, authorized contracts are foundational paperwork that outline the relationships, obligations, and duties between events. Whether or not it’s a partnership settlement, an NDA, or a provider contract, these paperwork usually comprise important info that drives decision-making, threat administration, and compliance. Nonetheless, navigating and extracting insights from these contracts generally is a complicated and time-consuming course of.
On this publish, we’ll discover how we will streamline the method of understanding and dealing with authorized contracts by implementing an end-to-end resolution utilizing Agentic Graphrag. I see GraphRAG as an umbrella time period for any methodology that retrieves or causes over info saved in a data graph, enabling extra structured and context-aware responses.
By structuring authorized contracts right into a data graph in Neo4j, we will create a robust repository of data that’s straightforward to question and analyze. From there, we’ll construct a LangGraph agent that permits customers to ask particular questions concerning the contracts, making it attainable to quickly uncover new insights.
The code is accessible on this GitHub repository.
Why structuring knowledge issues
Some domains work effectively with naive RAG, however authorized contracts current distinctive challenges.
As proven within the picture, relying solely on a vector index to retrieve related chunks can introduce dangers, resembling pulling info from irrelevant contracts. It is because authorized language is very structured, and comparable wording throughout totally different agreements can result in incorrect or deceptive retrieval. These limitations spotlight the necessity for a extra structured strategy, resembling GraphRAG, to make sure exact and context-aware retrieval.
To implement GraphRAG, we first must assemble a data graph.
To construct a data graph for authorized contracts, we’d like a method to extract structured info from paperwork and retailer it alongside the uncooked textual content. An LLM will help by studying by contracts and figuring out key particulars resembling events, dates, contract varieties, and necessary clauses. As an alternative of treating the contract as only a block of textual content, we break it down into structured elements that replicate its underlying authorized that means. For instance, an LLM can acknowledge that “ACME Inc. agrees to pay $10,000 monthly beginning January 1, 2024” accommodates each a fee obligation and a begin date, which we will then retailer in a structured format.
As soon as we’ve this structured knowledge, we retailer it in a data graph, the place entities like firms, agreements, and clauses are represented as represented together with their relationships. The unstructured textual content stays accessible, however now we will use the structured layer to refine our searches and make retrieval much more exact. As an alternative of simply fetching probably the most related textual content chunks, we will filter contracts primarily based on their attributes. This implies we will reply questions that naive RAG would wrestle with, resembling what number of contracts had been signed final month or whether or not we’ve any energetic agreements with a selected firm. These questions require aggregation and filtering, which isn’t attainable with commonplace vector-based retrieval alone.
By combining structured and unstructured knowledge, we additionally make retrieval extra context-aware. If a person asks a few contract’s fee phrases, we make sure that the search is constrained to the correct settlement quite than counting on textual content similarity, which could pull in phrases from unrelated contracts. This hybrid strategy overcomes the restrictions of naive RAG and permits for a a lot deeper and extra dependable evaluation of authorized paperwork.
Graph building
We’ll leverage an LLM to extract structured info from authorized paperwork, utilizing the CUAD (Contract Understanding Atticus Dataset), a broadly used benchmark dataset for contract evaluation licensed below CC BY 4.0. CUAD dataset accommodates over 500 contracts, making it an excellent dataset for evaluating our structured extraction pipeline.
The token depend distribution for the contracts is visualized under.
Most contracts on this dataset are comparatively brief, with token counts under 10,000. Nonetheless, there are some for much longer contracts, with a couple of reaching as much as 80,000 tokens. These lengthy contracts are uncommon, whereas shorter ones make up the bulk. The distribution reveals a steep drop-off, that means lengthy contracts are the exception quite than the rule.
We’re utilizing Gemini-2.0-Flash for extraction, which has a 1 million token enter restrict, so dealing with these contracts isn’t an issue. Even the longest contracts in our dataset (round 80,000 tokens) match effectively throughout the mannequin’s capability. Since most contracts are a lot shorter, we don’t have to fret about truncation or breaking paperwork into smaller chunks for processing.
