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Learn to develop a LangChain agent that has a number of methods of interacting with the Neo4j database
ChatGPT impressed the world and began a brand new AI revolution. Nonetheless, it appears that evidently the most recent pattern is supplying ChatGPT with exterior info to extend its accuracy and provides it the power to reply questions the place the solutions will not be current in public datasets. One other pattern round giant language fashions (LLMs) is to show them into brokers, the place they’ve a capability to work together with their setting by means of varied API calls or different integrations.
Since enhancing LLMs is comparatively new, there aren’t a number of open-source libraries but. Nonetheless, it appears that evidently the go-to library for constructing purposes round LLMs like ChatGPT known as LangChain. The library offers the power to reinforce an LLM by giving it entry to numerous instruments and exterior information sources. Not solely can it enhance its responses by accessing exterior information, however it may well additionally act as an agent and manipulate its setting by means of exterior endpoints.
I randomly stumbled upon a LangChain challenge by Ibis Prevedello that makes use of graph search to reinforce the LLMs by offering extra exterior context.
The challenge by Ibis makes use of NetworkX library to retailer the graph info. I actually preferred his method and the way simple it was to combine graph search into the LangChain ecosystem. Due to this fact, I’ve determined to develop a challenge that may combine Neo4j, a graph database, into the LangChain ecosystem.
After two weeks of coding, the challenge now permits a LangChain agent to work together with Neo4j in three totally different modes:
- Producing Cypher statements to question the database
- Full-text key phrase search of related entities
- Vector similarity search
On this weblog put up, I’ll stroll you thru the reasoning and implementation of every method I developed.
Atmosphere setup
First, we are going to configure the Neo4j setting. We’ll use the dataset obtainable because the recommendations project within the Neo4j sandbox. The simplest answer is solely to create a Neo4j Sandbox occasion by following this link. Nonetheless, when you would like an area occasion of Neo4j, you can too restore a database dump that is available on GitHub. The dataset is a part of the MovieLens datasets [1], particularly the small model.
After the Neo4j database is instantiated, we must always have a graph with the next schema populated.
Subsequent, you want to clone the langchain2neo4j repository by executing the next command:
git clone https://github.com/tomasonjo/langchain2neo4j
Within the subsequent step, you want to create an .env
file and populate the neo4j and OpenAI credentials as proven within the .env.instance
file.
Lastly, you want to create a full-text index in Neo4j and import film title embeddings by working:
sh seed_db.sh
In case you are a Home windows consumer, the seed_db
script in all probability gained’t work. In that case, I’ve ready a Jupyter pocket book that may assist you to seed the database as a substitute for the shell script.
Now, let’s leap to the LangChain integration.
LangChain agent
So far as I’ve seen, the most typical information circulate of utilizing a LangChain agent to reply a consumer query is the next:
The agent information circulate is initiated when it receives enter from a consumer. The agent then sends a request to an LLM mannequin that features the consumer query together with the agent immediate, which is a set of directions in a pure language the agent ought to observe. In flip, the LLM responds with additional directions to the agent. Most frequently, the primary response is to make use of any obtainable instruments to realize extra info from exterior sources. Nonetheless, instruments will not be restricted to read-only operations. For instance, you could possibly use them to replace a database. After the instrument returns extra context, one other name is made to an LLM that features the newly gained info. The LLM now has the choice to provide a last reply that’s returned to a consumer, or it may well resolve it must carry out extra actions by means of its obtainable instruments.
A LangChain agent makes use of LLMs for its reasoning. Due to this fact, step one is to outline which mannequin to make use of. In the mean time, the langchain2neo4j challenge helps solely OpenAI’s chat completion fashions, particularly GPT-3.5-turbo, and GPT-4 fashions.
if model_name in ['gpt-3.5-turbo', 'gpt-4']:
llm = ChatOpenAI(temperature=0, model_name=model_name)
else:
increase Exception(f"Mannequin {model_name} is presently not supported")
I haven’t but explored different LLMs in addition to OpenAI’s. Nonetheless, with LangChain, it needs to be simple, because it has integration with greater than ten different LLMs. I didn’t know that that many existed.
