mirror of
https://github.com/trustgraph-ai/trustgraph.git
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Prompt refactor (#125)
* Prompt manager integrated and working with 6 tests * Updated templates to for prompt-template update
This commit is contained in:
cybermaggedon
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parent
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commit
1e137768ca
19 changed files with 649 additions and 479 deletions
25
trustgraph-flow/trustgraph/model/prompt/template/README.md
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25
trustgraph-flow/trustgraph/model/prompt/template/README.md
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@ -0,0 +1,25 @@
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prompt-template \
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-p pulsar://localhost:6650 \
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--system-prompt 'You are a {{attitude}}, you are called {{name}}' \
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--global-term \
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'name=Craig' \
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'attitude=LOUD, SHOUTY ANNOYING BOT' \
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--prompt \
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'question={{question}}' \
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'french-question={{question}}' \
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"analyze=Find the name and age in this text, and output a JSON structure containing just the name and age fields: {{description}}. Don't add markup, just output the raw JSON object." \
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"graph-query=Study the following knowledge graph, and then answer the question.\\n\nGraph:\\n{% for edge in knowledge %}({{edge.0}})-[{{edge.1}}]->({{edge.2}})\\n{%endfor%}\\nQuestion:\\n{{question}}" \
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"extract-definition=Analyse the text provided, and then return a list of terms and definitions. The output should be a JSON array, each item in the array is an object with fields 'term' and 'definition'.Don't add markup, just output the raw JSON object. Here is the text:\\n{{text}}" \
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--prompt-response-type \
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'question=text' \
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'analyze=json' \
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'graph-query=text' \
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'extract-definition=json' \
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--prompt-term \
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'question=name:Bonny' \
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'french-question=attitude:French-speaking bot' \
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--prompt-schema \
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'analyze={ "type" : "object", "properties" : { "age": { "type" : "number" }, "name": { "type" : "string" } } }' \
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'extract-definition={ "type": "array", "items": { "type": "object", "properties": { "term": { "type": "string" }, "definition": { "type": "string" } }, "required": [ "term", "definition" ] } }'
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@ -0,0 +1,95 @@
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import ibis
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import json
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from jsonschema import validate
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import re
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from trustgraph.clients.llm_client import LlmClient
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class PromptConfiguration:
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def __init__(self, system_template, global_terms={}, prompts={}):
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self.system_template = system_template
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self.global_terms = global_terms
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self.prompts = prompts
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class Prompt:
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def __init__(self, template, response_type = "text", terms=None, schema=None):
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self.template = template
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self.response_type = response_type
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self.terms = terms
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self.schema = schema
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class PromptManager:
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def __init__(self, llm, config):
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self.llm = llm
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self.config = config
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self.terms = config.global_terms
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self.prompts = config.prompts
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try:
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self.system_template = ibis.Template(config.system_template)
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except:
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raise RuntimeError("Error in system template")
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self.templates = {}
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for k, v in self.prompts.items():
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try:
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self.templates[k] = ibis.Template(v.template)
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except:
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raise RuntimeError(f"Error in template: {k}")
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if v.terms is None:
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v.terms = {}
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def parse_json(self, text):
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json_match = re.search(r'```(?:json)?(.*?)```', text, re.DOTALL)
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if json_match:
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json_str = json_match.group(1).strip()
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else:
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# If no delimiters, assume the entire output is JSON
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json_str = text.strip()
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return json.loads(json_str)
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def invoke(self, id, input):
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if id not in self.prompts:
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raise RuntimeError("ID invalid")
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terms = self.terms | self.prompts[id].terms | input
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resp_type = self.prompts[id].response_type
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prompt = {
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"system": self.system_template.render(terms),
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"prompt": self.templates[id].render(terms)