Structured knowledge extraction
Most business LLMs have the choice to make use of Pydantic objects to outline the schema of the output. An instance for location:
class Location(BaseModel):
"""
Represents a bodily location together with handle, metropolis, state, and nation.
"""
handle: Non-obligatory[str] = Discipline(
..., description="The road handle of the situation.Use None if not offered"
)
metropolis: Non-obligatory[str] = Discipline(
..., description="Town of the situation.Use None if not offered"
)
state: Non-obligatory[str] = Discipline(
..., description="The state or area of the situation.Use None if not offered"
)
nation: str = Discipline(
...,
description="The nation of the situation. Use the two-letter ISO commonplace.",
)When utilizing LLMs for structured output, Pydantic helps outline a transparent schema by specifying the varieties of attributes and offering descriptions that information the mannequin’s responses. Every subject has a kind, resembling str or Non-obligatory[str], and an outline that tells the LLM precisely find out how to format the output.
For instance, in a Location mannequin, we outline key attributes like handle, metropolis, state, and nation, specifying what knowledge is anticipated and the way it needs to be structured. The nation subject, for example, follows two-letter nation code commonplace like "US", "FR", or "JP", as a substitute of inconsistent variations like “United States” or “USA.” This precept applies to different structured knowledge as effectively, ISO 8601 retains dates in a regular format (YYYY-MM-DD), and so forth.
By defining structured output with Pydantic, we make LLM responses extra dependable, machine-readable, and simpler to combine into databases or APIs. Clear subject descriptions additional assist the mannequin generate accurately formatted knowledge, lowering the necessity for post-processing.
The Pydantic schema fashions will be extra refined just like the Contract mannequin under, which captures key particulars of a authorized settlement, guaranteeing the extracted knowledge follows a standardized construction.
class Contract(BaseModel):
"""
Represents the important thing particulars of the contract.
"""
abstract: str = Discipline(
...,
description=("Excessive degree abstract of the contract with related details and particulars. Embrace all related info to supply full image."
"Do no use any pronouns"),
)
contract_type: str = Discipline(
...,
description="The kind of contract being entered into.",
enum=CONTRACT_TYPES,
)
events: Record[Organization] = Discipline(
...,
description="Record of events concerned within the contract, with particulars of every get together's function.",
)
effective_date: str = Discipline(
...,
description=(
"Enter the date when the contract turns into efficient in yyyy-MM-dd format."
"If solely the 12 months (e.g., 2015) is understood, use 2015-01-01 because the default date."
"All the time fill in full date"
),
)
contract_scope: str = Discipline(
...,
description="Description of the scope of the contract, together with rights, duties, and any limitations.",
)
period: Non-obligatory[str] = Discipline(
None,
description=(
"The period of the settlement, together with provisions for renewal or termination."
"Use ISO 8601 durations commonplace"
),
)
end_date: Non-obligatory[str] = Discipline(
None,
description=(
"The date when the contract expires. Use yyyy-MM-dd format."
"If solely the 12 months (e.g., 2015) is understood, use 2015-01-01 because the default date."
"All the time fill in full date"
),
)
total_amount: Non-obligatory[float] = Discipline(
None, description="Complete worth of the contract."
)
governing_law: Non-obligatory[Location] = Discipline(
None, description="The jurisdiction's legal guidelines governing the contract."
)
clauses: Non-obligatory[List[Clause]] = Discipline(
None, description=f"""Related summaries of clause varieties. Allowed clause varieties are {CLAUSE_TYPES}"""
)This contract schema organizes key particulars of authorized agreements in a structured approach, making it simpler to research with LLMs. It contains several types of clauses, resembling confidentiality or termination, every with a brief abstract. The events concerned are listed with their names, areas, and roles, whereas contract particulars cowl issues like begin and finish dates, complete worth, and governing regulation. Some attributes, resembling governing regulation, will be outlined utilizing nested fashions, enabling extra detailed and complicated outputs.