Subsequent, we have to add a conversational reminiscence with the next line:
reminiscence = ConversationBufferMemory(
memory_key="chat_history", return_messages=True)
LangChain help multiple types of agents. For instance, some brokers can use the reminiscence element, whereas others can not. For the reason that object was to construct a chatbot, I selected the Conversation Agent (for Chat Models) agent sort. What’s fascinating concerning the LangChain library is that half the code is written in Python, whereas the opposite half is immediate engineering. We will discover the prompts that the conversational agent uses. For instance, the brokers has some fundamental directions it should observe:
Assistant is a big language mannequin educated by OpenAI. Assistant is designed to have the ability to help with a variety of duties, from answering easy inquiries to offering in-depth explanations and discussions on a variety of matters. As a language mannequin, Assistant is ready to generate human-like textual content based mostly on the enter it receives, permitting it to have interaction in natural-sounding conversations and supply responses which might be coherent and related to the subject at hand. Assistant is continually studying and enhancing, and its capabilities are always evolving. It is ready to course of and perceive giant quantities of textual content, and might use this information to supply correct and informative responses to a variety of questions. Moreover, Assistant is ready to generate its personal textual content based mostly on the enter it receives, permitting it to have interaction in discussions and supply explanations and descriptions on a variety of matters. General, Assistant is a strong system that may assist with a variety of duties and supply helpful insights and knowledge on a variety of matters. Whether or not you need assistance with a selected query or simply need to have a dialog a few explicit matter, Assistant is right here to help.
Moreover, the agent has directions to make use of any of the desired instruments if wanted.
Assistant can ask the consumer to make use of instruments to lookup info
which may be useful in answering the customers authentic query.
The instruments the human can use are:
{{instruments}}
{format_instructions}
USER'S INPUT - - - - - - - - - -
Right here is the consumer's enter
(keep in mind to reply with a markdown code snippet of a
json blob with a single motion, and NOTHING else):
{{{{enter}}}}
Apparently, the immediate states that the assistant can ask the consumer to lookup extra info utilizing instruments. Nonetheless, the consumer shouldn’t be a human however an utility constructed on high of the LangChain library. Due to this fact, your entire strategy of discovering additional info is finished routinely with none human within the loop. In fact, we are able to change the prompts if wanted. The immediate additionally contains the format the LLMs ought to use to speak with the agent.
Observe that the agent immediate doesn’t embrace that the agent shouldn’t reply a query if the reply shouldn’t be offered within the context returned by instruments.
Now, all we’ve to do is to outline the obtainable instruments. As talked about, I’ve ready three strategies of interacting with Neo4j database.
instruments = [
Tool(
name="Cypher search",
func=cypher_tool.run,
description="""
Utilize this tool to search within a movie database,
specifically designed to answer movie-related questions.
This specialized tool offers streamlined search capabilities
to help you find the movie information you need with ease.
Input should be full question.""",
),
Tool(
name="Keyword search",
func=fulltext_tool.run,
description="Utilize this tool when explicitly told to use
keyword search.Input should be a list of relevant movies
inferred from the question.",
),
Tool(
name="Vector search",
func=vector_tool.run,
description="Utilize this tool when explicity told to use
vector search.Input should be full question.Do not include
agent instructions.",
),]
The outline of a instrument is used to specify the capabilities of the instrument in addition to to tell the agent when to make use of it. Moreover, we have to specify the format of the enter a instrument expects. For instance, each the Cypher and vector search count on a full query as an enter, whereas the key phrase search expects a listing of related motion pictures as enter.
LangChain is sort of totally different from what I’m used to in coding. It makes use of prompts to instruct the LLMs to do the be just right for you as a substitute of coding it your self. For instance, the key phrase search instructs the ChatGPT to extract related motion pictures and use that as enter. I spent 2 hours debugging the instrument enter format earlier than realizing I may specify it utilizing pure language, and the LLM will deal with the remaining.
Keep in mind how I discussed that the agent doesn’t have directions that it shouldn’t reply questions the place the data shouldn’t be offered within the context? Let’s study the next dialogue.
The LLM determined that based mostly on the instrument descriptions, it can not use any of them to retrieve related context. Nonetheless, the LLM is aware of loads by default, and for the reason that agent has no constraints that it ought to solely depend on exterior sources, the LLM can type the reply independently. We would want to vary the agent immediate if we wished to implement totally different habits.
Producing Cypher statements
I’ve already developed a chatbot interacting with a Neo4j database by producing Cypher statements utilizing the OpenAI’s conversational models like the GPT-3.5-turbo and GPT-4. Due to this fact, I may borrow many of the concepts to implement a instrument that enables the LangChain agent to retrieve info from the Neo4j database by developing Cypher statements.
The older fashions like text-davinci-003 and GPT-3.5-turbo work higher as a few-shot Cypher generator, the place we offer a few Cypher examples that the mannequin can use to generate new Cypher statements. Nonetheless, it appears the GPT-4 works effectively once we solely current the graph schema. Consequently, since graph schema might be extracted with a Cypher question, the GPT-4 might be theoretically used on any graph schema with none guide work required by a human.