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}
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resp = self.llm.request(**prompt)
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print(resp, flush=True)
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if resp_type == "text":
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return resp
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if resp_type != "json":
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raise RuntimeError(f"Response type {resp_type} not known")
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try:
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obj = self.parse_json(resp)
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except:
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raise RuntimeError("JSON parse fail")
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print(obj, flush=True)
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if self.prompts[id].schema:
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try:
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print(self.prompts[id].schema)
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validate(instance=obj, schema=self.prompts[id].schema)
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except Exception as e:
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raise RuntimeError(f"Schema validation fail: {e}")
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return obj
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@ -1,47 +0,0 @@
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def to_relationships(template, text):
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return template.format(text=text)
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def to_definitions(template, text):
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return template.format(text=text)
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def to_topics(template, text):
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return template.format(text=text)
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def to_rows(template, schema, text):
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field_schema = [
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f"- Name: {f.name}\n Type: {f.type}\n Definition: {f.description}"
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for f in schema.fields
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]
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field_schema = "\n".join(field_schema)
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return template.format(schema=schema, text=text)
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schema = f"""Object name: {schema.name}
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Description: {schema.description}
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Fields:
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{schema}"""
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prompt = f""""""
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return prompt
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def get_cypher(kg):
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sg2 = []
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for f in kg:
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sg2.append(f"({f.s})-[{f.p}]->({f.o})")
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kg = "\n".join(sg2)
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kg = kg.replace("\\", "-")
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return kg
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def to_kg_query(template, query, kg):
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cypher = get_cypher(kg)
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return template.format(query=query, graph=cypher)
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def to_document_query(template, query, docs):
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docs = "\n\n".join(docs)
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return template.format(query=query, documents=docs)
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@ -16,8 +16,7 @@ from .... schema import prompt_request_queue, prompt_response_queue
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from .... base import ConsumerProducer
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from .... clients.llm_client import LlmClient
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from . prompts import to_definitions, to_relationships, to_rows
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from . prompts import to_kg_query, to_document_query, to_topics
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from . prompt_manager import PromptConfiguration, Prompt, PromptManager
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module = ".".join(__name__.split(".")[1:-1])
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@ -29,6 +28,82 @@ class Processor(ConsumerProducer):
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def __init__(self, **params):
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prompt_base = {}
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# Parsing the prompt information to the prompt configuration
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# structure
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prompt_arg = params.get("prompt", [])
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if prompt_arg:
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for p in prompt_arg:
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toks = p.split("=", 1)
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if len(toks) < 2:
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raise RuntimeError(f"Prompt string not well-formed: {p}")
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prompt_base[toks[0]] = {
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"template": toks[1]
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}
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prompt_response_type_arg = params.get("prompt_response_type", [])
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if prompt_response_type_arg:
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for p in prompt_response_type_arg:
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toks = p.split("=", 1)
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if len(toks) < 2:
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raise RuntimeError(f"Response type not well-formed: {p}")
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if toks[0] not in prompt_base:
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raise RuntimeError(f"Response-type, {toks[0]} not known")
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prompt_base[toks[0]]["response_type"] = toks[1]
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prompt_schema_arg = params.get("prompt_schema", [])
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if prompt_schema_arg:
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for p in prompt_schema_arg:
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toks = p.split("=", 1)
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if len(toks) < 2:
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raise RuntimeError(f"Schema arg not well-formed: {p}")
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if toks[0] not in prompt_base:
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raise RuntimeError(f"Schema, {toks[0]} not known")
