From 2c90a5e6427b9b9b607c0ba4ce97bcba8dd659e3 Mon Sep 17 00:00:00 2001 From: owlmoonss Date: Fri, 14 Mar 2025 19:47:56 -0400 Subject: [PATCH 01/23] Update .gitignore to exclude .qodo files --- .gitignore | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index a2efa49..f9bf23b 100644 --- a/.gitignore +++ b/.gitignore @@ -13,4 +13,5 @@ __pycache__ # secrets.toml # Dependency managers -*.lock \ No newline at end of file +*.lock +.qodo From ee934478f809fb630decc353a1f5ac4c6ac48e5e Mon Sep 17 00:00:00 2001 From: owlmoonss Date: Sat, 15 Mar 2025 00:11:06 -0400 Subject: [PATCH 02/23] Add secrets configuration and update to use Ollama for embeddings --- .streamlit/secrets.toml | 5 ++++ rag_demo/graph_cypher_chain.py | 44 +++++++++++++--------------------- rag_demo/main.py | 2 +- rag_demo/rag_agent.py | 6 +++-- rag_demo/vector_chain.py | 37 +++------------------------- rag_demo/vector_graph_chain.py | 12 +++------- 6 files changed, 33 insertions(+), 73 deletions(-) create mode 100644 .streamlit/secrets.toml diff --git a/.streamlit/secrets.toml b/.streamlit/secrets.toml new file mode 100644 index 0000000..ba889ad --- /dev/null +++ b/.streamlit/secrets.toml @@ -0,0 +1,5 @@ +NEO4J_URI="neo4j://localhost:7687" +NEO4J_USERNAME="neo4j" +NEO4J_PASSWORD="neo4j123" +SEGMENT_WRITE_KEY="" +OPENAI_API_KEY="" \ No newline at end of file diff --git a/rag_demo/graph_cypher_chain.py b/rag_demo/graph_cypher_chain.py index 55af946..f1916c2 100644 --- a/rag_demo/graph_cypher_chain.py +++ b/rag_demo/graph_cypher_chain.py @@ -2,7 +2,7 @@ from langchain.chains.conversation.memory import ConversationBufferMemory from langchain_community.graphs import Neo4jGraph from langchain.prompts.prompt import PromptTemplate -from langchain_openai import ChatOpenAI +from langchain_ollama import OllamaEmbeddings as Ollama from retry import retry import logging import streamlit as st @@ -14,7 +14,7 @@ 3. Always do a case-insensitive and fuzzy search for any properties related search. Eg: to search for a Company name use `toLower(c.name) contains 'neo4j'` 4. Always use aliases to refer the node in the query 5. Always return count(DISTINCT n) for aggregations to avoid duplicates -6. `OWNS_STOCK_IN` relationship is syonymous with `OWNS` and `OWNER` +6. `OWNS_STOCK_IN` relationship is synonymous with `OWNS` and `OWNER` 7. Use examples of questions and accurate Cypher statements below to guide you. Schema: @@ -57,35 +57,27 @@ username = st.secrets["NEO4J_USERNAME"] password = st.secrets["NEO4J_PASSWORD"] -if "USER_OPENAI_API_KEY" in st.session_state: - openai_key = st.session_state["USER_OPENAI_API_KEY"] -else: - openai_key = st.secrets["OPENAI_API_KEY"] - graph = Neo4jGraph( url=url, username=username, password=password, - sanitize = True + sanitize=True ) -# Official API doc for GraphCypherQAChain at: https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.base.GraphQAChain.html# +# Using Ollama's DeepSeek R model graph_chain = GraphCypherQAChain.from_llm( - cypher_llm=ChatOpenAI( - openai_api_key=openai_key, - temperature=0, - model_name="gpt-4" + cypher_llm=Ollama( + model="deepseek-r1", # Replace with the appropriate Ollama model + temperature=0, ), - qa_llm=ChatOpenAI( - openai_api_key=openai_key, - temperature=0, - model_name="gpt-4" + qa_llm=Ollama( + model="deepseek-r1", # Replace with the appropriate Ollama model + temperature=0, ), - validate_cypher= True, + validate_cypher=True, graph=graph, - verbose=True, - # return_intermediate_steps = True, - return_direct = True + verbose=True, + return_direct=True ) @retry(tries=2, delay=12) @@ -100,10 +92,9 @@ def get_results(question) -> str: """ logging.info(f'Using Neo4j database at url: {url}') - graph.refresh_schema() - prompt=CYPHER_GENERATION_PROMPT.format(schema=graph.get_schema, question=question) + prompt = CYPHER_GENERATION_PROMPT.format(schema=graph.get_schema, question=question) print('Prompt:', prompt) chain_result = None @@ -112,10 +103,10 @@ def get_results(question) -> str: chain_result = graph_chain.invoke({ "query": question}, prompt=prompt, - return_only_outputs = True, + return_only_outputs=True, ) except Exception as e: - # Occurs when the chain can not generate a cypher statement + # Occurs when the chain can not generate a Cypher statement # for the question with the given database schema logging.warning(f'Handled exception running graphCypher chain: {e}') @@ -125,5 +116,4 @@ def get_results(question) -> str: return "Sorry, I couldn't find an answer to your question" result = chain_result.get("result", None) - - return result \ No newline at end of file + return result diff --git a/rag_demo/main.py b/rag_demo/main.py index 4c1fb1b..db5c571 100644 --- a/rag_demo/main.py +++ b/rag_demo/main.py @@ -39,7 +39,7 @@ st.session_state.messages = [ { "role": "ai", - "content": f"This is a Proof of Concept application which shows how GenAI can be used with Neo4j to build and consume Knowledge Graphs using both vectors and structured data.\nSee the sidebar for more information!", + "content": f"This is a Proof of Concept agpplication which shows how GenAI can be used with Neo4j to build and consume Knowledge Graphs using both vectors and structured data.\nSee the sidebar for more information!", }, # {"role": "ai", "content": f"""This the schema in which the EDGAR filings are stored in Neo4j: \n """}, # {"role": "ai", "content": f"""This is how the Chatbot flow goes: \n """} diff --git a/rag_demo/rag_agent.py b/rag_demo/rag_agent.py index 6e544d4..2dc4fa8 100644 --- a/rag_demo/rag_agent.py +++ b/rag_demo/rag_agent.py @@ -1,15 +1,17 @@ from langchain.agents import AgentExecutor, create_react_agent from langchain_community.agent_toolkits.load_tools import load_tools from langchain import hub # requires langchainhub package -from langchain_openai import OpenAI +from pydantic_ai.models.openai import OpenAIModel as OpenAI +from pydantic_ai.providers.openai import OpenAIProvider from vector_graph_tool import vector_graph_tool from graph_cypher_tool import graph_cypher_tool from vector_tool import vector_tool from retry import retry + # Setup tools the agent will use -llm = OpenAI(temperature=0) +llm = OpenAI(temperature=0, model_name='deepseek-r1', provider=OpenAIProvider(base_url='http://localhost:11434/v')) tools = load_tools([], llm=llm) tools = tools + [vector_graph_tool, graph_cypher_tool, vector_tool] diff --git a/rag_demo/vector_chain.py b/rag_demo/vector_chain.py index afd89c0..c2499ef 100644 --- a/rag_demo/vector_chain.py +++ b/rag_demo/vector_chain.py @@ -4,7 +4,7 @@ from langchain_community.vectorstores import Neo4jVector from langchain.chains import RetrievalQAWithSourcesChain from langchain.chains.conversation.memory import ConversationBufferMemory -from langchain_openai import ChatOpenAI, OpenAIEmbeddings +from langchain_ollama import OllamaEmbeddings as Ollama from retry import retry import logging import streamlit as st @@ -33,12 +33,7 @@ input_variables=["input", "context"], template=VECTOR_PROMPT_TEMPLATE ) -if "USER_OPENAI_API_KEY" in st.session_state: - openai_key = st.session_state["USER_OPENAI_API_KEY"] -else: - openai_key = st.secrets["OPENAI_API_KEY"] - -EMBEDDING_MODEL = OpenAIEmbeddings(openai_api_key=openai_key) +EMBEDDING_MODEL = Ollama(model="deepseek-r1") # Replace OpenAIEmbeddings with Ollama MEMORY = ConversationBufferMemory( memory_key="chat_history", input_key="question", @@ -52,7 +47,6 @@ username = st.secrets["NEO4J_USERNAME"] password = st.secrets["NEO4J_PASSWORD"] - vector_store = None try: logging.debug(f"Attempting to retrieve existing vector index: {index_name}...") @@ -91,7 +85,7 @@ vector_retriever = vector_store.as_retriever() vector_chain = RetrievalQAWithSourcesChain.from_chain_type( - ChatOpenAI(temperature=0, openai_api_key=openai_key), + Ollama(model="deepseek-r1"), # Replace ChatOpenAI with Ollama chain_type="stuff", retriever=vector_retriever, memory=MEMORY, @@ -99,7 +93,6 @@ max_tokens_limit=3000, ) - @retry(tries=2, delay=5) def get_results(question) -> str: """Generate response using Neo4jVector using vector index only @@ -132,27 +125,3 @@ def get_results(question) -> str: result += f"\n - [{source}]({source})" return result - - -# Using the vector store directly. But this could blow out the token count -# @retry(tries=5, delay=5) -# def get_results(question)-> str: -# """Generate response using Neo4jVector using vector index only - -# Args: -# question (str): User query - -# Returns: -# str: Formatted string answer with citations, if available. -# """ - -# logging.info(f'Using Neo4j url: {url}') - -# # Returns a dict with keys: answer, sources -# vector_result = vector_store.similarity_search(question, k=3) - -# logging.debug(f'chain_result: {vector_result}') - -# result = vector_result - -# return result diff --git a/rag_demo/vector_graph_chain.py b/rag_demo/vector_graph_chain.py index 4b5afad..887052b 100644 --- a/rag_demo/vector_graph_chain.py +++ b/rag_demo/vector_graph_chain.py @@ -4,6 +4,7 @@ from langchain.chains import RetrievalQAWithSourcesChain from langchain.chains.conversation.memory import ConversationBufferMemory from langchain_openai import ChatOpenAI, OpenAIEmbeddings +from langchain_ollama import OllamaEmbeddings as Ollama from retry import retry import logging import streamlit as st @@ -22,12 +23,7 @@ input_variables=["question"], template=VECTOR_GRAPH_PROMPT_TEMPLATE ) -if "USER_OPENAI_API_KEY" in st.session_state: - 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-[package.dependencies] -idna = ">=2.0" -multidict = ">=4.0" - -[metadata] -lock-version = "2.0" -python-versions = "^3.11" -content-hash = "af4886df01a8c54015e91f9ea534a4bd806e755a988a61de04ddcdd39ecd9c22" diff --git a/requirements.txt b/requirements.txt index accc275..b6d836e 100644 --- a/requirements.txt +++ b/requirements.txt @@ -49,8 +49,8 @@ pillow==10.2.0 ; python_version >= "3.11" and python_version < "4.0" protobuf==4.25.3 ; python_version >= "3.11" and python_version < "4.0" py==1.11.0 ; python_version >= "3.11" and python_version < "4.0" pyarrow==15.0.1 ; python_version >= "3.11" and python_version < "4.0" -pydantic-core==2.16.3 ; python_version >= "3.11" and python_version < "4.0" -pydantic==2.6.3 ; python_version >= "3.11" and python_version < "4.0" +pydantic-core==2.18.4 ; python_version >= "3.11" and python_version < "4.0" +pydantic==2.7.4 ; python_version >= "3.11" and python_version < "4.0" pydeck==0.8.0 ; python_version >= "3.11" and python_version < "4.0" pygments==2.17.2 ; python_version >= "3.11" and python_version < "4.0" pyjwt==2.8.0 ; python_version >= "3.11" and python_version < "4.0" From db74ab98a1f255600a0165c50c7206dc2398d3ea Mon Sep 17 00:00:00 2001 From: owlmoonss Date: Thu, 20 Mar 2025 00:53:03 -0400 Subject: [PATCH 04/23] Refactor to use ChatOllama for embeddings in graph_cypher_chain.py --- rag_demo/graph_cypher_chain.py | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/rag_demo/graph_cypher_chain.py b/rag_demo/graph_cypher_chain.py index f1916c2..1c581ed 100644 --- a/rag_demo/graph_cypher_chain.py +++ b/rag_demo/graph_cypher_chain.py @@ -2,7 +2,7 @@ from langchain.chains.conversation.memory import ConversationBufferMemory from langchain_community.graphs import Neo4jGraph from langchain.prompts.prompt import PromptTemplate -from langchain_ollama import OllamaEmbeddings as Ollama +from langchain_ollama import ChatOllama from retry import retry import logging import streamlit as st @@ -66,18 +66,19 @@ # Using Ollama's DeepSeek R model graph_chain = GraphCypherQAChain.from_llm( - cypher_llm=Ollama( - model="deepseek-r1", # Replace with the appropriate Ollama model + cypher_llm=ChatOllama( + model="llama2", # Replace with the appropriate Ollama model temperature=0, ), - qa_llm=Ollama( - model="deepseek-r1", # Replace with the appropriate Ollama model + qa_llm=ChatOllama( + model="llama2", # Replace with the appropriate Ollama model temperature=0, ), validate_cypher=True, graph=graph, verbose=True, - return_direct=True + return_direct=True, + allow_dangerous_requests=True ) @retry(tries=2, delay=12) From 0e4eaa83c2953fc757fdf5587edb29244fba3e95 Mon Sep 17 00:00:00 2001 From: owlmoonss Date: Thu, 20 Mar 2025 00:53:08 -0400 Subject: [PATCH 05/23] Refactor to use ChatOllama for language model in rag_agent.py --- rag_demo/rag_agent.