Semantic graph driven heterogeneous workflow hint word collaborative compilation adaptation method

Abstract

This invention discloses a semantic graph-driven method for collaborative compilation and adaptation of heterogeneous workflow prompts, relating to the field of workflow prompt orchestration technology. The method includes: acquiring workflow metadata, target model information, and runtime statistics; parsing node connections and establishing variable lineage, intent, and constraints; identifying streaming bottlenecks and parallel convergence points based on the output template variable reference sequence to obtain semantic graph data; matching contractual prompt modules with the semantic graph data and binding variable placeholders; adapting the format according to model capabilities and performing contract verification to obtain a prompt package; calculating the budget based on context length and writing back parameter patches; generating structural patches or output orchestration strategies based on streaming bottlenecks and parallel convergence points to achieve segmented output; performing secondary contract verification, automatic repair, and incremental compilation; and outputting a contract verification report to improve consistency, model compatibility, and reduce streaming pauses.

Description

Technical Field

[0001] This invention relates to the field of workflow prompt orchestration technology, specifically a semantic graph-driven method for collaborative compilation and adaptation of heterogeneous workflow prompts. Background Technology

[0002] Enterprise-level intelligent customer service, knowledge base Q&A, operation and maintenance work order processing, business analysis report generation, and compliance review assistance applications often employ node-based orchestration platforms such as DIfy to organize knowledge retrieval, external interface calls, data cleaning and template shaping, inference from multiple large language models, and parallel / iterative control into heterogeneous workflows, and output them to the front end in a streaming response. Existing platforms generally support introducing variable placeholders to reference upstream node outputs in model nodes or output nodes, and using template syntax to assemble retrieval fragments, tool return objects, and intermediate conclusions to form prompt configurations and output templates tailored to specific business needs. Furthermore, requirements such as hierarchical access control, sensitive information processing, audit logging, and service level agreements need to be superimposed, making the prompt configuration directly impact the generation, traceability, and operational stability.

[0003] In such scenarios, the inconsistency between the prompt configuration and the workflow execution behavior mainly manifests during workflow iteration, parallel convergence, and model replacement. Firstly, adding or deleting nodes, adjusting parameters, or changing data sources alters variable lineage and data format. If the prompt words or output templates still assume and reference variable names, field paths, or list structures according to existing settings, errors such as missing variables, type mismatches, and unmet structural constraints may occur. Secondly, the readiness times of parallel branches differ, and output template evaluation and replacement depend on variable readiness. If unready variables are introduced at the beginning, the streaming output will be blocked at that point, resulting in a prolonged period without effective incremental content, delays in subsequent paragraphs, or even session timeouts. Thirdly, during multi-model switching, different models have different requirements for system instruction positions, structured output constraints, context length, and truncation strategies. This can lead to field drifting, truncation of key contexts, or output formats that do not meet requirements.

[0004] If we rely on manual node-by-node troubleshooting and rewriting of prompts for a long time, it will lead to increased version maintenance costs, longer change regression cycles, and difficulties in online fault repair. External services will also suffer from interaction delays, unstable results, and compliance audit risks.

[0005] Therefore, during the generation and operation of heterogeneous workflow prompts, especially in actual deployment and operation, how can we ensure the consistency and verifiability of prompt configuration with node parameter constraints, variable data dependencies, and output template operation behavior under the conditions of continuous workflow evolution, multi-model switching, and parallel and streaming output, and prevent operation interruption, streaming blockage, or output structure mismatch caused by differences in variable lineage, parallel readiness, and model capabilities? Summary of the Invention

[0006] (a) Technical problems to be solved

[0007] To address the shortcomings of existing technologies, this invention provides a semantic graph-driven heterogeneous workflow prompt word collaborative compilation and adaptation method. It identifies streaming bottlenecks and parallel convergence points based on the output template variable reference sequence to obtain semantic graph data; matches contractual prompt word modules with the semantic graph data and binds variable placeholders; adapts the format according to model capabilities and performs contract verification to obtain a prompt word package; calculates the budget based on the context length and writes back parameter patches; generates structural patches or output orchestration strategies based on streaming bottlenecks and parallel convergence points to achieve segmented output; performs secondary contract verification, automatic repair, and incremental compilation, and outputs a contract verification report to improve consistency, thus solving the technical problems described in the background art.

[0008] (II) Technical Solution

[0009] To achieve the above objectives, the present invention provides the following technical solution:

[0010] A semantic graph-driven method for collaborative compilation and adaptation of heterogeneous workflow prompts generates prompts based on workflow metadata. This includes acquiring workflow metadata and target model information, parsing workflow metadata and establishing variable lineage, intent, and constraints, identifying streaming bottlenecks and parallel convergence points based on the output template variable reference sequence, and obtaining semantic graph data.

[0011] Based on semantic graph data and target model information, a contractual prompt word module library is matched and the prompt word modules are combined. Variable placeholders are bound and contractual verification is performed on the existence, type and model capabilities of variables to obtain a prompt word package.

[0012] The maximum context length is calculated based on semantic graph data, cue word packs and target model information, and parameter patches are generated. Structural patches or output orchestration strategies are generated based on streaming blockage points and parallel convergence points.

[0013] Secondary contract verification is performed based on prompt word packages, parameter patches, and structural patches or output orchestration strategies, and the output patch and contract verification report are generated.

[0014] Furthermore, the node set and edge set of the workflow metadata are normalized and a topological order is generated to identify parallel and iterative structures, establish a mapping between variables and producer and consumer nodes, infer the variable type, and record the reference anchor points in the output template.

[0015] Furthermore, based on the variable reference sequence of the output template, the streaming blocking point is determined, the set of triggering variables and the dependency path of the streaming blocking point are recorded, and the parallel convergence point and its dependency variable set are determined. The streaming blocking point and the parallel convergence point are written as entities into the semantic graph data.

[0016] Furthermore, the contractual prompt word module includes applicable conditions, pre-contract, post-contract, insertion position, conflict priority, and degradation strategy. It also matches and combines nodes, intentions, and variable lineages based on semantic graph data to generate prompt word packages that are bound one-to-one with variable placeholders.

[0017] Furthermore, based on the capability profile of the target model information, the prompt organization method is selected. When the target model supports structured output, structured constraints are added to the prompt word package. When the target model does not support structured output, explicit field constraints and self-checking error correction segments are added, and contract verification is performed to output issues such as missing variables and type mismatch.

[0018] Furthermore, the budget usage of prompt fragments and data products is estimated based on the prompt word package and semantic graph data, and parameter patches are generated when the maximum context length is exceeded. The parameter patches include parameters for the number of retrievals, the length of the retrieval fragment, the whitelist of fields returned by the tool, the timeout of the tool, the maximum output length of the model, the sampling temperature of the model, and the structured output switch.

[0019] Furthermore, based on the runtime statistics, the ready level of variables in the parallel branches is predicted. When the waiting threshold of the streaming blockage point is met, a structural patch is generated. When there are different ready levels of variables in the parallel branches, a structural patch is generated. The parallel group is divided into early and late layers and intermediate convergence points are set. The first output segment node is inserted after the intermediate convergence point and the second output segment node is inserted after the final convergence point.

[0020] Furthermore, secondary contract verification is performed on the prompt word package, parameter patch, and structure patch, and variable readiness verification is performed on the output segment in conjunction with the output orchestration strategy. The secondary contract verification includes variable traceability verification, variable type consistency verification, and model capability consistency verification, and a contract verification report is output.