The nested object strategy works effectively with some AI fashions that deal with complicated knowledge relationships, whereas others might wrestle with deeply nested particulars.
We will take a look at our strategy utilizing the next instance. We’re utilizing the LangChain framework to orchestrate LLMs.
llm = ChatGoogleGenerativeAI(mannequin="gemini-2.0-flash")
llm.with_structured_output(Contract).invoke(
"Tomaz works with Neo4j since 2017 and can make a billion greenback till 2030."
"The contract was signed in Las Vegas"
)which outputs
Contract(
abstract="Tomaz works with Neo4j since 2017 and can make a billion greenback till 2030.",
contract_type="Service",
events=[
Organization(
name="Tomaz",
location=Location(
address=None,
city="Las Vegas",
state=None,
country="US"
),
role="employee"
),
Organization(
name="Neo4j",
location=Location(
address=None,
city=None,
state=None,
country="US"
),
role="employer"
)
],
effective_date="2017-01-01",
contract_scope="Tomaz will work with Neo4j",
period=None,
end_date="2030-01-01",
total_amount=1_000_000_000.0,
governing_law=None,
clauses=None
)Now that our contract knowledge is in a structured format, we will outline the Cypher question wanted to import it into Neo4j, mapping entities, relationships, and key clauses right into a graph construction. This step transforms uncooked extracted knowledge right into a queryable data graph, enabling environment friendly traversal and retrieval of contract insights.
UNWIND $knowledge AS row
MERGE (c:Contract {file_id: row.file_id})
SET c.abstract = row.abstract,
c.contract_type = row.contract_type,
c.effective_date = date(row.effective_date),
c.contract_scope = row.contract_scope,
c.period = row.period,
c.end_date = CASE WHEN row.end_date IS NOT NULL THEN date(row.end_date) ELSE NULL END,
c.total_amount = row.total_amount
WITH c, row
CALL (c, row) {
WITH c, row
WHERE row.governing_law IS NOT NULL
MERGE (c)-[:HAS_GOVERNING_LAW]->(l:Location)
SET l += row.governing_law
}
FOREACH (get together IN row.events |
MERGE (p:Celebration {title: get together.title})
MERGE (p)-[:HAS_LOCATION]->(pl:Location)
SET pl += get together.location
MERGE (p)-[pr:PARTY_TO]->(c)
SET pr.function = get together.function
)
FOREACH (clause IN row.clauses |
MERGE (c)-[:HAS_CLAUSE]->(cl:Clause {kind: clause.clause_type})
SET cl.abstract = clause.abstract
)This Cypher question imports structured contract knowledge into Neo4j by creating Contract nodes with attributes resembling abstract, contract_type, effective_date, period, and total_amount. If a governing regulation is specified, it hyperlinks the contract to a Location node. Events concerned within the contract are saved as Celebration nodes, with every get together linked to a Location and assigned a task in relation to the contract. The question additionally processes clauses, creating Clause nodes and linking them to the contract whereas storing their kind and abstract.
After processing and importing the contracts, the ensuing graph follows the next graph schema.
Let’s additionally check out a single contract.
This graph represents a contract construction the place a contract (orange node) connects to varied clauses (purple nodes), events (blue nodes), and areas (violet nodes). The contract has three clauses: Renewal & Termination, Legal responsibility & Indemnification, and Confidentiality & Non-Disclosure. Two events, Modus Media Worldwide and Dragon Techniques, Inc., are concerned, every linked to their respective areas, Netherlands (NL) and United States (US). The contract is ruled by U.S. regulation. The contract node additionally accommodates further metadata, together with dates and different related particulars.
A public read-only occasion containing CUAD authorized contracts is accessible with the next credentials.