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try:
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prompt_base[toks[0]]["schema"] = json.loads(toks[1])
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except:
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raise RuntimeError(f"Failed to parse JSON schema: {p}")
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prompt_term_arg = params.get("prompt_term", [])
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if prompt_term_arg:
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for p in prompt_term_arg:
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toks = p.split("=", 1)
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if len(toks) < 2:
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raise RuntimeError(f"Term arg not well-formed: {p}")
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if toks[0] not in prompt_base:
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raise RuntimeError(f"Term, {toks[0]} not known")
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kvtoks = toks[1].split(":", 1)
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if len(kvtoks) < 2:
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raise RuntimeError(f"Term not well-formed: {toks[1]}")
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k, v = kvtoks
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if "terms" not in prompt_base[toks[0]]:
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prompt_base[toks[0]]["terms"] = {}
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prompt_base[toks[0]]["terms"][k] = v
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global_terms = {}
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global_term_arg = params.get("global_term", [])
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if global_term_arg:
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for t in global_term_arg:
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toks = t.split("=", 1)
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if len(toks) < 2:
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raise RuntimeError(f"Global term arg not well-formed: {t}")
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global_terms[toks[0]] = toks[1]
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print(global_terms)
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prompts = {
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k: Prompt(**v)
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for k, v in prompt_base.items()
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}
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prompt_configuration = PromptConfiguration(
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system_template = params.get("system_prompt", ""),
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global_terms = global_terms,
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prompts = prompts
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)
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input_queue = params.get("input_queue", default_input_queue)
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output_queue = params.get("output_queue", default_output_queue)
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subscriber = params.get("subscriber", default_subscriber)
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@ -64,23 +139,21 @@ class Processor(ConsumerProducer):
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pulsar_host = self.pulsar_host
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)
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self.definition_template = definition_template
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self.topic_template = topic_template
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self.relationship_template = relationship_template
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self.rows_template = rows_template
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self.knowledge_query_template = knowledge_query_template
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self.document_query_template = document_query_template
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# System prompt hack
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class Llm:
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def __init__(self, llm):
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self.llm = llm
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def request(self, system, prompt):
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print(system)
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print(prompt, flush=True)
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return self.llm.request(system + "\n\n" + prompt)
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def parse_json(self, text):
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json_match = re.search(r'```(?:json)?(.*?)```', text, re.DOTALL)
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if json_match:
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json_str = json_match.group(1).strip()
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else:
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# If no delimiters, assume the entire output is JSON
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json_str = text.strip()
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self.llm = Llm(self.llm)
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return json.loads(json_str)
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self.manager = PromptManager(
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llm = self.llm,
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config = prompt_configuration,
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)
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def handle(self, msg):
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@ -90,88 +163,52 @@ class Processor(ConsumerProducer):
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id = msg.properties()["id"]
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kind = v.kind
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print(f"Handling kind {kind}...", flush=True)
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if kind == "extract-definitions":
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self.handle_extract_definitions(id, v)
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return
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elif kind == "extract-topics":
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self.handle_extract_topics(id, v)
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return
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elif kind == "extract-relationships":
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self.handle_extract_relationships(id, v)
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return
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elif kind == "extract-rows":
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self.handle_extract_rows(id, v)
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return
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elif kind == "kg-prompt":
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self.handle_kg_prompt(id, v)
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return
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elif kind == "document-prompt":