[0021] Furthermore, based on the contract verification report, automatic repair actions are generated according to priority. These automatic repair actions include adjusting output segment boundaries, inserting integer prompt word modules into the prompt word package and configuring summary rules, reducing structured output constraints and adding self-checking and error correction segments, and writing back parameter patches to trim the search and tool returned data.

[0022] Furthermore, the mapping relationship between nodes, variables, prompt word modules and output segments is maintained. When the workflow metadata changes, the affected domain is located and incremental compilation is performed only on the prompt word modules and output segments related to the affected domain to generate differential parameter patches and structural patches. Node time consumption, number of blocking points, and token usage statistics are collected to update the variable readiness level prediction.

[0023] (III) Beneficial Effects

[0024] This invention provides a semantic graph-driven method for collaborative compilation and adaptation of heterogeneous workflow prompts, which has the following advantages:

[0025] By parsing heterogeneous workflow metadata, establishing variable lineage, extracting intent constraints, and representing variable reference sequences in output templates as streaming bottlenecks and convergence points, the workflow's operational dependencies are transformed from inexperienced data into semantic graph data. Based on the semantic graph data, a contractualized prompt word module library and variable placeholders are matched, incorporating variable existence, variable type, and target model capabilities into contractual verification. This ensures that prompt word packages under different nodes and models have the same reference caliber and the same output constraint caliber. The budget allocation for prompt fragments and data products is calculated using the maximum context length as a boundary. Adjustments are written back as parameter patches, and the input scale is controlled through the number of knowledge base retrievals, the length of retrieval fragments, and a whitelist of fields returned by the tool. This ensures that budget control and prompt word constraints are interconnected on the same configuration path.

[0026] By coupling streaming bottlenecks and parallel convergence points to structural patches or output orchestration strategies, and stratifying parallel branches into output segments according to variable readiness levels, the workflow structure, template layout, and variable readiness are made consistent. This involves secondary contract verification of cue word packages, parameter patches, and structural patches or output orchestration strategies, referencing anchor points back to variables and field paths, and generating consistency repair suggestions for field trimming, type mismatches, and unready segment references. Nodes, variables, cue word modules, and output segments are mapped and incrementally compiled, with differential write-back only for affected local cue words and patches. This allows for stable reuse of existing binding and verification results based on semantic graph data during workflow execution, achieving closed-loop collaboration from parsing, generation, write-back, and verification. Attached Figure Description

[0027] Figure 1 This is a schematic diagram of the collaborative compilation and adaptation process of heterogeneous workflow prompt words driven by semantic graphs in this invention. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] Please see Figure 1 This invention provides a semantic graph-driven method for collaborative compilation and adaptation of heterogeneous workflow prompts, including:

[0030] Step 1: Configure workflow metadata The execution structure, variable flow, and output template evaluation order in the data are uniformly extracted into semantic graph data. This allows subsequent steps to reference nodes, variables, constraints, and blocking points within the same semantic coordinate system.

[0031] In heterogeneous workflows within a workflow orchestration platform, prompts and output templates often reference upstream node artifacts simultaneously. When nodes are added, deleted, or rearranged in parallel, the prompt configuration references variables based on old assumptions, potentially leading to missing variables, type mismatches, or blocking at the streaming frontend. To reduce reliance on experience-based troubleshooting, structural information and runtime semantics are jointly written into the semantic graph data. This enables the variable source, variable form, first reference position, and parallel waiting relationship to have a computable expression.

[0032] Structural parsing must come first to obtain the topological order index and define the parallel and iterative groups; variable lineage must follow to convert placeholder strings into traceable variables and provide data entry points for the placement of subsequent intentions and constraints.

[0033] Heterogeneous workflow semantic graph modeling engine reads workflow metadata The process involves unifying nodes into workflow nodes and variables into variables; forming an execution skeleton based on connection relationships and outputting a topological order index; then extracting variable reference anchors from prompt text and template text to form a producer-consumer-reference location lineage; and finally generating type fingerprints for variables to compare the morphological differences between text, objects, lists, and fragment collections.

[0034] To emphasize recalculation, structure parsing goes beyond simply reading nodes and edges; it explicitly defines the control structure. The structure parsing unit retrieves data from workflow metadata. Extract node identifiers, node types, node parameter keys, and port definitions, and perform a normalization mapping on node types to ensure that nodes with similar capabilities are mapped in the semantic graph data. The process involves enumerating fixed node types, then extracting an edge set along the connection relationships, and labeling conditional edges, parallel branching edges, parallel converging edges, iteration entry edges, and iteration return edges as edge types. A topological sort is then performed on the edge set to obtain the topological order index. This topological sorting can be implemented using the Kahn queue method or the depth-first reverse postorder method. Branch sets derived from the same branching point and converging at the same convergence point are marked as parallel group identifiers. The iterative structure binds the iteration entry node to the iteration body node set to avoid misinterpreting the iteration body output as a single output.

[0035] The process involves normalizing node types and explicitly labeling edge types, then outputting the topological order index, parallel group identifier, and iteration group identifier. The topological order index fixes the order of production and consumption, and can be verified using topological order consistency checks; the parallel group identifier and iteration group identifier are explicitly recorded, and branch boundaries can be verified using fork-convergence closure checks.

[0036] Workflow metadata It consists of JSON / equivalent structured data returned by configuration files or interfaces exported by the workflow platform. It should include at least the following: Node table: node identifier, node type, node name, node parameter key-value pairs, input port definition, output port definition; Edge table: starting node identifier, ending node identifier, edge type (normal / conditional / parallel branching / parallel convergence / iterative back edge); Output template: template text belonging to output nodes or response nodes; Business objective description: task description or scenario description stored in text field form.

[0037] Furthermore, the lineage of variables is organized with the constraint that references must be traceable. The variable lineage unit first reads the explicit output variables from the port definition, then scans the placeholders in the prompt words and template text and records the reference position anchor. The reference position anchor contains the node identifier and the character range, thereby fixing the first reference in the template on the reusable coordinate. Then, the producer of each variable is determined: if the variable appears in a node output port, then the node is the producer; if the variable only appears in the template and there is no producer, then it is marked as an external input variable and points to the workflow entry point.

[0038] To avoid conflicts between variables with the same name in different scopes, the variable identifier is taken as the producer node identifier plus the variable name, and an alias mapping is established when passing variables across nodes. Finally, the variable structure token sequence is encoded as a type fingerprint. This is to enable subsequent type compatibility checks without relying on field names:

[0039]

[0040] In the formula: type fingerprint : Variable type fingerprint; range Used to compare the form of variables; variables : Variable identifier; scope is the set of variables within the workflow, used to index the encoded object; token sequence length : Number of structure tokens; range Preferred This is used to balance recognizability and computational cost;

[0041] Token value function :index The token mapping value; range Used to convert a structure into a sequence of integers; base number : Rolling base; range Used to reduce collisions; Module : Take the prime number modulo; the range is The intrinsic prime number is used to determine the fingerprint space;

[0042] In this context, producers and consumers are connected by reference location anchors, and type fingerprints are used to connect them. Fixed variable form and scope; type fingerprint It enables type compatibility and comparability, and allows verification of incompatible references based on fingerprint differences; the reference position anchor allows the first reference point to be replayed, and the template evaluation position can be verified by replaying the character range.