I gained’t stroll you thru what LangChain does below the hood. We’ll simply take a look at the perform that will get executed when the LangChain brokers decides to work together with the Neo4j database utilizing Cypher statements.
def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
chat_prompt = ChatPromptTemplate.from_messages(
[self.system_prompt] + inputs['chat_history'] + [self.human_prompt])
cypher_executor = LLMChain(
immediate=chat_prompt, llm=self.llm, callback_manager=self.callback_manager
)
cypher_statement = cypher_executor.predict(
query=inputs[self.input_key], cease=["Output:"])
# If Cypher assertion was not generated resulting from lack of context
if not "MATCH" in cypher_statement:
return {'reply': 'Lacking context to create a Cypher assertion'}
context = self.graph.question(cypher_statement)return {'reply': context}
The Cypher producing instrument will get the query together with the chat historical past as enter. The enter to the LLM is then mixed through the use of the system message, chat historical past, and the present query. I’ve ready the next system message immediate for the Cypher producing instrument.
SYSTEM_TEMPLATE = """
You might be an assistant with a capability to generate Cypher queries based mostly off
instance Cypher queries. Instance Cypher queries are:n""" + examples + """n
Don't response with any clarification or some other info besides the
Cypher question. You don't ever apologize and strictly generate cypher statements
based mostly of the offered Cypher examples. Don't present any Cypher statements
that may't be inferred from Cypher examples. Inform the consumer when you possibly can't
infer the cypher assertion as a result of lack of context of the dialog
and state what's the lacking context.
"""
Immediate engineering feels extra like artwork than science. On this instance, we offer the LLM with a few Cypher assertion examples and let it generate Cypher statements based mostly on that info. Moreover, we place a few constraints, like permitting it to assemble solely Cypher statements that may very well be inferred from coaching examples. Moreover, we don’t let the mannequin apologize or clarify its ideas (nevertheless, GPT-3.5-turbo gained’t take heed to that directions). Lastly, if the query lacks context, we permit the mannequin to reply with that info as a substitute of forcing it to generate Cypher statements.
After the LLM assemble a Cypher statements, we merely use it to question a Neo4j database, and return the outcomes to the Agent. Right here is an instance circulate.
When a consumer inputs their query, it will get despatched to an LLM together with the agent immediate. On this instance, the LLM responds that it wants to make use of the Cypher search instrument. The Cypher search instrument constructs a Cypher assertion and makes use of it to question Neo4j. The outcomes of the question are then handed again to the agent. Subsequent, the agent sends one other request to an LLM together with the brand new context. Because the context accommodates the wanted info to assemble a solution, the LLM varieties the ultimate reply and instructs the agent to return it to the consumer.
In fact, we are able to now ask observe up questions.
For the reason that agent has reminiscence, it’s conscious of who’s the second actor and, due to this fact, can move the data alongside to the Cypher search instrument to assemble applicable Cypher statements.
Key phrase search of related triples
I received the concept for key phrase search from present information graph index implementations in each LangChain and GPT-index libraries. Each implementations are pretty comparable. They ask an LLM to extract related entities from a query and search the graph for any triples that include these entities. So I figured we may do one thing comparable with Neo4j. Nonetheless, whereas we may seek for entities with a easy MATCH assertion, I’ve determined that utilizing Neo4j’s full-text index can be higher. After related entities are discovered utilizing the full-text index, we return the triples and hope the related info to reply the query is there.
def _call(self, inputs: Dict[str, str]) -> Dict[str, Any]:
"""Extract entities, lookup information and reply query."""
query = inputs[self.input_key]
params = generate_params(query)
context = self.graph.question(
fulltext_search, {'question': params})
return {self.output_key: context}
Keep in mind, the agent has directions to parse out related film titles already and use that as enter to the Key phrase search instrument. Due to this fact, we don’t need to cope with that. Nonetheless, since a number of entities may exist within the query, we should assemble applicable Lucene question parameters because the full-text index relies on Lucene. Then, we merely question the full-text index and return hopefully related triples. The Cypher assertion we use is the next:
CALL db.index.fulltext.queryNodes("film", $question)
YIELD node, rating
WITH node, rating LIMIT 5
CALL {
WITH node
MATCH (node)-[r:!RATED]->(goal)
RETURN coalesce(node.title, node.title) + " " + sort(r) + " " + coalesce(goal.title, goal.title) AS outcome
UNION
WITH node
MATCH (node)<-[r:!RATED]-(goal)
RETURN coalesce(goal.title, goal.title) + " " + sort(r) + " " + coalesce(node.title, node.title) AS outcome
}
RETURN outcome LIMIT 100
So, we take the highest 5 related entities returned by the full-text index. Subsequent, we generate triples by traversing to their neighbors. I’ve particularly excluded the RATED relationships from being traversed as a result of they include irrelevant info. I haven’t explored it, however I’ve a very good feeling we may additionally instruct the LLM to supply a listing of related relationships to be investigated together with the suitable entities, which might make our key phrase search extra centered. The key phrase search might be initiated by explicitly instructing the agent.
The LLM is instructed to make use of the key phrase search instrument. Moreover, the agent is informed to supply the key phrases search a listing of related entities as enter, which is just Pokemon on this instance. The Lucene parameter is then used to question Neo4j. This method casts a broader web and hopes the extracted triples include related info. For instance, the retrieved context contains info on the style of Pokemon, which is irrelevant. Nonetheless, it additionally has details about who acted within the film, which permits the agent to reply the consumer’s query.
As talked about, we may instruct the LLM to provide a listing of related relationship sorts together with applicable entities, which may assist the agent retrieve extra related info.
Vector similarity search
The vector similarity search is the final mode to work together with a Neo4j database we are going to study. Vector search is fashionable in the intervening time. For instance, LangChain affords integrations with more than ten vector databases. The thought behind vector similarity search is to embed a query into embedding house and discover related paperwork based mostly on the similarity of the embeddings of the query and paperwork. We solely have to be cautious to make use of the identical embedding mannequin to provide the vector illustration of paperwork and the query. I’ve used the OpenAI’s embeddings within the vector search implementation.
def _call(self, inputs: Dict[str, str]) -> Dict[str, Any]:
"""Embed a query and do vector search."""
query = inputs[self.input_key]
embedding = self.embeddings.embed_query(query)
context = self.graph.question(
vector_search, {'embedding': embedding})
return {self.output_key: context}
So, the very first thing we do is embed the query. Subsequent, we use the embedding to search out related motion pictures within the database. Normally, the vector databases return the textual content of a related doc. Nonetheless, we’re coping with a graph database. Due to this fact, I’ve determined to provide related info utilizing the triple construction. The Cypher assertion used is:
WITH $embedding AS e
MATCH (m:Film)
WHERE m.embedding IS NOT NULL AND dimension(m.embedding) = 1536
WITH m, gds.similarity.cosine(m.embedding, e) AS similarity
ORDER BY similarity DESC LIMIT 5
CALL {
WITH m
MATCH (m)-[r:!RATED]->(goal)
RETURN coalesce(m.title, m.title) + " " + sort(r) + " " + coalesce(goal.title, goal.title) AS outcome
UNION
WITH m
MATCH (m)<-[r:!RATED]-(goal)
RETURN coalesce(goal.title, goal.title) + " " + sort(r) + " " + coalesce(m.title, m.title) AS outcome
}
RETURN outcome LIMIT 100
The Cypher assertion is just like the key phrase search instance. The one distinction is that we use cosine similarity as a substitute of a full-text index to establish related motion pictures. This method is sweet sufficient when coping with as much as tens of hundreds of paperwork, possibly a whole lot of hundreds. Keep in mind, the bottleneck is normally LLM, particularly when you use GPT-4. Due to this fact, if you’re not coping with hundreds of thousands of paperwork, you don’t have to think about polyglot implementations the place you could have each a vector and a graph database to have the ability to produce related info by traversing the graph.
When an agent is instructed to make use of the vector search instrument, step one is to embed the query as a vector. The OpenAI’s embedding mannequin produces vector representations with a dimension of 1536. So, the following step is to make use of the constructed vector and seek for related info within the database by calculating the cosine similarity between the query and related paperwork or nodes. Once more, since we’re coping with a graph database, I’ve determined to return the data to the agent within the type of a triple.
What’s fascinating about vector search is that regardless that we instructed the agent to seek for the Lord of the Ring motion pictures, the vector similarity search additionally returned details about the Hobbit motion pictures. It appears like that Lord of the Ring and Hobbit motion pictures are shut within the embedded house, which is comprehensible.
Abstract
It appears like chatbots and generative brokers that may entry exterior instruments and knowledge are the following wave that follows the unique ChatGPT hype. Being able to supply extra context to an LLM can enormously enhance its outcomes. Moreover, the agent’s instruments will not be restricted to read-only operations, which implies they’ll replace a database and even make orders on Amazon. For probably the most half, it appears that evidently the LangChain library is the first library in the intervening time for use to implement generative brokers. Whenever you begin utilizing LangChain, you would possibly want a little bit of a shift within the coding course of, as you want to mix LLM prompts with code to finish duties. For instance, messages between LLMs and instruments might be formed and reshaped with pure language directions as prompts as a substitute of Python code. I hope this challenge will assist you to implement the capabilities of a graph database like Neo4j into your LangChain challenge.
As all the time, the code is on the market on GitHub.
References
[1] F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: Historical past and Context. ACM Transactions on Interactive Clever Programs (TiiS) 5, 4: 19:1–19:19. https://doi.org/10.1145/2827872
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