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/rag_demo/rag_agent.py b/rag_demo/rag_agent.py index 2dc4fa8..6bb3300 100644 --- a/rag_demo/rag_agent.py +++ b/rag_demo/rag_agent.py @@ -7,11 +7,15 @@ from graph_cypher_tool import graph_cypher_tool from vector_tool import vector_tool from retry import retry +from langchain_ollama import ChatOllama # Setup tools the agent will use -llm = OpenAI(temperature=0, model_name='deepseek-r1', provider=OpenAIProvider(base_url='http://localhost:11434/v')) +llm = ChatOllama( + model="llama2", # Replace with the appropriate Ollama model + temperature=0, + ) tools = load_tools([], llm=llm) tools = tools + [vector_graph_tool, graph_cypher_tool, vector_tool] From 50d6bcae354e785a3855de8150e28eff097ee785 Mon Sep 17 00:00:00 2001 From: owlmoonss Date: Thu, 20 Mar 2025 00:53:12 -0400 Subject: [PATCH 06/23] Update embedding model in vector_chain.py to use Ollama with llama2 --- rag_demo/vector_chain.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/rag_demo/vector_chain.py b/rag_demo/vector_chain.py index c2499ef..8baacb8 100644 --- a/rag_demo/vector_chain.py +++ b/rag_demo/vector_chain.py @@ -33,7 +33,7 @@ input_variables=["input", "context"], template=VECTOR_PROMPT_TEMPLATE ) -EMBEDDING_MODEL = Ollama(model="deepseek-r1") # Replace OpenAIEmbeddings with Ollama +EMBEDDING_MODEL = Ollama(model="llama2") # Replace OpenAIEmbeddings with Ollama MEMORY = ConversationBufferMemory( memory_key="chat_history", input_key="question", @@ -76,16 +76,16 @@ ) logging.debug(f"Created new index: {index_name}") except Exception as e: - logging.error(f"Failed to retrieve existing or to create a Neo4jVector: {e}") + logging.error(f"[vector_chain]: Failed to retrieve existing or to create a Neo4jVector: {e}") if vector_store is None: - logging.error(f"Failed to retrieve or create a Neo4jVector. Exiting.") + logging.error(f"[vector_chain]: Failed to retrieve or create a Neo4jVector. Exiting.") exit() vector_retriever = vector_store.as_retriever() vector_chain = RetrievalQAWithSourcesChain.from_chain_type( - Ollama(model="deepseek-r1"), # Replace ChatOpenAI with Ollama + Ollama(model="llama2"), # Replace ChatOpenAI with Ollama chain_type="stuff", retriever=vector_retriever, memory=MEMORY, From 7205ece8c595d111b114350b2cf2c5e92e52ef57 Mon Sep 17 00:00:00 2001 From: owlmoonss Date: Thu, 20 Mar 2025 00:53:17 -0400 Subject: [PATCH 07/23] Update vector_graph_chain.py to use ChatOllama with llama2 for embeddings and language model --- rag_demo/vector_graph_chain.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/rag_demo/vector_graph_chain.py b/rag_demo/vector_graph_chain.py index 887052b..b712d54 100644 --- a/rag_demo/vector_graph_chain.py +++ b/rag_demo/vector_graph_chain.py @@ -5,6 +5,7 @@ from langchain.chains.conversation.memory import ConversationBufferMemory from langchain_openai import ChatOpenAI, OpenAIEmbeddings from langchain_ollama import OllamaEmbeddings as Ollama +from langchain_ollama import ChatOllama from retry import retry import logging import streamlit as st @@ -23,7 +24,7 @@ input_variables=["question"], template=VECTOR_GRAPH_PROMPT_TEMPLATE ) -EMBEDDING_MODEL = Ollama(model="deepseek-r1") # Replace OpenAIEmbeddings with Ollama +EMBEDDING_MODEL = Ollama(model="llama2") # Replace OpenAIEmbeddings with Ollama MEMORY = ConversationBufferMemory( memory_key="chat_history", input_key="question", @@ -100,7 +101,10 @@ vector_graph_retriever = vector_store.as_retriever() vector_graph_chain = RetrievalQAWithSourcesChain.from_chain_type( - ChatOpenAI(temperature=0, model='deepseek-r1', base_url='http://localhost:11434/v1'), + ChatOllama( + model="llama2", # Replace with the appropriate Ollama model + temperature=0, + ), chain_type="stuff", retriever=vector_graph_retriever, memory=MEMORY, From b0d66c9bf8240d81cfae307508d4a51068c1f424 Mon Sep 17 00:00:00 2001 From: owlmoonss Date: Thu, 20 Mar 2025 01:11:08 -0400 Subject: [PATCH 08/23] Update models in graph_cypher_chain.py, rag_agent.py, vector_chain.py, and vector_graph_chain.py to use gemma2 --- rag_demo/graph_cypher_chain.py | 4 ++-- rag_demo/rag_agent.py | 2 +- rag_demo/vector_chain.py | 4 ++-- rag_demo/vector_graph_chain.py | 4 ++-- 4 files changed, 7 insertions(+), 7 deletions(-) diff --git a/rag_demo/graph_cypher_chain.py b/rag_demo/graph_cypher_chain.py index 1c581ed..9b5cc95 100644 --- a/rag_demo/graph_cypher_chain.py +++ b/rag_demo/graph_cypher_chain.py @@ -67,11 +67,11 @@ # Using Ollama's DeepSeek R model graph_chain = GraphCypherQAChain.from_llm( cypher_llm=ChatOllama( - model="llama2", # Replace with the appropriate Ollama model + model="gemma2", # Replace with the appropriate Ollama model temperature=0, ), qa_llm=ChatOllama( - model="llama2", # Replace with the appropriate Ollama model + model="gemma2", # Replace with the appropriate Ollama model temperature=0, ), validate_cypher=True, diff --git a/rag_demo/rag_agent.py b/rag_demo/rag_agent.py index 6bb3300..d67e6fc 100644 --- a/rag_demo/rag_agent.py +++ b/rag_demo/rag_agent.py @@ -13,7 +13,7 @@ # Setup tools the agent will use llm = ChatOllama( - model="llama2", # Replace with the appropriate Ollama model + model="gemma2", # Replace with the appropriate Ollama model temperature=0, ) tools = load_tools([], llm=llm) diff --git a/rag_demo/vector_chain.py b/rag_demo/vector_chain.py index 8baacb8..e037d12 100644 --- a/rag_demo/vector_chain.py +++ b/rag_demo/vector_chain.py @@ -33,7 +33,7 @@ input_variables=["input", "context"], template=VECTOR_PROMPT_TEMPLATE ) -EMBEDDING_MODEL = Ollama(model="llama2") # Replace OpenAIEmbeddings with Ollama +EMBEDDING_MODEL = Ollama(model="gemma2") # Replace OpenAIEmbeddings with Ollama MEMORY = ConversationBufferMemory( memory_key="chat_history", input_key="question", @@ -85,7 +85,7 @@ vector_retriever = vector_store.as_retriever() vector_chain = RetrievalQAWithSourcesChain.from_chain_type( - Ollama(model="llama2"), # Replace ChatOpenAI with Ollama + Ollama(model="gemma2"), # Replace ChatOpenAI with Ollama chain_type="stuff", retriever=vector_retriever, memory=MEMORY, diff --git a/rag_demo/vector_graph_chain.py b/rag_demo/vector_graph_chain.py index b712d54..dac5caa 100644 --- a/rag_demo/vector_graph_chain.py +++ b/rag_demo/vector_graph_chain.py @@ -24,7 +24,7 @@ input_variables=["question"], template=VECTOR_GRAPH_PROMPT_TEMPLATE ) -EMBEDDING_MODEL = Ollama(model="llama2") # Replace OpenAIEmbeddings with Ollama +EMBEDDING_MODEL = Ollama(model="gemma2") # Replace OpenAIEmbeddings with Ollama MEMORY = ConversationBufferMemory( memory_key="chat_history", input_key="question", @@ -102,7 +102,7 @@ vector_graph_chain = RetrievalQAWithSourcesChain.from_chain_type( ChatOllama( - model="llama2", # Replace with the appropriate Ollama model + model="gemma2", # Replace with the appropriate Ollama model temperature=0, ), chain_type="stuff", From e5555f2e4a8f718f837c8f7cb8d791c90a088712 Mon Sep 17 00:00:00 2001 From: owlmoonss Date: Thu, 27 Mar 2025 15:35:22 -0400 Subject: [PATCH 09/23] Update models in graph_cypher_chain.py, rag_agent.py, vector_chain.py, and vector_graph_chain.py to use qwen2 --- rag_demo/graph_cypher_chain.py | 4 ++-- rag_demo/rag_agent.py | 24 ++++++++++++++++++++---- rag_demo/vector_chain.py | 4 ++-- rag_demo/vector_graph_chain.py | 4 ++-- 4 files changed, 26 insertions(+), 10 deletions(-) diff --git a/rag_demo/graph_cypher_chain.py b/rag_demo/graph_cypher_chain.py index 9b5cc95..e9d8f52 100644 --- a/rag_demo/graph_cypher_chain.py +++ b/rag_demo/graph_cypher_chain.py @@ -67,11 +67,11 @@ # Using Ollama's DeepSeek R model graph_chain = GraphCypherQAChain.from_llm( cypher_llm=ChatOllama( - model="gemma2", # Replace with the appropriate Ollama model + model="qwen2", # Replace with the appropriate Ollama model temperature=0, ), qa_llm=ChatOllama( - model="gemma2", # Replace with the appropriate Ollama model + model="qwen2", # Replace with the appropriate Ollama model temperature=0, ), validate_cypher=True, diff --git a/rag_demo/rag_agent.py b/rag_demo/rag_agent.py index d67e6fc..0e910fe 100644 --- a/rag_demo/rag_agent.py +++ b/rag_demo/rag_agent.py @@ -13,7 +13,7 @@ # Setup tools the agent will use llm = ChatOllama( - model="gemma2", # Replace with the appropriate Ollama model + model="qwen2", # Replace with the appropriate Ollama model temperature=0, ) tools = load_tools([], llm=llm) @@ -37,7 +37,8 @@ @retry(tries=2, delay=20) def get_results(question, callbacks) -> dict: - """Starts a LangChain agent to generate an answer using one of several Neo4j RAG tools. + """ + Starts a LangChain agent to generate an answer using one of several Neo4j RAG tools. Args: question (str): User query @@ -46,6 +47,21 @@ def get_results(question, callbacks) -> dict: Returns: dict: Final answer as a dict with the keys: input, output, intermediate_steps """ - response = agent_executor.invoke({"input": question}, callbacks=callbacks) - return response + + # Extract more info from the response + full_input = response.get("input") + final_output = response.get("output") + intermediate_steps = response.get("intermediate_steps") + + # Optional: extract tool names used + tools_used = [step[0] for step in intermediate_steps] if intermediate_steps else [] + + return { + "input": full_input, + "output": final_output, + "intermediate_steps": intermediate_steps, + "tools_used": tools_used, + } + + diff --git a/rag_demo/vector_chain.py b/rag_demo/vector_chain.py index e037d12..c015d28 100644 --- a/rag_demo/vector_chain.py +++ b/rag_demo/vector_chain.py @@ -33,7 +33,7 @@ input_variables=["input", "context"], template=VECTOR_PROMPT_TEMPLATE ) -EMBEDDING_MODEL = Ollama(model="gemma2") # Replace OpenAIEmbeddings with Ollama +EMBEDDING_MODEL = Ollama(model="qwen2") # Replace OpenAIEmbeddings with Ollama MEMORY = ConversationBufferMemory( memory_key="chat_history", input_key="question", @@ -85,7 +85,7 @@ vector_retriever = vector_store.as_retriever() vector_chain = RetrievalQAWithSourcesChain.from_chain_type( - Ollama(model="gemma2"), # Replace ChatOpenAI with Ollama + Ollama(model="qwen2"), # Replace ChatOpenAI with Ollama chain_type="stuff", retriever=vector_retriever, memory=MEMORY, diff --git a/rag_demo/vector_graph_chain.py b/rag_demo/vector_graph_chain.py index dac5caa..b354f2e 100644 --- a/rag_demo/vector_graph_chain.py +++ b/rag_demo/vector_graph_chain.py @@ -24,7 +24,7 @@ input_variables=["question"], template=VECTOR_GRAPH_PROMPT_TEMPLATE ) -EMBEDDING_MODEL = Ollama(model="gemma2") # Replace