[0043] For example, in an operations and maintenance work order workflow, the workflow includes a knowledge base retrieval branch and an external work order system HTTP query branch, and the output template simultaneously references both the retrieval results and the work order details. Engineers export workflow metadata. The data is then read by the heterogeneous workflow semantic graph modeling engine: the structure parsing unit assigns the same parallel group identifier to the two branches based on the bifurcation and convergence relationships; the variable lineage unit records two reference anchor points in the output template and traces their producer nodes, while generating a type fingerprint for the work order details. And mark it as an object type. Once completed, the semantic graph data... By presenting the parallel structure, variable sources, and reference anchors, on-site personnel can determine that directly concatenating object form variables into text will trigger a template evaluation exception.

[0044] The origins of structure and lineage resolution still require the explanation of intent and constraints to determine why they are necessary. Simultaneously, streaming pauses arise from the coupling between template evaluation order and variable readiness conditions, necessitating the inclusion of parallel convergence points and streaming blocking points as runtime semantic objects in the semantic graph data. This is for subsequent steps to organize and output paragraphs.

[0045] Above the topological order index and variable lineage, the intent mining unit extracts intent phrases from business objective descriptions, node names, key names, and prompt fragments, and extracts hard and soft constraints as constraint conditions; the semantic extraction unit scans the output template and response node content to obtain variable reference sequences, and derives the convergence point and parallel convergence point by combining the parallel group identifier, and locates the streaming blockage point where slow-ready variables are first introduced; the association modeling unit writes the above entities and relationships into the semantic graph data. The entity list and the relationship list.

[0046] Furthermore, the intent mining unit first segments the business objective description into a set of intent phrases and generates node description text for each node. The node description text is obtained by concatenating the node type, node name, and key name, thereby reducing ambiguity caused by nodes with the same name. Vectorization can use TF-IDF normalized vectors or sentence vector models. Node description text: formed by concatenating the node type, node name, parameter key name set, and the names of the Variables adjacent to the node. Intent phrases: obtained by segmenting the business objective description into sentences according to punctuation and then into segments according to keywords. Word segmentation: Chinese can be segmented using maximum forward matching, and English can be segmented by spaces and punctuation. TF-IDF: a dictionary is constructed using all node description texts and all intent phrases, and the TF-IDF vector is calculated. Dimensionality: if the dictionary dimension is too large, a hashing technique can be used to project the vector to a larger dimension. The fixed dimension.

[0047] Next, the semantic similarity between the intent phrase and the node description text is calculated, and topological distance decay is applied to separate the input intent closer to the entry point from the summary presentation intent closer to the output. Intent placement is then achieved using association scoring. Based on intent weight With distance attenuation coefficient control:

[0048]

[0049] In the formula: related score Intent and node placement scoring; Scope Used to select the set of landing nodes; Intent vector Intent Index Corresponding phrase vector, range Dimension Pick Node vectors Node index The corresponding descriptive vector, range Used for semantic comparison; cosine similarity function Vector similarity; range ;form ;

[0050] Intent weight Semantic item weights; range Used to balance semantic and structural terms; distance decay coefficient Attenuation intensity; range Used to control the exponential term The decay rate, where the natural constant is... A fixed value of approximately 2.718; structural distance. : Shortest edge distance; range is a non-negative integer, used to express structural closeness;

[0051] Structural distance This represents the number of edges in the shortest path of the graph when the intended node is located. The graph is defined as an undirected version of the Node-Edge directed graph. When counting distances: ordinary edges and conditional edges are counted as 1; parallel branching edges and parallel converging edges are counted as 1; iterative back edges are counted as 1. However, the maximum distance counting depth can be limited within the iteration body (e.g., a maximum depth of 6) to avoid distance anomalies caused by iterative loops.

[0052] Among them, the correlation score Select the nodes to be assigned, and bind hard and soft constraints as constraints to the nodes and data outputs; the business intent and constraints have been assigned to the nodes, and correlation scoring can be used. Threshold checks verify placement results; distance decay ensures stable intention sorting, facilitating verification and backtracking.

[0053] The semantic extraction unit scans the output template and response node content sequentially according to character ranges to form a reference sequence, and associates each reference entry with a variable. It then derives the set of parallel branches that each reference entry depends on, based on the parallel group identifier, and generates a convergence point (parallel convergence point). The parallel convergence point records the set of branch output variables that must wait. Next, it calculates the template prefix dependency closure and marks the anchor point where the reference first contains a slow-ready variable as a blocking point (streaming blocking point).

[0054] Slow ready variable determination in the absence of runtime statistics The system uses a rule mapping between node type and input size, and includes runtime statistics. Historical time quantiles were used for inference.

[0055] To facilitate subsequent selection of which blockage point to process first, the semantic extraction unit calculates the blocking potential energy for each streaming blockage point. The blocking potential energy is determined by the position weight function. With branch load function Jointly determined, and via an S-shaped function compression:

[0056]

[0057] Where: blocking potential energy : Number of blocking points to sort; range Used to sort streaming choke points in the graph; choke points : Streaming blockage identifier; range is the set of streaming blockage points, used to index the computed object; S-shaped function : Compression function; range ;form Position weight function Prefix weight; range ;form ;

[0058] Branch load function Branch load; range ;form ,in Take the natural logarithm; weighting coefficients : Position term coefficient; range Used to adjust the sensitivity of front-end blocking; weighting coefficient : Load term coefficient; range , used to adjust the contribution of parallel dependencies;

[0059] Slow Readiness Level Coefficient Slow Readiness Level Coefficient; Range Used to differentiate between fast and slow readiness effects; dependency branch set : Dependency branch set; Scope is the set of branches in the parallel group, used to express the parallel waiting scale; Prefix length : Number of characters before the blocking point; positive integer, used for position weight calculation; total template length : Total number of characters in the template; a positive integer used to normalize position weights;

[0060] The association modeling unit writes workflow nodes, variables, business intents, constraints, parallel convergence points, and streaming congestion points into the semantic graph data. Workflow nodes generate relational connections to connect variables; these variables consume relational connections to reference anchor points and are blocked at relational connection streaming blocking points; parallel groups converge at relational connection parallel convergence points. Semantic graph data. A dual-table serialization approach is adopted, consisting of an entity list and a relationship list. The entity list records the entity identifier, type, key attributes, and source fragments, while the relationship list records the start point, end point, and relationship type. This ensures that subsequent steps referencing the same entity do not depend on the original file format.

[0061] Convergence relation refers to the relationship in semantic graph data where the outputs of parallel branches are merged at a parallel convergence point. Blocking relation refers to the relationship in semantic graph data where a variable or its dependency path causes output waiting at a streaming blocking point. Consumption relation refers to the relationship in semantic graph data where workflow nodes use variables or data products as input. Generation relation refers to the relationship in semantic graph data where workflow nodes produce variables or data products. Response node refers to a node in the workflow that references the output variables of upstream nodes and generates return content according to a template. Business intent refers to the set of task objectives and subtasks extracted from the business objective description, node configuration, and prompt summary in the workflow metadata, and includes corresponding output constraints.

[0062] Specifically: Parallel convergence points and streaming choke points are generated according to the reference sequence, and the choke potential energy is calculated. Then write semantic graph data The entity list and relationship list. Blocking potential energy. The system provides a sorting order of blocking points, which can be compared with the reference sequence to verify the location of blocking points. Parallel convergence points are explicitly defined as waiting branches, and the ready paths of variables can be verified using intra-segment lineage closure checks.

[0063] The prompt word module dynamic combination and multi-dimensional adaptation engine refers to a processing unit that matches prompt word modules based on semantic graph data, performs combination and assembly, variable placeholder binding and contract verification, and adapts the prompt organization method and output constraint method according to the target model information to generate prompt word packages.

