
)
|
||||
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}
|
||||
if self.return_intermediate_steps:
|
||||
|
||||
@ -60,7 +60,7 @@ def test_graph_cypher_qa_chain_prompt_selection_1() -> None:
|
||||
qa_prompt=qa_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
|
||||
|
||||
|
||||
@ -72,7 +72,7 @@ def test_graph_cypher_qa_chain_prompt_selection_2() -> None:
|
||||
verbose=True,
|
||||
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
|
||||
|
||||
|
||||
@ -88,7 +88,7 @@ def test_graph_cypher_qa_chain_prompt_selection_3() -> None:
|
||||
cypher_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
|
||||
|
||||
|
||||
@ -108,7 +108,7 @@ def test_graph_cypher_qa_chain_prompt_selection_4() -> None:
|
||||
cypher_llm_kwargs={"prompt": cypher_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
|
||||
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user