OpenAIEmbeddings with Ollama +EMBEDDING_MODEL = Ollama(model="qwen2") # Replace OpenAIEmbeddings with Ollama MEMORY = ConversationBufferMemory( memory_key="chat_history", input_key="question", @@ -102,7 +102,7 @@ vector_graph_chain = RetrievalQAWithSourcesChain.from_chain_type( ChatOllama( - model="gemma2", # Replace with the appropriate Ollama model + model="qwen2", # Replace with the appropriate Ollama model temperature=0, ), chain_type="stuff", From 9fc2031572385f97a09d2c0a70e8a689e6c534e9 Mon Sep 17 00:00:00 2001 From: owlmoonss Date: Thu, 3 Apr 2025 01:16:07 -0400 Subject: [PATCH 10/23] Refactor graph_cypher_chain and rag_agent to improve result extraction and update imports --- rag_demo/graph_cypher_chain.py | 21 +++++++++++++++++++++ rag_demo/graph_tool.py | 2 +- rag_demo/rag_agent.py | 3 ++- 3 files changed, 24 insertions(+), 2 deletions(-) diff --git a/rag_demo/graph_cypher_chain.py b/rag_demo/graph_cypher_chain.py index e9d8f52..205cfb0 100644 --- a/rag_demo/graph_cypher_chain.py +++ b/rag_demo/graph_cypher_chain.py @@ -116,5 +116,26 @@ def get_results(question) -> str: if chain_result is None: return "Sorry, I couldn't find an answer to your question" + logging.info(f'Chain result: {chain_result}') + # Extract the result from the chain result + # The result is typically in the "result" key of the chain result + # Adjust this based on the actual structure of the chain result + # For example, if the result is in the "output" key, use that instead + # If the chain result is a list, extract the first element + # if isinstance(chain_result, list) and len(chain_result) > 0: + # result = chain_result[0] + # else: + # result = chain_result + # If the chain result is a dictionary, extract the "result" key + # If the chain result is a string, return it directly + # If the chain result is None or empty, return a default message + # or raise an exception + # Adjust this based on the actual structure of the chain result + # For example, if the result is in the "output" key, use that instead + # If the chain result is a dictionary, extract the "result" key + # If the chain result is a string, return it directly + # If the chain result is None or empty, return a default message + # or raise an exception + result = chain_result.get("result", None) return result diff --git a/rag_demo/graph_tool.py b/rag_demo/graph_tool.py index 0b85b85..bd7ea08 100644 --- a/rag_demo/graph_tool.py +++ b/rag_demo/graph_tool.py @@ -1,5 +1,5 @@ from langchain.tools import tool -from graph_chain import get_results +from graph_cypher_chain import get_results @tool("graph-tool") def graph_tool(query:str) -> str: diff --git a/rag_demo/rag_agent.py b/rag_demo/rag_agent.py index 0e910fe..6d9aaaa 100644 --- a/rag_demo/rag_agent.py +++ b/rag_demo/rag_agent.py @@ -5,6 +5,7 @@ from pydantic_ai.providers.openai import OpenAIProvider from vector_graph_tool import vector_graph_tool from graph_cypher_tool import graph_cypher_tool +from graph_tool import graph_tool from vector_tool import vector_tool from retry import retry from langchain_ollama import ChatOllama @@ -17,7 +18,7 @@ temperature=0, ) tools = load_tools([], llm=llm) -tools = tools + [vector_graph_tool, graph_cypher_tool, vector_tool] +tools = tools + [graph_cypher_tool, graph_tool] # REACT AGENT EXECUTOR From 8fa12db931e509d885c61cce8004150db6845703 Mon Sep 17 00:00:00 2001 From: owlmoonss Date: Thu, 3 Apr 2025 02:04:13 -0400 Subject: [PATCH 11/23] Update Cypher generation template to enhance clarity and remove example queries --- rag_demo/graph_cypher_chain.py | 25 +++---------------------- 1 file changed, 3 insertions(+), 22 deletions(-) diff --git a/rag_demo/graph_cypher_chain.py b/rag_demo/graph_cypher_chain.py index 205cfb0..0502363 100644 --- a/rag_demo/graph_cypher_chain.py +++ b/rag_demo/graph_cypher_chain.py @@ -7,7 +7,7 @@ import logging import streamlit as st -CYPHER_GENERATION_TEMPLATE = """Task: Generate Cypher statement to query a graph database strictly based on the schema and instructions provided. +CYPHER_GENERATION_TEMPLATE = """Task: You are a master of technology especially cypher language. Generate Cypher statement to query a graph database strictly based on the schema and instructions provided. Instructions: 1. Use only nodes, relationships, and properties mentioned in the schema. 2. Always enclose the Cypher output inside 3 backticks. Do not add 'cypher' after the backticks. @@ -20,25 +20,6 @@ Schema: {schema} -Examples: Here are a few examples of generated Cypher statements for particular questions: - -# How many Managers own Companies? -MATCH (m:Manager)-[:OWNS_STOCK_IN]->(c:Company) -RETURN count(DISTINCT m) - -# How many companies are in filings? -MATCH (c:Company) -RETURN count(DISTINCT c) - -# Which companies are vulnerable to material shortage? -MATCH (co:Company)-[fi]-(f:Form)-[po]-(c:Chunk) -WHERE toLower(c.text) CONTAINS "material" -RETURN DISTINCT count(c) as chunks, co.name ORDER BY chunks desc - -# Which companies are in a specific industry? -MATCH (co:Company)-[fi]-(f:Form)-[po]-(c:Chunk) -WHERE toLower(c.text) CONTAINS "industryName" -RETURN DISTINCT count(c) as chunks, co.name ORDER BY chunks desc The question is: {question}""" @@ -111,8 +92,6 @@ def get_results(question) -> str: # for the question with the given database schema logging.warning(f'Handled exception running graphCypher chain: {e}') - logging.debug(f'chain_result: {chain_result}') - if chain_result is None: return "Sorry, I couldn't find an answer to your question" @@ -138,4 +117,6 @@ def get_results(question) -> str: # or raise an exception result = chain_result.get("result", None) + logging.debug(f'chain_result: {result}') + return result From 312c855a48e0bfb4defa820ec5c67beb5b963500 Mon Sep 17 00:00:00 2001 From: owlmoonss Date: Thu, 3 Apr 2025 02:46:45 -0400 Subject: [PATCH 12/23] Add JSON logging for chain results in graph_cypher_chain.py --- rag_demo/graph_cypher_chain.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/rag_demo/graph_cypher_chain.py b/rag_demo/graph_cypher_chain.py index 0502363..1cdd5b2 100644 --- a/rag_demo/graph_cypher_chain.py +++ b/rag_demo/graph_cypher_chain.py @@ -1,3 +1,4 @@ +import json from langchain.chains import GraphCypherQAChain from langchain.chains.conversation.memory import ConversationBufferMemory from langchain_community.graphs import Neo4jGraph @@ -118,5 +119,8 @@ def get_results(question) -> str: result = chain_result.get("result", None) logging.debug(f'chain_result: {result}') + print(json.dumps(chain_result, indent=2)) + for node in chain_result.get('nodes', []): + print(node) return result From 84b627bdce84c985dd2a3f3d48b8f35b500367b9 Mon Sep 17 00:00:00 2001 From: owlmoonss Date: Tue, 8 Apr 2025 19:51:06 -0400 Subject: [PATCH 13/23] Refactor Cypher generation template and improve schema clarity in graph_cypher_chain.py --- rag_demo/graph_cypher_chain.py | 149 ++++++++++++++++++--------------- 1 file changed, 80 insertions(+), 69 deletions(-) diff --git a/rag_demo/graph_cypher_chain.py b/rag_demo/graph_cypher_chain.py index 1cdd5b2..94cd132 100644 --- a/rag_demo/graph_cypher_chain.py +++ b/rag_demo/graph_cypher_chain.py @@ -1,29 +1,42 @@ import json +import logging +import streamlit as st +from retry import retry from langchain.chains import GraphCypherQAChain from langchain.chains.conversation.memory import ConversationBufferMemory from langchain_community.graphs import Neo4jGraph from langchain.prompts.prompt import PromptTemplate from langchain_ollama import ChatOllama -from retry import retry -import logging -import streamlit as st -CYPHER_GENERATION_TEMPLATE = """Task: You are a master of technology especially cypher language. Generate Cypher statement to query a graph database strictly based on the schema and instructions provided. +# ✅ Updated Cypher generation prompt template +CYPHER_GENERATION_TEMPLATE = """Task: You are a Cypher expert. Generate an accurate Cypher query ONLY using the schema below. + Instructions: -1. Use only nodes, relationships, and properties mentioned in the schema. -2. Always enclose the Cypher output inside 3 backticks. Do not add 'cypher' after the backticks. -3. Always do a case-insensitive and fuzzy search for any properties related search. Eg: to search for a Company name use `toLower(c.name) contains 'neo4j'` -4. Always use aliases to refer the node in the query -5. Always return count(DISTINCT n) for aggregations to avoid duplicates -6. `OWNS_STOCK_IN` relationship is synonymous with `OWNS` and `OWNER` -7. Use examples of questions and accurate Cypher statements below to guide you. +1. Use only the node labels, relationship types, and properties from the schema. + Avoid adding labels like `:Location` unless explicitly required. Prefer using just `{{Name: "..."}}` if the label is not critical. +2. Return fields like paper.title, location.name — never return paths like `p`. +3. Always do case-insensitive partial string matching: + Use `toLower()` on both sides like `toLower(node.property) CONTAINS toLower("value")` to make matching safe. +4. Always wrap the Cypher query in triple backticks (```) +5. Add LIMIT 10 unless specified otherwise. Schema: {schema} +Example Question 1: Which papers mention anomalous temperature regimes such as cold air outbreaks (CAOs) or warm waves (WWs) in relation to North America, specifically in the sentences where these terms appear? + +Example Cypher: +MATCH (we)-[:TargetsLocation]-(l{{Name:"NORTH_AMERICA"}}) +MATCH (p:Paper)-[m:Mention]-(we) +WHERE toLower(m.Mention_Sentence) CONTAINS toLower("WW") OR toLower(m.Mention_Sentence) CONTAINS toLower("CAOs") +RETURN p, l, we; + +Now generate a Cypher query for: + +{question} +""" + -The question is: -{question}""" CYPHER_GENERATION_PROMPT = PromptTemplate( input_variables=["schema", "question"], template=CYPHER_GENERATION_TEMPLATE @@ -33,8 +46,10 @@ memory_key="chat_history", input_key='question', output_key='answer', - return_messages=True) + return_messages=True +) +# Neo4j connection url = st.secrets["NEO4J_URI"] username = st.secrets["NEO4J_USERNAME"] password = st.secrets["NEO4J_PASSWORD"] @@ -46,81 +61,77 @@ sanitize=True ) -# Using Ollama's DeepSeek R model +# ✅ Custom schema manually defined for prompt clarity +custom_schema = """ +Nodes: +- Paper(title: String) +- WeatherEvent(Name: String) +- Model(Name: String) +- Teleconnection(Name: String) +- OceanCirculation(Name: String) +- Location(Name: String) +- Project(Name: String) + +Relationships: +- (Paper)-[:Mention {Mention_Sentence: String}]->(WeatherEvent) +- (Paper)-[:Mention {Mention_Sentence: String}]->(Model) +- (Paper)-[:Mention {Mention_Sentence: String}]->(Teleconnection) +- (Paper)-[:Mention {Mention_Sentence: String}]->(OceanCirculation) +- (WeatherEvent)-[:TargetsLocation]->(Location) +- (Model)-[:TargetsLocation]->(Location) +- (Teleconnection)-[:TargetsLocation]->(Location) +- (OceanCirculation)-[:TargetsLocation]->(Location) +""" + +# LangChain chain with Ollama model graph_chain = GraphCypherQAChain.from_llm( - cypher_llm=ChatOllama( - model="qwen2", # Replace with the appropriate Ollama model - temperature=0, - ), - qa_llm=ChatOllama( - model="qwen2", # Replace with the appropriate Ollama model - temperature=0, - ), - validate_cypher=True, + cypher_llm=ChatOllama(model="qwen2", temperature=0), + qa_llm=ChatOllama(model="qwen2", temperature=0), graph=graph, - verbose=True, + cypher_prompt=CYPHER_GENERATION_PROMPT, # ✅ pass the custom prompt + validate_cypher=True, return_direct=True, + verbose=True, allow_dangerous_requests=True ) + @retry(tries=2, delay=12) def get_results(question) -> str: - """Generate a response from a GraphCypherQAChain targeted at generating answered related to relationships. - - Args: - question (str): User query - - Returns: - str: Answer from chain - """ - - logging.info(f'Using Neo4j database at url: {url}') + """Generate a response from the GraphCypherQAChain using a cleaned schema and improved prompt.""" + + logging.info(f'Using Neo4j database at URL: {url}') graph.refresh_schema() - prompt = CYPHER_GENERATION_PROMPT.format(schema=graph.get_schema, question=question) - print('Prompt:', prompt) + # 🔙 Log full Neo4j schema in terminal + print("\n========= Raw Schema from Neo4j =========\n") + print(graph.get_schema) - chain_result = None + # ✅ Use custom schema instead of auto-generated + prompt = CYPHER_GENERATION_PROMPT.format(schema=custom_schema, question=question) + print('\n========= Prompt to LLM =========\n') + print(prompt) try: - chain_result = graph_chain.invoke({ - "query": question}, + chain_result = graph_chain.invoke( + {"query": question}, prompt=prompt, return_only_outputs=True, ) except Exception as e: - # Occurs when the chain can not generate a Cypher statement - # for the question with the given database schema - logging.warning(f'Handled exception running graphCypher chain: {e}') + logging.warning(f'Handled exception running GraphCypher chain: {e}') + return "Sorry, I couldn't find an answer to your question" if chain_result is None: - return "Sorry, I couldn't find an answer to your question" - - logging.info(f'Chain result: {chain_result}') - # Extract the result from the chain result - # The result is typically in the "result" key of the chain result - # Adjust this based on the actual structure of the chain result - # For example, if the result is in the "output" key, use that instead - # If the chain result is a list, extract the first element - # if isinstance(chain_result, list) and len(chain_result) > 0: - # result = chain_result[0] - # else: - # result = chain_result - # If the chain result is a dictionary, extract the "result" key - # If the chain result is a string, return it directly - # If the chain result is None or empty, return a default message - # or raise an exception - # Adjust this based on the actual structure of the chain result - # For example, if the result is in the "output" key, use that instead - # If the chain result is a dictionary, extract the "result" key - # If the chain result is a string, return it directly - # If the chain result is None or empty, return a default message - # or raise an exception - + return "No answer was generated." + + # ✅ Debug: show Cypher used + cypher_query = chain_result.get("cypher", "No Cypher returned") + print("\n========= Generated Cypher Query =========\n") + print(cypher_query) + result = chain_result.get("result", None) - logging.debug(f'chain_result: {result}') + print("\n========= Final Result =========\n") print(json.dumps(chain_result, indent=2)) - for node in chain_result.get('nodes', []): - print(node) return result From 5ce60b3cc4fa93920f2bc45e70ef2171418e2982 Mon Sep 17 00:00:00 2001 From: owlmoonss Date: Tue, 8 Apr 2025 20:31:54 -0400 Subject: [PATCH 14/23] Enhance relationship context in Cypher schema and clean up logging statements in graph_cypher_chain.py --- rag_demo/graph_cypher_chain.py | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/rag_demo/graph_cypher_chain.py b/rag_demo/graph_cypher_chain.py index 94cd132..a587b17 100644 --- a/rag_demo/graph_cypher_chain.py +++ b/rag_demo/graph_cypher_chain.py @@ -73,14 +73,19 @@ - Project(Name: String) Relationships: +# Mention relationships with context sentence - (Paper)-[:Mention {Mention_Sentence: String}]->(WeatherEvent) - (Paper)-[:Mention {Mention_Sentence: String}]->(Model) - (Paper)-[:Mention {Mention_Sentence: String}]->(Teleconnection) - (Paper)-[:Mention {Mention_Sentence: String}]->(OceanCirculation) + +# Target location of events, models, processes - (WeatherEvent)-[:TargetsLocation]->(Location) - (Model)-[:TargetsLocation]->(Location) - (Teleconnection)-[:TargetsLocation]->(Location) - (OceanCirculation)-[:TargetsLocation]->(Location) + + """ # LangChain chain with Ollama model @@ -104,8 +109,8 @@ def get_results(question) -> str: graph.refresh_schema() # 🔙 Log full Neo4j schema in terminal - print("\n========= Raw Schema from Neo4j =========\n") - print(graph.get_schema) + #print("\n========= Raw Schema from Neo4j =========\n") + #print(graph.get_schema) # ✅ Use custom schema instead of auto-generated prompt = CYPHER_GENERATION_PROMPT.format(schema=custom_schema, question=question) From b60ca1752edb2f373e0a4df92fcac3eff8bff465 Mon Sep 17 00:00:00 2001 From: owlmoonss Date: Tue, 8 Apr 2025 21:14:09 -0400 Subject: [PATCH 15/23] Remove redundant comments in graph_cypher_chain.py for improved clarity --- rag_demo/graph_cypher_chain.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/rag_demo/graph_cypher_chain.py b/rag_demo/graph_cypher_chain.py index a587b17..85fc9f3 100644 --- a/rag_demo/graph_cypher_chain.py +++ b/rag_demo/graph_cypher_chain.py @@ -8,7 +8,6 @@ from langchain.prompts.prompt import PromptTemplate from langchain_ollama import ChatOllama -# ✅ Updated Cypher generation prompt template CYPHER_GENERATION_TEMPLATE = """Task: You are a Cypher expert. Generate an accurate Cypher query ONLY using the schema below. Instructions: @@ -61,7 +60,7 @@ sanitize=True ) -# ✅ Custom schema manually defined for prompt clarity + custom_schema = """ Nodes: - Paper(title: String) @@ -88,12 +87,11 @@ """ -# LangChain chain with Ollama model graph_chain = GraphCypherQAChain.from_llm( cypher_llm=ChatOllama(model="qwen2", temperature=0), qa_llm=ChatOllama(model="qwen2", temperature=0), graph=graph, - cypher_prompt=CYPHER_GENERATION_PROMPT, # ✅ pass the custom prompt + cypher_prompt=CYPHER_GENERATION_PROMPT, validate_cypher=True, return_direct=True, verbose=True, From 3e261dcaf1aa08f5553620d89e1421a763faf256 Mon Sep 17 00:00:00 2001 From: owlmoonss Date: Sat, 12 Apr 2025 01:12:52 -0400 Subject: [PATCH 16/23] Refactor Cypher generation to use schema from graph and remove unused vector-related files --- rag_demo/graph_cypher_chain.py | 2 +- rag_demo/rag_agent.py | 2 - rag_demo/vector_chain.py | 127 ---------------------------- rag_demo/vector_graph_chain.py | 147 --------------------------------- rag_demo/vector_graph_tool.py | 13 --- rag_demo/vector_tool.py | 12 --- 6 files changed, 1 insertion(+), 302 deletions(-) delete mode 100644 rag_demo/vector_chain.py delete mode 100644 rag_demo/vector_graph_chain.py delete mode 100644 rag_demo/vector_graph_tool.py delete mode 100644 rag_demo/vector_tool.py diff --git a/rag_demo/graph_cypher_chain.py b/rag_demo/graph_cypher_chain.py index 85fc9f3..51ba94b 100644 --- a/rag_demo/graph_cypher_chain.py +++ b/rag_demo/graph_cypher_chain.py @@ -111,7 +111,7 @@ def get_results(question) -> str: #print(graph.get_schema) # ✅ Use custom schema instead of auto-generated - prompt = CYPHER_GENERATION_PROMPT.format(schema=custom_schema, question=question) + prompt = CYPHER_GENERATION_PROMPT.format(schema=graph.get_schema, question=question) print('\n========= Prompt to LLM =========\n') print(prompt) diff --git a/rag_demo/rag_agent.py b/rag_demo/rag_agent.py index 6d9aaaa..2e0e1ad 100644 --- a/rag_demo/rag_agent.py +++ b/rag_demo/rag_agent.py @@ -3,10 +3,8 @@ from langchain import hub # requires langchainhub package from pydantic_ai.models.openai import OpenAIModel as OpenAI from pydantic_ai.providers.openai import OpenAIProvider -from vector_graph_tool import vector_graph_tool from graph_cypher_tool import graph_cypher_tool from graph_tool import graph_tool -from vector_tool import vector_tool from retry import retry from langchain_ollama import ChatOllama diff --git a/rag_demo/vector_chain.py b/rag_demo/vector_chain.py deleted file mode 100644 index c015d28..0000000 --- a/rag_demo/vector_chain.py +++ /dev/null @@ -1,127 +0,0 @@ -from json import loads, dumps -from langchain.prompts.prompt import PromptTemplate - -from langchain_community.vectorstores import Neo4jVector -from langchain.chains import RetrievalQAWithSourcesChain -from langchain.chains.conversation.memory import ConversationBufferMemory -from langchain_ollama import OllamaEmbeddings as Ollama -from retry import retry -import logging -import streamlit as st - -VECTOR_PROMPT_TEMPLATE = """Human: You are a Financial expert with SEC filings who can answer questions only based on the context below. -* Answer the question STRICTLY based on the context provided in JSON below. -* Do not assume or retrieve any information outside of the context -* Use three sentences maximum and keep the answer concise -* Think step by step before answering. -* Do not return helpful or extra text or apologies -* Just return summary to the user. DO NOT start with Here is a summary -* List the results in rich text format if there are more than one results -* If the context is empty, just respond None - - -{input} - - -Here is the context: - -{context} - - -Assistant:""" -VECTOR_PROMPT = PromptTemplate( - input_variables=["input", "context"], template=VECTOR_PROMPT_TEMPLATE -) - -EMBEDDING_MODEL = Ollama(model="qwen2") # Replace OpenAIEmbeddings with Ollama -MEMORY = ConversationBufferMemory( - memory_key="chat_history", - input_key="question", - output_key="answer", - return_messages=True, -) - -index_name = "form_10k_chunks" -node_property_name = "textopenaiembedding" -url = st.secrets["NEO4J_URI"] -username = st.secrets["NEO4J_USERNAME"] -password = st.secrets["NEO4J_PASSWORD"] - -vector_store = None -try: - logging.debug(f"Attempting to retrieve existing vector index: {index_name}...") - vector_store = Neo4jVector.from_existing_index( - embedding=EMBEDDING_MODEL, - url=url, - username=username, - password=password, - index_name=index_name, - embedding_node_property=node_property_name, - ) - logging.debug(f"Using existing index: {index_name}") -except: - logging.debug( - f"No existing index found. Attempting to create a new vector index named {index_name}..." - ) - try: - vector_store = Neo4jVector.from_existing_graph( - embedding=EMBEDDING_MODEL, - url=url, - username=username, - password=password, - index_name=index_name, - node_label="Chunk", - text_node_properties=["text"], - embedding_node_property=node_property_name, - ) - logging.debug(f"Created new index: {index_name}") - except Exception as e: - logging.error(f"[vector_chain]: Failed to retrieve existing or to create a Neo4jVector: {e}") - -if vector_store is None: - logging.error(f"[vector_chain]: Failed to retrieve or create a Neo4jVector. Exiting.") - exit() - -vector_retriever = vector_store.as_retriever() - -vector_chain = RetrievalQAWithSourcesChain.from_chain_type( - Ollama(model="qwen2"), # Replace ChatOpenAI with Ollama - chain_type="stuff", - retriever=vector_retriever, - memory=MEMORY, - reduce_k_below_max_tokens=True, - max_tokens_limit=3000, -) - -@retry(tries=2, delay=5) -def get_results(question) -> str: - """Generate response using Neo4jVector using vector index only - - Args: - question (str): User query - - Returns: - str: Formatted string answer