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self.handle_document_prompt(id, v)
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return
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else:
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print("Invalid kind.", flush=True)
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return
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def handle_extract_definitions(self, id, v):
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kind = v.id
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try:
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prompt = to_definitions(self.definition_template, v.chunk)
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print(v.terms)
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ans = self.llm.request(prompt)
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input = {
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k: json.loads(v)
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for k, v in v.terms.items()
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}
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print(f"Handling kind {kind}...", flush=True)
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print(input, flush=True)
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# Silently ignore JSON parse error
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try:
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defs = self.parse_json(ans)
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except:
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print("JSON parse error, ignored", flush=True)
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defs = []
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resp = self.manager.invoke(kind, input)
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output = []
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if isinstance(resp, str):
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for defn in defs:
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print("Send text response...", flush=True)
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print(resp, flush=True)
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try:
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e = defn["entity"]
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d = defn["definition"]
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r = PromptResponse(
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text=resp,
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object=None,
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error=None,
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)
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if e == "": continue
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if e is None: continue
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if d == "": continue
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if d is None: continue
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self.producer.send(r, properties={"id": id})
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output.append(
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Definition(
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name=e, definition=d
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)
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)
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return
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except:
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print("definition fields missing, ignored", flush=True)
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else:
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print("Send response...", flush=True)
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r = PromptResponse(definitions=output, error=None)
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self.producer.send(r, properties={"id": id})
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print("Send object response...", flush=True)
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print(json.dumps(resp, indent=4), flush=True)
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print("Done.", flush=True)
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r = PromptResponse(
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text=None,
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object=json.dumps(resp),
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error=None,
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)
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self.producer.send(r, properties={"id": id})
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return
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except Exception as e:
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print(f"Exception: {e}")
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@ -188,122 +225,6 @@ class Processor(ConsumerProducer):
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self.producer.send(r, properties={"id": id})
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def handle_extract_topics(self, id, v):
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try:
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prompt = to_topics(self.topic_template, v.chunk)
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ans = self.llm.request(prompt)
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# Silently ignore JSON parse error
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try:
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defs = self.parse_json(ans)
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except:
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print("JSON parse error, ignored", flush=True)
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defs = []
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output = []
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for defn in defs:
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try:
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e = defn["topic"]
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d = defn["definition"]
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if e == "": continue
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if e is None: continue
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if d == "": continue
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if d is None: continue
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output.append(
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Topic(
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name=e, definition=d
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)
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)
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except:
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print("definition fields missing, ignored", flush=True)
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print("Send response...", flush=True)
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r = PromptResponse(topics=output, error=None)
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self.producer.send(r, properties={"id": id})
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print("Done.", flush=True)
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except Exception as e:
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print(f"Exception: {e}")