[0064] Step 2: Based on semantic graph data The prompts are transformed from manually concatenated text into a set of prompt fragments containing traceable variables of lineage and verifiable contractual constraints, and then sorted according to the target model information. Generate a prompt word package that can be directly distributed.

[0065] Semantic graph data Workflow nodes, variables, business intents, constraints, parallel convergence points, and streaming blockade points have been solidified into entities and relationships, but they remain at the level of structural description. During workflow execution, prompts must simultaneously satisfy three types of constraints: variables originate from traceable producers and their forms match; constraint statements fall on corresponding nodes and data products; and target model information is required. The capability boundaries and output structure must be consistent. Without unified module selection and contract verification, engineers repeatedly rewriting model nodes and output templates can easily introduce structural errors into the deployed version. Target model information. The model is selected by the user or read from the system configuration. It should include at least: model identifier; context upper bound. ; Ability predicate set (system message availability / structured output availability / whether fixed role format is supported / maximum output length limit, etc.).

[0066] Therefore, through a single-chain process of candidate generation, contract gating, assembly, and capability implementation, semantic graph data is... Convert to a hint word package and generate a replayable contract verification report. .

[0067] If the prompt word module library is searched only by keywords, mismatches between module and node types or variable forms are likely to occur; if hard constraints are only exposed after combination, it will lead to prompt reordering. First, limit the candidate set to those that can be satisfied simultaneously in terms of node type, intent, lineage, and ability, thereby converging the assembly input space.

[0068] semantic graph data The workflow nodes, variables, business intents, and constraints serve as inputs. First, a candidate module set is generated. Then, modules that do not meet the hard constraints are eliminated using contract gating. Finally, the retained modules are written into binding records. Candidate generation employs a dual-channel intersection approach: the structural channel filters usable modules based on node type and data product type as primary keys, while the semantic channel scores modules based on intent placement. Related modules for constraint label filtering; contract gating using variable lineage closure and type fingerprinting. Verify that the required variables exist and are morphologically compatible, and use the target model information. Verify that the required capabilities exist.

[0069] To make semantic graph data Structural facts are introduced into candidate generation, and intentional facts are incorporated into the same candidate space.

[0070] First, the structural channel reads the node type enumeration of the target node and its upstream data product type set, and filters it in the module library using the applicable node type field + required product field to obtain the structural candidate set; second, the semantic channel reads the intent and constraints associated with the target node, and scores it based on intent placement. Select the leading intent tags, and then filter them in the module library using the trigger intent field + constraint tag field to obtain a semantic candidate set; then take the intersection of the two sets as the initial candidate set to avoid cross-type misselection due to semantic similarity alone.

[0071] When the module library is large, the structural channel is preferably implemented using key-value indexes or inverted indexes, and the semantic channel is preferably implemented using a vector retrieval system. Intersection operations are performed on the candidate identifier set. To ensure the candidate ranking is verifiable, a module selection score is calculated. Intent relevance, lineage coverage, and token cost estimation are written into the same scale, where the token cost estimation is derived from the upper bound of the module template character count and variable expansion.

[0072]

[0073] Module selection score Module identifier With node identifier The selection score, the value Used for candidate sorting and truncation; module identifier : A unique identifier for the prompt word module, part of a set of module library identifiers used to index the evaluated module; Node identifier Semantic graph data The unique identifier of the target node, whose value is a set of workflow node entities, is used to index the assembled node; contract gating value. Hard contract satisfaction gate; value is 0 or 1; 1 is taken when all preceding contracts are satisfied, otherwise 0; the preceding contract predicates are defined as three categories: a. Existence predicates: b. Type predicate: Or satisfy the preset compatibility relationship; c. Capability predicate: If all three types of predicates are true, then... ,otherwise .

[0074] Intent Item Weight Intent item weight, value When the intention is to place a score Take a larger value when reliable to emphasize task consistency; lineage item weight : Weight of bloodline item, value And satisfy 1. Used to balance variable availability; intent relevance function Intent relevance, values ; in the form of .

[0075] Intended placement score Intent Index With node identifier The placement score, with values... From semantic graph data Intent - Node association attributes; Intent set Node identifier The associated intent set takes the value as a subset of the set of business intent entities; intent index. : Intent identifier, whose value is a set of intents. Used to enumerate intent candidates; lineage coverage function Bloodline coverage, value ; in the form of ;

[0076] Required variable set for a module : The set of required variables declared in the module's pre-contract, whose values ​​are a subset of the variable entity set, used to determine missing variables; the set of available variables for a node. Node identifier The set of available variables within the lineage closure, whose values ​​are subsets of the variable entity set, is used to determine referenceable variables. Token cost estimation. Module token cost estimation, value The preferred approach is to map the template character count plus variable expansion upper bound to an approximate token count and use this to decay long modules.

[0077] 1. The template text cost is determined by the number of characters in the template text and the upper bound of the placeholder expansion: The estimated number of tokens is obtained by dividing the number of Unicode characters by the character-to-token ratio factor, where the ratio factor can take a range of values. The preferred value is 3; Upper bound of placeholder expansion: For variables referenced by placeholders, read their data product scale attributes (such as the number of fragments, the upper bound of fragment length, and the upper bound of the number of fields) to deduce the upper bound of the expansion token.

[0078] Specifically, a candidate set is generated by utilizing the intersection of structural and semantic channels, and module selection scores are used. After sorting, a candidate sequence is generated; the candidate set is restricted by node type and data product type, preventing rearrangement due to cross-type misselection; module selection scores are used. Indicative of intended placement and scoring The coverage of closures related to bloodlines can be used to trace the reasons for sorting.

[0079] Furthermore, the verification results are solidified in the binding records and include the reference location anchor. Contract gating verification takes the module's pre-contract as input, which includes at least three types of predicates: required variables, variable type requirements, and required model capabilities. This verification process is preferably executed by a rule engine or expression tree evaluator, making the predicates versionable and stored along with the binding records. During verification, a lineage closure lookup is first performed on the required variables: from node identifiers... By tracing back along consumption and production relationships, the set of available variables is obtained and compared one by one with the set of required variables for each module; when a variable exists but its form may be incompatible, type fingerprinting is used. A consistency comparison is performed; if the type fingerprints are not equal, the module is marked as needing to be inserted into the shaping module. Subsequently, capability predicates are compared for the required model capabilities; the capability predicates are derived from the target model information. Provided.

[0080] The binding record is then updated, writing the mapping from placeholder names to variable identifiers and the anchor point for reference, as part of the contract verification report. Evidence items. Bloodline closure presence comparison + type fingerprint. Consistency comparison + target model information The capability predicate comparison completes the contract gating verification and outputs the binding record from placeholder to variable identifier.

[0081] When used, the binding record includes the variable source node identifier and the reference position anchor, so that the source of each variable reference in the prompt word package can be replayed to verify the source. In the case of type incompatibility, the path is reserved with an integer mark, so that the integer module can be inserted in sub-step 202 without triggering a global rollback.

[0082] For example, semantic graph data in the workflow for generating business analysis reports. One branch outputs the object returned by the external interface, and the other branch outputs a collection of knowledge base retrieval fragments. The summary node needs to generate the report text and field list. Engineers then input the target model information. Configured to support system messages without using function calls, the prompt word module dynamically combines with the multi-dimensional adaptation engine PM-DCME to first select candidates such as the knowledge context injection module, object field pruning description module, and field constraint module. Then, it excludes the function call instruction module through contract gating verification and generates binding records, based on the contract verification report on-site. It directly locates the source node identifier of the missing field.