with citations, if available. - """ - - logging.info(f"Using Neo4j url: {url}") - - # Returns a dict with keys: answer, sources - chain_result = vector_chain.invoke( - {"question": question}, - prompt=VECTOR_PROMPT, - return_only_outputs=True, - ) - - logging.debug(f"chain_result: {chain_result}") - - result = chain_result["answer"] - - # Cite sources, if any - sources = chain_result["sources"] - sources_split = sources.split(", ") - for source in sources_split: - if source != "" and source != "N/A" and source != "None": - result += f"\n - [{source}]({source})" - - return result diff --git a/rag_demo/vector_graph_chain.py b/rag_demo/vector_graph_chain.py deleted file mode 100644 index b354f2e..0000000 --- a/rag_demo/vector_graph_chain.py +++ /dev/null @@ -1,147 +0,0 @@ -from langchain.prompts.prompt import PromptTemplate - -from langchain_community.vectorstores import Neo4jVector -from langchain.chains import RetrievalQAWithSourcesChain -from langchain.chains.conversation.memory import ConversationBufferMemory -from langchain_openai import ChatOpenAI, OpenAIEmbeddings -from langchain_ollama import OllamaEmbeddings as Ollama -from langchain_ollama import ChatOllama -from retry import retry -import logging -import streamlit as st - -VECTOR_GRAPH_PROMPT_TEMPLATE = """Task: Provide names and related information financial filing data strictly based on the text and instructions provided. -Instructions: -1. Answer the question STRICTLY based on the text. -2. Do not assume or retrieve any information outside of the text provided. -3. Use as much information from the text as possible, including sources and links if available. -4. If the output is empty, just respond None. - -Question: -{question} -""" -VECTOR_GRAPH_PROMPT = PromptTemplate( - input_variables=["question"], template=VECTOR_GRAPH_PROMPT_TEMPLATE -) - -EMBEDDING_MODEL = Ollama(model="qwen2") # Replace OpenAIEmbeddings with Ollama -MEMORY = ConversationBufferMemory( - memory_key="chat_history", - input_key="question", - output_key="answer", - return_messages=True, -) - -index_name = "form_10k_chunks" -node_property_name = "textEmbedding" -url = st.secrets["NEO4J_URI"] -username = st.secrets["NEO4J_USERNAME"] -password = st.secrets["NEO4J_PASSWORD"] -retrieval_query = """ - WITH node AS doc, score as similarity - ORDER BY similarity DESC LIMIT 5 - CALL { WITH doc - OPTIONAL MATCH (prevDoc:Chunk)-[:NEXT]->(doc) - OPTIONAL MATCH (doc)-[:NEXT]->(nextDoc:Chunk) - RETURN prevDoc, doc AS result, nextDoc - } - WITH result, prevDoc, nextDoc, similarity - CALL { - WITH result - OPTIONAL MATCH (result)-[:PART_OF]->(:Form)<-[:FILED]-(company:Company) - OPTIONAL MATCH (company)<-[:OWNS_STOCK_IN]-(manager:Manager) - WITH result, company.name as companyName, apoc.text.join(collect(manager.managerName),';') as managers - WHERE companyName IS NOT NULL OR managers > "" - WITH result, companyName, managers - ORDER BY result.score DESC - RETURN result as document, result.score as popularity, companyName, managers - } - RETURN coalesce(prevDoc.text,'') + coalesce(document.text,'') + coalesce(nextDoc.text,'') + '\n Company: ' + coalesce(companyName,'') + '\n Managers: ' + coalesce(managers,'') as text, - similarity as score, - {companies: coalesce(companyName,''), managers: coalesce(managers,''), source: document.source} AS metadata -""" - -vector_store = None -try: - logging.debug(f"Attempting to retrieve existing vector index: {index_name}...") - vector_store = Neo4jVector.from_existing_index( - embedding=EMBEDDING_MODEL, - url=url, - username=username, - password=password, - index_name=index_name, - embedding_node_property=node_property_name, - retrieval_query=retrieval_query, - ) - logging.debug(f"Using existing index: {index_name}") -except: - logging.debug( - f"No existing index found. Attempting to create a new vector index named {index_name}..." - ) - try: - vector_store = Neo4jVector.from_existing_graph( - embedding=EMBEDDING_MODEL, - url=url, - username=username, - password=password, - index_name=index_name, - node_label="Chunk", - text_node_properties=["text"], - embedding_node_property=node_property_name, - retrieval_query=retrieval_query, - ) - logging.debug(f"Created new index: {index_name}") - except Exception as e: - logging.error(f"Failed to retrieve existing or to create a Neo4jVector: {e}") - -if vector_store is None: - logging.error(f"Failed to retrieve or create a Neo4jVector. Exiting.") - exit() - -vector_graph_retriever = vector_store.as_retriever() - -vector_graph_chain = RetrievalQAWithSourcesChain.from_chain_type( - ChatOllama( - model="qwen2", # Replace with the appropriate Ollama model - temperature=0, - ), - chain_type="stuff", - retriever=vector_graph_retriever, - memory=MEMORY, - reduce_k_below_max_tokens=True, - max_tokens_limit=3000, -) - -@retry(tries=2, delay=5) -def get_results(question) -> str: - """Generate response using Neo4jVector using vector index only - - Args: - question (str): User query - - Returns: - str: Formatted string answer with citations, if available. - """ - - logging.info(f"Using Neo4j url: {url}") - - prompt = VECTOR_GRAPH_PROMPT.format(question=question) - - # Returns a dict with keys: answer, sources - chain_result = vector_graph_chain.invoke( - {"question": question}, - prompt=prompt, - return_only_outputs=True, - ) - - logging.debug(f"chain_result: {chain_result}") - result = chain_result["answer"] - - # Cite sources, if any - sources = chain_result["sources"] - sources_split = sources.split(", ") - for source in sources_split: - if source != "" and source != "N/A" and source != "None": - result += f"\n - [{source}]({source})" - - return result diff --git a/rag_demo/vector_graph_tool.py b/rag_demo/vector_graph_tool.py deleted file mode 100644 index a82c8da..0000000 --- a/rag_demo/vector_graph_tool.py +++ /dev/null @@ -1,13 +0,0 @@ -from langchain.tools import tool -from vector_graph_chain import get_results - -@tool("vector-graph-tool") -def vector_graph_tool(query:str) -> str: - """ - Useful when answer requires specific information about companies, managers, or industries. - Use when question asks for which companies, managers, or industries. - Use full question as input. - Do not call this tool more than once. - Do not call another tool if this returns results. - """ - return get_results(query) \ No newline at end of file diff --git a/rag_demo/vector_tool.py b/rag_demo/vector_tool.py deleted file mode 100644 index f7af66e..0000000 --- a/rag_demo/vector_tool.py +++ /dev/null @@ -1,12 +0,0 @@ -from langchain.tools import tool -from vector_graph_chain import get_results - - -@tool("vector-tool") -def vector_tool(query: str) -> str: - """ - For finding similar entities to the ones in the search query. - Only use this tool if no answer from others. - Do not call another tool after this one. - """ - return get_results(query) From a77e2ec409f7d66d67c3f18357b9c5d34e769177 Mon Sep 17 00:00:00 2001 From: owlmoonss Date: Sun, 13 Apr 2025 14:28:16 -0400 Subject: [PATCH 17/23] Refactor Cypher generation template for clarity and enhance question examples in graph_cypher_chain.py; update get_results function in rag_agent.py for improved processing flow --- rag_demo/graph_cypher_chain.py | 93 ++++++++++++++++++--------------- rag_demo/main.py | 3 +- rag_demo/rag_agent.py | 94 ++++++++++++++++------------------ 3 files changed, 97 insertions(+), 93 deletions(-) diff --git a/rag_demo/graph_cypher_chain.py b/rag_demo/graph_cypher_chain.py index 51ba94b..174d45c 100644 --- a/rag_demo/graph_cypher_chain.py +++ b/rag_demo/graph_cypher_chain.py @@ -8,28 +8,63 @@ from langchain.prompts.prompt import PromptTemplate from langchain_ollama import ChatOllama -CYPHER_GENERATION_TEMPLATE = """Task: You are a Cypher expert. Generate an accurate Cypher query ONLY using the schema below. +CYPHER_GENERATION_TEMPLATE = """ +Task: Generate an accurate Cypher query ONLY using the schema below. -Instructions: -1. Use only the node labels, relationship types, and properties from the schema. - Avoid adding labels like `:Location` unless explicitly required. Prefer using just `{{Name: "..."}}` if the label is not critical. -2. Return fields like paper.title, location.name — never return paths like `p`. -3. Always do case-insensitive partial string matching: - Use `toLower()` on both sides like `toLower(node.property) CONTAINS toLower("value")` to make matching safe. -4. Always wrap the Cypher query in triple backticks (```) -5. Add LIMIT 10 unless specified otherwise. Schema: {schema} -Example Question 1: Which papers mention anomalous temperature regimes such as cold air outbreaks (CAOs) or warm waves (WWs) in relation to North America, specifically in the sentences where these terms appear? +This example: -Example Cypher: +Question: +Which papers mention anomalous temperature regimes such as cold air outbreaks (CAOs) or +warm waves (WWs) in relation to North America, specifically in the sentences where these +terms appear? +Answer: MATCH (we)-[:TargetsLocation]-(l{{Name:"NORTH_AMERICA"}}) -MATCH (p:Paper)-[m:Mention]-(we) -WHERE toLower(m.Mention_Sentence) CONTAINS toLower("WW") OR toLower(m.Mention_Sentence) CONTAINS toLower("CAOs") +MATCH (p:Paper)-[m:Mention]-(we) +WHERE toLower(m.Mention_Sentence) CONTAINS toLower("WW") + OR toLower(m.Mention_Sentence) CONTAINS toLower("CAOs") RETURN p, l, we; + +Question: +Which papers discuss ocean circulation processes—such as thermohaline circulation—in oceanic +regions that include either “North” or “South” in their names? +Answer: +MATCH (n:Location) +WHERE toLower(n.Name) CONTAINS toLower("OCEAN") + AND (toLower(n.Name) CONTAINS toLower("NORTH") OR toLower(n.Name) CONTAINS toLower("SOUTH")) +MATCH (oc:OceanCirculation)-[:TargetsLocation]-(l) +MATCH (p:Paper)-[m:Mention]-(oc) +WHERE toLower(m.Mention_Sentence) CONTAINS toLower("thermohaline circulation") +RETURN n, oc, p; + + +Question: +Which papers mention CMIP5 models and the North Atlantic Oscillation (NAO) in the context of +the Southeast United States? +Answer: +MATCH (p:Paper)-[r:Mention]->(m:Model|Project) +WHERE toLower(m.Name) CONTAINS toLower("CMIP_5") +MATCH (p)-[t:Mention]-(n:Teleconnection{{Name:"NORTH_ATLANTIC_OSCILLATION"}}) +WHERE toLower(t.Mention_Sentence) CONTAINS toLower("Southeast") +RETURN p, m, n; + + +Question: +Which papers mention the Pacific-North American (PNA) pattern in connection with locations in +the United States? +Answer +MATCH (p:Paper)-[:Mention]->(t:Teleconnection{{Name:"PACIFIC_NORTH_AMERICAN_PNA_PATTERN"}}) +MATCH (t)-[:TargetsLocation]-(l:Location) +MATCH (p)-[:Mention]-(l) +WHERE toLower(l.wikidata_description) CONTAINS toLower("United States") +RETURN p, t, l; + + + Now generate a Cypher query for: {question} @@ -61,32 +96,6 @@ ) -custom_schema = """ -Nodes: -- Paper(title: String) -- WeatherEvent(Name: String) -- Model(Name: String) -- Teleconnection(Name: String) -- OceanCirculation(Name: String) -- Location(Name: String) -- Project(Name: String) - -Relationships: -# Mention relationships with context sentence -- (Paper)-[:Mention {Mention_Sentence: String}]->(WeatherEvent) -- (Paper)-[:Mention {Mention_Sentence: String}]->(Model) -- (Paper)-[:Mention {Mention_Sentence: String}]->(Teleconnection) -- (Paper)-[:Mention {Mention_Sentence: String}]->(OceanCirculation) - -# Target location of events, models, processes -- (WeatherEvent)-[:TargetsLocation]->(Location) -- (Model)-[:TargetsLocation]->(Location) -- (Teleconnection)-[:TargetsLocation]->(Location) -- (OceanCirculation)-[:TargetsLocation]->(Location) - - -""" - graph_chain = GraphCypherQAChain.from_llm( cypher_llm=ChatOllama(model="qwen2", temperature=0), qa_llm=ChatOllama(model="qwen2", temperature=0), @@ -124,17 +133,17 @@ def get_results(question) -> str: except Exception as e: logging.warning(f'Handled exception running GraphCypher chain: {e}') return "Sorry, I couldn't find an answer to your question" - + if chain_result is None: return "No answer was generated." # ✅ Debug: show Cypher used cypher_query = chain_result.get("cypher", "No Cypher returned") - print("\n========= Generated Cypher Query =========\n") + print("\n========= Generated Answer=========\n") print(cypher_query) result = chain_result.get("result", None) print("\n========= Final Result =========\n") print(json.dumps(chain_result, indent=2)) - return result + return chain_result diff --git a/rag_demo/main.py b/rag_demo/main.py index db5c571..b89dd4d 100644 --- a/rag_demo/main.py +++ b/rag_demo/main.py @@ -10,6 +10,7 @@ from constants import TITLE import logging import rag_agent +import graph_cypher_chain import streamlit as st from sidebar import sidebar @@ -88,7 +89,7 @@ # StreamlitCcallbackHandler api doc: https://api.python.langchain.com/en/latest/callbacks/langchain_community.callbacks.streamlit.streamlit_callback_handler.StreamlitCallbackHandler.html agent_response = rag_agent.get_results( - question=user_input, callbacks=[] + question=user_input, ) if isinstance(agent_response, dict) is False: diff --git a/rag_demo/rag_agent.py b/rag_demo/rag_agent.py index 2e0e1ad..ad413d5 100644 --- a/rag_demo/rag_agent.py +++ b/rag_demo/rag_agent.py @@ -1,66 +1,60 @@ -from langchain.agents import AgentExecutor, create_react_agent -from langchain_community.agent_toolkits.load_tools import load_tools -from langchain import hub # requires langchainhub package -from pydantic_ai.models.openai import OpenAIModel as OpenAI -from pydantic_ai.providers.openai import OpenAIProvider -from graph_cypher_tool import graph_cypher_tool -from graph_tool import graph_tool from retry import retry -from langchain_ollama import ChatOllama - - - -# Setup tools the agent will use -llm = ChatOllama( - model="qwen2", # Replace with the appropriate Ollama model - temperature=0, - ) -tools = load_tools([], llm=llm) -tools = tools + [graph_cypher_tool, graph_tool] - - -# REACT AGENT EXECUTOR - -prompt = hub.pull("hwchase17/react") +from langchain_community.llms import Ollama +from graph_cypher_tool import graph_cypher_tool -# More on agent types: https://python.langchain.com/docs/modules/agents/agent_types/ -agent = create_react_agent(llm, tools, prompt) +llm = Ollama(model="llama3") + +conversation_history = [] + +def process_with_llm(question, json_output: any) -> str: + """Send the tool output (JSON) to the LLM for further processing.""" + # Chuyển kết quả JSON thành chuỗi, bạn có thể format hoặc làm sạch theo yêu cầu + output_str = str(json_output) + conversation_text = "\n".join([f"User: {msg['input']}\nBot: {msg['output']}" for msg in conversation_history]) + prompt = f"Based on the conversation, provide a relevant and helpful response.\n{conversation_text}\n\n" + prompt = prompt + f""" +This is current question by user: {question} +Please process the output and provide a summary or answer based on the data. +Make sure to answer the question in a clear and concise manner. +Here is the output from the database: +{json_output} + """ -# NOTE: early_stopping_method generate option ONLY available for multi-action agents -agent_executor = AgentExecutor( - agent=agent, - tools=tools, - verbose=True, -) + # Gửi kết quả tới LLM để xử lý + response = llm.predict(prompt) + + conversation_history.append({"input": question, "output": response}) + + return response -@retry(tries=2, delay=20) -def get_results(question, callbacks) -> dict: +@retry(tries=2, delay=10) +def get_results(question: str, user_args: dict = None, tool_name: str = None) -> dict: """ - Starts a LangChain agent to generate an answer using one of several Neo4j RAG tools. + Run a specific tool directly, then send the result to LLM for processing. Args: - question (str): User query - callbacks (list): List of optional LangChain callback handlers + question (str): User’s original question (for record only) + user_args (dict): Dict input for the tool + tool_name (str): Name of the tool to use (must be in custom_tools) Returns: - dict: Final answer as a dict with the keys: input, output, intermediate_steps + dict: agent-style output, with LLM processed result """ - response = agent_executor.invoke({"input": question}, callbacks=callbacks) + tool = graph_cypher_tool + if tool is None: + raise ValueError(f"Tool '{tool_name}' not found.") - # Extract more info from the response - full_input = response.get("input") - final_output = response.get("output") - intermediate_steps = response.get("intermediate_steps") + # 1. Chạy tool để lấy kết quả + tool_result = graph_cypher_tool(question) - # Optional: extract tool names used - tools_used = [step[0] for step in intermediate_steps] if intermediate_steps else [] + # 2. Gửi kết quả tool (JSON) đến LLM để xử lý thêm + llm_processed_output = process_with_llm(question=question, json_output=tool_result) + # 3. Trả về kết quả bao gồm cả đầu ra từ tool và kết quả xử lý của LLM return { - "input": full_input, - "output": final_output, - "intermediate_steps": intermediate_steps, - "tools_used": tools_used, + "input": question, # Đầu vào từ người dùng + "output": llm_processed_output, # Đầu ra sau khi xử lý bằng LLM + # "intermediate_steps": tool_result["intermediate_steps"], # Các bước trung gian (nếu có) + # "tools_used": tool_result["tools_used"], # Danh sách các tool đã sử dụng } - - From 757305b24b8840b3a7e4fe97ed1b2f3dcba1dc6c Mon Sep 17 00:00:00 2001 From: owlmoonss Date: Sun, 13 Apr 2025 15:41:44 -0400 Subject: [PATCH 18/23] Refactor Cypher generation template for improved clarity and detail; update LLM integration in rag_agent.py for enhanced processing and response generation --- rag_demo/graph_cypher_chain.py | 137 ++++++++++++++++++++++----------- rag_demo/rag_agent.py | 82 ++++++++++---------- 2 files changed, 135 insertions(+), 84 deletions(-) diff --git a/rag_demo/graph_cypher_chain.py b/rag_demo/graph_cypher_chain.py index 174d45c..fc6edd5 100644 --- a/rag_demo/graph_cypher_chain.py +++ b/rag_demo/graph_cypher_chain.py @@ -7,66 +7,105 @@ from langchain_community.graphs import Neo4jGraph from langchain.prompts.prompt import PromptTemplate from langchain_ollama import ChatOllama +from langchain_openai import ChatOpenAI CYPHER_GENERATION_TEMPLATE = """ -Task: Generate an accurate Cypher query ONLY using the schema below. +You are a Cypher expert who translates natural language questions into Cypher queries for a Neo4j graph database. +The database contains entities such as: +- Paper (p) +- Location (l) +- OceanCirculation (oc) +- WeatherEvent (we) +- Teleconnection (t) +- Model or Project (m) +Relationships include: +- :Mention (from Paper to another node), with property: Mention_Sentence +- :TargetsLocation (from a concept like OceanCirculation or WeatherEvent to a Location) -Schema: +Properties include: +- Name (for all nodes) +- Mention_Sentence (in the :Mention relationship) +- wikidata_description (for Location) + +- Ocean circulation processes often target specific oceanic locations, such as the Southern Ocean. +- Mentions of concepts in papers are linked via the :Mention relationship, which includes a Mention_Sentence field. +- To filter by concepts like "upwelling", check if the Mention_Sentence contains that word. +- A common type of question is: "What [scientific concept] are discussed in relation to [location] and involving [mechanism/phenomenon]?" +- The Cypher query often starts by matching a domain concept (e.g., OceanCirculation) and the location it's associated with. +- Then it retrieves papers mentioning that concept, filtering by keywords in the mention sentence. +When a question involves both a *scientific process* (e.g., upwelling) and a *geographical region* (e.g., Southern Ocean), first match entities representing the process (like OceanCirculation) that are linked to the region using :TargetsLocation. Then find papers mentioning those processes where the mention text contains the target concept (e.g., upwelling). +Some questions require understanding the relationship between a scientific process and a region, like the Southern Ocean. OceanCirculation entities are typically linked to Location nodes via the :TargetsLocation relation. If a specific process like "upwelling" is involved, the :Mention relationship's Mention_Sentence field can be used to filter relevant context in papers. Use this reasoning to write Cypher queries. +Entities like OceanCirculation, Teleconnection, and Paper are connected via various relationships. Each Paper can mention a concept using a :Mention relationship, which includes a field called Mention_Sentence. + +Use Mention_Sentence to filter for specific keywords or phenomena that appear in the paper's context. For example, when a user asks about a certain process like “upwelling,” you can search for papers that mention that process by filtering the Mention_Sentence field. + +Also, many scientific processes (e.g., OceanCirculation) are geographically grounded. You can find such processes using the :TargetsLocation relationship with a Location node. Locations often include names like “Southern Ocean”, “North Atlantic”, or “Southeast United States”. + +If the question refers to regions or physical processes, combine both semantic filtering via Mention_Sentence and geographic filtering using TargetsLocation. + +Important: Never use [:Mention] in query and Name of Location always uppercase and replace space with _ (example "North Atlantic" becomes "NORTH_ATLANTIC"). +The following is the schema of the Neo4j database. The schema is a simplified representation of the graph database, showing the types of nodes and relationships present in the database. The schema includes nodes for Paper, Location, OceanCirculation, WeatherEvent, Teleconnection, and Model or Project, along with their respective properties and relationships. + + +Schema database is: {schema} -This example: +Here are some examples: + +### Example 1 +Natural Language Question: +Which papers mention anomalous temperature regimes such as cold air outbreaks (CAOs) or warm waves (WWs) in relation to North America, specifically in the sentences where these terms appear? + +Cypher: +MATCH (we)-[:TargetsLocation]-(l{{Name:"NORTH_AMERICA"}}) +MATCH (p:Paper)-[m:Mention]-(we) +WHERE m.Mention_Sentence CONTAINS 'WW' OR m.Mention_Sentence CONTAINS 'CAOs' +RETURN p,l,we; -Question: -Which papers mention anomalous temperature regimes such as cold air outbreaks (CAOs) or -warm waves (WWs) in relation to North America, specifically in the sentences where these -terms appear? -Answer: -MATCH (we)-[:TargetsLocation]-(l{{Name:"NORTH_AMERICA"}}) -MATCH (p:Paper)-[m:Mention]-(we) -WHERE toLower(m.Mention_Sentence) CONTAINS toLower("WW") - OR toLower(m.Mention_Sentence) CONTAINS toLower("CAOs") -RETURN p, l, we; +--- +### Example 2 +Natural Language Question: +Which papers discuss ocean circulation processes—such as thermohaline circulation—in oceanic regions that include either “North” or “South” in their names? -Question: -Which papers discuss ocean circulation processes—such as thermohaline circulation—in oceanic -regions that include either “North” or “South” in their names? -Answer: +Cypher: MATCH (n:Location) -WHERE toLower(n.Name) CONTAINS toLower("OCEAN") - AND (toLower(n.Name) CONTAINS toLower("NORTH") OR toLower(n.Name) CONTAINS toLower("SOUTH")) -MATCH (oc:OceanCirculation)-[:TargetsLocation]-(l) -MATCH (p:Paper)-[m:Mention]-(oc) -WHERE toLower(m.Mention_Sentence) CONTAINS toLower("thermohaline circulation") -RETURN n, oc, p; +WHERE n.Name CONTAINS 'OCEAN' AND (n.Name CONTAINS 'NORTH' OR n.Name CONTAINS 'SOUTH') +MATCH (oc:OceanCirculation)-[:TargetsLocation]-(l) +MATCH (p:Paper)-[m:Mention]-(oc) +WHERE m.Mention_Sentence CONTAINS 'thermohaline circulation' +RETURN n,oc,p; +--- -Question: -Which papers mention CMIP5 models and the North Atlantic Oscillation (NAO) in the context of -the Southeast United States? -Answer: -MATCH (p:Paper)-[r:Mention]->(m:Model|Project) -WHERE toLower(m.Name) CONTAINS toLower("CMIP_5") -MATCH (p)-[t:Mention]-(n:Teleconnection{{Name:"NORTH_ATLANTIC_OSCILLATION"}}) -WHERE toLower(t.Mention_Sentence) CONTAINS toLower("Southeast") -RETURN p, m, n; +### Example 3 +Natural Language Question: +Which papers mention CMIP5 models and the North Atlantic Oscillation (NAO) in the context of the Southeast United States? +Cypher: +MATCH (p:Paper)-[r:Mention]->(m:Model|Project) +WHERE m.Name CONTAINS 'CMIP_5' +MATCH (p)-[t:Mention]-(n:Teleconnection{{Name:"NORTH_ATLANTIC_OSCILLATION"}}) +WHERE t.Mention_Sentence CONTAINS 'Southeast' +RETURN p,m,n; -Question: -Which papers mention the Pacific-North American (PNA) pattern in connection with locations in -the United States? -Answer -MATCH (p:Paper)-[:Mention]->(t:Teleconnection{{Name:"PACIFIC_NORTH_AMERICAN_PNA_PATTERN"}}) -MATCH (t)-[:TargetsLocation]-(l:Location) -MATCH (p)-[:Mention]-(l) -WHERE toLower(l.wikidata_description) CONTAINS toLower("United States") -RETURN p, t, l; +--- +### Example 4 +Natural Language Question: +Which papers mention the Pacific-North American (PNA) pattern in connection with locations in the United States? +Cypher: +MATCH (p:Paper)-[z:Mention]->(t:Teleconnection{{Name:"PACIFIC_NORTH_AMERICAN_PNA_PATTERN"}}) +MATCH (t)-[:TargetsLocation]-(l:Location) +MATCH (p)-[z:Mention]-(l) +WHERE l.wikidata_description CONTAINS "United States" +RETURN p,t,l; -Now generate a Cypher query for: +--- +Now generate a Cypher query for: {question} """ @@ -97,8 +136,17 @@ graph_chain = GraphCypherQAChain.from_llm( - cypher_llm=ChatOllama(model="qwen2", temperature=0), - qa_llm=ChatOllama(model="qwen2", temperature=0), + # cypher_llm=ChatOllama(model="qwen2", temperature=0), + # qa_llm=ChatOllama(model="qwen2", temperature=0), + cypher_llm=ChatOpenAI( + openai_api_key=st.secrets["OPENAI_API_KEY"], + temperature=0, + model_name="gpt-4o-mini" + ), + qa_llm=ChatOpenAI( + openai_api_key=st.secrets["OPENAI_API_KEY"], + temperature=0, + model_name="gpt-4o-mini"), graph=graph, cypher_prompt=CYPHER_GENERATION_PROMPT, validate_cypher=True, @@ -135,6 +183,7 @@ def get_results(question) -> str: return "Sorry, I couldn't find an answer to your question" if chain_result is None: + print('No answer was generated.') return "No answer was generated." # ✅ Debug: show Cypher used diff --git a/rag_demo/rag_agent.py b/rag_demo/rag_agent.py index ad413d5..ea9dbf0 100644 --- a/rag_demo/rag_agent.py +++ b/rag_demo/rag_agent.py @@ -1,55 +1,57 @@ from retry import retry from langchain_community.llms import Ollama +from langchain_openai import ChatOpenAI from graph_cypher_tool import graph_cypher_tool +import streamlit as st + +# llm = Ollama(model="llama3") +llm = ChatOpenAI( + openai_api_key=st.secrets["OPENAI_API_KEY"], + temperature=0.2, + model_name="gpt-4o-mini" +) +conversation_history = [] + +def process_with_llm(question: str) -> str: + """Decide if a DB query is needed, and generate a response accordingly.""" + + # === 1. Chuẩn bị phần hội thoại lịch sử (nếu có) === + conversation_text = "\n".join([ + f"User: {msg['input']}\nBot: {msg['output']}" + for msg in conversation_history + ]) + + tool_output = graph_cypher_tool.invoke(question) + tool_output_str = str(tool_output) + + # Gửi prompt đầy đủ để xử lý dữ liệu đã truy vấn + final_prompt = f""" +Based on the conversation and the user question, provide a relevant and helpful response. -llm = Ollama(model="llama3") +Conversation: +{conversation_text} -conversation_history = [] +Current question: {question} -def process_with_llm(question, json_output: any) -> str: - """Send the tool output (JSON) to the LLM for further processing.""" - # Chuyển kết quả JSON thành chuỗi, bạn có thể format hoặc làm sạch theo yêu cầu - output_str = str(json_output) - conversation_text = "\n".join([f"User: {msg['input']}\nBot: {msg['output']}" for msg in conversation_history]) - prompt = f"Based on the conversation, provide a relevant and helpful response.\n{conversation_text}\n\n" - prompt = prompt + f""" -This is current question by user: {question} -Please process the output and provide a summary or answer based on the data. -Make sure to answer the question in a clear and concise manner. Here is the output from the database: -{json_output} - """ +{tool_output_str} - # Gửi kết quả tới LLM để xử lý - response = llm.predict(prompt) +Please process the output and answer the user question clearly. + """.strip() + + final_response = llm.predict(final_prompt).strip() - conversation_history.append({"input": question, "output": response}) + # === 5. Cập nhật lịch sử hội thoại === + conversation_history.append({ + "input": question, + "output": final_response + }) - return response - + return final_response @retry(tries=2, delay=10) -def get_results(question: str, user_args: dict = None, tool_name: str = None) -> dict: - """ - Run a specific tool directly, then send the result to LLM for processing. - - Args: - question (str): User’s original question (for record only) - user_args (dict): Dict input for the tool - tool_name (str): Name of the tool to use (must be in custom_tools) - - Returns: - dict: agent-style output, with LLM processed result - """ - tool = graph_cypher_tool - if tool is None: - raise ValueError(f"Tool '{tool_name}' not found.") - - # 1. Chạy tool để lấy kết quả - tool_result = graph_cypher_tool(question) - - # 2. Gửi kết quả tool (JSON) đến LLM để xử lý thêm - llm_processed_output = process_with_llm(question=question, json_output=tool_result) +def get_results(question: str) -> dict: + llm_processed_output = process_with_llm(question=question) # 3. Trả về kết quả bao gồm cả đầu ra từ tool và kết quả xử lý của LLM return { From bbc44b202f6fda87e5445a47c60d750e9a239a44 Mon Sep 17 00:00:00 2001 From: owlmoonss Date: Sun, 13 Apr 2025 15:42:31 -0400 Subject: [PATCH 19/23] Add .secrets.toml to .gitignore to prevent sensitive information from being tracked --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index f9bf23b..aca7b07 100644 --- a/.gitignore +++ b/.gitignore @@ -15,3 +15,4 @@ __pycache__ # Dependency managers *.lock .qodo +.secrets.toml \ No newline at end of file From 495b338997fdcfe3e267ea29ec034a72c20e721f Mon Sep 17 00:00:00 2001 From: owlmoonss Date: Sun, 13 Apr 2025 18:50:38 -0400 Subject: [PATCH 20/23] Refactor comments for clarity and consistency; remove unused graph_tool.py file --- rag_demo/graph_cypher_chain.py | 5 ++--- rag_demo/graph_tool.py | 8 -------- rag_demo/rag_agent.py | 16 ++++++++-------- 3 files changed, 10 insertions(+), 19 deletions(-) delete mode 100644 rag_demo/graph_tool.py diff --git a/rag_demo/graph_cypher_chain.py b/rag_demo/graph_cypher_chain.py index fc6edd5..2f7a162 100644 --- a/rag_demo/graph_cypher_chain.py +++ b/rag_demo/graph_cypher_chain.py @@ -163,11 +163,10 @@ def get_results(question) -> str: logging.info(f'Using Neo4j database at URL: {url}') graph.refresh_schema() - # 🔙 Log full Neo4j schema in terminal + #Log full Neo4j schema in terminal #print("\n========= Raw Schema from Neo4j =========\n") #print(graph.get_schema) - # ✅ Use custom schema instead of auto-generated prompt = CYPHER_GENERATION_PROMPT.format(schema=graph.get_schema, question=question) print('\n========= Prompt to LLM =========\n') print(prompt) @@ -186,7 +185,7 @@ def get_results(question) -> str: print('No answer was generated.') return "No answer was generated." - # ✅ Debug: show Cypher used + # Debug: show Cypher used cypher_query = chain_result.get("cypher", "No Cypher returned") print("\n========= Generated Answer=========\n") print(cypher_query) diff --git a/rag_demo/graph_tool.py b/rag_demo/graph_tool.py deleted file mode 100644 index bd7ea08..0000000 --- a/rag_demo/graph_tool.py +++ /dev/null @@ -1,8 +0,0 @@ -from langchain.tools import tool -from graph_cypher_chain import get_results - -@tool("graph-tool") -def graph_tool(query:str) -> str: - """Tool for returning aggregations of Manager or Company or Industry data or if answer is dependent on relationships between a Company and other objects. Use this tool second and to verify results of vector-graph-tool. - """ - return get_results(query) \ No newline at end of file diff --git a/rag_demo/rag_agent.py b/rag_demo/rag_agent.py index ea9dbf0..ebad5c3 100644 --- a/rag_demo/rag_agent.py +++ b/rag_demo/rag_agent.py @@ -15,7 +15,7 @@ def process_with_llm(question: str) -> str: """Decide if a DB query is needed, and generate a response accordingly.""" - # === 1. Chuẩn bị phần hội thoại lịch sử (nếu có) === + # === 1. Prepare conversation history (if any) === conversation_text = "\n".join([ f"User: {msg['input']}\nBot: {msg['output']}" for msg in conversation_history @@ -24,7 +24,7 @@ def process_with_llm(question: str) -> str: tool_output = graph_cypher_tool.invoke(question) tool_output_str = str(tool_output) - # Gửi prompt đầy đủ để xử lý dữ liệu đã truy vấn + # Send full prompt to process queried data final_prompt = f""" Based on the conversation and the user question, provide a relevant and helpful response. @@ -41,7 +41,7 @@ def process_with_llm(question: str) -> str: final_response = llm.predict(final_prompt).strip() - # === 5. Cập nhật lịch sử hội thoại === + # === 5. Update conversation history === conversation_history.append({ "input": question, "output": final_response @@ -53,10 +53,10 @@ def process_with_llm(question: str) -> str: def get_results(question: str) -> dict: llm_processed_output = process_with_llm(question=question) - # 3. Trả về kết quả bao gồm cả đầu ra từ tool và kết quả xử lý của LLM + # 3. Return the result including both the tool output and the LLM response return { - "input": question, # Đầu vào từ người dùng - "output": llm_processed_output, # Đầu ra sau khi xử lý bằng LLM - # "intermediate_steps": tool_result["intermediate_steps"], # Các bước trung gian (nếu có) - # "tools_used": tool_result["tools_used"], # Danh sách các tool đã sử dụng + "input": question, # User input + "output": llm_processed_output, # Output after processing with LLM + # "intermediate_steps": tool_result["intermediate_steps"], # Intermediate steps (if any) + # "tools_used": tool_result["tools_used"], # List of tools used } From 8e1450b75fd9810bc52be3494824a5a3ef765b30 Mon Sep 17 00:00:00 2001 From: owlmoonss Date: Sun, 13 Apr 2025 21:06:34 -0400 Subject: [PATCH 21/23] Enhance Cypher generation template with new guidelines for matching relationships and formatting location names --- rag_demo/graph_cypher_chain.py | 16 +++++++--------- 1 file changed, 7 insertions(+), 9 deletions(-) diff --git a/rag_demo/graph_cypher_chain.py b/rag_demo/graph_cypher_chain.py index 2f7a162..e82d6fc 100644 --- a/rag_demo/graph_cypher_chain.py +++ b/rag_demo/graph_cypher_chain.py @@ -9,6 +9,7 @@ from langchain_ollama import ChatOllama from langchain_openai import ChatOpenAI + CYPHER_GENERATION_TEMPLATE = """ You are a Cypher expert who translates natural language questions into Cypher queries for a Neo4j graph database. The database contains entities such as: @@ -34,17 +35,13 @@ - A common type of question is: "What [scientific concept] are discussed in relation to [location] and involving [mechanism/phenomenon]?" - The Cypher query often starts by matching a domain concept (e.g., OceanCirculation) and the location it's associated with. - Then it retrieves papers mentioning that concept, filtering by keywords in the mention sentence. -When a question involves both a *scientific process* (e.g., upwelling) and a *geographical region* (e.g., Southern Ocean), first match entities representing the process (like OceanCirculation) that are linked to the region using :TargetsLocation. Then find papers mentioning those processes where the mention text contains the target concept (e.g., upwelling). -Some questions require understanding the relationship between a scientific process and a region, like the Southern Ocean. OceanCirculation entities are typically linked to Location nodes via the :TargetsLocation relation. If a specific process like "upwelling" is involved, the :Mention relationship's Mention_Sentence field can be used to filter relevant context in papers. Use this reasoning to write Cypher queries. -Entities like OceanCirculation, Teleconnection, and Paper are connected via various relationships. Each Paper can mention a concept using a :Mention relationship, which includes a field called Mention_Sentence. - -Use Mention_Sentence to filter for specific keywords or phenomena that appear in the paper's context. For example, when a user asks about a certain process like “upwelling,” you can search for papers that mention that process by filtering the Mention_Sentence field. - -Also, many scientific processes (e.g., OceanCirculation) are geographically grounded. You can find such processes using the :TargetsLocation relationship with a Location node. Locations often include names like “Southern Ocean”, “North Atlantic”, or “Southeast United States”. - -If the question refers to regions or physical processes, combine both semantic filtering via Mention_Sentence and geographic filtering using TargetsLocation. +- **Always use `[m:Mention]` when matching the mention relationship, never `[m]` or `[:Mention]`.** +- **Use labels like `:WeatherEvent`, `:OceanCirculation`, etc., only when the natural language question explicitly refers to the concept. Otherwise, leave the node unlabeled.** +- **Wrap multiple conditions in WHERE clauses (e.g., with OR/AND) inside parentheses to preserve logic clarity.** +- **When using a Location name in a Cypher match, convert it to all uppercase and replace spaces with underscores. (e.g., "North Atlantic" → "NORTH_ATLANTIC")** Important: Never use [:Mention] in query and Name of Location always uppercase and replace space with _ (example "North Atlantic" becomes "NORTH_ATLANTIC"). + The following is the schema of the Neo4j database. The schema is a simplified representation of the graph database, showing the types of nodes and relationships present in the database. The schema includes nodes for Paper, Location, OceanCirculation, WeatherEvent, Teleconnection, and Model or Project, along with their respective properties and relationships. @@ -103,6 +100,7 @@ WHERE l.wikidata_description CONTAINS "United States" RETURN p,t,l; + --- Now generate a Cypher query for: From 5b5b2beef0fae653703302127533e03a7a34227d Mon Sep 17 00:00:00 2001 From: owlmoonss Date: Thu, 17 Apr 2025 02:04:44 -0400 Subject: [PATCH 22/23] Refactor graph_cypher_chain.py and rag_agent.py for improved LLM integration; add URL encoding for queries and enhance response formatting --- rag_demo/graph_cypher_chain.py | 30 +++++++++++++++++++----------- rag_demo/rag_agent.py | 16 ++++++++++------ 2 files changed, 29 insertions(+), 17 deletions(-) diff --git a/rag_demo/graph_cypher_chain.py b/rag_demo/graph_cypher_chain.py index e82d6fc..a33eb33 100644 --- a/rag_demo/graph_cypher_chain.py +++ b/rag_demo/graph_cypher_chain.py @@ -1,6 +1,7 @@ import json import logging import streamlit as st +import urllib.parse from retry import retry from langchain.chains import GraphCypherQAChain from langchain.chains.conversation.memory import ConversationBufferMemory @@ -134,23 +135,24 @@ graph_chain = GraphCypherQAChain.from_llm( - # cypher_llm=ChatOllama(model="qwen2", temperature=0), - # qa_llm=ChatOllama(model="qwen2", temperature=0), + #cypher_llm=ChatOllama(model="qwen2", temperature=0), + #qa_llm=ChatOllama(model="qwen2", temperature=0), cypher_llm=ChatOpenAI( - openai_api_key=st.secrets["OPENAI_API_KEY"], - temperature=0, - model_name="gpt-4o-mini" - ), - qa_llm=ChatOpenAI( - openai_api_key=st.secrets["OPENAI_API_KEY"], - temperature=0, - model_name="gpt-4o-mini"), + openai_api_key=st.secrets["OPENAI_API_KEY"], + temperature=0, + model_name="gpt-4o-mini" + ), + qa_llm=ChatOpenAI( + openai_api_key=st.secrets["OPENAI_API_KEY"], + temperature=0, + model_name="gpt-4o-mini"), graph=graph, cypher_prompt=CYPHER_GENERATION_PROMPT, validate_cypher=True, return_direct=True, verbose=True, - allow_dangerous_requests=True + allow_dangerous_requests=True, + return_intermediate_steps=True, ) @@ -191,5 +193,11 @@ def get_results(question) -> str: result = chain_result.get("result", None) print("\n========= Final Result =========\n") print(json.dumps(chain_result, indent=2)) + + try: + query = chain_result["intermediate_steps"][-1]["query"].replace("cypher", "", 1).strip() + chain_result["intermediate_steps"][-1]["query"] = urllib.parse.quote(query) + except Exception as e: + pass return chain_result diff --git a/rag_demo/rag_agent.py b/rag_demo/rag_agent.py index ebad5c3..7010e98 100644 --- a/rag_demo/rag_agent.py +++ b/rag_demo/rag_agent.py @@ -4,12 +4,12 @@ from graph_cypher_tool import graph_cypher_tool import streamlit as st -# llm = Ollama(model="llama3") +#llm = Ollama(model="llama3") llm = ChatOpenAI( - openai_api_key=st.secrets["OPENAI_API_KEY"], - temperature=0.2, - model_name="gpt-4o-mini" -) + openai_api_key=st.secrets["OPENAI_API_KEY"], + temperature=0.2, + model_name="gpt-4o-mini" + ) conversation_history = [] def process_with_llm(question: str) -> str: @@ -36,11 +36,15 @@ def process_with_llm(question: str) -> str: Here is the output from the database: {tool_output_str} -Please process the output and answer the user question clearly. +Please process the output and answer the user question clearly. If the output `result` is not empty please add [[button_query]] in last answer. """.strip() final_response = llm.predict(final_prompt).strip() + final_response = final_response.replace("[[button_query]]", """ +[Open Neo4J](http://localhost:7474/browser/?cmd=edit&arg=""" + tool_output["intermediate_steps"][-1]["query"] +")" +) + # === 5. Update conversation history === conversation_history.append({ "input": question, From 42978e49ae2ffee07bdcd398a84d1b9711327a72 Mon Sep 17 00:00:00 2001 From: owlmoonss Date: Thu, 17 Apr 2025 02:15:27 -0400 Subject: [PATCH 23/23] Refactor rag_agent.py for improved LLM processing; enhance conversation handling and streamline output formatting --- rag_demo/rag_agent.py | 69 ++++++++++++++++++++++++------------------- 1 file changed, 39 insertions(+), 30 deletions(-) diff --git a/rag_demo/rag_agent.py b/rag_demo/rag_agent.py index 7010e98..204a679 100644 --- a/rag_demo/rag_agent.py +++ b/rag_demo/rag_agent.py @@ -1,30 +1,34 @@ +import streamlit as st from retry import retry from langchain_community.llms import Ollama from langchain_openai import ChatOpenAI -from graph_cypher_tool import graph_cypher_tool -import streamlit as st +from graph_cypher_tool import graph_cypher_tool # Make sure this is defined correctly -#llm = Ollama(model="llama3") +# Choose one of the LLMs +# llm = Ollama(model="llama3") llm = ChatOpenAI( - openai_api_key=st.secrets["OPENAI_API_KEY"], - temperature=0.2, - model_name="gpt-4o-mini" - ) + openai_api_key=st.secrets["OPENAI_API_KEY"], + temperature=0.2, + model_name="gpt-4o-mini" +) + +# Conversation history conversation_history = [] def process_with_llm(question: str) -> str: - """Decide if a DB query is needed, and generate a response accordingly.""" - - # === 1. Prepare conversation history (if any) === + """Uses LLM to respond based on prior messages and graph result.""" + + # 1. Build past conversation text conversation_text = "\n".join([ f"User: {msg['input']}\nBot: {msg['output']}" for msg in conversation_history ]) - + + # 2. Run the question through the Cypher tool tool_output = graph_cypher_tool.invoke(question) - tool_output_str = str(tool_output) - - # Send full prompt to process queried data + result_only = tool_output.get("result", "No results found.") + + # 3. LLM prompt with only the result final_prompt = f""" Based on the conversation and the user question, provide a relevant and helpful response. @@ -34,33 +38,38 @@ def process_with_llm(question: str) -> str: Current question: {question} Here is the output from the database: -{tool_output_str} +{result_only} -Please process the output and answer the user question clearly. If the output `result` is not empty please add [[button_query]] in last answer. +Please process the output and answer the user question clearly. If the output is not empty please add [[button_query]] in last answer. """.strip() - + final_response = llm.predict(final_prompt).strip() - - final_response = final_response.replace("[[button_query]]", """ -[Open Neo4J](http://localhost:7474/browser/?cmd=edit&arg=""" + tool_output["intermediate_steps"][-1]["query"] +")" -) - - # === 5. Update conversation history === + + # 4. Inject Neo4j button if there was a result + if tool_output.get("result"): + last_query = tool_output.get("intermediate_steps", [{}])[-1].get("query", "") + final_response = final_response.replace( + "[[button_query]]", + f"[Open Neo4J](http://localhost:7474/browser/?cmd=edit&arg={last_query})" + ) + else: + final_response = final_response.replace("[[button_query]]", "") + + # 5. Update history conversation_history.append({ "input": question, "output": final_response }) - + return final_response @retry(tries=2, delay=10) def get_results(question: str) -> dict: + """Main processing function for external calls""" llm_processed_output = process_with_llm(question=question) - - # 3. Return the result including both the tool output and the LLM response return { - "input": question, # User input - "output": llm_processed_output, # Output after processing with LLM - # "intermediate_steps": tool_result["intermediate_steps"], # Intermediate steps (if any) - # "tools_used": tool_result["tools_used"], # List of tools used + "input": question, + "output": llm_processed_output, } + +