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print("Send error response...", flush=True)
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r = PromptResponse(
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error=Error(
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type = "llm-error",
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message = str(e),
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),
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response=None,
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)
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self.producer.send(r, properties={"id": id})
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def handle_extract_relationships(self, id, v):
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try:
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prompt = to_relationships(self.relationship_template, v.chunk)
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ans = self.llm.request(prompt)
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# Silently ignore JSON parse error
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try:
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defs = self.parse_json(ans)
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except:
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print("JSON parse error, ignored", flush=True)
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defs = []
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output = []
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for defn in defs:
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try:
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s = defn["subject"]
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p = defn["predicate"]
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o = defn["object"]
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o_entity = defn["object-entity"]
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||||
|
||||
if s == "": continue
|
||||
if s is None: continue
|
||||
|
||||
if p == "": continue
|
||||
if p is None: continue
|
||||
|
||||
if o == "": continue
|
||||
if o is None: continue
|
||||
|
||||
if o_entity == "" or o_entity is None:
|
||||
o_entity = False
|
||||
|
||||
output.append(
|
||||
Relationship(
|
||||
s = s,
|
||||
p = p,
|
||||
o = o,
|
||||
o_entity = o_entity,
|
||||
)
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
print("relationship fields missing, ignored", flush=True)
|
||||
|
||||
print("Send response...", flush=True)
|
||||
r = PromptResponse(relationships=output, error=None)
|
||||
self.producer.send(r, properties={"id": id})
|
||||
|
||||
print("Done.", flush=True)
|
||||
|
||||
except Exception as e:
|
||||
|
||||
print(f"Exception: {e}")
|
||||
|
|
@ -320,147 +241,6 @@ class Processor(ConsumerProducer):
|
|||
|
||||
self.producer.send(r, properties={"id": id})
|
||||
|
||||
def handle_extract_rows(self, id, v):
|
||||
|
||||
try:
|
||||
|
||||
fields = v.row_schema.fields
|
||||
|
||||
prompt = to_rows(self.rows_template, v.row_schema, v.chunk)
|
||||
|
||||
print(prompt)
|
||||
|
||||
ans = self.llm.request(prompt)
|
||||
|
||||
print(ans)
|
||||
|
||||
# Silently ignore JSON parse error
|
||||
try:
|
||||
objs = self.parse_json(ans)
|
||||
except:
|
||||
print("JSON parse error, ignored", flush=True)
|
||||
objs = []
|
||||
|
||||
output = []
|
||||
|
||||
for obj in objs:
|
||||
|
||||
try:
|
||||
|
||||
row = {}
|
||||
|
||||
for f in fields:
|
||||
|
||||
if f.name not in obj:
|
||||
print(f"Object ignored, missing field {f.name}")
|
||||
row = {}
|
||||
break
|
||||
|
||||
row[f.name] = obj[f.name]
|
||||
|
||||
if row == {}:
|
||||
continue
|
||||
|
||||
output.append(row)
|
||||
|
||||
except Exception as e:
|
||||
print("row fields missing, ignored", flush=True)
|
||||
|
||||
for row in output:
|
||||
print(row)
|
||||
|
||||
print("Send response...", flush=True)
|
||||
r = PromptResponse(rows=output, error=None)
|
||||
self.producer.send(r, properties={"id": id})
|
||||
|
||||
print("Done.", flush=True)
|
||||
|
||||
except Exception as e:
|
||||
|
||||
print(f"Exception: {e}")
|
||||
|
||||
print("Send error response...", flush=True)
|
||||
|
||||
r = PromptResponse(
|
||||
error=Error(
|
||||
type = "llm-error",
|
||||
message = str(e),
|
||||
),
|
||||
response=None,
|
||||
)
|
||||
|
||||
self.producer.send(r, properties={"id": id})
|
||||
|
||||
def handle_kg_prompt(self, id, v):
|
||||
|
||||
try:
|
||||
|
||||
prompt = to_kg_query(self.knowledge_query_template, v.query, v.kg)
|
||||
|
||||
print(prompt)
|
||||
|
||||
ans = self.llm.request(prompt)
|
||||
|
||||
print(ans)
|
||||
|
||||
print("Send response...", flush=True)
|
||||
r = PromptResponse(answer=ans, error=None)
|
||||
self.producer.send(r, properties={"id": id})
|
||||
|
||||
print("Done.", flush=True)
|
||||
|
||||
except Exception as e:
|
||||
|
||||
print(f"Exception: {e}")
|
||||
|
||||
print("Send error response...", flush=True)
|
||||
|
||||
r = PromptResponse(
|
||||
error=Error(
|
||||
type = "llm-error",
|
||||
message = str(e),
|
||||
),
|
||||
response=None,
|
||||
)
|
||||
|
||||
self.producer.send(r, properties={"id": id})
|
||||
|
||||
def handle_document_prompt(self, id, v):
|
||||
|
||||
try:
|
||||
|
||||
prompt = to_document_query(
|
||||
self.document_query_template, v.query, v.documents
|
||||
)
|
||||
|
||||
print(prompt)
|
||||
|
||||
ans = self.llm.request(prompt)
|
||||
|
||||
print(ans)
|
||||
|
||||
print("Send response...", flush=True)
|
||||
r = PromptResponse(answer=ans, error=None)
|
||||
self.producer.send(r, properties={"id": id})
|
||||
|
||||
print("Done.", flush=True)
|
||||
|
||||
except Exception as e:
|
||||
|
||||
print(f"Exception: {e}")
|
||||
|
||||
print("Send error response...", flush=True)
|
||||
|
||||
r = PromptResponse(
|
||||
error=Error(
|
||||
type = "llm-error",
|
||||
message = str(e),
|
||||
),
|
||||
response=None,
|
||||
)
|
||||
|
||||
self.producer.send(r, properties={"id": id})
|
||||
|
||||
@staticmethod
|
||||
def add_args(parser):
|
||||
|
||||
|
|
@ -482,39 +262,33 @@ class Processor(ConsumerProducer):
|
|||
)
|
||||
|
||||
parser.add_argument(
|
||||
'--definition-template',
|
||||
required=True,
|
||||
help=f'Definition extraction template',
|
||||
'--prompt', nargs='*',
|
||||
help=f'Prompt template form id=template',
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
'--topic-template',
|
||||
required=True,
|
||||
help=f'Topic extraction template',
|
||||
'--prompt-response-type', nargs='*',
|
||||
help=f'Prompt response type, form id=json|text',
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
'--rows-template',
|
||||
required=True,
|
||||
help=f'Rows extraction template',
|
||||
'--prompt-term', nargs='*',
|
||||
help=f'Prompt response type, form id=key:value',
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
'--relationship-template',
|
||||
required=True,
|
||||
help=f'Relationship extraction template',
|
||||
'--prompt-schema', nargs='*',
|
||||
help=f'Prompt response schema, form id=schema',
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
'--knowledge-query-template',
|
||||
required=True,
|
||||
help=f'Knowledge query template',
|
||||
'--system-prompt',
|
||||
help=f'System prompt template',
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
'--document-query-template',
|
||||
required=True,
|
||||
help=f'Document query template',
|
||||
'--global-term', nargs='+',
|
||||
help=f'Global term, form key:value'
|
||||
)
|
||||
|
||||
def run():
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue