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Merge pull request #27 from fsa-simpleqt/HuyDN
Browse files
app/modules/matching_cv/models/matching_cv_logic.py
CHANGED
@@ -42,7 +42,7 @@ def result_matching_cv_jd(cv_text, jd_text):
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# create the chat message
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chat_message = chat_template.format_messages(cv=cv_text, jd=jd_text)
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llm = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.3, convert_system_message_to_human=True, api_key=GOOGLE_API_KEY)
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chain = llm | parser
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result = chain.invoke(chat_message)
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# create the chat message
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chat_message = chat_template.format_messages(cv=cv_text, jd=jd_text)
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llm = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.3, convert_system_message_to_human=True, api_key=GOOGLE_API_KEY, request_timeout=120)
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chain = llm | parser
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result = chain.invoke(chat_message)
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app/modules/question_tests_retrieval/models/jd2text.py
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@@ -17,7 +17,7 @@ parser = JsonOutputParser()
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def jobdes2text(jobdes):
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# setup the gemini pro
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llm = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.3, convert_system_message_to_human=True, api_key=GOOGLE_API_KEY)
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# create the prompt template
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finnal_jd_chat_template = ChatPromptTemplate.from_messages(
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@@ -27,7 +27,7 @@ def jobdes2text(jobdes):
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"""Return Job title, level(Fresher, Junior, Senior, ...) and Brief summary of required skills about 20 words from the job description. Use the following format: Job Title is {job title}, Level is {level}, and Brief summary of required skills is {brief summary of required skills}."""
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)
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),
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HumanMessagePromptTemplate.from_template("{text}"),
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]
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)
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def jobdes2text(jobdes):
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# setup the gemini pro
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llm = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.3, convert_system_message_to_human=True, api_key=GOOGLE_API_KEY, request_timeout=120)
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# create the prompt template
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finnal_jd_chat_template = ChatPromptTemplate.from_messages(
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"""Return Job title, level(Fresher, Junior, Senior, ...) and Brief summary of required skills about 20 words from the job description. Use the following format: Job Title is {job title}, Level is {level}, and Brief summary of required skills is {brief summary of required skills}."""
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)
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),
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HumanMessagePromptTemplate.from_template("{text}"),
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]
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)
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app/modules/question_tests_retrieval/models/question_tests_logic.py
CHANGED
@@ -18,7 +18,7 @@ os.environ['GOOGLE_API_KEY'] = os.getenv('GOOGLE_API_KEY')
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GOOGLE_API_KEY = os.environ.get("GOOGLE_API_KEY")
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# Setting model embedding
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embedding_model = GoogleGenerativeAIEmbeddings(model="models/embedding-001", google_api_key=GOOGLE_API_KEY)
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gemini_evaluator = load_evaluator("embedding_distance", distance_metric=EmbeddingDistance.COSINE, embeddings=embedding_model)
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# def compare_vector(vector_extract, vector_des):
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GOOGLE_API_KEY = os.environ.get("GOOGLE_API_KEY")
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# Setting model embedding
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embedding_model = GoogleGenerativeAIEmbeddings(model="models/embedding-001", google_api_key=GOOGLE_API_KEY, request_timeout=120)
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gemini_evaluator = load_evaluator("embedding_distance", distance_metric=EmbeddingDistance.COSINE, embeddings=embedding_model)
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# def compare_vector(vector_extract, vector_des):
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app/modules/question_tests_retrieval/models/text2vector.py
CHANGED
@@ -10,6 +10,6 @@ os.environ['GOOGLE_API_KEY'] = os.getenv('GOOGLE_API_KEY')
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GOOGLE_API_KEY = os.environ.get("GOOGLE_API_KEY")
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def text2vector(text):
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embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001", google_api_key=GOOGLE_API_KEY)
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vector = embeddings.embed_query(text)
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return vector
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GOOGLE_API_KEY = os.environ.get("GOOGLE_API_KEY")
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def text2vector(text):
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embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001", google_api_key=GOOGLE_API_KEY, request_timeout=120)
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vector = embeddings.embed_query(text)
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return vector
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