[0083] While candidate and contract gating resolves the selection criteria, the assembly and implementation processes still need to be addressed. If assembly merely strings modules together into a long text, hard constraints may be scattered throughout the paragraphs, leading to the loss of crucial instructions when encountering context truncation. Furthermore, differences in model support for system messages, field constraints, and message segments can result in unusable assembly formats. Therefore, assembly should be centered around bound records, with execution capabilities implemented at the end of the assembly process.

[0084] Read the binding records and candidate module sequences, first determine the assembly order based on the topological order index, and then organize the prompt fragments according to the rules of hard constraint pre-processing, dynamic data segmentation, and result verification closing; during the assembly process, replace placeholders with variable identifiers referencing expressions, and retain the reference position anchor points for replay; subsequently, based on the target model information... The prompt fragments are mapped to message structures acceptable to the target model to generate a prompt word package; finally, a contract verification based on the output structure is performed on the assembled prompt word package to generate a contract verification report. Consistent entries.

[0085] To fix the assembly sequence to interpretable rules, thus preventing the order of cue fragments from drifting with module library iterations, the assembly unit reads semantic graph data. The topological order index identifies nodes with the same node. The relevant modules are divided into four sections: input understanding, context injection, tool result interpretation, and output constraints. The hard constraint section includes security constraints, reference constraints, and field constraints, and is placed at the beginning of the prompt. The dynamic data section contains references to the retrieved fragment set and the objects returned by the tool. When replacing placeholders, the full text is not directly expanded; instead, variable identifiers are used to identify reference expressions and label the data product type, facilitating subsequent write-back based on the budget in step three. The result constraint section contains self-checking and error-correcting instructions, and its position is fixed at the end to constrain the final output. To prevent hard constraints from being overridden by subsequent modules, similar constraints are merged, indexed by the constraint key name and ranked by maximum strength.

[0086] The assembly order is fixed by using a topological index, hard constraint segments are encapsulated in the first prompt segment, and dynamic data is retained by using variable identifiers to reference expressions. Hard constraints are displayed centrally in the first prompt segment to avoid loss of constraints due to context truncation. Dynamic data is defined by using variable identifiers to reference expressions, which makes it easy to write back the upper limit of the number of search items, the length of the search fragment, and the maximum number of output tokens in step three without modifying the assembly rules.

[0087] Furthermore, the differences between the multiple models are translated into executable mapping rules. The mapping is based on the target model information. The capability predicate is an input-output message structure. The capability landing mapping unit reads the target model information. The capability predicate set is used to map the assembled prompt fragments to the target model input structure: when the capability predicate indicates support for system messages, the hard constraint paragraph is written to the system message position; when the capability predicate indicates no support for system messages, the hard constraint paragraph is written to the first user message and an identifier line is added at the beginning of the paragraph; when the capability predicate indicates support for structured output, the field constraints are written as field set and field type constraints; when the capability predicate indicates no support for structured output, the field constraints are written as a combination of field order, field example, and field validation prompt instructions.

[0088] After mapping is complete, a hint word package is generated, and a contract verification report is also generated. The report uses binding records as evidence entries, capability predicate comparison results as consistency entries, and constraint merging results as coverage entries. The node identifiers and variable identifiers of these three types of entries facilitate finding the corresponding node configuration and prompt fragments after runtime log errors.

[0089] Among them, the target model information Capability predicates drive message structure mapping, and generate contract verification reports using three types of entries: evidence entries, consistency entries, and coverage entries. .

[0090] The message structure of the prompt word package is consistent with the capability predicate, and can be written into the corresponding model node configuration without relying on manual rewriting of the separator. Contract Validation Report Linking the binding record and the capability implementation result to the node identifier makes it easier to use the same identifier system when writing back and reconstructing in step three.

[0091] Step 3: Assuming the prompt word package has been generated, proceed based on the semantic graph data. Operational statistics Calculate the budget difference and write back the upstream parameters, while generating a set of structural patches based on the parallel convergence point and the streaming congestion point. Output orchestration strategy This fixes the unfold and wait behaviors to a verifiable configuration object.

[0092] Two types of phenomena may occur during runtime with the prompt word package: the expanded dynamic data segment exceeds the target model information. The context upper limit and hard constraint paragraphs are truncated; the difference in parallel branch readiness causes the front part of the response node to reference slow variables, resulting in waiting and streaming pauses at the streaming blockage point. Step 3 assigns the processing actions to parameter write-back and aggregation structure insertion, and integrates them with semantic graph data. The variable lineage closure binding facilitates item-by-item verification. (Running statistics) The data collected from the runtime logs and call returns includes at least: a sequence of node time consumption samples (aggregated by node identifier); node failure results (success / failure code / exception type); token usage statistics; and variable ready timestamps (timestamps of variable production completion events or node end events).

[0093] If runtime truncation is relied upon, the truncation position is affected by session history and template layout. The budget difference is calculated during the compilation phase. The shrinking action is mapped to the parameter keys of the retrieval node, tool node, and model node, forming a parameter patch set. .

[0094] Read semantic graph data The data product size attribute and variable lineage closure, and the token cost estimation for reading the prompt word packet. Bind records and read target model information. upper limit of context Budgetary accounting unit calculation segment cost With aggregation cost Get the budget difference ;when The system generates write-back items for the maximum number of search results, search fragment length, whitelist of fields returned by the tool, timeout threshold, maximum number of output tokens, sampling temperature, and structured output switch, and writes them to the parameter patch set. .

[0095] Furthermore, the cue word package is split into Segment, segment cost Cost of prompt module and cost of data products The composition, in which the cost of the prompt module is estimated using token cost. The cost of data product decomposition is estimated using the number of decomposition tokens. To demonstrate the mechanism by which excessively long single segments lead to the truncation of hard constraints, aggregation is performed using a smooth maximum form of log-sum-exp. , and then with With budget security boundary form ,in:

[0096]

[0097] Where: Aggregation cost : Cost of aggregated tokens, value Used to determine the risk of exceeding limits; smoothing coefficient Smoothing coefficient, values Used to adjust sensitivity to excessively long single segments; segment cost : No. Segment token cost, value ;Depend on and Segment number obtained by accumulation; : Number of segments, which takes a positive integer value and is used to limit the size of the segments;

[0098] Budget difference Budget difference, value ; Indicates the need for write-back shrinkage; context upper limit Context upper limit, with a value of a positive integer; information from the target model. Budgetary safety boundaries Budget safety boundary, a positive integer value, is used to reserve system and constraint overhead;

[0099] Cost estimation based on prompt word module token Cost estimation with data products Segment Cost Aggregated cost Budget difference Determine if a write-back has been triggered. Budget difference. Regression to segment cost This then points back to the dominant data products and producer workflow nodes. More sensitive to extremely long single segments, causing contraction to preferentially occur at the dominant sources leading to truncation. Data product unfolding cost. : Data output Agreed-upon minimum feasible estimation method: If For text: estimated using character-to-token ratio; if For a collection of fragments: It is obtained by mapping the maximum number of search results to the length of the search segment; if For objects: It is obtained by mapping the upper bound of the number of fields to the upper bound of the average length of the fields. The upper bound of the number of fields can be obtained by the number of fields in the whitelist returned by the parameter tool.