URI: neo4j+s://demo.neo4jlabs.com
username: legalcontracts
password: legalcontracts
database: legalcontractsEntity decision
Entity decision in authorized contracts is difficult attributable to variations in how firms, people, and areas are referenced. An organization may seem as “Acme Inc.” in a single contract and “Acme Company” in one other, requiring a course of to find out whether or not they check with the identical entity.
One strategy is to generate candidate matches utilizing textual content embeddings or string distance metrics like Levenshtein distance. Embeddings seize semantic similarity, whereas string distance measures character-level variations. As soon as candidates are recognized, further analysis is required, evaluating metadata resembling addresses or tax IDs, analyzing shared relationships within the graph, or incorporating human evaluate for important circumstances.
For resolving entities at scale, each open-source options like Dedupe and business instruments like Senzing provide automated strategies. Selecting the best strategy relies on knowledge high quality, accuracy necessities, and whether or not guide oversight is possible.
With the authorized graph constructed, we will transfer onto the agentic GraphRAG implementation.
Agentic GraphRAG
Agentic architectures range broadly in complexity, modularity, and reasoning capabilities. At their core, these architectures contain an LLM performing as a central reasoning engine, usually supplemented with instruments, reminiscence, and orchestration mechanisms. The important thing differentiator is how a lot autonomy the LLM has in making selections and the way interactions with exterior techniques are structured.
One of many easiest and only designs, significantly for chatbot-like implementations, is a direct LLM-with-tools strategy. On this setup, the LLM serves because the decision-maker, dynamically choosing which instruments to invoke (if any), retrying operations when crucial, and executing a number of instruments in sequence to meet complicated requests.
The diagram represents a easy LangGraph agent workflow. It begins at __start__, transferring to the assistant node, the place the LLM processes person enter. From there, the assistant can both name instruments to fetch related info or transition on to __end__ to finish the interplay. If a device is used, the assistant processes the response earlier than deciding whether or not to name one other device or finish the session. This construction permits the agent to autonomously decide when exterior info is required earlier than responding.
This strategy is especially well-suited to stronger business fashions like Gemini or GPT-4o, which excel at reasoning and self-correction.
Instruments
LLMs are highly effective reasoning engines, however their effectiveness usually relies on how effectively they’re geared up with exterior instruments. These instruments , whether or not database queries, APIs, or search capabilities, lengthen an LLM’s potential to retrieve details, carry out calculations, or work together with structured knowledge.
Designing instruments which can be each common sufficient to deal with numerous queries and exact sufficient to return significant outcomes is extra artwork than science. What we’re actually constructing is a semantic layer between the LLM and the underlying knowledge. Somewhat than requiring the LLM to know the precise construction of a Neo4j data graph or a database schema, we outline instruments that summary away these complexities.
With this strategy, the LLM doesn’t must know whether or not contract info is saved as graph nodes and relationships or as uncooked textual content in a doc retailer. It solely must invoke the correct device to fetch related knowledge primarily based on a person’s query.
In our case, the contract retrieval device serves as this semantic interface. When a person asks about contract phrases, obligations, or events, the LLM calls a structured question device that interprets the request right into a database question, retrieves related info, and presents it in a format the LLM can interpret and summarize. This permits a versatile, model-agnostic system the place totally different LLMs can work together with contract knowledge with no need direct data of its storage or construction.
There’s no one-size-fits-all commonplace for designing an optimum toolset. What works effectively for one mannequin might fail for an additional. Some fashions deal with ambiguous device directions gracefully, whereas others wrestle with complicated parameters or require specific prompting. The trade-off between generality and task-specific effectivity means device design requires iteration, testing, and fine-tuning for the LLM in use.
For contract evaluation, an efficient device ought to retrieve contracts and summarize key phrases with out requiring customers to phrase queries rigidly. Reaching this flexibility relies on considerate immediate engineering, sturdy schema design, and adaptation to totally different LLM capabilities. As fashions evolve, so do methods for making instruments extra intuitive and efficient.
