Add more evaluations (#2)

- First past experience draft (8d3f0f0d422c586d6e334471a6c3ba394e36e48e)


Co-authored-by: Richard Cosemans <[email protected]>

recruiting_assistant.py

@@ -4,6 +4,8 @@ from langchain.chat_models import ChatOpenAI
 from langchain.prompts import ChatPromptTemplate
 from langchain.chains import LLMChain, SequentialChain
 
+from validation import validate_dict_value, validate_string_value
+
 # os.environ["OPENA"]
 
 
@@ -70,8 +72,8 @@ llm = ChatOpenAI(temperature=0.0, openai_api_key=os.environ["OPENAI"])
 def create_intro(vacancy=vacancy, resume=resume):
 
     template_vacancy_get_skills = """
-    Can you generate me a list of the skills that a candidate supposed to have for the below vacancy delimited by three backticks. 
-    If you do not know if skills are available menion that you do not know and do not make up an answer. 
+    Can you generate me a list of the skills that a candidate is supposed to have for the below vacancy delimited by three backticks. 
+    If you do not know if skills are available mention that you do not know and do not make up an answer. 
     Mention the skills in 1 to maximum three words for each skill. Return the skills as a JSON list.
 
     ```
@@ -91,7 +93,7 @@ def create_intro(vacancy=vacancy, resume=resume):
     Can you create a JSON object based on the below keys each starting with '-', with respect to the resume below delimited by three backticks?
 
     - "skills_present": <list the skills present. If no skills are present return an empty list, do not make up an answer. >
-    - "skills_not_present": <list the skills not present. If all skills are present return here empty list, do not make up an answer.>
+    - "skills_not_present": <list the skills not present. If all skills are present return an empty list, do not make up an answer.>
     - "score": <calculate a percentage of the number of skills present with respect to the total skills requested>
 
     ```
@@ -102,6 +104,54 @@ def create_intro(vacancy=vacancy, resume=resume):
     prompt_resume_check_skills = ChatPromptTemplate.from_template(template=template_resume_check_skills)
     resume_skills = LLMChain(llm=llm, prompt=prompt_resume_check_skills, output_key="resume_skills")
 
+    template_resume_check_information = """
+    I have a resume below the delimiter <RESUME> and it ends with </RESUME>. Can you fill in the JSON below and respond with just that JSON object and no other text around it?
+    
+    JSON:
+    
+    {
+        "Naam": "<fill in the name of the candidate here>"
+        "Beschikbaarheid": "<fill in the availability here>",
+        "Tarief": "<fill in the day price here>",
+        "Woonplaats": "<fill in here the location where the candidate lives>",
+        "Talen": "<fill in the languages the candidate speaks>",
+        "Presentatie": "<describe the person, what's their role, their experience and skills in miminum 3 sentences and maximum 5 sentences. Keep the jargon in english.>"
+    }
+
+    """
+
+    template_resume_past_experiences = """
+    Can you generate me a list of the past work experiences that the candidate has based on the resume below enclosed by three backticks.
+    Mention the experiences in one sentence of medium length. Return the experiences as a JSON list.
+
+    ```
+    {resume}
+    ```
+    """
+
+    prompt_resume_past_experiences = ChatPromptTemplate.from_template(template=template_resume_past_experiences)
+    past_experiences = LLMChain(llm=llm, prompt=prompt_resume_past_experiences, output_key="past_experiences")
+
+    template_vacancy_check_past_experiences = """
+    ```
+    {past_experiences}
+    ```
+
+    Based on the above list of past experiences by a vacancy delimited by backticks,
+    Can you create a JSON object based on the below keys each starting with '-', with respect to the vacancy below delimited by three backticks?
+
+    - "relevant_experiences": <list the relevant experiences. If no experiences are relevant return an empty list, do not make up an answer. >
+    - "irrelevant_experiences": <list the irrelevant experiences. If all experiences are relevant return an empty list, do not make up an answer.>
+    - "score": <calculate a percentage of the number of skills present with respect to the total skills requested>
+
+    ```
+    {resume}
+    ```
+    """
+
+    prompt_vacancy_check_past_experiences = ChatPromptTemplate.from_template(template=template_vacancy_check_past_experiences)
+    check_past_experiences = LLMChain(llm=llm, prompt=prompt_vacancy_check_past_experiences, output_key="check_past_experiences")
+
     template_introduction_email = """
        You are a recruitment specialist that tries to place the right profiles for the right job.
        I have a vacancy below the delimiter <VACANCY> and ends with </VACANCY> 
@@ -115,13 +165,13 @@ def create_intro(vacancy=vacancy, resume=resume):
        {resume}
        </RESUME>
 
-       Can you fill in the introduction email below and only return as answer this introduction email?
+       Can you fill in the introduction below and only return as answer this introduction?
 
        Role: < the role of the vacancy >
        Candidate: < name of the candidate >
        Education: < name the education of the candidate >
        Responsibilities: < did the candidate worked as an individual contributor or did het take on leadership postitions? >
-       Experience: < name 2 most relevant experiences from the candidate for this vacancy in a short compact sentence. Add these as a bullet list >
+       Experience: < name the 2 most relevant experiences from the candidate for this vacancy. Get them from the "relevant_experiences" key of the JSON object {past_experiences}. If there are less than 2 relevant, leave this empty. Do not make up an answer or get them from the irrelevant experiences. >
        Skills: print here a comma seperated list of the "skills_present" key of the JSON object {resume_skills}
        """
 
@@ -129,15 +179,17 @@ def create_intro(vacancy=vacancy, resume=resume):
     introduction_email = LLMChain(llm=llm, prompt=prompt_introduction_email, output_key="introduction_email")
 
     match_resume_vacancy_skills_chain = SequentialChain(
-        chains=[vacancy_skills, resume_skills, introduction_email],
+        chains=[vacancy_skills, resume_skills, past_experiences, check_past_experiences, introduction_email],
         input_variables=["vacancy", "resume"],
-        output_variables=["vacancy_skills", "resume_skills", "introduction_email"],
+        output_variables=["vacancy_skills", "resume_skills", "past_experiences", "check_past_experiences", "introduction_email"],
         verbose=False
     )
 
     result = match_resume_vacancy_skills_chain({"vacancy": vacancy, "resume": resume})
     print(result)
-    return result["introduction_email"], json.dumps(json.loads(result['resume_skills']), indent=4)
+    return result["introduction_email"], json.dumps(json.loads(result['resume_skills']), indent=4), json.dumps(json.loads(result['check_past_experiences']), indent=4)
+
+    
 
 if __name__ == '__main__':
     create_intro(vacancy=vacancy,resume=resume)