text analysis

Author: manuelporrasojeda

CHANGELOG.rst

@@ -6,6 +6,9 @@ v3.7.1
 
 *Release date: In development*
 
+- Add text analysis tool to get an overall match of a text against a list of expected caractersitics
+  using AI libraries that come with the `ai` extra dependency
+
 v3.7.0
 ------
 

toolium/utils/ai_utils/openai.py

@@ -49,12 +49,16 @@ def openai_request(system_message, user_message, model_name=None, azure=False, *
     model_name = model_name or config.get_optional('AI', 'openai_model', 'gpt-4o-mini')
     logger.info(f"Calling to OpenAI API with model {model_name}")
     client = AzureOpenAI(**kwargs) if azure else OpenAI(**kwargs)
+    msg = []
+    if isinstance(system_message, list):
+        for prompt in system_message:
+            msg.append({"role": "system", "content": prompt})
+    else:
+        msg.append({"role": "system", "content": system_message})
+    msg.append({"role": "user", "content": user_message})
     completion = client.chat.completions.create(
         model=model_name,
-        messages=[
-            {"role": "system", "content": system_message},
-            {"role": "user", "content": user_message},
-        ],
+        messages=msg,
     )
     response = completion.choices[0].message.content
     logger.debug(f"OpenAI response: {response}")

toolium/utils/ai_utils/text_analysis.py

@@ -0,0 +1,155 @@
+# -*- coding: utf-8 -*-
+"""
+Copyright 2025 Telefónica Innovación Digital, S.L.
+This file is part of Toolium.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+"""
+import logging
+
+try:
+    from sentence_transformers import SentenceTransformer, util
+except ImportError:
+    SentenceTransformer = None
+
+from toolium.utils.ai_utils.openai import openai_request
+from toolium.driver_wrappers_pool import DriverWrappersPool
+
+# Configure logger
+logger = logging.getLogger(__name__)
+
+def build_system_message(characteristics):
+    """
+    Build system message for text criteria analysis prompt.
+
+    :param characteristics: list of target characteristics to evaluate
+    """
+    feature_list = "\n".join(f"- {c}" for c in characteristics)
+    base_prompt = f"""
+        You are an assistant that scores how well a given text matches a set of target characteristics and returns a JSON object.
+
+        You will receive a user message that contains ONLY the text to analyze.
+
+        Target characteristics:
+        {feature_list}
+
+        Tasks:
+        1) For EACH characteristic, decide how well the text satisfies it on a scale from 0.0 (does not satisfy it at all) to 1.0 (perfectly satisfies it). Consider style, tone and content when relevant.
+        2) Only for each low scored characteristic (<=0.2), output:
+           - "name": the exact characteristic name as listed above.
+           - "score": a float between 0.0 and 0.2.
+        3) Compute an overall score "overall_match" between 0.0 and 1.0 that summarizes how well the text matches the whole set. It does not have to be a simple arithmetic mean, but must still be in [0.0, 1.0].
+        4) Produce a "data" object that can contain extra structured analysis sections:
+           - "data" MUST always be present.
+           - "data" MUST be a JSON object.
+           - Each key in "data" is the title/name of a section (e.g. "genres", "entities", "style_breakdown").
+           - Each value is a JSON array (the structure of its objects will be defined by additional system instructions).
+
+        Output format (IMPORTANT):
+        Return ONLY a single valid JSON object with this exact top-level structure and property names:
+
+        {{
+          "overall_match": float,
+          "features": [
+            {{
+              "name": string,
+              "score": float
+            }}
+          ],
+          "data": {{
+            "<section_title>": [
+              {{}}
+            ]
+          }}
+        }}
+
+        Constraints:
+        - The "data" field must ALWAYS be present. If there are no extra sections, it MUST be: "data": {{}}.
+        - Use a dot as decimal separator (e.g. 0.75, not 0,75).
+        - Use at most 2 decimal places for all scores.
+        - Do NOT include any text outside the JSON (no Markdown, no comments, no explanations).
+        - If a characteristic is not applicable to the text, give it a low score (<= 0.2).
+        """
+    return base_prompt.strip()
+
+
+def get_text_criteria_analysis_openai(text_input, target_features, extra_tasks=None, model_name=None, azure=False, **kwargs):
+    """
+    Get text criteria analysis using Azure OpenAI. To analyze how well a given text
+    matches a set of target characteristics.
+    The response is a structured JSON object with overall match score, individual feature scores,
+    and additional data sections.
+
+    :param text_input: text to analyze
+    :param target_features: list of target characteristics to evaluate
+    :param extra_tasks: additional system messages for extra analysis sections (optional)
+    :param model_name: name of the Azure OpenAI model to use
+    :param azure: whether to use Azure OpenAI or standard OpenAI
+    :param kwargs: additional parameters to be used by Azure OpenAI client
+    :returns: response from Azure OpenAI
+    """
+    # Build prompt using base prompt and target features
+    system_message = build_system_message(target_features)
+    msg = [system_message]
+    if extra_tasks:
+        if isinstance(extra_tasks, list):
+            for task in extra_tasks:
+                msg.append(task)
+        else:
+            msg.append(extra_tasks)
+    return openai_request(system_message, text_input, model_name, azure, **kwargs)
+
+
+def get_text_criteria_analysis_sentence_transformers(text_input, target_features, extra_tasks=None,
+                                                     model_name=None, azure=True, **kwargs):
+    """
+    Get text criteria analysis using Sentence Transformers. To analyze how well a given text
+    matches a set of target characteristics.
+
+    :param text_input: text to analyze
+    :param target_features: list of target characteristics to evaluate
+    :param extra_tasks: additional system messages for extra analysis sections (not used here, for compatibility)
+    :param model_name: name of the Sentence Transformers model to use
+    :param azure: whether to use Azure OpenAI or standard OpenAI (not used here, for compatibility)
+    :param kwargs: additional parameters to be used by Sentence Transformers client
+    """
+    if SentenceTransformer is None:
+        raise ImportError("Sentence Transformers is not installed. Please run 'pip install toolium[ai]'"
+                          " to use Sentence Transformers features")
+    config = DriverWrappersPool.get_default_wrapper().config
+    model_name = model_name or config.get_optional('AI', 'sentence_transformers_model', 'all-mpnet-base-v2')
+    model = SentenceTransformer(model_name, **kwargs)
+    # Pre-compute feature embeddings
+    feature_embs = model.encode([f for f in target_features], normalize_embeddings=True)
+    # text_input embedding
+    text_emb = model.encode(text_input, normalize_embeddings=True)
+    # Computes cosine-similarities between the text and features tensors (range [-1, 1])
+    sims = util.cos_sim(text_emb, feature_embs)[0].tolist()
+    results = []
+    # Generate contracted results
+    for f, sim in zip(target_features, sims):
+        # Normalize similarity from [-1, 1] to [0, 1]
+        score = (sim + 1.0) / 2.0
+        results.append({
+            "name": f,
+            "score": round(score, 2),
+        })
+
+    # overall score as average of feature scores
+    overall = sum(r["score"] for r in results) / len(results)
+
+    return {
+        "overall_match": round(overall, 2),
+        "features": results,
+        "data": {}
+    }