On this part, we’ll discover totally different approaches to device implementation, evaluating their flexibility, effectiveness, and compatibility with varied LLMs.
My most popular strategy is to dynamically and deterministically assemble a Cypher question and execute it towards the database. This methodology ensures constant and predictable question technology whereas sustaining implementation flexibility. By structuring queries this fashion, we reinforce the semantic layer, permitting person inputs to be seamlessly translated into database retrievals. This retains the LLM targeted on retrieving related info quite than understanding the underlying knowledge mannequin.
Our device is meant to determine related contracts, so we have to present the LLM with choices to go looking contracts primarily based on varied attributes. The enter description is once more offered as a Pydantic object.
class ContractInput(BaseModel):
min_effective_date: Non-obligatory[str] = Discipline(
None, description="Earliest contract efficient date (YYYY-MM-DD)"
)
max_effective_date: Non-obligatory[str] = Discipline(
None, description="Newest contract efficient date (YYYY-MM-DD)"
)
min_end_date: Non-obligatory[str] = Discipline(
None, description="Earliest contract finish date (YYYY-MM-DD)"
)
max_end_date: Non-obligatory[str] = Discipline(
None, description="Newest contract finish date (YYYY-MM-DD)"
)
contract_type: Non-obligatory[str] = Discipline(
None, description=f"Contract kind; legitimate varieties: {CONTRACT_TYPES}"
)
events: Non-obligatory[List[str]] = Discipline(
None, description="Record of events concerned within the contract"
)
summary_search: Non-obligatory[str] = Discipline(
None, description="Examine abstract of the contract"
)
nation: Non-obligatory[str] = Discipline(
None, description="Nation the place the contract applies. Use the two-letter ISO commonplace."
)
energetic: Non-obligatory[bool] = Discipline(None, description="Whether or not the contract is energetic")
monetary_value: Non-obligatory[MonetaryValue] = Discipline(
None, description="The overall quantity or worth of a contract"
)With LLM instruments, attributes can take varied types relying on their function. Some fields are easy strings, resembling contract_type and nation, which retailer single values. Others, like events, are lists of strings, permitting a number of entries (e.g., a number of entities concerned in a contract).
Past primary knowledge varieties, attributes may also signify complicated objects. For instance, monetary_value makes use of a MonetaryValue object, which incorporates structured knowledge resembling foreign money kind and the operator. Whereas attributes with nested objects provide a transparent and structured illustration of information, fashions are inclined to wrestle to deal with them successfully, so we should always preserve them easy.
As a part of this venture, we’re experimenting with an extra cypher_aggregation attribute, offering the LLM with better flexibility for eventualities that require particular filtering or aggregation.
cypher_aggregation: Non-obligatory[str] = Discipline(
None,
description="""Customized Cypher assertion for superior aggregations and analytics.
This shall be appended to the bottom question:
```
MATCH (c:Contract)
WITH c, abstract, contract_type, contract_scope, effective_date, end_date, events, energetic, monetary_value, contract_id, international locations
```
Examples:
1. Depend contracts by kind:
```
RETURN contract_type, depend(*) AS depend ORDER BY depend DESC
```
2. Calculate common contract period by kind:
```
WITH contract_type, effective_date, end_date
WHERE effective_date IS NOT NULL AND end_date IS NOT NULL
WITH contract_type, period.between(effective_date, end_date).days AS period
RETURN contract_type, avg(period) AS avg_duration ORDER BY avg_duration DESC
```
3. Calculate contracts per efficient date 12 months:
```
RETURN effective_date.12 months AS 12 months, depend(*) AS depend ORDER BY 12 months
```
4. Counts the get together with the very best variety of energetic contracts:
```
UNWIND events AS get together
WITH get together.title AS party_name, energetic, depend(*) AS contract_count
WHERE energetic = true
RETURN party_name, contract_count
ORDER BY contract_count DESC
LIMIT 1
```
""" The cypher_aggregation attribute permits LLMs to outline customized Cypher statements for superior aggregations and analytics. It extends the bottom question by appending question-specified aggregation logic, enabling versatile filtering and computation.
This characteristic helps use circumstances resembling counting contracts by kind, calculating common contract period, analyzing contract distributions over time, and figuring out key events primarily based on contract exercise. By leveraging this attribute, the LLM can dynamically generate insights tailor-made to particular analytical wants with out requiring predefined question buildings.
Whereas this flexibility is efficacious, it needs to be fastidiously evaluated, as elevated adaptability comes at the price of decreased consistency and robustness because of the added complexity of the operation.
We should clearly outline the operate’s title and outline when presenting it to the LLM. A well-structured description helps information the mannequin in utilizing the operate accurately, guaranteeing it understands its function, anticipated inputs, and outputs. This reduces ambiguity and improves the LLM’s potential to generate significant and dependable queries.
class ContractSearchTool(BaseTool):
title: str = "ContractSearch"
description: str = (
"helpful for when you might want to reply questions associated to any contracts"
)
args_schema: Kind[BaseModel] = ContractInputLastly, we have to implement a operate that processes the given inputs, constructs the corresponding Cypher assertion, and executes it effectively.
The core logic of the operate facilities on developing the Cypher assertion. We start by matching the contract as the inspiration of the question.
cypher_statement = "MATCH (c:Contract) "Subsequent, we have to implement the operate that processes the enter parameters. On this instance, we primarily use attributes to filter contracts primarily based on the given standards.
Easy property filtering
For instance, the contract_type attribute is used to carry out easy node property filtering.
if contract_type:
filters.append("c.contract_type = $contract_type")
params["contract_type"] = contract_typeThis code provides a Cypher filter for contract_type whereas utilizing question parameters for values to forestall question injection safety subject.
Because the attainable contract kind values are offered within the attribute description
contract_type: Non-obligatory[str] = Discipline(
None, description=f"Contract kind; legitimate varieties: {CONTRACT_TYPES}"
)we don’t have to fret about mapping values from enter to legitimate contract varieties because the LLM will deal with that.
Inferred property filtering
We’re constructing instruments for an LLM to work together with a data graph, the place the instruments function an abstraction layer over structured queries. A key characteristic is the flexibility to make use of inferred properties at runtime, just like an ontology however dynamically computed.
if energetic is just not None:
operator = ">=" if energetic else "Right here, energetic acts as a runtime classification, figuring out whether or not a contract is ongoing (>= date()) or expired (). This logic extends structured KG queries by computing properties solely when wanted, enabling extra versatile LLM reasoning. By dealing with logic like this inside instruments, we make sure the LLM interacts with simplified, intuitive operations, holding it targeted on reasoning quite than question formulation.
Neighbor filtering
Typically filtering relies on neighboring nodes, resembling limiting outcomes to contracts involving particular events. The events attribute is an non-compulsory listing, and when offered, it ensures solely contracts linked to these entities are thought-about:
if events:
parties_filter = []
for i, get together in enumerate(events):
party_param_name = f"party_{i}"
parties_filter.append(
f"""EXISTS {{
MATCH (c)This code filters contracts primarily based on their related events, treating the logic as AND, that means all specified circumstances should be met for a contract to be included. It iterates by the offered events listing and constructs a question the place every get together situation should maintain.
For every get together, a singular parameter title is generated to keep away from conflicts. The EXISTS clause ensures that the contract has a PARTY_TO relationship to a celebration whose title accommodates the desired worth. The title is transformed to lowercase to permit case-insensitive matching. Every get together situation is added individually, imposing an implicit AND between them.
If extra complicated logic had been wanted, resembling supporting OR circumstances or permitting totally different matching standards, the enter would wish to alter. As an alternative of a easy listing of get together names, a structured enter format specifying operators could be required.
Moreover, we might implement a party-matching methodology that tolerates minor typos, bettering the person expertise by dealing with variations in spelling and formatting.
Customized operator filtering
So as to add extra flexibility, we will introduce an operator object as a nested attribute, permitting extra management over filtering logic. As an alternative of hardcoding comparisons, we outline an enumeration for operators and use it dynamically.
For instance, with financial values, a contract may should be filtered primarily based on whether or not its complete quantity is larger than, lower than, or precisely equal to a specified worth. As an alternative of assuming a hard and fast comparability logic, we outline an enum that represents the attainable operators:
class NumberOperator(str, Enum):
EQUALS = "="
GREATER_THAN = ">"
LESS_THAN = "This strategy makes the system extra expressive. As an alternative of inflexible filtering guidelines, the device interface permits the LLM to specify not only a worth however the way it needs to be in contrast, making it simpler to deal with a broader vary of queries whereas holding the LLM’s interplay easy and declarative.
Some LLMs wrestle with nested objects as inputs, making it tougher to deal with structured operator-based filtering. Including a between operator introduces further complexity because it requires two separate values, which may result in ambiguity in parsing and enter validation.
Min and Max attributes
To maintain issues easier, I are inclined to gravitate towards utilizing min and max attributes for dates, as this naturally helps vary filtering and makes the between logic simple.
if min_effective_date:
filters.append("c.effective_date >= date($min_effective_date)")
params["min_effective_date"] = min_effective_date
if max_effective_date:
filters.append("c.effective_date This operate filters contracts primarily based on an efficient date vary by including an non-compulsory decrease and higher sure situation when min_effective_date and max_effective_date are offered, guaranteeing that solely contracts throughout the specified date vary are included.
Semantic search
An attribute can be used for semantic search, the place as a substitute of counting on a vector index upfront, we use a post-filtering strategy to metadata filtering. First, structured filters, like date ranges, financial values, or events, are utilized to slim down the candidate set. Then, vector search is carried out over this filtered subset to rank outcomes primarily based on semantic similarity.
if summary_search:
cypher_statement += (
"WITH c, vector.similarity.cosine(c.embedding, $embedding) "
"AS rating ORDER BY rating DESC WITH c, rating WHERE rating > 0.9 "
) # Outline a threshold restrict
params["embedding"] = embeddings.embed_query(summary_search)
else: # Else we kind by newest
cypher_statement += "WITH c ORDER BY c.effective_date DESC "This code applies semantic search when summary_search is offered by computing cosine similarity between the contract’s embedding and the question embedding, ordering outcomes by relevance, and filtering out low-scoring matches with a threshold of 0.9. In any other case, it defaults to sorting contracts by the latest effective_date.
Dynamic queries
The cypher aggregation attribute is an experiment I wished to check that offers the LLM a level of partial text2cypher functionality, permitting it to dynamically generate aggregations after the preliminary structured filtering. As an alternative of predefining each attainable aggregation, this strategy lets the LLM specify calculations like counts, averages, or grouped summaries on demand, making queries extra versatile and expressive. Nonetheless, since this shifts extra question logic to the LLM, guaranteeing all generated queries work accurately turns into difficult, as malformed or incompatible Cypher statements can break execution. This trade-off between flexibility and reliability is a key consideration in designing the system.
if cypher_aggregation:
cypher_statement += """WITH c, c.abstract AS abstract, c.contract_type AS contract_type,
c.contract_scope AS contract_scope, c.effective_date AS effective_date, c.end_date AS end_date,
[(c)= date() AS active, c.total_amount as monetary_value, c.file_id AS contract_id,
apoc.coll.toSet([(c)(country) | country.name]) AS international locations """
cypher_statement += cypher_aggregationIf no cypher aggregation is offered, we return the whole depend of recognized contracts together with solely 5 instance contracts to keep away from overwhelming the immediate. Dealing with extreme rows is essential, as an LLM scuffling with an enormous consequence set isn’t helpful. Moreover, LLM producing solutions with 100 contract titles isn’t a superb person expertise both.
cypher_statement += """WITH accumulate(c) AS nodes
RETURN
country.name])
]
AS output"""This cypher assertion collects all matching contracts into a listing, returning the whole depend and as much as 5 instance contracts with key attributes, together with abstract, kind, scope, dates, financial worth, related events with roles, and distinctive nation areas.