[0100] In the budget difference At that time, the writing-back positioning unit is used to remove the polymerization cost after the product is eliminated. The decrease in value defines the marginal contribution, used to identify the dominant data product that is cited in multiple places; subsequently, along the semantic graph data... The generation relationship is used to locate the producer workflow node and generate write-back items according to the node type.

[0101] When the producer is a retrieval node, the maximum number of retrieval records and the length of the retrieval fragment are written back. The preferred window is: the maximum number of retrieval records value. A better range To preserve multiple pieces of evidence; the range of values ​​for the retrieval fragment length. A better range To preserve paragraph boundaries, when the producer is a tool node, the tool's returned field whitelist and timeout threshold are written back. The field whitelist only retains field paths referenced downstream in the bound records. Field path evidence extraction is preferably implemented using structured data field path expressions, consistent with placeholder paths in the bound records. When the producer is a model node, the maximum output token count and sampling temperature are written back, and paragraph tags that are produced first and then referenced are written into the summary variable to avoid repeatedly pasting the full text.

[0102] For each data product Its marginal contribution is defined as:

[0103]

[0104] in This means all references segment cost Zhongyu The aggregate cost is recalculated after the relevant expanded cost items are set to zero. Write-back iteration strategy: [Followed by...] Choose from largest to smallest Generate its producer Node Write-back items; recalculated after each write-back. ,when Stop when the write-back reaches the preset limit (e.g., 5 times) and is still not satisfied, then the default entries that require structured segmentation or summary nodes will be processed in step four.

[0105] Using marginal contribution to define the dominant data output, the parameter keys are mapped along the lineage closure to the producer workflow nodes to generate... And retain field path evidence. Parameter patch set Each item is traceable to the producer workflow node and field path. Step four verifies that the write-back has not broken the bound records. External inputs and retrieved inputs are written back first to keep hard constraint paragraphs and task instruction paragraphs stable. Parameter patch set. Even after controlling the data size, parallel branches may still exhibit readiness discrepancies due to external dependencies; when a slow variable is referenced in the front end of the response node, a streaming blockage triggers a wait. This is discussed in relation to parallel convergence points, streaming blockage points, and blocking potential. As input, determine whether to insert an intermediate convergence point and split the output paragraphs to form a set of structural patches. Output orchestration strategy .

[0106] Read semantic graph data Parallel convergence points and streaming congestion points, and read runtime statistics. Time quantile With time quantile The stratification determination unit calculates the stratification trigger factor. and with the hierarchical trigger threshold Compare and determine the ready level after triggering. ;Structural patch generation unit data Divide the early-layer variable set and the late-layer variable set, generate intermediate convergence points, first output segment nodes, final convergence points, and second output segment nodes, and write them into the structural patch set. The output layout unit writes the paragraph position and citation sequence into the text. And write back the output segment node relationships to Layered trigger threshold Range of values .

[0107] Intermediate convergence points are convergence nodes used to wait for earlier branches to complete during the parallel branch hierarchical convergence process; final convergence points are convergence nodes used to wait for later branches to complete and merge with earlier results; the first output segment node and the second output segment node are used to generate and output corresponding segment content after the intermediate convergence point and the final convergence point, respectively. Data artifacts refer to data objects generated by workflow nodes that can be referenced by downstream nodes or output templates, including text, objects, lists, sets of search fragments, or objects returned by tools, etc. Output segment nodes are nodes in the workflow used to generate and output a segment of returned content according to a template. The variables referenced by output segment nodes are limited to the set of variables that are ready at their triggering position.

[0108] Furthermore, the blocking potential energy of the same parallel group Aggregation into a single hierarchical triggering factor This avoids the accumulation of multiple medium-sized blocks within the same session without triggering structure processing.

[0109] Aggregation uses the complement multiplication form; when the hierarchical trigger factor Once the threshold is reached, statistics will be generated during operation. Time quantile With time quantile The conservative time consumption is obtained and compared with the waiting threshold of the streaming blockage point. The branch is then divided into fast layer and slow layer to form a ready layer. .

[0110] Readiness level range The optimal value is 2, to reduce the complexity of inter-segment dependencies:

[0111]

[0112] Where: stratification triggering factor : Layered triggering factor, value Used to trigger the structural patch set Set of blocking points : Set of blocking points, whose value is the set of streaming blocking points associated with the parallel group; blocking potential energy : Blocking potential function, values From semantic graph data .

[0113] Blockage point : Blockage point, value Multiplication operator : Multiplication operator; calculates the product of elements within a set, used to aggregate multiple blockages; based on a set of blockage points. Blocking potential energy Calculate the stratification trigger factor and combined With the flow blocking point waiting threshold to determine the ready level ;

[0114] Stratified triggering factor By compressing the impact of multiple streaming blockages into a single quantity, the trigger boundaries of structured processing become recordable and verifiable, and the ready level becomes more efficient. Introducing time quantiles ensures that the fast and slow segments are consistent with the historical running path, reducing the need for repeated parameter tuning after segmentation.

[0115] exist At that time, the structural patch generation unit is arranged according to the ready level. The system divides the early-layer variable set into an early-layer variable set and extracts the waiting path from the parallel convergence point dependency set. Then, it inserts an intermediate convergence point that only waits for the early-layer variable set and inserts the first output segment node after the intermediate convergence point. The template only references the early-layer variable set and the summary variable. The original parallel convergence point is retained as the final convergence point, and the second output segment node is inserted after the final convergence point. The template references the late-layer variable set and the summary variable.

[0116] Summary variables are generated from integer nodes through field extraction and sentence rewriting, and are also found in the semantic graph. Generate relations within the framework. Output orchestration strategy. The paragraph order of output segment nodes is fixed, and slow-layer variables are not referenced in the first paragraph of the response node; if no new output nodes are added to the platform, the output orchestration strategy is as follows. Degenerates into segmented templates within the same response node, shifting the first reference of slow variables to the next level by the reference location anchor. This is done according to the ready level. Generate a set of structural patches and with It solidifies the paragraph order and citation sequence, while providing two implementation paths: structural insertion and response node segmentation templates. The set of reference variables for the output segment nodes is written into the semantic graph data. Step four allows for segment-by-segment verification that the trigger point variables are ready. The first segment of the response node only depends on the early layer variable set; the streaming output does not wait for slow layer variables in the first segment.

[0117] In the after-sales inquiry workflow, engineering personnel rely on... Based on the location results, fill in the whitelist of tool return fields and adjust the timeout threshold in the tool node, and adjust the upper limit of the number of search results and the length of the search fragment in the search node; then, based on the structural patch set... Insert an intermediate convergence point and the first output segment node, so that the first segment of the response node references the retrieved branch variables to output policy key points. After the order query branch is ready, the second output segment node outputs the verification conclusion. Engineers in the contract verification report... The supplementary entries show that each write-back includes a producer workflow node identifier and field path evidence, allowing for backtracking of the configuration source using the same identifier system. Evaluation metrics may include: context truncation alerts, number of streaming blockage triggers, and waiting events before the first segment of the response node appears.

[0118] Step 4: Configure the prompt word package - parameter patch set -Structural patch collection - Output orchestration strategy The combination of states is used for secondary contract verification, and a write-back differential object is generated after a breach is detected, so that variable references, paragraph triggers and model capability constraints are consistent.

