From e4b38e2822f2eefbf1c3469cb4ce991d3600cb19 Mon Sep 17 00:00:00 2001 From: Tomaz Bratanic Date: Fri, 19 Apr 2024 20:33:32 +0200 Subject: [PATCH] Update neo4j cypher templates to the function callback (#20515) Update Neo4j Cypher templates to use function callback to pass context instead of passing it in user prompt. Co-authored-by: Erick Friis --- .../neo4j-cypher-ft/neo4j_cypher_ft/chain.py | 67 ++++-- templates/neo4j-cypher-ft/poetry.lock | 195 ++++++++++++++++-- templates/neo4j-cypher-ft/pyproject.toml | 2 + .../neo4j_cypher_memory/chain.py | 66 ++++-- templates/neo4j-cypher-memory/poetry.lock | 195 ++++++++++++++++-- templates/neo4j-cypher-memory/pyproject.toml | 2 + templates/neo4j-cypher/neo4j_cypher/chain.py | 63 ++++-- templates/neo4j-cypher/poetry.lock | 195 ++++++++++++++++-- templates/neo4j-cypher/pyproject.toml | 2 + 9 files changed, 699 insertions(+), 88 deletions(-) diff --git a/templates/neo4j-cypher-ft/neo4j_cypher_ft/chain.py b/templates/neo4j-cypher-ft/neo4j_cypher_ft/chain.py index fe4953a2c7..5079f216f4 100644 --- a/templates/neo4j-cypher-ft/neo4j_cypher_ft/chain.py +++ b/templates/neo4j-cypher-ft/neo4j_cypher_ft/chain.py @@ -1,13 +1,21 @@ -from typing import List, Optional +from typing import List, Optional, Union from langchain.chains.graph_qa.cypher_utils import CypherQueryCorrector, Schema -from langchain.chains.openai_functions import create_structured_output_chain -from langchain_community.chat_models import ChatOpenAI 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 +from langchain_core.prompts import ( + ChatPromptTemplate, + HumanMessagePromptTemplate, + MessagesPlaceholder, +) from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.runnables import RunnablePassthrough +from langchain_openai import ChatOpenAI # Connection to Neo4j graph = Neo4jGraph() @@ -66,7 +74,7 @@ def map_to_database(entities: Entities) -> Optional[str]: return result -entity_chain = create_structured_output_chain(Entities, qa_llm, prompt) +entity_chain = prompt | qa_llm.with_structured_output(Entities) # 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: @@ -89,7 +97,7 @@ cypher_prompt = ChatPromptTemplate.from_messages( cypher_response = ( RunnablePassthrough.assign(names=entity_chain) | RunnablePassthrough.assign( - entities_list=lambda x: map_to_database(x["names"]["function"]), + entities_list=lambda x: map_to_database(x["names"]), schema=lambda _: graph.get_schema, ) | cypher_prompt @@ -98,26 +106,51 @@ cypher_response = ( ) # Generate natural language response based on database results -response_template = """Based on the the question, Cypher query, and Cypher response, write a natural language response: -Question: {question} -Cypher query: {query} -Cypher Response: {response}""" # noqa: E501 +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( [ - ( - "system", - "Given an input question and Cypher response, convert it to a natural" - " language answer. No pre-amble.", - ), - ("human", response_template), + 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( - response=lambda x: graph.query(cypher_validation(x["query"])), + function_response=lambda x: get_function_response(x["query"], x["question"]) ) | response_prompt | qa_llm diff --git a/templates/neo4j-cypher-ft/poetry.lock b/templates/neo4j-cypher-ft/poetry.lock index bf39e85086..b63623f174 100644 --- a/templates/neo4j-cypher-ft/poetry.lock +++ b/templates/neo4j-cypher-ft/poetry.lock @@ -722,19 +722,19 @@ uvicorn = ">=0.23.2,<0.24.0" [[package]] name = "langchain-community" -version = "0.0.28" +version = "0.0.33" description = "Community contributed LangChain integrations." optional = false -python-versions = ">=3.8.1,<4.0" +python-versions = "<4.0,>=3.8.1" files = [ - {file = "langchain_community-0.0.28-py3-none-any.whl", hash = "sha256:bdb015ac455ae68432ea104628717583dce041e1abdfcefe86e39f034f5e90b8"}, - {file = "langchain_community-0.0.28.tar.gz", hash = "sha256:8664d243a90550fc5ddc137b712034e02c8d43afc8d4cc832ba5842b44c864ce"}, + {file = "langchain_community-0.0.33-py3-none-any.whl", hash = "sha256:830f0d5f4ff9638b99ca01820c26abfa4b65fa705ef89b5ce55ac9aa3a7d83af"}, + {file = "langchain_community-0.0.33.tar.gz", hash = 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langchain.chains.graph_qa.cypher_utils import CypherQueryCorrector, Schema from langchain.memory import ChatMessageHistory -from langchain_community.chat_models import ChatOpenAI 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, MessagesPlaceholder +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() @@ -56,7 +65,9 @@ def get_history(input: Dict[str, Any]) -> ChatMessageHistory: def save_history(input): - input.pop("response") + print(input) + if input.get("function_response"): + input.pop("function_response") # store history to database graph.query( """MERGE (u:User {id: $user_id}) @@ -107,26 +118,51 @@ cypher_response = ( ) # Generate natural language response based on database results -response_template = """Based on the the question, Cypher query, and Cypher response, write a natural language response: -Question: {question} -Cypher query: {query} -Cypher Response: {response}""" # noqa: E501 +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( [ - ( - "system", - "Given an input question and Cypher response, convert it to a " - "natural language answer. No pre-amble.", - ), - ("human", response_template), + 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( - response=lambda x: graph.query(cypher_validation(x["query"])), + function_response=lambda x: get_function_response(x["query"], x["question"]), ) | 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= "835db3f73c8fc07e54293bb8c8e56e1d5c9be403844eac8f8025793c8e6c242b" +content-hash = "09bce7f4cc85481c418acf3d43764152ba55a29aec1172ecca1408591fec0ab6" diff --git a/templates/neo4j-cypher-memory/pyproject.toml b/templates/neo4j-cypher-memory/pyproject.toml index 2560c9c33f..6f628e8d7e 100644 --- a/templates/neo4j-cypher-memory/pyproject.toml +++ b/templates/neo4j-cypher-memory/pyproject.toml @@ -12,6 +12,8 @@ python = ">=3.8.1,<4.0" langchain = "^0.1" neo4j = ">5.12" openai = "<2" +langchain-community = "^0.0.33" +langchain-openai = "^0.1.3" [tool.poetry.group.dev.dependencies] langchain-cli = ">=0.0.21" diff --git a/templates/neo4j-cypher/neo4j_cypher/chain.py b/templates/neo4j-cypher/neo4j_cypher/chain.py index c6f9e4f0d9..effa02fb63 100644 --- a/templates/neo4j-cypher/neo4j_cypher/chain.py +++ b/templates/neo4j-cypher/neo4j_cypher/chain.py @@ -1,10 +1,21 @@ +from typing import List, Union + from langchain.chains.graph_qa.cypher_utils import CypherQueryCorrector, Schema -from langchain_community.chat_models import ChatOpenAI 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 +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() @@ -46,27 +57,51 @@ cypher_response = ( | StrOutputParser() ) -# Generate natural language response based on database results -response_template = """Based on the the question, Cypher query, and Cypher response, write a natural language response: -Question: {question} -Cypher query: {query} -Cypher Response: {response}""" # noqa: E501 +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( [ - ( - "system", - "Given an input question and Cypher response, convert it to a " - "natural language answer. No pre-amble.", - ), - ("human", response_template), + 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( - response=lambda x: graph.query(cypher_validation(x["query"])), + function_response=lambda x: get_function_response(x["query"], x["question"]) ) | response_prompt | qa_llm 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