Now that our contract search device is constructed, we hand it off to the LLM and similar to that, we’ve agentic GraphRAG carried out.
Agent Benchmark
For those who’re severe about implementing agentic GraphRAG, you want an analysis dataset, not simply as a benchmark however as a basis for your complete venture. A well-constructed dataset helps outline the scope of what the system ought to deal with, guaranteeing that preliminary growth aligns with real-world use circumstances. Past that, it turns into a useful device for evaluating efficiency, permitting you to measure how effectively the LLM interacts with the graph, retrieves info, and applies reasoning. It’s additionally important for immediate engineering optimizations, letting you iteratively refine queries, device use, and response formatting with clear suggestions quite than guesswork. And not using a structured dataset, you’re flying blind, making enhancements tougher to quantify and inconsistencies harder to catch.
The code for the benchmark is available on GitHub.
I’ve compiled a listing of twenty-two questions which we are going to use to judge the system. Moreover, we’re going to introduce a brand new metric known as answer_satisfaction the place we shall be present a customized immediate.
answer_satisfaction = AspectCritic(
title="answer_satisfaction",
definition="""You'll consider an ANSWER to a authorized QUESTION primarily based on a offered SOLUTION.
Price the reply on a scale from 0 to 1, the place:
- 0 = incorrect, considerably incomplete, or deceptive
- 1 = appropriate and sufficiently full
Contemplate these analysis standards:
1. Factual correctness is paramount - the reply should not contradict the answer
2. The reply should handle the core components of the answer
3. Extra related info past the answer is suitable and will improve the reply
4. Technical authorized terminology needs to be used appropriately if current within the resolution
5. For quantitative authorized analyses, correct figures should be offered
+ fewshots
"""Many questions can return a considerable amount of info. For instance, asking for contracts signed earlier than 2020 may yield tons of of outcomes. Because the LLM receives each the whole depend and some instance entries, our analysis ought to concentrate on the whole depend, quite than which particular examples the LLM chooses to indicate.
The offered outcomes point out that each one evaluated fashions (Gemini 1.5 Professional, Gemini 2.0 Flash, and GPT-4o) carry out equally effectively for many device calls, with GPT-4o barely outperforming the Gemini fashions (0.82 vs. 0.77). The noticeable distinction emerges primarily when partial text2cypher is used, significantly for varied aggregation operations.
Word that that is solely 22 pretty easy questions, so we didn’t actually discover reasoning capabilities of LLMs.
Moreover, I’ve seen initiatives the place accuracy will be improved considerably by leveraging Python for aggregations, as LLMs sometimes deal with Python code technology and execution higher than producing complicated Cypher queries immediately.
Net Software
We’ve additionally constructed a easy React internet software, powered by LangGraph hosted on FastAPI, which streams responses on to the frontend. Particular because of Anej Gorkic for creating the online app.
You may launch your complete stack with the next command:
docker compose upAnd navigate to localhost:5173
Abstract
As LLMs achieve stronger reasoning capabilities, they, when paired with the correct instruments, can change into highly effective brokers for navigating complicated domains like authorized contracts. On this publish, we’ve solely scratched the floor, specializing in core contract attributes whereas barely touching the wealthy number of clauses present in real-world agreements. There’s important room for development, from increasing clause protection to refining device design and interplay methods.
The code is accessible on GitHub.
Photographs
All photographs on this publish had been created by the writer.