[0119] After step three involves writing back parameters to the upstream retrieval and tool nodes, the field set, fragment set, and upper length bound of the data output will change; simultaneously, the structure patch set will also change. It may insert intermediate convergence points, final convergence points, and output segment nodes, and the output orchestration strategy will determine the final node. The initial reference position of variables is rearranged. This change triggers two types of operational mechanisms: First, the field path referenced by the bound record is pruned by the field whitelist returned by the tool, and template evaluation produces null values ​​or exceptions at that field path; second, if the first output segment node references a slow-layer variable after the intermediate convergence point, the streaming output waits at the streaming blockage point, and the front end can only receive incomplete segments. Since the above phenomena do not directly correspond to the surface content of the prompt text, semantic graph data is needed. The bloodline closure and the anchor point of the reference position are used as evidence for secondary verification, and the breach is located to the writeable object.

[0120] First, complete the secondary contract verification and breach location, and then check the parameter patch set. Whether the consumption requirements of the placeholder-bound record are met; on the other hand, check the structural patch set. and output orchestration strategy Has it been ensured that the set of reference variables for each output segment node trigger point has been completed, and then a repair action template is generated based on the breach evidence entries, using the set of influence domains? Incremental compilation is restricted. Lineage closures are recalculated in a supply-then-consumption order, with the contract verification unit reading the parameter patch set line by line. The write-back item is based on the target node identifier in the semantic graph data. The producer workflow node is located in the middle, and its generation relationship is recalculated by limiting the reachable path with topological order index, so as to obtain the set of available variables and the set of available fields after writing back.

[0121] Parameter patch collection Each patch must include at least: target node identifier, parameter key name, original value, suggested value / write-back value, evidence (corresponding DataArtifact or bound record path), and scope of effect (current run / version). Structure Patch Collection Each patch should include at least: operation type (add node / add edge / replace template / redirect edge), target location (node ​​identifier / edge identifier), newly added object identifier, and a description of changes to the reference variable set. Output orchestration strategy. It should include at least: the output segment sequence, the corresponding template fragment for each segment, the set of variables that can be referenced in each segment, and the target position of the anchor point for the first reference position of the slow variable.

[0122] For retrieval nodes, during recalculation, the upper limit of the number of retrievals and the length of the retrieval fragments are read, and the fragment sequence number range is marked; for tool nodes, during recalculation, the whitelist of fields returned by the tool is read, and the expression is normalized into a tree path set using the JSONPath path expression parser or the JMESPath path expression parser; for model nodes, during recalculation, the structured output switch is read to determine whether the field path evaluation uses object field rules or field text rules.

[0123] Subsequently, the consumer demand set is extracted from the binding records of the prompt word package and compared item by item with the supply set to form a consumer availability tag. :

[0124]

[0125] In the formula: Consumption can be marked : Consumption available flag; value is 0 or 1, used to indicate a write-back item. Can post-application consumption needs be met? Write-back items : Write-back item, value is the set of parameter patches A set of elements used to index a single write-back record; index function : Index function, takes the value 0 or 1, takes the value 1 when the proposition in parentheses is true, otherwise takes the value 0;

[0126] Consumer demand set The consumer demand set, whose values ​​are variable identifiers and field paths, is extracted from the bound records; the supply set can also be used. The available supply set takes the value of a variable identifier and a set of field paths, which is obtained by recalculating the lineage closure.

[0127] Consumption factors Consumption factors, with values ​​ranging from 1 to 2. ; Indicates the variable identifier or field path being validated; Set of consumer demands Extract the consumed variable identifier and field path from the binding record of the prompt word package, and perform operations on the field path. Available supply set From semantic graph data Generate relational relationship and parameter patch set The write-back result is used to recalculate the producible variable identifier and field path, and then the field path is executed. ;

[0128] Constructing the available supply set using bloodline closure recalculation and with consumption available tags Mark each write-back item Consumability, and then mark the consumable value. The entries are written into the default list along with the field path and the anchor point for the reference location.

[0129] When in use, the default is located at the write-back item. Consumption factors By identifying the producer workflow node, placeholders are left empty for write-back key names. Field path normalization allows different ways of writing JSONPath and JMESPath to be aligned and compared, facilitating review and regression.

[0130] Define path normalization operator The input is a field representation (JSOnPath or JMESPath), and the output is a normalized sequence of field paths. The normalization process includes at least removing whitespace and quotation marks, and standardizing the Bracket format to Dot format (e.g., ...). ), unify array indices to wildcards (Unless the bound record explicitly uses a fixed index); combine selections (e.g.) Expand into multiple paths sorted lexicographically; the output is a sequence of path strings joined by periods (.). Consumption can be marked with a flag. Verification includes a set of consumer demand. and available supply set First by Normalized set inclusion judgment.

[0131] Paragraph readiness check unit reads structure patch set The template fragment of each output segment node is used to form the segment reference set, and the variable reference sequence of the fragment is extracted from the reference position anchor point to form the segment reference set; the waiting set from the intermediate convergence point, the final convergence point and the summary variable set generated from the preceding and following output segment nodes form the segment ready set.

[0132] If the output orchestration strategy Using a segmentation template within the response node, the paragraph readiness verification unit recalculates the first reference position of the slow variable using the reference position anchor point, and requires that the paragraph reference set before that position does not contain slow layer variables; if the output arrangement strategy... Adding a new output node requires the first output segment node to contain the set of paragraph references. Its paragraph ready set, where the set contains relations. This indicates that every element of the left set belongs to the right set. Slow-level variable determination prioritizes runtime statistics. Time quantile The comparison result with the waiting threshold of the streaming congestion point is used to revert to the node type rule if the condition is not met. For each variable identifier that does not satisfy the inclusion relationship, its first occurrence anchor point and corresponding parallel convergence point identifier are recorded to form a paragraph violation entry.

[0133] Specifically, the inclusion relationship is checked between the paragraph reference set and the paragraph ready set, combined with the time consumption quantile. Paragraph breach entries are generated, and then these entries are referenced back to the output paragraph node identifier and the anchor point of the reference position. Before being written back, paragraph breaches are located at the template text position, so that the first paragraph wait can be directly repaired by moving the reference or adjusting the wait set.

[0134] Paragraph readiness verification relies solely on semantic graph data. Structural patch collection Output orchestration strategy Operational statistics It can be executed offline without relying on online reproduction. It solidifies the coupling between parallel readiness differences and template evaluation order into inspectable rules, providing definitive evidence for subsequent remediation actions.

[0135] In the order verification and policy response workflow, engineers used a tool to return a whitelist of fields to filter order objects and inserted the first and second output segment nodes. Output consumption is marked with an option. The default entry points to the field path "Invoice Information". The header and its referenced anchor point allow engineers to add the field path to the whitelist or remove the reference from the prompt fragment. Subsequently, the output paragraph default entry indicates that the first output paragraph node references the slow-layer variable "Logistics Trajectory" and that this variable is not in the intermediate convergence point waiting set. The first reference to the logistics trajectory is moved to the second output paragraph node to ensure that the first paragraph only references early-layer variables.

[0136] Default entries are categorized into three classes: consumption missing, paragraph ready, and capability predicate, and each class is mapped to an executable repair template. The consumption missing class is marked with a consumption available tag. The trigger and repair template selects between expanding the field whitelist and removing field references, and writes the selection result into a new parameter patch set. Or it may prompt for differential; the paragraph ready class is triggered by the paragraph default entry, the repair template chooses between moving the variable to its first reference and adjusting the intermediate convergence point waiting set, and writes the result as a structural patch set. Or output orchestration strategy The difference;

[0137] The ability predicate class is derived from the target model information. When the capability predicate is triggered, the repair template will downgrade the structured output switch and simultaneously rewrite the expression of field constraint fragments. To avoid repairs overwriting each other, a repair priority is introduced. Repair priority The corresponding issue must be fixed and write-back must be blocked; repair priority. Corresponding recommendations for repair, repair priority Corresponding processing can be delayed; the preferred rule is to prioritize repair for cases with missing consumption data. Paragraphs that are ready for repair should be prioritized. Prioritize repair for capability predicate classes. .

