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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/rag_demo/graph_cypher_chain.py b/rag_demo/graph_cypher_chain.py index 55af946..a33eb33 100644 --- a/rag_demo/graph_cypher_chain.py +++ b/rag_demo/graph_cypher_chain.py @@ -1,47 +1,114 @@ +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 from langchain_community.graphs import Neo4jGraph from langchain.prompts.prompt import PromptTemplate +from langchain_ollama import ChatOllama from langchain_openai import ChatOpenAI -from retry import retry -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. -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 syonymous with `OWNS` and `OWNER` -7. Use examples of questions and accurate Cypher statements below to guide you. - -Schema: + +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: +- 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) + +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. +- **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. + + +Schema database is: {schema} -Examples: Here are a few examples of generated Cypher statements for particular questions: +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; + +--- + +### 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? + +Cypher: +MATCH (n:Location) +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; + +--- + +### 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; -# 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) +### Example 4 +Natural Language Question: +Which papers mention the Pacific-North American (PNA) pattern in connection with locations in the United States? -# 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 +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: +{question} +""" -# 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}""" CYPHER_GENERATION_PROMPT = PromptTemplate( input_variables=["schema", "question"], template=CYPHER_GENERATION_TEMPLATE @@ -51,79 +118,86 @@ 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"] -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# + graph_chain = GraphCypherQAChain.from_llm( + #cypher_llm=ChatOllama(model="qwen2", temperature=0), + #qa_llm=ChatOllama(model="qwen2", temperature=0), cypher_llm=ChatOpenAI( - openai_api_key=openai_key, - temperature=0, - model_name="gpt-4" - ), - qa_llm=ChatOpenAI( - openai_api_key=openai_key, - temperature=0, - model_name="gpt-4" - ), - validate_cypher= True, + 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, - verbose=True, - # return_intermediate_steps = True, - return_direct = True + cypher_prompt=CYPHER_GENERATION_PROMPT, + validate_cypher=True, + return_direct=True, + verbose=True, + allow_dangerous_requests=True, + return_intermediate_steps=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 + prompt = CYPHER_GENERATION_PROMPT.format(schema=graph.get_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, + 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.debug(f'chain_result: {chain_result}') - - if chain_result is None: + 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: + print('No answer was generated.') + return "No answer was generated." + + # Debug: show Cypher used + cypher_query = chain_result.get("cypher", "No Cypher returned") + 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)) + + 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 result \ No newline at end of file + return chain_result diff --git a/rag_demo/graph_tool.py b/rag_demo/graph_tool.py deleted file mode 100644 index 0b85b85..0000000 --- a/rag_demo/graph_tool.py +++ /dev/null @@ -1,8 +0,0 @@ -from langchain.tools import tool -from graph_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/main.py b/rag_demo/main.py index 4c1fb1b..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 @@ -39,7 +40,7 @@ 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 """} @@ -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 6e544d4..204a679 100644 --- a/rag_demo/rag_agent.py +++ b/rag_demo/rag_agent.py @@ -1,45 +1,75 @@ -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 vector_graph_tool import vector_graph_tool -from graph_cypher_tool import graph_cypher_tool -from vector_tool import vector_tool +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 # Make sure this is defined correctly +# 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" +) -# Setup tools the agent will use -llm = OpenAI(temperature=0) -tools = load_tools([], llm=llm) -tools = tools + [vector_graph_tool, graph_cypher_tool, vector_tool] +# Conversation history +conversation_history = [] +def process_with_llm(question: str) -> str: + """Uses LLM to respond based on prior messages and graph result.""" -# REACT AGENT EXECUTOR + # 1. Build past conversation text + conversation_text = "\n".join([ + f"User: {msg['input']}\nBot: {msg['output']}" + for msg in conversation_history + ]) -prompt = hub.pull("hwchase17/react") + # 2. Run the question through the Cypher tool + tool_output = graph_cypher_tool.invoke(question) + result_only = tool_output.get("result", "No results found.") -# More on agent types: https://python.langchain.com/docs/modules/agents/agent_types/ -agent = create_react_agent(llm, tools, prompt) + # 3. LLM prompt with only the result + final_prompt = f""" +Based on the conversation and the user question, provide a relevant and helpful response. -# NOTE: early_stopping_method generate option ONLY available for multi-action agents -agent_executor = AgentExecutor( - agent=agent, - tools=tools, - verbose=True, -) +Conversation: +{conversation_text} + +Current question: {question} + +Here is the output from the database: +{result_only} + +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() + + # 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 + }) -@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. + return final_response - Args: - question (str): User query - callbacks (list): List of optional LangChain callback handlers +@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) + return { + "input": question, + "output": llm_processed_output, + } - Returns: - dict: Final answer as a dict with the keys: input, output, intermediate_steps - """ - response = agent_executor.invoke({"input": question}, callbacks=callbacks) - return response diff --git a/rag_demo/vector_chain.py b/rag_demo/vector_chain.py deleted file mode 100644 index afd89c0..0000000 --- a/rag_demo/vector_chain.py +++ /dev/null @@ -1,158 +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_openai import ChatOpenAI, OpenAIEmbeddings -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 -) - -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) -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"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_retriever = vector_store.as_retriever() - -vector_chain = RetrievalQAWithSourcesChain.from_chain_type( - ChatOpenAI(temperature=0, openai_api_key=openai_key), - 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 - - -# 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 deleted file mode 100644 index 4b5afad..0000000 --- a/rag_demo/vector_graph_chain.py +++ /dev/null @@ -1,149 +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 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 -) - -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) -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( - ChatOpenAI(temperature=0, openai_api_key=openai_key), - 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) 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"