[0138] Among them, they are categorized by default and prioritized for remediation. Sort and generate repair action templates, output parameter patch set Structural patch collection By differentiating the suggestions, each repair corresponds one-to-one with a specific evidence entry. Repair actions are applied to key names, structural connections, or suggested fragments to avoid broken links where the problem is known but cannot be rewritten. Repair priority. This ensures that multiple breaches maintain a definite order within the same writing cycle, reducing version drift caused by repeated trial and error.

[0139] The influence domain slicing unit starts from the node identifier in the default entry and is located in the semantic graph data. The relationship between generation and consumption in the middle affects the number of hops. Skip traversal to obtain the set of influence domains ; Affects the number of jumps The range of values ​​is The preferred value is 2, so as to cover direct upstream and downstream nodes and parallel convergence nodes without introducing irrelevant branches.

[0140] Incremental compilation units exist only in the set of influence domains. The internal recompiles the prompt fragments and merges the differences, reuses the text skeleton of the unchanged module, and only replaces the placeholders and constraint expressions of the bound records that have changed.

[0141] To ensure consistent results from repeated executions, template hashing is introduced. As a criterion for whether to change, a polynomial rolling form is adopted:

[0142]

[0143] Where: Template hash Template hash, value Used to determine whether the skeleton of the prompt fragment text has changed; template character sequence : Template character sequence, with values ​​from a set of Unicode strings; represents the hashed hint fragment; character length : Character length, takes a positive integer value; indicates the upper bound of the summation;

[0144] Code point mapping Code point mapping, value retrieval , indicating the first Code points of each character; rolling base : Rolling base, value Used to reduce collisions and maintain coding stability; Modulus Modulus, with a value of Prime numbers, used to define the hash space; summation operator Summation operator; for to Summation; Modulo operator Modulo operator, used to limit the range of results.

[0145] After completing incremental compilation, the final contract verification report is generated. The report should include at least the counts of unbound placeholders, missing field paths, paragraph readiness violations, and capability predicate violations; and can be verified offline by using historical input to drive the template evaluator to retrieve values ​​at the reference anchor points, confirming that all paragraph trigger point variables are ready after differential write-back. (Affects jump count) Generate an impact domain set Template hash jump Output revised prompt word packs and parameter patch sets for invariant entries. Structural patch collection and the final contract verification report set of influence domains Controllable incremental compilation range, facilitating regression writing and audit trail creation, template hashing. The repeated compilation outputs are consistent, making it convenient for version comparison tools to verify whether the difference meets the evidence items.

[0146] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0147] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0148] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0149] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0150] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A semantic graph-driven collaborative compilation and adaptation method for heterogeneous workflow prompts, which generates prompts based on workflow metadata, characterized by: include, Acquire workflow metadata and target model information, parse workflow metadata and establish variable lineage, intent and constraints, identify streaming bottlenecks and parallel convergence points based on output template variable reference sequences, and obtain semantic graph data; Based on semantic graph data and target model information, a contractual prompt word module library is matched and the prompt word modules are combined. Variable placeholders are bound and contractual verification is performed on the existence, type and model capabilities of variables to obtain a prompt word package. The maximum context length is calculated based on semantic graph data, cue word packs and target model information, and parameter patches are generated. Structural patches or output orchestration strategies are generated based on streaming blockage points and parallel convergence points. Secondary contract verification is performed based on prompt word packages, parameter patches, and structural patches or output orchestration strategies, and the output patch and contract verification report are generated.

2. The heterogeneous workflow prompt word collaborative compilation and adaptation method according to claim 1, characterized in that: The node and edge sets of workflow metadata are normalized and a topological order is generated. Parallel and iterative structures are identified, a mapping between variables and producer and consumer nodes is established, the variable type of variables is inferred, and the anchor points of reference positions in the output template are recorded.

3. The heterogeneous workflow prompt word collaborative compilation and adaptation method according to claim 2, characterized in that: Based on the variable reference sequence of the output template, the streaming blocking point is determined, the set of triggering variables and the dependency path of the streaming blocking point are recorded, and the parallel convergence point and its dependency variable set are determined. The streaming blocking point and the parallel convergence point are written as entities into the semantic graph data.

4. The heterogeneous workflow prompt word collaborative compilation adaptation method according to claim 3, characterized in that: The contractual prompt word module includes applicable conditions, pre-contract, post-contract, insertion position, conflict priority, and degradation strategy. It also matches and combines nodes, intentions, and variable lineages based on semantic graph data to generate prompt word packages that are bound one-to-one with variable placeholders.

5. The heterogeneous workflow prompt word collaborative compilation and adaptation method according to claim 4, characterized in that: Based on the capability profile of the target model information, select the prompt organization method. When the target model supports structured output, add structured constraints to the prompt word package. When the target model does not support structured output, add explicit field constraints and self-checking error correction segments, and perform contract verification to output issues such as missing variables and type mismatch.

6. The heterogeneous workflow prompt word collaborative compilation and adaptation method according to claim 5, characterized in that: Based on the cue word package and semantic graph data, the budget usage of cue fragments and data products is estimated, and parameter patches are generated when the maximum context length is exceeded. The parameter patches include parameters for the number of retrievals, the length of retrieval fragments, the whitelist of fields returned by the tool, the timeout of the tool, the maximum output length of the model, the sampling temperature of the model, and the structured output switch.

7. The heterogeneous workflow prompt word collaborative compilation adaptation method according to claim 6, characterized in that: Based on the variable readiness level of the parallel branch predicted by the runtime statistics, a structural patch is generated when the waiting threshold of the streaming blockage point is met. A structural patch is generated when there are different variable readiness levels in the parallel branch. The parallel group is divided into early and late layers and intermediate convergence points are set. The first output segment node is inserted after the intermediate convergence point and the second output segment node is inserted after the final convergence point.

8. The heterogeneous workflow prompt word collaborative compilation adaptation method according to claim 7, characterized in that: Secondary contract verification is performed on the prompt word package, parameter patch, and structure patch. In conjunction with the output orchestration strategy, variable readiness verification is performed on the output segments. The secondary contract verification includes variable traceability verification, variable type consistency verification, and model capability consistency verification. A contract verification report is then output.

9. The heterogeneous workflow prompt word collaborative compilation adaptation method according to claim 8, characterized in that: Automatic repair actions are generated based on the contract verification report according to priority. The automatic repair actions include adjusting the output segment boundaries, inserting an integer prompt word module into the prompt word package and configuring summary rules, reducing structured output constraints and adding a self-checking and error correction segment, and writing back parameter patches to trim the search and tool returned data.

10. The heterogeneous workflow prompt word collaborative compilation adaptation method according to claim 9, characterized in that: Maintain the mapping relationship between nodes, variables, prompt word modules and output segments. When workflow metadata changes, locate the affected domain and perform incremental compilation only on prompt word modules and output segments related to the affected domain to generate differential parameter patches and structural patches. Collect node time, number of blocking points and token usage statistics to update variable readiness level prediction.

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